system

Abstract

The system according to the embodiment aims to provide comprehensive support so that the senior generation and the elderly can smoothly carry out end-of-life arrangements. 【Solution means】The system according to the embodiment includes an analysis unit, a roadmap creation unit, an asset management proposal unit, a digital legacy arrangement unit, a legal procedure support unit, and a memory digitization unit. The analysis unit analyzes the user's values and interests. The roadmap creation unit creates a roadmap for achieving goals based on the information analyzed by the analysis unit. The asset management proposal unit proposes an asset management plan based on the roadmap created by the roadmap creation unit. The digital legacy arrangement unit arranges digital legacies based on the plan proposed by the asset management proposal unit. The legal procedure support unit supports legal procedures based on the information arranged by the digital legacy arrangement unit. The memory digitization unit digitizes memories based on the information supported by the legal procedure support unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when senior generations or the elderly conduct end-of-life affairs, there is a lack of a system that consistently supports goal setting, asset management, digital legacy arrangement, legal procedures, etc., and there is room for improvement.

[0005] The system according to the embodiment aims to provide comprehensive support so that senior generations or the elderly can smoothly conduct end-of-life affairs.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a roadmap creation unit, an asset management proposal unit, a digital heritage arrangement unit, a legal procedure support unit, and a memory digitization unit. The analysis unit analyzes the user's values and interests. The roadmap creation unit creates a roadmap for achieving goals based on the information analyzed by the analysis unit. The asset management proposal unit proposes an asset management plan based on the roadmap created by the roadmap creation unit. The digital heritage arrangement unit arranges digital heritage based on the plan proposed by the asset management proposal unit. The legal procedure support unit supports legal procedures based on the information arranged by the digital heritage arrangement unit. The memory digitization unit digitizes memories based on the information supported by the legal procedure support unit.

Effects of the Invention

[0007] The system according to the embodiment can provide comprehensive support so that the senior generation and the elderly can smoothly carry out estate settlement.

Brief Description of the Drawings

[0008] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It 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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 integrated end-of-life planning AI agent system according to an embodiment of the present invention is a system designed for senior citizens and local governments facing aging population issues. This system provides individual users with comprehensive support related to end-of-life planning, including setting life goals, optimizing asset management, managing digital assets, assisting with legal procedures, and digitizing memories. Furthermore, for local governments, it supports regional aging measures by streamlining local elderly support policies and providing data analysis and policy proposals to address residents' end-of-life needs. For example, the integrated end-of-life planning AI agent system analyzes the individual user's values, interests, and current assets and living situation, proposes a "to-achieve list," and creates a roadmap for achieving those goals. It also provides reminders based on progress to support the user in achieving their goals. Additionally, it offers a function to reflect on the user's life events and preserve memories as digital albums and memorial videos, generating tools to share emotional value with family and convey messages to future generations. Regarding asset management, the AI ​​analyzes the user's asset situation and proposes an investment plan to ensure a stable life in old age. It also designs plans to utilize surplus assets for donations and investments, maximizing economic and social value. In legal procedure support, legal procedure agents assist with estate division and account deletion, reducing the user's burden by proposing estate distribution, generating legal documents, and coordinating with lawyers and experts. As autonomous end-of-life management, AI centrally manages data monitoring, asset management, surviving family access design, and legal procedure support, organizing and deleting the user's digital data in real time and supporting appropriate handover to family. The regional collaboration platform collaborates with local experts and services to provide users with customized end-of-life plans, preventing isolation through support involving family and the community. For local governments, data analysis and policy proposals are provided to streamline local elderly support measures and address residents' end-of-life needs, supporting aging measures throughout the region. Specifically, AI analyzes data on local elderly people and proposes optimal support measures. This allows local governments to effectively implement elderly support measures and optimize resources. It also contributes to preventing isolation among the elderly and revitalizing local communities.This allows the integrated end-of-life planning AI agent system to provide comprehensive end-of-life support to senior citizens and municipalities facing aging population issues.

[0029] The integrated end-of-life AI agent system according to the embodiment includes an analysis unit, a roadmap creation unit, an asset management proposal unit, a digital legacy arrangement unit, a legal procedure support unit, and a memory digitization unit. The analysis unit analyzes the user's values and interests. For example, the analysis unit identifies values and interests based on the user's past behavior history and questionnaire results. In addition, the analysis unit can also analyze the posted content on social media and the accounts being followed. Furthermore, the analysis unit can identify values and interests based on the user's purchase history and browsing history. The roadmap creation unit creates a roadmap for achieving goals based on the information analyzed by the analysis unit. For example, the roadmap creation unit specifically shows the steps for the user to achieve their goals. In addition, the roadmap creation unit can also provide reminders according to the user's progress. Furthermore, the roadmap creation unit can also propose an optimal plan considering the user's lifestyle rhythm and schedule. The asset management proposal unit proposes an asset management plan based on the roadmap created by the roadmap creation unit. For example, the asset management proposal unit analyzes the user's asset situation through data analysis and proposes an operation plan to ensure a stable life in old age. In addition, the asset management proposal unit can also design a plan to utilize surplus assets for donations and investments. Furthermore, the asset management proposal unit can also propose an optimal investment strategy based on risk management and profit goals. The digital legacy arrangement unit arranges digital legacies based on the plan proposed by the asset management proposal unit. For example, the digital legacy arrangement unit proposes to organize and delete the user's digital data in real time. In addition, the digital legacy arrangement unit can also provide support for appropriately transferring the user's digital data to their family. Furthermore, the digital legacy arrangement unit can also propose a method for safely storing the user's digital data. The legal procedure support unit supports legal procedures based on the information arranged by the digital legacy arrangement unit. For example, the legal procedure support unit supports inheritance division and account deletion. In addition, the legal procedure support unit can also propose the distribution of inheritance and generate legal documents. Furthermore, the legal procedure support unit can also reduce the burden on the user through cooperation with lawyers and experts.The Memory Digitization Unit digitizes memories based on information supported by the Legal Procedure Support Unit. For example, the Memory Digitization Unit reviews the user's life events and preserves memories as digital albums and memorial videos. The Memory Digitization Unit can also generate tools to share emotional value with family and convey messages to future generations. Furthermore, the Memory Digitization Unit can suggest the optimal method for digitizing the user's memories. As a result, the integrated end-of-life AI agent system according to this embodiment can analyze the user's values ​​and interests, create a roadmap for achieving goals, propose asset management plans, organize digital assets, support legal procedures, and digitize memories.

[0030] The analytics department analyzes users' values ​​and interests. For example, it identifies values ​​and interests based on users' past behavioral history and survey results. Specifically, it collects the history of events users have participated in, products they have purchased, and websites they have visited, and statistically analyzes this data to reveal users' preferences and interests. The analytics department can also analyze the content of social media posts and accounts that users follow. For example, it analyzes the types of posts users frequently post and the trends in posts they "like" to understand the direction of users' interests. Furthermore, the analytics department can identify values ​​and interests based on users' purchase and browsing history. For example, it analyzes purchase history on online shopping sites and viewing history on video streaming services to identify what kinds of products and content users are interested in. This data is then highly analyzed using AI to precisely understand users' values ​​and interests. AI uses natural language processing technology to analyze text data and understand users' emotions and intentions. It can also use machine learning algorithms to learn users' behavioral patterns and predict future interests and concerns. This allows the analytics department to comprehensively analyze users' values ​​and interests, providing a foundation for making optimal suggestions to individual users.

[0031] The Roadmap Creation Department creates a roadmap for achieving goals based on the information analyzed by the Analysis Department. For example, the Roadmap Creation Department outlines specific steps for the user to achieve their goals. Specifically, it proposes concrete steps and schedules for achieving the goals set by the user. For instance, if a user's goal is health management, it proposes daily exercise and meal plans and provides reminders for regular health checks. The Roadmap Creation Department can also provide reminders based on the user's progress. For example, it can periodically check the user's progress toward their goals and send reminders to encourage action toward achieving those goals. Furthermore, the Roadmap Creation Department can propose an optimal plan considering the user's lifestyle and schedule. For example, it can propose a plan for achieving goals that is manageable, taking into account the user's work and family schedules. This allows the Roadmap Creation Department to provide flexible plans tailored to each user's individual circumstances and support goal achievement. Additionally, the Roadmap Creation Department can use AI to analyze user behavior data and automatically generate the optimal plan. The AI ​​learns from the user's past behavior data and goal achievement history to propose the most effective plan. This allows the roadmap creation department to efficiently support users in achieving their goals and improve user satisfaction.

[0032] The Asset Management Proposal Department proposes asset management plans based on roadmaps created by the Roadmap Creation Department. For example, the Asset Management Proposal Department analyzes data on the user's asset situation and proposes investment plans to ensure a stable life in retirement. Specifically, it collects data on the user's current asset situation, income, and expenses, and uses this data to predict future asset situations. The Asset Management Proposal Department can also design plans to utilize surplus assets for donations or investments. For example, if a user wishes to contribute to society, it proposes appropriate donation or investment destinations and provides an asset management plan that matches the user's values. Furthermore, the Asset Management Proposal Department can also propose optimal investment strategies based on risk management and return targets. For example, it proposes investment strategies using diversification and risk hedging techniques, taking into account the user's risk tolerance and return targets. In this way, the Asset Management Proposal Department can efficiently manage the user's assets and support a stable life in the future. In addition, the Asset Management Proposal Department can use AI to analyze market data and economic indicators and propose optimal investment timing and investment destinations. The AI ​​learns from past market data and economic indicators and predicts future market trends. Furthermore, the system considers each user's investment history and risk tolerance to propose the most suitable investment strategy for each individual user. This allows the asset management proposal department to provide advanced support for users' asset management and maximize the value of their assets.

[0033] The Digital Estate Management Department organizes digital assets based on plans proposed by the Asset Management Proposal Department. For example, the Digital Estate Management Department proposes real-time organization and deletion of the user's digital data. Specifically, it classifies the user's digital data and identifies important and unnecessary data. The Digital Estate Management Department can also assist in the proper transfer of the user's digital data to family members. For example, it supports the process of transferring specific data to family members based on the user's wishes. Furthermore, the Digital Estate Management Department can propose methods for securely storing the user's digital data. For example, it proposes secure storage methods using cloud storage and encryption technology. This allows the Digital Estate Management Department to efficiently organize and properly manage the user's digital data. Additionally, the Digital Estate Management Department can automate the classification and organization of digital data using AI. The AI ​​analyzes the user's digital data and automatically identifies important and unnecessary data. It can also automatically transfer or delete data based on the user's wishes. This allows the Digital Estate Management Department to reduce the burden on users and efficiently organize digital data.

[0034] The Legal Procedure Support Department provides legal assistance based on information organized by the Digital Estate Management Department. For example, it assists with estate division and account deletion. Specifically, it supports procedures for the proper division of a user's estate and handles the deletion of online accounts. The Legal Procedure Support Department can also propose estate distribution and generate legal documents. For instance, it can propose the proper distribution of a user's estate to heirs and automatically generate necessary legal documents. Furthermore, the Legal Procedure Support Department can reduce the user's burden by collaborating with lawyers and experts. For example, if complex legal procedures are required, it can reduce the user's burden by collaborating with lawyers and experts. This allows the Legal Procedure Support Department to efficiently support users' legal procedures and reduce their burden. Additionally, the Legal Procedure Support Department can utilize AI to automate legal procedures. AI manages the generation of legal documents and the progress of procedures, automatically advancing necessary steps. It can also propose the most suitable legal procedures based on the user's wishes. This allows the Legal Procedure Support Department to provide advanced support for users' legal procedures and minimize their burden.

[0035] The Memory Digitization Department digitizes memories based on information supported by the Legal Procedure Support Department. For example, the Memory Digitization Department looks back on a user's life events and preserves memories as digital albums and memorial videos. Specifically, it collects the user's past photos and videos, edits them, and creates digital albums and memorial videos. The Memory Digitization Department can also generate tools to share emotional value with family and convey messages to future generations. For example, users can record messages they want to convey to their families, digitize them, and pass them on to future generations. Users can also record their life stories in text and video and share them with family and friends. Furthermore, the Memory Digitization Department can suggest the best way to digitize a user's memories. For example, it can suggest using cloud storage or digital archiving services to safely store memories. This allows the Memory Digitization Department to digitize users' precious memories and preserve them for the future. In addition, the Memory Digitization Department can automate the digitization of memories using AI. The AI ​​analyzes photos and videos and automatically performs optimal editing and organization. Furthermore, the memory digitization process can be optimized based on the user's preferences. This allows the memory digitization unit to efficiently digitize the user's memories and preserve them for the future.

[0036] The reminder service can provide reminders that are tailored to the user's progress. For example, the reminder service can monitor the user's progress toward achieving their goals in real time and provide reminders at the appropriate time. It can also set reminders based on the user's schedule. Furthermore, the reminder service can customize the content and format of reminders according to the user's preferences. This allows the service to support the user in achieving their goals by providing reminders tailored to their progress. Some or all of the above processes in the reminder service may be performed using AI, for example, or without AI. For example, the reminder service can input user progress data into a generating AI and have the generating AI suggest the content and timing of reminders.

[0037] The emotional value sharing unit can share emotional value with family members. For example, it can preserve the user's memories as digital albums or memorial videos, thereby sharing emotional value with family members. It can also generate tools to reflect on the user's life events and convey messages to future generations. Furthermore, the emotional value sharing unit can suggest the optimal way to share the user's emotional value. This allows for deeper family bonds through the sharing of emotional value. Some or all of the above-described processes in the emotional value sharing unit may be performed using AI, for example, or without AI. For instance, the emotional value sharing unit can input the user's memory data into a generating AI and have the generating AI execute methods for sharing emotional value.

[0038] The Asset Utilization Department can utilize surplus assets for donations or investments. For example, the Asset Utilization Department can analyze a user's surplus assets and propose the most suitable donation or investment destinations. Furthermore, the Asset Utilization Department can design plans to maximize the user's economic and social value. In addition, the Asset Utilization Department can propose the most suitable asset utilization method based on risk management and profit targets. This allows for the maximization of economic and social value by utilizing surplus assets for donations or investments. Some or all of the above-described processes in the Asset Utilization Department may be performed using AI, for example, or without AI. For example, the Asset Utilization Department can input the user's surplus asset data into a generating AI and have the generating AI generate suggestions for donation or investment destinations.

[0039] The inheritance distribution proposal unit can make proposals for the distribution of inheritances. For example, the inheritance distribution proposal unit can analyze data on the user's inheritance situation and propose the optimal distribution method. The inheritance distribution proposal unit can also generate legal documents and coordinate with lawyers. Furthermore, the inheritance distribution proposal unit can provide support to reduce the burden on the user. In this way, the burden on the user can be reduced by making inheritance distribution proposals. Some or all of the above processes in the inheritance distribution proposal unit may be performed using AI, for example, or not using AI. For example, the inheritance distribution proposal unit can input the user's inheritance data into a generating AI and have the generating AI execute a proposal for a distribution method.

[0040] The data organization unit can organize and delete data in real time. For example, the data organization unit can monitor a user's digital data in real time and suggest deleting unnecessary data. It can also organize a user's digital data and suggest methods for prioritizing the storage of important data. Furthermore, the data organization unit can suggest methods for securely storing a user's digital data. This allows for proper management of a user's digital data by providing real-time organization and deletion suggestions. Some or all of the above processes in the data organization unit may be performed using AI, for example, or without AI. For example, the data organization unit can input a user's digital data into a generating AI and have the generating AI execute organization and deletion suggestions.

