system

Abstract

The system according to this embodiment aims to comprehensively support users in organizing their affairs before death. [Solution] The system according to the embodiment comprises a sales unit, a sales property management unit, a property management unit, a document management unit, a data management unit, and a message transmission unit. The sales unit organizes the user's items. The sales property management unit manages information on items sold by the sales unit. The property management unit organizes and manages the user's assets. The document management unit organizes and manages the user's documents. The data management unit organizes and manages the user's data and accounts. The message transmission unit sends messages based on the user's relationships.

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, a system that comprehensively supports a user's pre-mortem arrangement has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively support a user's pre-mortem arrangement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sales unit, a sales property management unit, an asset management unit, a document management unit, a data management unit, and a message transmission unit. The sales unit organizes the user's items. The sales property management unit manages information on items sold by the sales unit. The asset management unit organizes and manages the user's assets. The document management unit organizes and manages the user's documents. The data management unit organizes and manages the user's data and accounts. The message transmission unit sends messages based on the user's relationships. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively support the user's pre-death arrangements. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The pre-death planning support system according to an embodiment of the present invention is a system that efficiently supports families in planning their affairs before death. This pre-death planning support system can organize and manage the user's belongings, assets, documents, data, and relationships. For example, the pre-death planning support system includes a sales unit that organizes the user's belongings, such as everyday items, furniture, clothes, and hobby equipment, and allows the user to discard or give away unwanted items. For example, unwanted items can be sold using an e-commerce site. Next, there is a sales property management unit that manages information on items sold by the sales unit, centrally managing information on sold items. Next, there is an asset management unit that organizes and manages the user's assets, such as bank accounts, real estate, stocks, and insurance. It also provides support considering cases such as dementia, and supports the creation of wills using a generation AI as needed. For example, a will can be created using a generation AI. Furthermore, there is a document management unit that organizes and manages the user's documents, such as insurance policies, pension-related documents, and important contracts, and supports where to store them. It also provides organization support including data stored on electronic devices such as personal computers and smartphones. Next, there is a data management section that organizes and manages user data and accounts, supporting the management of online accounts, social media accounts, email addresses, and data stored in the cloud. It helps to consolidate necessary information and provide access methods to trusted individuals. Furthermore, there is a messaging section that allows users to send messages based on their relationships, enabling them to express gratitude to people they haven't seen in a long time or to those they have lingering feelings for. For example, they can send thank-you messages using messaging apps. Finally, as a way of organizing one's life, organizing belongings and assets according to one's own will can lead to emotional closure and reduce the burden on surviving family members. In this way, the pre-death planning support system efficiently supports families in organizing their belongings, assets, documents, data, and relationships.

[0029] The pre-death arrangement support system according to this embodiment comprises a sales unit, a sales property management unit, an asset management unit, a document management unit, a data management unit, and a message transmission unit. The sales unit organizes the user's belongings. For example, the sales unit organizes everyday items, furniture, clothes, hobby equipment, etc., and allows the user to discard or give away unwanted items. For example, the sales unit can sell unwanted items using e-commerce sites. The sales unit can also analyze the frequency of use and condition of items to set the optimal selling price. For example, the sales unit can use AI to analyze the frequency of use of items and sell frequently used items at a high price. The sales unit can also use AI to evaluate the condition of items and sell items in good condition at a high price. The sales unit can also use AI to analyze the usage history of items and sell infrequently used items at a low price. The sales property management unit manages information on items sold by the sales unit. For example, the sales property management unit centrally manages information on sold items. The sales property management unit can also analyze the sales history of items and select the optimal management method. For example, the Property Sales Management Department uses AI to analyze the sales history of items and select the optimal management method. The Property Sales Management Department can also use AI to propose efficient management methods based on past sales data. The Property Sales Management Department can also use AI to analyze the sales history of items and optimize management methods. The Asset Management Department organizes and manages the user's assets. The Asset Management Department organizes assets such as bank accounts, real estate, stocks, and insurance. The Asset Management Department also provides support considering situations such as dementia, and supports the creation of wills using AI generation as needed. For example, the Asset Management Department can create wills using AI generation. The Asset Management Department can also perform asset risk assessments and select the optimal management method. For example, the Asset Management Department uses AI to perform asset risk assessments and select the optimal management method. The Asset Management Department can also use AI to perform risk assessments based on past asset data. The Asset Management Department can also use AI to analyze market data and perform asset risk assessments. The Document Management Department organizes and manages the user's documents. The document management department helps organize and store documents such as insurance policies, pension-related documents, and important contracts.The Document Management Department provides support for organizing data, including data stored on electronic devices such as PCs and smartphones. The Document Management Department can also digitize documents and achieve efficient management. For example, the Document Management Department can use AI to scan and digitize documents. The Document Management Department can also use AI to analyze document content and save it as digital data. The Document Management Department can also use AI to digitize documents and provide search functionality. The Data Management Department organizes and manages user data and accounts. For example, the Data Management Department supports the management of online accounts, social media accounts, email addresses, and data stored in the cloud. The Data Management Department helps users consolidate necessary information and provide access instructions to trusted individuals. The Data Management Department can also automatically back up data to ensure data security. For example, the Data Management Department can use AI to automatically back up data and ensure data security. The Data Management Department can also use AI to set data backup schedules and perform backups automatically. The Data Management Department can also use AI to back up data and distribute it across multiple storage locations. The Message Sending Department sends messages based on the user's relationships. The message sending unit can, for example, convey feelings of gratitude to people you haven't seen in a long time or to people you have lingering regrets about. The message sending unit can send thank-you messages using a messaging app. The message sending unit can also create the most appropriate message considering the relationship with the recipient. For example, the message sending unit's AI can analyze the relationship with the recipient and suggest appropriate message content. The message sending unit's AI can also create the most suitable message for the recipient based on past message history. The message sending unit's AI can also analyze the recipient's profile and suggest a message appropriate to the relationship. As a result, the pre-death arrangement support system according to this embodiment can efficiently organize and manage the user's belongings, assets, documents, data, and relationships.

[0030] The selling department helps users organize their belongings. For example, it can organize everyday items, furniture, clothing, and hobby equipment, allowing users to discard or give away unwanted items. The selling department can also sell unwanted items using e-commerce sites. Furthermore, the selling department can analyze the frequency of use and condition of items to set optimal selling prices. For example, the selling department can use AI to analyze the frequency of use of items and sell frequently used items at a higher price. The selling department can also use AI to evaluate the condition of items and sell items in good condition at a higher price. The selling department can also use AI to analyze the usage history of items and sell infrequently used items at a lower price. The selling department creates a detailed list of the user's belongings and registers photos and descriptions of each item in a database. This allows users to see their belongings at a glance and easily provide the necessary information when selling. In addition, the selling department collects data from online marketplaces and uses AI to analyze it in order to understand the market value of items in real time. This allows the selling department to suggest the optimal timing for selling items. For example, seasonal or trendy items can be sold at a higher price by selling them during periods of high demand. Furthermore, the sales department proposes multiple selling methods for items the user wishes to sell. For instance, users can choose the most suitable method based on their needs, such as selling through an auction, using an instant buyback service, or selling at a local flea market. This allows the sales department to efficiently and effectively organize and sell the user's items.

[0031] The Sales Management Department manages information on items sold by the Sales Department. For example, the Sales Management Department centrally manages information on sold items. The Sales Management Department can also analyze the sales history of items to select the optimal management method. For example, the Sales Management Department can use AI to analyze the sales history of items and select the optimal management method. The Sales Management Department can also use AI to suggest efficient management methods based on past sales data. The Sales Management Department can also use AI to analyze the sales history of items and optimize management methods. The Sales Management Department builds a detailed database of sold items, recording information such as the sale price, sale date, and buyer for each item. This allows users to easily refer to information on items they have sold in the past and use it to help with future sales activities. Furthermore, the Sales Management Department analyzes users' sales trends and preferences based on the data of sold items. This allows the Sales Management Department to provide users with more personalized sales suggestions. For example, users who frequently sell items in a specific category can be provided with sales information and market trends related to that category. Furthermore, the Sales Property Management Department shares data on sold items with other departments to improve the overall efficiency of the system. For example, it collaborates with the Asset Management Department and the Document Management Department to support the organization of assets and documents related to sold items. This allows the Sales Property Management Department to comprehensively support users' item selling activities and achieve efficient management.

[0032] The Asset Management Department organizes and manages users' assets. For example, it organizes assets such as bank accounts, real estate, stocks, and insurance. The Asset Management Department also provides support, taking into account situations like dementia, and assists with will creation using AI generation as needed. For example, the Asset Management Department can create wills using AI generation. The Asset Management Department can also perform asset risk assessments and select the optimal management method. For example, the Asset Management Department's AI can perform asset risk assessments and select the optimal management method. The Asset Management Department's AI can also perform risk assessments based on past asset data. The Asset Management Department's AI can also analyze market data to perform asset risk assessments. The Asset Management Department provides a digital platform for centralized management of users' asset information, allowing users to understand their asset status in real time. This enables users to efficiently manage their assets and respond quickly when needed. Furthermore, the Asset Management Department supports legal procedures related to users' assets. For example, it provides support for will creation and inheritance procedures, allowing users to manage their assets with peace of mind. Furthermore, the Asset Management Department provides advice to minimize risks related to users' assets. For example, it makes specific suggestions to protect users' assets, such as evaluating investment risks and reviewing insurance policies. In this way, the Asset Management Department can comprehensively manage users' assets and provide optimal management methods while minimizing risks.

[0033] The Document Management Department organizes and manages users' documents. For example, it helps users organize and store documents such as insurance policies, pension documents, and important contracts. It also provides support for organizing data stored on electronic devices such as PCs and smartphones. The Document Management Department can also digitize documents to achieve efficient management. For example, it can use AI to scan and digitize documents. It can also use AI to analyze document content and save it as digital data. Furthermore, it can use AI to digitize documents and provide search functionality. The Document Management Department provides a digital archiving system for efficient document management, allowing users to quickly search and access necessary documents. This significantly reduces the risk of document loss and the effort required for management. In addition, the Document Management Department provides advice on optimizing document storage locations. For example, it recommends storing important documents in fireproof and waterproof locations and suggests storing documents used daily in easily accessible locations. The Document Management Department also strengthens security measures for users' documents. For example, encryption technology can be applied to digitized documents to prevent unauthorized access and information leaks. This allows the document management department to manage users' documents securely and efficiently, providing an environment where they can be quickly accessed when needed.

