System

Abstract

An object of the system according to the embodiment is to propose an optimal inheritance asset allocation that satisfies all family members.SOLUTION: A system includes an opinion collection part, an analysis part, a proposal part, and an opinion exchange support part. An opinion collection part collects opinions of the respective relatives. The analysis unit analyzes the opinions of the relatives collected by the opinion collection unit. The proposal unit proposes an optimal inheritance asset allocation based on the result analyzed by the analysis unit. An opinion exchange support part smoothly advances opinion exchange between the successors on the basis of the contents proposed by the proposal part.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] With conventional technology, it was difficult to achieve a property division that all relatives could agree on in the event of inheritance.

[0005] The system according to the embodiment aims to propose an optimal division of inheritance property that satisfies all relatives. [Means for solving the problem]

[0006] The system according to the embodiment includes an opinion collection unit, an analysis unit, a proposal unit, and an opinion exchange support unit. The opinion collection unit collects the opinions of each relative. The analysis unit analyzes the opinions of the relatives collected by the opinion collection unit. The proposal unit proposes an optimal division of inheritance property based on the results of the analysis by the analysis unit. The opinion exchange support unit facilitates the exchange of opinions between the heirs based on the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal division of inheritance property that satisfies all relatives. [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) In the inheritance support system according to an embodiment of the present invention, a generation AI summarizes the arguments of each family member in advance, proposes the optimal division of inheritance assets, and facilitates the smooth exchange of opinions between heirs. As a result, the inheritance support system can resolve the complex issues that arise during inheritance and achieve a division of inheritance assets that all family members can agree on.

[0029] An inheritance support system according to an embodiment includes an opinion collection unit, an analysis unit, a proposal unit, and an opinion exchange support unit. The opinion collection unit collects opinions from each relative. For example, it records oral opinions from relatives and converts them into text data. It can also scan written opinions submitted by relatives and convert them into digital data. It can also directly collect opinions submitted online by relatives. For example, the opinion collection unit automatically collects opinions entered by relatives into an online form. The analysis unit analyzes the opinions of relatives collected by the opinion collection unit. For example, the generation AI analyzes the opinions of relatives using text analysis technology. It can also analyze the emotions of relatives using emotion analysis technology. It can also aggregate the opinions of relatives using statistical analysis technology. For example, the analysis unit classifies the opinions of relatives using text analysis technology, evaluates their emotions using emotion analysis technology, and aggregates the results using statistical analysis technology. The proposal unit proposes an optimal division of inheritance assets based on the results of the analysis by the analysis unit. For example, the generation AI proposes the optimal property division by taking into account the opinions and feelings of relatives. The generation AI can also propose property division by referring to legal standards and past inheritance cases. The generation AI can also propose property division by taking into account the wishes and emotional satisfaction of relatives. For example, the proposal unit proposes the optimal property division based on the opinions and feelings of relatives, referring to legal standards and past inheritance cases. The opinion exchange support unit facilitates the exchange of opinions between heirs based on the content proposed by the proposal unit. For example, the generation AI conducts opinion exchanges between relatives through an online platform and records and analyzes the history. The generation AI can also hold opinion exchanges between relatives in the form of regular meetings and manage their progress. The generation AI can also make suggestions to avoid emotional conflicts during opinion exchanges, thereby facilitating the exchange of opinions. For example, the opinion exchange support unit conducts opinion exchanges between relatives through an online platform, records and analyzes the history, holds opinion exchanges in the form of regular meetings, and makes suggestions to avoid emotional conflicts. As a result, the inheritance support system according to the embodiment can solve the complex problems that arise during inheritance and realize the division of inheritance property that is acceptable to all relatives.For example, it can avoid disputes between family members and ensure a smooth inheritance. It can also create a will that reflects the opinions of all family members while respecting the wishes of the deceased.

[0030] The opinion collection unit can analyze the relative's past communication history or social media posts to extract the relative's true feelings and latent wishes. For example, the opinion collection unit uses a generation AI to analyze the relative's past communication history to extract the relative's true feelings and latent wishes. For example, the opinions and wishes expressed by the relative in the past are used as a reference when collecting current opinions. The opinion collection unit can also analyze the relative's social media posts to extract the relative's true feelings and latent wishes. For example, it analyzes the content posted by the relative on social media to understand the relative's true feelings and latent wishes. The opinion collection unit can also analyze the relative's past email and chat history to extract the relative's true feelings and latent wishes. For example, it analyzes the content of emails and chats sent by the relative in the past to understand the relative's true feelings and latent wishes. This makes it possible to understand the relative's true feelings and latent wishes.

[0031] The opinion collection unit can perform a more precise analysis by taking into account individual background information such as the living situation or health condition of relatives. For example, the opinion collection unit allows the generation AI to consider the living situation of relatives and reflect that background information when collecting opinions. For example, when a relative describes their current living situation, opinions are collected based on that information. The opinion collection unit can also consider the health condition of relatives and reflect that background information when collecting opinions. For example, when a relative describes their current health condition, opinions are collected based on that information. The opinion collection unit can also collect opinions by taking into account the living situation of relatives, such as their income and family composition. For example, when a relative describes their current income and family composition, opinions are collected based on that information. This makes it possible to collect opinions that take into account the individual background information of relatives.

[0032] The proposal unit can analyze past inheritance cases and legal precedents and make optimal property division proposals based on the results. For example, the proposal unit uses a generation AI to analyze past inheritance cases and make optimal property division proposals based on the results. For example, the proposal unit can make optimal property division proposals based on past successful and unsuccessful cases. The proposal unit can also analyze legal precedents and make optimal property division proposals based on the results. For example, the proposal unit can analyze past court records and legal documents and make optimal property division proposals. The proposal unit can also refer to past inheritance cases and legal precedents to make property division proposals that take into account the wishes and emotional satisfaction of relatives. For example, the proposal unit can make optimal property division proposals based on past inheritance cases and legal precedents and make optimal property division proposals that take into account the wishes and emotional satisfaction of relatives based on past inheritance cases and legal precedents. This makes it possible to make optimal property division proposals based on past inheritance cases and legal precedents.

[0033] The proposal unit can predict the relative's future lifestyle plans or economic situation and make asset division proposals based on that. For example, the proposal unit uses a generation AI to predict the relative's future lifestyle plans and make asset division proposals based on that. For example, it makes optimal asset division proposals based on the relative's future lifestyle plans. The proposal unit can also predict the relative's future economic situation and make asset division proposals based on that. For example, it makes optimal asset division proposals based on the relative's future income predictions and asset valuations. The proposal unit can also adjust the content of the asset division proposal taking into account the relative's future lifestyle plans and economic situation. For example, it fine-tunes the content of the proposal based on the relative's future lifestyle plans and economic situation and makes optimal asset division proposals. This makes it possible to make asset division proposals that take into account the relative's future lifestyle plans and economic situation.

