system

Abstract

The system according to this embodiment aims to provide advice tailored to the user's financial literacy and support household and asset management. [Solution] The system according to the embodiment comprises a collection unit, a dialogue unit, an analysis unit, and a provision unit. The collection unit collects the user's household and asset data and a wide range of external information. The dialogue unit engages in dialogue tailored to the user's literacy level based on the information collected by the collection unit. The analysis unit proposes specific savings methods and investment plans, taking into account the user's financial situation and goals based on the information provided by the dialogue unit. The provision unit updates the advice based on the latest information, even if the user's income or expenses change, based on the information proposed 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that a household and asset management application was not linked with other financial institutions, making it difficult to provide advice according to the user's financial literacy.

[0005] The system according to the embodiment aims to provide advice according to the user's financial literacy and support household and asset management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a dialogue unit, an analysis unit, and a provision unit. The collection unit collects the user's household and asset data and a wide range of external information. The dialogue unit engages in dialogue tailored to the user's literacy level based on the information collected by the collection unit. The analysis unit proposes specific savings methods and investment plans, taking into account the user's financial situation and goals based on the information provided by the dialogue unit. The provision unit updates the advice based on the latest information, even if the user's income or expenses change, based on the information proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide advice tailored to the user's financial literacy and support household and asset management. [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 manages communication between a plurality of 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 household and asset management system according to an embodiment of the present invention evolves a wallet into a household and asset management app that can also link with other financial institutions, and further incorporates a generating AI to provide users with advanced and user-friendly advice. The household and asset management system takes the user's household and asset data and a wide range of external information (financial, tax, and local information, etc.) as input, and the generating AI performs Q&A tailored to the user's literacy level, thereby deriving appropriate answers from ambiguous needs by understanding the background, context, and surroundings, something that was previously only possible for human financial planners. As a result, users can consult anytime, anywhere, in an easy-to-understand manner, just as if they were consulting a person (financial planner). Furthermore, it provides advice that is not one-time, but always takes into account the user's situation and the latest external information. For example, the household and asset management system takes the user's household and asset data and a wide range of external information as input. At this time, it collects the user's financial asset information, tax information, local information, etc., and inputs them into the generating AI. For example, it collects the user's bank account information, credit card usage history, tax return information, local subsidy information, etc. This allows the generating AI to understand the user's overall financial situation. Next, the generating AI conducts a series of Q&A sessions tailored to the user's literacy level. The generating AI engages in dialogue that takes the user's literacy into account in order to provide appropriate answers to the user's questions. For example, it provides basic financial knowledge and specific advice to users with low financial literacy, while providing more specialized advice to users with high financial literacy. This allows users to receive appropriate advice tailored to their own literacy level. Furthermore, the generating AI can derive appropriate answers from ambiguous needs by understanding the background, context, and surrounding factors, something that was previously only possible for human financial planners. For example, if a user asks a vague question such as "How should I save for the future?", the generating AI will consider the user's financial situation and goals and propose specific savings methods and investment plans. This allows the user to create a concrete action plan. This system allows users to consult anytime, anywhere, in an easy-to-understand way, just as if they were consulting a human financial planner.For example, even if a user wants to discuss their household finances in the middle of the night, the generating AI is available 24 hours a day to provide appropriate advice. Furthermore, instead of providing one-time advice, it constantly provides advice based on the user's situation and the latest external information, allowing users to continuously receive appropriate advice. For example, even if the user's income or expenses fluctuate, the generating AI updates the advice based on the latest information. This allows users to always manage their finances optimally. In this way, the household finance and asset management system can efficiently manage the user's household finances and assets and provide appropriate advice.

[0029] The household and asset management system according to this embodiment comprises a collection unit, a dialogue unit, an analysis unit, and a provision unit. The collection unit collects the user's household and asset data and a wide range of external information. For example, the collection unit collects the user's financial asset information, tax information, and regional information. For example, the collection unit can collect bank account information and credit card usage history. The collection unit can also collect tax return information and regional subsidy information. For example, the collection unit obtains the user's bank account information via an API and credit card usage history from a database. The collection unit obtains tax return information from the tax office's database and collects regional subsidy information from local government websites. The dialogue unit conducts a dialogue tailored to the user's literacy level based on the information collected by the collection unit. For example, the dialogue unit adjusts the content of the dialogue according to the user's financial literacy. For example, the dialogue unit conducts a dialogue while providing basic financial knowledge to users with low financial literacy. The dialogue unit can provide more specialized advice to users with high financial literacy. The dialogue unit, for example, conducts conversations that take the user's literacy into consideration in order to provide appropriate answers to the user's questions. The dialogue unit can adjust the tone and content of the conversation according to the user's literacy. The analysis unit considers the user's financial situation and goals based on the information provided by the dialogue unit and proposes specific savings methods and investment plans. The analysis unit performs analysis considering information such as the user's income, expenses, assets, and liabilities. For example, the analysis unit can analyze the balance between the user's income and expenses and propose the optimal savings method. The analysis unit can analyze the user's asset and liability situation and propose the optimal investment plan. For example, the analysis unit can analyze the user's income and expense data and set a savings target amount. The analysis unit can analyze the user's asset and liability data and propose a low-risk investment plan. The service provider updates the advice based on the latest information, even if the user's income or expenses change, based on the information proposed by the analysis unit. For example, the service provider updates the advice based on the latest information if the user's income or expenses change. The service provider, for example, updates the savings target amount if the user's income increases.The service provider can suggest a review of the user's spending if their spending increases. For example, if the user's income increases, the service provider can reset the savings target. If the user's spending increases, the service provider can suggest ways to reduce spending. In this way, the household and asset management system according to the embodiment can efficiently manage the user's household and asset finances and provide appropriate advice.

[0030] The data collection unit collects users' household and asset data as well as a wide range of external information. Specifically, it collects users' financial asset information, tax information, and local information. For example, the data collection unit obtains users' bank account information via API and credit card usage history from a database. This allows for real-time tracking of detailed data on users' income and expenses. The data collection unit also obtains tax return information from tax office databases and collects local subsidy information from local government websites. This allows users to understand available subsidies and tax incentives, supporting optimal asset management. Furthermore, the data collection unit can also collect users' investment account information and insurance contract information. This enables a comprehensive understanding of the user's overall asset situation and allows for the provision of more accurate advice. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and dialogue units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The dialogue unit conducts conversations tailored to the user's literacy level based on information collected by the data collection unit. Specifically, it adjusts the content of the conversation according to the user's financial literacy. For example, for users with low financial literacy, the conversation provides basic financial knowledge, including the basics of saving, how to use credit cards, and how to repay loans. On the other hand, for users with high financial literacy, more specialized advice can be provided, such as investment risk management, how to utilize tax incentives, and how to choose complex financial products. The dialogue unit conducts conversations that take the user's literacy into consideration in order to provide appropriate answers to the user's questions. This allows users to obtain information according to their level of understanding and manage their household finances and assets more effectively. The dialogue unit can adjust the tone and content of the conversation according to the user's literacy level. For example, it explains things in simple terms and avoids jargon for beginners, while providing detailed technical explanations for advanced users. Furthermore, the dialogue unit can collect user feedback and continuously improve the accuracy and effectiveness of the conversation content. This allows the dialogue unit to provide users with optimal information and strengthen support for household finances and asset management.