[0041] The Regional Collaboration Department can collaborate with local experts. For example, it can collaborate with local legal, medical, and welfare professionals to provide users with customized end-of-life planning plans. The Regional Collaboration Department can also collaborate with local services to provide support tailored to user needs. Furthermore, the Regional Collaboration Department can prevent user isolation through support involving family and the community. This allows the Regional Collaboration Department to provide users with customized end-of-life planning plans by collaborating with local experts. Some or all of the above processes in the Regional Collaboration Department may be performed using AI, or not. For example, the Regional Collaboration Department can input user needs data into a generating AI and have the AI ​​suggest the most suitable experts and services.

[0042] The analytics department can analyze a user's past behavioral history and predict changes in their values ​​and interests. For example, it can predict future changes in interests based on events and activities that a user has frequently participated in in the past. It can also analyze a user's past purchasing history and predict future consumption trends. Furthermore, it can predict places a user might want to visit next and their interests based on their past travel history. In this way, by analyzing a user's past behavioral history, it is possible to predict changes in their values ​​and interests. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input the user's past behavioral history data into a generating AI and have the generating AI perform predictions of changes in values ​​and interests.

[0043] The analysis unit can analyze a user's values ​​and interests by considering their living environment and social background. For example, the analysis unit can analyze values ​​by considering the culture and customs of the area where the user lives. It can also analyze interests by considering the user's occupation and work environment. Furthermore, the analysis unit can analyze values ​​and interests by considering the user's family structure and friendships. This allows for a more accurate analysis of values ​​and interests by considering the user's living environment and social background. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's living environment and social background into a generating AI and have the generating AI perform the analysis of values ​​and interests.

[0044] The analysis unit can analyze a user's values ​​and interests while considering their geographical location. For example, the analysis unit can analyze interests based on tourist spots and events in the user's area of ​​residence. It can also analyze values ​​while considering the user's commute or school route. Furthermore, the analysis unit can analyze interests based on the user's travel destinations and places they have visited. This allows for a more accurate analysis of values ​​and interests by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the analysis of values ​​and interests.

[0045] The analytics department can analyze a user's social media activity and identify their values ​​and interests. For example, the analytics department can identify interests based on the content and hashtags a user frequently posts. It can also identify values ​​based on the accounts and groups a user follows. Furthermore, it can identify interests based on a user's comment and like history. In this way, by analyzing a user's social media activity, it is possible to identify their values ​​and interests. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input the user's social media data into a generating AI and have the generating AI perform the identification of values ​​and interests.

[0046] The roadmap creation unit can reflect the user's progress toward achieving their goals in real time when creating a roadmap. For example, the roadmap creation unit updates the progress in real time each time the user approaches their goal. Furthermore, the roadmap creation unit can also reflect the user's achievement status in real time once they have achieved their goal. In addition, the roadmap creation unit can reflect the progress in real time if the user moves further away from their goal. This allows the system to always provide the most up-to-date information by reflecting the user's progress toward achieving their goals in real time. Some or all of the above processes in the roadmap creation unit may be performed using AI, for example, or without AI. For example, the roadmap creation unit can input user progress data into a generating AI and have the generating AI perform the real-time reflection of the progress.

[0047] The roadmap creation unit can propose an optimal plan when creating a roadmap, taking into account the user's lifestyle and schedule. For example, the roadmap creation unit can propose an optimal plan by considering the user's sleep patterns and activity times. It can also propose an optimal plan by considering the user's work or school schedule. Furthermore, it can propose an optimal plan by considering the user's schedule with family and friends. In this way, by considering the user's lifestyle and schedule, it can propose an optimal plan. Some or all of the above processing in the roadmap creation unit may be performed using AI, for example, or not using AI. For example, the roadmap creation unit can input the user's lifestyle and schedule data into a generating AI and have the generating AI propose an optimal plan.

[0048] The roadmap creation unit can propose an optimal plan when creating a roadmap, taking into account the user's geographical location information. For example, the roadmap creation unit can propose an optimal plan based on tourist spots and events in the user's area of ​​residence. It can also propose an optimal plan considering the user's commute or school route. Furthermore, it can propose an optimal plan based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose an optimal plan. Some or all of the above processing in the roadmap creation unit may be performed using AI, for example, or without AI. For example, the roadmap creation unit can input the user's geographical location data into a generating AI and have the generating AI propose an optimal plan.

[0049] The roadmap creation unit can analyze a user's social media activity and suggest relevant goals when creating a roadmap. For example, the roadmap creation unit can suggest relevant goals based on the content and hashtags that the user frequently posts. It can also suggest relevant goals based on the accounts and groups that the user follows. Furthermore, it can suggest relevant goals based on the user's comment and like history. In this way, relevant goals can be suggested by analyzing the user's social media activity. Some or all of the above processing in the roadmap creation unit may be performed using AI, for example, or not using AI. For example, the roadmap creation unit can input the user's social media data into a generating AI and have the generating AI suggest relevant goals.

[0050] The asset management proposal department can analyze a user's past asset management history and propose the optimal plan when making an asset management proposal. For example, the asset management proposal department can propose the optimal plan based on asset management plans the user has used in the past. Furthermore, the asset management proposal department can also propose a risk-averse plan based on the user's past asset management history. In addition, the asset management proposal department can analyze the user's past asset management history and propose the most efficient plan. Thus, by analyzing the user's past asset management history, the optimal plan can be proposed. Some or all of the above processes in the asset management proposal department may be performed using AI, for example, or without AI. For example, the asset management proposal department can input the user's past asset management history data into a generating AI and have the generating AI execute the proposal of the optimal plan.

[0051] The asset management proposal department can customize plans when proposing asset management, taking into account the user's living situation and future goals. For example, the asset management proposal department can propose an optimal plan by considering the user's current income and expenses. It can also customize plans by considering the user's future goals and dreams. Furthermore, it can customize plans by considering the user's family structure and life stage. In this way, it can customize the optimal plan by considering the user's living situation and future goals. Some or all of the above processes in the asset management proposal department may be performed using AI, for example, or not using AI. For example, the asset management proposal department can input data on the user's living situation and future goals into a generating AI and have the generating AI perform the plan customization.

[0052] The asset management proposal department can propose the optimal plan when making asset management proposals, taking into account the user's geographical location information. For example, the asset management proposal department can propose the optimal plan by considering the economic conditions of the area where the user lives. It can also propose the optimal plan by considering the user's commute route or school route. Furthermore, it can propose the optimal plan based on the user's travel destinations or places visited. In this way, by considering the user's geographical location information, it is possible to propose the optimal plan. Some or all of the above processing in the asset management proposal department may be performed using AI, for example, or without using AI. For example, the asset management proposal department can input the user's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal plan.

[0053] The asset management proposal department can analyze a user's social media activity and propose relevant plans when making asset management proposals. For example, the asset management proposal department can propose relevant plans based on the content and hashtags that the user frequently posts. It can also propose relevant plans based on the accounts and groups that the user follows. Furthermore, it can propose relevant plans based on the user's comment and like history. In this way, it can propose relevant plans by analyzing the user's social media activity. Some or all of the above processing in the asset management proposal department may be performed using AI, for example, or not using AI. For example, the asset management proposal department can input the user's social media data into a generating AI and have the generating AI execute the proposal of relevant plans.

[0054] The Digital Estate Management Department can analyze a user's past digital data during the digital estate management process and propose the optimal organization method. For example, the Digital Estate Management Department can analyze photos and videos previously saved by the user and prioritize the organization of important data. It can also analyze the user's past emails and messages and organize important contacts. Furthermore, the Digital Estate Management Department can analyze the user's past files and documents and organize important documents. In this way, by analyzing the user's past digital data, it can propose the optimal organization method. Some or all of the above processes in the Digital Estate Management Department may be performed using AI, for example, or not. For example, the Digital Estate Management Department can input the user's past digital data into a generating AI and have the generating AI propose the optimal organization method.

[0055] The Digital Estate Cleanup Department can customize the cleanup method during the digital estate cleanup process, taking into account the user's living situation and family structure. For example, the Digital Estate Cleanup Department can prioritize the cleanup of data important to the family, taking into account the user's family structure. It can also prioritize the cleanup of data used on a daily basis, taking into account the user's living situation. Furthermore, the Digital Estate Cleanup Department can prioritize the cleanup of data related to the user's hobbies and interests, taking into account the user's hobbies and interests. In this way, the optimal cleanup method can be customized by taking into account the user's living situation and family structure. Some or all of the above processes in the Digital Estate Cleanup Department may be performed using AI, for example, or not. For example, the Digital Estate Cleanup Department can input the user's living situation and family structure data into a generating AI and have the generating AI perform the customization of the cleanup method.

[0056] The Digital Estate Management Department can propose the optimal organization method when organizing a user's digital belongings, taking into account the user's geographical location information. For example, the Digital Estate Management Department can propose the optimal organization method by considering the culture and customs of the area where the user lives. It can also propose the optimal organization method by considering the user's commute route or school route. Furthermore, the Digital Estate Management Department can propose the optimal organization method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose the optimal organization method. Some or all of the above processes in the Digital Estate Management Department may be performed using AI, for example, or not using AI. For example, the Digital Estate Management Department can input the user's geographical location information data into a generating AI and have the generating AI execute a proposal for the optimal organization method.

[0057] The Digital Estate Cleanup Department can analyze a user's social media activity and organize relevant data during the digital estate cleanup process. For example, the Digital Estate Cleanup Department can organize relevant data based on the content and hashtags that the user frequently posts. It can also organize relevant data based on the accounts and groups that the user follows. Furthermore, the Digital Estate Cleanup Department can organize relevant data based on the user's comment and like history. In this way, relevant data can be organized by analyzing the user's social media activity. Some or all of the above processes in the Digital Estate Cleanup Department may be performed using AI, for example, or not. For example, the Digital Estate Cleanup Department can input the user's social media data into a generating AI and have the generating AI perform the organization of the relevant data.

[0058] The Legal Procedure Support Unit can analyze a user's past legal procedure history and propose the most suitable support method when providing legal procedure support. For example, the Legal Procedure Support Unit can propose the most suitable support method based on the legal procedure methods the user has used in the past. Furthermore, the Legal Procedure Support Unit can also propose support methods that avoid risks based on the user's past legal procedure history. In addition, the Legal Procedure Support Unit can analyze the user's past legal procedure history and propose the most efficient support method. Thus, by analyzing the user's past legal procedure history, the optimal support method can be proposed. Some or all of the above processing in the Legal Procedure Support Unit may be performed using AI, for example, or without AI. For example, the Legal Procedure Support Unit can input the user's past legal procedure history data into a generating AI and have the generating AI propose the most suitable support method.

[0059] The Legal Procedure Support Unit can customize its support methods when providing legal assistance, taking into account the user's living situation and family structure. For example, the Legal Procedure Support Unit can consider the user's family structure and prioritize support for legal procedures that are important to the family. It can also consider the user's living situation and prioritize support for legal procedures that are necessary on a daily basis. Furthermore, the Legal Procedure Support Unit can consider the user's hobbies and interests and prioritize support for relevant legal procedures. In this way, the optimal support method can be customized by considering the user's living situation and family structure. Some or all of the above processing in the Legal Procedure Support Unit may be performed using AI, for example, or not using AI. For example, the Legal Procedure Support Unit can input the user's living situation and family structure data into a generating AI and have the generating AI perform the customization of the support method.

[0060] The Legal Procedure Support Unit can propose the most suitable support method when providing legal assistance, taking into account the user's geographical location. For example, the Legal Procedure Support Unit can propose the most suitable support method by considering the laws and regulations of the area where the user lives. It can also propose the most suitable support method by considering the user's commute route or school route. Furthermore, the Legal Procedure Support Unit can propose the most suitable support method based on the user's travel destinations or places visited. In this way, by considering the user's geographical location, the unit can propose the most suitable support method. Some or all of the above processing in the Legal Procedure Support Unit may be performed using AI, for example, or without AI. For example, the Legal Procedure Support Unit can input the user's geographical location data into a generating AI and have the generating AI propose the most suitable support method.

[0061] The Legal Procedure Support Unit can analyze a user's social media activity and provide support for relevant legal procedures. For example, the Legal Procedure Support Unit can provide support based on the content and hashtags that the user frequently posts. It can also provide support based on the accounts and groups that the user follows. Furthermore, the Legal Procedure Support Unit can provide support based on the user's comment and like history. In this way, by analyzing the user's social media activity, it can provide support for relevant procedures. Some or all of the above processing in the Legal Procedure Support Unit may be performed using AI, for example, or not using AI. For example, the Legal Procedure Support Unit can input the user's social media data into a generating AI and have the generating AI perform support for relevant procedures.

[0062] The Memory Digitization Unit can analyze the user's past life events and propose the optimal digitization method during the memory digitization process. For example, the Memory Digitization Unit can analyze the user's past photos and videos and prioritize the digitization of important life events. It can also analyze the user's past emails and messages and digitize important contacts. Furthermore, the Memory Digitization Unit can analyze the user's past files and documents and digitize important documents. In this way, by analyzing the user's past life events, it can propose the optimal digitization method. Some or all of the above processing in the Memory Digitization Unit may be performed using AI, for example, or without AI. For example, the Memory Digitization Unit can input the user's past life event data into a generating AI and have the generating AI propose the optimal digitization method.

[0063] The memory digitization unit can customize the digitization method when digitizing memories, taking into account the user's living situation and family structure. For example, the memory digitization unit can prioritize digitizing memories that are important to the family, taking into account the user's family structure. The memory digitization unit can also prioritize digitizing data that is used on a daily basis, taking into account the user's living situation. Furthermore, the memory digitization unit can prioritize digitizing data related to the user's hobbies and interests, taking into account the user's hobbies and interests. In this way, the optimal digitization method can be customized by taking into account the user's living situation and family structure. Some or all of the above processing in the memory digitization unit may be performed using AI, for example, or without AI. For example, the memory digitization unit can input the user's living situation and family structure data into a generating AI and have the generating AI perform the customization of the digitization method.

[0064] The Memory Digitization Unit can propose the optimal digitization method when digitizing memories, taking into account the user's geographical location information. For example, the Memory Digitization Unit can propose the optimal digitization method by considering the culture and customs of the area where the user lives. It can also propose the optimal digitization method by considering the user's commute route or school route. Furthermore, the Memory Digitization Unit can propose the optimal digitization method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose the optimal digitization method. Some or all of the above processing in the Memory Digitization Unit may be performed using AI, for example, or without AI. For example, the Memory Digitization Unit can input the user's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal digitization method.

[0065] The Memory Digitization Unit can analyze a user's social media activity and digitize relevant memories during the memory digitization process. For example, the Memory Digitization Unit can digitize relevant memories based on the content and hashtags that the user frequently posts. It can also digitize relevant memories based on the accounts and groups that the user follows. Furthermore, the Memory Digitization Unit can digitize relevant memories based on the user's comment and like history. In this way, relevant memories can be digitized by analyzing the user's social media activity. Some or all of the above processing in the Memory Digitization Unit may be performed using AI, for example, or without AI. For example, the Memory Digitization Unit can input the user's social media data into a generating AI and have the generating AI perform the digitization of relevant memories.

