system

Abstract

The system according to this embodiment aims to centrally manage information from multiple credit cards and bank accounts, and to track and analyze income and expenses in real time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a management unit, and an adjustment unit. The collection unit collects information on the user's multiple credit cards and bank accounts. The analysis unit analyzes the information collected by the collection unit to grasp the overall picture of income and expenses. The proposal unit proposes an optimal savings plan or investment plan based on the analysis results obtained by the analysis unit. The management unit manages all financial accounts, including multi-currency accounts and overseas accounts. The adjustment unit autonomously adjusts the balance of income and expenses.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to centrally manage information of multiple credit cards and bank accounts and track and analyze income and expenses in real time.

[0005] The system according to the embodiment aims to centrally manage information of multiple credit cards and bank accounts and track and analyze income and expenses in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a management unit, and an adjustment unit. The collection unit collects information from the user's multiple credit cards and bank accounts. The analysis unit analyzes the information collected by the collection unit to grasp the overall picture of income and expenses. The proposal unit proposes an optimal savings plan or investment plan based on the analysis results obtained by the analysis unit. The management unit manages all financial accounts, including multi-currency accounts and overseas accounts. The adjustment unit autonomously adjusts the balance of income and expenses. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage information from multiple credit cards and bank accounts, and track and analyze income and expenses in real time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The financial management system according to an embodiment of the present invention is a platform that uses AI to centrally manage information from multiple credit cards and bank accounts and automatically tracks and analyzes income and expenses in real time. This financial management system centrally manages information from a user's multiple credit cards and bank accounts and automatically tracks and analyzes income and expenses in real time. For example, the financial management system collects transaction data from each financial account to grasp the overall picture of income and expenses. For example, it can analyze credit card usage history and bank account deposit and withdrawal history in real time to understand the user's income and expense situation. Next, the financial management system uses AI to automatically propose an optimal savings plan and investment / management plan based on the user's spending patterns and income. For example, it analyzes the user's past spending history and income fluctuations to predict future income and expenses. Based on this, it can propose an optimal savings plan and investment strategy to the user. For example, it proposes a low-risk savings plan to users who are expected to have a stable income, and a diversified investment plan to users with unstable income. Furthermore, the financial management system's AI agent tracks the user's income and expenses in real time and manages all financial accounts, including multi-currency accounts and overseas accounts. For example, if a user trades in multiple currencies, the AI ​​analyzes exchange rate fluctuations in real time and proposes an optimal asset allocation. For users with overseas accounts, it optimizes the costs of international remittances and foreign exchange transactions, supporting asset management from a global perspective. This system allows users to centrally manage multiple financial accounts and understand the overall picture of their income and expenses. Furthermore, the AI ​​automatically proposes optimal savings plans and investment strategies, enabling users to manage their assets effectively. In addition, specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback features can be added to provide users with more advanced financial advice. For example, specialized investment simulations allow users to simulate specific investment scenarios and develop investment strategies based on the results. AI chatbot support allows users to ask questions to the AI ​​in real time and receive immediate answers.Community advice allows users to share information and receive advice from other users. Sustainable investment options suggest investments that are environmentally and socially responsible. Personalized feedback provides individual feedback based on the user's income and expenses and investment performance. As a result, the financial management system can centrally manage a user's multiple financial accounts, understand the overall picture of income and expenses, suggest optimal savings and investment plans, and autonomously adjust the balance of income and expenses.

[0029] The financial management system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a management unit, and an adjustment unit. The collection unit collects information from the user's multiple credit cards and bank accounts. For example, the collection unit collects credit card usage history and bank account deposit and withdrawal history. For example, the collection unit can collect information such as transaction date and time, transaction amount, and store used as credit card usage history. The collection unit can also collect information such as deposit date and time, withdrawal date and time, and transaction details as bank account deposit and withdrawal history. The analysis unit analyzes the information collected by the collection unit to grasp the overall picture of income and expenses. For example, the analysis unit analyzes the collected transaction data to grasp the balance between income and expenses. For example, the analysis unit can create a monthly income and expense report and provide it to the user. The analysis unit can also perform category-based analysis of income and expenses in order to grasp the overall picture of income and expenses. The proposal unit proposes an optimal savings plan or investment plan based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes an optimal savings plan based on the user's spending patterns and income. The proposal unit can propose an optimal investment plan, for example, by considering the user's risk tolerance, target amount, and investment period. The management unit manages all financial accounts, including multi-currency accounts and overseas accounts. The management unit manages transactions in multiple currencies and analyzes exchange rate fluctuations, for example. The management unit can propose an optimal asset allocation, for example, by considering the types of currencies and methods for obtaining exchange rates. The adjustment unit autonomously adjusts the balance of income and expenses. The adjustment unit performs tasks such as automatic adjustment of income and expenses and budget setting. The adjustment unit can optimize the balance of income and expenses and create monthly income and expense reports, for example. As a result, the financial management system according to this embodiment can centrally manage a user's multiple financial accounts, grasp the overall picture of income and expenses, propose optimal savings and investment plans, and autonomously adjust the balance of income and expenses.

