system

Abstract

The system according to this embodiment aims to provide high-quality and efficient support for inheritance planning and will preparation. [Solution] The system according to this embodiment comprises a learning unit, an analysis unit, and a provision unit. The learning unit learns customer information, the company's internal information and knowledge, and external information. The analysis unit analyzes the customer's consultation content based on the information learned by the learning unit. The provision unit proposes optimal inheritance measures and will drafting based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are variations in the skills and experiences of the persons in charge in succession countermeasures and will preparation, and there is a problem that it is difficult to provide an efficient and high-quality response.

[0005] The system according to the embodiment aims to provide a high-quality and efficient response in succession countermeasures and will preparation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, an analysis unit, and a provision unit. The learning unit learns customer information, the company's internal information and knowledge, and external information. The analysis unit analyzes the customer's consultation content based on the information learned by the learning unit. The provision unit proposes optimal inheritance measures and will drafting based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide high-quality and efficient support for inheritance planning and will preparation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The consultation service provision system according to an embodiment of the present invention is a system that provides high-quality, highly efficient, and low-cost consultation services to wealthy clients of financial institutions and tax accounting firms by utilizing a generating AI agent in inheritance planning and will drafting services. This consultation service provision system trains the generating AI with customer information, the company's internal information and knowledge (past case examples, the experience of highly skilled personnel), and external information (tax, legal, financial, real estate, etc.). The generating AI analyzes the customer's consultation content and proposes the optimal inheritance planning and will drafting. This mechanism reproduces the skills of highly skilled personnel, improves work efficiency, and frees up time for customer service. In addition, by having the same person (AI agent) continuously handle the case, consistent service to the customer becomes possible. For example, the generating AI is trained with customer information, the company's internal information and knowledge, and external information. In this process, the generating AI learns broad and accurate knowledge of tax, legal, financial, real estate, etc., and maintains updated information. For example, by learning past case examples and the consultation skills of highly skilled personnel, the generating AI can acquire the ability to extract the customer's latent needs. Next, the generating AI analyzes the client's consultation content. The generating AI understands the client's consultation content and proposes the most suitable inheritance strategy and will creation. For example, if a client requests an inheritance tax simulation, the generating AI can perform a simulation based on the client's asset information and propose the most suitable inheritance strategy. Furthermore, the AI ​​provides consultation to the client based on the proposal it has created. The generating AI creates and provides appropriate materials and documents according to the client's consultation content. For example, by providing guidance on the documents and procedures necessary for creating a will, the burden on the client can be reduced. This system allows for the reproduction of the skills of highly skilled staff, improves operational efficiency, and frees up time for client consultations. In addition, having the same staff member (AI agent) continuously handle the client ensures consistent service. For example, even if a client changes their consultation content, the generating AI retains past consultation details, allowing for a smooth response. In this way, by utilizing the generating AI agent, financial institutions and tax accounting firms can provide high-quality consultation services to a larger number of affluent clients.This will lead to improved customer satisfaction and increased operational efficiency, enhancing the competitiveness of financial institutions and tax accounting firms. The consultation service system will then be able to analyze customer inquiries and propose optimal inheritance strategies and will drafting plans.

[0029] The consultation service provision system according to this embodiment comprises a learning unit, an analysis unit, and a provision unit. The learning unit learns customer information, the company's internal information and knowledge, and external information. For example, the learning unit learns personal information, transaction history, and consultation history as customer information. For example, it learns past case data, specialized knowledge, and know-how as company's internal information and knowledge. For example, it learns market data, legal and regulatory information, and competitor information as external information. The learning unit uses generative AI to learn this information extensively and accurately and maintains updated information. The analysis unit analyzes the customer's consultation content based on the information learned by the learning unit. For example, the analysis unit understands the customer's consultation content and proposes the optimal inheritance measures and will creation. The analysis unit uses generative AI to analyze the customer's consultation content using natural language processing technology and data mining methods. The provision unit proposes the optimal inheritance measures and will creation based on the information analyzed by the analysis unit. For example, the provision unit creates and provides appropriate materials and documents according to the customer's consultation content. The service provider uses a generation AI to automatically create and provide materials and documents tailored to the customer's consultation. As a result, the consultation service provision system according to this embodiment can analyze the customer's consultation and propose optimal inheritance planning and will creation.

[0030] The learning unit learns customer information, internal company information and knowledge, and external information. For example, as customer information, the learning unit learns personal information, transaction history, and consultation history. Specifically, in addition to basic personal information such as the customer's name, address, and contact information, it learns detailed past transaction and consultation history. This allows the unit to understand customer needs and trends and provide more personalized services. As internal company information and knowledge, it learns past case data, specialized knowledge, and know-how. For example, it systematically learns and accumulates data on inheritance cases handled in the past, as well as the knowledge and know-how of experts. This allows the unit to make more effective proposals by referring to past success and failure cases. As external information, it learns market data, legal and regulatory information, and competitor information. For example, it can constantly keep up-to-date with the latest market trends, changes in legal regulations, and the activities of competitors, and provide customers with the latest and most optimal information. The learning unit uses generative AI to learn this information extensively and accurately, and maintains updated information. Generative AI has the ability to efficiently process large amounts of data and extract important information. For example, natural language processing techniques can be used to extract useful information from text data, and data mining methods can be used to analyze the relationships between data. This allows the learning unit to always maintain up-to-date information and build a foundation for providing optimal services to customers. Furthermore, by regularly updating information and incorporating new data and insights, the learning unit can improve the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes customer consultations based on information learned by the learning unit. For example, the analysis unit understands customer consultations and proposes optimal inheritance strategies and will drafting. Specifically, it analyzes the consultation content provided by customers using natural language processing technology to accurately grasp the customer's intentions and needs. The generation AI takes customer consultation content as text data and identifies the information and solutions the customer is seeking by analyzing the context and keywords. For example, if a customer consults about inheritance, the generation AI refers to inheritance-related laws and regulations and past cases to propose the optimal inheritance strategy. Also, if a customer consults about will drafting, the generation AI considers the customer's intentions, family structure, and financial situation to propose appropriate will content. The analysis unit uses the generation AI to analyze customer consultations using natural language processing technology and data mining techniques. This allows the analysis unit to quickly and accurately analyze customer consultations and make optimal proposals. Furthermore, the analysis unit can refer to past consultation history and success stories to make more effective proposals to customers. For example, it can refer to how similar consultations were handled and the results in the past to provide specific advice to customers. Furthermore, the analysis department can collect customer feedback and improve the accuracy of analysis results and the content of recommendations. This allows the analysis department to consistently provide optimal recommendations based on the latest information and customer needs, thereby improving customer satisfaction.

