system

Abstract

The system according to this embodiment aims to efficiently create and store wills and propose inheritance plans. [Solution] The system according to the embodiment comprises a collection unit, a proposal unit, a creation unit, a storage unit, and a notification unit. The collection unit automatically collects asset information. The proposal unit analyzes the asset information collected by the collection unit and proposes the optimal inheritance plan. The creation unit creates a will based on the inheritance plan proposed by the proposal unit. The storage unit stores the will created by the creation unit on the web. The notification unit notifies heirs and other relevant parties of the contents of the will stored by the storage unit at a specified time.

Description

Technical Field

[0006]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is time-consuming and difficult to efficiently create, store a will, and propose an inheritance plan.

[0005] <​​​​​​​The system according to this embodiment comprises a collection unit, a proposal unit, a creation unit, a storage unit, and a notification unit. The collection unit automatically collects asset information. The proposal unit analyzes the asset information collected by the collection unit and proposes the optimal inheritance plan. The creation unit creates a will based on the inheritance plan proposed by the proposal unit. The storage unit stores the will created by the creation unit on the web. The notification unit notifies heirs and other relevant parties of the contents of the will stored by the storage unit at a specified time. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create and store wills and propose inheritance plans. [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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Web Will Planner System, according to an embodiment of the present invention, is a system that securely creates and stores wills online and provides them to heirs as legally binding official documents upon death. This Web Will Planner System automatically collects asset information (bank accounts, securities, real estate) and uses AI to propose an optimal inheritance plan. A future notification function automatically notifies heirs and related parties of the contents of the will at a specified time. It also provides support to ensure that legal requirements are met. For example, when a user creates a will with the Web Will Planner System, they need to input asset information. For example, they input information about their bank accounts and details of their real estate. This information is automatically collected by the AI. Next, the AI ​​analyzes the collected asset information and proposes an optimal inheritance plan. For example, based on the user's asset information, the AI ​​proposes a plan that considers minimizing inheritance tax and ensuring fair distribution among heirs. This allows users to easily create an optimal inheritance plan. Furthermore, the future notification function automatically notifies heirs and related parties of the contents of the will at a specified time. For example, it can be set so that the contents of the will are notified to the heirs when the user dies. This ensures that inheritance procedures proceed smoothly and prevents problems. Furthermore, the system provides support to ensure compliance with legal requirements. For example, it offers advice on meeting the legal requirements for creating a will and assists in preparing necessary documents. This allows users to create and store their wills with peace of mind. As a result, the Web Will Planner system makes it easy to create and store wills, and ensures that inheritance procedures proceed smoothly. Users simply input their asset information, and the AI ​​proposes the optimal inheritance plan, with a future notification function that automatically notifies them of the contents of their will. In addition, support to ensure compliance with legal requirements is provided, so users can use the system with confidence.

[0029] The Web Will Planner system according to this embodiment comprises a collection unit, a proposal unit, a creation unit, a storage unit, and a notification unit. The collection unit automatically collects asset information. Asset information includes, but is not limited to, bank accounts, securities, and real estate. For example, the collection unit automatically collects bank account information. The collection unit can also automatically collect securities information. The collection unit can also automatically collect real estate information. For example, the collection unit automatically obtains bank account balances and transaction history. For securities information, it automatically obtains details of held stocks and bonds. For real estate information, it automatically obtains the location and appraised value of owned real estate. The proposal unit analyzes the asset information collected by the collection unit and proposes an optimal inheritance plan. For example, the proposal unit proposes a plan that takes into account minimizing inheritance tax. The proposal unit can also propose a plan that takes into account fair distribution among heirs. The proposal unit can also propose a plan that aligns with the user's wishes. For example, the proposal unit proposes a plan that applies inheritance tax deductions and special provisions. Fair distribution among heirs considers equal distribution of assets or distribution based on specific conditions. Plans tailored to the user's wishes reflect requests such as preferential distribution of specific assets to specific heirs. The creation unit creates a will based on the inheritance plan proposed by the proposal unit. For example, the creation unit creates a will based on information entered by the user. The creation unit can also create a will that reflects the plan proposed by the proposal unit. Furthermore, the creation unit can create a will in a format that meets legal requirements. For example, the creation unit creates a will based on asset information and heir information entered by the user. Plans proposed by the proposal unit are reflected in the content of the will. Formats that meet legal requirements are created to meet the requirements for signatures and witnesses on the will. The storage unit stores the wills created by the creation unit on the web. For example, the storage unit stores the wills encrypted. Furthermore, the storage unit can implement access control to prevent tampering with the wills. Furthermore, the storage unit can perform regular backups to prevent data loss. For example, the storage unit encrypts and securely stores the will. Access control is set up so that only specific users can access the will.Regular backups are performed to protect against data loss or corruption. The notification unit notifies heirs and other relevant parties of the contents of the will stored by the storage unit at a specified time. For example, the notification unit notifies the heirs of the contents of the will when the user dies. The notification unit can also notify the contents of the will at a specific date and time. Furthermore, the notification unit can provide notifications according to the progress of the inheritance procedure. For example, the notification unit notifies the heirs of the contents of the will when the user dies. If notification is to be given at a specific date and time, the contents of the will will be notified at the date and time specified by the user. Notifications according to the progress of the inheritance procedure provide the necessary information at each stage of the inheritance procedure. As a result, the web will planner system according to this embodiment can handle everything from asset information collection to will creation, storage, and notification in an integrated manner.

[0030] The data collection unit automatically collects asset information. Asset information includes, but is not limited to, bank accounts, securities, and real estate. For example, the data collection unit automatically collects bank account information. It can also automatically collect securities information. It can also automatically collect real estate information. For example, the data collection unit automatically retrieves bank account balances and transaction history. For securities information, it automatically retrieves details of held stocks and bonds. For real estate information, it automatically retrieves the location and appraised value of owned real estate. To obtain this information, the data collection unit uses APIs that link with various financial institutions, securities companies, and real estate management systems. Bank account information is obtained directly from the bank's online banking system based on authentication information provided by the user. Securities information is obtained in real time from the securities company's trading system, reflecting the latest appraised value of held stocks and bonds. Real estate information is obtained from real estate registration systems and appraisal agency databases, collecting the latest appraised value and location information of owned real estate. This allows the data collection unit to collect user asset information accurately and quickly, improving the overall reliability of the system. Furthermore, the data collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the proposal and creation departments. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The proposal department analyzes asset information collected by the collection department and proposes the optimal inheritance plan. For example, the proposal department proposes a plan that considers minimizing inheritance tax. It can also propose a plan that considers fair distribution among heirs. Furthermore, the proposal department can propose a plan that aligns with the user's wishes. For example, the proposal department proposes a plan that applies inheritance tax deductions and special provisions. Fair distribution among heirs considers equal distribution of assets or distribution based on specific conditions. A plan that aligns with the user's wishes reflects requests such as preferential distribution of specific assets to specific heirs. The proposal department uses AI to analyze the collected asset information and generates the optimal plan based on the user's asset situation and heir information. The AI ​​refers to databases of past inheritance cases and tax laws and proposes the best way to achieve inheritance tax minimization and fair distribution. For example, the AI ​​considers the user's asset composition, the number of heirs, and their relationships to generate a plan that makes the most of inheritance tax deductions and special provisions. The AI ​​also proposes equal distribution of assets or distribution based on specific conditions to achieve fair distribution among heirs. Furthermore, the proposal department considers the user's wishes and requests to generate a plan that reflects the user's intentions. For example, if there is a request to preferentially distribute certain assets to a specific heir, the AI ​​will generate a plan that reflects that request. In this way, the proposal department can propose the optimal inheritance plan that meets the user's needs and improve user satisfaction.

[0032] The creation unit creates a will based on the inheritance plan proposed by the proposal unit. For example, the creation unit creates a will based on information entered by the user. It can also create a will that reflects the plan proposed by the proposal unit. Furthermore, the creation unit can create a will in a format that meets legal requirements. For example, the creation unit creates a will based on asset information and heir information entered by the user. The plan proposed by the proposal unit is reflected in the content of the will. A format that meets legal requirements is created to satisfy the requirements for signatures and witnesses. The creation unit uses templates and guidelines to ensure legal requirements are met when creating a will. This ensures that the will is legally valid. For example, to meet the requirements for signatures and witnesses, it provides the necessary procedures for the user to sign the will and how to select witnesses. The creation unit also automatically generates the content of the will based on the information entered by the user. This allows the user to easily create a will. Furthermore, the creation department will strengthen its collaboration with the proposal department to create wills that reflect the plans proposed by the proposal department. For example, it will automatically import the inheritance plan generated by the proposal department and reflect it in the content of the will. This will enable the creation department to create wills that accurately reflect the user's intentions and the proposal department's plans.

[0033] The storage unit stores wills created by the creation unit on the web. The storage unit can, for example, encrypt and store the wills. It can also implement access control to prevent tampering with the wills. Furthermore, the storage unit can perform regular backups to prevent data loss. For example, the storage unit securely stores the wills in an encrypted format. Access control is set so that only specific users can access the wills. Regular backups are performed to prepare for data loss or corruption. The storage unit implements the latest security technologies to ensure the safety and reliability of the wills. For example, strong encryption algorithms are used to encrypt the wills to prevent unauthorized access and data leakage. Multi-factor authentication and access log monitoring are implemented for access control to ensure that only specific users can access the wills. In addition, the storage unit performs regular backups to prepare for data loss or corruption. Backup data is stored in a different location, allowing for data recovery even in the event of disasters or system failures. This ensures the safety and reliability of the wills and protects users' important information.

