System

Abstract

An object of a system according to an embodiment is to provide knowledge related to an event and to propose an event distribution reflecting a user's intention.SOLUTION: A system according to an embodiment includes a knowledge providing unit, a thought learning unit, and a distribution proposal unit. The literacy providing unit provides literacy related to the legacy by using the generated AI. The thought learning unit learns the thought of the user based on the knowledge provided by the knowledge providing unit. The distribution proposal unit proposes the estate distribution on the basis of the user's thought or desire learned by the thought learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide knowledge about inheritance distribution or propose distribution methods that reflect the user's wishes, so there is room for improvement.

[0005] The system according to the embodiment aims to provide knowledge related to inheritances and propose inheritance distribution that reflects the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a knowledge providing unit, a thought learning unit, and a distribution suggestion unit. The knowledge providing unit provides knowledge related to inheritances using a generation AI. The thought learning unit learns the user's thoughts based on the knowledge provided by the knowledge providing unit. The distribution suggestion unit makes inheritance distribution suggestions based on the user's thoughts and wishes learned by the thought learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide knowledge related to inheritances and make inheritance distribution proposals that reflect the user's wishes. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The inheritance distribution proposal system according to an embodiment of the present invention provides users with custom-made inheritance-related knowledge, and the AI ​​generator learns the user's wishes and proposes inheritance distribution. This allows the inheritance distribution proposal system to propose inheritance distribution that reflects the user's wishes, eliminating the need for tedious inheritance-related tasks in one stop.

[0029] An inheritance distribution proposal system according to an embodiment includes a knowledge providing unit, a wish learning unit, and a distribution proposal unit. The knowledge providing unit provides knowledge related to inheritance using a generation AI. For example, the generation AI explains basic inheritance procedures, how to calculate inheritance tax, and how to prepare a will. The generation AI also provides appropriate information based on prompts containing instructions about the content the user wants to know. The wish learning unit learns the user's wishes based on the knowledge provided by the knowledge providing unit. For example, the generation AI learns the user's wishes and desires through dialogue with the user. If the user expresses a wish such as "I want to divide my inheritance equally among my children," the generation AI understands that wish and reflects it in the inheritance distribution proposal. The distribution proposal unit proposes inheritance distribution based on the user's wishes and desires learned by the wish learning unit. For example, if the user expresses a wish such as "I want to give a house to my eldest son and more cash to my second son," the generation AI proposes a specific distribution plan based on that wish. This enables the inheritance distribution proposal system according to an embodiment to propose inheritance distribution that reflects the user's wishes.

[0030] The knowledge provision unit can provide customized information based on the user's past inheritance experience and family composition. For example, the generation AI in the knowledge provision unit retrieves the user's past inheritance experience from a database and provides customized inheritance procedures based on that information. For example, it takes into account the type of assets inherited in the past and the distribution method. The knowledge provision unit also collects the user's family composition as input data, and the generation AI proposes the optimal inheritance distribution method based on that information. For example, it provides a distribution plan that takes into account the number of family members, their ages, and their relationships. The knowledge provision unit also provides customized information on specific laws and tax systems based on the user's past inheritance experience and family composition. For example, it presents a method for calculating inheritance tax that is suitable for a specific family composition. By providing customized information based on the user's past inheritance experience and family composition, it becomes possible to propose more appropriate inheritance distribution plans.

[0031] The knowledge provision unit can provide information based on the laws and tax systems specific to the user's region. For example, the generation AI identifies the user's region of residence and provides information on inheritance laws and tax systems specific to that region. For example, it explains the inheritance tax rates and legal requirements for wills for each region. The knowledge provision unit also allows the generation AI to propose a customized inheritance distribution plan based on the laws and tax systems specific to the user's region. For example, it provides a method for creating an inheritance division agreement in accordance with local laws. The knowledge provision unit also allows the generation AI to collect the latest information on the laws and tax systems specific to the user's region and provide appropriate advice to the user based on that information. For example, it presents a method for calculating inheritance tax that corresponds to local tax system reforms. As a result, by providing information based on the laws and tax systems specific to the user's region, it becomes possible to propose an inheritance distribution plan that is appropriate for the region.

[0032] The knowledge provision unit can use visual and interactive content to deepen understanding when providing knowledge about inheritance. For example, when the generation AI provides knowledge about inheritance, the knowledge provision unit uses visual content to provide explanations. For example, it may provide diagrams showing a flow chart of inheritance procedures or an example of inheritance tax calculation. The knowledge provision unit also uses interactive content to enable users to learn about inheritance while operating the system themselves. For example, it may provide an interface that provides step-by-step guidance on the procedure for creating a will. The knowledge provision unit also uses visual and interactive content to enable users to gain a deeper understanding of knowledge about inheritance. For example, it may use videos and animations to explain the flow of inheritance procedures. In this way, the use of visual and interactive content can deepen users' understanding.

[0033] The knowledge provision unit provides knowledge in different languages, making it possible to accommodate international users. For example, the knowledge provision unit builds a system in which the generation AI provides knowledge about inheritances in different languages. For example, it provides information in multiple languages, such as English, French, and Chinese. Furthermore, in order to accommodate international users, the generation AI provides knowledge in multiple languages. For example, it provides inheritance procedures and legal information based on the language selected by the user. Furthermore, the knowledge provision unit accommodates international users by providing knowledge in different languages. For example, it provides information on the inheritance laws and tax systems of each country in multiple languages. In this way, by providing knowledge in different languages, it is possible to accommodate international users.

[0034] The thought learning unit can learn a user's thoughts and wishes not only from the content of their dialogue but also from their past behavioral history and choices. For example, the thought learning unit has the generation AI analyze the content of the user's dialogue and learn their thoughts and wishes from their past behavioral history and choices. For example, it learns based on previously selected inheritance distribution methods and consultation details. The thought learning unit also retrieves the user's past behavioral history from a database, and the generation AI learns their thoughts and wishes based on that information. For example, it analyzes past consultation history and choices to understand the user's wishes. The thought learning unit also builds a system in which the generation AI learns the user's thoughts and wishes not only from the content of the user's dialogue but also from their past behavioral history and choices. For example, it predicts the user's wishes based on past choices and behavior patterns. This makes it possible to propose more accurate inheritance distribution by learning the user's thoughts and wishes not only from the content of the user's dialogue but also from their past behavioral history and choices.