[0030] The data collection unit collects information from a user's multiple credit cards and bank accounts. Specifically, it collects credit card usage history and bank account deposit and withdrawal history. For credit card usage history, it can collect detailed information such as transaction date and time, transaction amount, and store where the transaction was made. This allows for an accurate understanding of how much a user spent at which stores. For bank account deposit and withdrawal history, it collects information such as deposit date and time, withdrawal date and time, and transaction details. This allows for a detailed understanding of the user's income sources and spending destinations. The data collection unit collects this information in real time and transmits it to a central database. Furthermore, with the user's permission, the data collection unit can automatically retrieve data using financial institution APIs. This eliminates the need for users to manually enter data. The data collection unit centrally manages the collected data and can link with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the data collection unit to understand the overall picture of income and expenses. Specifically, it analyzes the collected transaction data to understand the balance between income and expenses. For example, the analysis unit can create and provide monthly income and expense reports to users. These reports include total income and expenses, income and expenses by category, and monthly trends in income and expenses. This allows users to understand their own financial situation at a glance. The analysis unit can also perform category-based analysis of income and expenses to understand the overall picture of income and expenses. For example, it can classify expenses by category, such as food expenses, transportation expenses, and entertainment expenses, and calculate the percentage of expenses in each category. This allows users to understand which categories are concentrating the most spending and take measures to reduce unnecessary expenses. Furthermore, the analysis unit can also analyze long-term income and expense trends by utilizing historical data and statistical information. For example, based on income and expense data from the past few years, it can predict trends in increases and decreases in income and expenses and provide reference information for future financial planning. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal transactions, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term revenue and expenditure management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The Proposal Department proposes optimal savings and investment plans based on the analysis results obtained by the Analysis Department. Specifically, it proposes the optimal savings plan based on the user's spending patterns and income. For example, it analyzes the user's monthly income and expenses and sets a savings target within a reasonable range. The Proposal Department can also propose the optimal investment plan considering the user's risk tolerance, target amount, and investment period. For example, for users who want stable investments with reduced risk, it proposes low-risk investment products such as bonds and time deposits, and for users who aim for high returns, it proposes high-risk, high-return investment products such as stocks and mutual funds. The Proposal Department presents these proposals to users in an easy-to-understand manner and provides concrete action plans. For example, it shows specific monthly savings and investment amounts and makes proposals in a way that is easy for users to implement. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it revises savings and investment plans based on feedback from users who have implemented the proposals, and makes more effective proposals. This allows the proposal department to provide users with optimal savings and investment plans tailored to their needs, thereby supporting their asset building.

[0033] The Management Department manages all financial accounts, including multi-currency accounts and overseas accounts. Specifically, it manages transactions in multiple currencies and analyzes exchange rate fluctuations. For example, if a user trades in multiple currencies, the Management Department centrally manages the transaction history for each currency and understands the impact of exchange rate fluctuations in real time. The Management Department can also propose optimal asset allocations, taking into account the types of currencies supported and how exchange rates are obtained. For example, to minimize exchange rate risk, it proposes a balanced asset allocation that is not biased towards any particular currency. Furthermore, the Management Department centrally manages all financial accounts, including overseas accounts, and can accurately understand how much assets a user holds in each account. This allows users to grasp their asset status at a glance and manage their assets efficiently. In addition, the Management Department securely manages users' financial information in accordance with the security policies of each financial institution. For example, it implements data encryption and multi-factor authentication to prevent unauthorized access and information leaks. This allows the Management Department to manage users' financial information safely and efficiently, improving the reliability of the entire system.