[0034] The notification unit notifies heirs and other relevant parties of the contents of the will stored by the storage unit at a specified time. For example, the notification unit notifies the heirs of the contents of the will when the user dies. The notification unit can also notify the contents of the will at a specific date and time. Furthermore, the notification unit can provide notifications according to the progress of the inheritance procedure. For example, the notification unit notifies the heirs of the contents of the will when the user dies. If notification is to be made at a specific date and time, the contents of the will will be notified at the date and time specified by the user. Notifications according to the progress of the inheritance procedure provide necessary information at each stage of the inheritance procedure. The notification unit uses multiple communication methods to enhance the reliability and certainty of notifications. For example, it combines methods such as email, SMS, telephone, and postal mail to ensure that heirs and other relevant parties are notified. The notification unit also provides a function to confirm and track the contents of notifications, so that heirs and other relevant parties can confirm that they have received the notification. In addition, the notification unit can flexibly provide notifications based on conditions and timings specified by the user. For example, the user can set it to prioritize notifying specific heirs of specific assets. This allows the notification unit to provide notifications in line with the user's wishes, thereby supporting the smooth progress of inheritance procedures.

[0035] The data collection unit can automatically collect asset information such as bank accounts, securities, and real estate. For example, the data collection unit can automatically collect bank account information. The data collection unit can also automatically collect securities information. The data collection unit can also automatically collect real estate information. For example, the data collection unit can automatically retrieve bank account balances and transaction history. The data collection unit can automatically retrieve details of owned stocks and bonds. The data collection unit can automatically retrieve the location and appraised value of owned real estate. By automatically collecting asset information, the data collection unit can reduce the effort required from the user. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, in order to obtain bank account information, the data collection unit can use AI to access bank databases and collect the necessary information.

[0036] The proposal unit can propose inheritance plans that take into account minimizing inheritance tax and ensuring fair distribution among heirs. For example, the proposal unit can propose a plan that takes into account minimizing inheritance tax. The proposal unit can also propose a plan that takes into account fair distribution among heirs. The proposal unit can also propose a plan that aligns with the user's wishes. For example, the proposal unit can propose a plan that applies inheritance tax deductions and special provisions. The proposal unit can consider equal distribution of assets or distribution based on specific conditions. The proposal unit can reflect requests such as preferentially distributing specific assets to specific heirs. By proposing plans that take into account minimizing inheritance tax and ensuring fair distribution, the proposal unit can provide the user with the optimal inheritance plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can propose an inheritance plan using an AI model that takes the user's asset information as input and outputs a plan that takes into account minimizing inheritance tax and ensuring fair distribution.

[0037] The creation unit can create a will based on the inheritance plan proposed by the proposal unit. The creation unit can, for example, create a will based on information entered by the user. The creation unit can also, for example, create a will that reflects the plan proposed by the proposal unit. The creation unit can also, for example, create a will in a format that meets legal requirements. For example, the creation unit can create a will based on asset information and heir information entered by the user. The creation unit can, for example, reflect the plan proposed by the proposal unit in the content of the will. The creation unit can, for example, create a will that meets the requirements for signatures and witnesses. In this way, by creating a will based on the proposed inheritance plan, a will that is in line with the user's wishes can be created. Some or all of the above processes in the creation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the creation unit can input information entered by the user as a prompt to the generation AI, and the generation AI can generate the content of the will.

[0038] The storage unit can securely store wills created by the creation unit on the web. The storage unit can, for example, encrypt and store the wills. The storage unit can also, for example, implement access control to prevent tampering with the wills. The storage unit can also, for example, perform regular backups to prevent data loss. For example, the storage unit can securely store the wills by encrypting them. The storage unit can, for example, configure it so that only specific users can access the wills. The storage unit can, for example, perform regular backups to prepare for data loss or corruption. This reduces the risk of loss or tampering by securely storing wills on the web. Some or all of the above processes in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can use AI to optimize security protocols for will encryption and access control.

[0039] The notification unit can automatically notify heirs and other relevant parties of the contents of a will stored by the storage unit at a specified time. For example, the notification unit notifies the heirs of the contents of the will when the user dies. The notification unit can also notify the contents of the will at a specific date and time. The notification unit can also provide notifications according to the progress of the inheritance procedure. For example, the notification unit notifies the heirs of the contents of the will when the user dies. The notification unit notifies the contents of the will at a date and time specified by the user. The notification unit notifies necessary information at each stage of the inheritance procedure. This allows the inheritance procedure to proceed smoothly by automatically notifying the contents of the will at a specified time. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to confirm the submission of a death certificate in order to detect the user's death and then notify the heirs of the contents of the will.

[0040] The notification unit can notify the heirs of the contents of a will when a user dies. For example, the notification unit notifies the heirs of the contents of a will when a user dies. For example, the notification unit can confirm the submission of a death certificate to confirm the death of a user. For example, the notification unit can confirm the death of a user based on the passage of a specific period of time. For example, the notification unit notifies the heirs of the contents of a will when a user dies. For example, the notification unit notifies the heirs of the contents of a will after confirming the submission of a death certificate. For example, the notification unit notifies the heirs of the contents of a will after a specific period of time has elapsed. This allows inheritance procedures to be started quickly by notifying the heirs of the contents of a will when a user dies. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to confirm the submission of a death certificate to detect the death of a user and notify the heirs of the contents of a will.

[0041] The proposal unit can propose the optimal inheritance plan based on the user's asset information. For example, the proposal unit can propose a plan that considers minimizing inheritance tax based on the user's asset information. The proposal unit can also propose a plan that considers fair distribution among heirs based on the user's asset information. The proposal unit can also propose a plan that aligns with the user's wishes. For example, the proposal unit can propose a plan that applies inheritance tax deductions and special provisions based on the user's asset information. The proposal unit can consider equal distribution of assets or distribution based on specific conditions. The proposal unit can reflect requests such as preferentially distributing specific assets to specific heirs. In this way, by proposing the optimal inheritance plan based on the user's asset information, the proposal unit can provide the user with the best possible inheritance plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can propose an inheritance plan using an AI model that takes the user's asset information as input and outputs a plan that considers minimizing inheritance tax and fair distribution.

[0042] The data collection unit can automatically collect information on bank accounts and real estate owned by the user. For example, the data collection unit can automatically collect information on bank accounts owned by the user. The data collection unit can also automatically collect details on real estate owned by the user. The data collection unit can also automatically collect information on securities owned by the user. For example, the data collection unit can automatically obtain the balance and transaction history of bank accounts owned by the user. For example, the data collection unit can automatically obtain the location and appraised value of real estate owned by the user. For example, the data collection unit can automatically obtain details on securities owned by the user. This makes the collection of asset information more efficient by automatically collecting information on bank accounts and real estate owned by the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, in order to obtain information on bank accounts owned by the user, the data collection unit can use AI to access bank databases and collect the necessary information.

[0043] The data collection unit can analyze the user's past asset information collection history and select the optimal collection method. The data collection unit can, for example, prioritize suggesting collection methods the user has used in the past (manual input, automatic collection, etc.). The data collection unit can also, for example, suggest a method for collecting data during a specific time period based on the user's past collection history. The data collection unit can also, for example, analyze the user's past collection history and select the most efficient collection method. For example, the data collection unit can, for example, prioritize suggesting collection methods the user has used in the past (manual input, automatic collection, etc.). The data collection unit can, for example, suggest a method for collecting data during a specific time period based on the user's past collection history. The data collection unit can, for example, analyze the user's past collection history and select the most efficient collection method. This allows the optimal collection method to be selected by analyzing the user's past collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select a collection method using an AI model that takes the user's past collection history as input and outputs the optimal collection method.

[0044] The data collection unit can filter asset information based on the user's current financial situation and areas of interest. For example, the data collection unit can prioritize the collection of important asset information, taking into account the user's current financial situation. The data collection unit can also collect relevant asset information based on the user's areas of interest (e.g., real estate, securities). The data collection unit can also filter and collect unnecessary information based on the user's financial situation and areas of interest. For example, the data collection unit can prioritize the collection of important asset information, taking into account the user's current financial situation. The data collection unit can collect relevant asset information based on the user's areas of interest (e.g., real estate, securities). The data collection unit can filter and collect unnecessary information based on the user's financial situation and areas of interest. This allows for the priority collection of important asset information by filtering based on the user's financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform filtering using an AI model that prioritizes the collection of important asset information, taking the user's financial situation and areas of interest as input.

[0045] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting asset information. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of real estate information related to that region. For example, if the user is traveling, the data collection unit can also collect asset information related to the user's current location. For example, the data collection unit can also collect highly relevant bank account information based on the user's geographical location. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of real estate information related to that region. For example, if the user is traveling, the data collection unit will collect asset information related to the user's current location. For example, the data collection unit can collect highly relevant bank account information based on the user's geographical location. This allows for the priority collection of highly relevant asset information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform collection using an AI model that prioritizes the collection of highly relevant asset information, taking the user's geographical location as input.

[0046] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting asset information. For example, the data collection unit can collect relevant asset information based on information shared by the user on social media. The data collection unit can also, for example, prioritize the collection of asset information of interest from the user's social media activity. The data collection unit can also, for example, analyze the user's social media activity and propose the optimal collection method. For example, the data collection unit can collect relevant asset information based on information shared by the user on social media. For example, the data collection unit can prioritize the collection of asset information of interest from the user's social media activity. The data collection unit can, for example, analyze the user's social media activity and propose the optimal collection method. This allows for the efficient collection of relevant asset information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform collection using an AI model that collects relevant asset information using the user's social media activity as input.

[0047] The proposal unit can adjust the level of detail of its proposals based on the importance of the assets. For example, the proposal unit can provide detailed proposals for highly important assets. For example, the proposal unit can provide concise proposals for less important assets. The proposal unit can also adjust the level of detail of its proposals according to the importance of the assets. For example, the proposal unit can provide detailed proposals for highly important assets. For example, the proposal unit can provide concise proposals for less important assets. The proposal unit can adjust the level of detail of its proposals according to the importance of the assets. By adjusting the level of detail of proposals based on the importance of the assets, it becomes possible to provide the best possible proposals for the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use an AI model that adjusts the level of detail of proposals based on the importance of the assets as input to make proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the asset category when making a proposal. For example, for a proposal concerning real estate, the proposal unit can apply an algorithm that takes regional market trends into account. For example, for a proposal concerning securities, the proposal unit can also apply an algorithm that takes past performance into account. For example, for a proposal concerning bank accounts, the proposal unit can also apply an algorithm that takes interest rates and fees into account. For example, for a proposal concerning real estate, the proposal unit can apply an algorithm that takes regional market trends into account. For example, for a proposal concerning securities, the proposal unit can apply an algorithm that takes past performance into account. For example, for a proposal concerning bank accounts, the proposal unit can apply an algorithm that takes interest rates and fees into account. This makes it possible to make more accurate proposals by applying different proposal algorithms depending on the asset category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can make proposals using an AI model that takes the asset category as input and applies different proposal algorithms.