[0035] The Thought Learning Unit also learns the content of conversations between the user and family and friends, enabling a more comprehensive understanding of thoughts. For example, the Thought Learning Unit constructs a system in which the generation AI learns the content of conversations between the user and family and friends, and understands thoughts more comprehensively. For example, it analyzes the content of conversations between family members to understand the user's wishes. The Thought Learning Unit also retrieves the content of conversations between the user and family and friends from a database, and the generation AI learns the user's thoughts and wishes based on that information. For example, it proposes an inheritance distribution plan that takes into account the opinions and wishes of the family. The Thought Learning Unit also develops a system in which the generation AI learns the content of conversations between the user and family and friends, and understands thoughts more comprehensively. For example, it provides an inheritance distribution plan that reflects the opinions of all family members. In this way, by learning the content of conversations between the user and family and friends, it is possible to understand thoughts more comprehensively and propose appropriate inheritance distribution.

[0036] The distribution proposal unit can perform a detailed analysis of the user's family structure and financial situation and propose an optimal distribution plan. For example, the distribution proposal unit uses a generation AI to perform a detailed analysis of the user's family structure and propose an optimal inheritance distribution plan based on that information. For example, it provides a distribution plan that takes into account the number of family members, ages, and relationships. The distribution proposal unit also retrieves the user's financial situation from a database, and the generation AI proposes an optimal inheritance distribution plan based on that information. For example, it provides a method for optimally distributing assets such as cash, land, and stocks. The distribution proposal unit also builds a system in which the generation AI performs a detailed analysis of the user's family structure and financial situation and proposes an optimal inheritance distribution plan based on that information. For example, it provides a distribution plan that reflects the opinions of all family members. This makes it possible to propose an optimal inheritance distribution plan through a detailed analysis of the user's family structure and financial situation.

[0037] The distribution proposal unit can learn from past inheritance distribution cases and make proposals based on them. For example, the generation AI in the distribution proposal unit learns from a database of past inheritance distribution cases and proposes the optimal inheritance distribution plan based on that information. For example, it provides a distribution method that refers to past successful and unsuccessful cases. The distribution proposal unit also analyzes past inheritance distribution cases and builds a system in which the generation AI proposes the optimal inheritance distribution plan based on that information. For example, it provides a distribution plan based on similar family structures and financial situations. The distribution proposal unit also learns from past inheritance distribution cases and proposes the optimal inheritance distribution plan based on that knowledge. For example, it provides a distribution method that reflects lessons learned from past cases. In this way, by learning from past inheritance distribution cases, it becomes possible to propose more appropriate inheritance distribution plans.

[0038] The distribution proposal unit can propose multiple distribution plans based on different scenarios and allow the user to select from them. The distribution proposal unit, for example, builds a system in which a generation AI proposes multiple inheritance distribution plans based on different scenarios and allows the user to select from them. For example, it provides a plan that distributes equally to all family members and a plan that distributes more to specific children. The distribution proposal unit also proposes multiple inheritance distribution plans based on the user's wishes and thoughts. For example, it presents multiple distribution methods according to the scenario desired by the user. The distribution proposal unit also develops a system in which a generation AI proposes multiple inheritance distribution plans based on different scenarios and allows the user to select from them. For example, it provides a distribution plan according to the scenario selected by the user. This allows the user to select the optimal plan by proposing multiple distribution plans based on different scenarios.

[0039] The distribution proposal unit can propose a distribution plan that incorporates feedback from the user's family and friends. The distribution proposal unit, for example, builds a system in which a generation AI proposes an inheritance distribution plan that incorporates feedback from the user's family and friends. For example, it provides a distribution method that reflects the opinions of all family members. The distribution proposal unit also obtains feedback from the user's family and friends from a database, and the generation AI proposes an optimal inheritance distribution plan based on that information. For example, it provides a distribution plan that takes into account the opinions and wishes of the family. The distribution proposal unit also develops a system in which a generation AI proposes an inheritance distribution plan that incorporates feedback from the user's family and friends. For example, it provides a distribution method that reflects the opinions of all family members. In this way, by incorporating feedback from the user's family and friends, it becomes possible to propose a more appropriate inheritance distribution.

[0040] The system can automate all procedures related to inheritances, eliminating the need for users to go through any hassle. For example, the system builds a system in which a generating AI automates all procedures related to inheritances, eliminating the need for users to go through any hassle. For example, it automates the creation of wills, inheritance tax returns, and the creation of estate division agreements. The system also automates all procedures related to inheritances, eliminating the need for users to go through any hassle. For example, it automatically creates and submits necessary documents. The system also develops a system in which a generating AI automates all procedures related to inheritances, eliminating the need for users to go through any hassle. For example, it automatically handles estate distribution procedures and tax returns. This automates all procedures related to inheritances, eliminating the need for users to go through any hassle.

[0041] The system can propose an optimal procedure schedule that matches the user's schedule. For example, the system builds a system in which a generation AI analyzes a user's schedule and proposes an optimal procedure schedule based on that information. For example, it provides a procedure schedule that avoids the user's busy hours. The system also retrieves the user's schedule from a database, and the generation AI proposes an optimal procedure schedule based on that information. For example, it provides a procedure schedule that matches the user's free time. The system also develops a system in which a generation AI proposes an optimal procedure schedule that matches the user's schedule. For example, it provides a procedure schedule that matches the user's plans. This allows the procedure to proceed smoothly by proposing an optimal procedure schedule that matches the user's schedule.