[0034] The opinion exchange support unit can analyze the past communication history between the heirs and provide advice for a smooth exchange of opinions. For example, the generation AI analyzes the past communication history between the heirs and provides advice for a smooth exchange of opinions. For example, based on the content of past dialogue, it proposes a way to proceed with an opinion exchange that is easy for the heirs to agree with. The opinion exchange support unit can also analyze the past email and chat history between the heirs and provide advice for a smooth exchange of opinions. For example, based on the content of past emails and chats, it proposes a way to proceed with an opinion exchange that is easy for the heirs to agree with. The opinion exchange support unit can also provide advice for avoiding conflicts based on the past communication history between the heirs. For example, based on the content of past dialogues, it can detect situations where conflicts are likely to occur and provide appropriate advice. This makes it possible to provide advice for a smooth exchange of opinions based on the past communication history between the heirs.

[0035] The opinion exchange support unit monitors the exchange of opinions between heirs in real time and intervenes as necessary to ensure the exchange of opinions proceeds smoothly. For example, the generation AI monitors the exchange of opinions between heirs in real time and intervenes as necessary. For example, when the exchange of opinions reaches an impasse, it provides appropriate advice to ensure the exchange of opinions proceeds smoothly. The opinion exchange support unit can also monitor the exchange of opinions between heirs in real time and intervene when a conflict arises. For example, when a conflict arises, it can mediate appropriately to ensure the exchange of opinions proceeds smoothly. The opinion exchange support unit can also monitor the exchange of opinions between heirs in real time and manage the progress. For example, it can grasp the progress of the exchange of opinions in real time and manage the progress appropriately. This allows the exchange of opinions between heirs to be monitored in real time and intervene as necessary to ensure the exchange of opinions proceeds smoothly.

[0036] The opinion exchange support unit can conduct opinion exchanges between heirs through an online platform and record and analyze the history of the opinion exchanges. For example, the opinion exchange support unit uses a generation AI to conduct opinion exchanges between heirs through an online platform and record and analyze the history of the opinion exchanges. For example, the history of online chats and video conferences is saved and later analyzed. The opinion exchange support unit can also conduct opinion exchanges between heirs through an online platform and analyze the history in real time. For example, it can analyze the history of online chats and video conferences in real time and provide appropriate advice. The opinion exchange support unit can also conduct opinion exchanges between heirs through an online platform and provide advice to avoid conflicts based on the history. For example, it can detect situations where conflicts are likely to occur in advance based on the history of online chats and video conferences and provide appropriate advice. This allows opinion exchanges between heirs to be conducted through an online platform and record and analyze the history, thereby facilitating the smooth exchange of opinions.

[0037] The opinion exchange support unit can hold opinion exchanges between heirs in the form of regular meetings and manage the progress of the opinion exchanges. For example, the generation AI can hold opinion exchanges between heirs in the form of regular meetings and manage the progress of the opinion exchanges. For example, the generation AI can set up regular online meetings and record and analyze the progress. The opinion exchange support unit can also hold opinion exchanges between heirs in the form of regular meetings and manage the progress in real time. For example, the generation AI can set up regular online meetings, grasp the progress in real time, and manage the progress appropriately. The opinion exchange support unit can also hold opinion exchanges between heirs in the form of regular meetings and provide advice to avoid conflicts based on the progress. For example, the generation AI can detect situations where conflicts are likely to occur in advance and provide appropriate advice based on the progress of regular online meetings. This allows opinion exchanges between heirs to be held in the form of regular meetings and manage the progress, thereby smoothly promoting opinion exchanges.

[0038] The suggestion unit can propose multiple different scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit uses a generation AI to propose multiple different property division scenarios, allowing relatives to compare and consider the options. For example, it presents multiple scenarios, allowing relatives to select the optimal scenario. The suggestion unit can also propose multiple different inheritance procedure scenarios, allowing relatives to compare and consider the options. For example, it presents multiple inheritance procedure scenarios, allowing relatives to select the optimal scenario. The suggestion unit can also visually present the options based on the different scenarios, allowing relatives to compare and consider the options. For example, it can visually present different scenarios using graphs and charts, allowing relatives to compare and consider the options. In this way, it is possible to propose optimal property division by proposing multiple different scenarios and allowing relatives to compare and consider the options.

[0039] The proposal unit can simulate property division based on the opinions of relatives and propose it in a visually easy-to-understand format. For example, the proposal unit uses a generation AI to simulate property division based on the opinions of relatives and propose it in a visually easy-to-understand format. For example, the proposal unit displays the results of the property division simulation using graphs and charts. The proposal unit can also simulate inheritance procedures based on the opinions of relatives and propose it in a visually easy-to-understand format. For example, the proposal unit displays the results of the inheritance procedures simulation using graphs and charts. The proposal unit can also visually present the results of the property division simulation based on the opinions of relatives and propose it in a format that is easy for relatives to understand. For example, the proposal unit visually presents the results of the property division simulation using graphs and charts and propose it in a format that is easy for relatives to understand. In this way, by simulating property division based on the opinions of relatives and proposing it in a visually easy-to-understand format, it is possible to propose a property division that is easy for relatives to understand.

[0040] The proposal unit can analyze past inheritance cases and legal precedents and propose inheritance procedures that are highly satisfactory based on the results. For example, the proposal unit uses a generation AI to analyze past inheritance cases and propose inheritance procedures that are highly satisfactory based on the results. For example, the proposal unit proposes inheritance procedures that are highly satisfactory based on past successes and failures. The proposal unit can also analyze legal precedents and propose inheritance procedures that are highly satisfactory based on the results. For example, the proposal unit can analyze past court records and legal documents and propose inheritance procedures that are highly satisfactory. The proposal unit can also refer to past inheritance cases and legal precedents and propose inheritance procedures that take into account the wishes and emotional satisfaction of relatives. For example, the proposal unit proposes inheritance procedures that are highly satisfactory based on past inheritance cases and legal precedents and proposes inheritance procedures that take into account the wishes and emotional satisfaction of relatives. In this way, by proposing inheritance procedures that are highly satisfactory based on past inheritance cases and legal precedents, it is possible to realize inheritance procedures that are satisfactory to all relatives.

[0041] The proposal unit can predict the relative's future lifestyle plans or economic situation and propose convincing inheritance procedures based on that. For example, the proposal unit uses a generation AI to predict the relative's future lifestyle plans and propose convincing inheritance procedures based on that. For example, the proposal unit proposes convincing inheritance procedures based on the relative's future lifestyle plans. The proposal unit can also predict the relative's future economic situation and propose convincing inheritance procedures based on that. For example, the proposal unit proposes convincing inheritance procedures based on the relative's future income forecast and asset valuation. The proposal unit can also adjust the proposed inheritance procedures taking into account the relative's future lifestyle plans and economic situation. For example, the proposal can fine-tune the proposed contents based on the relative's future lifestyle plans and economic situation and propose convincing inheritance procedures. In this way, by predicting the relative's future lifestyle plans and economic situation and proposing convincing inheritance procedures based on that, it is possible to realize inheritance procedures that are convincing to all relatives.