[0034] The adjustment unit autonomously adjusts the balance of income and expenses. Specifically, it automatically adjusts income and expenses and sets budgets. For example, it automatically sets a monthly budget based on the user's income and expense data to optimize the balance of income and expenses. The adjustment unit monitors the balance of income and expenses in real time and issues a warning if the budget is exceeded. Furthermore, the adjustment unit can flexibly adjust the budget in response to fluctuations in the user's income and expenses. For example, it can increase savings if income increases and reduce other expenses if expenses increase. In addition, the adjustment unit can create income and expense plans that are aligned with the user's long-term goals. For example, it can set a monthly savings amount to prepare for large future expenses and systematically accumulate funds. The adjustment unit can also collect user feedback and continuously improve the accuracy and effectiveness of income and expense adjustments. For example, it can survey user satisfaction with budget settings and the results of income and expense adjustments and review the adjustment methods as needed. In this way, the adjustment unit can optimize the user's balance of income and expenses and support efficient asset management.

[0035] The Simulation Unit performs specialized investment simulations. For example, the Simulation Unit can simulate a specific investment scenario and develop an investment strategy based on the results. For example, the Simulation Unit can propose an optimal investment strategy by considering the investment products and scenario settings to be simulated. For example, the Simulation Unit can provide the user with an optimal investment scenario by considering the balance between risk and return. In this way, by performing specialized investment simulations, users can simulate specific investment scenarios and develop investment strategies based on the results. Some or all of the above-described processes in the Simulation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Simulation Unit inputs a specific investment scenario into a generative AI, and the generative AI outputs the simulation results.

[0036] The Support Department provides AI chatbot support. For example, the Support Department allows users to ask questions to the AI ​​in real time and receive immediate answers. The Support Department can provide optimal support by considering the type of question and the accuracy of the answer. The Support Department can provide quick and accurate answers to user questions. Thus, by providing AI chatbot support, users can ask questions to the AI ​​in real time and receive immediate answers. Some or all of the above-described processes in the Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Support Department inputs the user's question into a generative AI, and the generative AI outputs an answer.

[0037] The Advice Unit provides community advice. For example, the Advice Unit allows users to share information with other users and receive advice. The Advice Unit can provide optimal advice by considering, for example, the selection criteria for users providing advice and the format of the advice. The Advice Unit allows users to share information with each other and provide advice to one another. In this way, by providing community advice, users can share information with other users and receive advice. Some or all of the above processing in the Advice Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Advice Unit inputs user information into a generative AI, and the generative AI outputs advice.

[0038] The Options Unit proposes sustainable investment options. For example, the Options Unit proposes investment destinations that are environmentally and socially conscious. For example, the Options Unit can propose the optimal investment destination by considering the specific content and criteria of sustainable investment options. For example, the Options Unit can propose socially responsible investments (SRI) or environmentally conscious investment destinations. In this way, by proposing sustainable investment options, users can choose investment destinations that are environmentally and socially conscious. Some or all of the above processing in the Options Unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the Options Unit inputs information on sustainable investment options into a generating AI, and the generating AI outputs the optimal investment destination.

[0039] The Feedback Unit provides personalized feedback. The Feedback Unit provides individual feedback based, for example, on the user's financial situation and investment results. The Feedback Unit can provide optimal feedback by considering, for example, the criteria for feedback and the content of individual advice. The Feedback Unit can analyze the user's financial situation and investment results and provide individual feedback. By providing personalized feedback, users can receive individual feedback based on their financial situation and investment results. Some or all of the above processing in the Feedback Unit may be performed using, for example, a generating AI, or without a generating AI. For example, the Feedback Unit inputs data on the user's financial situation and investment results into a generating AI, and the generating AI outputs individual feedback.

[0040] The data collection unit can collect credit card usage history and bank account deposit and withdrawal history. For example, the data collection unit collects information such as transaction date and time, transaction amount, and store used as part of the credit card usage history. The data collection unit can also collect information such as deposit date and time, withdrawal date and time, and transaction details as part of the bank account deposit and withdrawal history. By collecting credit card usage history and bank account deposit and withdrawal history, it is possible to accurately understand the user's financial transaction information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs data on credit card usage history and bank account deposit and withdrawal history into a generating AI, and the generating AI collects the information.