[0042] The system allows inheritance procedures to be completed online, making it possible to use the system from remote locations. For example, the system builds a system that allows generation AI to complete inheritance procedures online, making it possible to use the system from remote locations. For example, creating a will or filing an inheritance tax return online. The system also provides an online procedure system that can be used from remote locations. For example, inheritance division negotiations are conducted using video calls. The system also develops a system that allows generation AI to complete inheritance procedures online, making it possible to use the system from remote locations. For example, submitting necessary documents online. This allows inheritance procedures to be completed online, making it possible to use the system from remote locations.

[0043] The system can provide a function that allows a user to collaborate with their family and friends and proceed with procedures together. For example, the system builds a system in which a generating AI provides a function that allows a user to collaborate with their family and friends and proceed with procedures together. For example, a platform is provided where all family members can check procedures online. The system also provides a function in which a generating AI provides a function that allows a user to collaborate with their family and friends and proceed with procedures together. For example, a system is provided where all family members can check the progress of procedures in real time. The system also develops a system in which a generating AI provides a function that allows a user to collaborate with their family and friends and proceed with procedures together. For example, a platform is provided where all family members can share the progress of procedures. This provides a function that allows a user to collaborate with their family and friends and proceed with procedures together, allowing procedures to proceed more smoothly.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The knowledge provision unit can provide customized information based on the user's past inheritance experience and family composition. For example, the generation AI retrieves the user's past inheritance experience from a database and provides customized inheritance procedures based on that information. The type of assets inherited in the past and the distribution method are taken into consideration. The knowledge provision unit also collects the user's family composition as input data, and the generation AI proposes the optimal inheritance distribution method based on that information. A distribution plan is provided that takes into account the number of family members, their ages, and their relationships. The knowledge provision unit also provides customized information on specific laws and tax systems based on the user's past inheritance experience and family composition. The generation AI presents an inheritance tax calculation method suitable for a specific family composition. By providing customized information based on the user's past inheritance experience and family composition, it is possible to propose more appropriate inheritance distribution plans.

[0046] The knowledge provision unit can provide information based on the laws and tax systems specific to the user's region. For example, the generation AI identifies the user's region of residence and provides information on inheritance laws and tax systems specific to that region. It explains the inheritance tax rates and legal requirements for wills for each region. The knowledge provision unit also allows the generation AI to propose a customized inheritance distribution plan based on the laws and tax systems specific to the user's region. It provides a method for creating an inheritance division agreement in accordance with local laws. The knowledge provision unit also allows the generation AI to collect the latest information on the laws and tax systems specific to the user's region and provide appropriate advice to the user based on that information. It presents a method for calculating inheritance tax that corresponds to local tax system reforms. As a result, by providing information based on the laws and tax systems specific to the user's region, it is possible to propose an inheritance distribution plan that is appropriate for the region.

[0047] When providing knowledge about inheritances, the knowledge provision unit can use visual and interactive content to deepen understanding. For example, when the generative AI provides knowledge about inheritances, it uses visual content to provide explanations. It can provide diagrams showing a flow chart of inheritance procedures and examples of inheritance tax calculations. The knowledge provision unit also uses interactive content to allow users to learn about inheritances while operating the system themselves. It provides an interface that guides users step-by-step through the process of creating a will. The knowledge provision unit also uses visual and interactive content to help users gain a deeper understanding of inheritances. It uses videos and animations to explain the flow of inheritance procedures. In this way, the use of visual and interactive content can deepen users' understanding.

[0048] The knowledge provision unit can provide knowledge in different languages ​​and accommodate international users. For example, a system can be built in which the generation AI provides knowledge about inheritances in different languages. Information can be provided in multiple languages, such as English, French, and Chinese. The knowledge provision unit also provides knowledge in multiple languages ​​to accommodate international users. Information on inheritance procedures and legal information is provided based on the language selected by the user. The knowledge provision unit also accommodates international users by providing knowledge in different languages. Information on the inheritance laws and tax systems of each country is provided in multiple languages. This allows knowledge to be provided in different languages, making it possible to accommodate international users.

[0049] The Thought Learning Unit can learn a user's thoughts and wishes not only from the content of their dialogue, but also from their past behavioral history and choices. For example, the generation AI analyzes the content of the user's dialogue and learns their thoughts and wishes from their past behavioral history and choices. Learning is performed based on previously selected inheritance distribution methods and consultation details. The Thought Learning Unit also retrieves the user's past behavioral history from a database, and the generation AI learns their thoughts and wishes based on that information. Past consultation history and choices are analyzed to understand the user's wishes. The Thought Learning Unit also builds a system in which the generation AI learns the user's thoughts and wishes not only from the content of the user's dialogue, but also from their past behavioral history and choices. The user's wishes are predicted based on past choices and behavior patterns. This makes it possible to propose more accurate inheritance distribution by learning the user's thoughts and wishes not only from the content of the user's dialogue, but also from their past behavioral history and choices.

[0050] The Thought Learning Unit also learns the content of conversations between the user and family and friends, allowing for a more comprehensive understanding of their thoughts. For example, a system is constructed in which the generation AI learns the content of conversations between the user and family and friends, and a more comprehensive understanding of their thoughts is achieved. The content of conversations between family members is analyzed to understand the user's wishes. The Thought Learning Unit also retrieves the content of conversations between the user and family and friends from a database, and the generation AI learns the user's thoughts and wishes based on that information. An inheritance distribution plan is proposed that takes into account the opinions and wishes of the family. The Thought Learning Unit also develops a system in which the generation AI learns the content of conversations between the user and family and friends, and a more comprehensive understanding of their thoughts is achieved. An inheritance distribution plan is proposed that reflects the opinions of all family members. In this way, by learning the content of conversations between the user and family and friends, a more comprehensive understanding of their thoughts is achieved, making it possible to propose appropriate inheritance distribution plans.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The knowledge provider uses the generation AI to provide knowledge related to inheritance. For example, the generation AI may explain basic procedures for inheritance, how to calculate inheritance tax, and how to write a will. The generation AI also provides appropriate information based on prompts containing instructions on what the user wants to know. Step 2: The desire learning unit learns the user's desires based on the knowledge provided by the knowledge provider. For example, the generation AI learns the user's desires and wishes through dialogue with the user. If the user expresses a desire such as "I want to divide my inheritance equally among my children," the generation AI will understand that desire and reflect it in its inheritance distribution proposals. Step 3: The distribution proposal unit proposes inheritance distribution based on the user's wishes and desires learned by the desire learning unit. For example, if the user expresses a wish such as "I want to give a house to my eldest son and a large amount of cash to my second son," the generation AI will propose a specific distribution plan based on those wishes.