[0034] The Data Management Department organizes and manages user data and accounts. For example, it supports the management of online accounts, social media accounts, email addresses, and data stored in the cloud. The Data Management Department helps users consolidate necessary information and provide access instructions to trusted individuals. The Data Management Department can also ensure data security by automatically backing up data. For example, the Data Management Department can use AI to automatically back up data and ensure its security. The Data Management Department can also use AI to set backup schedules and perform backups automatically. The Data Management Department can also use AI to back up data and distribute it across multiple storage locations. The Data Management Department provides a platform for centralized management of users' digital assets, enabling users to efficiently manage their data. This makes it easy for users to organize their online accounts and digital data. Furthermore, the Data Management Department strengthens security measures for user data. For example, it provides password management tools, allowing users to generate and manage secure passwords. The Data Management Department also supports user data privacy settings and can delete data or restrict access as needed. This allows the data management department to securely and efficiently manage users' digital assets and provide an environment where they can be quickly accessed when needed.

[0035] The message sending function sends messages based on the user's relationships. For example, it can be used to express gratitude to someone you haven't seen in a long time or to someone you have lingering feelings for. The message sending function can send thank-you messages using messaging apps. It can also create optimal messages considering the recipient's relationship. For example, the AI ​​analyzes the recipient's relationship and suggests appropriate message content. The AI ​​can also create the best message for the recipient based on past message history. The AI ​​can analyze the recipient's profile and suggest a message appropriate to the relationship. The message sending function provides an interface for users to input the content of the message they want to send, making it easy for users to create messages. This allows users to easily convey feelings of gratitude and memories. Furthermore, the message sending function monitors the recipient's response and provides feedback to the user. For example, it notifies the user whether the recipient received the message and what their response was. The message sending function also optimizes the timing and content of messages based on the recipient's relationship and past interactions. This allows the message sending function to smooth user relationships and effectively convey feelings of gratitude.

[0036] The sales department can sell unwanted items using e-commerce sites. The sales department can, for example, sell unwanted items using e-commerce sites. The sales department can also, for example, sell unwanted items using online marketplaces. The sales department can also, for example, sell unwanted items using auction sites. This allows for the efficient sale of unwanted items. Some or all of the above processes in the sales department may be performed using AI, for example, or not using AI. For example, the sales department can input information about the items into e-commerce sites and have AI perform the setting of the selling price and the selling procedures.

[0037] The Asset Management Department can create wills using a generative AI. The Asset Management Department can, for example, create wills using a generative AI. The Asset Management Department can also, for example, automatically generate the content of wills using a generative AI. The Asset Management Department can also, for example, optimize the format and content of wills using a generative AI. This allows for the efficient creation of wills. Some or all of the above processes in the Asset Management Department may be performed using AI, for example, or without AI. For example, the Asset Management Department can input the user's asset information into a generative AI and have the generative AI automatically generate the content of a will.

[0038] The document management department can organize insurance certificates or pension-related documents. For example, the document management department can organize insurance certificates. The document management department can also organize pension-related documents. For example, the document management department can organize insurance certificates and pension-related documents together. This allows for the efficient organization of important documents. Some or all of the above processes in the document management department may be performed using AI, for example, or not using AI. For example, the document management department can scan insurance certificates and pension-related documents and save them as digital data.

[0039] The data management unit can organize data stored on electronic devices. For example, the data management unit can organize data stored on a personal computer. The data management unit can also organize data stored on a smartphone. The data management unit can also organize data stored on a tablet. This allows for efficient organization of data stored on electronic devices. Some or all of the above-described processes in the data management unit may be performed using AI, for example, or without AI. For example, the data management unit can have AI analyze data stored on electronic devices and automatically delete unnecessary data.

[0040] The data management department can manage online accounts or social media accounts. For example, the data management department can manage online accounts. The data management department can also manage social media accounts. The data management department can also manage email accounts. This allows for efficient management of online accounts and social media accounts. Some or all of the above processes in the data management department may be performed using AI, for example, or not. For example, the data management department can input information about online accounts and social media accounts into an AI and have the AI ​​manage the accounts.

[0041] The sales unit can analyze the frequency of use and condition of items to set an appropriate selling price. For example, the sales unit can use AI to analyze the frequency of use of items and sell frequently used items at a higher price. For example, the sales unit can use AI to evaluate the condition of items and sell items in good condition at a higher price. For example, the sales unit can use AI to analyze the usage history of items and sell infrequently used items at a lower price. This allows for setting an optimal selling price based on the frequency of use and condition of items. Some or all of the above processes in the sales unit may be performed using AI, for example, or without AI. For example, the sales unit can input data on the frequency of use and condition of items into a generating AI and have the generating AI set the selling price.

[0042] The sales department can assess the market value of goods in real time and determine the appropriate timing for sale. For example, the sales department can use AI to collect market data in real time and assess the market value of goods. The sales department can also use AI to analyze market supply and demand and suggest the optimal timing for sale. For example, the sales department can use AI to predict price fluctuations of goods based on historical market data and determine the timing for sale. This allows the sales department to assess the market value of goods in real time and determine the optimal timing for sale. Some or all of the above processes in the sales department may be performed using AI, for example, or without AI. For example, the sales department can input market data into a generating AI and have the generating AI perform the assessment of the market value of goods and the determination of the timing for sale.

[0043] The sales department can assess the emotional value of an item by considering its history and the memories associated with it. For example, the sales department can use AI to analyze the item's purchase and usage history and assess its emotional value. The sales department can also use AI to analyze photos and notes related to the item and assess its emotional value. The sales department can also use AI to collect memories and anecdotes from the item's owner and assess its emotional value. This allows for appropriate sales by assessing the emotional value of an item. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For example, the sales department can input data on the item's history and memories into a generating AI and have the generating AI perform the emotional value assessment.

[0044] The sales department can select buyers for goods and prioritize reliable buyers. For example, the sales department can use AI to analyze buyer ratings and reviews to select reliable buyers. For example, the sales department can use AI to prioritize reliable buyers based on past transaction history. For example, the sales department can use AI to analyze buyer profiles and transaction history to select reliable buyers. This allows for the sale of goods with peace of mind by selecting reliable buyers. Some or all of the above processes in the sales department may be performed using AI, or not. For example, the sales department can input buyer rating and review data into a generating AI and have the generating AI perform the selection of reliable buyers.

[0045] The property sales management department can analyze the sales history of items and select an appropriate management method. For example, the property sales management department can use AI to analyze the sales history of items and select the optimal management method. For example, the property sales management department can use AI to propose an efficient management method based on past sales data. For example, the property sales management department can use AI to analyze the sales history of items and optimize the management method. In this way, the optimal management method can be selected by analyzing the sales history of items. Some or all of the above processes in the property sales management department may be performed using AI, for example, or without AI. For example, the property sales management department can input sales history data of items into a generating AI and have the generating AI perform the selection of a management method.

[0046] The property sales management department can monitor the storage status of items in real time and propose appropriate storage methods. For example, the property sales management department can use AI to monitor the storage status of items in real time and propose the optimal storage method. For example, the property sales management department can use AI to analyze the storage environment of items and propose the optimal storage method. For example, the property sales management department can use AI to propose efficient storage methods based on the storage history of items. This allows for the proposal of the optimal storage method by monitoring the storage status of items in real time. Some or all of the above processes in the property sales management department may be performed using AI, for example, or without AI. For example, the property sales management department can input item storage status data into a generating AI and have the generating AI execute storage method proposals.

[0047] The property sales management department can appropriately adjust the storage locations of items and manage them efficiently. For example, the property sales management department can use AI to analyze the storage locations of items and propose the optimal storage locations. For example, the property sales management department can use AI to analyze the storage environment of items and propose efficient management methods. For example, the property sales management department can use AI to propose the optimal storage locations based on the storage history of items. This allows for efficient management by optimizing the storage locations of items. Some or all of the above processes in the property sales management department may be performed using AI, or not. For example, the property sales management department can input item storage location data into a generating AI and have the generating AI perform storage location optimization.

[0048] The property sales management department can automatically collect relevant information about items and enrich its management data. For example, the property sales management department can use AI to automatically collect relevant information about items and enrich its management data. For example, the property sales management department can use AI to analyze the usage history of items and collect relevant information. For example, the property sales management department can use AI to collect relevant information based on the storage history of items. In this way, by automatically collecting relevant information about items, the management data can be enriched. Some or all of the above processes in the property sales management department may be performed using AI, for example, or without AI. For example, the property sales management department can input relevant information data about items into a generating AI and have the generating AI perform the collection of relevant information.

[0049] The Asset Management Department can perform asset risk assessments and select appropriate management methods. For example, the Asset Management Department can use AI to perform asset risk assessments and select the optimal management methods. For example, the Asset Management Department can use AI to perform risk assessments based on historical asset data. For example, the Asset Management Department can use AI to analyze market data and perform asset risk assessments. By performing asset risk assessments, the Asset Management Department can select the optimal management methods. Some or all of the above processes in the Asset Management Department may be performed using AI, for example, or without AI. For example, the Asset Management Department can input asset risk data into a generating AI and have the generating AI perform risk assessments and select management methods.

[0050] The asset management department can propose diversified asset investments to reduce risk. For example, the asset management department can use AI to propose diversified asset investments and minimize risk. For example, the asset management department can use AI to analyze market data and propose the optimal method of diversification. For example, the asset management department can use AI to evaluate the risk of diversification based on past investment data. This allows for the minimization of risk by proposing diversified asset investments. Some or all of the above processes in the asset management department may be performed using AI, for example, or without AI. For example, the asset management department can input diversification data into a generating AI and have the generating AI perform diversification proposals and risk assessments.