[0042] The suggestion unit can propose multiple different scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit uses a generation AI to propose multiple different inheritance procedure scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit presents multiple scenarios, allowing relatives to select the optimal scenario. The suggestion unit can also propose multiple different property division scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit presents multiple property division scenarios, allowing relatives to select the optimal scenario. The suggestion unit can also visually present the options based on the different scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit can visually present different scenarios using graphs and charts, allowing relatives to compare and consider the options. In this way, the optimal inheritance procedure can be proposed by proposing multiple different scenarios and allowing relatives to compare and consider the options.

[0043] The proposal unit can simulate inheritance procedures based on the opinions of relatives and propose them in a visually easy-to-understand format. For example, the proposal unit uses a generation AI to simulate inheritance procedures based on the opinions of relatives and propose them in a visually easy-to-understand format. For example, the proposal unit displays the results of the inheritance procedure simulation using graphs and charts. The proposal unit can also simulate property division based on the opinions of relatives and propose them in a visually easy-to-understand format. For example, the proposal unit displays the results of the property division simulation using graphs and charts. The proposal unit can also visually present the results of the inheritance procedure simulation based on the opinions of relatives and propose them in a format that is easy for relatives to understand. For example, the proposal unit visually presents the results of the inheritance procedure simulation using graphs and charts and proposes them in a format that is easy for relatives to understand. In this way, by simulating inheritance procedures based on the opinions of relatives and proposing them in a visually easy-to-understand format, it is possible to propose inheritance procedures that are easy for relatives to understand.

[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] When collecting opinions of relatives, the opinion collection unit can convert the opinions into text data in real time using voice recognition technology. For example, opinions expressed by relatives during a conversation can be instantly converted into text and saved as data for later analysis. The opinion collection unit can also record opinions expressed by relatives in video calls and convert the audio into text data. For example, the contents of a video call can be automatically recorded and converted into text using voice recognition technology. The opinion collection unit can also record opinions expressed by relatives over the phone and convert the audio into text data. For example, the contents of a phone call can be recorded and converted into text using voice recognition technology. In this way, opinions of relatives can be collected in real time and saved as data for later analysis.

[0046] The opinion collection unit can analyze the relative's past communication history or social media posts to extract the relative's true feelings and latent hopes. For example, the opinions and hopes expressed by the relative in the past can be used as a reference when collecting current opinions. The opinion collection unit can also analyze the relative's social media posts to extract the relative's true feelings and latent hopes. For example, it analyzes the content posted by the relative on social media to understand the relative's true feelings and latent hopes. The opinion collection unit can also analyze the relative's past email and chat history to extract the relative's true feelings and latent hopes. For example, it analyzes the content of emails and chats sent by the relative in the past to understand the relative's true feelings and latent hopes. This makes it possible to understand the relative's true feelings and latent hopes.

[0047] The opinion collection unit can perform more precise analysis by taking into account individual background information such as the living situation or health condition of relatives. For example, the generation AI takes into account the living situation of relatives and reflects that background information when collecting opinions. For example, when a relative describes their current living situation, opinions are collected based on that information. The opinion collection unit can also take into account the health condition of relatives and reflect that background information when collecting opinions. For example, when a relative describes their current health condition, opinions are collected based on that information. The opinion collection unit can also collect opinions by taking into account the living situation of relatives, such as their income and family composition. For example, when a relative describes their current income and family composition, opinions are collected based on that information. This makes it possible to collect opinions that take into account the individual background information of relatives.

[0048] The proposal unit can analyze past inheritance cases and legal precedents and make optimal property division proposals based on the results. For example, the generation AI analyzes past inheritance cases and makes optimal property division proposals based on the results. For example, it proposes optimal property division proposals based on past successful and unsuccessful cases. The proposal unit can also analyze legal precedents and make optimal property division proposals based on the results. For example, it analyzes past court records and legal documents and makes optimal property division proposals. The proposal unit can also refer to past inheritance cases and legal precedents to make property division proposals that take into account the wishes and emotional satisfaction of relatives. For example, it proposes optimal property division proposals based on past inheritance cases and legal precedents and makes optimal property division proposals that take into account the wishes and emotional satisfaction of relatives. This makes it possible to propose optimal property division proposals based on past inheritance cases and legal precedents.

[0049] The proposal unit can predict the relative's future lifestyle plans or economic situation and make asset division proposals based on that. For example, the generation AI predicts the relative's future lifestyle plans and makes asset division proposals based on that. For example, it makes optimal asset division proposals based on the relative's future lifestyle plans. The proposal unit can also predict the relative's future economic situation and make asset division proposals based on that. For example, it makes optimal asset division proposals based on the relative's future income predictions and asset valuations. The proposal unit can also adjust the content of the asset division proposal taking into account the relative's future lifestyle plans and economic situation. For example, it fine-tunes the content of the proposal based on the relative's future lifestyle plans and economic situation and makes optimal asset division proposals. This makes it possible to make asset division proposals that take into account the relative's future lifestyle plans and economic situation.

[0050] The opinion exchange support unit can analyze the past communication history between heirs and provide advice for a smooth exchange of opinions. For example, the generation AI can analyze the past communication history between heirs and provide advice for a smooth exchange of opinions. For example, based on the content of past dialogue, it can suggest how to proceed with an opinion exchange that is easy for the heirs to agree with. The opinion exchange support unit can also analyze the past email and chat history between heirs and provide advice for a smooth exchange of opinions. For example, based on the content of past emails and chats, it can suggest how to proceed with an opinion exchange that is easy for the heirs to agree with. The opinion exchange support unit can also provide advice for avoiding conflicts based on the past communication history between heirs. For example, based on the content of past dialogues, it can detect situations where conflicts are likely to occur and provide appropriate advice. This makes it possible to provide advice for a smooth exchange of opinions based on the past communication history between heirs.