[0032] The analysis unit considers the user's financial situation and goals based on the information provided by the dialogue unit, and proposes specific savings methods and investment plans. Specifically, it performs analysis considering information such as the user's income, expenses, assets, and liabilities. For example, it analyzes the balance between the user's income and expenses and proposes the optimal savings method. This includes monthly savings targets and specific means for saving (e.g., setting up an automatic savings plan). It can also analyze the user's asset and liability situation and propose the optimal investment plan. This includes selecting low-risk investments and constructing a portfolio for risk diversification. The analysis unit analyzes the user's income and expense data and sets savings targets. For example, if the user's income is above a certain level, it sets a higher savings target; if income is unstable, it proposes a flexible savings plan. The analysis unit also analyzes the user's asset and liability data and proposes low-risk investment plans. For example, if the user wants to avoid risk, it recommends safe bonds or time deposits; if the user seeks high returns even with risk, it proposes stocks or mutual funds. Furthermore, the analysis unit can propose long-term asset management plans by considering past data and market trends. This allows the analysis unit to provide users with optimal savings methods and investment plans tailored to their financial situation, supporting their asset management.

[0033] The service provider updates advice based on the latest information, even if the user's income or expenses change, using the information proposed by the analysis department. Specifically, when the user's income or expenses change, the service provider updates the advice based on the latest information. For example, if the user's income increases, the service provider updates the savings target amount. This includes specific advice on how to allocate the increased income to savings or investments. Also, if the user's expenses increase, the service provider can suggest a review of expenses. This includes methods for reducing unnecessary expenses and reviewing spending priorities. The service provider resets the savings target amount when the user's income increases. For example, if there is a bonus or a raise, the service provider will specifically suggest how to use the increase. Also, if the user's expenses increase, the service provider will suggest ways to reduce expenses. For example, they will provide suggestions for reviewing fixed costs and specific methods for saving money (e.g., how to save on electricity bills or reduce food expenses). Furthermore, the service provider can update advice in accordance with the user's life events (e.g., marriage, childbirth, job change, etc.). This allows the service provider to offer up-to-date advice tailored to the user's situation, enabling efficient household and asset management. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to provide optimal advice to users and strengthen support for household and asset management.

[0034] The data collection unit can collect user financial asset information, tax information, and regional information. For example, the data collection unit collects financial asset information such as the user's bank account balance, stock holdings, and bond holdings. For example, the data collection unit can obtain bank account balances via APIs and stock holdings from securities company databases. The data collection unit can obtain bond holdings from financial institution databases. The data collection unit can also collect tax information such as the user's income tax information, resident tax information, and deduction information. For example, the data collection unit can obtain income tax information from tax office databases and resident tax information from local government databases. The data collection unit can obtain deduction information from tax office databases. Furthermore, the data collection unit can also collect regional information such as the user's regional economic situation, regional cost of living, and regional tax rates. For example, the data collection unit can obtain regional economic situation information from local government websites and regional cost of living information from statistical data. The data collection unit can obtain regional tax rates from local government databases. This allows the data collection unit to collect diverse user information and achieve comprehensive financial management. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's financial asset information into an AI model and optimize the timing of when the AI ​​model collects the information.

[0035] The dialogue unit can conduct conversations tailored to the user's literacy level and provide appropriate answers. For example, the dialogue unit adjusts the content of the conversation according to the user's financial literacy. For example, the dialogue unit can conduct conversations with users with low financial literacy while providing basic financial knowledge. For users with moderate financial literacy, the dialogue unit can conduct conversations while providing specific advice. For users with high financial literacy, the dialogue unit can conduct conversations while providing expert advice. For example, the dialogue unit conducts conversations that take the user's literacy into consideration in order to provide appropriate answers to the user's questions. The dialogue unit can adjust the tone and content of the conversation according to the user's literacy level. This allows the dialogue unit to provide appropriate advice according to the user's literacy level. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's literacy level into an AI model, and the AI ​​model can optimize the content of the conversation.

[0036] The analysis unit can consider the user's financial situation and goals and propose specific savings methods and investment plans. For example, the analysis unit performs analysis considering information such as the user's income, expenses, assets, and liabilities. For example, the analysis unit can analyze the balance between the user's income and expenses and propose the optimal savings method. The analysis unit can analyze the user's asset and liability situation and propose the optimal investment plan. For example, the analysis unit can analyze the user's income and expense data and set a savings target amount. The analysis unit can analyze the user's asset and liability data and propose a low-risk investment plan. This allows the analysis unit to provide specific advice based on the user's financial situation. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's financial data into an AI model, which can then propose the optimal savings methods and investment plans.

[0037] The service provider can update its advice based on the latest information, even if the user's income or expenses fluctuate. For example, if the user's income increases, the service provider can update the savings target. If the user's expenses increase, the service provider can suggest a review of expenses. For example, if the user's income increases, the service provider can reset the savings target. If the user's expenses increase, the service provider can suggest ways to reduce expenses. This allows the service provider to provide up-to-date advice tailored to the user's situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's income and expense data into an AI model, and the AI ​​model can provide up-to-date advice.

[0038] The data collection unit can analyze the user's past financial transaction history and select the optimal data collection method. For example, the data collection unit prioritizes collecting information from financial institutions that the user has frequently used in the past. The data collection unit can achieve efficient information collection by collecting data from the user's transaction history at specific time periods. The data collection unit analyzes the user's transaction patterns and prioritizes collecting the most relevant information. This enables the data collection unit to achieve efficient information collection based on the user's transaction history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's financial transaction history into a generating AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current living situation and areas of interest during collection. For example, the data collection unit can prioritize collecting highly relevant financial information based on the user's current living situation. The data collection unit can filter and collect specific investment or tax-saving information based on the user's areas of interest. The data collection unit eliminates unnecessary information and collects only the necessary information according to the user's living situation and areas of interest. This allows the data collection unit to collect information that is relevant to the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's living situation and areas of interest into a generating AI, which can then perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user lives in a specific region, the data collection unit can prioritize the collection of financial information related to that region. If the user is traveling, the data collection unit can prioritize the collection of financial information related to the travel destination. Based on the user's geographical location, the data collection unit can collect region-specific tax information and subsidy information. This allows the data collection unit to collect information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI, which can then prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect information related to financial products that the user has shown interest in on social media. The data collection unit can analyze specific investment trends from the user's social media activity and collect relevant information. The data collection unit can collect and refer to the opinions of financial experts that the user follows on social media. This allows the data collection unit to collect information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant information.