[0066] The reminder service can provide optimal reminders by analyzing the user's past behavioral history when providing reminders. For example, the reminder service can provide reminders based on tasks that the user tends to forget in the past. It can also analyze the user's past behavioral patterns and provide reminders at the optimal time. Furthermore, the reminder service can remind users of important tasks based on their past schedules. In this way, it can provide optimal reminders by analyzing the user's past behavioral history. Some or all of the above processes in the reminder service may be performed using AI, for example, or without AI. For example, the reminder service can input the user's past behavioral history data into a generating AI and have the generating AI execute the provision of optimal reminders.

[0067] The reminder provisioning unit can provide optimal reminders by considering the user's geographical location information when providing reminders. For example, the reminder provisioning unit can provide reminders based on events and tasks in the user's residential area. It can also provide reminders considering the user's commute or school route. Furthermore, it can provide reminders based on the user's travel destinations or places visited. In this way, by considering the user's geographical location information, it is possible to provide optimal reminders. Some or all of the above processing in the reminder provisioning unit may be performed using AI, for example, or without AI. For example, the reminder provisioning unit can input the user's geographical location information data into a generation AI and have the generation AI perform the task of providing optimal reminders.

[0068] The emotional value sharing unit can analyze the user's past emotional history and propose the optimal sharing method when sharing emotional value. For example, the emotional value sharing unit can analyze the user's past emotional events and prioritize sharing important emotional values. It can also propose the optimal sharing timing based on the user's past emotional history. Furthermore, the emotional value sharing unit can analyze the user's past emotional history and propose the most effective sharing method. In this way, it can propose the optimal sharing method by analyzing the user's past emotional history. Some or all of the above processing in the emotional value sharing unit may be performed using AI, for example, or without AI. For example, the emotional value sharing unit can input the user's past emotional history data into a generating AI and have the generating AI propose the optimal sharing method.

[0069] The emotional value sharing unit can propose the optimal sharing method when sharing emotional value, taking into account the user's geographical location information. For example, the emotional value sharing unit can propose the optimal sharing method by considering the culture and customs of the area where the user lives. It can also propose the optimal sharing method by considering the user's commute route or school route. Furthermore, the emotional value sharing unit can propose the optimal sharing method based on the user's travel destinations or places visited. In this way, by considering the user's geographical location information, it is possible to propose the optimal sharing method. Some or all of the above processing in the emotional value sharing unit may be performed using AI, for example, or without AI. For example, the emotional value sharing unit can input the user's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal sharing method.

[0070] The Asset Utilization Department can analyze a user's past asset management history and propose the optimal utilization method when utilizing assets. For example, the Asset Utilization Department can propose the optimal utilization method based on the asset management plans the user has used in the past. Furthermore, the Asset Utilization Department can propose risk-averse utilization methods based on the user's past asset management history. In addition, the Asset Utilization Department can analyze the user's past asset management history and propose the most efficient utilization method. Thus, by analyzing the user's past asset management history, the optimal utilization method can be proposed. Some or all of the above-described processes in the Asset Utilization Department may be performed using AI, for example, or without AI. For example, the Asset Utilization Department can input the user's past asset management history data into a generating AI and have the generating AI propose the optimal utilization method.

[0071] The asset utilization unit can propose the optimal utilization method when utilizing assets, taking into account the user's geographical location information. For example, the asset utilization unit can propose the optimal utilization method by considering the economic situation of the area where the user lives. It can also propose the optimal utilization method by considering the user's commute route or school route. Furthermore, the asset utilization unit can propose the optimal utilization method based on the user's travel destinations or places visited. In this way, by considering the user's geographical location information, it is possible to propose the optimal utilization method. Some or all of the above processing in the asset utilization unit may be performed using AI, for example, or without using AI. For example, the asset utilization unit can input the user's geographical location information data into a generating AI and have the generating AI execute a proposal for the optimal utilization method.

[0072] The inheritance distribution proposal unit can analyze the user's past inheritance distribution history and propose the optimal distribution method when proposing an inheritance distribution. For example, the inheritance distribution proposal unit can propose the optimal distribution method based on the inheritance distribution methods the user has used in the past. The inheritance distribution proposal unit can also propose a method that avoids risk based on the user's past inheritance distribution history. Furthermore, the inheritance distribution proposal unit can analyze the user's past inheritance distribution history and propose the most efficient distribution method. In this way, the optimal distribution method can be proposed by analyzing the user's past inheritance distribution history. Some or all of the above processing in the inheritance distribution proposal unit may be performed using AI, for example, or without AI. For example, the inheritance distribution proposal unit can input the user's past inheritance distribution history data into a generating AI and have the generating AI execute a proposal for the optimal distribution method.

[0073] The inheritance distribution proposal unit can customize its proposal method when proposing inheritance distribution, taking into account the user's living situation and family structure. For example, the inheritance distribution proposal unit can consider the user's family structure and prioritize proposing inheritance distribution methods that are important to the family. It can also consider the user's living situation and prioritize proposing inheritance distribution methods that are needed on a daily basis. Furthermore, the inheritance distribution proposal unit can consider the user's hobbies and interests and prioritize proposing related inheritance distribution methods. In this way, the optimal proposal method can be customized by taking into account the user's living situation and family structure. Some or all of the above processing in the inheritance distribution proposal unit may be performed using AI, for example, or not using AI. For example, the inheritance distribution proposal unit can input the user's living situation and family structure data into a generating AI and have the generating AI perform the customization of the proposal method.

[0074] The inheritance distribution proposal unit can propose the optimal distribution method when proposing inheritance distribution, taking into account the user's geographical location information. For example, the inheritance distribution proposal unit can propose the optimal distribution method by considering the laws and regulations of the area where the user lives. It can also propose the optimal distribution method by considering the user's commute route or school route. Furthermore, the inheritance distribution proposal unit can propose the optimal distribution method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose the optimal distribution method. Some or all of the above processing in the inheritance distribution proposal unit may be performed using AI, for example, or without using AI. For example, the inheritance distribution proposal unit can input the user's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal distribution method.

[0075] The inheritance distribution proposal unit can analyze the user's social media activity and make relevant proposals when proposing inheritance distribution. For example, the inheritance distribution proposal unit can make relevant proposals based on the content and hashtags that the user frequently posts. It can also make relevant proposals based on the accounts and groups that the user follows. Furthermore, the inheritance distribution proposal unit can make relevant proposals based on the user's comment and like history. In this way, it can make relevant proposals by analyzing the user's social media activity. Some or all of the above processing in the inheritance distribution proposal unit may be performed using AI, for example, or not using AI. For example, the inheritance distribution proposal unit can input the user's social media data into a generating AI and have the generating AI execute relevant proposals.

[0076] The data organization unit can analyze the user's past data organization history and propose the optimal organization method during data organization. For example, the data organization unit can propose the optimal organization method based on the data organization methods the user has used in the past. Furthermore, the data organization unit can propose organization methods that avoid risks based on the user's past data organization history. In addition, the data organization unit can analyze the user's past data organization history and propose the most efficient organization method. Thus, by analyzing the user's past data organization history, the optimal organization method can be proposed. Some or all of the above processing in the data organization unit may be performed using AI, for example, or without AI. For example, the data organization unit can input the user's past data organization history data into a generating AI and have the generating AI propose the optimal organization method.

[0077] The data organization unit can customize the organization method when organizing data, taking into account the user's lifestyle and family structure. For example, the data organization unit can prioritize organizing data that is important to the family, taking into account the user's family structure. It can also prioritize organizing data that the user uses on a daily basis, taking into account the user's lifestyle. Furthermore, the data organization unit can prioritize organizing data related to the user's hobbies and interests, taking into account the user's hobbies and interests. In this way, the optimal organization method can be customized by taking into account the user's lifestyle and family structure. Some or all of the above processing in the data organization unit may be performed using AI, for example, or not using AI. For example, the data organization unit can input the user's lifestyle and family structure data into a generating AI and have the generating AI perform the customization of the organization method.

[0078] The data organization unit can propose the optimal organization method when organizing data, taking into account the user's geographical location information. For example, the data organization unit can propose the optimal organization method by considering the culture and customs of the area where the user lives. It can also propose the optimal organization method by considering the user's commute route or school route. Furthermore, the data organization unit can propose the optimal organization method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose the optimal organization method. Some or all of the above processing in the data organization unit may be performed using AI, for example, or without AI. For example, the data organization unit can input the user's geographical location information data into a generating AI and have the generating AI execute a proposal for the optimal organization method.

[0079] The data organization unit can analyze a user's social media activity and organize relevant data during data organization. For example, the data organization unit can organize relevant data based on the content and hashtags that the user frequently posts. It can also organize relevant data based on the accounts and groups that the user follows. Furthermore, the data organization unit can organize relevant data based on the user's comment and like history. In this way, relevant data can be organized by analyzing the user's social media activity. Some or all of the above processing in the data organization unit may be performed using AI, for example, or without AI. For example, the data organization unit can input the user's social media data into a generating AI and have the generating AI perform the organization of relevant data.

[0080] The Regional Collaboration Department can analyze a user's past regional collaboration history and propose the optimal collaboration method during regional collaboration. For example, the Regional Collaboration Department can propose the optimal collaboration method based on the regional collaboration methods the user has used in the past. Furthermore, the Regional Collaboration Department can propose collaboration methods that avoid risks based on the user's past regional collaboration history. In addition, the Regional Collaboration Department can analyze the user's past regional collaboration history and propose the most efficient collaboration method. Thus, by analyzing the user's past regional collaboration history, the optimal collaboration method can be proposed. Some or all of the above processing in the Regional Collaboration Department may be performed using AI, for example, or without AI. For example, the Regional Collaboration Department can input the user's past regional collaboration history data into a generating AI and have the generating AI propose the optimal collaboration method.

[0081] The Regional Collaboration Department can customize the collaboration method during regional collaboration, taking into account the user's living situation and family structure. For example, the Regional Collaboration Department can consider the user's family structure and prioritize suggesting regional collaboration methods that are important to the family. It can also consider the user's living situation and prioritize suggesting regional collaboration methods that are needed on a daily basis. Furthermore, the Regional Collaboration Department can consider the user's hobbies and interests and prioritize suggesting relevant regional collaboration methods. In this way, the optimal collaboration method can be customized by considering the user's living situation and family structure. Some or all of the above processing in the Regional Collaboration Department may be performed using AI, for example, or not. For example, the Regional Collaboration Department can input user living situation and family structure data into a generating AI and have the generating AI perform the customization of the collaboration method.

[0082] The Regional Collaboration Department can propose the optimal collaboration method when collaborating with a local community, taking into account the user's geographical location. For example, the Regional Collaboration Department can propose the optimal collaboration method by considering the culture and customs of the area where the user lives. It can also propose the optimal collaboration method by considering the user's commute or school route. Furthermore, the Regional Collaboration Department can propose the optimal collaboration method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location, it can propose the optimal collaboration method. Some or all of the above processing in the Regional Collaboration Department may be performed using AI, for example, or without AI. For example, the Regional Collaboration Department can input the user's geographical location data into a generating AI and have the generating AI propose the optimal collaboration method.

[0083] The Regional Collaboration Department can analyze a user's social media activity and propose relevant collaboration methods during regional collaboration. For example, the Regional Collaboration Department can propose relevant collaboration methods based on the content and hashtags that the user frequently posts. It can also propose relevant collaboration methods based on the accounts and groups that the user follows. Furthermore, the Regional Collaboration Department can propose relevant collaboration methods based on the user's comment and like history. In this way, relevant collaboration methods can be proposed by analyzing the user's social media activity. Some or all of the above processing in the Regional Collaboration Department may be performed using AI, for example, or not using AI. For example, the Regional Collaboration Department can input the user's social media data into a generating AI and have the generating AI propose relevant collaboration methods.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The integrated end-of-life planning AI agent system can also include a health management department. This department monitors the user's health status and provides advice for maintaining good health. For example, it analyzes the user's diet and exercise records and proposes balanced meal plans and exercise programs. It can also suggest necessary medical tests and treatments based on the user's regular health checkup results. Furthermore, it can monitor the user's stress levels and suggest relaxation methods and stress-relieving activities. This allows for comprehensive management of the user's health status and support for maintaining good health.

[0086] The integrated end-of-life planning AI agent system can also be equipped with a hobby recommendation function. This function analyzes the user's interests and past activity history to suggest new hobbies and activities. For example, it can suggest related new hobbies based on activities and events the user has enjoyed in the past. It can also analyze the user's social media posts and followed accounts to suggest activities that might interest them. Furthermore, it can suggest events and workshops held in the user's local area, helping the user discover new hobbies. This provides new enjoyment to the user's life, allowing them to spend their time more fulfilling.

[0087] The integrated end-of-life planning AI agent system can also include a community liaison unit. This unit helps users participate in local community activities. For example, it can suggest local volunteer activities and club activities based on the user's interests and skills. It can also provide information for users to participate in local events and gatherings. Furthermore, it can help users connect with local professionals and services to receive necessary support. This allows users to deepen their connections with their local community and prevent isolation.

[0088] The integrated end-of-life planning AI agent system can also be equipped with a learning support unit. This unit helps users acquire new skills and knowledge. For example, it can suggest online courses and workshops based on the user's interests and goals. It can also monitor the user's learning progress and provide appropriate feedback and advice. Furthermore, it can suggest projects and activities to help users put what they have learned into practice. This allows users to effectively acquire new skills and knowledge and promote their personal growth.

[0089] The integrated end-of-life planning AI agent system can also include a travel planning section. This section proposes optimal travel plans based on the user's interests and preferences. For example, it analyzes the user's past travel history and interests to suggest new destinations and activities. It can also customize travel plans based on the user's budget and schedule. Furthermore, it can provide advice and support to ensure the user's safety and comfort during their trip. This allows the user to enjoy a fulfilling travel experience.

[0090] The integrated end-of-life planning AI agent system can also include a hobby sharing section. This section helps users share their hobbies and interests with family and friends. For example, it suggests messages and photos to introduce the user's hobbies and activities to family and friends. It can also suggest activities for users to enjoy with family and friends when starting a new hobby. Furthermore, it can help users share events and workshops related to their hobbies with family and friends. This allows users to share their hobbies and interests with family and friends and find common ground.

[0091] The following briefly describes the processing flow for example form 1.

[0092] Step 1: The analytics department analyzes the user's values ​​and interests. For example, they identify values ​​and interests based on the user's past behavior history, survey results, social media posts and followed accounts, purchase history, and browsing history. Step 2: The Roadmap Creation Department creates a roadmap for achieving the goal based on the information analyzed by the Analysis Department. For example, it will specifically outline the steps for the user to achieve their goal, provide reminders according to their progress, and propose an optimal plan that takes into account their lifestyle and schedule. Step 3: The Asset Management Proposal Department proposes asset management plans based on the roadmap created by the Roadmap Creation Department. For example, they analyze the user's asset situation using data and propose investment plans to ensure a stable life in retirement, plans to utilize surplus assets for donations or investments, and optimal investment strategies based on risk management and return targets. Step 4: The Digital Estate Management Department organizes the digital estate based on the plan proposed by the Asset Management Proposal Department. For example, they propose organizing and deleting the user's digital data in real time, provide support for properly handing it over to family members, and suggest methods for secure storage. Step 5: The Legal Procedure Support Department assists with legal procedures based on the information organized by the Digital Estate Management Department. For example, it assists with estate division and account deletion, reduces the user's burden by proposing estate distribution, generating legal documents, and coordinating with lawyers and experts. Step 6: The Memory Digitization Department digitizes memories based on information supported by the Legal Procedure Support Department. For example, it looks back on the user's life events, preserves memories as digital albums and memorial videos, generates tools to share emotional value with family and convey messages to future generations, and proposes the optimal method.