[0051] The opinion exchange support unit monitors the exchange of opinions between heirs in real time and intervenes as necessary to ensure the exchange of opinions proceeds smoothly. For example, the generation AI monitors the exchange of opinions between heirs in real time and intervenes as necessary. For example, when the exchange of opinions reaches an impasse, it provides appropriate advice to ensure the exchange of opinions proceeds smoothly. The opinion exchange support unit can also monitor the exchange of opinions between heirs in real time and intervene when a conflict arises. For example, when a conflict arises, it can mediate appropriately to ensure the exchange of opinions proceeds smoothly. The opinion exchange support unit can also monitor the exchange of opinions between heirs in real time and manage the progress. For example, it can grasp the progress of the exchange of opinions in real time and manage the progress appropriately. This allows the exchange of opinions between heirs to be monitored in real time and intervene as necessary to ensure the exchange of opinions proceeds smoothly.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The opinion collection unit collects the opinions of each relative. For example, the opinion collection unit may record the oral opinions of the relatives and convert them into text data. It may also scan the written opinions submitted by the relatives and convert them into digital data. It may also directly collect opinions submitted by the relatives online. For example, the opinion collection unit may automatically collect opinions entered by the relatives into an online form. Step 2: The analysis unit analyzes the opinions of relatives collected by the opinion collection unit. For example, the generation AI analyzes the opinions of relatives using text analysis technology. The generation AI can also analyze the emotions of relatives using emotion analysis technology. The generation AI can also aggregate the opinions of relatives using statistical analysis technology. For example, the analysis unit classifies the opinions of relatives using text analysis technology, evaluates emotions using emotion analysis technology, and aggregates them using statistical analysis technology. Step 3: The proposal unit proposes the optimal division of inheritance assets based on the results of the analysis by the analysis unit. For example, the generation AI proposes the optimal division of assets by taking into account the opinions and feelings of relatives. The generation AI can also propose a division of assets by referring to legal standards and past inheritance cases. The generation AI can also propose a division of assets by taking into account the wishes and emotional satisfaction of relatives. For example, the proposal unit proposes the optimal division of assets based on the opinions and feelings of relatives, by referring to legal standards and past inheritance cases. Step 4: The opinion exchange support unit facilitates the exchange of opinions between the heirs based on the content proposed by the proposal unit. For example, the generation AI conducts opinion exchanges between relatives through an online platform, recording and analyzing the history. The generation AI can also hold opinion exchanges between relatives in the form of regular meetings and manage their progress. The generation AI can also make suggestions to avoid emotional conflicts when opinion exchanges occur between relatives, thereby facilitating the exchange of opinions. For example, the opinion exchange support unit conducts opinion exchanges between relatives through an online platform, recording and analyzing the history, holding opinion exchanges in the form of regular meetings, and making suggestions to avoid emotional conflicts.

[0054] (Example 2) In the inheritance support system according to an embodiment of the present invention, a generation AI summarizes the arguments of each family member in advance, proposes the optimal division of inheritance assets, and facilitates the smooth exchange of opinions between heirs. As a result, the inheritance support system can resolve the complex issues that arise during inheritance and achieve a division of inheritance assets that all family members can agree on.

[0055] An inheritance support system according to an embodiment includes an opinion collection unit, an analysis unit, a proposal unit, and an opinion exchange support unit. The opinion collection unit collects opinions from each relative. For example, it records oral opinions from relatives and converts them into text data. It can also scan written opinions submitted by relatives and convert them into digital data. It can also directly collect opinions submitted online by relatives. For example, the opinion collection unit automatically collects opinions entered by relatives into an online form. The analysis unit analyzes the opinions of relatives collected by the opinion collection unit. For example, the generation AI analyzes the opinions of relatives using text analysis technology. It can also analyze the emotions of relatives using emotion analysis technology. It can also aggregate the opinions of relatives using statistical analysis technology. For example, the analysis unit classifies the opinions of relatives using text analysis technology, evaluates their emotions using emotion analysis technology, and aggregates the results using statistical analysis technology. The proposal unit proposes an optimal division of inheritance assets based on the results of the analysis by the analysis unit. For example, the generation AI proposes the optimal property division by taking into account the opinions and feelings of relatives. The generation AI can also propose property division by referring to legal standards and past inheritance cases. The generation AI can also propose property division by taking into account the wishes and emotional satisfaction of relatives. For example, the proposal unit proposes the optimal property division based on the opinions and feelings of relatives, referring to legal standards and past inheritance cases. The opinion exchange support unit facilitates the exchange of opinions between heirs based on the content proposed by the proposal unit. For example, the generation AI conducts opinion exchanges between relatives through an online platform and records and analyzes the history. The generation AI can also hold opinion exchanges between relatives in the form of regular meetings and manage their progress. The generation AI can also make suggestions to avoid emotional conflicts during opinion exchanges, thereby facilitating the exchange of opinions. For example, the opinion exchange support unit conducts opinion exchanges between relatives through an online platform, records and analyzes the history, holds opinion exchanges in the form of regular meetings, and makes suggestions to avoid emotional conflicts. As a result, the inheritance support system according to the embodiment can solve the complex problems that arise during inheritance and realize the division of inheritance property that is acceptable to all relatives.For example, it can avoid disputes between family members and ensure a smooth inheritance. It can also create a will that reflects the opinions of all family members while respecting the wishes of the deceased.

[0056] The opinion collection unit can analyze the emotions of relatives using the emotion estimation function and collect opinions taking into account their emotional background. For example, when the generation AI collects opinions from relatives, the opinion collection unit uses the emotion estimation function to analyze the emotions of relatives in real time. For example, the opinion collection unit analyzes the facial expressions and tone of voice when relatives express their opinions and collects opinions taking into account their emotional background. The opinion collection unit can also scan opinions submitted in writing by relatives and analyze their emotions using the emotion estimation function. For example, the opinion collection unit analyzes the context and wording of the opinions submitted in writing by relatives and collects opinions taking into account their emotional background. The opinion collection unit can also collect opinions submitted online by relatives and analyze their emotions using the emotion estimation function. For example, the opinion collection unit analyzes the text of opinions entered by relatives in an online form and collects opinions taking into account their emotional background. This makes it possible to collect opinions taking into account the emotions of relatives.

[0057] The opinion collection unit can analyze the relative's past communication history or social media posts to extract the relative's true feelings and latent wishes. For example, the opinion collection unit uses a generation AI to analyze the relative's past communication history to extract the relative's true feelings and latent wishes. For example, the opinions and wishes expressed by the relative in the past are used as a reference when collecting current opinions. The opinion collection unit can also analyze the relative's social media posts to extract the relative's true feelings and latent wishes. For example, it analyzes the content posted by the relative on social media to understand the relative's true feelings and latent wishes. The opinion collection unit can also analyze the relative's past email and chat history to extract the relative's true feelings and latent wishes. For example, it analyzes the content of emails and chats sent by the relative in the past to understand the relative's true feelings and latent wishes. This makes it possible to understand the relative's true feelings and latent wishes.

[0058] The opinion collection unit can perform a more precise analysis by taking into account individual background information such as the living situation or health condition of relatives. For example, the opinion collection unit allows the generation AI to consider the living situation of relatives and reflect that background information when collecting opinions. For example, when a relative describes their current living situation, opinions are collected based on that information. The opinion collection unit can also consider the health condition of relatives and reflect that background information when collecting opinions. For example, when a relative describes their current health condition, opinions are collected based on that information. The opinion collection unit can also collect opinions by taking into account the living situation of relatives, such as their income and family composition. For example, when a relative describes their current income and family composition, opinions are collected based on that information. This makes it possible to collect opinions that take into account the individual background information of relatives.