[0049] The proposal unit can determine the priority of proposals based on the acquisition date of the assets when making a proposal. For example, the proposal unit will prioritize proposals for recently acquired assets. For example, the proposal unit may postpone proposals for assets that have been held for a long period of time. The proposal unit can also determine the priority of proposals according to the acquisition date of the assets. For example, the proposal unit will prioritize proposals for recently acquired assets. For example, the proposal unit will postpone proposals for assets that have been held for a long period of time. For example, the proposal unit will determine the priority of proposals according to the acquisition date of the assets. This makes it possible to make optimal proposals for the user by determining the priority of proposals based on the acquisition date of the assets. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can make proposals using an AI model that takes the acquisition date of assets as input and determines the priority of proposals.

[0050] The proposal unit can adjust the order of proposals based on the relevance of assets when making a proposal. For example, the proposal unit can prioritize proposals for highly relevant assets. For example, the proposal unit can postpone proposals for less relevant assets. The proposal unit can also adjust the order of proposals according to the relevance of assets. For example, the proposal unit can prioritize proposals for highly relevant assets. For example, the proposal unit can postpone proposals for less relevant assets. For example, the proposal unit can adjust the order of proposals according to the relevance of assets. This makes it possible to make optimal proposals for the user by adjusting the order of proposals based on the relevance of assets. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can make proposals using an AI model that adjusts the order of proposals, taking the relevance of assets as input.

[0051] The creation unit can analyze the user's past will creation history and select the optimal creation method during creation. For example, the creation unit may prioritize suggesting creation methods the user has used in the past (manual input, voice input, etc.). The creation unit may also suggest a method for creating a will at a specific time based on the user's past creation history. The creation unit may also analyze the user's past creation history and select the most efficient creation method. For example, the creation unit may prioritize suggesting creation methods the user has used in the past (manual input, voice input, etc.). The creation unit may, for example, suggest a method for creating a will at a specific time based on the user's past creation history. The creation unit may, for example, analyze the user's past creation history and select the most efficient creation method. This allows the optimal creation method to be selected by analyzing the user's past creation history. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can use an AI model that selects the optimal creation method based on the user's past creation history as input to perform the creation.

[0052] The creation unit can customize the contents of the will based on the user's current living situation during creation. For example, the creation unit considers the user's current financial situation and proposes the optimal contents of the will. The creation unit can also customize the contents of the will based on the user's family structure and living environment. The creation unit can also automatically add necessary information based on the user's living situation. For example, the creation unit considers the user's current financial situation and proposes the optimal contents of the will. The creation unit customizes the contents of the will based on the user's family structure and living environment. The creation unit automatically adds necessary information based on the user's living situation. This allows for the creation of a will that is optimal for the user by customizing the contents of the will based on the user's living situation. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can use an AI model that customizes the contents of the will using the user's living situation as input.

[0053] The creation unit can select the most appropriate will content by considering the user's geographical location information during creation. For example, if the user lives in a specific region, the creation unit will prioritize creating will content related to that region. For example, if the user is traveling, the creation unit can also create will content related to the user's current location. The creation unit can also select highly relevant content based on the user's geographical location information. For example, if the user lives in a specific region, the creation unit will prioritize creating will content related to that region. For example, if the user is traveling, the creation unit will create will content related to the user's current location. For example, the creation unit can select highly relevant content based on the user's geographical location information. This allows the creation of highly relevant will content by considering the user's geographical location information. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can use an AI model that selects the most appropriate will content using the user's geographical location information as input.

[0054] The creation unit can analyze the user's social media activity and propose the contents of the will during the creation process. For example, the creation unit can propose relevant will contents based on information shared by the user on social media. The creation unit can also prioritize proposing content of interest based on the user's social media activity. The creation unit can also analyze the user's social media activity and propose the most suitable content. For example, the creation unit can propose relevant will contents based on information shared by the user on social media. The creation unit can prioritize proposing content of interest based on the user's social media activity. The creation unit can also analyze the user's social media activity and propose the most suitable content. This allows for the efficient proposal of relevant will contents by analyzing the user's social media activity. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can make suggestions using an AI model that takes the user's social media activity as input and proposes relevant will contents.

[0055] The storage unit can analyze the user's past storage history to select the optimal storage method during storage. For example, the storage unit may prioritize suggesting storage methods the user has used in the past (cloud, local, etc.). The storage unit may also suggest a method for storing data during a specific time period based on the user's past storage history. The storage unit may also analyze the user's past storage history to select the most efficient storage method. For example, the storage unit may prioritize suggesting storage methods the user has used in the past (cloud, local, etc.). The storage unit may also suggest a method for storing data during a specific time period based on the user's past storage history. The storage unit may also analyze the user's past storage history to select the most efficient storage method. This allows the storage unit to select the optimal storage method by analyzing the user's past storage history. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can perform storage using an AI model that selects the optimal storage method based on the user's past storage history as input.

[0056] The storage unit can customize storage methods based on the user's current living situation during storage. For example, the storage unit considers the user's current financial situation and proposes the optimal storage method. The storage unit can also customize storage methods based on the user's family structure and living environment. The storage unit can also automatically add necessary information based on the user's living situation. For example, the storage unit considers the user's current financial situation and proposes the optimal storage method. The storage unit customizes storage methods based on the user's family structure and living environment. The storage unit automatically adds necessary information based on the user's living situation. This makes it possible to provide the optimal storage method for the user by customizing storage methods based on the user's living situation. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can perform storage using an AI model that customizes storage methods based on the user's living situation as input.

[0057] The storage unit can select the optimal storage method when storing data, taking into account the user's geographical location information. For example, if the user lives in a specific region, the storage unit will prioritize suggesting storage methods related to that region. For example, if the user is traveling, the storage unit can also suggest storage methods related to their current location. The storage unit can also select a highly relevant storage method based on the user's geographical location information. For example, if the user lives in a specific region, the storage unit will prioritize suggesting storage methods related to that region. For example, if the user is traveling, the storage unit will suggest storage methods related to their current location. For example, the storage unit can select a highly relevant storage method based on the user's geographical location information. This allows the storage unit to select a highly relevant storage method by considering the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can perform storage using an AI model that selects the optimal storage method based on the user's geographical location information as input.

[0058] The storage unit can analyze the user's social media activity and suggest storage methods during storage. For example, the storage unit can suggest relevant storage methods based on information shared by the user on social media. The storage unit can also prioritize suggesting storage methods of interest based on the user's social media activity. The storage unit can also analyze the user's social media activity and suggest the optimal storage method. For example, the storage unit can suggest relevant storage methods based on information shared by the user on social media. The storage unit can prioritize suggesting storage methods of interest based on the user's social media activity. The storage unit can analyze the user's social media activity and suggest the optimal storage method. This allows for the efficient suggestion of relevant storage methods by analyzing the user's social media activity. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can use an AI model that takes the user's social media activity as input to suggest relevant storage methods.

[0059] The notification unit can analyze the user's past notification history to select the optimal notification method when sending a notification. For example, the notification unit may prioritize suggesting notification methods the user has used in the past (email, SMS, etc.). The notification unit can also suggest a method for sending notifications at a specific time based on the user's past notification history. The notification unit can also analyze the user's past notification history to select the most efficient notification method. For example, the notification unit may prioritize suggesting notification methods the user has used in the past (email, SMS, etc.). The notification unit may suggest a method for sending notifications at a specific time based on the user's past notification history. The notification unit can also analyze the user's past notification history to select the most efficient notification method. This allows the notification unit to select the optimal notification method by analyzing the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that selects the optimal notification method based on the user's past notification history as input.

[0060] The notification unit can customize notification content based on the user's current living situation when it sends a notification. For example, the notification unit can consider the user's current financial situation and suggest the most suitable notification content. The notification unit can also customize notification content based on the user's family structure and living environment. The notification unit can also automatically add necessary information based on the user's living situation. For example, the notification unit can consider the user's current financial situation and suggest the most suitable notification content. The notification unit can customize notification content based on the user's family structure and living environment. The notification unit can automatically add necessary information based on the user's living situation. This makes it possible to provide notifications that are optimal for the user by customizing notification content based on the user's living situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that customizes notification content based on the user's living situation as input.

[0061] The notification unit can select the most appropriate notification method when sending a notification, taking into account the user's geographical location. For example, if the user lives in a specific region, the notification unit will prioritize suggesting a notification method relevant to that region. For example, if the user is traveling, the notification unit can also suggest a notification method relevant to their current location. The notification unit can also select a highly relevant notification method based on the user's geographical location. For example, if the user lives in a specific region, the notification unit will prioritize suggesting a notification method relevant to that region. For example, if the user is traveling, the notification unit will suggest a notification method relevant to their current location. For example, the notification unit can select a highly relevant notification method based on the user's geographical location. This allows the notification unit to select a highly relevant notification method by considering the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that selects the most appropriate notification method based on the user's geographical location as input.

[0062] The notification unit can analyze the user's social media activity and suggest notification content when sending a notification. For example, the notification unit can suggest relevant notification content based on information shared by the user on social media. The notification unit can also prioritize suggesting notification content of interest based on the user's social media activity. The notification unit can also analyze the user's social media activity and suggest the most appropriate notification content. For example, the notification unit can suggest relevant notification content based on information shared by the user on social media. The notification unit can prioritize suggesting notification content of interest based on the user's social media activity. The notification unit can analyze the user's social media activity and suggest the most appropriate notification content. This allows for the efficient suggestion of relevant notification content by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can make suggestions using an AI model that takes the user's social media activity as input and suggests relevant notification content.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit can analyze the user's past asset information collection history and select the optimal collection method. For example, it can prioritize suggesting collection methods the user has used in the past (manual input, automated collection, etc.). It can also suggest methods for collecting data during specific time periods based on the user's past collection history. Furthermore, it can analyze the user's past collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past collection history. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can select a collection method using an AI model that takes the user's past collection history as input and outputs the optimal collection method.