[0051] The Asset Management Department can propose appropriate management methods considering the liquidity of assets. For example, the Asset Management Department can use AI to evaluate asset liquidity and propose the optimal management method. For example, the Asset Management Department can use AI to analyze market data and propose management methods that take asset liquidity into consideration. For example, the Asset Management Department can use AI to evaluate liquidity based on historical asset data and propose management methods. This allows for the proposal of the optimal management method by considering asset liquidity. Some or all of the above processes in the Asset Management Department may be performed using AI, for example, or without AI. For example, the Asset Management Department can input asset liquidity data into a generating AI and have the generating AI execute a proposal for a liquidity-considering management method.

[0052] The asset management department can predict the future value of assets and formulate appropriate management plans. For example, the asset management department can use AI to predict the future value of assets and formulate long-term management plans. For example, the asset management department can use AI to analyze market data and predict the future value of assets. For example, the asset management department can use AI to predict future value and formulate management plans based on past asset data. This allows for the formulation of long-term management plans by predicting the future value of assets. Some or all of the above processes in the asset management department may be performed using AI, for example, or without AI. For example, the asset management department can input future asset value data into a generating AI and have the generating AI perform future value prediction and management plan formulation.

[0053] The document management department can assess the importance of documents and propose appropriate storage methods. For example, the document management department can use AI to analyze the content of documents and assess their importance. For example, the document management department can use AI to assess the importance of documents based on their frequency of use. For example, the document management department can use AI to collect relevant information about documents and assess their importance. By assessing the importance of documents, the department can propose the optimal storage method. Some or all of the above processes in the document management department may be performed using AI, for example, or without AI. For example, the document management department can input document importance data into a generating AI and have the generating AI perform importance assessment and propose storage methods.

[0054] The document management department can digitize documents and achieve efficient management. For example, the document management department can use AI to scan and digitize documents. For example, the document management department can use AI to analyze the contents of documents and save them as digital data. For example, the document management department can use AI to digitize documents and provide a search function. In this way, efficient management can be achieved by digitizing documents. Some or all of the above processes in the document management department may be performed using AI, for example, or without AI. For example, the document management department can input digitized document data into a generating AI and have the generating AI perform the digitization and management.

[0055] The document management department can automatically collect relevant information about documents and enrich its management data. For example, the document management department can use AI to automatically collect relevant information about documents and enrich its management data. For example, the document management department can use AI to analyze the usage history of documents and collect relevant information. For example, the document management department can use AI to collect relevant information based on the storage history of documents. In this way, by automatically collecting relevant information about documents, the management data can be enriched. Some or all of the above processes in the document management department may be performed using AI, for example, or without AI. For example, the document management department can input relevant information data about documents into a generating AI and have the generating AI perform the collection of relevant information.

[0056] The document management department can appropriately adjust the storage locations of documents and manage them efficiently. For example, the document management department can use AI to analyze document storage locations and propose the optimal storage location. For example, the document management department can use AI to analyze the document storage environment and propose efficient management methods. For example, the document management department can use AI to propose the optimal storage location based on the document storage history. This allows for efficient management by optimizing document storage locations. Some or all of the above processes in the document management department may be performed using AI, for example, or without AI. For example, the document management department can input document storage location data into a generating AI and have the generating AI perform storage location optimization.

[0057] The data management department can assess the importance of data and propose appropriate storage methods. For example, the data management department may use AI to analyze the data content and assess its importance. Alternatively, the AI ​​may assess the importance based on the frequency of data use. The data management department may also use AI to collect relevant data information and assess its importance. This allows the department to propose optimal storage methods by assessing data importance. Some or all of the above processes in the data management department may be performed using AI, or not. For example, the data management department may input data importance data into a generating AI and have the generating AI perform importance assessment and propose storage methods.

[0058] The data management department can ensure data security by automatically backing up data. For example, the data management department can use AI to automatically back up data and ensure data security. For example, the data management department can use AI to set a data backup schedule and perform backups automatically. For example, the data management department can use AI to back up data and distribute it to multiple storage locations. This ensures data security by automating data backups. Some or all of the above processes in the data management department may be performed using AI, for example, or without AI. For example, the data management department can input data backup data into a generating AI and have the generating AI perform the backup execution and management.

[0059] The data management department can automatically collect relevant data information and enrich the managed data. For example, the data management department can use AI to automatically collect relevant data information and enrich the managed data. For example, the data management department can use AI to analyze data usage history and collect relevant information. For example, the data management department can use AI to collect relevant information based on data storage history. In this way, the managed data can be enriched by automatically collecting relevant data information. Some or all of the above processes in the data management department may be performed using AI, for example, or without AI. For example, the data management department can input relevant data information into a generating AI and have the generating AI perform the collection of relevant information.

[0060] The data management department can appropriately adjust data storage locations and manage them efficiently. For example, the data management department can use AI to analyze data storage locations and propose the optimal storage location. The data management department can also use AI to analyze the data storage environment and propose efficient management methods. For example, the data management department can use AI to propose the optimal storage location based on the data storage history. By optimizing data storage locations, efficient management can be achieved. Some or all of the above processes in the data management department may be performed using AI, for example, or without AI. For example, the data management department can input data storage location data into a generating AI and have the generating AI perform storage location optimization.

[0061] The message sending unit can create an optimal message by considering the relationship with the recipient. For example, the message sending unit can use AI to analyze the relationship with the recipient and suggest appropriate message content. For example, the message sending unit can use AI to create an optimal message for the recipient based on past message history. For example, the message sending unit can use AI to analyze the recipient's profile and suggest a message appropriate to the relationship. In this way, the optimal message can be created by considering the relationship with the recipient. Some or all of the above processing in the message sending unit may be performed using AI, for example, or without AI. For example, the message sending unit can input the recipient's relationship data into a generating AI and have the generating AI create message content based on the relationship.

[0062] The message sending unit can optimize the sending timing and achieve effective communication. For example, the message sending unit can use AI to analyze the recipient's activity time and propose the optimal sending timing. For example, the message sending unit can use AI to determine the most effective sending timing based on past sending history. For example, the message sending unit can use AI to consider the recipient's time zone and propose the optimal sending timing. By optimizing the sending timing, effective communication can be achieved. Some or all of the above-described processes in the message sending unit may be performed using AI, for example, or without AI. For example, the message sending unit can input sending timing data into a generating AI and have the generating AI determine the optimal sending timing.

[0063] The message sending unit can create an optimal message by considering the recipient's geographical location information. For example, the message sending unit can use AI to analyze the recipient's geographical location information and propose appropriate message content. The message sending unit can also use AI to create an optimal message based on the recipient's activity area. For example, the message sending unit can use AI to consider the recipient's geographical conditions and propose a message appropriate to the relationship. In this way, an optimal message can be created by considering the recipient's geographical location information. Some or all of the above processing in the message sending unit may be performed using AI, for example, or without AI. For example, the message sending unit can input the recipient's geographical location information data into a generating AI and have the generating AI create message content based on the geographical location information.

[0064] The message sending unit can create an optimal message by referring to the recipient's past communication history. For example, the message sending unit can use AI to analyze the recipient's past communication history and suggest appropriate message content. For example, the message sending unit can use AI to create an optimal message for the recipient based on past message history. For example, the message sending unit can use AI to analyze the recipient's communication patterns and suggest a message appropriate to the relationship. This allows the message sending unit to create an optimal message by referring to the recipient's past communication history. Some or all of the above processing in the message sending unit may be performed using AI, for example, or without AI. For example, the message sending unit can input the recipient's past communication history data into a generating AI and have the generating AI create message content based on the past history.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] A pre-death planning support system can monitor the user's health and determine priorities for organizing based on that health status. For example, if health deteriorates, the system will prioritize organizing important items and documents. If health is good, more detailed organizing can be performed. Furthermore, if health changes suddenly, the system can proceed with organizing quickly. This allows for flexible organizing tailored to the user's health condition. Health monitoring may be performed using wearable devices or medical data. For example, data from wearable devices can be analyzed to understand health status in real time. Health status can also be evaluated based on data provided by medical institutions. This allows for the creation of an optimal organizing plan based on the user's health condition.

[0067] The pre-death planning support system can propose organization plans that take into account the user's hobbies and interests. For example, it can prioritize organizing items related to hobbies and sell unnecessary items. It can also organize documents and data related to hobbies and store necessary items. Furthermore, it can organize relationships related to hobbies and send messages of gratitude. This makes it possible to organize based on the user's hobbies and interests. Information on hobbies and interests can be obtained by analyzing user input and social media data. For example, social media posts can be analyzed to understand the user's hobbies and interests. It can also evaluate hobbies and interests based on the information the user has entered. This allows for the creation of an optimal organization plan tailored to the user's hobbies and interests.

[0068] The pre-death decluttering support system can propose decluttering plans that take into account the user's living environment. For example, it can suggest decluttering based on the size of the residence and available storage space. If the residence is small, it can prioritize selling unnecessary items to free up space. If the residence is large, it can perform detailed decluttering and propose efficient storage methods. This enables decluttering based on the user's living environment. Information about the living environment can be obtained by analyzing user input and data from smart home devices. For example, data from smart home devices can be analyzed to understand the size of the residence and available storage space. It can also evaluate the living environment based on the information entered by the user. This allows for the creation of an optimal decluttering plan tailored to the user's living environment.

[0069] The pre-death planning support system can propose organization strategies that take into account the user's family structure. For example, it can suggest how to organize belongings based on the number and ages of family members. If there are many family members, it can prioritize organizing shared items and selling unnecessary ones. If there are few family members, it can organize individual belongings in detail and store only necessary items. This allows for organization tailored to the user's family structure. Family structure information may be obtained by user input or by analyzing family profile data. For example, family profile data can be analyzed to understand the family structure. It can also evaluate the family structure based on the information entered by the user. This allows for the creation of an optimal organization plan tailored to the user's family structure.