[0059] The proposal unit can use the emotion estimation function to make a property division proposal that takes into account the emotional satisfaction of the relatives. For example, the generation AI uses the emotion estimation function to make a property division proposal that takes into account the emotional satisfaction of the relatives. For example, the proposal unit can make a property division proposal that is emotionally convincing based on the emotion scores of the relatives. The proposal unit can also evaluate the emotional satisfaction of the relatives and make a property division proposal based on the evaluation results. For example, the proposal unit can propose multiple property division scenarios that are emotionally satisfying to the relatives and allow the relatives to select one. The proposal unit can also adjust the content of the property division proposal taking into account the emotional satisfaction of the relatives. For example, the proposal content can be fine-tuned based on the emotion scores of the relatives to make a proposal that is emotionally convincing. This makes it possible to make a property division proposal that takes into account the emotional satisfaction of the relatives.

[0060] The proposal unit can analyze past inheritance cases and legal precedents and make optimal property division proposals based on the results. For example, the proposal unit uses a generation AI to analyze past inheritance cases and make optimal property division proposals based on the results. For example, the proposal unit can make optimal property division proposals based on past successful and unsuccessful cases. The proposal unit can also analyze legal precedents and make optimal property division proposals based on the results. For example, the proposal unit can analyze past court records and legal documents and make optimal property division proposals. The proposal unit can also refer to past inheritance cases and legal precedents to make property division proposals that take into account the wishes and emotional satisfaction of relatives. For example, the proposal unit can make optimal property division proposals based on past inheritance cases and legal precedents and make optimal property division proposals that take into account the wishes and emotional satisfaction of relatives based on past inheritance cases and legal precedents. This makes it possible to make optimal property division proposals based on past inheritance cases and legal precedents.

[0061] The proposal unit can predict the relative's future lifestyle plans or economic situation and make asset division proposals based on that. For example, the proposal unit uses a generation AI to predict the relative's future lifestyle plans and make asset division proposals based on that. For example, it makes optimal asset division proposals based on the relative's future lifestyle plans. The proposal unit can also predict the relative's future economic situation and make asset division proposals based on that. For example, it makes optimal asset division proposals based on the relative's future income predictions and asset valuations. The proposal unit can also adjust the content of the asset division proposal taking into account the relative's future lifestyle plans and economic situation. For example, it fine-tunes the content of the proposal based on the relative's future lifestyle plans and economic situation and makes optimal asset division proposals. This makes it possible to make asset division proposals that take into account the relative's future lifestyle plans and economic situation.

[0062] The opinion exchange support unit can use the emotion estimation function to predict emotional conflicts between heirs and promote an exchange of opinions to avoid conflicts. For example, the generation AI can use the emotion estimation function to predict emotional conflicts between heirs and promote an exchange of opinions to avoid conflicts. For example, based on the emotion score, it can detect situations where conflicts are likely to occur in advance and take appropriate measures. The opinion exchange support unit can also predict emotional conflicts between heirs and make suggestions to avoid conflicts. For example, based on the emotion score, it can detect situations where conflicts are likely to occur in advance and make appropriate suggestions. The opinion exchange support unit can also suggest communication methods to avoid emotional conflicts between heirs. For example, based on the emotion score, it can suggest communication methods to avoid conflicts. This makes it possible to predict emotional conflicts between heirs and exchange opinions to avoid conflicts.

[0063] The opinion exchange support unit can analyze the past communication history between the heirs and provide advice for a smooth exchange of opinions. For example, the generation AI analyzes the past communication history between the heirs and provides advice for a smooth exchange of opinions. For example, based on the content of past dialogue, it proposes a way to proceed with an opinion exchange that is easy for the heirs to agree with. The opinion exchange support unit can also analyze the past email and chat history between the heirs and provide advice for a smooth exchange of opinions. For example, based on the content of past emails and chats, it proposes a way to proceed with an opinion exchange that is easy for the heirs to agree with. The opinion exchange support unit can also provide advice for avoiding conflicts based on the past communication history between the heirs. For example, based on the content of past dialogues, it can detect situations where conflicts are likely to occur and provide appropriate advice. This makes it possible to provide advice for a smooth exchange of opinions based on the past communication history between the heirs.

[0064] The opinion exchange support unit monitors the exchange of opinions between heirs in real time and intervenes as necessary to ensure the exchange of opinions proceeds smoothly. For example, the generation AI monitors the exchange of opinions between heirs in real time and intervenes as necessary. For example, when the exchange of opinions reaches an impasse, it provides appropriate advice to ensure the exchange of opinions proceeds smoothly. The opinion exchange support unit can also monitor the exchange of opinions between heirs in real time and intervene when a conflict arises. For example, when a conflict arises, it can mediate appropriately to ensure the exchange of opinions proceeds smoothly. The opinion exchange support unit can also monitor the exchange of opinions between heirs in real time and manage the progress. For example, it can grasp the progress of the exchange of opinions in real time and manage the progress appropriately. This allows the exchange of opinions between heirs to be monitored in real time and intervene as necessary to ensure the exchange of opinions proceeds smoothly.

[0065] The opinion exchange support unit can conduct opinion exchanges between heirs through an online platform and record and analyze the history of the opinion exchanges. For example, the opinion exchange support unit uses a generation AI to conduct opinion exchanges between heirs through an online platform and record and analyze the history of the opinion exchanges. For example, the history of online chats and video conferences is saved and later analyzed. The opinion exchange support unit can also conduct opinion exchanges between heirs through an online platform and analyze the history in real time. For example, it can analyze the history of online chats and video conferences in real time and provide appropriate advice. The opinion exchange support unit can also conduct opinion exchanges between heirs through an online platform and provide advice to avoid conflicts based on the history. For example, it can detect situations where conflicts are likely to occur in advance based on the history of online chats and video conferences and provide appropriate advice. This allows opinion exchanges between heirs to be conducted through an online platform and record and analyze the history, thereby facilitating the smooth exchange of opinions.

[0066] The opinion exchange support unit can hold opinion exchanges between heirs in the form of regular meetings and manage the progress of the opinion exchanges. For example, the generation AI can hold opinion exchanges between heirs in the form of regular meetings and manage the progress of the opinion exchanges. For example, the generation AI can set up regular online meetings and record and analyze the progress. The opinion exchange support unit can also hold opinion exchanges between heirs in the form of regular meetings and manage the progress in real time. For example, the generation AI can set up regular online meetings, grasp the progress in real time, and manage the progress appropriately. The opinion exchange support unit can also hold opinion exchanges between heirs in the form of regular meetings and provide advice to avoid conflicts based on the progress. For example, the generation AI can detect situations where conflicts are likely to occur in advance and provide appropriate advice based on the progress of regular online meetings. This allows opinion exchanges between heirs to be held in the form of regular meetings and manage the progress, thereby smoothly promoting opinion exchanges.