[0093] (Example of form 2) The integrated end-of-life planning AI agent system according to an embodiment of the present invention is a system designed for senior citizens and local governments facing aging population issues. This system provides individual users with comprehensive support related to end-of-life planning, including setting life goals, optimizing asset management, managing digital assets, assisting with legal procedures, and digitizing memories. Furthermore, for local governments, it supports regional aging measures by streamlining local elderly support policies and providing data analysis and policy proposals to address residents' end-of-life needs. For example, the integrated end-of-life planning AI agent system analyzes the individual user's values, interests, and current assets and living situation, proposes a "to-achieve list," and creates a roadmap for achieving those goals. It also provides reminders based on progress to support the user in achieving their goals. Additionally, it offers a function to reflect on the user's life events and preserve memories as digital albums and memorial videos, generating tools to share emotional value with family and convey messages to future generations. Regarding asset management, the AI ​​analyzes the user's asset situation and proposes an investment plan to ensure a stable life in old age. It also designs plans to utilize surplus assets for donations and investments, maximizing economic and social value. In legal procedure support, legal procedure agents assist with estate division and account deletion, reducing the user's burden by proposing estate distribution, generating legal documents, and coordinating with lawyers and experts. As autonomous end-of-life management, AI centrally manages data monitoring, asset management, surviving family access design, and legal procedure support, organizing and deleting the user's digital data in real time and supporting appropriate handover to family. The regional collaboration platform collaborates with local experts and services to provide users with customized end-of-life plans, preventing isolation through support involving family and the community. For local governments, data analysis and policy proposals are provided to streamline local elderly support measures and address residents' end-of-life needs, supporting aging measures throughout the region. Specifically, AI analyzes data on local elderly people and proposes optimal support measures. This allows local governments to effectively implement elderly support measures and optimize resources. It also contributes to preventing isolation among the elderly and revitalizing local communities.This allows the integrated end-of-life planning AI agent system to provide comprehensive end-of-life support to senior citizens and municipalities facing aging population issues.

[0094] The integrated end-of-life AI agent system according to the embodiment includes an analysis unit, a roadmap creation unit, an asset management proposal unit, a digital legacy arrangement unit, a legal procedure support unit, and a memory digitization unit. The analysis unit analyzes the user's values and interests. For example, the analysis unit identifies values and interests based on the user's past behavior history and questionnaire results. Also, the analysis unit can analyze the content of posts on social media and the accounts the user follows. Furthermore, the analysis unit can identify values and interests based on the user's purchase history and browsing history. The roadmap creation unit creates a roadmap for achieving goals based on the information analyzed by the analysis unit. For example, the roadmap creation unit specifically shows the steps for the user to achieve their goals. Also, the roadmap creation unit can provide reminders according to the user's progress. Furthermore, the roadmap creation unit can propose an optimal plan considering the user's lifestyle and schedule. The asset management proposal unit proposes an asset management plan based on the roadmap created by the roadmap creation unit. For example, the asset management proposal unit analyzes the user's asset situation through data analysis and proposes an operation plan to ensure a stable life in old age. Also, the asset management proposal unit can design a plan to utilize surplus assets for donations and investments. Furthermore, the asset management proposal unit can propose an optimal investment strategy based on risk management and profit goals. The digital legacy arrangement unit arranges digital legacies based on the plan proposed by the asset management proposal unit. For example, the digital legacy arrangement unit proposes to organize and delete the user's digital data in real time. Also, the digital legacy arrangement unit can provide support for appropriately transferring the user's digital data to their family. Furthermore, the digital legacy arrangement unit can propose a method for safely storing the user's digital data. The legal procedure support unit supports legal procedures based on the information arranged by the digital legacy arrangement unit. For example, the legal procedure support unit supports inheritance division and account deletion. Also, the legal procedure support unit can propose distribution of inheritance and generate legal documents. Furthermore, the legal procedure support unit can reduce the user's burden through cooperation with lawyers and experts.The Memory Digitization Unit digitizes memories based on information supported by the Legal Procedure Support Unit. For example, the Memory Digitization Unit reviews the user's life events and preserves memories as digital albums and memorial videos. The Memory Digitization Unit can also generate tools to share emotional value with family and convey messages to future generations. Furthermore, the Memory Digitization Unit can suggest the optimal method for digitizing the user's memories. As a result, the integrated end-of-life AI agent system according to this embodiment can analyze the user's values ​​and interests, create a roadmap for achieving goals, propose asset management plans, organize digital assets, support legal procedures, and digitize memories.

[0095] The analytics department analyzes users' values ​​and interests. For example, it identifies values ​​and interests based on users' past behavioral history and survey results. Specifically, it collects the history of events users have participated in, products they have purchased, and websites they have visited, and statistically analyzes this data to reveal users' preferences and interests. The analytics department can also analyze the content of social media posts and accounts that users follow. For example, it analyzes the types of posts users frequently post and the trends in posts they "like" to understand the direction of users' interests. Furthermore, the analytics department can identify values ​​and interests based on users' purchase and browsing history. For example, it analyzes purchase history on online shopping sites and viewing history on video streaming services to identify what kinds of products and content users are interested in. This data is then highly analyzed using AI to precisely understand users' values ​​and interests. AI uses natural language processing technology to analyze text data and understand users' emotions and intentions. It can also use machine learning algorithms to learn users' behavioral patterns and predict future interests and concerns. This allows the analytics department to comprehensively analyze users' values ​​and interests, providing a foundation for making optimal suggestions to individual users.

[0096] The Roadmap Creation Department creates a roadmap for achieving goals based on the information analyzed by the Analysis Department. For example, the Roadmap Creation Department outlines specific steps for the user to achieve their goals. Specifically, it proposes concrete steps and schedules for achieving the goals set by the user. For instance, if a user's goal is health management, it proposes daily exercise and meal plans and provides reminders for regular health checks. The Roadmap Creation Department can also provide reminders based on the user's progress. For example, it can periodically check the user's progress toward their goals and send reminders to encourage action toward achieving those goals. Furthermore, the Roadmap Creation Department can propose an optimal plan considering the user's lifestyle and schedule. For example, it can propose a plan for achieving goals that is manageable, taking into account the user's work and family schedules. This allows the Roadmap Creation Department to provide flexible plans tailored to each user's individual circumstances and support goal achievement. Additionally, the Roadmap Creation Department can use AI to analyze user behavior data and automatically generate the optimal plan. The AI ​​learns from the user's past behavior data and goal achievement history to propose the most effective plan. This allows the roadmap creation department to efficiently support users in achieving their goals and improve user satisfaction.

[0097] The Asset Management Proposal Department proposes asset management plans based on roadmaps created by the Roadmap Creation Department. For example, the Asset Management Proposal Department analyzes data on the user's asset situation and proposes investment plans to ensure a stable life in retirement. Specifically, it collects data on the user's current asset situation, income, and expenses, and uses this data to predict future asset situations. The Asset Management Proposal Department can also design plans to utilize surplus assets for donations or investments. For example, if a user wishes to contribute to society, it proposes appropriate donation or investment destinations and provides an asset management plan that matches the user's values. Furthermore, the Asset Management Proposal Department can also propose optimal investment strategies based on risk management and return targets. For example, it proposes investment strategies using diversification and risk hedging techniques, taking into account the user's risk tolerance and return targets. In this way, the Asset Management Proposal Department can efficiently manage the user's assets and support a stable life in the future. In addition, the Asset Management Proposal Department can use AI to analyze market data and economic indicators and propose optimal investment timing and investment destinations. The AI ​​learns from past market data and economic indicators and predicts future market trends. Furthermore, the system considers each user's investment history and risk tolerance to propose the most suitable investment strategy for each individual user. This allows the asset management proposal department to provide advanced support for users' asset management and maximize the value of their assets.

[0098] The Digital Estate Management Department organizes digital assets based on plans proposed by the Asset Management Proposal Department. For example, the Digital Estate Management Department proposes real-time organization and deletion of the user's digital data. Specifically, it classifies the user's digital data and identifies important and unnecessary data. The Digital Estate Management Department can also assist in the proper transfer of the user's digital data to family members. For example, it supports the process of transferring specific data to family members based on the user's wishes. Furthermore, the Digital Estate Management Department can propose methods for securely storing the user's digital data. For example, it proposes secure storage methods using cloud storage and encryption technology. This allows the Digital Estate Management Department to efficiently organize and properly manage the user's digital data. Additionally, the Digital Estate Management Department can automate the classification and organization of digital data using AI. The AI ​​analyzes the user's digital data and automatically identifies important and unnecessary data. It can also automatically transfer or delete data based on the user's wishes. This allows the Digital Estate Management Department to reduce the burden on users and efficiently organize digital data.

[0099] The Legal Procedure Support Department provides legal assistance based on information organized by the Digital Estate Management Department. For example, it assists with estate division and account deletion. Specifically, it supports procedures for the proper division of a user's estate and handles the deletion of online accounts. The Legal Procedure Support Department can also propose estate distribution and generate legal documents. For instance, it can propose the proper distribution of a user's estate to heirs and automatically generate necessary legal documents. Furthermore, the Legal Procedure Support Department can reduce the user's burden by collaborating with lawyers and experts. For example, if complex legal procedures are required, it can reduce the user's burden by collaborating with lawyers and experts. This allows the Legal Procedure Support Department to efficiently support users' legal procedures and reduce their burden. Additionally, the Legal Procedure Support Department can utilize AI to automate legal procedures. AI manages the generation of legal documents and the progress of procedures, automatically advancing necessary steps. It can also propose the most suitable legal procedures based on the user's wishes. This allows the Legal Procedure Support Department to provide advanced support for users' legal procedures and minimize their burden.

[0100] The Memory Digitization Department digitizes memories based on information supported by the Legal Procedure Support Department. For example, the Memory Digitization Department looks back on a user's life events and preserves memories as digital albums and memorial videos. Specifically, it collects the user's past photos and videos, edits them, and creates digital albums and memorial videos. The Memory Digitization Department can also generate tools to share emotional value with family and convey messages to future generations. For example, users can record messages they want to convey to their families, digitize them, and pass them on to future generations. Users can also record their life stories in text and video and share them with family and friends. Furthermore, the Memory Digitization Department can suggest the best way to digitize a user's memories. For example, it can suggest using cloud storage or digital archiving services to safely store memories. This allows the Memory Digitization Department to digitize users' precious memories and preserve them for the future. In addition, the Memory Digitization Department can automate the digitization of memories using AI. The AI ​​analyzes photos and videos and automatically performs optimal editing and organization. Furthermore, the memory digitization process can be optimized based on the user's preferences. This allows the memory digitization unit to efficiently digitize the user's memories and preserve them for the future.

[0101] The reminder service can provide reminders that are tailored to the user's progress. For example, the reminder service can monitor the user's progress toward achieving their goals in real time and provide reminders at the appropriate time. It can also set reminders based on the user's schedule. Furthermore, the reminder service can customize the content and format of reminders according to the user's preferences. This allows the service to support the user in achieving their goals by providing reminders tailored to their progress. Some or all of the above processes in the reminder service may be performed using AI, for example, or without AI. For example, the reminder service can input user progress data into a generating AI and have the generating AI suggest the content and timing of reminders.

[0102] The emotional value sharing unit can share emotional value with family members. For example, it can preserve the user's memories as digital albums or memorial videos, thereby sharing emotional value with family members. It can also generate tools to reflect on the user's life events and convey messages to future generations. Furthermore, the emotional value sharing unit can suggest the optimal way to share the user's emotional value. This allows for deeper family bonds through the sharing of emotional value. Some or all of the above-described processes in the emotional value sharing unit may be performed using AI, for example, or without AI. For instance, the emotional value sharing unit can input the user's memory data into a generating AI and have the generating AI execute methods for sharing emotional value.

[0103] The Asset Utilization Department can utilize surplus assets for donations or investments. For example, the Asset Utilization Department can analyze a user's surplus assets and propose the most suitable donation or investment destinations. Furthermore, the Asset Utilization Department can design plans to maximize the user's economic and social value. In addition, the Asset Utilization Department can propose the most suitable asset utilization method based on risk management and profit targets. This allows for the maximization of economic and social value by utilizing surplus assets for donations or investments. Some or all of the above-described processes in the Asset Utilization Department may be performed using AI, for example, or without AI. For example, the Asset Utilization Department can input the user's surplus asset data into a generating AI and have the generating AI generate suggestions for donation or investment destinations.

[0104] The inheritance distribution proposal unit can make proposals for the distribution of inheritances. For example, the inheritance distribution proposal unit can analyze data on the user's inheritance situation and propose the optimal distribution method. The inheritance distribution proposal unit can also generate legal documents and coordinate with lawyers. Furthermore, the inheritance distribution proposal unit can provide support to reduce the burden on the user. In this way, the burden on the user can be reduced by making inheritance distribution proposals. Some or all of the above processes in the inheritance distribution proposal unit may be performed using AI, for example, or not using AI. For example, the inheritance distribution proposal unit can input the user's inheritance data into a generating AI and have the generating AI execute a proposal for a distribution method.

[0105] The data organization unit can organize and delete data in real time. For example, the data organization unit can monitor a user's digital data in real time and suggest deleting unnecessary data. It can also organize a user's digital data and suggest methods for prioritizing the storage of important data. Furthermore, the data organization unit can suggest methods for securely storing a user's digital data. This allows for proper management of a user's digital data by providing real-time organization and deletion suggestions. Some or all of the above processes in the data organization unit may be performed using AI, for example, or without AI. For example, the data organization unit can input a user's digital data into a generating AI and have the generating AI execute organization and deletion suggestions.

[0106] The Regional Collaboration Department can collaborate with local experts. For example, it can collaborate with local legal, medical, and welfare professionals to provide users with customized end-of-life planning plans. The Regional Collaboration Department can also collaborate with local services to provide support tailored to user needs. Furthermore, the Regional Collaboration Department can prevent user isolation through support involving family and the community. This allows the Regional Collaboration Department to provide users with customized end-of-life planning plans by collaborating with local experts. Some or all of the above processes in the Regional Collaboration Department may be performed using AI, or not. For example, the Regional Collaboration Department can input user needs data into a generating AI and have the AI ​​suggest the most suitable experts and services.

[0107] The analysis unit can estimate the user's emotions and adjust the analysis method for values ​​and interests based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize activities in a relaxing environment. If the user is excited, the analysis unit can also emphasize active activities and challenging goals. Furthermore, if the user is calm, the analysis unit can also emphasize long-term goals and plans. This allows for a more appropriate analysis by adjusting the analysis method for values ​​and interests based on the user's emotions. Emotion estimation can be performed, for example, by estimating the emotions of an emotional engine and adjusting the analysis method for values ​​and interests based on the estimated user emotions. This allows for a more appropriate analysis by adjusting the analysis method for values ​​and interests based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can input user emotional data into a generating AI and have the AI ​​adjust the methods for analyzing values ​​and interests.

[0108] The analytics department can analyze a user's past behavioral history and predict changes in their values ​​and interests. For example, it can predict future changes in interests based on events and activities that a user has frequently participated in in the past. It can also analyze a user's past purchasing history and predict future consumption trends. Furthermore, it can predict places a user might want to visit next and their interests based on their past travel history. In this way, by analyzing a user's past behavioral history, it is possible to predict changes in their values ​​and interests. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input the user's past behavioral history data into a generating AI and have the generating AI perform predictions of changes in values ​​and interests.