[0032] The service department provides optimal inheritance planning and will drafting proposals based on information analyzed by the analysis department. Specifically, it creates and provides appropriate materials and documents according to the client's consultation. The service department uses generation AI to automatically create and provide materials and documents tailored to the client's consultation. For example, the generation AI automatically creates inheritance planning proposals and will drafts and provides them to the client. The generation AI selects the appropriate format and content based on the client's consultation and analysis results, and generates the documents. This allows the service department to quickly and accurately create and provide materials and documents to the client. Furthermore, the service department can collect client feedback and continuously improve the content of the materials and documents it provides. For example, it can revise the proposal content and document format to reflect client requests and opinions. In addition, the service department can reliably transmit information using multiple communication methods. For example, it can provide materials and documents to clients using email and cloud storage, and also conduct online meetings and telephone explanations as needed. This allows the service department to provide information to clients quickly and reliably, improving customer satisfaction. Furthermore, the service department can provide customized services according to the client's needs. For example, by providing individualized consulting and advice to specific customers, a more personalized service can be achieved. This allows the service provider to propose optimal inheritance planning and will drafting solutions to customers, thereby improving customer satisfaction.

[0033] The learning unit can learn from past case studies and the consulting skills of highly skilled personnel. For example, the learning unit learns from past case studies such as success stories, failure stories, and countermeasures. The learning unit learns from the consulting skills of highly skilled personnel such as communication techniques and problem-solving abilities. As a result, the accuracy of learning is improved by learning from past case studies and the consulting skills of highly skilled personnel. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input past case studies and the consulting skills of highly skilled personnel into a generative AI, and the generative AI can learn this information.

[0034] The analysis unit can understand the customer's consultation content and propose the most suitable inheritance strategies and will drafting. For example, the analysis unit can understand customer consultation content such as questions about inheritance and consultations about will drafting. The analysis unit uses natural language processing technology and semantic analysis methods to understand the customer's consultation content. This improves the accuracy of proposals to customers by understanding the customer's consultation content and proposing the most suitable inheritance strategies and will drafting. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the customer's consultation content into a generative AI, and the generative AI can analyze this information.

[0035] The service provider can create and provide appropriate materials and documents according to the customer's consultation. For example, the service provider can create appropriate materials and documents such as legal documents, explanatory materials, and guidelines. By creating and providing appropriate materials and documents according to the customer's consultation, the service provider reduces the burden on the customer. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the customer's consultation into a generation AI, and the generation AI can create appropriate materials and documents.

[0036] The service provider can perform inheritance tax simulations based on the customer's asset information. For example, the service provider can perform inheritance tax simulations using tax rate calculation methods and simulation tools. By performing inheritance tax simulations based on the customer's asset information, it can propose the optimal inheritance tax strategy. Some or all of the above processing in the service provider may be performed using a generating AI, or it may be performed without a generating AI. For example, the service provider can input the customer's asset information into a generating AI, and the generating AI can perform inheritance tax simulations.

[0037] The service provider can provide guidance on the documents and procedures necessary for creating a will. For example, the service provider can provide guidance on legal documents, application procedures, and necessary certificates required for creating a will. This reduces the burden on the customer by providing guidance on the documents and procedures necessary for creating a will. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input guidance on the documents and procedures necessary for creating a will into a generation AI, and the generation AI can provide this information.

[0038] The learning unit can improve the accuracy of its learning by considering customer attribute information when learning past case examples and the consultation skills of highly skilled personnel. For example, the learning unit can improve the accuracy of its learning by selecting past case examples that match the age group of the customer. The learning unit can prioritize learning cases related to the customer's occupation to make more appropriate suggestions. The learning unit can learn cases that match the size of the customer's assets to provide optimal inheritance planning. In this way, by improving the accuracy of learning by considering customer attribute information, it can make more appropriate suggestions. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input customer attribute information into a generative AI, which can then improve the accuracy of its learning.

[0039] The learning unit can customize the learning content based on the customer's life events during the learning process. For example, if a customer is about to get married, the learning unit will learn about asset management after marriage. If a customer is planning to have a child, the learning unit will learn about funding for the child's education. If a customer is planning to retire, the learning unit will learn about planning for life after retirement. By customizing the learning content based on the customer's life events, more appropriate information can be provided. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the customer's life event information into a generative AI, which can then customize the learning content.

[0040] The learning unit can learn region-specific information by considering the customer's geographical location during the learning process. For example, the learning unit can learn information about the tax system in the area where the customer lives. The learning unit can learn information about the real estate market in the area where the customer lives. The learning unit can learn information about legal matters in the area where the customer lives. By learning region-specific information while considering the customer's geographical location, it is possible to provide more appropriate information. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the customer's geographical location information into a generative AI, which can then learn region-specific information.