[0067] The opinion exchange support unit uses the emotion estimation function to make suggestions to avoid emotional conflicts when heirs exchange opinions, allowing the exchange to proceed smoothly. For example, the generation AI uses the emotion estimation function to make suggestions to avoid emotional conflicts when heirs exchange opinions. For example, based on the emotion score, it detects situations where conflicts are likely to occur in advance and takes appropriate measures. The opinion exchange support unit can also make suggestions to avoid emotional conflicts when heirs exchange opinions. For example, based on the emotion score, it detects situations where conflicts are likely to occur in advance and takes appropriate suggestions. The opinion exchange support unit can also suggest communication methods to avoid emotional conflicts when heirs exchange opinions. For example, based on the emotion score, it suggests communication methods to avoid conflicts. This makes suggestions to avoid emotional conflicts when heirs exchange opinions, allowing the exchange to proceed smoothly.

[0068] The suggestion unit can propose multiple different scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit uses a generation AI to propose multiple different property division scenarios, allowing relatives to compare and consider the options. For example, it presents multiple scenarios, allowing relatives to select the optimal scenario. The suggestion unit can also propose multiple different inheritance procedure scenarios, allowing relatives to compare and consider the options. For example, it presents multiple inheritance procedure scenarios, allowing relatives to select the optimal scenario. The suggestion unit can also visually present the options based on the different scenarios, allowing relatives to compare and consider the options. For example, it can visually present different scenarios using graphs and charts, allowing relatives to compare and consider the options. In this way, it is possible to propose optimal property division by proposing multiple different scenarios and allowing relatives to compare and consider the options.

[0069] The proposal unit can simulate property division based on the opinions of relatives and propose it in a visually easy-to-understand format. For example, the proposal unit uses a generation AI to simulate property division based on the opinions of relatives and propose it in a visually easy-to-understand format. For example, the proposal unit displays the results of the property division simulation using graphs and charts. The proposal unit can also simulate inheritance procedures based on the opinions of relatives and propose it in a visually easy-to-understand format. For example, the proposal unit displays the results of the inheritance procedures simulation using graphs and charts. The proposal unit can also visually present the results of the property division simulation based on the opinions of relatives and propose it in a format that is easy for relatives to understand. For example, the proposal unit visually presents the results of the property division simulation using graphs and charts and propose it in a format that is easy for relatives to understand. In this way, by simulating property division based on the opinions of relatives and proposing it in a visually easy-to-understand format, it is possible to propose a property division that is easy for relatives to understand.

[0070] The proposal unit uses the emotion estimation function to propose property division in a way that is most convincing to relatives, making the proposal content easier to emotionally accept. For example, the generation AI uses the emotion estimation function to propose property division in a way that is most convincing to relatives. For example, it generates emotionally acceptable proposal content based on the emotion scores of relatives. The proposal unit can also evaluate the emotional satisfaction of relatives and make property division proposals based on the evaluation results. For example, it can propose multiple property division scenarios that are emotionally satisfying to relatives and allow relatives to select from them. The proposal unit can also adjust the property division proposal content taking into account the emotional satisfaction of relatives. For example, it can fine-tune the proposal content based on the emotion scores of relatives to make a proposal that is emotionally acceptable. This makes it possible to propose property division in a way that is most convincing to relatives and makes the proposal content easier to emotionally accept, thereby making it possible to make property division proposals that are acceptable to all relatives.

[0071] The opinion exchange support unit uses the emotion estimation function to make suggestions to avoid emotional conflicts when heirs exchange opinions, allowing the exchange to proceed smoothly. For example, the generation AI uses the emotion estimation function to make suggestions to avoid emotional conflicts when heirs exchange opinions. For example, based on the emotion score, it detects situations where conflicts are likely to occur in advance and takes appropriate measures. The opinion exchange support unit can also make suggestions to avoid emotional conflicts when heirs exchange opinions. For example, based on the emotion score, it detects situations where conflicts are likely to occur in advance and takes appropriate suggestions. The opinion exchange support unit can also suggest communication methods to avoid emotional conflicts when heirs exchange opinions. For example, based on the emotion score, it suggests communication methods to avoid conflicts. This makes suggestions to avoid emotional conflicts when heirs exchange opinions, allowing the exchange to proceed smoothly.

[0072] The suggestion unit can use the emotion estimation function to propose inheritance procedures that take into account the emotional satisfaction of relatives. For example, the generation AI uses the emotion estimation function to propose inheritance procedures that take into account the emotional satisfaction of all relatives. For example, the suggestion unit can propose inheritance procedures that are emotionally acceptable based on the emotion scores of relatives. The suggestion unit can also evaluate the emotional satisfaction of relatives and propose inheritance procedures based on the evaluation results. For example, the suggestion unit can propose multiple inheritance procedure scenarios that are emotionally satisfying for relatives and allow them to select one. The suggestion unit can also adjust the proposed content of inheritance procedures taking into account the emotional satisfaction of relatives. For example, the suggestion unit can fine-tune the proposed content based on the emotion scores of relatives and make a proposal that is emotionally acceptable. In this way, by proposing inheritance procedures that take into account the emotional satisfaction of relatives, it is possible to realize inheritance procedures that are acceptable to all relatives.

[0073] The proposal unit can analyze past inheritance cases and legal precedents and propose inheritance procedures that are highly satisfactory based on the results. For example, the proposal unit uses a generation AI to analyze past inheritance cases and propose inheritance procedures that are highly satisfactory based on the results. For example, the proposal unit proposes inheritance procedures that are highly satisfactory based on past successes and failures. The proposal unit can also analyze legal precedents and propose inheritance procedures that are highly satisfactory based on the results. For example, the proposal unit can analyze past court records and legal documents and propose inheritance procedures that are highly satisfactory. The proposal unit can also refer to past inheritance cases and legal precedents and propose inheritance procedures that take into account the wishes and emotional satisfaction of relatives. For example, the proposal unit proposes inheritance procedures that are highly satisfactory based on past inheritance cases and legal precedents and proposes inheritance procedures that take into account the wishes and emotional satisfaction of relatives. In this way, by proposing inheritance procedures that are highly satisfactory based on past inheritance cases and legal precedents, it is possible to realize inheritance procedures that are satisfactory to all relatives.

[0074] The proposal unit can predict the relative's future lifestyle plans or economic situation and propose convincing inheritance procedures based on that. For example, the proposal unit uses a generation AI to predict the relative's future lifestyle plans and propose convincing inheritance procedures based on that. For example, the proposal unit proposes convincing inheritance procedures based on the relative's future lifestyle plans. The proposal unit can also predict the relative's future economic situation and propose convincing inheritance procedures based on that. For example, the proposal unit proposes convincing inheritance procedures based on the relative's future income forecast and asset valuation. The proposal unit can also adjust the proposed inheritance procedures taking into account the relative's future lifestyle plans and economic situation. For example, the proposal can fine-tune the proposed contents based on the relative's future lifestyle plans and economic situation and propose convincing inheritance procedures. In this way, by predicting the relative's future lifestyle plans and economic situation and proposing convincing inheritance procedures based on that, it is possible to realize inheritance procedures that are convincing to all relatives.