[0065] The data collection unit can filter asset information based on the user's current financial situation and areas of interest. For example, it can prioritize the collection of important asset information, taking into account the user's current financial situation. It can also collect relevant asset information based on the user's areas of interest (e.g., real estate, securities). Furthermore, it can filter out unnecessary information based on the user's financial situation and areas of interest. This allows for the priority collection of important asset information by filtering based on the user's financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model that prioritizes the collection of important asset information, taking the user's financial situation and areas of interest as input, to perform filtering.

[0066] The proposal unit can adjust the level of detail in its proposals based on the importance of the assets. For example, it can provide detailed proposals for highly important assets and concise proposals for less important assets. Furthermore, it can adjust the level of detail in its proposals according to the importance of the assets. This allows for optimal proposals for the user by adjusting the level of detail based on the importance of the assets. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can use an AI model that adjusts the level of detail in proposals based on the importance of the assets as input.

[0067] The creation unit can analyze the user's past will-creation history to select the optimal creation method during the creation process. For example, it can prioritize suggesting creation methods the user has used in the past (manual input, voice input, etc.). It can also suggest methods for creating a will at specific times based on the user's past creation history. Furthermore, it can analyze the user's past creation history to select the most efficient creation method. In this way, the optimal creation method can be selected by analyzing the user's past creation history. Some or all of the above processes in the creation unit may be performed using AI, or not. For example, the creation unit can use an AI model that selects the optimal creation method based on the user's past creation history as input to perform the creation.

[0068] The storage unit can analyze the user's past storage history to select the optimal storage method during storage. For example, it can prioritize suggesting storage methods the user has used in the past (cloud, local, etc.). It can also suggest storage methods for specific time periods based on the user's past storage history. Furthermore, it can analyze the user's past storage history to select the most efficient storage method. In this way, the optimal storage method can be selected by analyzing the user's past storage history. Some or all of the above processes in the storage unit may be performed using AI, or not. For example, the storage unit can use an AI model that selects the optimal storage method based on the user's past storage history as input, and then perform storage.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The collection unit automatically collects asset information. Asset information includes, for example, bank accounts, securities, and real estate. The collection unit automatically retrieves bank account balances and transaction history, details of held stocks and bonds, and the location and appraised value of owned real estate. Step 2: The proposal department analyzes the asset information collected by the collection department and proposes the optimal inheritance plan. The proposal department proposes a plan that minimizes inheritance tax, ensures fair distribution among heirs, and aligns with the user's wishes. For example, it will reflect requests such as plans that apply inheritance tax deductions and special provisions, equal distribution of assets or distribution based on specific conditions, and preferential distribution of specific assets to specific heirs. Step 3: The creation department prepares the will based on the inheritance plan proposed by the proposal department. The creation department prepares the will based on the information entered by the user and creates a will that reflects the plan proposed by the proposal department. It also prepares the will in a format that meets legal requirements. Step 4: The storage unit stores the wills created by the creation unit online. The storage unit encrypts the wills, implements access control, and prevents tampering. It also performs regular backups to prevent data loss. Step 5: The notification unit notifies heirs and other relevant parties of the contents of the will stored by the storage unit at the specified time. The notification unit notifies the contents of the will when the user dies or at a specific date and time, and also notifies them according to the progress of the inheritance procedures.

[0071] (Example of form 2) The Web Will Planner System, according to an embodiment of the present invention, is a system that securely creates and stores wills online and provides them to heirs as legally binding official documents upon death. This Web Will Planner System automatically collects asset information (bank accounts, securities, real estate) and uses AI to propose an optimal inheritance plan. A future notification function automatically notifies heirs and related parties of the contents of the will at a specified time. It also provides support to ensure that legal requirements are met. For example, when a user creates a will with the Web Will Planner System, they need to input asset information. For example, they input information about their bank accounts and details of their real estate. This information is automatically collected by the AI. Next, the AI ​​analyzes the collected asset information and proposes an optimal inheritance plan. For example, based on the user's asset information, the AI ​​proposes a plan that considers minimizing inheritance tax and ensuring fair distribution among heirs. This allows users to easily create an optimal inheritance plan. Furthermore, the future notification function automatically notifies heirs and related parties of the contents of the will at a specified time. For example, it can be set so that the contents of the will are notified to the heirs when the user dies. This ensures that inheritance procedures proceed smoothly and prevents problems. Furthermore, the system provides support to ensure compliance with legal requirements. For example, it offers advice on meeting the legal requirements for creating a will and assists in preparing necessary documents. This allows users to create and store their wills with peace of mind. As a result, the Web Will Planner system makes it easy to create and store wills, and ensures that inheritance procedures proceed smoothly. Users simply input their asset information, and the AI ​​proposes the optimal inheritance plan, with a future notification function that automatically notifies them of the contents of their will. In addition, support to ensure compliance with legal requirements is provided, so users can use the system with confidence.

[0072] The Web Will Planner system according to this embodiment comprises a collection unit, a proposal unit, a creation unit, a storage unit, and a notification unit. The collection unit automatically collects asset information. Asset information includes, but is not limited to, bank accounts, securities, and real estate. For example, the collection unit automatically collects bank account information. The collection unit can also automatically collect securities information. The collection unit can also automatically collect real estate information. For example, the collection unit automatically obtains bank account balances and transaction history. For securities information, it automatically obtains details of held stocks and bonds. For real estate information, it automatically obtains the location and appraised value of owned real estate. The proposal unit analyzes the asset information collected by the collection unit and proposes an optimal inheritance plan. For example, the proposal unit proposes a plan that takes into account minimizing inheritance tax. The proposal unit can also propose a plan that takes into account fair distribution among heirs. The proposal unit can also propose a plan that aligns with the user's wishes. For example, the proposal unit proposes a plan that applies inheritance tax deductions and special provisions. Fair distribution among heirs considers equal distribution of assets or distribution based on specific conditions. Plans tailored to the user's wishes reflect requests such as preferential distribution of specific assets to specific heirs. The creation unit creates a will based on the inheritance plan proposed by the proposal unit. For example, the creation unit creates a will based on information entered by the user. The creation unit can also create a will that reflects the plan proposed by the proposal unit. Furthermore, the creation unit can create a will in a format that meets legal requirements. For example, the creation unit creates a will based on asset information and heir information entered by the user. Plans proposed by the proposal unit are reflected in the content of the will. Formats that meet legal requirements are created to meet the requirements for signatures and witnesses on the will. The storage unit stores the wills created by the creation unit on the web. For example, the storage unit stores the wills encrypted. Furthermore, the storage unit can implement access control to prevent tampering with the wills. Furthermore, the storage unit can perform regular backups to prevent data loss. For example, the storage unit encrypts and securely stores the will. Access control is set up so that only specific users can access the will.Regular backups are performed to protect against data loss or corruption. The notification unit notifies heirs and other relevant parties of the contents of the will stored by the storage unit at a specified time. For example, the notification unit notifies the heirs of the contents of the will when the user dies. The notification unit can also notify the contents of the will at a specific date and time. Furthermore, the notification unit can provide notifications according to the progress of the inheritance procedure. For example, the notification unit notifies the heirs of the contents of the will when the user dies. If notification is to be given at a specific date and time, the contents of the will will be notified at the date and time specified by the user. Notifications according to the progress of the inheritance procedure provide the necessary information at each stage of the inheritance procedure. As a result, the web will planner system according to this embodiment can handle everything from asset information collection to will creation, storage, and notification in an integrated manner.

[0073] The data collection unit automatically collects asset information. Asset information includes, but is not limited to, bank accounts, securities, and real estate. For example, the data collection unit automatically collects bank account information. It can also automatically collect securities information. It can also automatically collect real estate information. For example, the data collection unit automatically retrieves bank account balances and transaction history. For securities information, it automatically retrieves details of held stocks and bonds. For real estate information, it automatically retrieves the location and appraised value of owned real estate. To obtain this information, the data collection unit uses APIs that link with various financial institutions, securities companies, and real estate management systems. Bank account information is obtained directly from the bank's online banking system based on authentication information provided by the user. Securities information is obtained in real time from the securities company's trading system, reflecting the latest appraised value of held stocks and bonds. Real estate information is obtained from real estate registration systems and appraisal agency databases, collecting the latest appraised value and location information of owned real estate. This allows the data collection unit to collect user asset information accurately and quickly, improving the overall reliability of the system. Furthermore, the data collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the proposal and creation departments. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0074] The proposal department analyzes asset information collected by the collection department and proposes the optimal inheritance plan. For example, the proposal department proposes a plan that considers minimizing inheritance tax. It can also propose a plan that considers fair distribution among heirs. Furthermore, the proposal department can propose a plan that aligns with the user's wishes. For example, the proposal department proposes a plan that applies inheritance tax deductions and special provisions. Fair distribution among heirs considers equal distribution of assets or distribution based on specific conditions. A plan that aligns with the user's wishes reflects requests such as preferential distribution of specific assets to specific heirs. The proposal department uses AI to analyze the collected asset information and generates the optimal plan based on the user's asset situation and heir information. The AI ​​refers to databases of past inheritance cases and tax laws and proposes the best way to achieve inheritance tax minimization and fair distribution. For example, the AI ​​considers the user's asset composition, the number of heirs, and their relationships to generate a plan that makes the most of inheritance tax deductions and special provisions. The AI ​​also proposes equal distribution of assets or distribution based on specific conditions to achieve fair distribution among heirs. Furthermore, the proposal department considers the user's wishes and requests to generate a plan that reflects the user's intentions. For example, if there is a request to preferentially distribute certain assets to a specific heir, the AI ​​will generate a plan that reflects that request. In this way, the proposal department can propose the optimal inheritance plan that meets the user's needs and improve user satisfaction.