[0053] (Example 2) The inheritance distribution proposal system according to an embodiment of the present invention provides users with custom-made inheritance-related knowledge, and the AI ​​generator learns the user's wishes and proposes inheritance distribution. This allows the inheritance distribution proposal system to propose inheritance distribution that reflects the user's wishes, eliminating the need for tedious inheritance-related tasks in one stop.

[0054] An inheritance distribution proposal system according to an embodiment includes a knowledge providing unit, a wish learning unit, and a distribution proposal unit. The knowledge providing unit provides knowledge related to inheritance using a generation AI. For example, the generation AI explains basic inheritance procedures, how to calculate inheritance tax, and how to prepare a will. The generation AI also provides appropriate information based on prompts containing instructions about the content the user wants to know. The wish learning unit learns the user's wishes based on the knowledge provided by the knowledge providing unit. For example, the generation AI learns the user's wishes and desires through dialogue with the user. If the user expresses a wish such as "I want to divide my inheritance equally among my children," the generation AI understands that wish and reflects it in the inheritance distribution proposal. The distribution proposal unit proposes inheritance distribution based on the user's wishes and desires learned by the wish learning unit. For example, if the user expresses a wish such as "I want to give a house to my eldest son and more cash to my second son," the generation AI proposes a specific distribution plan based on that wish. This enables the inheritance distribution proposal system according to an embodiment to propose inheritance distribution that reflects the user's wishes.

[0055] The knowledge provision unit can provide customized information based on the user's past inheritance experience and family composition. For example, the generation AI in the knowledge provision unit retrieves the user's past inheritance experience from a database and provides customized inheritance procedures based on that information. For example, it takes into account the type of assets inherited in the past and the distribution method. The knowledge provision unit also collects the user's family composition as input data, and the generation AI proposes the optimal inheritance distribution method based on that information. For example, it provides a distribution plan that takes into account the number of family members, their ages, and their relationships. The knowledge provision unit also provides customized information on specific laws and tax systems based on the user's past inheritance experience and family composition. For example, it presents a method for calculating inheritance tax that is suitable for a specific family composition. By providing customized information based on the user's past inheritance experience and family composition, it becomes possible to propose more appropriate inheritance distribution plans.

[0056] The knowledge provision unit can provide information based on the laws and tax systems specific to the user's region. For example, the generation AI identifies the user's region of residence and provides information on inheritance laws and tax systems specific to that region. For example, it explains the inheritance tax rates and legal requirements for wills for each region. The knowledge provision unit also allows the generation AI to propose a customized inheritance distribution plan based on the laws and tax systems specific to the user's region. For example, it provides a method for creating an inheritance division agreement in accordance with local laws. The knowledge provision unit also allows the generation AI to collect the latest information on the laws and tax systems specific to the user's region and provide appropriate advice to the user based on that information. For example, it presents a method for calculating inheritance tax that corresponds to local tax system reforms. As a result, by providing information based on the laws and tax systems specific to the user's region, it becomes possible to propose an inheritance distribution plan that is appropriate for the region.

[0057] The knowledge provision unit can use the emotion estimation function to identify parts of the content where the user feels anxious or confused, and provide detailed explanations for those parts. For example, the generation AI analyzes the content of the user's dialogue to identify parts of the content where the user feels anxious or confused. For example, if the user expresses anxiety about a particular legal term, the knowledge provision unit provides a detailed explanation of that term. The knowledge provision unit also uses the emotion estimation function to provide additional information for parts of the content where the user feels anxious. For example, if the user feels anxious about how to calculate inheritance tax, the knowledge provision unit presents a specific example of the calculation. The knowledge provision unit also uses the emotion estimation function to identify parts of the content where the user feels confused, and the generation AI provides a detailed explanation for those parts. For example, if the user has questions about the legal effect of a will, the knowledge provision unit uses a specific example to explain. This allows the user to deepen their understanding by providing detailed explanations for parts of the content where the user feels anxious or confused.

[0058] The knowledge provision unit can use visual and interactive content to deepen understanding when providing knowledge about inheritance. For example, when the generation AI provides knowledge about inheritance, the knowledge provision unit uses visual content to provide explanations. For example, it may provide diagrams showing a flow chart of inheritance procedures or an example of inheritance tax calculation. The knowledge provision unit also uses interactive content to enable users to learn about inheritance while operating the system themselves. For example, it may provide an interface that provides step-by-step guidance on the procedure for creating a will. The knowledge provision unit also uses visual and interactive content to enable users to gain a deeper understanding of knowledge about inheritance. For example, it may use videos and animations to explain the flow of inheritance procedures. In this way, the use of visual and interactive content can deepen users' understanding.

[0059] The knowledge provision unit provides knowledge in different languages, making it possible to accommodate international users. For example, the knowledge provision unit builds a system in which the generation AI provides knowledge about inheritances in different languages. For example, it provides information in multiple languages, such as English, French, and Chinese. Furthermore, in order to accommodate international users, the generation AI provides knowledge in multiple languages. For example, it provides inheritance procedures and legal information based on the language selected by the user. Furthermore, the knowledge provision unit accommodates international users by providing knowledge in different languages. For example, it provides information on the inheritance laws and tax systems of each country in multiple languages. In this way, by providing knowledge in different languages, it is possible to accommodate international users.