[0042] The dialogue unit can apply different dialogue algorithms depending on the user's literacy level during a conversation. For example, the dialogue unit can engage in a conversation with a user with low financial literacy while providing basic financial knowledge. For a user with moderate financial literacy, the dialogue unit can engage in a conversation while providing specific advice. For a user with high financial literacy, the dialogue unit can engage in a conversation while providing expert advice. In this way, the dialogue unit can provide a conversation that is appropriate to the user's literacy level. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's literacy level into a generating AI, and the generating AI can apply a dialogue algorithm.

[0043] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's preferred dialogue style in the past. The dialogue unit can analyze specific question patterns from the user's past dialogue history and select the optimal dialogue method. The dialogue unit refers to the user's past dialogue history and selects a dialogue method that is easy for the user to understand. In this way, the dialogue unit can provide the optimal dialogue based on the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI, which can then select the optimal dialogue method.

[0044] The dialogue unit can determine the priority of a dialogue based on the user's submission timing during the dialogue. For example, if the user is in a hurry, the dialogue unit will increase the priority of the dialogue, taking the submission timing into consideration. If the user has ample time, the dialogue unit can adjust the priority of the dialogue based on the submission timing. The dialogue unit conducts the dialogue at the optimal time based on the user's submission timing. In this way, the dialogue unit can provide a dialogue priority based on the user's submission timing. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input user submission timing data into a generating AI, and the generating AI can determine the dialogue priority.

[0045] The dialogue unit can adjust the order of conversations based on the user's relevance during a conversation. For example, the dialogue unit can prioritize topics that the user has shown interest in and incorporate them into the conversation order. The dialogue unit can also discuss important topics first based on the user's relevance. The dialogue unit adjusts the order of conversations according to the user's level of interest to provide the most optimal conversation. In this way, the dialogue unit can provide a conversation order based on the user's relevance. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input user interest data into a generating AI, which can then adjust the order of conversations.

[0046] The analysis unit can analyze the user's past financial behavior during analysis and select the optimal analysis method. For example, the analysis unit can select the optimal analysis method based on the user's past financial behavior. The analysis unit can analyze specific patterns from the user's past financial behavior and select the optimal analysis method. The analysis unit refers to the user's past financial behavior and selects an analysis method that is easy for the user to understand. In this way, the analysis unit can provide the optimal analysis method based on the user's past financial behavior. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past financial behavior data into a generating AI, and the generating AI can select the optimal analysis method.

[0047] The analysis unit can customize the analysis methods based on the user's current living situation during the analysis. For example, the analysis unit can provide highly relevant analysis methods based on the user's current living situation. The analysis unit can eliminate unnecessary information and analyze only the necessary information according to the user's living situation. The analysis unit customizes the optimal analysis methods based on the user's living situation. As a result, the analysis unit can provide the optimal analysis methods according to the user's living situation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's living situation data into a generating AI, and the generating AI can customize the analysis methods.

[0048] The analysis unit can select the optimal analysis method during analysis, taking into account the user's geographical location information. For example, if the user lives in a specific region, the analysis unit can select an analysis method relevant to that region. If the user is traveling, the analysis unit can select an analysis method that takes into account information about the travel destination. The analysis unit selects a region-specific analysis method based on the user's geographical location information. In this way, the analysis unit can provide the optimal analysis method based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then select the optimal analysis method.

[0049] The analysis unit can analyze a user's social media activity during analysis and propose analysis methods. For example, the analysis unit can propose analysis methods related to topics the user has shown interest in on social media. The analysis unit can analyze specific trends from the user's social media activity and propose relevant analysis methods. The analysis unit can propose analysis methods by referring to the opinions of experts the user follows on social media. In this way, the analysis unit can provide analysis methods based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI, and the generating AI can propose analysis methods.

[0050] The service provider can select the optimal service delivery method by referring to the user's past advice history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the advice style the user has preferred in the past. The service provider can analyze specific patterns from the user's past advice history and select the optimal service delivery method. The service provider can refer to the user's past advice history and select a service delivery method that is easy for the user to understand. In this way, the service provider can provide the optimal service delivery method based on the user's past advice history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past advice history into a generating AI, and the generating AI can select the optimal service delivery method.

[0051] The service provider can customize the means of advice based on the user's current living situation at the time of delivery. For example, the service provider can provide highly relevant advice based on the user's current living situation. The service provider can exclude unnecessary information and provide only necessary information according to the user's living situation. The service provider can customize the optimal means of advice based on the user's living situation. In this way, the service provider can provide the optimal means of advice tailored to the user's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's living situation data into a generating AI, and the generating AI can customize the means of advice.

[0052] The service provider can select the most appropriate advice method at the time of delivery, taking into account the user's geographical location information. For example, if the user lives in a specific region, the service provider can select an advice method relevant to that region. If the user is traveling, the service provider can select an advice method that takes into account information about the travel destination. The service provider selects a region-specific advice method based on the user's geographical location information. This allows the service provider to provide the most appropriate advice method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, which can then select the most appropriate advice method.

[0053] The service provider can analyze the user's social media activity and propose advice methods at the time of delivery. For example, the service provider can propose advice methods related to topics the user has shown interest in on social media. The service provider can analyze specific trends from the user's social media activity and propose relevant advice methods. The service provider can propose advice methods by referring to the opinions of experts the user follows on social media. In this way, the service provider can provide advice methods based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI, and the generating AI can propose advice methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The household and asset management system can also collect user health data and incorporate it into financial advice. For example, the data collection unit can acquire health data from the user's fitness tracker or smartwatch, and the analysis unit can take this into consideration to suggest health-related spending and insurance reviews. The dialogue unit can suggest savings plans for maintaining health based on the user's health status. The provision unit can suggest investments and insurance products for maintaining health based on the user's health data. This allows users to receive optimal advice on both health and finances.

[0056] A household finance and asset management system can provide customized advice based on the user's hobbies and interests. For example, the data collection unit collects data on the user's hobbies and interests, and the analysis unit considers this data to suggest saving methods and investment plans related to those hobbies. The dialogue unit can suggest the optimal financial plan for enjoying the user's hobbies based on their interests. The provision unit can provide information on events and products related to the user's hobbies, thereby attracting the user's interest. As a result, the user can receive financial advice tailored to their hobbies and interests.