[0075] The creation unit creates a will based on the inheritance plan proposed by the proposal unit. For example, the creation unit creates a will based on information entered by the user. It can also create a will that reflects the plan proposed by the proposal unit. Furthermore, the creation unit can create a will in a format that meets legal requirements. For example, the creation unit creates a will based on asset information and heir information entered by the user. The plan proposed by the proposal unit is reflected in the content of the will. A format that meets legal requirements is created to satisfy the requirements for signatures and witnesses. The creation unit uses templates and guidelines to ensure legal requirements are met when creating a will. This ensures that the will is legally valid. For example, to meet the requirements for signatures and witnesses, it provides the necessary procedures for the user to sign the will and how to select witnesses. The creation unit also automatically generates the content of the will based on the information entered by the user. This allows the user to easily create a will. Furthermore, the creation department will strengthen its collaboration with the proposal department to create wills that reflect the plans proposed by the proposal department. For example, it will automatically import the inheritance plan generated by the proposal department and reflect it in the content of the will. This will enable the creation department to create wills that accurately reflect the user's intentions and the proposal department's plans.

[0076] The storage unit stores wills created by the creation unit on the web. The storage unit can, for example, encrypt and store the wills. It can also implement access control to prevent tampering with the wills. Furthermore, the storage unit can perform regular backups to prevent data loss. For example, the storage unit securely stores the wills in an encrypted format. Access control is set so that only specific users can access the wills. Regular backups are performed to prepare for data loss or corruption. The storage unit implements the latest security technologies to ensure the safety and reliability of the wills. For example, strong encryption algorithms are used to encrypt the wills to prevent unauthorized access and data leakage. Multi-factor authentication and access log monitoring are implemented for access control to ensure that only specific users can access the wills. In addition, the storage unit performs regular backups to prepare for data loss or corruption. Backup data is stored in a different location, allowing for data recovery even in the event of disasters or system failures. This ensures the safety and reliability of the wills and protects users' important information.

[0077] The notification unit notifies heirs and other relevant parties of the contents of the will stored by the storage unit at a specified time. For example, the notification unit notifies the heirs of the contents of the will when the user dies. The notification unit can also notify the contents of the will at a specific date and time. Furthermore, the notification unit can provide notifications according to the progress of the inheritance procedure. For example, the notification unit notifies the heirs of the contents of the will when the user dies. If notification is to be made at a specific date and time, the contents of the will will be notified at the date and time specified by the user. Notifications according to the progress of the inheritance procedure provide necessary information at each stage of the inheritance procedure. The notification unit uses multiple communication methods to enhance the reliability and certainty of notifications. For example, it combines methods such as email, SMS, telephone, and postal mail to ensure that heirs and other relevant parties are notified. The notification unit also provides a function to confirm and track the contents of notifications, so that heirs and other relevant parties can confirm that they have received the notification. In addition, the notification unit can flexibly provide notifications based on conditions and timings specified by the user. For example, the user can set it to prioritize notifying specific heirs of specific assets. This allows the notification unit to provide notifications in line with the user's wishes, thereby supporting the smooth progress of inheritance procedures.

[0078] The data collection unit can automatically collect asset information such as bank accounts, securities, and real estate. For example, the data collection unit can automatically collect bank account information. The data collection unit can also automatically collect securities information. The data collection unit can also automatically collect real estate information. For example, the data collection unit can automatically retrieve bank account balances and transaction history. The data collection unit can automatically retrieve details of owned stocks and bonds. The data collection unit can automatically retrieve the location and appraised value of owned real estate. By automatically collecting asset information, the data collection unit can reduce the effort required from the user. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, in order to obtain bank account information, the data collection unit can use AI to access bank databases and collect the necessary information.

[0079] The proposal unit can propose inheritance plans that take into account minimizing inheritance tax and ensuring fair distribution among heirs. For example, the proposal unit can propose a plan that takes into account minimizing inheritance tax. The proposal unit can also propose a plan that takes into account fair distribution among heirs. The proposal unit can also propose a plan that aligns with the user's wishes. For example, the proposal unit can propose a plan that applies inheritance tax deductions and special provisions. The proposal unit can consider equal distribution of assets or distribution based on specific conditions. The proposal unit can reflect requests such as preferentially distributing specific assets to specific heirs. By proposing plans that take into account minimizing inheritance tax and ensuring fair distribution, the proposal unit can provide the user with the optimal inheritance plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can propose an inheritance plan using an AI model that takes the user's asset information as input and outputs a plan that takes into account minimizing inheritance tax and ensuring fair distribution.

[0080] The creation unit can create a will based on the inheritance plan proposed by the proposal unit. The creation unit can, for example, create a will based on information entered by the user. The creation unit can also, for example, create a will that reflects the plan proposed by the proposal unit. The creation unit can also, for example, create a will in a format that meets legal requirements. For example, the creation unit can create a will based on asset information and heir information entered by the user. The creation unit can, for example, reflect the plan proposed by the proposal unit in the content of the will. The creation unit can, for example, create a will that meets the requirements for signatures and witnesses. In this way, by creating a will based on the proposed inheritance plan, a will that is in line with the user's wishes can be created. Some or all of the above processes in the creation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the creation unit can input information entered by the user as a prompt to the generation AI, and the generation AI can generate the content of the will.

[0081] The storage unit can securely store wills created by the creation unit on the web. The storage unit can, for example, encrypt and store the wills. The storage unit can also, for example, implement access control to prevent tampering with the wills. The storage unit can also, for example, perform regular backups to prevent data loss. For example, the storage unit can securely store the wills by encrypting them. The storage unit can, for example, configure it so that only specific users can access the wills. The storage unit can, for example, perform regular backups to prepare for data loss or corruption. This reduces the risk of loss or tampering by securely storing wills on the web. Some or all of the above processes in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can use AI to optimize security protocols for will encryption and access control.

[0082] The notification unit can automatically notify heirs and other relevant parties of the contents of a will stored by the storage unit at a specified time. For example, the notification unit notifies the heirs of the contents of the will when the user dies. The notification unit can also notify the contents of the will at a specific date and time. The notification unit can also provide notifications according to the progress of the inheritance procedure. For example, the notification unit notifies the heirs of the contents of the will when the user dies. The notification unit notifies the contents of the will at a date and time specified by the user. The notification unit notifies necessary information at each stage of the inheritance procedure. This allows the inheritance procedure to proceed smoothly by automatically notifying the contents of the will at a specified time. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to confirm the submission of a death certificate in order to detect the user's death and then notify the heirs of the contents of the will.

[0083] The notification unit can notify the heirs of the contents of a will when a user dies. For example, the notification unit notifies the heirs of the contents of a will when a user dies. For example, the notification unit can confirm the submission of a death certificate to confirm the death of a user. For example, the notification unit can confirm the death of a user based on the passage of a specific period of time. For example, the notification unit notifies the heirs of the contents of a will when a user dies. For example, the notification unit notifies the heirs of the contents of a will after confirming the submission of a death certificate. For example, the notification unit notifies the heirs of the contents of a will after a specific period of time has elapsed. This allows inheritance procedures to be started quickly by notifying the heirs of the contents of a will when a user dies. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to confirm the submission of a death certificate to detect the death of a user and notify the heirs of the contents of a will.

[0084] The proposal unit can propose the optimal inheritance plan based on the user's asset information. For example, the proposal unit can propose a plan that considers minimizing inheritance tax based on the user's asset information. The proposal unit can also propose a plan that considers fair distribution among heirs based on the user's asset information. The proposal unit can also propose a plan that aligns with the user's wishes. For example, the proposal unit can propose a plan that applies inheritance tax deductions and special provisions based on the user's asset information. The proposal unit can consider equal distribution of assets or distribution based on specific conditions. The proposal unit can reflect requests such as preferentially distributing specific assets to specific heirs. In this way, by proposing the optimal inheritance plan based on the user's asset information, the proposal unit can provide the user with the best possible inheritance plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can propose an inheritance plan using an AI model that takes the user's asset information as input and outputs a plan that considers minimizing inheritance tax and fair distribution.

[0085] The data collection unit can automatically collect information on bank accounts and real estate owned by the user. For example, the data collection unit can automatically collect information on bank accounts owned by the user. The data collection unit can also automatically collect details on real estate owned by the user. The data collection unit can also automatically collect information on securities owned by the user. For example, the data collection unit can automatically obtain the balance and transaction history of bank accounts owned by the user. For example, the data collection unit can automatically obtain the location and appraised value of real estate owned by the user. For example, the data collection unit can automatically obtain details on securities owned by the user. This makes the collection of asset information more efficient by automatically collecting information on bank accounts and real estate owned by the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, in order to obtain information on bank accounts owned by the user, the data collection unit can use AI to access bank databases and collect the necessary information.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of asset information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect the information when the user is relaxed. For example, if the user is relaxed, the data collection unit can immediately collect asset information and make suggestions quickly. For example, if the user is in a hurry, the data collection unit can accelerate collection to quickly collect asset information. For example, if the user is stressed, the data collection unit can delay collection and collect the information when the user is relaxed. For example, if the user is relaxed, the data collection unit can immediately collect asset information and make suggestions quickly. For example, if the user is in a hurry, the data collection unit can accelerate collection to quickly collect asset information. This reduces the burden on the user by adjusting the timing of asset information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may use AI to perform facial recognition and voice analysis to estimate the user's emotions and adjust the data collection timing based on those emotions.

[0087] The data collection unit can analyze the user's past asset information collection history and select the optimal collection method. The data collection unit can, for example, prioritize suggesting collection methods the user has used in the past (manual input, automatic collection, etc.). The data collection unit can also, for example, suggest a method for collecting data during a specific time period based on the user's past collection history. The data collection unit can also, for example, analyze the user's past collection history and select the most efficient collection method. For example, the data collection unit can, for example, prioritize suggesting collection methods the user has used in the past (manual input, automatic collection, etc.). The data collection unit can, for example, suggest a method for collecting data during a specific time period based on the user's past collection history. The data collection unit can, for example, analyze the user's past collection history and select the most efficient collection method. This allows the optimal collection method to be selected by analyzing the user's past collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select a collection method using an AI model that takes the user's past collection history as input and outputs the optimal collection method.