[0075] The suggestion unit can propose multiple different scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit uses a generation AI to propose multiple different inheritance procedure scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit presents multiple scenarios, allowing relatives to select the optimal scenario. The suggestion unit can also propose multiple different property division scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit presents multiple property division scenarios, allowing relatives to select the optimal scenario. The suggestion unit can also visually present the options based on the different scenarios, allowing relatives to compare and consider the options. For example, the suggestion unit can visually present different scenarios using graphs and charts, allowing relatives to compare and consider the options. In this way, the optimal inheritance procedure can be proposed by proposing multiple different scenarios and allowing relatives to compare and consider the options.

[0076] The proposal unit can simulate inheritance procedures based on the opinions of relatives and propose them in a visually easy-to-understand format. For example, the proposal unit uses a generation AI to simulate inheritance procedures based on the opinions of relatives and propose them in a visually easy-to-understand format. For example, the proposal unit displays the results of the inheritance procedure simulation using graphs and charts. The proposal unit can also simulate property division based on the opinions of relatives and propose them in a visually easy-to-understand format. For example, the proposal unit displays the results of the property division simulation using graphs and charts. The proposal unit can also visually present the results of the inheritance procedure simulation based on the opinions of relatives and propose them in a format that is easy for relatives to understand. For example, the proposal unit visually presents the results of the inheritance procedure simulation using graphs and charts and proposes them in a format that is easy for relatives to understand. In this way, by simulating inheritance procedures based on the opinions of relatives and proposing them in a visually easy-to-understand format, it is possible to propose inheritance procedures that are easy for relatives to understand.

[0077] The suggestion unit uses the emotion estimation function to propose inheritance procedures in a way that is most convincing to relatives, making the procedure content easier to emotionally accept. For example, the suggestion unit uses the emotion estimation function to propose inheritance procedures in a way that is most convincing to relatives. For example, it generates procedure content that is emotionally easy to accept based on the emotion scores of relatives. The suggestion unit can also evaluate the emotional satisfaction of relatives and propose inheritance procedures based on the evaluation results. For example, it can propose multiple inheritance procedure scenarios that are emotionally satisfying to relatives and allow relatives to select. The suggestion unit can also adjust the proposed inheritance procedure content taking into account the emotional satisfaction of relatives. For example, it can fine-tune the proposed content based on the emotion scores of relatives and make a proposal that is emotionally easy to accept. This makes it possible to propose inheritance procedures in a way that is most convincing to relatives, making the procedure content easier to emotionally accept, thereby realizing inheritance procedures that are acceptable to all relatives.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] When collecting opinions of relatives, the opinion collection unit can convert the opinions into text data in real time using voice recognition technology. For example, opinions expressed by relatives during a conversation can be instantly converted into text and saved as data for later analysis. The opinion collection unit can also record opinions expressed by relatives in video calls and convert the audio into text data. For example, the contents of a video call can be automatically recorded and converted into text using voice recognition technology. The opinion collection unit can also record opinions expressed by relatives over the phone and convert the audio into text data. For example, the contents of a phone call can be recorded and converted into text using voice recognition technology. In this way, opinions of relatives can be collected in real time and saved as data for later analysis.

[0080] The opinion collection unit can use the emotion estimation function to analyze the emotions of relatives and collect opinions taking into account their emotional backgrounds. For example, the opinion collection unit can analyze facial expressions and tone of voice when relatives express their opinions, and collect opinions taking into account their emotional backgrounds. The opinion collection unit can also scan opinions submitted in writing by relatives and analyze their emotions using the emotion estimation function. For example, the opinion collection unit can analyze the context and wording of the opinions submitted in writing by relatives, and collect opinions taking into account their emotional backgrounds. The opinion collection unit can also collect opinions submitted online by relatives and analyze their emotions using the emotion estimation function. For example, the text of opinions entered by relatives in online forms can be analyzed, and opinions can be collected taking into account their emotional backgrounds. This makes it possible to collect opinions taking into account the emotions of relatives.

[0081] The opinion collection unit can analyze the relative's past communication history or social media posts to extract the relative's true feelings and latent hopes. For example, the opinions and hopes expressed by the relative in the past can be used as a reference when collecting current opinions. The opinion collection unit can also analyze the relative's social media posts to extract the relative's true feelings and latent hopes. For example, it analyzes the content posted by the relative on social media to understand the relative's true feelings and latent hopes. The opinion collection unit can also analyze the relative's past email and chat history to extract the relative's true feelings and latent hopes. For example, it analyzes the content of emails and chats sent by the relative in the past to understand the relative's true feelings and latent hopes. This makes it possible to understand the relative's true feelings and latent hopes.

[0082] The opinion collection unit can perform more precise analysis by taking into account individual background information such as the living situation or health condition of relatives. For example, the generation AI takes into account the living situation of relatives and reflects that background information when collecting opinions. For example, when a relative describes their current living situation, opinions are collected based on that information. The opinion collection unit can also take into account the health condition of relatives and reflect that background information when collecting opinions. For example, when a relative describes their current health condition, opinions are collected based on that information. The opinion collection unit can also collect opinions by taking into account the living situation of relatives, such as their income and family composition. For example, when a relative describes their current income and family composition, opinions are collected based on that information. This makes it possible to collect opinions that take into account the individual background information of relatives.

[0083] The proposal unit can use the emotion estimation function to make a property division proposal that takes into account the emotional satisfaction of relatives. For example, the generation AI uses the emotion estimation function to make a property division proposal that takes into account the emotional satisfaction of relatives. For example, it can make a property division proposal that is emotionally convincing based on the emotion scores of relatives. The proposal unit can also evaluate the emotional satisfaction of relatives and make a property division proposal based on the evaluation results. For example, it can propose multiple property division scenarios that are emotionally satisfying to relatives and allow them to select one. The proposal unit can also adjust the content of the property division proposal taking into account the emotional satisfaction of relatives. For example, it can fine-tune the content of the proposal based on the emotion scores of relatives and make a proposal that is emotionally convincing. This makes it possible to make a property division proposal that takes into account the emotional satisfaction of relatives.

[0084] The proposal unit can analyze past inheritance cases and legal precedents and make optimal property division proposals based on the results. For example, the generation AI analyzes past inheritance cases and makes optimal property division proposals based on the results. For example, it proposes optimal property division proposals based on past successful and unsuccessful cases. The proposal unit can also analyze legal precedents and make optimal property division proposals based on the results. For example, it analyzes past court records and legal documents and makes optimal property division proposals. The proposal unit can also refer to past inheritance cases and legal precedents to make property division proposals that take into account the wishes and emotional satisfaction of relatives. For example, it proposes optimal property division proposals based on past inheritance cases and legal precedents and makes optimal property division proposals that take into account the wishes and emotional satisfaction of relatives. This makes it possible to propose optimal property division proposals based on past inheritance cases and legal precedents.