[0057] A household finance and asset management system can provide customized advice based on the user's family structure and life stage. For example, the data collection unit collects data on the user's family structure and life stage, and the analysis unit considers this data to propose a financial plan for the entire family. The dialogue unit can provide specific advice on matters such as education expenses and mortgages based on the user's life stage. The provision unit can propose insurance and investment products for the entire family based on the user's family structure. This allows the user to receive optimal advice tailored to the financial situation of their entire family.

[0058] A household and asset management system can analyze a user's past financial behavior, predict future behavior, and provide advice. For example, the data collection unit collects the user's past income, expenditure, and investment history, and the analysis unit uses this to predict future income and expenditure. The dialogue unit can propose future financial plans based on the user's past behavioral patterns. The provision unit can provide advice to avoid future risks based on the user's past behavior. This allows the user to receive optimal advice that reflects their past actions.

[0059] A household finance and asset management system can provide region-specific advice by considering the user's geographical location. For example, the data collection unit collects economic conditions and living expenses in the user's area, and the analysis unit uses this information to propose region-specific saving methods and investment plans. The dialogue unit can provide regional tax and subsidy information based on the user's geographical location. The provision unit can propose region-specific financial products and services based on the user's geographical location. As a result, users can receive optimal advice tailored to their specific region.

[0060] The household finance and asset management system can analyze users' social media activity and provide trend-based advice. For example, the data collection unit collects topics that users have shown interest in on social media, and the analysis unit uses this to propose trend-based investment plans and saving methods. The dialogue unit can provide the latest trend information based on the user's social media activity. The advice provision unit can provide advice by referencing the opinions of financial experts that the user follows. This allows users to receive optimal advice based on the latest trends.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects the user's household and asset data, as well as a wide range of external information. For example, it collects the user's financial asset information, tax information, local information, bank account information, credit card usage history, tax return information, and local subsidy information. The data collection unit obtains this information through APIs and databases. Step 2: The dialogue unit conducts conversations tailored to the user's literacy level based on the information collected by the data collection unit. For example, it provides basic financial knowledge to users with low financial literacy and more specialized advice to users with high financial literacy. The dialogue unit provides appropriate answers to the user's questions and adjusts the tone and content of the conversation. Step 3: The analysis unit considers the user's financial situation and goals based on the information provided by the dialogue unit and proposes specific savings methods and investment plans. For example, it analyzes information such as the user's income, expenses, assets, and liabilities to propose the optimal savings methods and investment plans. Step 4: The service provider updates its advice based on the latest information, even if the user's income or expenses change, using the information suggested by the analysis department. For example, if the user's income increases, it updates the savings target, and if expenses increase, it suggests reviewing expenses.

[0063] (Example of form 2) The household and asset management system according to an embodiment of the present invention evolves a wallet into a household and asset management app that can also link with other financial institutions, and further incorporates a generating AI to provide users with advanced and user-friendly advice. The household and asset management system takes the user's household and asset data and a wide range of external information (financial, tax, and local information, etc.) as input, and the generating AI performs Q&A tailored to the user's literacy level, thereby deriving appropriate answers from ambiguous needs by understanding the background, context, and surroundings, something that was previously only possible for human financial planners. As a result, users can consult anytime, anywhere, in an easy-to-understand manner, just as if they were consulting a person (financial planner). Furthermore, it provides advice that is not one-time, but always takes into account the user's situation and the latest external information. For example, the household and asset management system takes the user's household and asset data and a wide range of external information as input. At this time, it collects the user's financial asset information, tax information, local information, etc., and inputs them into the generating AI. For example, it collects the user's bank account information, credit card usage history, tax return information, local subsidy information, etc. This allows the generating AI to understand the user's overall financial situation. Next, the generating AI conducts a series of Q&A sessions tailored to the user's literacy level. The generating AI engages in dialogue that takes the user's literacy into account in order to provide appropriate answers to the user's questions. For example, it provides basic financial knowledge and specific advice to users with low financial literacy, while providing more specialized advice to users with high financial literacy. This allows users to receive appropriate advice tailored to their own literacy level. Furthermore, the generating AI can derive appropriate answers from ambiguous needs by understanding the background, context, and surrounding factors, something that was previously only possible for human financial planners. For example, if a user asks a vague question such as "How should I save for the future?", the generating AI will consider the user's financial situation and goals and propose specific savings methods and investment plans. This allows the user to create a concrete action plan. This system allows users to consult anytime, anywhere, in an easy-to-understand way, just as if they were consulting a human financial planner.For example, even if a user wants to discuss their household finances in the middle of the night, the generating AI is available 24 hours a day to provide appropriate advice. Furthermore, instead of providing one-time advice, it constantly provides advice based on the user's situation and the latest external information, allowing users to continuously receive appropriate advice. For example, even if the user's income or expenses fluctuate, the generating AI updates the advice based on the latest information. This allows users to always manage their finances optimally. In this way, the household finance and asset management system can efficiently manage the user's household finances and assets and provide appropriate advice.

[0064] The household and asset management system according to this embodiment comprises a collection unit, a dialogue unit, an analysis unit, and a provision unit. The collection unit collects the user's household and asset data and a wide range of external information. For example, the collection unit collects the user's financial asset information, tax information, and regional information. For example, the collection unit can collect bank account information and credit card usage history. The collection unit can also collect tax return information and regional subsidy information. For example, the collection unit obtains the user's bank account information via an API and credit card usage history from a database. The collection unit obtains tax return information from the tax office's database and collects regional subsidy information from local government websites. The dialogue unit conducts a dialogue tailored to the user's literacy level based on the information collected by the collection unit. For example, the dialogue unit adjusts the content of the dialogue according to the user's financial literacy. For example, the dialogue unit conducts a dialogue while providing basic financial knowledge to users with low financial literacy. The dialogue unit can provide more specialized advice to users with high financial literacy. The dialogue unit, for example, conducts conversations that take the user's literacy into consideration in order to provide appropriate answers to the user's questions. The dialogue unit can adjust the tone and content of the conversation according to the user's literacy. The analysis unit considers the user's financial situation and goals based on the information provided by the dialogue unit and proposes specific savings methods and investment plans. The analysis unit performs analysis considering information such as the user's income, expenses, assets, and liabilities. For example, the analysis unit can analyze the balance between the user's income and expenses and propose the optimal savings method. The analysis unit can analyze the user's asset and liability situation and propose the optimal investment plan. For example, the analysis unit can analyze the user's income and expense data and set a savings target amount. The analysis unit can analyze the user's asset and liability data and propose a low-risk investment plan. The service provider updates the advice based on the latest information, even if the user's income or expenses change, based on the information proposed by the analysis unit. For example, the service provider updates the advice based on the latest information if the user's income or expenses change. The service provider, for example, updates the savings target amount if the user's income increases.The service provider can suggest a review of the user's spending if their spending increases. For example, if the user's income increases, the service provider can reset the savings target. If the user's spending increases, the service provider can suggest ways to reduce spending. In this way, the household and asset management system according to the embodiment can efficiently manage the user's household and asset finances and provide appropriate advice.