[0041] The learning unit can analyze customers' social media activity and learn relevant information during the learning process. For example, the learning unit can learn information related to topics that customers show interest in on social media. The learning unit can extract potential needs from customers' social media posts and learn information related to those needs. The learning unit can learn lifestyle-related information from customers' social media activity. This allows the learning unit to provide more appropriate information by analyzing customers' social media activity and learning relevant information. Some or all of the above processing in the learning unit may be performed using generative AI, or it may be performed without generative AI. For example, the learning unit can input customers' social media activity into a generative AI, which can then learn relevant information.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the customer's past consultation history when analyzing the customer's consultation content. For example, the analysis unit analyzes similar consultation content based on the customer's past consultation content. The analysis unit extracts specific patterns from the customer's past consultation history and incorporates them into the analysis. The analysis unit refers to the customer's past consultation history and selects the optimal analysis method. By improving the accuracy of the analysis by referring to the customer's past consultation history, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the customer's past consultation history into a generation AI, which can then improve the accuracy of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the customer's life stage when analyzing customer consultations. For example, if a customer is about to get married, the analysis unit applies an analysis algorithm related to asset management after marriage. If a customer is planning to have a child, the analysis unit applies an analysis algorithm related to children's education funds. If a customer is planning to retire, the analysis unit applies an analysis algorithm related to post-retirement life planning. By applying different analysis algorithms according to the customer's life stage, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input customer life stage information into a generating AI, and the generating AI can apply different analysis algorithms.

[0044] The analysis unit can perform region-specific analysis by considering the customer's geographical location when analyzing customer inquiries. For example, the analysis unit can perform analysis on the tax system of the area where the customer lives. The analysis unit can perform analysis on the real estate market of the area where the customer lives. The analysis unit can perform analysis on legal matters of the area where the customer lives. By performing region-specific analysis while considering the customer's geographical location, it is possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the customer's geographical location information into a generation AI, and the generation AI can perform region-specific analysis.

[0045] The analysis unit can analyze the customer's social media activity and reflect relevant information in the analysis when analyzing the customer's consultation content. For example, the analysis unit can reflect information related to topics the customer is interested in on social media. The analysis unit can extract potential needs from the customer's social media posts and reflect relevant information in the analysis. The analysis unit can reflect lifestyle-related information from the customer's social media activity in the analysis. In this way, by analyzing the customer's social media activity and reflecting relevant information in the analysis, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the customer's social media activity into generative AI, and the generative AI can reflect relevant information in the analysis.

[0046] The service provider can improve the accuracy of inheritance tax simulations based on customer asset information by referring to the customer's past asset fluctuation history. For example, the service provider can predict future asset fluctuations based on the customer's past asset fluctuation history and reflect them in the simulation. The service provider can extract specific patterns from the customer's past asset fluctuation history and reflect them in the simulation. The service provider can select the optimal simulation method by referring to the customer's past asset fluctuation history. By improving the accuracy of the simulation by referring to the customer's past asset fluctuation history, a more accurate inheritance tax simulation can be performed. Some or all of the above processes in the service provider may be performed using a generation AI, or they may not be performed using a generation AI. For example, the service provider can input the customer's past asset fluctuation history into a generation AI, which can then improve the accuracy of the simulation.

[0047] The service provider can provide customized guidance to clients regarding the documents and procedures required for creating a will, tailored to their legal circumstances. For example, the service provider can customize and provide a list of necessary documents according to the client's legal circumstances. The service provider can customize and guide clients through the procedural steps according to their legal circumstances. The service provider can highlight specific points of interest according to the client's legal circumstances. By providing customized guidance according to the client's legal circumstances, more appropriate information can be provided. Some or all of the above processes in the service provider may be performed using or without a generating AI. For example, the service provider can input the client's legal circumstances into a generating AI, which can then provide customized guidance.

[0048] The service provider can, when performing inheritance tax simulations based on customer asset information, take into account the customer's geographical location and reflect region-specific tax systems. For example, the service provider can perform inheritance tax simulations based on the tax system of the area where the customer lives. The service provider can perform inheritance tax simulations based on the real estate market of the area where the customer lives. The service provider can perform inheritance tax simulations based on the legal system of the area where the customer lives. By taking into account the customer's geographical location and reflecting region-specific tax systems, a more accurate inheritance tax simulation can be performed. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the customer's geographical location information into a generation AI, and the generation AI can reflect region-specific tax systems.

[0049] The service provider can analyze a customer's social media activity and reflect relevant information in the guidance when providing information on the documents and procedures necessary for creating a will. For example, the service provider can reflect information related to topics the customer is interested in on social media. The service provider can extract potential needs from the customer's social media posts and reflect relevant information in the guidance. The service provider can reflect lifestyle-related information from the customer's social media activity in the guidance. By analyzing the customer's social media activity and reflecting relevant information in the guidance, the service provider can provide more appropriate information. Some or all of the above processing in the service provider may be performed using or without generative AI. For example, the service provider can input the customer's social media activity into generative AI, which can then reflect relevant information in the guidance.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The learning unit can improve the accuracy of its learning by considering customer attribute information when learning past case examples and the consultation skills of highly skilled personnel. For example, the learning unit can improve the accuracy of its learning by selecting past case examples that match the age group of the customer. The learning unit can prioritize learning cases related to the customer's occupation to make more appropriate suggestions. The learning unit can learn cases that match the size of the customer's assets to provide optimal inheritance planning. In this way, by improving the accuracy of learning by considering customer attribute information, it can make more appropriate suggestions. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input customer attribute information into a generative AI, which can then improve the accuracy of its learning.

[0052] The learning unit can customize the learning content based on the customer's life events during the learning process. For example, if a customer is about to get married, the learning unit will learn about asset management after marriage. If a customer is planning to have a child, the learning unit will learn about funding for the child's education. If a customer is planning to retire, the learning unit will learn about planning for life after retirement. By customizing the learning content based on the customer's life events, more appropriate information can be provided. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the customer's life event information into a generative AI, which can then customize the learning content.

[0053] The learning unit can learn region-specific information by considering the customer's geographical location during the learning process. For example, the learning unit can learn information about the tax system in the area where the customer lives. The learning unit can learn information about the real estate market in the area where the customer lives. The learning unit can learn information about legal matters in the area where the customer lives. By learning region-specific information while considering the customer's geographical location, it is possible to provide more appropriate information. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the customer's geographical location information into a generative AI, which can then learn region-specific information.