[0070] The pre-death planning support system can propose organization strategies that take into account the user's future plans. For example, it can suggest organizing belongings according to future plans such as moving or changing jobs. If moving is planned, it can prioritize selling unnecessary items to reduce the amount of belongings. If a job change is planned, it can organize work-related documents and data and store necessary items. This allows for organization based on the user's future plans. Information on future plans may be obtained by analyzing user input or data from calendar apps. For example, data from calendar apps can be analyzed to understand future plans. It can also evaluate future plans based on the information entered by the user. This allows for the creation of an optimal organization plan tailored to the user's future plans.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The selling department organizes the user's belongings. The selling department organizes everyday items, furniture, clothes, hobby equipment, etc., and allows users to discard or give away unwanted items. The selling department can sell unwanted items using e-commerce sites. The selling department can also analyze the frequency of use and condition of items to set the optimal selling price. For example, the selling department can use AI to analyze the frequency of use of items and sell frequently used items at a higher price. The selling department can also use AI to evaluate the condition of items and sell items in good condition at a higher price. The selling department can also use AI to analyze the usage history of items and sell infrequently used items at a lower price. Step 2: The Sales Property Management Department manages information on items sold by the Sales Department. The Sales Property Management Department centrally manages information on sold items. The Sales Property Management Department can also analyze the sales history of items and select the optimal management method. For example, the Sales Property Management Department can use AI to analyze the sales history of items and select the optimal management method. The Sales Property Management Department can also use AI to suggest efficient management methods based on past sales data. The Sales Property Management Department can also use AI to analyze the sales history of items and optimize management methods. Step 3: The Asset Management Department organizes and manages the user's assets. The Asset Management Department organizes assets such as bank accounts, real estate, stocks, and insurance. The Asset Management Department also provides support considering situations such as dementia, and assists in the creation of wills using AI generation as needed. For example, the Asset Management Department can create wills using AI generation. The Asset Management Department can also perform asset risk assessments and select the optimal management method. For example, the Asset Management Department's AI can perform asset risk assessments and select the optimal management method. The Asset Management Department's AI can also perform risk assessments based on past asset data. The Asset Management Department's AI can also analyze market data to perform asset risk assessments. Step 4: The Document Management Department organizes and manages the user's documents. The Document Management Department helps users organize and store insurance policies, pension documents, important contracts, etc. The Document Management Department also provides support for organizing data stored on electronic devices such as PCs and smartphones. The Document Management Department can also digitize documents to enable efficient management. For example, the Document Management Department can use AI to scan and digitize documents. The Document Management Department can also use AI to analyze the content of documents and save it as digital data. The Document Management Department can also use AI to digitize documents and provide search functionality. Step 5: The Data Management Department organizes and manages user data and accounts. The Data Management Department supports the management of online accounts, social media accounts, email addresses, and data stored in the cloud. The Data Management Department helps to consolidate necessary information and provide access instructions to trusted individuals. The Data Management Department can also ensure data security by automatically backing up data. For example, the Data Management Department can use AI to automatically back up data and ensure data security. The Data Management Department can also use AI to set data backup schedules and perform backups automatically. The Data Management Department can also use AI to back up data and distribute it across multiple storage locations. Step 6: The message sending function sends messages based on the user's relationships. The message sending function can be used to express gratitude to people you haven't seen in a long time or to people you have lingering feelings for. The message sending function can send thank-you messages using messaging apps. The message sending function can also create the most suitable message considering the recipient's relationship. For example, the message sending function's AI analyzes the recipient's relationship and suggests appropriate message content. The message sending function's AI can also create the most suitable message for the recipient based on past message history. The message sending function's AI can also analyze the recipient's profile and suggest a message appropriate to the relationship.

[0073] (Example of form 2) The pre-death planning support system according to an embodiment of the present invention is a system that efficiently supports families in planning their affairs before death. This pre-death planning support system can organize and manage the user's belongings, assets, documents, data, and relationships. For example, the pre-death planning support system includes a sales unit that organizes the user's belongings, such as everyday items, furniture, clothes, and hobby equipment, and allows the user to discard or give away unwanted items. For example, unwanted items can be sold using an e-commerce site. Next, there is a sales property management unit that manages information on items sold by the sales unit, centrally managing information on sold items. Next, there is an asset management unit that organizes and manages the user's assets, such as bank accounts, real estate, stocks, and insurance. It also provides support considering cases such as dementia, and supports the creation of wills using a generation AI as needed. For example, a will can be created using a generation AI. Furthermore, there is a document management unit that organizes and manages the user's documents, such as insurance policies, pension-related documents, and important contracts, and supports where to store them. It also provides organization support including data stored on electronic devices such as personal computers and smartphones. Next, there is a data management section that organizes and manages user data and accounts, supporting the management of online accounts, social media accounts, email addresses, and data stored in the cloud. It helps to consolidate necessary information and provide access methods to trusted individuals. Furthermore, there is a messaging section that allows users to send messages based on their relationships, enabling them to express gratitude to people they haven't seen in a long time or to those they have lingering feelings for. For example, they can send thank-you messages using messaging apps. Finally, as a way of organizing one's life, organizing belongings and assets according to one's own will can lead to emotional closure and reduce the burden on surviving family members. In this way, the pre-death planning support system efficiently supports families in organizing their belongings, assets, documents, data, and relationships.

[0074] The pre-death arrangement support system according to this embodiment comprises a sales unit, a sales property management unit, an asset management unit, a document management unit, a data management unit, and a message transmission unit. The sales unit organizes the user's belongings. For example, the sales unit organizes everyday items, furniture, clothes, hobby equipment, etc., and allows the user to discard or give away unwanted items. For example, the sales unit can sell unwanted items using e-commerce sites. The sales unit can also analyze the frequency of use and condition of items to set the optimal selling price. For example, the sales unit can use AI to analyze the frequency of use of items and sell frequently used items at a high price. The sales unit can also use AI to evaluate the condition of items and sell items in good condition at a high price. The sales unit can also use AI to analyze the usage history of items and sell infrequently used items at a low price. The sales property management unit manages information on items sold by the sales unit. For example, the sales property management unit centrally manages information on sold items. The sales property management unit can also analyze the sales history of items and select the optimal management method. For example, the Property Sales Management Department uses AI to analyze the sales history of items and select the optimal management method. The Property Sales Management Department can also use AI to propose efficient management methods based on past sales data. The Property Sales Management Department can also use AI to analyze the sales history of items and optimize management methods. The Asset Management Department organizes and manages the user's assets. The Asset Management Department organizes assets such as bank accounts, real estate, stocks, and insurance. The Asset Management Department also provides support considering situations such as dementia, and supports the creation of wills using AI generation as needed. For example, the Asset Management Department can create wills using AI generation. The Asset Management Department can also perform asset risk assessments and select the optimal management method. For example, the Asset Management Department uses AI to perform asset risk assessments and select the optimal management method. The Asset Management Department can also use AI to perform risk assessments based on past asset data. The Asset Management Department can also use AI to analyze market data and perform asset risk assessments. The Document Management Department organizes and manages the user's documents. The document management department helps organize and store documents such as insurance policies, pension-related documents, and important contracts.The Document Management Department provides support for organizing data, including data stored on electronic devices such as PCs and smartphones. The Document Management Department can also digitize documents and achieve efficient management. For example, the Document Management Department can use AI to scan and digitize documents. The Document Management Department can also use AI to analyze document content and save it as digital data. The Document Management Department can also use AI to digitize documents and provide search functionality. The Data Management Department organizes and manages user data and accounts. For example, the Data Management Department supports the management of online accounts, social media accounts, email addresses, and data stored in the cloud. The Data Management Department helps users consolidate necessary information and provide access instructions to trusted individuals. The Data Management Department can also automatically back up data to ensure data security. For example, the Data Management Department can use AI to automatically back up data and ensure data security. The Data Management Department can also use AI to set data backup schedules and perform backups automatically. The Data Management Department can also use AI to back up data and distribute it across multiple storage locations. The Message Sending Department sends messages based on the user's relationships. The message sending unit can, for example, convey feelings of gratitude to people you haven't seen in a long time or to people you have lingering regrets about. The message sending unit can send thank-you messages using a messaging app. The message sending unit can also create the most appropriate message considering the relationship with the recipient. For example, the message sending unit's AI can analyze the relationship with the recipient and suggest appropriate message content. The message sending unit's AI can also create the most suitable message for the recipient based on past message history. The message sending unit's AI can also analyze the recipient's profile and suggest a message appropriate to the relationship. As a result, the pre-death arrangement support system according to this embodiment can efficiently organize and manage the user's belongings, assets, documents, data, and relationships.

[0075] The selling department helps users organize their belongings. For example, it can organize everyday items, furniture, clothing, and hobby equipment, allowing users to discard or give away unwanted items. The selling department can also sell unwanted items using e-commerce sites. Furthermore, the selling department can analyze the frequency of use and condition of items to set optimal selling prices. For example, the selling department can use AI to analyze the frequency of use of items and sell frequently used items at a higher price. The selling department can also use AI to evaluate the condition of items and sell items in good condition at a higher price. The selling department can also use AI to analyze the usage history of items and sell infrequently used items at a lower price. The selling department creates a detailed list of the user's belongings and registers photos and descriptions of each item in a database. This allows users to see their belongings at a glance and easily provide the necessary information when selling. In addition, the selling department collects data from online marketplaces and uses AI to analyze it in order to understand the market value of items in real time. This allows the selling department to suggest the optimal timing for selling items. For example, seasonal or trendy items can be sold at a higher price by selling them during periods of high demand. Furthermore, the sales department proposes multiple selling methods for items the user wishes to sell. For instance, users can choose the most suitable method based on their needs, such as selling through an auction, using an instant buyback service, or selling at a local flea market. This allows the sales department to efficiently and effectively organize and sell the user's items.

[0076] The Sales Management Department manages information on items sold by the Sales Department. For example, the Sales Management Department centrally manages information on sold items. The Sales Management Department can also analyze the sales history of items to select the optimal management method. For example, the Sales Management Department can use AI to analyze the sales history of items and select the optimal management method. The Sales Management Department can also use AI to suggest efficient management methods based on past sales data. The Sales Management Department can also use AI to analyze the sales history of items and optimize management methods. The Sales Management Department builds a detailed database of sold items, recording information such as the sale price, sale date, and buyer for each item. This allows users to easily refer to information on items they have sold in the past and use it to help with future sales activities. Furthermore, the Sales Management Department analyzes users' sales trends and preferences based on the data of sold items. This allows the Sales Management Department to provide users with more personalized sales suggestions. For example, users who frequently sell items in a specific category can be provided with sales information and market trends related to that category. Furthermore, the Sales Property Management Department shares data on sold items with other departments to improve the overall efficiency of the system. For example, it collaborates with the Asset Management Department and the Document Management Department to support the organization of assets and documents related to sold items. This allows the Sales Property Management Department to comprehensively support users' item selling activities and achieve efficient management.