[0109] The analysis unit can analyze a user's values ​​and interests by considering their living environment and social background. For example, the analysis unit can analyze values ​​by considering the culture and customs of the area where the user lives. It can also analyze interests by considering the user's occupation and work environment. Furthermore, the analysis unit can analyze values ​​and interests by considering the user's family structure and friendships. This allows for a more accurate analysis of values ​​and interests by considering the user's living environment and social background. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's living environment and social background into a generating AI and have the generating AI perform the analysis of values ​​and interests.

[0110] The analysis unit can estimate the user's emotions and determine the priority of values ​​and interests based on those estimated emotions. For example, if the user is stressed, the analysis unit will prioritize relaxing activities. It can also prioritize challenging goals if the user is excited. Furthermore, if the user is calm, it can prioritize long-term plans. This allows for more appropriate prioritization by determining the priority of values ​​and interests based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of values ​​and interests.

[0111] The analysis unit can analyze a user's values ​​and interests while considering their geographical location. For example, the analysis unit can analyze interests based on tourist spots and events in the user's area of ​​residence. It can also analyze values ​​while considering the user's commute or school route. Furthermore, the analysis unit can analyze interests based on the user's travel destinations and places they have visited. This allows for a more accurate analysis of values ​​and interests by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the analysis of values ​​and interests.

[0112] The analytics department can analyze a user's social media activity and identify their values ​​and interests. For example, the analytics department can identify interests based on the content and hashtags a user frequently posts. It can also identify values ​​based on the accounts and groups a user follows. Furthermore, it can identify interests based on a user's comment and like history. In this way, by analyzing a user's social media activity, it is possible to identify their values ​​and interests. Some or all of the above processing in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input the user's social media data into a generating AI and have the generating AI perform the identification of values ​​and interests.

[0113] The roadmap creation unit can estimate the user's emotions and adjust the way the roadmap is presented based on those emotions. For example, if the user is relaxed, the roadmap creation unit can create a roadmap that progresses at a leisurely pace. If the user is in a hurry, the roadmap creation unit can also create a roadmap that emphasizes the shortest route. Furthermore, if the user is excited, the roadmap creation unit can create a roadmap with visually stimulating effects. By adjusting the way the roadmap is presented based on the user's emotions, a more appropriate roadmap can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the roadmap creation unit may be performed using AI, or not using AI. For example, the roadmap creation unit can input user emotion data into the generative AI and have the generative AI adjust the way the roadmap is presented.

[0114] The roadmap creation unit can reflect the user's progress toward achieving their goals in real time when creating a roadmap. For example, the roadmap creation unit updates the progress in real time each time the user approaches their goal. Furthermore, the roadmap creation unit can also reflect the user's achievement status in real time once they have achieved their goal. In addition, the roadmap creation unit can reflect the progress in real time if the user moves further away from their goal. This allows the system to always provide the most up-to-date information by reflecting the user's progress toward achieving their goals in real time. Some or all of the above processes in the roadmap creation unit may be performed using AI, for example, or without AI. For example, the roadmap creation unit can input user progress data into a generating AI and have the generating AI perform the real-time reflection of the progress.

[0115] The roadmap creation unit can propose an optimal plan when creating a roadmap, taking into account the user's lifestyle and schedule. For example, the roadmap creation unit can propose an optimal plan by considering the user's sleep patterns and activity times. It can also propose an optimal plan by considering the user's work or school schedule. Furthermore, it can propose an optimal plan by considering the user's schedule with family and friends. In this way, by considering the user's lifestyle and schedule, it can propose an optimal plan. Some or all of the above processing in the roadmap creation unit may be performed using AI, for example, or not using AI. For example, the roadmap creation unit can input the user's lifestyle and schedule data into a generating AI and have the generating AI propose an optimal plan.

[0116] The roadmap creation unit can estimate the user's emotions and determine the priorities of the roadmap based on those estimated emotions. For example, if the user is stressed, the roadmap creation unit will prioritize relaxing activities. It can also prioritize challenging goals if the user is excited. Furthermore, if the user is calm, it can prioritize long-term plans. This allows for more appropriate prioritization by determining roadmap priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 roadmap creation unit may be performed using AI or not. For example, the roadmap creation unit can input user emotion data into a generative AI and have the generative AI determine the roadmap priorities.

[0117] The roadmap creation unit can propose an optimal plan when creating a roadmap, taking into account the user's geographical location information. For example, the roadmap creation unit can propose an optimal plan based on tourist spots and events in the user's area of ​​residence. It can also propose an optimal plan considering the user's commute or school route. Furthermore, it can propose an optimal plan based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose an optimal plan. Some or all of the above processing in the roadmap creation unit may be performed using AI, for example, or without AI. For example, the roadmap creation unit can input the user's geographical location data into a generating AI and have the generating AI propose an optimal plan.

[0118] The roadmap creation unit can analyze a user's social media activity and suggest relevant goals when creating a roadmap. For example, the roadmap creation unit can suggest relevant goals based on the content and hashtags that the user frequently posts. It can also suggest relevant goals based on the accounts and groups that the user follows. Furthermore, it can suggest relevant goals based on the user's comment and like history. In this way, relevant goals can be suggested by analyzing the user's social media activity. Some or all of the above processing in the roadmap creation unit may be performed using AI, for example, or not using AI. For example, the roadmap creation unit can input the user's social media data into a generating AI and have the generating AI suggest relevant goals.

[0119] The asset management proposal unit can estimate the user's emotions and adjust the presentation of the asset management plan based on the estimated emotions. For example, if the user is relaxed, the asset management proposal unit can propose an asset management plan that proceeds at a relaxed pace. If the user is in a hurry, the asset management proposal unit can also propose an asset management plan that emphasizes the shortest route. Furthermore, if the user is excited, the asset management proposal unit can propose an asset management plan with visually stimulating effects. In this way, by adjusting the presentation of the asset management plan based on the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the asset management proposal unit may be performed using AI, for example, or not using AI. For example, the asset management proposal unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the asset management plan.

[0120] The asset management proposal department can analyze a user's past asset management history and propose the optimal plan when making an asset management proposal. For example, the asset management proposal department can propose the optimal plan based on asset management plans the user has used in the past. Furthermore, the asset management proposal department can also propose a risk-averse plan based on the user's past asset management history. In addition, the asset management proposal department can analyze the user's past asset management history and propose the most efficient plan. Thus, by analyzing the user's past asset management history, the optimal plan can be proposed. Some or all of the above processes in the asset management proposal department may be performed using AI, for example, or without AI. For example, the asset management proposal department can input the user's past asset management history data into a generating AI and have the generating AI execute the proposal of the optimal plan.

[0121] The asset management proposal department can customize plans when proposing asset management, taking into account the user's living situation and future goals. For example, the asset management proposal department can propose an optimal plan by considering the user's current income and expenses. It can also customize plans by considering the user's future goals and dreams. Furthermore, it can customize plans by considering the user's family structure and life stage. In this way, it can customize the optimal plan by considering the user's living situation and future goals. Some or all of the above processes in the asset management proposal department may be performed using AI, for example, or not using AI. For example, the asset management proposal department can input data on the user's living situation and future goals into a generating AI and have the generating AI perform the plan customization.

[0122] The asset management proposal unit can estimate the user's emotions and determine the priority of asset management plans based on those estimated emotions. For example, if the user is stressed, the asset management proposal unit will prioritize low-risk plans. Conversely, if the user is agitated, the asset management proposal unit may prioritize high-risk plans. Furthermore, if the user is calm, the asset management proposal unit may prioritize long-term plans. This allows for more appropriate prioritization by determining the priority of asset management plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 asset management proposal unit may be performed using AI or not. For example, the asset management proposal unit can input user emotion data into a generative AI and have the generative AI determine the priority of asset management plans.

[0123] The asset management proposal department can propose the optimal plan when making asset management proposals, taking into account the user's geographical location information. For example, the asset management proposal department can propose the optimal plan by considering the economic conditions of the area where the user lives. It can also propose the optimal plan by considering the user's commute route or school route. Furthermore, it can propose the optimal plan based on the user's travel destinations or places visited. In this way, by considering the user's geographical location information, it is possible to propose the optimal plan. Some or all of the above processing in the asset management proposal department may be performed using AI, for example, or without using AI. For example, the asset management proposal department can input the user's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal plan.

[0124] The asset management proposal department can analyze a user's social media activity and propose relevant plans when making asset management proposals. For example, the asset management proposal department can propose relevant plans based on the content and hashtags that the user frequently posts. It can also propose relevant plans based on the accounts and groups that the user follows. Furthermore, it can propose relevant plans based on the user's comment and like history. In this way, it can propose relevant plans by analyzing the user's social media activity. Some or all of the above processing in the asset management proposal department may be performed using AI, for example, or not using AI. For example, the asset management proposal department can input the user's social media data into a generating AI and have the generating AI execute the proposal of relevant plans.

[0125] The Digital Estate Management Department can estimate the user's emotions and adjust the digital estate management method based on the estimated emotions. For example, if the user is sad, the Digital Estate Management Department can suggest a simple management method to alleviate the emotional burden. If the user is relaxed, the Digital Estate Management Department can also suggest a detailed management method. Furthermore, if the user is in a hurry, the Digital Estate Management Department can suggest a method that allows for quick management. In this way, by adjusting the digital estate management method based on the user's emotions, a more appropriate management method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Digital Estate Management Department may be performed using AI or not. For example, the Digital Estate Management Department can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the digital estate management method.

[0126] The Digital Estate Management Department can analyze a user's past digital data during the digital estate management process and propose the optimal organization method. For example, the Digital Estate Management Department can analyze photos and videos previously saved by the user and prioritize the organization of important data. It can also analyze the user's past emails and messages and organize important contacts. Furthermore, the Digital Estate Management Department can analyze the user's past files and documents and organize important documents. In this way, by analyzing the user's past digital data, it can propose the optimal organization method. Some or all of the above processes in the Digital Estate Management Department may be performed using AI, for example, or not. For example, the Digital Estate Management Department can input the user's past digital data into a generating AI and have the generating AI propose the optimal organization method.

[0127] When organizing digital legacy, the Digital Legacy Organization Department can customize the organizing method by considering the user's living situation and family composition. For example, the Digital Legacy Organization Department can consider the user's family composition and prioritize the organization of data important to the family. Also, the Digital Legacy Organization Department can consider the user's living situation and prioritize the organization of data used daily. Furthermore, the Digital Legacy Organization Department can consider the user's hobbies and interests and prioritize the organization of related data. By doing so, an optimal organizing method can be customized by considering the user's living situation and family composition. Some or all of the above-mentioned processes in the Digital Legacy Organization Department may be performed using, for example, AI or without using AI. For example, the Digital Legacy Organization Department can input the user's living situation and family composition data into a generative AI and have the generative AI execute the customization of the organizing method.

[0128] The Digital Legacy Organization Department can estimate the user's emotions and determine the priority order of digital legacy organization based on the estimated user emotions. For example, when the user is sad, the Digital Legacy Organization Department can prioritize the organization of data important for reducing emotional burden. Also, when the user is relaxed, the Digital Legacy Organization Department can prioritize the organization of detailed data. Furthermore, when the user is in a hurry, the Digital Legacy Organization Department can prioritize the organization of data that can be quickly organized. By determining the priority order of digital legacy organization based on the user's emotions, more appropriate prioritization becomes possible. The estimation of emotions is realized using, for example, an emotion engine or a generative AI with an emotion estimation function. The generative AI is, for example, a text generative AI (such as an LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the above-mentioned processes in the Digital Legacy Organization Department may be performed using, for example, AI or without using AI. For example, the Digital Legacy Organization Department can input the user's emotion data into a generative AI and have the generative AI execute the determination of the priority order of digital legacy organization.

[0129] The Digital Estate Management Department can propose the optimal organization method when organizing a user's digital belongings, taking into account the user's geographical location information. For example, the Digital Estate Management Department can propose the optimal organization method by considering the culture and customs of the area where the user lives. It can also propose the optimal organization method by considering the user's commute route or school route. Furthermore, the Digital Estate Management Department can propose the optimal organization method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose the optimal organization method. Some or all of the above processes in the Digital Estate Management Department may be performed using AI, for example, or not using AI. For example, the Digital Estate Management Department can input the user's geographical location information data into a generating AI and have the generating AI execute a proposal for the optimal organization method.

[0130] The Digital Estate Cleanup Department can analyze a user's social media activity and organize relevant data during the digital estate cleanup process. For example, the Digital Estate Cleanup Department can organize relevant data based on the content and hashtags that the user frequently posts. It can also organize relevant data based on the accounts and groups that the user follows. Furthermore, the Digital Estate Cleanup Department can organize relevant data based on the user's comment and like history. In this way, relevant data can be organized by analyzing the user's social media activity. Some or all of the above processes in the Digital Estate Cleanup Department may be performed using AI, for example, or not. For example, the Digital Estate Cleanup Department can input the user's social media data into a generating AI and have the generating AI perform the organization of the relevant data.

[0131] The legal procedure support unit can estimate the user's emotions and adjust the method of legal procedure support based on the estimated emotions. For example, if the user is nervous, the legal procedure support unit can provide a simple and easy-to-understand method of support. If the user is relaxed, the legal procedure support unit can also provide a method of support that includes detailed information. Furthermore, if the user is in a hurry, the legal procedure support unit can provide a concise method of support. In this way, by adjusting the method of legal procedure support based on the user's emotions, a more appropriate method of support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the legal procedure support unit may be performed using AI, for example, or not using AI. For example, the legal procedure support unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the method of legal procedure support.

[0132] The Legal Procedure Support Unit can analyze a user's past legal procedure history and propose the most suitable support method when providing legal procedure support. For example, the Legal Procedure Support Unit can propose the most suitable support method based on the legal procedure methods the user has used in the past. Furthermore, the Legal Procedure Support Unit can also propose support methods that avoid risks based on the user's past legal procedure history. In addition, the Legal Procedure Support Unit can analyze the user's past legal procedure history and propose the most efficient support method. Thus, by analyzing the user's past legal procedure history, the optimal support method can be proposed. Some or all of the above processing in the Legal Procedure Support Unit may be performed using AI, for example, or without AI. For example, the Legal Procedure Support Unit can input the user's past legal procedure history data into a generating AI and have the generating AI propose the most suitable support method.

[0133] The Legal Procedure Support Unit can customize its support methods when providing legal assistance, taking into account the user's living situation and family structure. For example, the Legal Procedure Support Unit can consider the user's family structure and prioritize support for legal procedures that are important to the family. It can also consider the user's living situation and prioritize support for legal procedures that are necessary on a daily basis. Furthermore, the Legal Procedure Support Unit can consider the user's hobbies and interests and prioritize support for relevant legal procedures. In this way, the optimal support method can be customized by considering the user's living situation and family structure. Some or all of the above processing in the Legal Procedure Support Unit may be performed using AI, for example, or not using AI. For example, the Legal Procedure Support Unit can input the user's living situation and family structure data into a generating AI and have the generating AI perform the customization of the support method.

[0134] The legal procedure support unit can estimate the user's emotions and determine the priority of legal procedures based on those estimated emotions. For example, if the user is stressed, the legal procedure support unit can prioritize important legal procedures to alleviate their emotional burden. If the user is relaxed, the legal procedure support unit can also prioritize detailed legal procedures. Furthermore, if the user is in a hurry, the legal procedure support unit can prioritize legal procedures that can be handled quickly. This allows for more appropriate prioritization by determining the priority of legal procedures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the legal procedure support unit may be performed using AI or not. For example, the legal procedure support unit can input user emotion data into a generative AI and have the generative AI determine the priority of legal procedures.