[0041] The analysis unit can analyze the collected transaction data to grasp the overall picture of income and expenses. For example, the analysis unit can analyze the collected transaction data to understand the balance between income and expenses. For example, the analysis unit can create and provide a monthly income and expense report to the user. For example, the analysis unit can also perform a categorized analysis of income and expenses to grasp the overall picture of income and expenses. In this way, by analyzing the collected transaction data, the overall picture of the user's income and expenses can be grasped. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit inputs the collected transaction data into a generating AI, and the generating AI grasps the overall picture of income and expenses.

[0042] The suggestion unit can propose optimal savings and investment plans based on the user's spending patterns and income. For example, the suggestion unit can propose an optimal savings plan based on the user's spending patterns and income. For example, the suggestion unit can propose an optimal investment plan considering the user's risk tolerance, target amount, investment period, etc. This allows the user to effectively manage their assets by proposing optimal savings and investment plans based on their spending patterns and income. Some or all of the above processing in the suggestion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the suggestion unit inputs data on the user's spending patterns and income into a generating AI, and the generating AI proposes an optimal savings and investment plan.

[0043] The management unit can manage transactions in multiple currencies and analyze fluctuations in exchange rates. For example, the management unit can manage transactions in multiple currencies and analyze fluctuations in exchange rates. The management unit can, for example, propose an optimal asset allocation considering the types of currencies and methods for obtaining exchange rates. This allows users to manage their assets from a global perspective by managing transactions in multiple currencies and analyzing fluctuations in exchange rates. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit inputs transaction data in multiple currencies into a generative AI, and the generative AI analyzes fluctuations in exchange rates.

[0044] The adjustment unit can autonomously adjust the balance of income and expenses. For example, the adjustment unit can automatically adjust income and expenses and set budgets. For example, the adjustment unit can optimize the balance of income and expenses and create a monthly income and expense report. In this way, by autonomously adjusting the balance of income and expenses, the user can optimize their monthly income and expense balance. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the adjustment unit inputs income and expense data into the generating AI, and the generating AI autonomously adjusts the balance of income and expenses.

[0045] 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 may prioritize collecting information on credit cards and bank accounts that the user has frequently used in the past. For example, the data collection unit may collect information from the user's past transaction history with a focus on a specific period. For example, the data collection unit can analyze the user's transaction patterns and select the most efficient data collection method. This enables efficient information collection by selecting the optimal data collection method through analysis of the user's past financial transaction history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs the user's past financial transaction history into a generative AI, which then selects the optimal data collection method.

[0046] 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 information on investment areas that the user is currently interested in. For example, the data collection unit can filter and collect necessary financial information according to the user's living situation. For example, the data collection unit can collect relevant financial information based on the user's areas of interest. This allows for efficient collection of necessary financial information by filtering based on 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, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs data on the user's current living situation and areas of interest into a generating AI, and the generating AI performs the filtering.

[0047] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during collection. For example, the data collection unit can prioritize the collection of financial information for the area where the user is currently located. For example, the data collection unit can collect relevant financial information based on the user's geographical location information. For example, the data collection unit can prioritize the collection of financial information for areas that the user frequently visits. By collecting information while considering the user's geographical location information, the data collection unit can prioritize the collection of information that is highly relevant to the user. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit inputs the user's geographical location information into a generating AI, and the generating AI prioritizes the collection of highly relevant information.

[0048] The data collection unit can analyze the user's social media activity and collect relevant financial information during collection. For example, the data collection unit can prioritize collecting financial information that the user has shown interest in on social media. The data collection unit can analyze the user's social media activity and collect relevant financial information. For example, the data collection unit can prioritize collecting information about financial experts that the user follows. This allows for the collection of financial information based on the user's interests by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs data on the user's social media activity into a generative AI, and the generative AI collects relevant financial information.

[0049] The analysis unit can adjust the level of detail of its analysis based on the importance of the financial transactions. For example, the analysis unit can perform a detailed analysis for important financial transactions and a simplified analysis for less important transactions. The analysis unit can also adjust the level of detail of its analysis based on the amount of the financial transactions. The analysis unit can also adjust the level of detail of its analysis based on the frequency of the financial transactions. This allows for detailed analysis of important transactions by adjusting the level of detail based on the importance of the financial transactions. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit inputs data on the importance of financial transactions into a generating AI, and the generating AI adjusts the level of detail of the analysis.