[0065] The data collection unit collects users' household and asset data as well as a wide range of external information. Specifically, it collects users' financial asset information, tax information, and local information. For example, the data collection unit obtains users' bank account information via API and credit card usage history from a database. This allows for real-time tracking of detailed data on users' income and expenses. The data collection unit also obtains tax return information from tax office databases and collects local subsidy information from local government websites. This allows users to understand available subsidies and tax incentives, supporting optimal asset management. Furthermore, the data collection unit can also collect users' investment account information and insurance contract information. This enables a comprehensive understanding of the user's overall asset situation and allows for the provision of more accurate advice. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and dialogue units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The dialogue unit conducts conversations tailored to the user's literacy level based on information collected by the data collection unit. Specifically, it adjusts the content of the conversation according to the user's financial literacy. For example, for users with low financial literacy, the conversation provides basic financial knowledge, including the basics of saving, how to use credit cards, and how to repay loans. On the other hand, for users with high financial literacy, more specialized advice can be provided, such as investment risk management, how to utilize tax incentives, and how to choose complex financial products. The dialogue unit conducts conversations that take the user's literacy into consideration in order to provide appropriate answers to the user's questions. This allows users to obtain information according to their level of understanding and manage their household finances and assets more effectively. The dialogue unit can adjust the tone and content of the conversation according to the user's literacy level. For example, it explains things in simple terms and avoids jargon for beginners, while providing detailed technical explanations for advanced users. Furthermore, the dialogue unit can collect user feedback and continuously improve the accuracy and effectiveness of the conversation content. This allows the dialogue unit to provide users with optimal information and strengthen support for household finances and asset management.

[0067] The analysis unit considers the user's financial situation and goals based on the information provided by the dialogue unit, and proposes specific savings methods and investment plans. Specifically, it performs analysis considering information such as the user's income, expenses, assets, and liabilities. For example, it analyzes the balance between the user's income and expenses and proposes the optimal savings method. This includes monthly savings targets and specific means for saving (e.g., setting up an automatic savings plan). It can also analyze the user's asset and liability situation and propose the optimal investment plan. This includes selecting low-risk investments and constructing a portfolio for risk diversification. The analysis unit analyzes the user's income and expense data and sets savings targets. For example, if the user's income is above a certain level, it sets a higher savings target; if income is unstable, it proposes a flexible savings plan. The analysis unit also analyzes the user's asset and liability data and proposes low-risk investment plans. For example, if the user wants to avoid risk, it recommends safe bonds or time deposits; if the user seeks high returns even with risk, it proposes stocks or mutual funds. Furthermore, the analysis unit can propose long-term asset management plans by considering past data and market trends. This allows the analysis unit to provide users with optimal savings methods and investment plans tailored to their financial situation, supporting their asset management.

[0068] The service provider updates advice based on the latest information, even if the user's income or expenses change, using the information proposed by the analysis department. Specifically, when the user's income or expenses change, the service provider updates the advice based on the latest information. For example, if the user's income increases, the service provider updates the savings target amount. This includes specific advice on how to allocate the increased income to savings or investments. Also, if the user's expenses increase, the service provider can suggest a review of expenses. This includes methods for reducing unnecessary expenses and reviewing spending priorities. The service provider resets the savings target amount when the user's income increases. For example, if there is a bonus or a raise, the service provider will specifically suggest how to use the increase. Also, if the user's expenses increase, the service provider will suggest ways to reduce expenses. For example, they will provide suggestions for reviewing fixed costs and specific methods for saving money (e.g., how to save on electricity bills or reduce food expenses). Furthermore, the service provider can update advice in accordance with the user's life events (e.g., marriage, childbirth, job change, etc.). This allows the service provider to offer up-to-date advice tailored to the user's situation, enabling efficient household and asset management. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to provide optimal advice to users and strengthen support for household and asset management.

[0069] The data collection unit can collect user financial asset information, tax information, and regional information. For example, the data collection unit collects financial asset information such as the user's bank account balance, stock holdings, and bond holdings. For example, the data collection unit can obtain bank account balances via APIs and stock holdings from securities company databases. The data collection unit can obtain bond holdings from financial institution databases. The data collection unit can also collect tax information such as the user's income tax information, resident tax information, and deduction information. For example, the data collection unit can obtain income tax information from tax office databases and resident tax information from local government databases. The data collection unit can obtain deduction information from tax office databases. Furthermore, the data collection unit can also collect regional information such as the user's regional economic situation, regional cost of living, and regional tax rates. For example, the data collection unit can obtain regional economic situation information from local government websites and regional cost of living information from statistical data. The data collection unit can obtain regional tax rates from local government databases. This allows the data collection unit to collect diverse user information and achieve comprehensive financial management. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's financial asset information into an AI model and optimize the timing of when the AI ​​model collects the information.

[0070] The dialogue unit can conduct conversations tailored to the user's literacy level and provide appropriate answers. For example, the dialogue unit adjusts the content of the conversation according to the user's financial literacy. For example, the dialogue unit can conduct conversations with users with low financial literacy while providing basic financial knowledge. For users with moderate financial literacy, the dialogue unit can conduct conversations while providing specific advice. For users with high financial literacy, the dialogue unit can conduct conversations while providing expert advice. For example, the dialogue unit conducts conversations that take the user's literacy into consideration in order to provide appropriate answers to the user's questions. The dialogue unit can adjust the tone and content of the conversation according to the user's literacy level. This allows the dialogue unit to provide appropriate advice according to the user's literacy level. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's literacy level into an AI model, and the AI ​​model can optimize the content of the conversation.

[0071] The analysis unit can consider the user's financial situation and goals and propose specific savings methods and investment plans. For example, the analysis unit performs analysis considering information such as the user's income, expenses, assets, and liabilities. For example, the analysis unit can analyze the balance between the user's income and expenses and propose the optimal savings method. The analysis unit can analyze the user's asset and liability situation and propose the optimal investment plan. For example, the analysis unit can analyze the user's income and expense data and set a savings target amount. The analysis unit can analyze the user's asset and liability data and propose a low-risk investment plan. This allows the analysis unit to provide specific advice based on the user's financial situation. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's financial data into an AI model, which can then propose the optimal savings methods and investment plans.