[0085] The proposal unit can predict the relative's future lifestyle plans or economic situation and make asset division proposals based on that. For example, the generation AI predicts the relative's future lifestyle plans and makes asset division proposals based on that. For example, it makes optimal asset division proposals based on the relative's future lifestyle plans. The proposal unit can also predict the relative's future economic situation and make asset division proposals based on that. For example, it makes optimal asset division proposals based on the relative's future income predictions and asset valuations. The proposal unit can also adjust the content of the asset division proposal taking into account the relative's future lifestyle plans and economic situation. For example, it fine-tunes the content of the proposal based on the relative's future lifestyle plans and economic situation and makes optimal asset division proposals. This makes it possible to make asset division proposals that take into account the relative's future lifestyle plans and economic situation.

[0086] The opinion exchange support unit can use the emotion estimation function to predict emotional conflicts between heirs and promote opinion exchanges to avoid conflicts. For example, the generation AI can use the emotion estimation function to predict emotional conflicts between heirs and promote opinion exchanges to avoid conflicts. For example, based on the emotion score, it can detect situations where conflicts are likely to occur in advance and take appropriate measures. The opinion exchange support unit can also predict emotional conflicts between heirs and make suggestions to avoid conflicts. For example, based on the emotion score, it can detect situations where conflicts are likely to occur in advance and make appropriate suggestions. The opinion exchange support unit can also suggest communication methods to avoid emotional conflicts between heirs. For example, based on the emotion score, it can suggest communication methods to avoid conflicts. This makes it possible to predict emotional conflicts between heirs and promote opinion exchanges to avoid conflicts.

[0087] The opinion exchange support unit can analyze the past communication history between heirs and provide advice for a smooth exchange of opinions. For example, the generation AI can analyze the past communication history between heirs and provide advice for a smooth exchange of opinions. For example, based on the content of past dialogue, it can suggest how to proceed with an opinion exchange that is easy for the heirs to agree with. The opinion exchange support unit can also analyze the past email and chat history between heirs and provide advice for a smooth exchange of opinions. For example, based on the content of past emails and chats, it can suggest how to proceed with an opinion exchange that is easy for the heirs to agree with. The opinion exchange support unit can also provide advice for avoiding conflicts based on the past communication history between heirs. For example, based on the content of past dialogues, it can detect situations where conflicts are likely to occur and provide appropriate advice. This makes it possible to provide advice for a smooth exchange of opinions based on the past communication history between heirs.

[0088] The opinion exchange support unit monitors the exchange of opinions between heirs in real time and intervenes as necessary to ensure the exchange of opinions proceeds smoothly. For example, the generation AI monitors the exchange of opinions between heirs in real time and intervenes as necessary. For example, when the exchange of opinions reaches an impasse, it provides appropriate advice to ensure the exchange of opinions proceeds smoothly. The opinion exchange support unit can also monitor the exchange of opinions between heirs in real time and intervene when a conflict arises. For example, when a conflict arises, it can mediate appropriately to ensure the exchange of opinions proceeds smoothly. The opinion exchange support unit can also monitor the exchange of opinions between heirs in real time and manage the progress. For example, it can grasp the progress of the exchange of opinions in real time and manage the progress appropriately. This allows the exchange of opinions between heirs to be monitored in real time and intervene as necessary to ensure the exchange of opinions proceeds smoothly.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The opinion collection unit collects the opinions of each relative. For example, the opinion collection unit may record the oral opinions of the relatives and convert them into text data. It may also scan the written opinions submitted by the relatives and convert them into digital data. It may also directly collect opinions submitted by the relatives online. For example, the opinion collection unit may automatically collect opinions entered by the relatives into an online form. Step 2: The analysis unit analyzes the opinions of relatives collected by the opinion collection unit. For example, the generation AI analyzes the opinions of relatives using text analysis technology. The generation AI can also analyze the emotions of relatives using emotion analysis technology. The generation AI can also aggregate the opinions of relatives using statistical analysis technology. For example, the analysis unit classifies the opinions of relatives using text analysis technology, evaluates emotions using emotion analysis technology, and aggregates them using statistical analysis technology. Step 3: The proposal unit proposes the optimal division of inheritance assets based on the results of the analysis by the analysis unit. For example, the generation AI proposes the optimal division of assets by taking into account the opinions and feelings of relatives. The generation AI can also propose a division of assets by referring to legal standards and past inheritance cases. The generation AI can also propose a division of assets by taking into account the wishes and emotional satisfaction of relatives. For example, the proposal unit proposes the optimal division of assets based on the opinions and feelings of relatives, by referring to legal standards and past inheritance cases. Step 4: The opinion exchange support unit facilitates the exchange of opinions between the heirs based on the content proposed by the proposal unit. For example, the generation AI conducts opinion exchanges between relatives through an online platform, recording and analyzing the history. The generation AI can also hold opinion exchanges between relatives in the form of regular meetings and manage their progress. The generation AI can also make suggestions to avoid emotional conflicts when opinion exchanges occur between relatives, thereby facilitating the exchange of opinions. For example, the opinion exchange support unit conducts opinion exchanges between relatives through an online platform, recording and analyzing the history, holding opinion exchanges in the form of regular meetings, and making suggestions to avoid emotional conflicts.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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. The system comprises an opinion collection unit that collects opinions of each relative, an analysis unit that analyzes the opinions of the relatives collected by the opinion collection unit, a proposal unit that proposes an optimal inheritance property division based on the results of the analysis by the analysis unit, and an opinion exchange support unit that smoothly promotes opinion exchange between the heirs based on the contents proposed by the proposal unit. A system characterized by:

2. The opinion collection unit Analyze the emotions of the relatives and collect opinions taking into account their emotional background.

2. The system of claim 1.

3. The proposal unit Propose property division that takes into account the emotional satisfaction of the family members 2. The system of claim 1.

4. The opinion exchange support unit Anticipate emotional conflicts between heirs and facilitate exchanges of opinions to avoid conflicts 2. The system of claim 1.

5. The proposal unit Propose the division of assets in a way that is most convincing to the relatives, making it easier for them to emotionally accept the proposal.

2. The system of claim 1.

6. The proposal unit Propose inheritance procedures that take into consideration the emotional satisfaction of the family members.

2. The system of claim 1.

7. The opinion exchange support unit Providing suggestions to avoid emotional conflicts when exchanging opinions between heirs and facilitating the exchange of opinions 2. The system of claim 1.

8. The proposal unit Propose inheritance procedures in a way that is most convincing to the relatives, making it easier for them to emotionally accept the procedures.

2. The system of claim 1.

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