[0135] The Legal Procedure Support Unit can propose the most suitable support method when providing legal assistance, taking into account the user's geographical location. For example, the Legal Procedure Support Unit can propose the most suitable support method by considering the laws and regulations of the area where the user lives. It can also propose the most suitable support method by considering the user's commute route or school route. Furthermore, the Legal Procedure Support Unit can propose the most suitable support method based on the user's travel destinations or places visited. In this way, by considering the user's geographical location, the unit can propose the most suitable support method. Some or all of the above processing in the Legal Procedure Support Unit may be performed using AI, for example, or without AI. For example, the Legal Procedure Support Unit can input the user's geographical location data into a generating AI and have the generating AI propose the most suitable support method.

[0136] The Legal Procedure Support Unit can analyze a user's social media activity and provide support for relevant legal procedures. For example, the Legal Procedure Support Unit can provide support based on the content and hashtags that the user frequently posts. It can also provide support based on the accounts and groups that the user follows. Furthermore, the Legal Procedure Support Unit can provide support based on the user's comment and like history. In this way, by analyzing the user's social media activity, it can provide support for relevant procedures. Some or all of the above processing in the Legal Procedure Support Unit may be performed using AI, for example, or not using AI. For example, the Legal Procedure Support Unit can input the user's social media data into a generating AI and have the generating AI perform support for relevant procedures.

[0137] The memory digitization unit can estimate the user's emotions and adjust the memory digitization method based on the estimated emotions. For example, if the user is emotional, the memory digitization unit can suggest a digitization method that incorporates emotional effects. It can also suggest a simple and highly visible digitization method if the user is relaxed. Furthermore, if the user is in a hurry, the memory digitization unit can suggest a method that allows for quick digitization. This allows for the provision of a more appropriate digitization method by adjusting the memory digitization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, 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 memory digitization unit may be performed using AI, or not. For example, the memory digitization unit can input user emotion data into a generative AI and have the generative AI adjust the memory digitization method.

[0138] The Memory Digitization Unit can analyze the user's past life events and propose the optimal digitization method during the memory digitization process. For example, the Memory Digitization Unit can analyze the user's past photos and videos and prioritize the digitization of important life events. It can also analyze the user's past emails and messages and digitize important contacts. Furthermore, the Memory Digitization Unit can analyze the user's past files and documents and digitize important documents. In this way, by analyzing the user's past life events, it can propose the optimal digitization method. Some or all of the above processing in the Memory Digitization Unit may be performed using AI, for example, or without AI. For example, the Memory Digitization Unit can input the user's past life event data into a generating AI and have the generating AI propose the optimal digitization method.

[0139] The memory digitization unit can customize the digitization method when digitizing memories, taking into account the user's living situation and family structure. For example, the memory digitization unit can prioritize digitizing memories that are important to the family, taking into account the user's family structure. The memory digitization unit can also prioritize digitizing data that is used on a daily basis, taking into account the user's living situation. Furthermore, the memory digitization unit can prioritize digitizing data related to the user's hobbies and interests, taking into account the user's hobbies and interests. In this way, the optimal digitization method can be customized by taking into account the user's living situation and family structure. Some or all of the above processing in the memory digitization unit may be performed using AI, for example, or without AI. For example, the memory digitization unit can input the user's living situation and family structure data into a generating AI and have the generating AI perform the customization of the digitization method.

[0140] The memory digitization unit can estimate the user's emotions and determine the priority of memory digitization based on the estimated emotions. For example, if the user is emotional, the memory digitization unit can prioritize digitizing important memories to reduce the emotional burden. It can also prioritize digitizing detailed memories if the user is relaxed. Furthermore, if the user is in a hurry, the memory digitization unit can prioritize digitizing memories that can be digitized quickly. This allows for more appropriate prioritization by determining the priority of memory digitization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 memory digitization unit may be performed using AI or not. For example, the memory digitization unit can input user emotion data into a generative AI and have the generative AI determine the priority of memory digitization.

[0141] The Memory Digitization Unit can propose the optimal digitization method when digitizing memories, taking into account the user's geographical location information. For example, the Memory Digitization Unit can propose the optimal digitization method by considering the culture and customs of the area where the user lives. It can also propose the optimal digitization method by considering the user's commute route or school route. Furthermore, the Memory Digitization Unit can propose the optimal digitization method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose the optimal digitization method. Some or all of the above processing in the Memory Digitization Unit may be performed using AI, for example, or without AI. For example, the Memory Digitization Unit can input the user's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal digitization method.

[0142] The Memory Digitization Unit can analyze a user's social media activity and digitize relevant memories during the memory digitization process. For example, the Memory Digitization Unit can digitize relevant memories based on the content and hashtags that the user frequently posts. It can also digitize relevant memories based on the accounts and groups that the user follows. Furthermore, the Memory Digitization Unit can digitize relevant memories based on the user's comment and like history. In this way, relevant memories can be digitized by analyzing the user's social media activity. Some or all of the above processing in the Memory Digitization Unit may be performed using AI, for example, or without AI. For example, the Memory Digitization Unit can input the user's social media data into a generating AI and have the generating AI perform the digitization of relevant memories.

[0143] The reminder delivery unit can estimate the user's emotions and adjust the way reminders are delivered based on the estimated emotions. For example, if the user is nervous, the reminder delivery unit can deliver reminders in a calm voice. It can also deliver reminders in a cheerful voice if the user is relaxed. Furthermore, if the user is in a hurry, the reminder delivery unit can deliver quick and concise reminders. This allows for the delivery of more appropriate reminders by adjusting the delivery method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 reminder delivery unit may be performed using AI or not. For example, the reminder delivery unit can input user emotion data into the generative AI and have the generative AI adjust the way reminders are delivered.

[0144] The reminder service can provide optimal reminders by analyzing the user's past behavioral history when providing reminders. For example, the reminder service can provide reminders based on tasks that the user tends to forget in the past. It can also analyze the user's past behavioral patterns and provide reminders at the optimal time. Furthermore, the reminder service can remind users of important tasks based on their past schedules. In this way, it can provide optimal reminders by analyzing the user's past behavioral history. Some or all of the above processes in the reminder service may be performed using AI, for example, or without AI. For example, the reminder service can input the user's past behavioral history data into a generating AI and have the generating AI execute the provision of optimal reminders.

[0145] The reminder provider can estimate the user's emotions and determine the priority of reminders based on those emotions. For example, if the user is stressed, the reminder provider will prioritize relaxing tasks. It can also prioritize challenging tasks if the user is excited. Furthermore, if the user is calm, it can prioritize long-term tasks. This allows for more appropriate prioritization by determining reminder priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 reminder provider may be performed using AI or not. For example, the reminder provider can input user emotion data into a generative AI and have the generative AI determine the priority of reminders.

[0146] The reminder provisioning unit can provide optimal reminders by considering the user's geographical location information when providing reminders. For example, the reminder provisioning unit can provide reminders based on events and tasks in the user's residential area. It can also provide reminders considering the user's commute or school route. Furthermore, it can provide reminders based on the user's travel destinations or places visited. In this way, by considering the user's geographical location information, it is possible to provide optimal reminders. Some or all of the above processing in the reminder provisioning unit may be performed using AI, for example, or without AI. For example, the reminder provisioning unit can input the user's geographical location information data into a generation AI and have the generation AI perform the task of providing optimal reminders.

[0147] The emotional value sharing unit can estimate the user's emotions and adjust the method of sharing emotional value based on the estimated user emotions. For example, if the user is moved, the emotional value sharing unit can suggest a sharing method with added emotional effects. It can also suggest a simple and highly visible sharing method if the user is relaxed. Furthermore, if the user is in a hurry, it can suggest a method that allows for quick sharing. This allows for the provision of more appropriate sharing methods by adjusting the method of sharing emotional value based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the emotional value sharing unit may be performed using AI, or not. For example, the emotional value sharing unit can input user emotion data into a generative AI and have the generative AI adjust the method of sharing emotional value.

[0148] The emotional value sharing unit can analyze the user's past emotional history and propose the optimal sharing method when sharing emotional value. For example, the emotional value sharing unit can analyze the user's past emotional events and prioritize sharing important emotional values. It can also propose the optimal sharing timing based on the user's past emotional history. Furthermore, the emotional value sharing unit can analyze the user's past emotional history and propose the most effective sharing method. In this way, it can propose the optimal sharing method by analyzing the user's past emotional history. Some or all of the above processing in the emotional value sharing unit may be performed using AI, for example, or without AI. For example, the emotional value sharing unit can input the user's past emotional history data into a generating AI and have the generating AI propose the optimal sharing method.

[0149] The emotional value sharing unit can estimate the user's emotions and determine the priority of emotional values ​​based on the estimated user emotions. For example, if the user is emotionally moved, the emotional value sharing unit will prioritize sharing emotional values. It can also prioritize sharing detailed emotional values ​​if the user is relaxed. Furthermore, if the user is in a hurry, the emotional value sharing unit can prioritize sharing emotional values ​​that can be shared quickly. This allows for more appropriate prioritization by determining the priority of emotional values ​​based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 emotional value sharing unit may be performed using AI, or not. For example, the emotional value sharing unit can input user emotion data into a generative AI and have the generative AI determine the priority of emotional values.

[0150] The emotional value sharing unit can propose the optimal sharing method when sharing emotional value, taking into account the user's geographical location information. For example, the emotional value sharing unit can propose the optimal sharing method by considering the culture and customs of the area where the user lives. It can also propose the optimal sharing method by considering the user's commute route or school route. Furthermore, the emotional value sharing unit can propose the optimal sharing method based on the user's travel destinations or places visited. In this way, by considering the user's geographical location information, it is possible to propose the optimal sharing method. Some or all of the above processing in the emotional value sharing unit may be performed using AI, for example, or without AI. For example, the emotional value sharing unit can input the user's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal sharing method.

[0151] The asset utilization unit can estimate the user's emotions and adjust the asset utilization method based on the estimated emotions. For example, if the user is relaxed, the asset utilization unit can suggest an asset utilization method that proceeds at a relaxed pace. If the user is in a hurry, the asset utilization unit can also suggest an asset utilization method that emphasizes the shortest route. Furthermore, if the user is excited, the asset utilization unit can suggest an asset utilization method that includes visually stimulating effects. In this way, by adjusting the asset utilization method based on the user's emotions, a more appropriate utilization method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the asset utilization unit may be performed using AI, for example, or not using AI. For example, the asset utilization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the asset utilization method.

[0152] The Asset Utilization Department can analyze a user's past asset management history and propose the optimal utilization method when utilizing assets. For example, the Asset Utilization Department can propose the optimal utilization method based on the asset management plans the user has used in the past. Furthermore, the Asset Utilization Department can propose risk-averse utilization methods based on the user's past asset management history. In addition, the Asset Utilization Department can analyze the user's past asset management history and propose the most efficient utilization method. Thus, by analyzing the user's past asset management history, the optimal utilization method can be proposed. Some or all of the above-described processes in the Asset Utilization Department may be performed using AI, for example, or without AI. For example, the Asset Utilization Department can input the user's past asset management history data into a generating AI and have the generating AI propose the optimal utilization method.

[0153] The asset utilization unit can estimate the user's emotions and determine the priority of asset utilization based on the estimated emotions. For example, if the user is stressed, the asset utilization unit will prioritize low-risk utilization methods. Conversely, if the user is excited, the asset utilization unit may prioritize high-risk utilization methods. Furthermore, if the user is calm, the asset utilization unit may prioritize long-term utilization methods. This allows for more appropriate prioritization by determining asset utilization priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 asset utilization unit may be performed using AI, or not. For example, the asset utilization unit can input user emotion data into a generative AI and have the generative AI determine the priority of asset utilization.

[0154] The asset utilization unit can propose the optimal utilization method when utilizing assets, taking into account the user's geographical location information. For example, the asset utilization unit can propose the optimal utilization method by considering the economic situation of the area where the user lives. It can also propose the optimal utilization method by considering the user's commute route or school route. Furthermore, the asset utilization unit can propose the optimal utilization method based on the user's travel destinations or places visited. In this way, by considering the user's geographical location information, it is possible to propose the optimal utilization method. Some or all of the above processing in the asset utilization unit may be performed using AI, for example, or without using AI. For example, the asset utilization unit can input the user's geographical location information data into a generating AI and have the generating AI execute a proposal for the optimal utilization method.

[0155] The inheritance distribution proposal unit can estimate the user's emotions and adjust the inheritance distribution proposal method based on the estimated user emotions. For example, if the user is nervous, the inheritance distribution proposal unit can provide a simple and highly visible proposal method. If the user is relaxed, the inheritance distribution proposal unit can also provide a proposal method that includes detailed information. Furthermore, if the user is in a hurry, the inheritance distribution proposal unit can provide a concise proposal method. In this way, by adjusting the inheritance distribution proposal method based on the user's emotions, a more appropriate proposal method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inheritance distribution proposal unit may be performed using AI or not using AI. For example, the inheritance distribution proposal unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the inheritance distribution proposal method.

[0156] The inheritance distribution proposal unit can analyze the user's past inheritance distribution history and propose the optimal distribution method when proposing an inheritance distribution. For example, the inheritance distribution proposal unit can propose the optimal distribution method based on the inheritance distribution methods the user has used in the past. The inheritance distribution proposal unit can also propose a method that avoids risk based on the user's past inheritance distribution history. Furthermore, the inheritance distribution proposal unit can analyze the user's past inheritance distribution history and propose the most efficient distribution method. In this way, the optimal distribution method can be proposed by analyzing the user's past inheritance distribution history. Some or all of the above processing in the inheritance distribution proposal unit may be performed using AI, for example, or without AI. For example, the inheritance distribution proposal unit can input the user's past inheritance distribution history data into a generating AI and have the generating AI execute a proposal for the optimal distribution method.

[0157] The inheritance distribution proposal unit can customize its proposal method when proposing inheritance distribution, taking into account the user's living situation and family structure. For example, the inheritance distribution proposal unit can consider the user's family structure and prioritize proposing inheritance distribution methods that are important to the family. It can also consider the user's living situation and prioritize proposing inheritance distribution methods that are needed on a daily basis. Furthermore, the inheritance distribution proposal unit can consider the user's hobbies and interests and prioritize proposing related inheritance distribution methods. In this way, the optimal proposal method can be customized by taking into account the user's living situation and family structure. Some or all of the above processing in the inheritance distribution proposal unit may be performed using AI, for example, or not using AI. For example, the inheritance distribution proposal unit can input the user's living situation and family structure data into a generating AI and have the generating AI perform the customization of the proposal method.

[0158] The inheritance distribution proposal unit can estimate the user's emotions and determine the priority of inheritance distribution based on the estimated emotions. For example, if the user is stressed, the inheritance distribution proposal unit will prioritize suggesting important inheritance distributions to alleviate their emotional burden. It can also prioritize suggesting detailed inheritance distributions if the user is relaxed. Furthermore, if the user is in a hurry, the inheritance distribution proposal unit can prioritize suggesting inheritance distributions that can be proposed quickly. This allows for more appropriate prioritization by determining inheritance distribution priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 inheritance distribution proposal unit may be performed using AI or not. For example, the inheritance distribution proposal unit can input user emotion data into a generative AI and have the generative AI determine the priority of inheritance distribution.