[0060] The knowledge providing unit can use the emotion estimation function to identify the topic in which the user is most interested and provide detailed information related to that topic preferentially. For example, the knowledge providing unit uses the emotion estimation function to identify the topic in which the user is most interested. For example, if the user shows a strong interest in inheritance tax, detailed information related to that topic is provided. The knowledge providing unit also analyzes the content of the user's dialogue and identifies topics of high interest using the emotion estimation function. For example, if the user shows interest in how to write a will, detailed guides related to that topic are provided. The knowledge providing unit also uses the emotion estimation function to provide information related to the topic in which the user is most interested preferentially. For example, if the user is highly interested in inheritance distribution among family members, specific examples related to that topic are presented. This allows for the user's satisfaction to be improved by providing detailed information related to the topic in which the user is most interested preferentially.

[0061] The thought learning unit can learn a user's thoughts and wishes not only from the content of their dialogue but also from their past behavioral history and choices. For example, the thought learning unit has the generation AI analyze the content of the user's dialogue and learn their thoughts and wishes from their past behavioral history and choices. For example, it learns based on previously selected inheritance distribution methods and consultation details. The thought learning unit also retrieves the user's past behavioral history from a database, and the generation AI learns their thoughts and wishes based on that information. For example, it analyzes past consultation history and choices to understand the user's wishes. The thought learning unit also builds a system in which the generation AI learns the user's thoughts and wishes not only from the content of the user's dialogue but also from their past behavioral history and choices. For example, it predicts the user's wishes based on past choices and behavior patterns. This makes it possible to propose more accurate inheritance distribution by learning the user's thoughts and wishes not only from the content of the user's dialogue but also from their past behavioral history and choices.

[0062] The emotion learning unit can monitor changes in a user's emotions in real time and respond accordingly. For example, the emotion learning unit builds a system in which a generation AI monitors changes in a user's emotions in real time and responds accordingly. For example, if a user feels anxious, it provides reassuring information. The emotion learning unit also analyzes changes in a user's emotions in real time, and the generation AI responds accordingly. For example, if a user feels joy, it makes suggestions to maintain that emotion. The emotion learning unit also develops a system in which a generation AI monitors changes in a user's emotions in real time and responds accordingly. For example, if a user has a question, it provides a detailed explanation for that question. In this way, by monitoring changes in a user's emotions in real time and responding accordingly, it is possible to improve user satisfaction.

[0063] The emotion learning unit uses the emotion estimation function to deeply understand the user's emotions and make suggestions based on those emotions. The emotion learning unit, for example, uses the emotion estimation function to build a system that deeply understands the user's emotions and makes suggestions based on those emotions. For example, if the user feels sad, the system makes suggestions that take those emotions into consideration. The emotion learning unit also analyzes the user's emotions using the emotion estimation function and makes suggestions based on those emotions based on the results. For example, if the user is seeking a sense of security, the system provides an inheritance distribution plan that corresponds to those emotions. The emotion learning unit also uses the emotion estimation function to develop a system that deeply understands the user's emotions and makes suggestions based on those emotions. For example, the system makes suggestions that make the user feel happy. In this way, by deeply understanding the user's emotions and making suggestions based on those emotions, it is possible to improve user satisfaction.

[0064] The Thought Learning Unit also learns the content of conversations between the user and family and friends, enabling a more comprehensive understanding of thoughts. For example, the Thought Learning Unit constructs a system in which the generation AI learns the content of conversations between the user and family and friends, and understands thoughts more comprehensively. For example, it analyzes the content of conversations between family members to understand the user's wishes. The Thought Learning Unit also retrieves the content of conversations between the user and family and friends from a database, and the generation AI learns the user's thoughts and wishes based on that information. For example, it proposes an inheritance distribution plan that takes into account the opinions and wishes of the family. The Thought Learning Unit also develops a system in which the generation AI learns the content of conversations between the user and family and friends, and understands thoughts more comprehensively. For example, it provides an inheritance distribution plan that reflects the opinions of all family members. In this way, by learning the content of conversations between the user and family and friends, it is possible to understand thoughts more comprehensively and propose appropriate inheritance distribution.

[0065] The Thought Learning Unit can also incorporate non-verbal information when learning a user's thoughts using voice and facial expression recognition technology. For example, the Thought Learning Unit will build a system that uses voice recognition technology to incorporate non-verbal information when the generative AI learns a user's thoughts. For example, it will analyze the tone and strength of the user's voice to understand their emotions. The Thought Learning Unit will also use facial expression recognition technology to read emotions from the user's facial expressions and learn their thoughts and wishes based on that information. For example, if the user smiles, it will make suggestions that take that emotion into consideration. The Thought Learning Unit will also develop a system that incorporates the user's non-verbal information using voice and facial expression recognition technology. For example, it will analyze changes in the user's voice tone and facial expression to make suggestions based on their emotions. In this way, by using voice and facial expression recognition technology, it will be possible to incorporate non-verbal information and gain a deeper understanding of the user's thoughts.

[0066] The emotion learning unit uses the emotion estimation function to provide reminders and alerts based on the user's emotions, thereby supporting important decisions. The emotion learning unit, for example, uses the emotion estimation function to build a system that provides reminders and alerts based on the user's emotions. For example, if the user feels anxious, a reminder to reassure them is sent. The emotion learning unit also analyzes the user's emotions and, based on the results, provides reminders and alerts based on the emotions. For example, an alert is sent at an appropriate time when the user is making an important decision. The emotion learning unit also uses the emotion estimation function to develop a system that provides reminders and alerts based on the user's emotions. For example, if the user feels stressed, a reminder to relieve the stress is sent. This makes it possible to support important decisions by providing reminders and alerts based on the user's emotions.

[0067] The distribution proposal unit can perform a detailed analysis of the user's family structure and financial situation and propose an optimal distribution plan. For example, the distribution proposal unit uses a generation AI to perform a detailed analysis of the user's family structure and propose an optimal inheritance distribution plan based on that information. For example, it provides a distribution plan that takes into account the number of family members, ages, and relationships. The distribution proposal unit also retrieves the user's financial situation from a database, and the generation AI proposes an optimal inheritance distribution plan based on that information. For example, it provides a method for optimally distributing assets such as cash, land, and stocks. The distribution proposal unit also builds a system in which the generation AI performs a detailed analysis of the user's family structure and financial situation and proposes an optimal inheritance distribution plan based on that information. For example, it provides a distribution plan that reflects the opinions of all family members. This makes it possible to propose an optimal inheritance distribution plan through a detailed analysis of the user's family structure and financial situation.