[0054] The learning unit can analyze customers' social media activity and learn relevant information during the learning process. For example, the learning unit can learn information related to topics that customers show interest in on social media. The learning unit can extract potential needs from customers' social media posts and learn information related to those needs. The learning unit can learn lifestyle-related information from customers' social media activity. This allows the learning unit to provide more appropriate information by analyzing customers' social media activity and learning relevant information. Some or all of the above processing in the learning unit may be performed using generative AI, or it may be performed without generative AI. For example, the learning unit can input customers' social media activity into a generative AI, which can then learn relevant information.

[0055] The analysis unit can improve the accuracy of its analysis by referring to the customer's past consultation history when analyzing the customer's consultation content. For example, the analysis unit analyzes similar consultation content based on the customer's past consultation content. The analysis unit extracts specific patterns from the customer's past consultation history and incorporates them into the analysis. The analysis unit refers to the customer's past consultation history and selects the optimal analysis method. By improving the accuracy of the analysis by referring to the customer's past consultation history, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the customer's past consultation history into a generation AI, which can then improve the accuracy of the analysis.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The learning unit learns customer information, internal company information and knowledge, and external information. Specifically, it learns personal information, transaction history, and consultation history as customer information, and past case data, specialized knowledge, and know-how as internal company information and knowledge. Furthermore, it learns market data, legal and regulatory information, and competitor information as external information. The learning unit uses generative AI to learn this information extensively and accurately, and maintains updated information. Step 2: The analysis unit analyzes the customer's consultation content based on the information learned by the learning unit. Specifically, it understands the customer's consultation content and proposes the most suitable inheritance plan and will drafting. The analysis unit uses generative AI to analyze the customer's consultation content using natural language processing technology and data mining techniques. Step 3: The service department proposes optimal inheritance planning and will drafting based on the information analyzed by the analysis department. Specifically, it creates and provides appropriate materials and documents according to the customer's consultation. The service department uses generation AI to automatically create and provide materials and documents according to the customer's consultation.

[0058] (Example of form 2) The consultation service provision system according to an embodiment of the present invention is a system that provides high-quality, highly efficient, and low-cost consultation services to wealthy clients of financial institutions and tax accounting firms by utilizing a generating AI agent in inheritance planning and will drafting services. This consultation service provision system trains the generating AI with customer information, the company's internal information and knowledge (past case examples, the experience of highly skilled personnel), and external information (tax, legal, financial, real estate, etc.). The generating AI analyzes the customer's consultation content and proposes the optimal inheritance planning and will drafting. This mechanism reproduces the skills of highly skilled personnel, improves work efficiency, and frees up time for customer service. In addition, by having the same person (AI agent) continuously handle the case, consistent service to the customer becomes possible. For example, the generating AI is trained with customer information, the company's internal information and knowledge, and external information. In this process, the generating AI learns broad and accurate knowledge of tax, legal, financial, real estate, etc., and maintains updated information. For example, by learning past case examples and the consultation skills of highly skilled personnel, the generating AI can acquire the ability to extract the customer's latent needs. Next, the generating AI analyzes the client's consultation content. The generating AI understands the client's consultation content and proposes the most suitable inheritance strategy and will creation. For example, if a client requests an inheritance tax simulation, the generating AI can perform a simulation based on the client's asset information and propose the most suitable inheritance strategy. Furthermore, the AI ​​provides consultation to the client based on the proposal it has created. The generating AI creates and provides appropriate materials and documents according to the client's consultation content. For example, by providing guidance on the documents and procedures necessary for creating a will, the burden on the client can be reduced. This system allows for the reproduction of the skills of highly skilled staff, improves operational efficiency, and frees up time for client consultations. In addition, having the same staff member (AI agent) continuously handle the client ensures consistent service. For example, even if a client changes their consultation content, the generating AI retains past consultation details, allowing for a smooth response. In this way, by utilizing the generating AI agent, financial institutions and tax accounting firms can provide high-quality consultation services to a larger number of affluent clients.This will lead to improved customer satisfaction and increased operational efficiency, enhancing the competitiveness of financial institutions and tax accounting firms. The consultation service system will then be able to analyze customer inquiries and propose optimal inheritance strategies and will drafting plans.

[0059] The consultation service provision system according to this embodiment comprises a learning unit, an analysis unit, and a provision unit. The learning unit learns customer information, the company's internal information and knowledge, and external information. For example, the learning unit learns personal information, transaction history, and consultation history as customer information. For example, it learns past case data, specialized knowledge, and know-how as company's internal information and knowledge. For example, it learns market data, legal and regulatory information, and competitor information as external information. The learning unit uses generative AI to learn this information extensively and accurately and maintains updated information. The analysis unit analyzes the customer's consultation content based on the information learned by the learning unit. For example, the analysis unit understands the customer's consultation content and proposes the optimal inheritance measures and will creation. The analysis unit uses generative AI to analyze the customer's consultation content using natural language processing technology and data mining methods. The provision unit proposes the optimal inheritance measures and will creation based on the information analyzed by the analysis unit. For example, the provision unit creates and provides appropriate materials and documents according to the customer's consultation content. The service provider uses a generation AI to automatically create and provide materials and documents tailored to the customer's consultation. As a result, the consultation service provision system according to this embodiment can analyze the customer's consultation and propose optimal inheritance planning and will creation.

[0060] The learning unit learns customer information, internal company information and knowledge, and external information. For example, as customer information, the learning unit learns personal information, transaction history, and consultation history. Specifically, in addition to basic personal information such as the customer's name, address, and contact information, it learns detailed past transaction and consultation history. This allows the unit to understand customer needs and trends and provide more personalized services. As internal company information and knowledge, it learns past case data, specialized knowledge, and know-how. For example, it systematically learns and accumulates data on inheritance cases handled in the past, as well as the knowledge and know-how of experts. This allows the unit to make more effective proposals by referring to past success and failure cases. As external information, it learns market data, legal and regulatory information, and competitor information. For example, it can constantly keep up-to-date with the latest market trends, changes in legal regulations, and the activities of competitors, and provide customers with the latest and most optimal information. The learning unit uses generative AI to learn this information extensively and accurately, and maintains updated information. Generative AI has the ability to efficiently process large amounts of data and extract important information. For example, natural language processing techniques can be used to extract useful information from text data, and data mining methods can be used to analyze the relationships between data. This allows the learning unit to always maintain up-to-date information and build a foundation for providing optimal services to customers. Furthermore, by regularly updating information and incorporating new data and insights, the learning unit can improve the accuracy and reliability of the entire system.