[0050] The analysis unit can apply different analysis algorithms depending on the category of financial transaction during analysis. For example, the analysis unit can apply a risk analysis algorithm to investment transactions and an expenditure pattern analysis algorithm to daily expenditures. The analysis unit can apply different analysis algorithms depending on the category of financial transaction. For example, the analysis unit can select the optimal analysis algorithm depending on the type of financial transaction. By applying different analysis algorithms depending on the category of financial transaction, the optimal analysis can be performed according to the type of transaction. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit inputs data on the category of financial transactions into a generation AI, and the generation AI applies different analysis algorithms.

[0051] The analysis unit can determine the priority of analysis based on the submission date of financial transactions during the analysis process. For example, the analysis unit may prioritize the analysis of recent financial transactions and postpone the analysis of past transactions. The analysis unit can determine the priority of analysis based on the submission date of financial transactions. For example, the analysis unit may prioritize the analysis of financial transactions that are of high urgency. This allows for the prioritization of analysis based on the submission date of financial transactions, thereby prioritizing the analysis of transactions that are of high urgency. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit inputs data on the submission date of financial transactions into a generating AI, and the generating AI determines the priority of analysis.

[0052] The analysis unit can adjust the order of analysis based on the relevance of financial transactions during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant financial transactions and postpone the analysis of less relevant transactions. The analysis unit can adjust the order of analysis based on the relevance of financial transactions. For example, the analysis unit can group highly relevant transactions for analysis. This allows for the prioritization of highly relevant transactions by adjusting the order of analysis based on the relevance of financial transactions. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs data on the relevance of financial transactions into a generating AI, and the generating AI adjusts the order of analysis.

[0053] The proposal unit can adjust the level of detail of its proposals based on the importance of the financial instruments. For example, it can provide detailed proposals for important financial instruments and simplified proposals for less important ones. The proposal unit can also adjust the level of detail of its proposals based on the monetary value of the financial instruments. For example, it can adjust the level of detail of its proposals based on the risk of the financial instruments. This allows for detailed proposals for important instruments by adjusting the level of detail based on the importance of the financial instruments. Some or all of the above processing in the proposal unit may be performed using, for example, a generating AI, or without a generating AI. For example, the proposal unit inputs data on the importance of financial instruments into a generating AI, and the generating AI adjusts the level of detail of the proposals.

[0054] The proposal unit can apply different proposal algorithms depending on the category of financial product when making a proposal. For example, the proposal unit can apply a risk analysis algorithm to investment products and a stability analysis algorithm to savings products. The proposal unit can apply different proposal algorithms depending on the category of financial product. For example, the proposal unit can select the optimal proposal algorithm depending on the type of financial product. By applying different proposal algorithms depending on the category of financial product, it is possible to make optimal proposals according to the type of product. Some or all of the above processing in the proposal unit may be performed using a generative AI, for example, or without a generative AI. For example, the proposal unit inputs data on the category of financial products into a generative AI, and the generative AI applies different proposal algorithms.

[0055] The proposal department can determine the priority of proposals based on the submission timing of financial products at the time of proposal. For example, the proposal department may prioritize recent financial products and postpone older products. The proposal department can determine the priority of proposals based on the submission timing of financial products. For example, the proposal department may prioritize financial products that are urgent. This allows for prioritizing urgent products by determining the priority of proposals based on the submission timing of financial products. Some or all of the above processing in the proposal department may be performed using, for example, a generating AI, or not using a generating AI. For example, the proposal department inputs data on the submission timing of financial products into a generating AI, and the generating AI determines the priority of proposals.

[0056] The proposal unit can adjust the order of proposals based on the relevance of financial products when making a proposal. For example, the proposal unit may prioritize proposing highly relevant financial products and postpone less relevant products. The proposal unit can adjust the order of proposals based on the relevance of financial products. For example, the proposal unit can group highly relevant products together and propose them. This allows the proposal unit to prioritize the proposition of highly relevant products by adjusting the order of proposals based on the relevance of financial products. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit inputs data on the relevance of financial products into a generative AI, and the generative AI adjusts the order of proposals.