[0068] The distribution proposal unit can learn from past inheritance distribution cases and make proposals based on them. For example, the generation AI in the distribution proposal unit learns from a database of past inheritance distribution cases and proposes the optimal inheritance distribution plan based on that information. For example, it provides a distribution method that refers to past successful and unsuccessful cases. The distribution proposal unit also analyzes past inheritance distribution cases and builds a system in which the generation AI proposes the optimal inheritance distribution plan based on that information. For example, it provides a distribution plan based on similar family structures and financial situations. The distribution proposal unit also learns from past inheritance distribution cases and proposes the optimal inheritance distribution plan based on that knowledge. For example, it provides a distribution method that reflects lessons learned from past cases. In this way, by learning from past inheritance distribution cases, it becomes possible to propose more appropriate inheritance distribution plans.

[0069] The distribution proposal unit can use the emotion estimation function to propose a distribution plan that takes into consideration the user's emotions. The distribution proposal unit, for example, uses the emotion estimation function to build a system that proposes an inheritance distribution plan that takes into consideration the user's emotions. For example, if a user feels anxious, a distribution method that alleviates that emotion is provided. The distribution proposal unit also analyzes the user's emotions and, based on the results, proposes an inheritance distribution plan that takes into consideration the emotions. For example, if the user desires a sense of security, a distribution plan that corresponds to that emotion is provided. The distribution proposal unit also uses the emotion estimation function to develop a system that proposes an inheritance distribution plan that takes into consideration the user's emotions. For example, a distribution method that makes the user feel happy is provided. In this way, by proposing a distribution plan that takes into consideration the user's emotions, user satisfaction can be improved.

[0070] The distribution proposal unit can propose multiple distribution plans based on different scenarios and allow the user to select from them. The distribution proposal unit, for example, builds a system in which a generation AI proposes multiple inheritance distribution plans based on different scenarios and allows the user to select from them. For example, it provides a plan that distributes equally to all family members and a plan that distributes more to specific children. The distribution proposal unit also proposes multiple inheritance distribution plans based on the user's wishes and thoughts. For example, it presents multiple distribution methods according to the scenario desired by the user. The distribution proposal unit also develops a system in which a generation AI proposes multiple inheritance distribution plans based on different scenarios and allows the user to select from them. For example, it provides a distribution plan according to the scenario selected by the user. This allows the user to select the optimal plan by proposing multiple distribution plans based on different scenarios.

[0071] The distribution proposal unit can propose a distribution plan that incorporates feedback from the user's family and friends. The distribution proposal unit, for example, builds a system in which a generation AI proposes an inheritance distribution plan that incorporates feedback from the user's family and friends. For example, it provides a distribution method that reflects the opinions of all family members. The distribution proposal unit also obtains feedback from the user's family and friends from a database, and the generation AI proposes an optimal inheritance distribution plan based on that information. For example, it provides a distribution plan that takes into account the opinions and wishes of the family. The distribution proposal unit also develops a system in which a generation AI proposes an inheritance distribution plan that incorporates feedback from the user's family and friends. For example, it provides a distribution method that reflects the opinions of all family members. In this way, by incorporating feedback from the user's family and friends, it becomes possible to propose a more appropriate inheritance distribution.

[0072] The distribution proposal unit uses the emotion estimation function to propose a distribution plan that takes into account the emotions of all family members, thereby avoiding disputes between family members. The distribution proposal unit, for example, uses the emotion estimation function to build a system that proposes an inheritance distribution plan that takes into account the emotions of all family members. For example, it provides a distribution method that all family members can agree on. The distribution proposal unit also analyzes the emotions of all family members and, based on the results, proposes an inheritance distribution plan that takes into account the emotions. For example, it provides a distribution method that avoids disputes between family members. The distribution proposal unit also uses the emotion estimation function to develop a system that proposes an inheritance distribution plan that takes into account the emotions of all family members. For example, it provides a distribution method that all family members can be satisfied with. In this way, by proposing a distribution plan that takes into account the emotions of all family members, it is possible to avoid disputes between family members.

[0073] The system can automate all procedures related to inheritances, eliminating the need for users to go through any hassle. For example, the system builds a system in which a generating AI automates all procedures related to inheritances, eliminating the need for users to go through any hassle. For example, it automates the creation of wills, inheritance tax returns, and the creation of estate division agreements. The system also automates all procedures related to inheritances, eliminating the need for users to go through any hassle. For example, it automatically creates and submits necessary documents. The system also develops a system in which a generating AI automates all procedures related to inheritances, eliminating the need for users to go through any hassle. For example, it automatically handles estate distribution procedures and tax returns. This automates all procedures related to inheritances, eliminating the need for users to go through any hassle.

[0074] The system can propose an optimal procedure schedule that matches the user's schedule. For example, the system builds a system in which a generation AI analyzes a user's schedule and proposes an optimal procedure schedule based on that information. For example, it provides a procedure schedule that avoids the user's busy hours. The system also retrieves the user's schedule from a database, and the generation AI proposes an optimal procedure schedule based on that information. For example, it provides a procedure schedule that matches the user's free time. The system also develops a system in which a generation AI proposes an optimal procedure schedule that matches the user's schedule. For example, it provides a procedure schedule that matches the user's plans. This allows the procedure to proceed smoothly by proposing an optimal procedure schedule that matches the user's schedule.