[0072] The service provider can update its advice based on the latest information, even if the user's income or expenses fluctuate. For example, if the user's income increases, the service provider can update the savings target. If the user's expenses increase, the service provider can suggest a review of expenses. For example, if the user's income increases, the service provider can reset the savings target. If the user's expenses increase, the service provider can suggest ways to reduce expenses. This allows the service provider to provide up-to-date advice tailored to the user's situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's income and expense data into an AI model, and the AI ​​model can provide up-to-date advice.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect data when the user is relaxed. If the user is relaxed, the data collection unit can immediately collect financial asset information and begin analysis quickly. If the user is in a hurry, the data collection unit can advance the collection timing to quickly collect the necessary information. This allows the data collection unit to collect information at the appropriate time according to the user'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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can optimize the data collection timing.

[0074] The data collection unit can analyze the user's past financial transaction history and select the optimal data collection method. For example, the data collection unit prioritizes collecting information from financial institutions that the user has frequently used in the past. The data collection unit can achieve efficient information collection by collecting data from the user's transaction history at specific time periods. The data collection unit analyzes the user's transaction patterns and prioritizes collecting the most relevant information. This enables the data collection unit to achieve efficient information collection based on the user's transaction history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's financial transaction history into a generating AI, which can then select the optimal data collection method.

[0075] The data collection unit can filter data based on the user's current living situation and areas of interest during collection. For example, the data collection unit can prioritize collecting highly relevant financial information based on the user's current living situation. The data collection unit can filter and collect specific investment or tax-saving information based on the user's areas of interest. The data collection unit eliminates unnecessary information and collects only the necessary information according to the user's living situation and areas of interest. This allows the data collection unit to collect information that is relevant to the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's living situation and areas of interest into a generating AI, which can then perform the filtering.

[0076] The data collection unit can estimate the user's emotions and prioritize the information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important information and prioritize collecting more important information. If the user is relaxed, the data collection unit can collect all information evenly and perform detailed analysis. If the user is in a hurry, the data collection unit will quickly collect the most important information and begin analysis. This allows the data collection unit to prioritize information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of the information.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user lives in a specific region, the data collection unit can prioritize the collection of financial information related to that region. If the user is traveling, the data collection unit can prioritize the collection of financial information related to the travel destination. Based on the user's geographical location, the data collection unit can collect region-specific tax information and subsidy information. This allows the data collection unit to collect information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI, which can then prioritize the collection of highly relevant information.

[0078] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect information related to financial products that the user has shown interest in on social media. The data collection unit can analyze specific investment trends from the user's social media activity and collect relevant information. The data collection unit can collect and refer to the opinions of financial experts that the user follows on social media. This allows the data collection unit to collect information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant information.

[0079] The dialogue unit can estimate the user's emotions and adjust the way the dialogue is expressed based on the estimated emotions. For example, if the user is nervous, the dialogue unit can use a calm tone to provide reassurance. If the user is relaxed, the dialogue unit can use a friendly tone to provide friendliness. If the user is in a hurry, the dialogue unit can use a concise and rapid dialogue to efficiently provide information. In this way, the dialogue unit can provide a way of expressing the dialogue that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI, which can then adjust the way the dialogue is expressed.

[0080] The dialogue unit can apply different dialogue algorithms depending on the user's literacy level during a conversation. For example, the dialogue unit can engage in a conversation with a user with low financial literacy while providing basic financial knowledge. For a user with moderate financial literacy, the dialogue unit can engage in a conversation while providing specific advice. For a user with high financial literacy, the dialogue unit can engage in a conversation while providing expert advice. In this way, the dialogue unit can provide a conversation that is appropriate to the user's literacy level. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's literacy level into a generating AI, and the generating AI can apply a dialogue algorithm.

[0081] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's preferred dialogue style in the past. The dialogue unit can analyze specific question patterns from the user's past dialogue history and select the optimal dialogue method. The dialogue unit refers to the user's past dialogue history and selects a dialogue method that is easy for the user to understand. In this way, the dialogue unit can provide the optimal dialogue based on the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI, which can then select the optimal dialogue method.

[0082] The dialogue unit can estimate the user's emotions and adjust the length of the dialogue based on the estimated emotions. For example, if the user is nervous, the dialogue unit can provide a short, to-the-point dialogue. If the user is relaxed, the dialogue unit can provide a longer dialogue with detailed explanations. If the user is in a hurry, the dialogue unit can provide a quick and concise dialogue. In this way, the dialogue unit can provide a dialogue length that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI, which can then adjust the length of the dialogue.

[0083] The dialogue unit can determine the priority of a dialogue based on the user's submission timing during the dialogue. For example, if the user is in a hurry, the dialogue unit will increase the priority of the dialogue, taking the submission timing into consideration. If the user has ample time, the dialogue unit can adjust the priority of the dialogue based on the submission timing. The dialogue unit conducts the dialogue at the optimal time based on the user's submission timing. In this way, the dialogue unit can provide a dialogue priority based on the user's submission timing. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input user submission timing data into a generating AI, and the generating AI can determine the dialogue priority.

[0084] The dialogue unit can adjust the order of conversations based on the user's relevance during a conversation. For example, the dialogue unit can prioritize topics that the user has shown interest in and incorporate them into the conversation order. The dialogue unit can also discuss important topics first based on the user's relevance. The dialogue unit adjusts the order of conversations according to the user's level of interest to provide the most optimal conversation. In this way, the dialogue unit can provide a conversation order based on the user's relevance. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input user interest data into a generating AI, which can then adjust the order of conversations.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis method. If the user is relaxed, the analysis unit can provide an analysis method that includes detailed information. If the user is in a hurry, the analysis unit can provide a concise analysis method. In this way, the analysis unit can provide an analysis method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the analysis method.

[0086] The analysis unit can analyze the user's past financial behavior during analysis and select the optimal analysis method. For example, the analysis unit can select the optimal analysis method based on the user's past financial behavior. The analysis unit can analyze specific patterns from the user's past financial behavior and select the optimal analysis method. The analysis unit refers to the user's past financial behavior and selects an analysis method that is easy for the user to understand. In this way, the analysis unit can provide the optimal analysis method based on the user's past financial behavior. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past financial behavior data into a generating AI, and the generating AI can select the optimal analysis method.

[0087] The analysis unit can customize the analysis methods based on the user's current living situation during the analysis. For example, the analysis unit can provide highly relevant analysis methods based on the user's current living situation. The analysis unit can eliminate unnecessary information and analyze only the necessary information according to the user's living situation. The analysis unit customizes the optimal analysis methods based on the user's living situation. As a result, the analysis unit can provide the optimal analysis methods according to the user's living situation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's living situation data into a generating AI, and the generating AI can customize the analysis methods.