[0061] The analysis unit analyzes customer consultations based on information learned by the learning unit. For example, the analysis unit understands customer consultations and proposes optimal inheritance strategies and will drafting. Specifically, it analyzes the consultation content provided by customers using natural language processing technology to accurately grasp the customer's intentions and needs. The generation AI takes customer consultation content as text data and identifies the information and solutions the customer is seeking by analyzing the context and keywords. For example, if a customer consults about inheritance, the generation AI refers to inheritance-related laws and regulations and past cases to propose the optimal inheritance strategy. Also, if a customer consults about will drafting, the generation AI considers the customer's intentions, family structure, and financial situation to propose appropriate will content. The analysis unit uses the generation AI to analyze customer consultations using natural language processing technology and data mining techniques. This allows the analysis unit to quickly and accurately analyze customer consultations and make optimal proposals. Furthermore, the analysis unit can refer to past consultation history and success stories to make more effective proposals to customers. For example, it can refer to how similar consultations were handled and the results in the past to provide specific advice to customers. Furthermore, the analysis department can collect customer feedback and improve the accuracy of analysis results and the content of recommendations. This allows the analysis department to consistently provide optimal recommendations based on the latest information and customer needs, thereby improving customer satisfaction.

[0062] The service department provides optimal inheritance planning and will drafting proposals based on information analyzed by the analysis department. Specifically, it creates and provides appropriate materials and documents according to the client's consultation. The service department uses generation AI to automatically create and provide materials and documents tailored to the client's consultation. For example, the generation AI automatically creates inheritance planning proposals and will drafts and provides them to the client. The generation AI selects the appropriate format and content based on the client's consultation and analysis results, and generates the documents. This allows the service department to quickly and accurately create and provide materials and documents to the client. Furthermore, the service department can collect client feedback and continuously improve the content of the materials and documents it provides. For example, it can revise the proposal content and document format to reflect client requests and opinions. In addition, the service department can reliably transmit information using multiple communication methods. For example, it can provide materials and documents to clients using email and cloud storage, and also conduct online meetings and telephone explanations as needed. This allows the service department to provide information to clients quickly and reliably, improving customer satisfaction. Furthermore, the service department can provide customized services according to the client's needs. For example, by providing individualized consulting and advice to specific customers, a more personalized service can be achieved. This allows the service provider to propose optimal inheritance planning and will drafting solutions to customers, thereby improving customer satisfaction.

[0063] The learning unit can learn from past case studies and the consulting skills of highly skilled personnel. For example, the learning unit learns from past case studies such as success stories, failure stories, and countermeasures. The learning unit learns from the consulting skills of highly skilled personnel such as communication techniques and problem-solving abilities. As a result, the accuracy of learning is improved by learning from past case studies and the consulting skills of highly skilled personnel. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input past case studies and the consulting skills of highly skilled personnel into a generative AI, and the generative AI can learn this information.

[0064] The analysis unit can understand the customer's consultation content and propose the most suitable inheritance strategies and will drafting. For example, the analysis unit can understand customer consultation content such as questions about inheritance and consultations about will drafting. The analysis unit uses natural language processing technology and semantic analysis methods to understand the customer's consultation content. This improves the accuracy of proposals to customers by understanding the customer's consultation content and proposing the most suitable inheritance strategies and will drafting. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the customer's consultation content into a generative AI, and the generative AI can analyze this information.

[0065] The service provider can create and provide appropriate materials and documents according to the customer's consultation. For example, the service provider can create appropriate materials and documents such as legal documents, explanatory materials, and guidelines. By creating and providing appropriate materials and documents according to the customer's consultation, the service provider reduces the burden on the customer. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the customer's consultation into a generation AI, and the generation AI can create appropriate materials and documents.

[0066] The service provider can perform inheritance tax simulations based on the customer's asset information. For example, the service provider can perform inheritance tax simulations using tax rate calculation methods and simulation tools. By performing inheritance tax simulations based on the customer's asset information, it can propose the optimal inheritance tax strategy. Some or all of the above processing in the service provider may be performed using a generating AI, or it may be performed without a generating AI. For example, the service provider can input the customer's asset information into a generating AI, and the generating AI can perform inheritance tax simulations.

[0067] The service provider can provide guidance on the documents and procedures necessary for creating a will. For example, the service provider can provide guidance on legal documents, application procedures, and necessary certificates required for creating a will. This reduces the burden on the customer by providing guidance on the documents and procedures necessary for creating a will. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input guidance on the documents and procedures necessary for creating a will into a generation AI, and the generation AI can provide this information.

[0068] The learning unit can estimate customer emotions and select training data based on the estimated customer emotions. For example, if a customer is feeling anxious, the learning unit will select data containing many success stories to provide reassurance. If a customer is excited, the learning unit will select data related to risk to encourage calm judgment. If a customer is relaxed, the learning unit will select data containing general information to provide broad knowledge. This allows for the provision of more appropriate information by selecting training data based on customer emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input customer emotion data into a generative AI, which can then select training data.

[0069] The learning unit can improve the accuracy of its learning by considering customer attribute information when learning past case examples and the consultation skills of highly skilled personnel. For example, the learning unit can improve the accuracy of its learning by selecting past case examples that match the age group of the customer. The learning unit can prioritize learning cases related to the customer's occupation to make more appropriate suggestions. The learning unit can learn cases that match the size of the customer's assets to provide optimal inheritance planning. In this way, by improving the accuracy of learning by considering customer attribute information, it can make more appropriate suggestions. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input customer attribute information into a generative AI, which can then improve the accuracy of its learning.