[0075] The system can use the emotion estimation function to identify areas where the user feels stressed and provide focused support for those areas. For example, the system uses the emotion estimation function to build a system that identifies areas where the user feels stressed and provides focused support for those areas. For example, it provides detailed explanations for procedures that make the user feel anxious. The system also analyzes the user's emotions and, based on the results, identifies areas where the user feels stressed and provides focused support. For example, it provides additional support for procedures that make the user feel stressed. The system also uses the emotion estimation function to develop a system that identifies areas where the user feels stressed and provides focused support for those areas. For example, it provides dedicated support for procedures that make the user feel stressed. In this way, the burden on the user can be reduced by identifying areas where the user feels stressed and providing focused support for those areas.

[0076] The system allows inheritance procedures to be completed online, making it possible to use the system from remote locations. For example, the system builds a system that allows generation AI to complete inheritance procedures online, making it possible to use the system from remote locations. For example, creating a will or filing an inheritance tax return online. The system also provides an online procedure system that can be used from remote locations. For example, inheritance division negotiations are conducted using video calls. The system also develops a system that allows generation AI to complete inheritance procedures online, making it possible to use the system from remote locations. For example, submitting necessary documents online. This allows inheritance procedures to be completed online, making it possible to use the system from remote locations.

[0077] The system can provide a function that allows a user to collaborate with their family and friends and proceed with procedures together. For example, the system builds a system in which a generating AI provides a function that allows a user to collaborate with their family and friends and proceed with procedures together. For example, a platform is provided where all family members can check procedures online. The system also provides a function in which a generating AI provides a function that allows a user to collaborate with their family and friends and proceed with procedures together. For example, a system is provided where all family members can check the progress of procedures in real time. The system also develops a system in which a generating AI provides a function that allows a user to collaborate with their family and friends and proceed with procedures together. For example, a platform is provided where all family members can share the progress of procedures. This provides a function that allows a user to collaborate with their family and friends and proceed with procedures together, allowing procedures to proceed more smoothly.

[0078] The system uses the emotion estimation function to provide reminders and alerts based on the user's emotions, allowing procedures to proceed smoothly. For example, the system uses the emotion estimation function to build a system that provides reminders and alerts based on the user's emotions. For example, if the user feels anxious, the system sends a reminder to reassure the user. The system also analyzes the user's emotions and provides reminders and alerts based on the emotions based on the results. For example, the system sends alerts at appropriate times to ensure the user does not forget an important procedure. The system also uses the emotion estimation function to develop a system that provides reminders and alerts based on the user's emotions. For example, if the user feels stressed, the system sends a reminder to relieve the stress. In this way, by providing reminders and alerts based on the user's emotions, procedures can proceed smoothly.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The knowledge provision unit can provide customized information based on the user's past inheritance experience and family composition. For example, the generation AI retrieves the user's past inheritance experience from a database and provides customized inheritance procedures based on that information. The type of assets inherited in the past and the distribution method are taken into consideration. The knowledge provision unit also collects the user's family composition as input data, and the generation AI proposes the optimal inheritance distribution method based on that information. A distribution plan is provided that takes into account the number of family members, their ages, and their relationships. The knowledge provision unit also provides customized information on specific laws and tax systems based on the user's past inheritance experience and family composition. The generation AI presents an inheritance tax calculation method suitable for a specific family composition. By providing customized information based on the user's past inheritance experience and family composition, it is possible to propose more appropriate inheritance distribution plans.

[0081] The knowledge provision unit can provide information based on the laws and tax systems specific to the user's region. For example, the generation AI identifies the user's region of residence and provides information on inheritance laws and tax systems specific to that region. It explains the inheritance tax rates and legal requirements for wills for each region. The knowledge provision unit also allows the generation AI to propose a customized inheritance distribution plan based on the laws and tax systems specific to the user's region. It provides a method for creating an inheritance division agreement in accordance with local laws. The knowledge provision unit also allows the generation AI to collect the latest information on the laws and tax systems specific to the user's region and provide appropriate advice to the user based on that information. It presents a method for calculating inheritance tax that corresponds to local tax system reforms. As a result, by providing information based on the laws and tax systems specific to the user's region, it is possible to propose an inheritance distribution plan that is appropriate for the region.

[0082] The knowledge provision unit can use the emotion estimation function to identify parts of a sentence that the user feels uneasy or confused about, and provide a detailed explanation for those parts. For example, the generation AI analyzes the content of the user's dialogue and identifies parts that the user feels uneasy or confused about. If the user expresses anxiety about a particular legal term, a detailed explanation of that term is provided. The knowledge provision unit also uses the emotion estimation function to provide additional information on parts that the user feels uneasy about. If the user feels uneasy about how to calculate inheritance tax, a specific calculation example is presented. The knowledge provision unit also uses the emotion estimation function to identify parts of a sentence that the user feels uneasy about, and the generation AI provides a detailed explanation for those parts. Specific examples are used to explain questions about the legal effect of a will. This allows the user to deepen their understanding by providing detailed explanations for parts that the user feels uneasy or confused about.

[0083] When providing knowledge about inheritances, the knowledge provision unit can use visual and interactive content to deepen understanding. For example, when the generative AI provides knowledge about inheritances, it uses visual content to provide explanations. It can provide diagrams showing a flow chart of inheritance procedures and examples of inheritance tax calculations. The knowledge provision unit also uses interactive content to allow users to learn about inheritances while operating the system themselves. It provides an interface that guides users step-by-step through the process of creating a will. The knowledge provision unit also uses visual and interactive content to help users gain a deeper understanding of inheritances. It uses videos and animations to explain the flow of inheritance procedures. In this way, the use of visual and interactive content can deepen users' understanding.

[0084] The knowledge provision unit can provide knowledge in different languages ​​and accommodate international users. For example, a system can be built in which the generation AI provides knowledge about inheritances in different languages. Information can be provided in multiple languages, such as English, French, and Chinese. The knowledge provision unit also provides knowledge in multiple languages ​​to accommodate international users. Information on inheritance procedures and legal information is provided based on the language selected by the user. The knowledge provision unit also accommodates international users by providing knowledge in different languages. Information on the inheritance laws and tax systems of each country is provided in multiple languages. This allows knowledge to be provided in different languages, making it possible to accommodate international users.