[0088] The analysis unit can estimate the user's emotions and determine the priority of analyses based on the estimated emotions. For example, if the user is stressed, the analysis unit will postpone less important analyses and prioritize more important ones. If the user is relaxed, the analysis unit can perform all analyses equally and provide detailed information. If the user is in a hurry, the analysis unit will quickly perform the most important analyses and provide results. In this way, the analysis unit can provide analysis priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can determine the priority of analyses.

[0089] The analysis unit can select the optimal analysis method during analysis, taking into account the user's geographical location information. For example, if the user lives in a specific region, the analysis unit can select an analysis method relevant to that region. If the user is traveling, the analysis unit can select an analysis method that takes into account information about the travel destination. The analysis unit selects a region-specific analysis method based on the user's geographical location information. In this way, the analysis unit can provide the optimal analysis method based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then select the optimal analysis method.

[0090] The analysis unit can analyze a user's social media activity during analysis and propose analysis methods. For example, the analysis unit can propose analysis methods related to topics the user has shown interest in on social media. The analysis unit can analyze specific trends from the user's social media activity and propose relevant analysis methods. The analysis unit can propose analysis methods by referring to the opinions of experts the user follows on social media. In this way, the analysis unit can provide analysis methods based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI, and the generating AI can propose analysis methods.

[0091] The service provider can estimate the user's emotions and adjust the way advice is delivered based on the estimated emotions. For example, if the user is tense, the service provider can provide advice in a calm tone. If the user is relaxed, the service provider can provide advice in a friendly tone. If the user is in a hurry, the service provider can provide concise and quick advice. In this way, the service provider can provide advice in a way that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then adjust the way advice is delivered.

[0092] The service provider can select the optimal service delivery method by referring to the user's past advice history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the advice style the user has preferred in the past. The service provider can analyze specific patterns from the user's past advice history and select the optimal service delivery method. The service provider can refer to the user's past advice history and select a service delivery method that is easy for the user to understand. In this way, the service provider can provide the optimal service delivery method based on the user's past advice history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past advice history into a generating AI, and the generating AI can select the optimal service delivery method.

[0093] The service provider can customize the means of advice based on the user's current living situation at the time of delivery. For example, the service provider can provide highly relevant advice based on the user's current living situation. The service provider can exclude unnecessary information and provide only necessary information according to the user's living situation. The service provider can customize the optimal means of advice based on the user's living situation. In this way, the service provider can provide the optimal means of advice tailored to the user's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's living situation data into a generating AI, and the generating AI can customize the means of advice.

[0094] The service provider can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the service provider will postpone less important advice and prioritize more important advice. If the user is relaxed, the service provider can provide all advice equally and offer detailed information. If the user is in a hurry, the service provider will quickly provide the most important advice. In this way, the service provider can provide advice prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then determine the priority of advice.

[0095] The service provider can select the most appropriate advice method at the time of delivery, taking into account the user's geographical location information. For example, if the user lives in a specific region, the service provider can select an advice method relevant to that region. If the user is traveling, the service provider can select an advice method that takes into account information about the travel destination. The service provider selects a region-specific advice method based on the user's geographical location information. This allows the service provider to provide the most appropriate advice method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, which can then select the most appropriate advice method.

[0096] The service provider can analyze the user's social media activity and propose advice methods at the time of delivery. For example, the service provider can propose advice methods related to topics the user has shown interest in on social media. The service provider can analyze specific trends from the user's social media activity and propose relevant advice methods. The service provider can propose advice methods by referring to the opinions of experts the user follows on social media. In this way, the service provider can provide advice methods based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI, and the generating AI can propose advice methods.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The household and asset management system can also collect user health data and incorporate it into financial advice. For example, the data collection unit can acquire health data from the user's fitness tracker or smartwatch, and the analysis unit can take this into consideration to suggest health-related spending and insurance reviews. The dialogue unit can suggest savings plans for maintaining health based on the user's health status. The provision unit can suggest investments and insurance products for maintaining health based on the user's health data. This allows users to receive optimal advice on both health and finances.

[0099] A household finance and asset management system can provide customized advice based on the user's hobbies and interests. For example, the data collection unit collects data on the user's hobbies and interests, and the analysis unit considers this data to suggest saving methods and investment plans related to those hobbies. The dialogue unit can suggest the optimal financial plan for enjoying the user's hobbies based on their interests. The provision unit can provide information on events and products related to the user's hobbies, thereby attracting the user's interest. As a result, the user can receive financial advice tailored to their hobbies and interests.

[0100] The household finance and asset management system can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, the data collection unit can temporarily delay providing advice if the user is feeling stressed. The dialogue unit can provide detailed advice if the user is relaxed. The advice delivery unit can provide concise and quick advice if the user is in a hurry. This allows users to receive advice at the optimal time according to their emotional state.

[0101] A household finance and asset management system can provide customized advice based on the user's family structure and life stage. For example, the data collection unit collects data on the user's family structure and life stage, and the analysis unit considers this data to propose a financial plan for the entire family. The dialogue unit can provide specific advice on matters such as education expenses and mortgages based on the user's life stage. The provision unit can propose insurance and investment products for the entire family based on the user's family structure. This allows the user to receive optimal advice tailored to the financial situation of their entire family.

[0102] A household finance and asset management system can estimate the user's emotions and adjust the advice based on those emotions. For example, if the data collection unit is feeling anxious, it can suggest a low-risk investment plan. If the user is confident, the dialogue unit can suggest a high-risk but high-return investment plan. If the user is emotionally stable, the delivery unit can suggest a long-term financial plan. This allows users to receive optimal advice tailored to their emotional state.

[0103] A household and asset management system can analyze a user's past financial behavior, predict future behavior, and provide advice. For example, the data collection unit collects the user's past income, expenditure, and investment history, and the analysis unit uses this to predict future income and expenditure. The dialogue unit can propose future financial plans based on the user's past behavioral patterns. The provision unit can provide advice to avoid future risks based on the user's past behavior. This allows the user to receive optimal advice that reflects their past actions.

[0104] The household finance and asset management system can estimate the user's emotions and adjust the format of advice based on those emotions. For example, if the user is feeling stressed, the data collection unit can provide advice using visually easy-to-understand graphs and charts. If the user is relaxed, the dialogue unit can provide detailed text-based advice. If the user is in a hurry, the delivery unit can provide advice in a concise list format. This allows users to receive advice in the format best suited to their emotional state.