[0070] The learning unit can customize the learning content based on the customer's life events during the learning process. For example, if a customer is about to get married, the learning unit will learn about asset management after marriage. If a customer is planning to have a child, the learning unit will learn about funding for the child's education. If a customer is planning to retire, the learning unit will learn about planning for life after retirement. By customizing the learning content based on the customer's life events, more appropriate information can be provided. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the customer's life event information into a generative AI, which can then customize the learning content.

[0071] The learning unit can estimate the customer's emotions and adjust the frequency of learning based on the estimated emotions. For example, if the customer is feeling anxious, the learning unit will learn frequently and provide up-to-date information. If the customer is relaxed, the learning unit will reduce the frequency of learning and provide only the necessary information. If the customer is excited, the learning unit will learn at a moderate frequency to encourage calm judgment. By adjusting the frequency of learning based on the customer's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input customer emotion data into a generative AI, which can then adjust the frequency of learning.

[0072] The learning unit can learn region-specific information by considering the customer's geographical location during the learning process. For example, the learning unit can learn information about the tax system in the area where the customer lives. The learning unit can learn information about the real estate market in the area where the customer lives. The learning unit can learn information about legal matters in the area where the customer lives. By learning region-specific information while considering the customer's geographical location, it is possible to provide more appropriate information. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the customer's geographical location information into a generative AI, which can then learn region-specific information.

[0073] The learning unit can analyze customers' social media activity and learn relevant information during the learning process. For example, the learning unit can learn information related to topics that customers show interest in on social media. The learning unit can extract potential needs from customers' social media posts and learn information related to those needs. The learning unit can learn lifestyle-related information from customers' social media activity. This allows the learning unit to provide more appropriate information by analyzing customers' social media activity and learning relevant information. Some or all of the above processing in the learning unit may be performed using generative AI, or it may be performed without generative AI. For example, the learning unit can input customers' social media activity into a generative AI, which can then learn relevant information.

[0074] The analysis unit can estimate the customer's emotions and adjust the analysis method based on the estimated emotions. For example, if the customer is feeling anxious, the analysis unit will prioritize positive analysis results to provide reassurance. If the customer is excited, the analysis unit will emphasize risk-related analysis results to encourage calm judgment. If the customer is relaxed, the analysis unit will provide analysis results that include general information. This allows for the provision of more appropriate analysis results by adjusting the analysis method based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input customer emotion data into a generative AI, which can then adjust the analysis method.

[0075] The analysis unit can improve the accuracy of its analysis by referring to the customer's past consultation history when analyzing the customer's consultation content. For example, the analysis unit analyzes similar consultation content based on the customer's past consultation content. The analysis unit extracts specific patterns from the customer's past consultation history and incorporates them into the analysis. The analysis unit refers to the customer's past consultation history and selects the optimal analysis method. By improving the accuracy of the analysis by referring to the customer's past consultation history, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the customer's past consultation history into a generation AI, which can then improve the accuracy of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the customer's life stage when analyzing customer consultations. For example, if a customer is about to get married, the analysis unit applies an analysis algorithm related to asset management after marriage. If a customer is planning to have a child, the analysis unit applies an analysis algorithm related to children's education funds. If a customer is planning to retire, the analysis unit applies an analysis algorithm related to post-retirement life planning. By applying different analysis algorithms according to the customer's life stage, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input customer life stage information into a generating AI, and the generating AI can apply different analysis algorithms.

[0077] The analysis unit can estimate the customer's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the customer is feeling anxious, the analysis unit will emphasize positive analysis results to provide reassurance. If the customer is excited, the analysis unit will emphasize risk-related analysis results to encourage calm judgment. If the customer is relaxed, the analysis unit will provide analysis results containing general information. This allows for the provision of more appropriate information by adjusting how the analysis results are displayed based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input customer emotion data into a generative AI, which can then adjust how the analysis results are displayed.

[0078] The analysis unit can perform region-specific analysis by considering the customer's geographical location when analyzing customer inquiries. For example, the analysis unit can perform analysis on the tax system of the area where the customer lives. The analysis unit can perform analysis on the real estate market of the area where the customer lives. The analysis unit can perform analysis on legal matters of the area where the customer lives. By performing region-specific analysis while considering the customer's geographical location, it is possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the customer's geographical location information into a generation AI, and the generation AI can perform region-specific analysis.

[0079] The analysis unit can analyze the customer's social media activity and reflect relevant information in the analysis when analyzing the customer's consultation content. For example, the analysis unit can reflect information related to topics the customer is interested in on social media. The analysis unit can extract potential needs from the customer's social media posts and reflect relevant information in the analysis. The analysis unit can reflect lifestyle-related information from the customer's social media activity in the analysis. In this way, by analyzing the customer's social media activity and reflecting relevant information in the analysis, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input the customer's social media activity into generative AI, and the generative AI can reflect relevant information in the analysis.

[0080] The service provider can estimate the customer's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the customer is feeling anxious, the service provider might use positive language to reassure them. If the customer is excited, the service provider might emphasize risk-related language to encourage calm judgment. If the customer is relaxed, the service provider might use language that includes general information. By adjusting the way it presents its suggestions based on the customer's emotions, it can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using or without generative AI. For example, the service provider can input customer emotion data into a generative AI, which can then adjust the way it presents its suggestions.

[0081] The service provider can improve the accuracy of inheritance tax simulations based on customer asset information by referring to the customer's past asset fluctuation history. For example, the service provider can predict future asset fluctuations based on the customer's past asset fluctuation history and reflect them in the simulation. The service provider can extract specific patterns from the customer's past asset fluctuation history and reflect them in the simulation. The service provider can select the optimal simulation method by referring to the customer's past asset fluctuation history. By improving the accuracy of the simulation by referring to the customer's past asset fluctuation history, a more accurate inheritance tax simulation can be performed. Some or all of the above processes in the service provider may be performed using a generation AI, or they may not be performed using a generation AI. For example, the service provider can input the customer's past asset fluctuation history into a generation AI, which can then improve the accuracy of the simulation.