[0159] The inheritance distribution proposal unit can propose the optimal distribution method when proposing inheritance distribution, taking into account the user's geographical location information. For example, the inheritance distribution proposal unit can propose the optimal distribution method by considering the laws and regulations of the area where the user lives. It can also propose the optimal distribution method by considering the user's commute route or school route. Furthermore, the inheritance distribution proposal unit can propose the optimal distribution method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose the optimal distribution method. Some or all of the above processing in the inheritance distribution proposal unit may be performed using AI, for example, or without using AI. For example, the inheritance distribution proposal unit can input the user's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal distribution method.

[0160] The inheritance distribution proposal unit can analyze the user's social media activity and make relevant proposals when proposing inheritance distribution. For example, the inheritance distribution proposal unit can make relevant proposals based on the content and hashtags that the user frequently posts. It can also make relevant proposals based on the accounts and groups that the user follows. Furthermore, the inheritance distribution proposal unit can make relevant proposals based on the user's comment and like history. In this way, it can make relevant proposals by analyzing the user's social media activity. Some or all of the above processing in the inheritance distribution proposal unit may be performed using AI, for example, or not using AI. For example, the inheritance distribution proposal unit can input the user's social media data into a generating AI and have the generating AI execute relevant proposals.

[0161] The data organization unit can estimate the user's emotions and adjust the data organization method based on the estimated emotions. For example, if the user is stressed, the data organization unit can provide a simple and highly visual organization method. If the user is relaxed, the data organization unit can also provide an organization method that includes detailed information. Furthermore, if the user is in a hurry, the data organization unit can provide an organization method that gets straight to the point. In this way, by adjusting the data organization method based on the user's emotions, a more appropriate organization method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data organization unit may be performed using AI, for example, or not using AI. For example, the data organization unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the data organization method.

[0162] The data organization unit can analyze the user's past data organization history and propose the optimal organization method during data organization. For example, the data organization unit can propose the optimal organization method based on the data organization methods the user has used in the past. Furthermore, the data organization unit can propose organization methods that avoid risks based on the user's past data organization history. In addition, the data organization unit can analyze the user's past data organization history and propose the most efficient organization method. Thus, by analyzing the user's past data organization history, the optimal organization method can be proposed. Some or all of the above processing in the data organization unit may be performed using AI, for example, or without AI. For example, the data organization unit can input the user's past data organization history data into a generating AI and have the generating AI propose the optimal organization method.

[0163] The data organization unit can customize the organization method when organizing data, taking into account the user's lifestyle and family structure. For example, the data organization unit can prioritize organizing data that is important to the family, taking into account the user's family structure. It can also prioritize organizing data that the user uses on a daily basis, taking into account the user's lifestyle. Furthermore, the data organization unit can prioritize organizing data related to the user's hobbies and interests, taking into account the user's hobbies and interests. In this way, the optimal organization method can be customized by taking into account the user's lifestyle and family structure. Some or all of the above processing in the data organization unit may be performed using AI, for example, or not using AI. For example, the data organization unit can input the user's lifestyle and family structure data into a generating AI and have the generating AI perform the customization of the organization method.

[0164] The data sorting unit can estimate the user's emotions and determine the priority of data sorting based on the estimated emotions. For example, if the user is stressed, the data sorting unit will prioritize sorting important data to reduce emotional burden. If the user is relaxed, the data sorting unit can also prioritize sorting detailed data. Furthermore, if the user is in a hurry, the data sorting unit can prioritize sorting data that can be sorted quickly. This allows for more appropriate prioritization by determining data sorting priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 data sorting unit may be performed using AI or not. For example, the data sorting unit can input user emotion data into a generative AI and have the generative AI determine the priority of data sorting.

[0165] The data organization unit can propose the optimal organization method when organizing data, taking into account the user's geographical location information. For example, the data organization unit can propose the optimal organization method by considering the culture and customs of the area where the user lives. It can also propose the optimal organization method by considering the user's commute route or school route. Furthermore, the data organization unit can propose the optimal organization method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location information, it can propose the optimal organization method. Some or all of the above processing in the data organization unit may be performed using AI, for example, or without AI. For example, the data organization unit can input the user's geographical location information data into a generating AI and have the generating AI execute a proposal for the optimal organization method.

[0166] The data organization unit can analyze a user's social media activity and organize relevant data during data organization. For example, the data organization unit can organize relevant data based on the content and hashtags that the user frequently posts. It can also organize relevant data based on the accounts and groups that the user follows. Furthermore, the data organization unit can organize relevant data based on the user's comment and like history. In this way, relevant data can be organized by analyzing the user's social media activity. Some or all of the above processing in the data organization unit may be performed using AI, for example, or without AI. For example, the data organization unit can input the user's social media data into a generating AI and have the generating AI perform the organization of relevant data.

[0167] The Regional Collaboration Unit can estimate the user's emotions and adjust the method of regional collaboration based on the estimated emotions. For example, if the user is stressed, the Regional Collaboration Unit can provide a simple and highly visible collaboration method. If the user is relaxed, the Regional Collaboration Unit can also provide a collaboration method that includes detailed information. Furthermore, if the user is in a hurry, the Regional Collaboration Unit can provide a concise collaboration method. In this way, by adjusting the method of regional collaboration based on the user's emotions, a more appropriate collaboration method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Regional Collaboration Unit may be performed using AI, for example, or not using AI. For example, the Regional Collaboration Unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the regional collaboration method.

[0168] The Regional Collaboration Department can analyze a user's past regional collaboration history and propose the optimal collaboration method during regional collaboration. For example, the Regional Collaboration Department can propose the optimal collaboration method based on the regional collaboration methods the user has used in the past. Furthermore, the Regional Collaboration Department can propose collaboration methods that avoid risks based on the user's past regional collaboration history. In addition, the Regional Collaboration Department can analyze the user's past regional collaboration history and propose the most efficient collaboration method. Thus, by analyzing the user's past regional collaboration history, the optimal collaboration method can be proposed. Some or all of the above processing in the Regional Collaboration Department may be performed using AI, for example, or without AI. For example, the Regional Collaboration Department can input the user's past regional collaboration history data into a generating AI and have the generating AI propose the optimal collaboration method.

[0169] The Regional Collaboration Department can customize the collaboration method during regional collaboration, taking into account the user's living situation and family structure. For example, the Regional Collaboration Department can consider the user's family structure and prioritize suggesting regional collaboration methods that are important to the family. It can also consider the user's living situation and prioritize suggesting regional collaboration methods that are needed on a daily basis. Furthermore, the Regional Collaboration Department can consider the user's hobbies and interests and prioritize suggesting relevant regional collaboration methods. In this way, the optimal collaboration method can be customized by considering the user's living situation and family structure. Some or all of the above processing in the Regional Collaboration Department may be performed using AI, for example, or not. For example, the Regional Collaboration Department can input user living situation and family structure data into a generating AI and have the generating AI perform the customization of the collaboration method.

[0170] The Regional Collaboration Department can estimate the user's emotions and determine the priority of regional collaborations based on the estimated emotions. For example, if the user is stressed, the Regional Collaboration Department will prioritize suggesting important regional collaborations to alleviate their emotional burden. If the user is relaxed, the Regional Collaboration Department can also prioritize suggesting more detailed regional collaborations. Furthermore, if the user is in a hurry, the Regional Collaboration Department can prioritize suggesting regional collaborations that can be proposed quickly. This allows for more appropriate prioritization by determining the priority of regional collaborations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 Regional Collaboration Department may be performed using AI or not. For example, the Regional Collaboration Department can input user emotion data into a generative AI and have the generative AI determine the priority of regional collaborations.

[0171] The Regional Collaboration Department can propose the optimal collaboration method when collaborating with a local community, taking into account the user's geographical location. For example, the Regional Collaboration Department can propose the optimal collaboration method by considering the culture and customs of the area where the user lives. It can also propose the optimal collaboration method by considering the user's commute or school route. Furthermore, the Regional Collaboration Department can propose the optimal collaboration method based on the user's travel destinations and places visited. In this way, by considering the user's geographical location, it can propose the optimal collaboration method. Some or all of the above processing in the Regional Collaboration Department may be performed using AI, for example, or without AI. For example, the Regional Collaboration Department can input the user's geographical location data into a generating AI and have the generating AI propose the optimal collaboration method.

[0172] The Regional Collaboration Department can analyze a user's social media activity and propose relevant collaboration methods during regional collaboration. For example, the Regional Collaboration Department can propose relevant collaboration methods based on the content and hashtags that the user frequently posts. It can also propose relevant collaboration methods based on the accounts and groups that the user follows. Furthermore, the Regional Collaboration Department can propose relevant collaboration methods based on the user's comment and like history. In this way, relevant collaboration methods can be proposed by analyzing the user's social media activity. Some or all of the above processing in the Regional Collaboration Department may be performed using AI, for example, or not using AI. For example, the Regional Collaboration Department can input the user's social media data into a generating AI and have the generating AI propose relevant collaboration methods.

[0173] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0174] The integrated end-of-life planning AI agent system can also include a health management department. This department monitors the user's health status and provides advice for maintaining good health. For example, it analyzes the user's diet and exercise records and proposes balanced meal plans and exercise programs. It can also suggest necessary medical tests and treatments based on the user's regular health checkup results. Furthermore, it can monitor the user's stress levels and suggest relaxation methods and stress-relieving activities. This allows for comprehensive management of the user's health status and support for maintaining good health.

[0175] The integrated end-of-life planning AI agent system can also be equipped with a hobby recommendation function. This function analyzes the user's interests and past activity history to suggest new hobbies and activities. For example, it can suggest related new hobbies based on activities and events the user has enjoyed in the past. It can also analyze the user's social media posts and followed accounts to suggest activities that might interest them. Furthermore, it can suggest events and workshops held in the user's local area, helping the user discover new hobbies. This provides new enjoyment to the user's life, allowing them to spend their time more fulfilling.

[0176] The integrated end-of-life planning AI agent system can also be equipped with an emotion monitoring unit. This unit monitors the user's emotional state in real time and provides appropriate support. For example, it analyzes the user's voice and facial expressions and suggests relaxation methods if the user is experiencing stress or anxiety. It can also suggest activities to further enhance positive emotions if the user is feeling joy or excitement. Furthermore, the unit can provide messages of gratitude and encouragement at appropriate times, depending on the user's emotional state. This comprehensively supports the user's emotional state and helps maintain their mental well-being.

[0177] The integrated end-of-life planning AI agent system can also include a community liaison unit. This unit helps users participate in local community activities. For example, it can suggest local volunteer activities and club activities based on the user's interests and skills. It can also provide information for users to participate in local events and gatherings. Furthermore, it can help users connect with local professionals and services to receive necessary support. This allows users to deepen their connections with their local community and prevent isolation.

[0178] The integrated end-of-life planning AI agent system can also be equipped with an emotional feedback unit. This unit adjusts the system's responses and suggestions based on the user's emotional state. For example, if the user is stressed, the emotional feedback unit can provide relaxing music or videos. If the user is agitated, it can suggest energetic activities. Furthermore, if the user is calm, it can suggest long-term goals and plans. This allows for appropriate support tailored to the user's emotional state, increasing user satisfaction.

[0179] The integrated end-of-life planning AI agent system can also be equipped with a learning support unit. This unit helps users acquire new skills and knowledge. For example, it can suggest online courses and workshops based on the user's interests and goals. It can also monitor the user's learning progress and provide appropriate feedback and advice. Furthermore, it can suggest projects and activities to help users put what they have learned into practice. This allows users to effectively acquire new skills and knowledge and promote their personal growth.

[0180] The integrated end-of-life AI agent system can also be equipped with an emotion-sharing function. This function helps users share their emotions with family and friends. For example, it suggests messages and photos to convey moments that moved the user or events that brought them joy to family and friends. It can also help users send encouraging messages to family and friends when they are facing difficult situations. Furthermore, based on the user's emotional state, it can suggest words of gratitude and messages of compassion at the appropriate time. This allows users to share their emotions with family and friends and deepen their bonds.

[0181] The integrated end-of-life planning AI agent system can also include a travel planning section. This section proposes optimal travel plans based on the user's interests and preferences. For example, it analyzes the user's past travel history and interests to suggest new destinations and activities. It can also customize travel plans based on the user's budget and schedule. Furthermore, it can provide advice and support to ensure the user's safety and comfort during their trip. This allows the user to enjoy a fulfilling travel experience.

[0182] The integrated end-of-life planning AI agent system can also be equipped with an emotion analysis unit. This unit meticulously analyzes the user's emotional data to understand emotional changes and patterns. For example, it analyzes the user's voice, facial expressions, and text data to monitor emotional changes in real time. Furthermore, it can identify emotional patterns and trends based on the user's past emotional data. In addition, the emotion analysis unit can provide appropriate support and advice according to the user's emotional state. This allows for a comprehensive understanding of the user's emotional state and the provision of appropriate support.

[0183] The integrated end-of-life planning AI agent system can also include a hobby sharing section. This section helps users share their hobbies and interests with family and friends. For example, it suggests messages and photos to introduce the user's hobbies and activities to family and friends. It can also suggest activities for users to enjoy with family and friends when starting a new hobby. Furthermore, it can help users share events and workshops related to their hobbies with family and friends. This allows users to share their hobbies and interests with family and friends and find common ground.

[0184] The following briefly describes the processing flow for example form 2.

[0185] Step 1: The analytics department analyzes the user's values ​​and interests. For example, they identify values ​​and interests based on the user's past behavior history, survey results, social media posts and followed accounts, purchase history, and browsing history. Step 2: The Roadmap Creation Department creates a roadmap for achieving the goal based on the information analyzed by the Analysis Department. For example, it will specifically outline the steps for the user to achieve their goal, provide reminders according to their progress, and propose an optimal plan that takes into account their lifestyle and schedule. Step 3: The Asset Management Proposal Department proposes asset management plans based on the roadmap created by the Roadmap Creation Department. For example, they analyze the user's asset situation using data and propose investment plans to ensure a stable life in retirement, plans to utilize surplus assets for donations or investments, and optimal investment strategies based on risk management and return targets. Step 4: The Digital Estate Management Department organizes the digital estate based on the plan proposed by the Asset Management Proposal Department. For example, they propose organizing and deleting the user's digital data in real time, provide support for properly handing it over to family members, and suggest methods for secure storage. Step 5: The Legal Procedure Support Department assists with legal procedures based on the information organized by the Digital Estate Management Department. For example, it assists with estate division and account deletion, reduces the user's burden by proposing estate distribution, generating legal documents, and coordinating with lawyers and experts. Step 6: The Memory Digitization Department digitizes memories based on information supported by the Legal Procedure Support Department. For example, it looks back on the user's life events, preserves memories as digital albums and memorial videos, generates tools to share emotional value with family and convey messages to future generations, and proposes the optimal method.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the plurality of elements including the above-described analysis unit, roadmap creation unit, asset management proposal unit, digital legacy arrangement unit, legal procedure support unit, memory digitization unit, reminder provision unit, emotional value sharing unit, asset utilization unit, inheritance distribution proposal unit, data arrangement unit, and regional cooperation unit is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and analyzes the user's values and interests. The roadmap creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a roadmap for goal achievement. The asset management proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an asset management plan. The digital legacy arrangement unit is realized by the control unit 46A of the smart device 14 and arranges digital legacy. The legal procedure support unit is realized by the specific processing unit 290 of the data processing device 12 and supports legal procedures. The memory digitization unit is realized by the control unit 46A of the smart device 14 and digitizes memories. The reminder provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides reminders according to progress. The emotional value sharing unit is realized by the control unit 46A of the smart device 14 and shares emotional value with family members. The asset utilization unit is realized by the specific processing unit 290 of the data processing device 12 and utilizes surplus assets for donations and investments. The inheritance distribution proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an inheritance distribution plan. The data arrangement unit is realized by the control unit 46A of the smart device 14 and proposes to arrange and delete data in real time. The regional cooperation unit is realized by the specific processing unit 290 of the data processing device 12 and cooperates with local experts. The correspondence between each unit and the device or control unit is not limited to the above-described examples, and various changes are possible.