[0077] The Asset Management Department organizes and manages users' assets. For example, it organizes assets such as bank accounts, real estate, stocks, and insurance. The Asset Management Department also provides support, taking into account situations like dementia, and assists with will creation using AI generation as needed. For example, the Asset Management Department can create wills using AI generation. The Asset Management Department can also perform asset risk assessments and select the optimal management method. For example, the Asset Management Department's AI can perform asset risk assessments and select the optimal management method. The Asset Management Department's AI can also perform risk assessments based on past asset data. The Asset Management Department's AI can also analyze market data to perform asset risk assessments. The Asset Management Department provides a digital platform for centralized management of users' asset information, allowing users to understand their asset status in real time. This enables users to efficiently manage their assets and respond quickly when needed. Furthermore, the Asset Management Department supports legal procedures related to users' assets. For example, it provides support for will creation and inheritance procedures, allowing users to manage their assets with peace of mind. Furthermore, the Asset Management Department provides advice to minimize risks related to users' assets. For example, it makes specific suggestions to protect users' assets, such as evaluating investment risks and reviewing insurance policies. In this way, the Asset Management Department can comprehensively manage users' assets and provide optimal management methods while minimizing risks.

[0078] The Document Management Department organizes and manages users' documents. For example, it helps users organize and store documents such as insurance policies, pension documents, and important contracts. It also provides support for organizing data stored on electronic devices such as PCs and smartphones. The Document Management Department can also digitize documents to achieve efficient management. For example, it can use AI to scan and digitize documents. It can also use AI to analyze document content and save it as digital data. Furthermore, it can use AI to digitize documents and provide search functionality. The Document Management Department provides a digital archiving system for efficient document management, allowing users to quickly search and access necessary documents. This significantly reduces the risk of document loss and the effort required for management. In addition, the Document Management Department provides advice on optimizing document storage locations. For example, it recommends storing important documents in fireproof and waterproof locations and suggests storing documents used daily in easily accessible locations. The Document Management Department also strengthens security measures for users' documents. For example, encryption technology can be applied to digitized documents to prevent unauthorized access and information leaks. This allows the document management department to manage users' documents securely and efficiently, providing an environment where they can be quickly accessed when needed.

[0079] The Data Management Department organizes and manages user data and accounts. For example, it supports the management of online accounts, social media accounts, email addresses, and data stored in the cloud. The Data Management Department helps users consolidate necessary information and provide access instructions to trusted individuals. The Data Management Department can also ensure data security by automatically backing up data. For example, the Data Management Department can use AI to automatically back up data and ensure its security. The Data Management Department can also use AI to set backup schedules and perform backups automatically. The Data Management Department can also use AI to back up data and distribute it across multiple storage locations. The Data Management Department provides a platform for centralized management of users' digital assets, enabling users to efficiently manage their data. This makes it easy for users to organize their online accounts and digital data. Furthermore, the Data Management Department strengthens security measures for user data. For example, it provides password management tools, allowing users to generate and manage secure passwords. The Data Management Department also supports user data privacy settings and can delete data or restrict access as needed. This allows the data management department to securely and efficiently manage users' digital assets and provide an environment where they can be quickly accessed when needed.

[0080] The message sending function sends messages based on the user's relationships. For example, it can be used to express gratitude to someone you haven't seen in a long time or to someone you have lingering feelings for. The message sending function can send thank-you messages using messaging apps. It can also create optimal messages considering the recipient's relationship. For example, the AI ​​analyzes the recipient's relationship and suggests appropriate message content. The AI ​​can also create the best message for the recipient based on past message history. The AI ​​can analyze the recipient's profile and suggest a message appropriate to the relationship. The message sending function provides an interface for users to input the content of the message they want to send, making it easy for users to create messages. This allows users to easily convey feelings of gratitude and memories. Furthermore, the message sending function monitors the recipient's response and provides feedback to the user. For example, it notifies the user whether the recipient received the message and what their response was. The message sending function also optimizes the timing and content of messages based on the recipient's relationship and past interactions. This allows the message sending function to smooth user relationships and effectively convey feelings of gratitude.

[0081] The sales department can sell unwanted items using e-commerce sites. The sales department can, for example, sell unwanted items using e-commerce sites. The sales department can also, for example, sell unwanted items using online marketplaces. The sales department can also, for example, sell unwanted items using auction sites. This allows for the efficient sale of unwanted items. Some or all of the above processes in the sales department may be performed using AI, for example, or not using AI. For example, the sales department can input information about the items into e-commerce sites and have AI perform the setting of the selling price and the selling procedures.

[0082] The Asset Management Department can create wills using a generative AI. The Asset Management Department can, for example, create wills using a generative AI. The Asset Management Department can also, for example, automatically generate the content of wills using a generative AI. The Asset Management Department can also, for example, optimize the format and content of wills using a generative AI. This allows for the efficient creation of wills. Some or all of the above processes in the Asset Management Department may be performed using AI, for example, or without AI. For example, the Asset Management Department can input the user's asset information into a generative AI and have the generative AI automatically generate the content of a will.

[0083] The document management department can organize insurance certificates or pension-related documents. For example, the document management department can organize insurance certificates. The document management department can also organize pension-related documents. For example, the document management department can organize insurance certificates and pension-related documents together. This allows for the efficient organization of important documents. Some or all of the above processes in the document management department may be performed using AI, for example, or not using AI. For example, the document management department can scan insurance certificates and pension-related documents and save them as digital data.

[0084] The data management unit can organize data stored on electronic devices. For example, the data management unit can organize data stored on a personal computer. The data management unit can also organize data stored on a smartphone. The data management unit can also organize data stored on a tablet. This allows for efficient organization of data stored on electronic devices. Some or all of the above-described processes in the data management unit may be performed using AI, for example, or without AI. For example, the data management unit can have AI analyze data stored on electronic devices and automatically delete unnecessary data.

[0085] The data management department can manage online accounts or social media accounts. For example, the data management department can manage online accounts. The data management department can also manage social media accounts. The data management department can also manage email accounts. This allows for efficient management of online accounts and social media accounts. Some or all of the above processes in the data management department may be performed using AI, for example, or not. For example, the data management department can input information about online accounts and social media accounts into an AI and have the AI ​​manage the accounts.

[0086] The sales unit can estimate the user's emotions and select items to sell based on those emotions. For example, if the emotion engine indicates that the user is stressed, the sales unit can prioritize selecting unwanted items to facilitate sales. If the emotion engine indicates that the user is relaxed, the sales unit can also carefully select sentimental items and suggest selling them. If the emotion engine indicates that the user is in a hurry, the sales unit can also select items that can be sold immediately and sell them quickly. This allows for the selection and sale of appropriate items 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-described processes in the sales unit may be performed using AI or not. For example, the sales unit can input user emotion data into a generative AI and have the generative AI perform emotion-based item selection.

[0087] The sales unit can analyze the frequency of use and condition of items to set an appropriate selling price. For example, the sales unit can use AI to analyze the frequency of use of items and sell frequently used items at a higher price. For example, the sales unit can use AI to evaluate the condition of items and sell items in good condition at a higher price. For example, the sales unit can use AI to analyze the usage history of items and sell infrequently used items at a lower price. This allows for setting an optimal selling price based on the frequency of use and condition of items. Some or all of the above processes in the sales unit may be performed using AI, for example, or without AI. For example, the sales unit can input data on the frequency of use and condition of items into a generating AI and have the generating AI set the selling price.

[0088] The sales department can assess the market value of goods in real time and determine the appropriate timing for sale. For example, the sales department can use AI to collect market data in real time and assess the market value of goods. The sales department can also use AI to analyze market supply and demand and suggest the optimal timing for sale. For example, the sales department can use AI to predict price fluctuations of goods based on historical market data and determine the timing for sale. This allows the sales department to assess the market value of goods in real time and determine the optimal timing for sale. Some or all of the above processes in the sales department may be performed using AI, for example, or without AI. For example, the sales department can input market data into a generating AI and have the generating AI perform the assessment of the market value of goods and the determination of the timing for sale.

[0089] The sales unit can estimate the user's emotions and determine sales priorities based on the estimated emotions. For example, if the emotion engine indicates that the user is stressed, the sales unit will prioritize selling unwanted items. For example, if the emotion engine indicates that the user is relaxed, the sales unit may postpone selling sentimental items. For example, if the emotion engine indicates that the user is in a hurry, the sales unit may prioritize selling items that can be sold immediately. This allows the sales unit to determine sales priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sales unit may be performed using AI, or not using AI. For example, the sales unit can input user emotion data into a generative AI and have the generative AI perform the determination of sales priorities based on emotions.

[0090] The sales department can assess the emotional value of an item by considering its history and the memories associated with it. For example, the sales department can use AI to analyze the item's purchase and usage history and assess its emotional value. The sales department can also use AI to analyze photos and notes related to the item and assess its emotional value. The sales department can also use AI to collect memories and anecdotes from the item's owner and assess its emotional value. This allows for appropriate sales by assessing the emotional value of an item. Some or all of the above processes in the sales department may be performed using AI, for example, or not. For example, the sales department can input data on the item's history and memories into a generating AI and have the generating AI perform the emotional value assessment.

[0091] The sales department can select buyers for goods and prioritize reliable buyers. For example, the sales department can use AI to analyze buyer ratings and reviews to select reliable buyers. For example, the sales department can use AI to prioritize reliable buyers based on past transaction history. For example, the sales department can use AI to analyze buyer profiles and transaction history to select reliable buyers. This allows for the sale of goods with peace of mind by selecting reliable buyers. Some or all of the above processes in the sales department may be performed using AI, or not. For example, the sales department can input buyer rating and review data into a generating AI and have the generating AI perform the selection of reliable buyers.