[0105] A household finance and asset management system can provide region-specific advice by considering the user's geographical location. For example, the data collection unit collects economic conditions and living expenses in the user's area, and the analysis unit uses this information to propose region-specific saving methods and investment plans. The dialogue unit can provide regional tax and subsidy information based on the user's geographical location. The provision unit can propose region-specific financial products and services based on the user's geographical location. As a result, users can receive optimal advice tailored to their specific region.

[0106] The household finance and asset management system can estimate the user's emotions and adjust the frequency of advice based on those emotions. For example, the data collection unit can reduce the frequency of advice to lessen the user's burden if the user is stressed. The dialogue unit can provide advice more frequently to maintain the user's interest if the user is relaxed. The delivery unit can provide only the minimum necessary advice if the user is in a hurry. This allows the user to receive advice at the optimal frequency according to their emotional state.

[0107] The household finance and asset management system can analyze users' social media activity and provide trend-based advice. For example, the data collection unit collects topics that users have shown interest in on social media, and the analysis unit uses this to propose trend-based investment plans and saving methods. The dialogue unit can provide the latest trend information based on the user's social media activity. The advice provision unit can provide advice by referencing the opinions of financial experts that the user follows. This allows users to receive optimal advice based on the latest trends.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects the user's household and asset data, as well as a wide range of external information. For example, it collects the user's financial asset information, tax information, local information, bank account information, credit card usage history, tax return information, and local subsidy information. The data collection unit obtains this information through APIs and databases. Step 2: The dialogue unit conducts conversations tailored to the user's literacy level based on the information collected by the data collection unit. For example, it provides basic financial knowledge to users with low financial literacy and more specialized advice to users with high financial literacy. The dialogue unit provides appropriate answers to the user's questions and adjusts the tone and content of the conversation. Step 3: The analysis unit considers the user's financial situation and goals based on the information provided by the dialogue unit and proposes specific savings methods and investment plans. For example, it analyzes information such as the user's income, expenses, assets, and liabilities to propose the optimal savings methods and investment plans. Step 4: The service provider updates its advice based on the latest information, even if the user's income or expenses change, using the information suggested by the analysis department. For example, if the user's income increases, it updates the savings target, and if expenses increase, it suggests reviewing expenses.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the collection unit, dialogue 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 collection unit collects the user's financial asset information and tax information using the camera 42 and communication I / F 44 of the smart device 14, and collects a wide range of external information using the specific processing unit 290 of the data processing unit 12. The dialogue unit, for example, uses the control unit 46A of the smart device 14 to engage in dialogue tailored to the user's literacy level and provide appropriate answers to the user's questions. The analysis unit, for example, uses the specific processing unit 290 of the data processing unit 12 to analyze the user's financial situation and propose specific savings methods and investment plans. The provision unit, for example, uses the control unit 46A of the smart device 14 to update advice based on the latest information when the user's income or expenses change. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the collection unit, dialogue unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's financial asset information and tax information using the camera 42 and communication I / F 44 of the smart glasses 214, and collects a wide range of external information using the identification processing unit 290 of the data processing unit 12. The dialogue unit, for example, uses the control unit 46A of the smart glasses 214 to engage in dialogue tailored to the user's literacy level and provide appropriate answers to the user's questions. The analysis unit, for example, uses the identification processing unit 290 of the data processing unit 12 to analyze the user's financial situation and propose specific savings methods and investment plans. The provision unit, for example, uses the control unit 46A of the smart glasses 214 to update advice based on the latest information when the user's income or expenses change. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, dialogue 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 collection unit collects the user's financial asset information and tax information using the camera 42 and communication I / F 44 of the headset terminal 314, and collects a wide range of external information using the specific processing unit 290 of the data processing unit 12. The dialogue unit, for example, uses the control unit 46A of the headset terminal 314 to conduct a dialogue tailored to the user's literacy level and provide appropriate answers to the user's questions. The analysis unit, for example, uses the specific processing unit 290 of the data processing unit 12 to analyze the user's financial situation and propose specific savings methods and investment plans. The provision unit, for example, uses the control unit 46A of the headset terminal 314 to update advice based on the latest information when the user's income or expenses change. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, dialogue unit, analysis unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's financial asset information and tax information using the camera 42 and communication I / F 44 of the robot 414, and collects a wide range of external information using the specific processing unit 290 of the data processing unit 12. The dialogue unit, for example, uses the control unit 46A of the robot 414 to engage in dialogue tailored to the user's literacy level and provide appropriate answers to the user's questions. The analysis unit, for example, uses the specific processing unit 290 of the data processing unit 12 to analyze the user's financial situation and propose specific savings methods and investment plans. The provision unit, for example, uses the control unit 46A of the robot 414 to update advice based on the latest information when the user's income or expenses change. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A collection unit that collects user household and asset data and a wide range of external information, A dialogue unit that conducts conversations tailored to the user's literacy level based on the information collected by the aforementioned collection unit, Based on the information provided by the aforementioned dialogue unit, the analysis unit considers the user's financial situation and goals and proposes specific savings methods and investment plans. The system includes a provisioning unit that updates advice based on the latest information, even if the user's income or expenses change, based on the information proposed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects user financial asset information, tax information, regional information, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned dialogue unit, It engages in dialogue tailored to the user's literacy level and provides appropriate answers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We take into account the user's financial situation and goals and propose specific savings methods and investment plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Even if a user's income or expenses change, the advice will be updated based on the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting financial asset information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past financial transaction history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned dialogue unit, During conversations, different dialogue algorithms are applied depending on the user's literacy level. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the length of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dialogue unit, During the interaction, the system prioritizes the conversation based on when the user submits their input. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned dialogue unit, During conversations, the order of dialogues is adjusted based on the relevance of the user. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the system analyzes the user's past financial behavior to select the most suitable analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the analysis methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the optimal analysis method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During the analysis, we analyze users' social media activity and propose analytical methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how advice is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past advice history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the advice will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal advice method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest ways to offer advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user household and asset data and a wide range of external information, A dialogue unit that conducts conversations tailored to the user's literacy level based on the information collected by the aforementioned collection unit, Based on the information provided by the aforementioned dialogue unit, the analysis unit considers the user's financial situation and goals and proposes specific savings methods and investment plans. The system includes a provisioning unit that updates advice based on the latest information, even if the user's income or expenses change, based on the information proposed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects user financial asset information, tax information, regional information, etc. The system according to feature 1.

3. The aforementioned dialogue unit, It engages in dialogue tailored to the user's literacy level and provides appropriate answers. The system according to feature 1.

4. The aforementioned analysis unit, We take into account the user's financial situation and goals and propose specific savings methods and investment plans. The system according to feature 1.

5. The aforementioned supply unit is, Even if a user's income or expenses change, the advice will be updated based on the latest information. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting financial asset information based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past financial transaction history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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