[0057] The management department can adjust the level of detail in management based on the importance of the financial accounts. For example, the management department can perform detailed management for important financial accounts and simplified management for less important accounts. For example, the management department can adjust the level of detail in management based on the amount of the financial accounts. For example, the management department can adjust the level of detail in management based on the transaction frequency of the financial accounts. This allows for detailed management of important accounts by adjusting the level of detail in management based on the importance of the financial accounts. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department inputs data on the importance of the financial accounts into a generative AI, and the generative AI adjusts the level of detail in management.

[0058] The management department can apply different management algorithms depending on the category of the financial account during management. For example, the management department can apply a risk management algorithm to investment accounts and a stability management algorithm to savings accounts. The management department can apply different management algorithms depending on the category of the financial account. For example, the management department can select the optimal management algorithm depending on the type of financial account. By applying different management algorithms depending on the category of the financial account, optimal management can be performed according to the type of account. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or without using a generative AI. For example, the management department inputs data on the category of financial accounts into a generative AI, and the generative AI applies different management algorithms.

[0059] The management department can determine the priority of management based on the submission date of financial accounts during management. For example, the management department may prioritize the management of recent financial accounts and postpone older accounts. The management department can determine the priority of management based on the submission date of financial accounts. For example, the management department may prioritize the management of financial accounts with high urgency. This allows for the prioritization of urgent accounts by determining the priority of management based on the submission date of financial accounts. Some or all of the above processes in the management department may be performed using, for example, a generating AI, or not using a generating AI. For example, the management department inputs data on the submission date of financial accounts into a generating AI, and the generating AI determines the priority of management.

[0060] The management department can adjust the order of management based on the relevance of financial accounts during management. For example, the management department can prioritize the management of highly relevant financial accounts and postpone the management of less relevant accounts. The management department can adjust the order of management based on the relevance of financial accounts. For example, the management department can group and manage highly relevant accounts. This allows for priority management of highly relevant accounts by adjusting the order of management based on the relevance of financial accounts. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department inputs data on the relevance of financial accounts into a generative AI, and the generative AI adjusts the order of management.

[0061] The adjustment unit can adjust the level of detail of the adjustment based on the importance of the balance of income and expenses during the adjustment process. For example, the adjustment unit can perform detailed adjustments for important balances of income and expenses and simplified adjustments for less important balances. For example, the adjustment unit can adjust the level of detail of the adjustment based on the amount of the balance of income and expenses. For example, the adjustment unit can adjust the level of detail of the adjustment based on the frequency of the balance of income and expenses. This allows for detailed adjustments to be made to important balances by adjusting the level of detail of the adjustment based on the importance of the balance of income and expenses. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit inputs data on the importance of the balance of income and expenses into the generating AI, and the generating AI adjusts the level of detail of the adjustment.

[0062] The adjustment unit can apply different adjustment algorithms depending on the category of the income and expenditure balance during the adjustment process. For example, the adjustment unit can apply a risk adjustment algorithm to investment income and expenditure balances and a stability adjustment algorithm to daily expenditure income and expenditure balances. The adjustment unit can apply different adjustment algorithms depending on the category of the income and expenditure balance. For example, the adjustment unit can select the optimal adjustment algorithm depending on the type of income and expenditure balance. By applying different adjustment algorithms depending on the category of the income and expenditure balance, the optimal adjustment can be made according to the type of balance. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit inputs data on the categories of income and expenditure balances into a generating AI, and the generating AI applies different adjustment algorithms.

[0063] The adjustment unit can determine the priority of adjustments based on the submission timing of the income and expenditure balances. For example, the adjustment unit may prioritize adjusting recent income and expenditure balances and postpone past balances. The adjustment unit can determine the priority of adjustments based on the submission timing of the income and expenditure balances. For example, the adjustment unit may prioritize adjusting income and expenditure balances that require urgent attention. This allows for prioritizing adjustments of urgent balances by determining the priority of adjustments based on the submission timing of the income and expenditure balances. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit inputs data on the submission timing of income and expenditure balances into the generating AI, and the generating AI determines the priority of adjustments.