[0190] [Second Embodiment] FIG. 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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).

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] Each of the plurality of elements including the above-described analysis unit, roadmap creation unit, asset management proposal unit, digital legacy arrangement unit, legal procedure support unit, memory digitization unit, reminder provision unit, emotional value sharing unit, asset utilization unit, inheritance distribution proposal unit, data arrangement unit, and regional cooperation unit is realized by at least one of, for example, the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and analyzes the user's values and interests. The roadmap creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a roadmap for achieving the goal. The asset management proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an asset management plan. The digital legacy arrangement unit is realized by the control unit 46A of the smart glasses 214 and arranges digital legacies. The legal procedure support unit is realized by the specific processing unit 290 of the data processing device 12 and supports legal procedures. The memory digitization unit is realized by the control unit 46A of the smart glasses 214 and digitizes memories. The reminder provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides reminders according to the progress. The emotional value sharing unit is realized by the control unit 46A of the smart glasses 214 and shares emotional values with family members. The asset utilization unit is realized by the specific processing unit 290 of the data processing device 12 and utilizes surplus assets for donations and investments. The inheritance distribution proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an inheritance distribution plan. The data arrangement unit is realized by the control unit 46A of the smart glasses 214 and proposes to arrange and delete data in real time. The regional cooperation unit is realized by the specific processing unit 290 of the data processing device 12 and cooperates with local experts. The correspondence relationship between each unit and the device or the control unit is not limited to the above-described example, and various changes are possible.

[0206] [Third Embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] Each of the plurality of elements including the above-described analysis unit, roadmap creation unit, asset management proposal unit, digital legacy arrangement unit, legal procedure support unit, memory digitization unit, reminder provision unit, emotional value sharing unit, asset utilization unit, inheritance distribution proposal unit, data arrangement unit, and regional cooperation unit is realized by, for example, at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset-type terminal 314 and analyzes the user's values and interests. The roadmap creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a roadmap for goal achievement. The asset management proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an asset management plan. The digital legacy arrangement unit is realized by the control unit 46A of the headset-type terminal 314 and arranges digital legacies. The legal procedure support unit is realized by the specific processing unit 290 of the data processing device 12 and supports legal procedures. The memory digitization unit is realized by the control unit 46A of the headset-type terminal 314 and digitizes memories. The reminder provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides reminders according to progress. The emotional value sharing unit is realized by the control unit 46A of the headset-type terminal 314 and shares emotional values with family members. The asset utilization unit is realized by the specific processing unit 290 of the data processing device 12 and utilizes surplus assets for donations and investments. The inheritance distribution proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes inheritance distribution. The data arrangement unit is realized by the control unit 46A of the headset-type terminal 314 and proposes to arrange and delete data in real time. The regional cooperation unit is realized by the specific processing unit 290 of the data processing device 12 and cooperates with local experts. The correspondence relationship between each unit and the device or control unit is not limited to the above-described examples, and various modifications are possible.

[0222] [Fourth Embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] Each of the plurality of elements including the above-described analysis unit, roadmap creation unit, asset management proposal unit, digital legacy arrangement unit, legal procedure support unit, memory digitization unit, reminder provision unit, emotional value sharing unit, asset utilization unit, inheritance distribution proposal unit, data arrangement unit, and regional cooperation unit is realized by, for example, at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and analyzes the user's values and interests. The roadmap creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a roadmap for goal achievement. The asset management proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an asset management plan. The digital legacy arrangement unit is realized by the control unit 46A of the robot 414 and arranges digital legacies. The legal procedure support unit is realized by the specific processing unit 290 of the data processing device 12 and supports legal procedures. The memory digitization unit is realized by the control unit 46A of the robot 414 and digitizes memories. The reminder provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides reminders according to progress. The emotional value sharing unit is realized by the control unit 46A of the robot 414 and shares emotional values with family members. The asset utilization unit is realized by the specific processing unit 290 of the data processing device 12 and utilizes surplus assets for donations and investments. The inheritance distribution proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes inheritance distribution. The data arrangement unit is realized by the control unit 46A of the robot 414 and proposes to arrange and delete data in real time. The regional cooperation unit is realized by the specific processing unit 290 of the data processing device 12 and cooperates with local experts. The correspondence relationship between each unit and the device or control unit is not limited to the above-described example, and various changes are possible.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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."

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] (Note 1) The analytics department analyzes users' values ​​and interests, A roadmap creation unit creates a roadmap for achieving the goal based on the information analyzed by the aforementioned analysis unit, The Asset Management Proposal Unit proposes an asset management plan based on the roadmap created by the aforementioned Roadmap Creation Unit, The Digital Estate Management Department organizes digital assets based on the plan proposed by the aforementioned Asset Management Proposal Department, Based on the information organized by the aforementioned Digital Estate Management Department, the Legal Procedure Support Department provides support for legal procedures, The system includes a memory digitization unit that digitizes memories based on information supported by the aforementioned legal procedure support unit. A system characterized by the following features. (Note 2) It includes a reminder service that provides reminders based on progress. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an emotional value sharing section for sharing emotional values ​​with family members. The system described in Appendix 1, characterized by the features described herein. (Note 4) The company has an asset utilization department that uses surplus assets for donations and investments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The company has a department dedicated to proposing the distribution of inherited assets. The system described in Appendix 1, characterized by the features described herein. (Note 6) It features a data organization unit that organizes and suggests data deletion in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) It has a community liaison department that works in cooperation with local experts. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis methods for values ​​and interests based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is By analyzing users' past behavioral history, we predict changes in their values ​​and interests. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is Analyze the user's values ​​and interests by considering their living environment and social background. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and determines the priority of values ​​and interests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Analyzes values ​​and interests by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is Analyze users' social media activity to identify their values ​​and interests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned roadmap creation unit, We estimate user sentiment and adjust how the roadmap is presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned roadmap creation unit, When creating a roadmap, the progress of users towards achieving their goals should be reflected in real time. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned roadmap creation unit, When creating a roadmap, we propose the optimal plan by taking into account the user's lifestyle and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 17) The roadmap creation unit estimates the user's sentiment and determines the priority order of the roadmap based on the estimated user sentiment. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 18) The roadmap creation unit proposes an optimal plan considering the user's geographical location information when creating the roadmap. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 19) The roadmap creation unit analyzes the user's social media activities and proposes relevant goals when creating the roadmap. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 20) The asset management plan proposal unit estimates the user's sentiment and adjusts the presentation method of the asset management plan based on the estimated user sentiment. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 21) The asset management plan proposal unit analyzes the user's past asset management history and proposes an optimal plan when proposing the asset management plan. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 22) The asset management plan proposal unit customizes the plan considering the user's living situation and future goals when proposing the asset management plan. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 23) The asset management plan proposal unit estimates the user's sentiment and determines the priority order of the asset management plan based on the estimated user sentiment. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 24) The asset management plan proposal unit proposes an optimal plan considering the user's geographical location information when proposing the asset management plan. The system according to appended note 1, characterized in that... (Appended note 25) The asset management proposal department Analyzes the user's social media activities and proposes relevant plans when making asset management proposals The system according to appended note 1, characterized in that... (Appended note 26) The digital legacy arrangement department Estimates the user's emotions and adjusts the method of digital legacy arrangement based on the estimated user emotions The system according to appended note 1, characterized in that... (Appended note 27) The digital legacy arrangement department Analyzes the user's past digital data and proposes an optimal arrangement method when arranging digital legacy The system according to appended note 1, characterized in that... (Appended note 28) The digital legacy arrangement department Considers the user's living situation and family composition and customizes the arrangement method when arranging digital legacy The system according to appended note 1, characterized in that... (Appended note 29) The digital legacy arrangement department Estimates the user's emotions and determines the priority of digital legacy arrangement based on the estimated user emotions The system according to appended note 1, characterized in that... (Appended note 30) The digital legacy arrangement department Considers the user's geographical location information and proposes an optimal arrangement method when arranging digital legacy The system according to appended note 1, characterized in that... (Appended note 31) The digital legacy arrangement department Analyzes the user's social media activities and arranges relevant data when arranging digital legacy The system according to appended note 1, characterized in that... (Appended note 32) The legal procedure support department Estimate the user's emotions and adjust the legal procedure support method based on the estimated user emotions The system according to appended note 1, characterized in that it does so (Appended note 33) The legal procedure support unit Analyzes the user's past legal procedure history during legal procedure support and proposes an optimal support method The system according to appended note 1, characterized in that it does so (Appended note 34) The legal procedure support unit Customizes the support method considering the user's living situation and family composition during legal procedure support The system according to appended note 1, characterized in that it does so (Appended note 35) The legal procedure support unit Estimate the user's emotions and determine the priority of legal procedures based on the estimated user emotions The system according to appended note 1, characterized in that it does so (Appended note 36)[[ID=X]] The legal procedure support unit Proposes an optimal support method considering the user's geographical location information during legal procedure support The system according to appended note 1, characterized in that it does so [[ID=Y]]The system according to appended note 1, characterized in that it does so The legal procedure support unit Analyzes the user's social media activities during legal procedure support and supports related procedures The system according to appended note 1, characterized in that it does so (Appended note 38) The memory digitization unit Estimate the user's emotions and adjust the memory digitization method based on the estimated user emotions The system according to appended note 1, characterized in that it does so (Appended note 39) The memory digitization unit Analyzes the user's past life events during memory digitization and proposes an optimal digitization method Note: There seems to be a formatting issue in the original text where the " " and " " sections are not properly separated in terms of the description structure. I've tried to make sense of it as best as possible in the translation. Also, the "[[ID=Y]]" seems to be an incorrect tag in the original, but I've translated the text as presented.The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned memory digitization unit is When digitizing memories, the digitization method is customized to take into account the user's living situation and family structure. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned memory digitization unit is It estimates the user's emotions and determines the priority of digitizing memories based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned memory digitization unit is When digitizing memories, we propose the optimal digitization method while taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned memory digitization unit is When digitizing memories, the system analyzes the user's social media activity and digitizes related memories. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned reminder provision unit is: It estimates the user's emotions and adjusts how reminders are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned reminder provision unit is: When providing reminders, the system analyzes the user's past behavior history to deliver the most appropriate reminders. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned reminder provision unit is: It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned reminder provision unit is: When providing reminders, the system will take into account the user's geographical location to deliver the most appropriate reminder. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned emotional value sharing unit is It estimates the user's emotions and adjusts how emotional value is shared based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned emotional value sharing unit is When sharing emotional values, the system analyzes the user's past emotional history and suggests the optimal sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 50) The aforementioned emotional value sharing unit is It estimates the user's emotions and determines the priority of emotional values ​​based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 51) The aforementioned emotional value sharing unit is When sharing emotional values, we propose the optimal sharing method while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned asset utilization unit is: It estimates user emotions and adjusts how assets are used based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned asset utilization unit is: When utilizing assets, we analyze the user's past asset management history and propose the optimal utilization method. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned asset utilization unit is: It estimates user emotions and determines asset utilization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned asset utilization unit is: When utilizing assets, we propose the optimal utilization method while taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 56) The aforementioned inheritance distribution proposal department, It estimates the user's emotions and adjusts the proposed inheritance distribution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 57) The aforementioned inheritance distribution proposal department, When proposing inheritance distribution, we analyze the user's past inheritance distribution history and propose the most suitable method. The system described in Appendix 1, characterized by the features described herein. (Note 58) The aforementioned inheritance distribution proposal department, When proposing inheritance distribution, the proposal method is customized to take into account the user's living situation and family structure. The system described in Appendix 1, characterized by the features described herein. (Note 59) The aforementioned inheritance distribution proposal department, It estimates the user's emotions and determines the priority of inheritance distribution based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 60) The aforementioned inheritance distribution proposal department, When proposing inheritance distribution, we take the user's geographical location into consideration to suggest the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 61) The aforementioned inheritance distribution proposal department, When proposing inheritance distribution, we analyze users' social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 62) The aforementioned data processing unit, We estimate user emotions and adjust the data processing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 63) The aforementioned data processing unit, When organizing data, we analyze the user's past data organization history and propose the optimal organization method. The system described in Appendix 1, characterized by the features described herein. (Note 64) The aforementioned data processing unit, When organizing data, the organization method is customized to take into account the user's lifestyle and family structure. The system described in Appendix 1, characterized by the features described herein. (Note 65) The aforementioned data processing unit, We estimate user emotions and determine data organization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 66) The aforementioned data processing unit, When organizing data, we propose the optimal organization method while considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 67) The aforementioned data processing unit, When organizing data, analyze users' social media activity and organize relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 68) The aforementioned Regional Cooperation Department It estimates user sentiment and adjusts the method of regional collaboration based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 69) The aforementioned Regional Cooperation Department When collaborating with a local community, the system analyzes the user's past community collaboration history and proposes the optimal collaboration method. The system described in Appendix 1, characterized by the features described herein. (Note 70) The aforementioned Regional Cooperation Department When collaborating with local communities, the collaboration method is customized to take into account the user's living situation and family structure. The system described in Appendix 1, characterized by the features described herein. (Note 71) The aforementioned Regional Cooperation Department It estimates user sentiment and determines the priority of regional collaborations based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 72) The aforementioned Regional Cooperation Department When collaborating with a regional network, we propose the optimal collaboration method while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 73) The aforementioned Regional Cooperation Department When collaborating with local communities, we analyze users' social media activity and propose relevant collaboration methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0258] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analytics department analyzes users' values ​​and interests, A roadmap creation unit creates a roadmap for achieving the goal based on the information analyzed by the aforementioned analysis unit, The Asset Management Proposal Unit proposes an asset management plan based on the roadmap created by the aforementioned Roadmap Creation Unit, The Digital Estate Management Department organizes digital assets based on the plan proposed by the aforementioned Asset Management Proposal Department, Based on the information organized by the aforementioned Digital Estate Management Department, the Legal Procedure Support Department provides support for legal procedures, The system includes a memory digitization unit that digitizes memories based on information supported by the aforementioned legal procedure support unit. A system characterized by the following features.

2. It includes a reminder service that provides reminders based on progress. The system according to feature 1.

3. It includes an emotional value sharing section for sharing emotional values ​​with family members. The system according to feature 1.

4. The company has an asset utilization department that uses surplus assets for donations and investments. The system according to feature 1.

5. The company has a department dedicated to proposing the distribution of inherited assets. The system according to feature 1.

6. It has a data organization department that organizes and suggests data deletion in real time. The system according to feature 1.

7. It has a community liaison department that works in cooperation with local experts. The system according to feature 1.

8. The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis methods for values ​​and interests based on the estimated user emotions. The system according to feature 1.

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