[0092] The property management department can estimate the user's emotions and adjust the management method of the property based on the estimated emotions. For example, if the emotion engine indicates that the user is stressed, the property management department can suggest a simple management method. If the emotion engine indicates that the user is relaxed, the property management department can also suggest a detailed management method. If the emotion engine indicates that the user is in a hurry, the property management department can also suggest a quick management method. This allows the management method of the property to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the property management department may be performed using AI or not. For example, the property management department can input user emotion data into a generative AI and have the generative AI perform the adjustment of the management method based on emotions.

[0093] The property sales management department can analyze the sales history of items and select an appropriate management method. For example, the property sales management department can use AI to analyze the sales history of items and select the optimal management method. For example, the property sales management department can use AI to propose an efficient management method based on past sales data. For example, the property sales management department can use AI to analyze the sales history of items and optimize the management method. In this way, the optimal management method can be selected by analyzing the sales history of items. Some or all of the above processes in the property sales management department may be performed using AI, for example, or without AI. For example, the property sales management department can input sales history data of items into a generating AI and have the generating AI perform the selection of a management method.

[0094] The property sales management department can monitor the storage status of items in real time and propose appropriate storage methods. For example, the property sales management department can use AI to monitor the storage status of items in real time and propose the optimal storage method. For example, the property sales management department can use AI to analyze the storage environment of items and propose the optimal storage method. For example, the property sales management department can use AI to propose efficient storage methods based on the storage history of items. This allows for the proposal of the optimal storage method by monitoring the storage status of items in real time. Some or all of the above processes in the property sales management department may be performed using AI, for example, or without AI. For example, the property sales management department can input item storage status data into a generating AI and have the generating AI execute storage method proposals.

[0095] The property sales management unit can estimate the user's emotions and adjust how properties are displayed based on those emotions. For example, if the emotion engine indicates that the user is stressed, the property sales management unit can suggest a simple display method. If the emotion engine indicates that the user is relaxed, the property sales management unit can also suggest a detailed display method. If the emotion engine indicates that the user is in a hurry, the property sales management unit can also suggest a fast display method. This allows the property sales management unit to adjust how properties are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the property sales management unit may be performed using AI or not. For example, the property sales management unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the display method.

[0096] The property sales management department can appropriately adjust the storage locations of items and manage them efficiently. For example, the property sales management department can use AI to analyze the storage locations of items and propose the optimal storage locations. For example, the property sales management department can use AI to analyze the storage environment of items and propose efficient management methods. For example, the property sales management department can use AI to propose the optimal storage locations based on the storage history of items. This allows for efficient management by optimizing the storage locations of items. Some or all of the above processes in the property sales management department may be performed using AI, or not. For example, the property sales management department can input item storage location data into a generating AI and have the generating AI perform storage location optimization.

[0097] The property sales management department can automatically collect relevant information about items and enrich its management data. For example, the property sales management department can use AI to automatically collect relevant information about items and enrich its management data. For example, the property sales management department can use AI to analyze the usage history of items and collect relevant information. For example, the property sales management department can use AI to collect relevant information based on the storage history of items. In this way, by automatically collecting relevant information about items, the management data can be enriched. Some or all of the above processes in the property sales management department may be performed using AI, for example, or without AI. For example, the property sales management department can input relevant information data about items into a generating AI and have the generating AI perform the collection of relevant information.

[0098] The asset management unit can estimate the user's emotions and adjust the asset management method based on the estimated user emotions. For example, if the emotion engine indicates that the user is stressed, the asset management unit can suggest a simple management method. For example, if the emotion engine indicates that the user is relaxed, the asset management unit can also suggest a detailed management method. For example, if the emotion engine indicates that the user is in a hurry, the asset management unit can also suggest a rapid management method. This allows the asset management method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 asset management unit may be performed using AI, for example, or without AI. For example, the asset management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the management method based on emotions.

[0099] The Asset Management Department can perform asset risk assessments and select appropriate management methods. For example, the Asset Management Department can use AI to perform asset risk assessments and select the optimal management methods. For example, the Asset Management Department can use AI to perform risk assessments based on historical asset data. For example, the Asset Management Department can use AI to analyze market data and perform asset risk assessments. By performing asset risk assessments, the Asset Management Department can select the optimal management methods. Some or all of the above processes in the Asset Management Department may be performed using AI, for example, or without AI. For example, the Asset Management Department can input asset risk data into a generating AI and have the generating AI perform risk assessments and select management methods.

[0100] The asset management department can propose diversified asset investments to reduce risk. For example, the asset management department can use AI to propose diversified asset investments and minimize risk. For example, the asset management department can use AI to analyze market data and propose the optimal method of diversification. For example, the asset management department can use AI to evaluate the risk of diversification based on past investment data. This allows for the minimization of risk by proposing diversified asset investments. Some or all of the above processes in the asset management department may be performed using AI, for example, or without AI. For example, the asset management department can input diversification data into a generating AI and have the generating AI perform diversification proposals and risk assessments.

[0101] The asset management unit can estimate the user's emotions and determine asset priorities based on those estimated emotions. For example, if the emotion engine indicates that the user is stressed, the asset management unit will prioritize managing important assets. For example, if the emotion engine indicates that the user is relaxed, the asset management unit can perform detailed asset management. For example, if the emotion engine indicates that the user is in a hurry, the asset management unit can quickly determine asset priorities. This allows for asset prioritization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 asset management unit may be performed using AI, for example, or without AI. For example, the asset management unit can input user emotion data into a generative AI and have the generative AI perform emotion-based asset prioritization.

[0102] The Asset Management Department can propose appropriate management methods considering the liquidity of assets. For example, the Asset Management Department can use AI to evaluate asset liquidity and propose the optimal management method. For example, the Asset Management Department can use AI to analyze market data and propose management methods that take asset liquidity into consideration. For example, the Asset Management Department can use AI to evaluate liquidity based on historical asset data and propose management methods. This allows for the proposal of the optimal management method by considering asset liquidity. Some or all of the above processes in the Asset Management Department may be performed using AI, for example, or without AI. For example, the Asset Management Department can input asset liquidity data into a generating AI and have the generating AI execute a proposal for a liquidity-considering management method.

[0103] The asset management department can predict the future value of assets and formulate appropriate management plans. For example, the asset management department can use AI to predict the future value of assets and formulate long-term management plans. For example, the asset management department can use AI to analyze market data and predict the future value of assets. For example, the asset management department can use AI to predict future value and formulate management plans based on past asset data. This allows for the formulation of long-term management plans by predicting the future value of assets. Some or all of the above processes in the asset management department may be performed using AI, for example, or without AI. For example, the asset management department can input future asset value data into a generating AI and have the generating AI perform future value prediction and management plan formulation.

[0104] The document management unit can estimate the user's emotions and adjust the document organization method based on the estimated emotions. For example, if the emotion engine indicates that the user is stressed, the document management unit can suggest a simple organization method. If the emotion engine indicates that the user is relaxed, the document management unit can also suggest a detailed organization method. If the emotion engine indicates that the user is in a hurry, the document management unit can also suggest a quick organization method. This allows the document organization method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the document management unit may be performed using AI, for example, or without AI. For example, the document management unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the organization method based on emotions.

[0105] The document management department can assess the importance of documents and propose appropriate storage methods. For example, the document management department can use AI to analyze the content of documents and assess their importance. For example, the document management department can use AI to assess the importance of documents based on their frequency of use. For example, the document management department can use AI to collect relevant information about documents and assess their importance. By assessing the importance of documents, the department can propose the optimal storage method. Some or all of the above processes in the document management department may be performed using AI, for example, or without AI. For example, the document management department can input document importance data into a generating AI and have the generating AI perform importance assessment and propose storage methods.

[0106] The document management department can digitize documents and achieve efficient management. For example, the document management department can use AI to scan and digitize documents. For example, the document management department can use AI to analyze the contents of documents and save them as digital data. For example, the document management department can use AI to digitize documents and provide a search function. In this way, efficient management can be achieved by digitizing documents. Some or all of the above processes in the document management department may be performed using AI, for example, or without AI. For example, the document management department can input digitized document data into a generating AI and have the generating AI perform the digitization and management.

[0107] The document management unit can estimate the user's emotions and prioritize documents based on those emotions. For example, if the emotion engine indicates that the user is stressed, the document management unit will prioritize organizing important documents. If the emotion engine indicates that the user is relaxed, the document management unit can also perform detailed document organization. If the emotion engine indicates that the user is in a hurry, the document management unit can also quickly prioritize documents. This allows for document prioritization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 document management unit may be performed using AI or not. For example, the document management unit can input user emotion data into a generative AI and have the generative AI perform emotion-based document prioritization.

[0108] The document management department can automatically collect relevant information about documents and enrich its management data. For example, the document management department can use AI to automatically collect relevant information about documents and enrich its management data. For example, the document management department can use AI to analyze the usage history of documents and collect relevant information. For example, the document management department can use AI to collect relevant information based on the storage history of documents. In this way, by automatically collecting relevant information about documents, the management data can be enriched. Some or all of the above processes in the document management department may be performed using AI, for example, or without AI. For example, the document management department can input relevant information data about documents into a generating AI and have the generating AI perform the collection of relevant information.

[0109] The document management department can appropriately adjust the storage locations of documents and manage them efficiently. For example, the document management department can use AI to analyze document storage locations and propose the optimal storage location. For example, the document management department can use AI to analyze the document storage environment and propose efficient management methods. For example, the document management department can use AI to propose the optimal storage location based on the document storage history. This allows for efficient management by optimizing document storage locations. Some or all of the above processes in the document management department may be performed using AI, for example, or without AI. For example, the document management department can input document storage location data into a generating AI and have the generating AI perform storage location optimization.

[0110] The data management unit can estimate the user's emotions and adjust the data organization method based on the estimated user emotions. For example, if the emotion engine indicates that the user is stressed, the data management unit may suggest a simple organization method. For example, if the emotion engine indicates that the user is relaxed, the data management unit may suggest a detailed organization method. For example, if the emotion engine indicates that the user is in a hurry, the data management unit may suggest a rapid organization method. This allows the data organization method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data management unit may be performed using AI, for example, or without AI. For example, the data management unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the organization method based on emotions.