[0088] The data collection unit can filter asset information based on the user's current financial situation and areas of interest. For example, the data collection unit can prioritize the collection of important asset information, taking into account the user's current financial situation. The data collection unit can also collect relevant asset information based on the user's areas of interest (e.g., real estate, securities). The data collection unit can also filter and collect unnecessary information based on the user's financial situation and areas of interest. For example, the data collection unit can prioritize the collection of important asset information, taking into account the user's current financial situation. The data collection unit can collect relevant asset information based on the user's areas of interest (e.g., real estate, securities). The data collection unit can filter and collect unnecessary information based on the user's financial situation and areas of interest. This allows for the priority collection of important asset information by filtering based on the user's financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform filtering using an AI model that prioritizes the collection of important asset information, taking the user's financial situation and areas of interest as input.

[0089] The data collection unit can estimate the user's emotions and determine the priority of asset information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important asset information. For example, if the user is relaxed, the data collection unit can collect all asset information equally. For example, if the user is in a hurry, the data collection unit can prioritize collecting highly important asset information. For example, if the user is stressed, the data collection unit will postpone collecting less important asset information. For example, if the user is relaxed, the data collection unit will collect all asset information equally. For example, if the user is in a hurry, the data collection unit will prioritize collecting highly important asset information. This reduces the user's burden by prioritizing asset information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to perform facial recognition and voice analysis to estimate the user's emotions, and then determine the priority of asset information to collect based on those emotions.

[0090] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting asset information. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of real estate information related to that region. For example, if the user is traveling, the data collection unit can also collect asset information related to the user's current location. For example, the data collection unit can also collect highly relevant bank account information based on the user's geographical location. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of real estate information related to that region. For example, if the user is traveling, the data collection unit will collect asset information related to the user's current location. For example, the data collection unit can collect highly relevant bank account information based on the user's geographical location. This allows for the priority collection of highly relevant asset information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform collection using an AI model that prioritizes the collection of highly relevant asset information, taking the user's geographical location as input.

[0091] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting asset information. For example, the data collection unit can collect relevant asset information based on information shared by the user on social media. The data collection unit can also, for example, prioritize the collection of asset information of interest from the user's social media activity. The data collection unit can also, for example, analyze the user's social media activity and propose the optimal collection method. For example, the data collection unit can collect relevant asset information based on information shared by the user on social media. For example, the data collection unit can prioritize the collection of asset information of interest from the user's social media activity. The data collection unit can, for example, analyze the user's social media activity and propose the optimal collection method. This allows for the efficient collection of relevant asset information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform collection using an AI model that collects relevant asset information using the user's social media activity as input.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and easy-to-understand suggestions. For example, if the user is relaxed, the suggestion unit may provide suggestions that include detailed information. For example, if the user is in a hurry, the suggestion unit may provide concise and quick suggestions. For example, if the user is stressed, the suggestion unit will provide simple and easy-to-understand suggestions. For example, if the user is relaxed, the suggestion unit may provide suggestions that include detailed information. For example, if the user is in a hurry, the suggestion unit may provide concise and quick suggestions. By adjusting the way it presents suggestions according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can use AI to perform facial recognition and voice analysis to estimate the user's emotions, and adjust the way the proposal is presented based on those emotions.

[0093] The proposal unit can adjust the level of detail of its proposals based on the importance of the assets. For example, the proposal unit can provide detailed proposals for highly important assets. For example, the proposal unit can provide concise proposals for less important assets. The proposal unit can also adjust the level of detail of its proposals according to the importance of the assets. For example, the proposal unit can provide detailed proposals for highly important assets. For example, the proposal unit can provide concise proposals for less important assets. The proposal unit can adjust the level of detail of its proposals according to the importance of the assets. By adjusting the level of detail of proposals based on the importance of the assets, it becomes possible to provide the best possible proposals for the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use an AI model that adjusts the level of detail of proposals based on the importance of the assets as input to make proposals.

[0094] The proposal unit can apply different proposal algorithms depending on the asset category when making a proposal. For example, for a proposal concerning real estate, the proposal unit can apply an algorithm that takes regional market trends into account. For example, for a proposal concerning securities, the proposal unit can also apply an algorithm that takes past performance into account. For example, for a proposal concerning bank accounts, the proposal unit can also apply an algorithm that takes interest rates and fees into account. For example, for a proposal concerning real estate, the proposal unit can apply an algorithm that takes regional market trends into account. For example, for a proposal concerning securities, the proposal unit can apply an algorithm that takes past performance into account. For example, for a proposal concerning bank accounts, the proposal unit can apply an algorithm that takes interest rates and fees into account. This makes it possible to make more accurate proposals by applying different proposal algorithms depending on the asset category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can make proposals using an AI model that takes the asset category as input and applies different proposal algorithms.

[0095] The suggestion section can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion section will make a short, to-the-point suggestion. For example, if the user is relaxed, the suggestion section may make a longer suggestion with more detailed explanations. For example, if the user is in a hurry, the suggestion section may make a quick and concise suggestion. For example, if the user is stressed, the suggestion section will make a short, to-the-point suggestion. For example, if the user is relaxed, the suggestion section will make a longer suggestion with more detailed explanations. For example, if the user is in a hurry, the suggestion section will make a quick and concise suggestion. By adjusting the length of the suggestion according to the user's emotions, it becomes possible to make the most appropriate suggestion for the user. Emotion estimation is achieved using an emotion estimation function, for example, 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 suggestion section may be performed using AI, for example, or without AI. For example, the proposal function can use AI to perform facial recognition and voice analysis to estimate the user's emotions, and adjust the length of the proposal based on those emotions.

[0096] The proposal unit can determine the priority of proposals based on the acquisition date of the assets when making a proposal. For example, the proposal unit will prioritize proposals for recently acquired assets. For example, the proposal unit may postpone proposals for assets that have been held for a long period of time. The proposal unit can also determine the priority of proposals according to the acquisition date of the assets. For example, the proposal unit will prioritize proposals for recently acquired assets. For example, the proposal unit will postpone proposals for assets that have been held for a long period of time. For example, the proposal unit will determine the priority of proposals according to the acquisition date of the assets. This makes it possible to make optimal proposals for the user by determining the priority of proposals based on the acquisition date of the assets. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can make proposals using an AI model that takes the acquisition date of assets as input and determines the priority of proposals.

[0097] The proposal unit can adjust the order of proposals based on the relevance of assets when making a proposal. For example, the proposal unit can prioritize proposals for highly relevant assets. For example, the proposal unit can postpone proposals for less relevant assets. The proposal unit can also adjust the order of proposals according to the relevance of assets. For example, the proposal unit can prioritize proposals for highly relevant assets. For example, the proposal unit can postpone proposals for less relevant assets. For example, the proposal unit can adjust the order of proposals according to the relevance of assets. This makes it possible to make optimal proposals for the user by adjusting the order of proposals based on the relevance of assets. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can make proposals using an AI model that adjusts the order of proposals, taking the relevance of assets as input.

[0098] The creation unit can estimate the user's emotions and adjust the will creation process based on those emotions. For example, if the user is stressed, the creation unit can provide a simple interface and minimize the creation steps. For example, if the user is relaxed, the creation unit can provide detailed input options and suggest a customizable creation method. For example, if the user is in a hurry, the creation unit can prioritize voice input to enable quick will creation. For example, if the user is stressed, the creation unit can provide a simple interface and minimize the creation steps. For example, if the user is relaxed, the creation unit can provide detailed input options and suggest a customizable creation method. For example, if the user is in a hurry, the creation unit can prioritize voice input to enable quick will creation. This allows for the creation of a will that is optimal for the user by adjusting the will creation process according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit may use AI to perform facial recognition and voice analysis to estimate the user's emotions and adjust the method of creating the will based on those emotions.

[0099] The creation unit can analyze the user's past will creation history and select the optimal creation method during creation. For example, the creation unit may prioritize suggesting creation methods the user has used in the past (manual input, voice input, etc.). The creation unit may also suggest a method for creating a will at a specific time based on the user's past creation history. The creation unit may also analyze the user's past creation history and select the most efficient creation method. For example, the creation unit may prioritize suggesting creation methods the user has used in the past (manual input, voice input, etc.). The creation unit may, for example, suggest a method for creating a will at a specific time based on the user's past creation history. The creation unit may, for example, analyze the user's past creation history and select the most efficient creation method. This allows the optimal creation method to be selected by analyzing the user's past creation history. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can use an AI model that selects the optimal creation method based on the user's past creation history as input to perform the creation.

[0100] The creation unit can customize the contents of the will based on the user's current living situation during creation. For example, the creation unit considers the user's current financial situation and proposes the optimal contents of the will. The creation unit can also customize the contents of the will based on the user's family structure and living environment. The creation unit can also automatically add necessary information based on the user's living situation. For example, the creation unit considers the user's current financial situation and proposes the optimal contents of the will. The creation unit customizes the contents of the will based on the user's family structure and living environment. The creation unit automatically adds necessary information based on the user's living situation. This allows for the creation of a will that is optimal for the user by customizing the contents of the will based on the user's living situation. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can use an AI model that customizes the contents of the will using the user's living situation as input.

[0101] The creation unit can estimate the user's emotions and determine the priorities of the will based on the estimated emotions. For example, if the user is stressed, the creation unit will postpone less important content. For example, if the user is relaxed, the creation unit can create all content equally. For example, if the user is in a hurry, the creation unit can prioritize creating highly important content. For example, if the user is stressed, the creation unit will postpone less important content. For example, if the user is relaxed, the creation unit will create all content equally. For example, if the user is in a hurry, the creation unit will prioritize creating highly important content. This allows for the creation of a will that is optimal for the user by determining the priorities of the will according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 creation unit may be performed using AI, for example, or without AI. For example, the creation unit can use AI to perform facial recognition and voice analysis to estimate the user's emotions, and then determine the priority of the will based on those emotions.