[0082] The service provider can provide customized guidance to clients regarding the documents and procedures required for creating a will, tailored to their legal circumstances. For example, the service provider can customize and provide a list of necessary documents according to the client's legal circumstances. The service provider can customize and guide clients through the procedural steps according to their legal circumstances. The service provider can highlight specific points of interest according to the client's legal circumstances. By providing customized guidance according to the client's legal circumstances, more appropriate information can be provided. Some or all of the above processes in the service provider may be performed using or without a generating AI. For example, the service provider can input the client's legal circumstances into a generating AI, which can then provide customized guidance.

[0083] The service provider can estimate the customer's emotions and prioritize suggestions based on those emotions. For example, if the customer is feeling anxious, the service provider will prioritize suggestions that provide reassurance. If the customer is excited, the service provider will prioritize suggestions that encourage calm judgment. If the customer is relaxed, the service provider will prioritize suggestions that include general information. By prioritizing suggestions based on the customer's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without generative AI. For example, the service provider can input customer emotion data into a generative AI, which can then determine the priority of suggestions.

[0084] The service provider can, when performing inheritance tax simulations based on customer asset information, take into account the customer's geographical location and reflect region-specific tax systems. For example, the service provider can perform inheritance tax simulations based on the tax system of the area where the customer lives. The service provider can perform inheritance tax simulations based on the real estate market of the area where the customer lives. The service provider can perform inheritance tax simulations based on the legal system of the area where the customer lives. By taking into account the customer's geographical location and reflecting region-specific tax systems, a more accurate inheritance tax simulation can be performed. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the customer's geographical location information into a generation AI, and the generation AI can reflect region-specific tax systems.

[0085] The service provider can analyze a customer's social media activity and reflect relevant information in the guidance when providing information on the documents and procedures necessary for creating a will. For example, the service provider can reflect information related to topics the customer is interested in on social media. The service provider can extract potential needs from the customer's social media posts and reflect relevant information in the guidance. The service provider can reflect lifestyle-related information from the customer's social media activity in the guidance. By analyzing the customer's social media activity and reflecting relevant information in the guidance, the service provider can provide more appropriate information. Some or all of the above processing in the service provider may be performed using or without generative AI. For example, the service provider can input the customer's social media activity into generative AI, which can then reflect relevant information in the guidance.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The learning unit can estimate customer emotions and select training data based on the estimated customer emotions. For example, if a customer is feeling anxious, the learning unit will select data containing many success stories to provide reassurance. If a customer is excited, the learning unit will select data related to risk to encourage calm judgment. If a customer is relaxed, the learning unit will select data containing general information to provide broad knowledge. This allows for the provision of more appropriate information by selecting training data based on customer emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input customer emotion data into a generative AI, which can then select training data.

[0088] The analysis unit can estimate the customer's emotions and adjust the analysis method based on the estimated emotions. For example, if the customer is feeling anxious, the analysis unit will prioritize positive analysis results to provide reassurance. If the customer is excited, the analysis unit will emphasize risk-related analysis results to encourage calm judgment. If the customer is relaxed, the analysis unit will provide analysis results that include general information. This allows for the provision of more appropriate analysis results by adjusting the analysis method based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input customer emotion data into a generative AI, which can then adjust the analysis method.

[0089] The service provider can estimate the customer's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the customer is feeling anxious, the service provider might use positive language to reassure them. If the customer is excited, the service provider might emphasize risk-related language to encourage calm judgment. If the customer is relaxed, the service provider might use language that includes general information. By adjusting the way it presents its suggestions based on the customer's emotions, it can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using or without generative AI. For example, the service provider can input customer emotion data into a generative AI, which can then adjust the way it presents its suggestions.

[0090] The service provider can estimate the customer's emotions and prioritize suggestions based on those emotions. For example, if the customer is feeling anxious, the service provider will prioritize suggestions that provide reassurance. If the customer is excited, the service provider will prioritize suggestions that encourage calm judgment. If the customer is relaxed, the service provider will prioritize suggestions that include general information. By prioritizing suggestions based on the customer's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without generative AI. For example, the service provider can input customer emotion data into a generative AI, which can then determine the priority of suggestions.

[0091] The learning unit can estimate the customer's emotions and adjust the frequency of learning based on the estimated emotions. For example, if the customer is feeling anxious, the learning unit will learn frequently and provide up-to-date information. If the customer is relaxed, the learning unit will reduce the frequency of learning and provide only the necessary information. If the customer is excited, the learning unit will learn at a moderate frequency to encourage calm judgment. By adjusting the frequency of learning based on the customer's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit can input customer emotion data into a generative AI, which can then adjust the frequency of learning.

[0092] The learning unit can improve the accuracy of its learning by considering customer attribute information when learning past case examples and the consultation skills of highly skilled personnel. For example, the learning unit can improve the accuracy of its learning by selecting past case examples that match the age group of the customer. The learning unit can prioritize learning cases related to the customer's occupation to make more appropriate suggestions. The learning unit can learn cases that match the size of the customer's assets to provide optimal inheritance planning. In this way, by improving the accuracy of learning by considering customer attribute information, it can make more appropriate suggestions. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input customer attribute information into a generative AI, which can then improve the accuracy of its learning.

[0093] The learning unit can customize the learning content based on the customer's life events during the learning process. For example, if a customer is about to get married, the learning unit will learn about asset management after marriage. If a customer is planning to have a child, the learning unit will learn about funding for the child's education. If a customer is planning to retire, the learning unit will learn about planning for life after retirement. By customizing the learning content based on the customer's life events, more appropriate information can be provided. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the customer's life event information into a generative AI, which can then customize the learning content.

[0094] The learning unit can learn region-specific information by considering the customer's geographical location during the learning process. For example, the learning unit can learn information about the tax system in the area where the customer lives. The learning unit can learn information about the real estate market in the area where the customer lives. The learning unit can learn information about legal matters in the area where the customer lives. By learning region-specific information while considering the customer's geographical location, it is possible to provide more appropriate information. Some or all of the above processing in the learning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the customer's geographical location information into a generative AI, which can then learn region-specific information.