[0111] The data management department can assess the importance of data and propose appropriate storage methods. For example, the data management department may use AI to analyze the data content and assess its importance. Alternatively, the AI ​​may assess the importance based on the frequency of data use. The data management department may also use AI to collect relevant data information and assess its importance. This allows the department to propose optimal storage methods by assessing data importance. Some or all of the above processes in the data management department may be performed using AI, or not. For example, the data management department may input data importance data into a generating AI and have the generating AI perform importance assessment and propose storage methods.

[0112] The data management department can ensure data security by automatically backing up data. For example, the data management department can use AI to automatically back up data and ensure data security. For example, the data management department can use AI to set a data backup schedule and perform backups automatically. For example, the data management department can use AI to back up data and distribute it to multiple storage locations. This ensures data security by automating data backups. Some or all of the above processes in the data management department may be performed using AI, for example, or without AI. For example, the data management department can input data backup data into a generating AI and have the generating AI perform the backup execution and management.

[0113] The data management unit can estimate the user's emotions and prioritize data based on the estimated emotions. For example, if the emotion engine indicates that the user is stressed, the data management unit can prioritize important data. If the emotion engine indicates that the user is relaxed, the data management unit can also perform detailed data organization. If the emotion engine indicates that the user is in a hurry, the data management unit can also quickly prioritize data. This allows data prioritization to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 management unit may be performed using AI or not. For example, the data management unit can input user emotion data into a generative AI and have the generative AI perform emotion-based data prioritization.

[0114] The data management department can automatically collect relevant data information and enrich the managed data. For example, the data management department can use AI to automatically collect relevant data information and enrich the managed data. For example, the data management department can use AI to analyze data usage history and collect relevant information. For example, the data management department can use AI to collect relevant information based on data storage history. In this way, the managed data can be enriched by automatically collecting relevant data information. Some or all of the above processes in the data management department may be performed using AI, for example, or without AI. For example, the data management department can input relevant data information into a generating AI and have the generating AI perform the collection of relevant information.

[0115] The data management department can appropriately adjust data storage locations and manage them efficiently. For example, the data management department can use AI to analyze data storage locations and propose the optimal storage location. The data management department can also use AI to analyze the data storage environment and propose efficient management methods. For example, the data management department can use AI to propose the optimal storage location based on the data storage history. By optimizing data storage locations, efficient management can be achieved. Some or all of the above processes in the data management department may be performed using AI, for example, or without AI. For example, the data management department can input data storage location data into a generating AI and have the generating AI perform storage location optimization.

[0116] The message sending unit can estimate the user's emotions and adjust the message content based on the estimated emotions. For example, if the emotion engine indicates that the user is tense, the message sending unit can suggest a calm message. For example, if the emotion engine indicates that the user is relaxed, the message sending unit can suggest a cheerful message. For example, if the emotion engine indicates that the user is in a hurry, the message sending unit can suggest a concise message. This allows the message content to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the message sending unit may be performed using AI, for example, or without AI. For example, the message sending unit can input user emotion data into a generative AI and have the generative AI perform emotion-based message content adjustments.

[0117] The message sending unit can create an optimal message by considering the relationship with the recipient. For example, the message sending unit can use AI to analyze the relationship with the recipient and suggest appropriate message content. For example, the message sending unit can use AI to create an optimal message for the recipient based on past message history. For example, the message sending unit can use AI to analyze the recipient's profile and suggest a message appropriate to the relationship. In this way, the optimal message can be created by considering the relationship with the recipient. Some or all of the above processing in the message sending unit may be performed using AI, for example, or without AI. For example, the message sending unit can input the recipient's relationship data into a generating AI and have the generating AI create message content based on the relationship.

[0118] The message sending unit can optimize the sending timing and achieve effective communication. For example, the message sending unit can use AI to analyze the recipient's activity time and propose the optimal sending timing. For example, the message sending unit can use AI to determine the most effective sending timing based on past sending history. For example, the message sending unit can use AI to consider the recipient's time zone and propose the optimal sending timing. By optimizing the sending timing, effective communication can be achieved. Some or all of the above-described processes in the message sending unit may be performed using AI, for example, or without AI. For example, the message sending unit can input sending timing data into a generating AI and have the generating AI determine the optimal sending timing.

[0119] The message sending unit can estimate the user's emotions and determine message priorities based on the estimated emotions. For example, if the emotion engine indicates that the user is tense, the message sending unit will prioritize sending important messages. For example, if the emotion engine indicates that the user is relaxed, the message sending unit can also send detailed messages. For example, if the emotion engine indicates that the user is in a hurry, the message sending unit can also quickly determine message priorities. This allows message priorities to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 message sending unit may be performed using AI or not using AI. For example, the message sending unit can input user emotion data into a generative AI and have the generative AI perform emotion-based message prioritization.

[0120] The message sending unit can create an optimal message by considering the recipient's geographical location information. For example, the message sending unit can use AI to analyze the recipient's geographical location information and propose appropriate message content. The message sending unit can also use AI to create an optimal message based on the recipient's activity area. For example, the message sending unit can use AI to consider the recipient's geographical conditions and propose a message appropriate to the relationship. In this way, an optimal message can be created by considering the recipient's geographical location information. Some or all of the above processing in the message sending unit may be performed using AI, for example, or without AI. For example, the message sending unit can input the recipient's geographical location information data into a generating AI and have the generating AI create message content based on the geographical location information.

[0121] The message sending unit can create an optimal message by referring to the recipient's past communication history. For example, the message sending unit can use AI to analyze the recipient's past communication history and suggest appropriate message content. For example, the message sending unit can use AI to create an optimal message for the recipient based on past message history. For example, the message sending unit can use AI to analyze the recipient's communication patterns and suggest a message appropriate to the relationship. This allows the message sending unit to create an optimal message by referring to the recipient's past communication history. Some or all of the above processing in the message sending unit may be performed using AI, for example, or without AI. For example, the message sending unit can input the recipient's past communication history data into a generating AI and have the generating AI create message content based on the past history.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] A pre-death planning support system can monitor the user's health and determine priorities for organizing based on that health status. For example, if health deteriorates, the system will prioritize organizing important items and documents. If health is good, more detailed organizing can be performed. Furthermore, if health changes suddenly, the system can proceed with organizing quickly. This allows for flexible organizing tailored to the user's health condition. Health monitoring may be performed using wearable devices or medical data. For example, data from wearable devices can be analyzed to understand health status in real time. Health status can also be evaluated based on data provided by medical institutions. This allows for the creation of an optimal organizing plan based on the user's health condition.

[0124] The pre-death planning support system can propose organization plans that take into account the user's hobbies and interests. For example, it can prioritize organizing items related to hobbies and sell unnecessary items. It can also organize documents and data related to hobbies and store necessary items. Furthermore, it can organize relationships related to hobbies and send messages of gratitude. This makes it possible to organize based on the user's hobbies and interests. Information on hobbies and interests can be obtained by analyzing user input and social media data. For example, social media posts can be analyzed to understand the user's hobbies and interests. It can also evaluate hobbies and interests based on the information the user has entered. This allows for the creation of an optimal organization plan tailored to the user's hobbies and interests.

[0125] The pre-death decluttering support system can propose decluttering plans that take into account the user's living environment. For example, it can suggest decluttering based on the size of the residence and available storage space. If the residence is small, it can prioritize selling unnecessary items to free up space. If the residence is large, it can perform detailed decluttering and propose efficient storage methods. This enables decluttering based on the user's living environment. Information about the living environment can be obtained by analyzing user input and data from smart home devices. For example, data from smart home devices can be analyzed to understand the size of the residence and available storage space. It can also evaluate the living environment based on the information entered by the user. This allows for the creation of an optimal decluttering plan tailored to the user's living environment.

[0126] The pre-death planning support system can propose organization strategies that take into account the user's family structure. For example, it can suggest how to organize belongings based on the number and ages of family members. If there are many family members, it can prioritize organizing shared items and selling unnecessary ones. If there are few family members, it can organize individual belongings in detail and store only necessary items. This allows for organization tailored to the user's family structure. Family structure information may be obtained by user input or by analyzing family profile data. For example, family profile data can be analyzed to understand the family structure. It can also evaluate the family structure based on the information entered by the user. This allows for the creation of an optimal organization plan tailored to the user's family structure.

[0127] The pre-death planning support system can propose organization strategies that take into account the user's future plans. For example, it can suggest organizing belongings according to future plans such as moving or changing jobs. If moving is planned, it can prioritize selling unnecessary items to reduce the amount of belongings. If a job change is planned, it can organize work-related documents and data and store necessary items. This allows for organization based on the user's future plans. Information on future plans may be obtained by analyzing user input or data from calendar apps. For example, data from calendar apps can be analyzed to understand future plans. It can also evaluate future plans based on the information entered by the user. This allows for the creation of an optimal organization plan tailored to the user's future plans.

[0128] The pre-death planning support system can estimate the user's emotions and adjust the pace of the planning process based on those emotions. For example, if the emotion engine indicates the user is feeling stressed, the system can slow down the process to reduce the burden. If the emotion engine indicates the user is relaxed, the system can speed up the process for greater efficiency. Furthermore, if the emotion engine indicates the user is in a hurry, the system can expedite the process. This enables flexible planning based on the user's emotions. Emotion estimation can be performed using an emotion engine or generative AI. For example, data from the emotion engine can be analyzed to understand the user's emotions in real time. Generative AI can also be used to evaluate emotions. This allows for the creation of an optimal planning plan tailored to the user's emotions.

[0129] The pre-death planning support system can estimate the user's emotions and determine priorities for organization based on those emotions. For example, if the emotion engine indicates the user is stressed, it will prioritize organizing important items and documents. If the emotion engine indicates the user is relaxed, it can proceed with more detailed organization. Furthermore, if the emotion engine indicates the user is in a hurry, it can expedite the organization process. This enables flexible organization based on the user's emotions. Emotion estimation can be performed using an emotion engine or generative AI. For example, data from the emotion engine can be analyzed to understand the user's emotions in real time. Generative AI can also be used to evaluate emotions. This allows for the creation of an optimal organization plan tailored to the user's emotions.