[0102] The creation unit can select the most appropriate will content by considering the user's geographical location information during creation. For example, if the user lives in a specific region, the creation unit will prioritize creating will content related to that region. For example, if the user is traveling, the creation unit can also create will content related to the user's current location. The creation unit can also select highly relevant content based on the user's geographical location information. For example, if the user lives in a specific region, the creation unit will prioritize creating will content related to that region. For example, if the user is traveling, the creation unit will create will content related to the user's current location. For example, the creation unit can select highly relevant content based on the user's geographical location information. This allows the creation of highly relevant will content by considering the user's geographical location information. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can use an AI model that selects the most appropriate will content using the user's geographical location information as input.

[0103] The creation unit can analyze the user's social media activity and propose the contents of the will during the creation process. For example, the creation unit can propose relevant will contents based on information shared by the user on social media. The creation unit can also prioritize proposing content of interest based on the user's social media activity. The creation unit can also analyze the user's social media activity and propose the most suitable content. For example, the creation unit can propose relevant will contents based on information shared by the user on social media. The creation unit can prioritize proposing content of interest based on the user's social media activity. The creation unit can also analyze the user's social media activity and propose the most suitable content. This allows for the efficient proposal of relevant will contents by analyzing the user's social media activity. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can make suggestions using an AI model that takes the user's social media activity as input and proposes relevant will contents.

[0104] The storage unit can estimate the user's emotions and adjust the storage method of the will based on the estimated emotions. For example, if the user is stressed, the storage unit can provide a simple and easy-to-understand storage method. For example, if the user is relaxed, the storage unit can provide detailed storage options and suggest a customizable storage method. For example, if the user is in a hurry, the storage unit can provide a method for quick storage. For example, if the user is stressed, the storage unit can provide a simple and easy-to-understand storage method. For example, if the user is relaxed, the storage unit can provide detailed storage options and suggest a customizable storage method. For example, if the user is in a hurry, the storage unit can provide a method for quick storage. This allows for the optimal storage method for the user by adjusting the storage method of the will according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 storage unit may be performed using AI, for example, or without AI. For example, the storage unit can use AI to perform facial recognition and voice analysis to estimate the user's emotions, and adjust the storage method of the will based on those emotions.

[0105] The storage unit can analyze the user's past storage history to select the optimal storage method during storage. For example, the storage unit may prioritize suggesting storage methods the user has used in the past (cloud, local, etc.). The storage unit may also suggest a method for storing data during a specific time period based on the user's past storage history. The storage unit may also analyze the user's past storage history to select the most efficient storage method. For example, the storage unit may prioritize suggesting storage methods the user has used in the past (cloud, local, etc.). The storage unit may also suggest a method for storing data during a specific time period based on the user's past storage history. The storage unit may also analyze the user's past storage history to select the most efficient storage method. This allows the storage unit to select the optimal storage method by analyzing the user's past storage history. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can perform storage using an AI model that selects the optimal storage method based on the user's past storage history as input.

[0106] The storage unit can customize storage methods based on the user's current living situation during storage. For example, the storage unit considers the user's current financial situation and proposes the optimal storage method. The storage unit can also customize storage methods based on the user's family structure and living environment. The storage unit can also automatically add necessary information based on the user's living situation. For example, the storage unit considers the user's current financial situation and proposes the optimal storage method. The storage unit customizes storage methods based on the user's family structure and living environment. The storage unit automatically adds necessary information based on the user's living situation. This makes it possible to provide the optimal storage method for the user by customizing storage methods based on the user's living situation. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can perform storage using an AI model that customizes storage methods based on the user's living situation as input.

[0107] The storage unit can estimate the user's emotions and determine the priority of storing the will based on the estimated emotions. For example, if the user is stressed, the storage unit will postpone less important items. For example, if the user is relaxed, the storage unit may store all items equally. For example, if the user is in a hurry, the storage unit may prioritize storing highly important items. For example, if the user is stressed, the storage unit will postpone less important items. For example, if the user is relaxed, the storage unit will store all items equally. For example, if the user is in a hurry, the storage unit will prioritize storing highly important items. This allows for the optimal storage method for the user by determining the priority of storing the will according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 storage unit may be performed using AI, for example, or without AI. For example, the storage unit can use AI to perform facial recognition and voice analysis to estimate the user's emotions, and then determine the priority for storing wills based on those emotions.

[0108] The storage unit can select the optimal storage method when storing data, taking into account the user's geographical location information. For example, if the user lives in a specific region, the storage unit will prioritize suggesting storage methods related to that region. For example, if the user is traveling, the storage unit can also suggest storage methods related to their current location. The storage unit can also select a highly relevant storage method based on the user's geographical location information. For example, if the user lives in a specific region, the storage unit will prioritize suggesting storage methods related to that region. For example, if the user is traveling, the storage unit will suggest storage methods related to their current location. For example, the storage unit can select a highly relevant storage method based on the user's geographical location information. This allows the storage unit to select a highly relevant storage method by considering the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can perform storage using an AI model that selects the optimal storage method based on the user's geographical location information as input.

[0109] The storage unit can analyze the user's social media activity and suggest storage methods during storage. For example, the storage unit can suggest relevant storage methods based on information shared by the user on social media. The storage unit can also prioritize suggesting storage methods of interest based on the user's social media activity. The storage unit can also analyze the user's social media activity and suggest the optimal storage method. For example, the storage unit can suggest relevant storage methods based on information shared by the user on social media. The storage unit can prioritize suggesting storage methods of interest based on the user's social media activity. The storage unit can analyze the user's social media activity and suggest the optimal storage method. This allows for the efficient suggestion of relevant storage methods by analyzing the user's social media activity. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can use an AI model that takes the user's social media activity as input to suggest relevant storage methods.

[0110] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can delay the notification timing and send it when the user is relaxed. For example, if the user is relaxed, the notification unit can also send an immediate notification. For example, if the user is in a hurry, the notification unit can speed up the notification timing and send a quick notification. For example, if the user is stressed, the notification unit can delay the notification timing and send it when the user is relaxed. For example, if the user is relaxed, the notification unit can send an immediate notification. For example, if the user is in a hurry, the notification unit can speed up the notification timing and send a quick notification. This allows for optimal notifications for the user by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to perform facial recognition and voice analysis to estimate the user's emotions, and adjust the timing of notifications based on those emotions.

[0111] The notification unit can analyze the user's past notification history to select the optimal notification method when sending a notification. For example, the notification unit may prioritize suggesting notification methods the user has used in the past (email, SMS, etc.). The notification unit can also suggest a method for sending notifications at a specific time based on the user's past notification history. The notification unit can also analyze the user's past notification history to select the most efficient notification method. For example, the notification unit may prioritize suggesting notification methods the user has used in the past (email, SMS, etc.). The notification unit may suggest a method for sending notifications at a specific time based on the user's past notification history. The notification unit can also analyze the user's past notification history to select the most efficient notification method. This allows the notification unit to select the optimal notification method by analyzing the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that selects the optimal notification method based on the user's past notification history as input.

[0112] The notification unit can customize notification content based on the user's current living situation when it sends a notification. For example, the notification unit can consider the user's current financial situation and suggest the most suitable notification content. The notification unit can also customize notification content based on the user's family structure and living environment. The notification unit can also automatically add necessary information based on the user's living situation. For example, the notification unit can consider the user's current financial situation and suggest the most suitable notification content. The notification unit can customize notification content based on the user's family structure and living environment. The notification unit can automatically add necessary information based on the user's living situation. This makes it possible to provide notifications that are optimal for the user by customizing notification content based on the user's living situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that customizes notification content based on the user's living situation as input.

[0113] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will postpone less important notifications. For example, if the user is relaxed, the notification unit may send all notifications equally. For example, if the user is in a hurry, the notification unit may prioritize more important notifications. For example, if the user is stressed, the notification unit will postpone less important notifications. For example, if the user is relaxed, the notification unit will send all notifications equally. For example, if the user is in a hurry, the notification unit will prioritize more important notifications. This allows for optimal notifications for the user by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to perform facial recognition and voice analysis to estimate the user's emotions, and then determine the priority of notifications based on those emotions.

[0114] The notification unit can select the most appropriate notification method when sending a notification, taking into account the user's geographical location. For example, if the user lives in a specific region, the notification unit will prioritize suggesting a notification method relevant to that region. For example, if the user is traveling, the notification unit can also suggest a notification method relevant to their current location. The notification unit can also select a highly relevant notification method based on the user's geographical location. For example, if the user lives in a specific region, the notification unit will prioritize suggesting a notification method relevant to that region. For example, if the user is traveling, the notification unit will suggest a notification method relevant to their current location. For example, the notification unit can select a highly relevant notification method based on the user's geographical location. This allows the notification unit to select a highly relevant notification method by considering the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that selects the most appropriate notification method based on the user's geographical location as input.

[0115] The notification unit can analyze the user's social media activity and suggest notification content when sending a notification. For example, the notification unit can suggest relevant notification content based on information shared by the user on social media. The notification unit can also prioritize suggesting notification content of interest based on the user's social media activity. The notification unit can also analyze the user's social media activity and suggest the most appropriate notification content. For example, the notification unit can suggest relevant notification content based on information shared by the user on social media. The notification unit can prioritize suggesting notification content of interest based on the user's social media activity. The notification unit can analyze the user's social media activity and suggest the most appropriate notification content. This allows for the efficient suggestion of relevant notification content by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can make suggestions using an AI model that takes the user's social media activity as input and suggests relevant notification content.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The WebWill Planner system can estimate the user's emotions and adjust the proposed inheritance plan based on those emotions. For example, if the user is stressed, the proposal unit can suggest a simple and easy-to-understand plan. If the user is relaxed, it can suggest a plan with more detailed information. Furthermore, if the user is in a hurry, it can suggest a concise and quick plan. This allows the system to provide the optimal inheritance plan according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the proposal unit may be performed using AI or not. For example, the proposal unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the proposed content based on those emotions.