[0095] The learning unit can analyze customers' social media activity and learn relevant information during the learning process. For example, the learning unit can learn information related to topics that customers show interest in on social media. The learning unit can extract potential needs from customers' social media posts and learn information related to those needs. The learning unit can learn lifestyle-related information from customers' social media activity. This allows the learning unit to provide more appropriate information by analyzing customers' social media activity and learning relevant information. Some or all of the above processing in the learning unit may be performed using generative AI, or it may be performed without generative AI. For example, the learning unit can input customers' social media activity into a generative AI, which can then learn relevant information.

[0096] The analysis unit can improve the accuracy of its analysis by referring to the customer's past consultation history when analyzing the customer's consultation content. For example, the analysis unit analyzes similar consultation content based on the customer's past consultation content. The analysis unit extracts specific patterns from the customer's past consultation history and incorporates them into the analysis. The analysis unit refers to the customer's past consultation history and selects the optimal analysis method. By improving the accuracy of the analysis by referring to the customer's past consultation history, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the customer's past consultation history into a generation AI, which can then improve the accuracy of the analysis.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The learning unit learns customer information, internal company information and knowledge, and external information. Specifically, it learns personal information, transaction history, and consultation history as customer information, and past case data, specialized knowledge, and know-how as internal company information and knowledge. Furthermore, it learns market data, legal and regulatory information, and competitor information as external information. The learning unit uses generative AI to learn this information extensively and accurately, and maintains updated information. Step 2: The analysis unit analyzes the customer's consultation content based on the information learned by the learning unit. Specifically, it understands the customer's consultation content and proposes the most suitable inheritance plan and will drafting. The analysis unit uses generative AI to analyze the customer's consultation content using natural language processing technology and data mining techniques. Step 3: The service department proposes optimal inheritance planning and will drafting based on the information analyzed by the analysis department. Specifically, it creates and provides appropriate materials and documents according to the customer's consultation. The service department uses generation AI to automatically create and provide materials and documents according to the customer's consultation.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the learning unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns customer information, the company's internal information and knowledge, and external information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the customer's consultation content based on the learned information. The provision unit is implemented by the control unit 46A of the smart device 14 and proposes optimal inheritance measures and will creation based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the learning unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns customer information, the company's internal information and knowledge, and external information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the customer's consultation content based on the learned information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and proposes optimal inheritance measures and will creation based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the learning unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns customer information, the company's internal information and knowledge, and external information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the customer's consultation content based on the learned information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and proposes optimal inheritance measures and will creation based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the learning unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns customer information, the company's internal information and knowledge, and external information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the customer's consultation content based on the learned information. The provision unit is implemented by the control unit 46A of the robot 414 and proposes optimal inheritance measures and will creation based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The learning department learns customer information, internal company information and knowledge, and external information. An analysis unit analyzes the customer's consultation content based on the information learned by the aforementioned learning unit, Based on the information analyzed by the aforementioned analysis unit, the provision unit proposes optimal inheritance strategies and will creation methods. Equipped with A system characterized by the following features. (Note 2) The aforementioned learning unit, Learn from past case studies and the consulting skills of highly skilled professionals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We understand the client's concerns and propose the most suitable inheritance strategies and will drafting solutions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We create and provide appropriate materials and documents based on the customer's inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We perform inheritance tax simulations based on the customer's asset information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide guidance on the documents and procedures required to create a will. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, The system estimates customer emotions and selects training data based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, When learning from past case studies and the consulting skills of highly skilled personnel, consider customer attribute information to improve the accuracy of the learning process. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, During the learning process, the learning content is customized based on the customer's life events. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, It estimates customer emotions and adjusts the learning frequency based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During the learning process, the system learns region-specific information by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, During the learning process, the system analyzes customers' social media activity and learns relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate customer emotions and adjust the analysis method based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing customer inquiries, we improve the accuracy of the analysis by referring to the customer's past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing customer inquiries, different analysis algorithms are applied depending on the customer's life stage. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates customer emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing customer inquiries, we perform region-specific analysis by taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing customer inquiries, we analyze the customer's social media activity and incorporate relevant information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the customer's emotions and adjust the way we present our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When performing inheritance tax simulations based on customer asset information, we improve the accuracy of the simulations by referring to the customer's past asset fluctuation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing guidance on the documents and procedures required to create a will, we offer customized guidance tailored to the client's legal situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, Estimate customer emotions and prioritize proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When performing inheritance tax simulations based on customer asset information, the system takes into account the customer's geographical location to reflect region-specific tax regulations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing guidance on the documents and procedures required to create a will, we analyze the customer's social media activity and incorporate relevant information into the guidance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The learning department learns customer information, internal company information and knowledge, and external information. An analysis unit analyzes the customer's consultation content based on the information learned by the aforementioned learning unit, Based on the information analyzed by the aforementioned analysis unit, the provision unit proposes optimal inheritance strategies and will creation methods. Equipped with A system characterized by the following features.

2. The aforementioned learning unit, Learn from past case studies and the consulting skills of highly skilled professionals. The system according to feature 1.

3. The aforementioned analysis unit, We understand the client's concerns and propose the most suitable inheritance strategies and will drafting solutions. The system according to feature 1.

4. The aforementioned supply unit is, We create and provide appropriate materials and documents based on the customer's inquiry. The system according to feature 1.

5. The aforementioned supply unit is, We perform inheritance tax simulations based on the customer's asset information. The system according to feature 1.

6. The aforementioned supply unit is, We provide guidance on the documents and procedures required to create a will. The system according to feature 1.

7. The aforementioned learning unit, The system estimates customer emotions and selects training data based on the estimated customer emotions. The system according to feature 1.

8. The aforementioned learning unit, When learning from past case studies and the consulting skills of highly skilled personnel, consider customer attribute information to improve the accuracy of the learning process. The system according to feature 1.

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