[0130] The pre-death planning support system can estimate the user's emotions and adjust the planning method based on those emotions. For example, if the emotion engine indicates the user is stressed, it can suggest a simple planning method. If the emotion engine indicates the user is relaxed, it can suggest a more detailed method. Furthermore, if the emotion engine indicates the user is in a hurry, it can suggest a quick planning method. This enables flexible planning based on the user's emotions. Emotion estimation can be performed using an emotion engine or generative AI. For example, data from the emotion engine can be analyzed to understand the user's emotions in real time. Emotions can also be evaluated using generative AI. This allows for the creation of an optimal planning plan tailored to the user's emotions.

[0131] The pre-death planning support system can estimate the user's emotions and adjust the timing of the planning process based on those emotions. For example, if the emotion engine indicates that the user is feeling stressed, the system can delay the planning process to reduce the burden. If the emotion engine indicates that the user is relaxed, the system can speed up the process to make it more efficient. Furthermore, if the emotion engine indicates that the user is in a hurry, the system can proceed quickly. This enables flexible planning based on the user's emotions. Emotion estimation can be performed using an emotion engine or generative AI. For example, data from the emotion engine can be analyzed to understand the user's emotions in real time. Generative AI can also be used to evaluate emotions. This allows for the creation of an optimal planning plan tailored to the user's emotions.

[0132] The pre-death planning support system can estimate the user's emotions and adjust the support content based on those emotions. For example, if the emotion engine indicates the user is stressed, it can suggest simple support. If the emotion engine indicates the user is relaxed, it can suggest more detailed support. Furthermore, if the emotion engine indicates the user is in a hurry, it can suggest quick support. This enables flexible support based on the user's emotions. Emotion estimation can be performed using an emotion engine or generative AI. For example, data from the emotion engine can be analyzed to understand the user's emotions in real time. Emotions can also be evaluated using generative AI. This allows for the creation of an optimal support plan tailored to the user's emotions.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The selling department organizes the user's belongings. The selling department organizes everyday items, furniture, clothes, hobby equipment, etc., and allows users to discard or give away unwanted items. The selling department can sell unwanted items using e-commerce sites. The selling department can also analyze the frequency of use and condition of items to set the optimal selling price. For example, the selling department can use AI to analyze the frequency of use of items and sell frequently used items at a higher price. The selling department can also use AI to evaluate the condition of items and sell items in good condition at a higher price. The selling department can also use AI to analyze the usage history of items and sell infrequently used items at a lower price. Step 2: The Sales Property Management Department manages information on items sold by the Sales Department. The Sales Property Management Department centrally manages information on sold items. The Sales Property Management Department can also analyze the sales history of items and select the optimal management method. For example, the Sales Property Management Department can use AI to analyze the sales history of items and select the optimal management method. The Sales Property Management Department can also use AI to suggest efficient management methods based on past sales data. The Sales Property Management Department can also use AI to analyze the sales history of items and optimize management methods. Step 3: The Asset Management Department organizes and manages the user's assets. The Asset Management Department organizes assets such as bank accounts, real estate, stocks, and insurance. The Asset Management Department also provides support considering situations such as dementia, and assists in the creation of wills using AI generation as needed. For example, the Asset Management Department can create wills using AI generation. The Asset Management Department can also perform asset risk assessments and select the optimal management method. For example, the Asset Management Department's AI can perform asset risk assessments and select the optimal management method. The Asset Management Department's AI can also perform risk assessments based on past asset data. The Asset Management Department's AI can also analyze market data to perform asset risk assessments. Step 4: The Document Management Department organizes and manages the user's documents. The Document Management Department helps users organize and store insurance policies, pension documents, important contracts, etc. The Document Management Department also provides support for organizing data stored on electronic devices such as PCs and smartphones. The Document Management Department can also digitize documents to enable efficient management. For example, the Document Management Department can use AI to scan and digitize documents. The Document Management Department can also use AI to analyze the content of documents and save it as digital data. The Document Management Department can also use AI to digitize documents and provide search functionality. Step 5: The Data Management Department organizes and manages user data and accounts. The Data Management Department supports the management of online accounts, social media accounts, email addresses, and data stored in the cloud. The Data Management Department helps to consolidate necessary information and provide access instructions to trusted individuals. The Data Management Department can also ensure data security by automatically backing up data. For example, the Data Management Department can use AI to automatically back up data and ensure data security. The Data Management Department can also use AI to set data backup schedules and perform backups automatically. The Data Management Department can also use AI to back up data and distribute it across multiple storage locations. Step 6: The message sending function sends messages based on the user's relationships. The message sending function can be used to express gratitude to people you haven't seen in a long time or to people you have lingering feelings for. The message sending function can send thank-you messages using messaging apps. The message sending function can also create the most suitable message considering the recipient's relationship. For example, the message sending function's AI analyzes the recipient's relationship and suggests appropriate message content. The message sending function's AI can also create the most suitable message for the recipient based on past message history. The message sending function's AI can also analyze the recipient's profile and suggest a message appropriate to the relationship.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] For example, the sales unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the property sales management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the asset management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the document management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the data management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the message transmission unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices or control units is not limited to the examples described above, and various changes are possible.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] For example, the sales unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the property sales management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the asset management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the document management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the data management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the message transmission unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples above, and various changes are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] For example, the sales unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the property sales management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the asset management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the document management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the data management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the message transmission unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] For example, the sales unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the property sales management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the asset management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the document management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the data management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the message transmission unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices or control units is not limited to the examples described above, and various changes are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) The sales department is responsible for organizing the user's belongings, The Sales Property Management Department manages information on items sold by the aforementioned Sales Department, The Asset Management Department is responsible for organizing and managing the assets of the aforementioned users. The document management department is responsible for organizing and managing the documents of the aforementioned users. A data management unit that organizes and manages the aforementioned user data and accounts, The system includes a message sending unit that sends messages based on the user's relationships. A system characterized by the following features. (Note 2) The aforementioned sales department, Sell ​​unwanted items using e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned property management department, Create a will using a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned document management department, Organize your insurance policy or pension-related documents. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned data management unit, Organize the data stored on electronic devices. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data management unit, Manage your online accounts or social media accounts. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sales department, The system estimates the user's emotions and selects items to sell based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned sales department, We analyze the frequency of use and condition of the items to determine an appropriate selling price. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned sales department, We assess the market value of items in real time and determine the appropriate time to sell them. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned sales department, It estimates user sentiment and determines the priority of sales based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned sales department, The emotional value of an item is assessed by considering its history and the memories associated with it. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned sales department, Select buyers for your goods and prioritize reliable buyers. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned property sales management department, The system estimates user sentiment and adjusts the management methods for properties for sale based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned property sales management department, Analyze the sales history of items to select the appropriate management method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned property sales management department, We monitor the storage status of items in real time and propose appropriate storage methods. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned property sales management department, The system estimates the user's emotions and adjusts how properties for sale are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned property sales management department, Properly adjust the storage locations of items and manage them efficiently. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned property sales management department, Automatically collect related information about items and enrich the management data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned property management department, It estimates the user's emotions and adjusts how assets are managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned property management department, Conduct a risk assessment of the assets and select appropriate management methods. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned property management department, We propose diversifying your assets to reduce risk. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned property management department, It estimates the user's emotions and determines the priority of assets based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned property management department, We propose appropriate management methods that take asset liquidity into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned property management department, Predict the future value of assets and develop an appropriate management plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned document management department, It estimates the user's emotions and adjusts the document organization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned document management department, We assess the importance of documents and propose appropriate storage methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned document management department, Digitizing documents enables efficient management. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned document management department, It estimates the user's emotions and prioritizes documents based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned document management department, Automatically collect relevant information from documents and enrich the management data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned document management department, Properly organize and efficiently manage document storage locations. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned data management unit, We estimate the user's emotions and adjust how the data is organized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned data management unit, We assess the importance of the data and propose appropriate storage methods. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned data management unit, Automatically backs up data to ensure data security. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned data management unit, It estimates user sentiment and prioritizes data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned data management unit, Automatically collect relevant data and enrich the management data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned data management unit, Properly adjust data storage locations and manage them efficiently. The system described in Appendix 1, characterized by the features described herein. (Note 37) The message transmission unit, It estimates the user's emotions and adjusts the message content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The message transmission unit, Create the optimal message considering the relationship with the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 39) The message transmission unit, Optimize transmission timing to achieve effective communication. The system described in Appendix 1, characterized by the features described herein. (Note 40) The message transmission unit, It estimates the user's emotions and prioritizes messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The message transmission unit, Create the optimal message considering the recipient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The message transmission unit, Create the most suitable message by referring to the recipient's past communication history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 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 sales department is responsible for organizing the user's belongings, The Sales Property Management Department manages information on items sold by the aforementioned Sales Department, The Asset Management Department is responsible for organizing and managing the assets of the aforementioned users. The document management department is responsible for organizing and managing the documents of the aforementioned users. A data management unit that organizes and manages the aforementioned user data and accounts, The system includes a message sending unit that sends messages based on the user's relationships. A system characterized by the following features.

2. The aforementioned sales department, Sell ​​unwanted items using e-commerce sites. The system according to feature 1.

3. The aforementioned property management department, Create a will using AI generation. The system according to feature 1.

4. The aforementioned document management department, Organize your insurance policy or pension-related documents. The system according to feature 1.

5. The aforementioned data management unit, Organize the data stored on electronic devices. The system according to feature 1.

6. The aforementioned data management unit, Manage your online accounts or social media accounts. The system according to feature 1.

7. The aforementioned sales department, The system estimates the user's emotions and selects items to sell based on the estimated user's emotions. The system according to feature 1.

8. The aforementioned sales department, We analyze the frequency of use and condition of the items to determine an appropriate selling price. The system according to feature 1.

9. The aforementioned sales department, We assess the market value of items in real time and determine the appropriate time to sell them. The system according to feature 1.

10. The aforementioned sales department, The system estimates the user's emotions and determines the priority of sales based on the estimated user's emotions. The system according to feature 1.

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