[0064] The adjustment unit can adjust the order of adjustments based on the relationships between income and expenditure balances during the adjustment process. For example, the adjustment unit may prioritize adjusting highly related income and expenditure balances and postpone adjusting less related balances. The adjustment unit can adjust the order of adjustments based on the relationships between income and expenditure balances. For example, the adjustment unit can group highly related balances and adjust them together. This allows for prioritizing the adjustment of highly related balances by adjusting the order of adjustments based on the relationships between income and expenditure balances. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit inputs data on the relationships between income and expenditure balances into a generating AI, and the generating AI adjusts the order of adjustments.

[0065] The simulation unit can adjust the level of detail of the simulation based on the importance of the financial instruments during the simulation. For example, the simulation unit can perform detailed simulations for important financial instruments and simplified simulations for less important instruments. The simulation unit can adjust the level of detail of the simulation based on the amount of the financial instruments, for example. The simulation unit can adjust the level of detail of the simulation based on the risk of the financial instruments, for example. This allows for detailed simulations of important instruments by adjusting the level of detail of the simulation based on the importance of the financial instruments. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit inputs data on the importance of financial instruments into the generative AI, and the generative AI adjusts the level of detail of the simulation.

[0066] The simulation unit can weight simulations based on the filing date of financial products. For example, the simulation unit may prioritize simulating recent financial products and postpone simulating older products. The simulation unit can weight simulations based on the filing date of financial products. For example, the simulation unit can prioritize simulating financial products with high urgency. This allows for prioritizing the simulation of products with high urgency by weighting simulations based on the filing date of financial products. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit inputs data on the filing date of financial products into a generative AI, and the generative AI performs the simulation weighting.

[0067] The support department can provide optimal support by referring to the user's past inquiry history during support. For example, the support department can provide optimal support based on the content of past inquiries the user has made. For example, the support department can provide relevant support information from the user's past inquiry history. For example, the support department can analyze the user's inquiry patterns and provide the most efficient support. In this way, by referring to the user's past inquiry history, the support department can provide the optimal support for the user. Some or all of the above processes in the support department may be performed using, for example, a generation AI, or not using a generation AI. For example, the support department inputs data from the user's past inquiry history into a generation AI, and the generation AI provides optimal support.

[0068] The support unit can provide optimal support by considering the user's device information during support. For example, if the user is using a smartphone, the support unit can provide support tailored to the screen size. For example, if the user is using a tablet, the support unit can provide support optimized for a larger screen. For example, if the user is using a smartwatch, the support unit can provide concise and highly visible support. In this way, by considering the user's device information, it is possible to provide support optimized for the device the user is using. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit inputs the user's device information data into a generative AI, and the generative AI provides optimal support.

[0069] The advice unit can provide optimal advice by referring to the user's past advice history when providing advice. For example, the advice unit can provide optimal advice based on advice the user has received in the past. For example, the advice unit can provide relevant advice information from the user's past advice history. For example, the advice unit can analyze the user's advice patterns and provide the most efficient advice. In this way, by referring to the user's past advice history, it is possible to provide the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the advice unit inputs data from the user's past advice history into a generative AI, and the generative AI provides optimal advice.

[0070] The advice unit can provide optimal advice by considering the user's device information when providing advice. For example, if the user is using a smartphone, the advice unit can provide advice tailored to the screen size. For example, if the user is using a tablet, the advice unit can provide advice optimized for a larger screen. For example, if the user is using a smartwatch, the advice unit can provide concise and easy-to-read advice. In this way, by considering the user's device information, it is possible to provide advice optimized for the device the user is using. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit inputs the user's device information data into a generative AI, and the generative AI provides optimal advice.

[0071] The options unit can provide the most suitable investment options by considering the user's geographical location when offering investment options. For example, the options unit can prioritize providing investment options for the region where the user is currently located. For example, the options unit can provide relevant investment options based on the user's geographical location. For example, the options unit can prioritize providing investment options for regions that the user frequently visits. This allows the options unit to provide investment options that are highly relevant to the user by considering the user's geographical location. Some or all of the above processing in the options unit may be performed using, for example, a generative AI, or without a generative AI. For example, the options unit inputs the user's geographical location data into a generative AI, and the generative AI provides the most suitable investment options.

[0072] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit can provide optimal feedback based on feedback the user has received in the past. For example, the feedback unit can provide relevant feedback information from the user's past feedback history. For example, the feedback unit can analyze the user's feedback patterns and provide the most efficient feedback. In this way, by referring to the user's past feedback history, it is possible to provide the optimal feedback for the user. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback unit inputs data from the user's past feedback history into a generative AI, and the generative AI provides optimal feedback.