[0118] The data collection unit can analyze the user's past asset information collection history and select the optimal collection method. For example, it can prioritize suggesting collection methods the user has used in the past (manual input, automated collection, etc.). It can also suggest methods for collecting data during specific time periods based on the user's past collection history. Furthermore, it can analyze the user's past collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past collection history. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can select a collection method using an AI model that takes the user's past collection history as input and outputs the optimal collection method.

[0119] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can offer simple and easy-to-understand suggestions. If the user is relaxed, it can offer suggestions that include detailed information. Furthermore, if the user is in a hurry, it can offer concise and quick suggestions. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the way suggestions are presented based on those emotions.

[0120] The creation unit can estimate the user's emotions and adjust the will creation process based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the creation steps. If the user is relaxed, it can provide detailed input options and suggest a customizable creation method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick will creation. This allows for the creation of a will that is optimal for the user by adjusting the will creation process according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the will creation process based on those emotions.

[0121] The storage unit can estimate the user's emotions and adjust the will storage method based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand storage method. If the user is relaxed, it can provide detailed storage options and suggest a customizable storage method. Furthermore, if the user is in a hurry, it can provide a method for quick storage. This allows for the optimal storage method for the user by adjusting the will storage method according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the will storage method based on those emotions.

[0122] The data collection unit can filter asset information based on the user's current financial situation and areas of interest. For example, it can prioritize the collection of important asset information, taking into account the user's current financial situation. It can also collect relevant asset information based on the user's areas of interest (e.g., real estate, securities). Furthermore, it can filter out unnecessary information based on the user's financial situation and areas of interest. This allows for the priority collection of important asset information by filtering based on the user's financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model that prioritizes the collection of important asset information, taking the user's financial situation and areas of interest as input, to perform filtering.

[0123] The proposal unit can adjust the level of detail in its proposals based on the importance of the assets. For example, it can provide detailed proposals for highly important assets and concise proposals for less important assets. Furthermore, it can adjust the level of detail in its proposals according to the importance of the assets. This allows for optimal proposals for the user by adjusting the level of detail based on the importance of the assets. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can use an AI model that adjusts the level of detail in proposals based on the importance of the assets as input.

[0124] The creation unit can analyze the user's past will-creation history to select the optimal creation method during the creation process. For example, it can prioritize suggesting creation methods the user has used in the past (manual input, voice input, etc.). It can also suggest methods for creating a will at specific times based on the user's past creation history. Furthermore, it can analyze the user's past creation history to select the most efficient creation method. In this way, the optimal creation method can be selected by analyzing the user's past creation history. Some or all of the above processes in the creation unit may be performed using AI, or not. For example, the creation unit can use an AI model that selects the optimal creation method based on the user's past creation history as input to perform the creation.

[0125] The storage unit can analyze the user's past storage history to select the optimal storage method during storage. For example, it can prioritize suggesting storage methods the user has used in the past (cloud, local, etc.). It can also suggest storage methods for specific time periods based on the user's past storage history. Furthermore, it can analyze the user's past storage history to select the most efficient storage method. In this way, the optimal storage method can be selected by analyzing the user's past storage history. Some or all of the above processes in the storage unit may be performed using AI, or not. For example, the storage unit can use an AI model that selects the optimal storage method based on the user's past storage history as input, and then perform storage.

[0126] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification timing can be delayed to deliver the notification when the user is relaxed. Alternatively, if the user is relaxed, the notification can be delivered immediately. Furthermore, if the user is in a hurry, the notification timing can be advanced to deliver the notification quickly. By adjusting the timing of notifications according to the user's emotions, it becomes possible to provide the most optimal notification for the user. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the timing of notifications based on those emotions.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The collection unit automatically collects asset information. Asset information includes, for example, bank accounts, securities, and real estate. The collection unit automatically retrieves bank account balances and transaction history, details of held stocks and bonds, and the location and appraised value of owned real estate. Step 2: The proposal department analyzes the asset information collected by the collection department and proposes the optimal inheritance plan. The proposal department proposes a plan that minimizes inheritance tax, ensures fair distribution among heirs, and aligns with the user's wishes. For example, it will reflect requests such as plans that apply inheritance tax deductions and special provisions, equal distribution of assets or distribution based on specific conditions, and preferential distribution of specific assets to specific heirs. Step 3: The creation department prepares the will based on the inheritance plan proposed by the proposal department. The creation department prepares the will based on the information entered by the user and creates a will that reflects the plan proposed by the proposal department. It also prepares the will in a format that meets legal requirements. Step 4: The storage unit stores the wills created by the creation unit online. The storage unit encrypts the wills, implements access control, and prevents tampering. It also performs regular backups to prevent data loss. Step 5: The notification unit notifies heirs and other relevant parties of the contents of the will stored by the storage unit at the specified time. The notification unit notifies the contents of the will when the user dies or at a specific date and time, and also notifies them according to the progress of the inheritance procedures.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, proposal unit, creation unit, storage unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and automatically collects information on bank accounts and real estate. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected asset information to propose an optimal inheritance plan. The creation unit is implemented by the control unit 46A of the smart device 14 and creates a will based on the proposed plan. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12 and securely stores the created will on the web. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the heirs and related parties of the contents of the will at a specified time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, proposal unit, creation unit, storage unit, and notification unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and automatically collects information on bank accounts and real estate. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected asset information to propose an optimal inheritance plan. The creation unit is implemented by, for example, the control unit 46A of the smart glasses 214 and creates a will based on the proposed plan. The storage unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and securely stores the created will on the web. The notification unit is implemented by, for example, the control unit 46A of the smart glasses 214 and notifies the heirs and related parties of the contents of the will at a specified time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, proposal unit, creation unit, storage unit, and notification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and automatically collects information on bank accounts and real estate. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected asset information to propose an optimal inheritance plan. The creation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and creates a will based on the proposed plan. The storage unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and securely stores the created will on the web. The notification unit is implemented by, for example, the control unit 46A of the headset terminal 314 and notifies the heirs and related parties of the contents of the will at a specified time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The 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.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the collection unit, proposal unit, creation unit, storage unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and automatically collects information on bank accounts and real estate. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected asset information to propose an optimal inheritance plan. The creation unit is implemented by, for example, the control unit 46A of the robot 414 and creates a will based on the proposed plan. The storage unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and securely stores the created will on the web. The notification unit is implemented by, for example, the control unit 46A of the robot 414 and notifies the heirs and related parties of the contents of the will at a specified time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A collection unit that automatically collects asset information, The asset information collected by the aforementioned collection unit is analyzed, and the proposal unit proposes the optimal inheritance plan. A drafting department prepares wills based on the inheritance plan proposed by the aforementioned proposal department, A storage unit that stores wills created by the aforementioned creation unit on the web, The system includes a notification unit that notifies heirs and other relevant parties of the contents of the will stored by the aforementioned storage unit at a specified time. A system characterized by the following features. (Note 2) The aforementioned collection unit is Automatically collects asset information such as bank accounts, securities, and real estate. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose inheritance plans that take into account minimizing inheritance tax and ensuring fair distribution among heirs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creation unit, A will will be prepared based on the inheritance plan proposed by the aforementioned proposal department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned storage unit is The will created by the aforementioned creation unit is securely stored on the web. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, The contents of the will stored by the aforementioned storage unit will be automatically notified to heirs and other relevant parties at a specified time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, The contents of the will will be notified to the heirs upon the user's death. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We propose the optimal inheritance plan based on the user's asset information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system automatically collects information about the user's bank accounts and real estate details. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of asset information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze the user's past asset information collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting asset information, filtering is performed based on the user's current financial situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and determines the priority of asset information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting asset information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting asset information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the assets. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the asset category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the assets were acquired. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the assets. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, It estimates the user's emotions and adjusts the will creation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, During creation, the system analyzes the user's past will-creation history to select the most suitable creation method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, When creating a will, the contents are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned creation unit, It estimates the user's emotions and determines the priority of wills based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned creation unit, During creation, the system selects the most suitable content for the will, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned creation unit, During the creation process, the system analyzes the user's social media activity to suggest the contents of the will. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned storage unit is The system estimates the user's emotions and adjusts how the will is stored based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned storage unit is During storage, the system analyzes the user's past storage history to select the optimal storage method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned storage unit is During storage, the storage method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned storage unit is The system estimates the user's emotions and determines the priority for storing wills based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned storage unit is When storing data, the system selects the optimal storage method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned storage unit is During storage, we analyze the user's social media activity and suggest storage methods. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When sending a notification, the system analyzes the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When a notification is sent, the content of the notification will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity to suggest appropriate notification content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0201] 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. A collection unit that automatically collects asset information, The asset information collected by the aforementioned collection unit is analyzed, and the proposal unit proposes the optimal inheritance plan. A drafting department prepares wills based on the inheritance plan proposed by the aforementioned proposal department, A storage unit that stores wills created by the aforementioned creation unit on the web, The system includes a notification unit that notifies heirs and other relevant parties of the contents of the will stored by the aforementioned storage unit at a specified time. A system characterized by the following features.

2. The aforementioned collection unit is Automatically collects asset information such as bank accounts, securities, and real estate. The system according to feature 1.

3. The aforementioned proposal section is, We propose inheritance plans that take into account minimizing inheritance tax and ensuring fair distribution among heirs. The system according to feature 1.

4. The aforementioned creation unit, A will will be prepared based on the inheritance plan proposed by the aforementioned proposal department. The system according to feature 1.

5. The aforementioned storage unit is The will created by the aforementioned creation unit is securely stored on the web. The system according to feature 1.

6. The aforementioned notification unit, The contents of the will stored by the aforementioned storage unit will be automatically notified to heirs and other relevant parties at a specified time. The system according to feature 1.

7. The aforementioned notification unit, The contents of the will will be notified to the heirs upon the user's death. The system according to feature 1.

8. The aforementioned proposal section is, We propose the optimal inheritance plan based on the user's asset information. The system according to feature 1.

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