[0085] The knowledge providing unit can use the emotion estimation function to identify the topic in which the user is most interested and provide detailed information on that topic preferentially. For example, the emotion estimation function is used to identify the topic in which the user is most interested. If the user shows a strong interest in inheritance tax, detailed information on that topic is provided. The knowledge providing unit also analyzes the content of the user's dialogue and uses the emotion estimation function to identify topics of high interest. If the user shows interest in how to write a will, detailed guides on that topic are provided. The knowledge providing unit also uses the emotion estimation function to provide information on the topic in which the user is most interested preferentially. If the user shows a high interest in inheritance distribution among family members, specific examples related to that topic are presented. This allows for the user's satisfaction to be improved by providing detailed information on the topic in which the user is most interested preferentially.

[0086] The Thought Learning Unit can learn a user's thoughts and wishes not only from the content of their dialogue, but also from their past behavioral history and choices. For example, the generation AI analyzes the content of the user's dialogue and learns their thoughts and wishes from their past behavioral history and choices. Learning is performed based on previously selected inheritance distribution methods and consultation details. The Thought Learning Unit also retrieves the user's past behavioral history from a database, and the generation AI learns their thoughts and wishes based on that information. Past consultation history and choices are analyzed to understand the user's wishes. The Thought Learning Unit also builds a system in which the generation AI learns the user's thoughts and wishes not only from the content of the user's dialogue, but also from their past behavioral history and choices. The user's wishes are predicted based on past choices and behavior patterns. This makes it possible to propose more accurate inheritance distribution by learning the user's thoughts and wishes not only from the content of the user's dialogue, but also from their past behavioral history and choices.

[0087] The Thought Learning Unit can monitor changes in a user's emotions in real time and respond accordingly. For example, we will build a system in which a generation AI monitors changes in a user's emotions in real time and responds accordingly. If the user feels anxious, it will provide reassuring information. The Thought Learning Unit will also analyze changes in a user's emotions in real time, and the generation AI will respond accordingly. If the user feels joy, it will make suggestions to maintain that emotion. The Thought Learning Unit will also develop a system in which a generation AI monitors changes in a user's emotions in real time and responds accordingly. If the user has a question, it will provide a detailed explanation for the question. In this way, by monitoring changes in a user's emotions in real time and responding accordingly, it is possible to improve user satisfaction.

[0088] The Thought Learning Unit also learns the content of conversations between the user and family and friends, allowing for a more comprehensive understanding of their thoughts. For example, a system is constructed in which the generation AI learns the content of conversations between the user and family and friends, and a more comprehensive understanding of their thoughts is achieved. The content of conversations between family members is analyzed to understand the user's wishes. The Thought Learning Unit also retrieves the content of conversations between the user and family and friends from a database, and the generation AI learns the user's thoughts and wishes based on that information. An inheritance distribution plan is proposed that takes into account the opinions and wishes of the family. The Thought Learning Unit also develops a system in which the generation AI learns the content of conversations between the user and family and friends, and a more comprehensive understanding of their thoughts is achieved. An inheritance distribution plan is proposed that reflects the opinions of all family members. In this way, by learning the content of conversations between the user and family and friends, a more comprehensive understanding of their thoughts is achieved, making it possible to propose appropriate inheritance distribution plans.

[0089] The emotion learning unit uses the emotion estimation function to deeply understand the user's emotions and make suggestions based on those emotions. For example, a system is constructed that uses the emotion estimation function to deeply understand the user's emotions and make suggestions based on those emotions. If the user feels sad, a suggestion is made that takes those emotions into consideration. The emotion learning unit also analyzes the user's emotions using the emotion estimation function and makes a suggestion based on the results. If the user is seeking a sense of security, an inheritance distribution plan is provided that corresponds to those emotions. The emotion learning unit also uses the emotion estimation function to develop a system that deeply understands the user's emotions and makes a suggestion based on those emotions. A suggestion is made that makes the user feel happy. In this way, by deeply understanding the user's emotions and making suggestions based on those emotions, user satisfaction can be improved.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The knowledge provider uses the generation AI to provide knowledge related to inheritance. For example, the generation AI may explain basic procedures for inheritance, how to calculate inheritance tax, and how to write a will. The generation AI also provides appropriate information based on prompts containing instructions on what the user wants to know. Step 2: The desire learning unit learns the user's desires based on the knowledge provided by the knowledge provider. For example, the generation AI learns the user's desires and wishes through dialogue with the user. If the user expresses a desire such as "I want to divide my inheritance equally among my children," the generation AI will understand that desire and reflect it in its inheritance distribution proposals. Step 3: The distribution proposal unit proposes inheritance distribution based on the user's wishes and desires learned by the desire learning unit. For example, if the user expresses a wish such as "I want to give a house to my eldest son and a large amount of cash to my second son," the generation AI will propose a specific distribution plan based on those wishes.

[0092] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Using generative AI, The Knowledge Provision Department provides knowledge related to heritage sites, a thought learning unit that learns the thoughts of a user based on the knowledge provided by the knowledge providing unit; A distribution suggestion unit is provided that proposes inheritance distribution based on the user's thoughts and wishes learned by the thought learning unit. A system characterized by:

2. The knowledge providing unit Provide customized information based on the user's past inheritance experience and family structure 2. The system of claim 1.

3. The knowledge providing unit Providing heritage knowledge through visual and interactive content to enhance understanding 2. The system of claim 1.

4. The thought learning unit Learns the user's thoughts and wishes not only from the content of their conversations but also from their past behavioral history and choices 2. The system of claim 1.

5. The distribution proposal unit A detailed analysis of the user's family structure and financial situation will be conducted, and an optimal distribution plan will be proposed.

2. The system of claim 1.

6. The knowledge providing unit Identify areas where the user has concerns or questions and provide detailed explanations for those areas 2. The system of claim 1.

7. The thought learning unit The system according to claim 1, wherein changes in the user's emotions are monitored in real time and a response is made in response to the changes.

8. The distribution proposal unit Propose a distribution plan that takes into consideration the user's feelings 2. The system of claim 1.

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