[0073] The feedback unit can provide optimal feedback by considering the user's device information when providing feedback. For example, if the user is using a smartphone, the feedback unit can provide feedback that is adapted to the screen size. For example, if the user is using a tablet, the feedback unit can provide feedback optimized for a larger screen. For example, if the user is using a smartwatch, the feedback unit can provide concise and highly visible feedback. In this way, by considering the user's device information, it is possible to provide feedback optimized for the device the user is using. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs user device information data into a generative AI, and the generative AI provides optimal feedback.

[0074] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0075] Financial management systems can also collect users' health data and provide financial advice based on their health status. For example, they can monitor users' exercise levels and sleep duration, suggesting high-risk investments if their health is good, and low-risk savings plans if their health is deteriorating. They can also analyze users' health data and provide financial plans for improving their health. For instance, they can recommend gym memberships or the purchase of health foods to maintain health. Furthermore, they can review existing insurance policies or suggest new insurance products based on the user's health status.

[0076] Financial management systems can also provide customized financial advice based on the user's hobbies and interests. For example, if a user enjoys traveling, the system can suggest savings plans for travel funds and travel insurance. It can also suggest investment products related to the user's hobbies. For instance, if a user is interested in art, it can suggest art-related investment products. Furthermore, it can provide information on specific events and seminars based on the user's hobbies and interests. For example, if a user is interested in fitness, it can provide information on fitness-related events and seminars.

[0077] Financial management systems can also provide financial advice tailored to the user's life stage. For example, if a user is about to get married, it can offer advice on saving for wedding expenses and managing household finances after marriage. If a user plans to have children, it can offer advice on saving for education expenses and suggest insurance products for children. Furthermore, if a user is about to retire, it can offer advice on planning for post-retirement living expenses and optimizing pensions. For example, it can provide estimates of living expenses after retirement and suggest pension payment methods.

[0078] Financial management systems can also provide local financial services and promotions based on a user's geographical location. For example, if a user is in a specific region, it can provide information on special offers from banks and financial institutions in that region. If a user is traveling, it can also provide information on financial services and exchange rates in their destination. Furthermore, it can guide users to nearby ATMs and bank branches based on their location. For instance, if a user needs to withdraw cash, it can guide them to the nearest ATM.

[0079] Financial management systems can also analyze users' social media activity and provide relevant financial advice. For example, if a user shows interest in a particular brand or product on social media, it can provide financial advice related to that brand or product. It can also provide appropriate financial advice based on life events shared by users on social media. For instance, if a user starts a new job, it can suggest savings plans and investment strategies related to that job. Furthermore, it can analyze users' social media activity and suggest financial products based on their interests.

[0080] The following briefly describes the processing flow for example form 1.

[0081] Step 1: The data collection unit collects information from the user's multiple credit cards and bank accounts. For example, it can collect credit card transaction history and bank account deposit and withdrawal history, and collect information such as transaction date and time, transaction amount, store used, deposit date and time, withdrawal date and time, and transaction details. Step 2: The analysis unit analyzes the information collected by the collection unit to grasp the overall picture of income and expenses. For example, it can analyze the collected transaction data to understand the balance between income and expenses, create monthly income and expense reports, and perform a categorized analysis of income and expenses. Step 3: The proposal unit proposes optimal savings and investment plans based on the analysis results obtained by the analysis unit. For example, it can propose an optimal savings plan based on the user's spending patterns and income, and an optimal investment plan considering the user's risk tolerance, target amount, investment period, etc. Step 4: The management department manages all financial accounts, including multi-currency accounts and overseas accounts. For example, it can manage transactions in multiple currencies, analyze exchange rate fluctuations, and propose optimal asset allocations considering the types of currencies and methods for obtaining exchange rates. Step 5: The adjustment unit autonomously adjusts the balance of income and expenses. For example, it can automatically adjust income and expenses, set budgets, optimize the balance of income and expenses, and generate monthly income and expense reports.

[0082] (Example of form 2) The financial management system according to an embodiment of the present invention is a platform that uses AI to centrally manage information from multiple credit cards and bank accounts and automatically tracks and analyzes income and expenses in real time. This financial management system centrally manages information from a user's multiple credit cards and bank accounts and automatically tracks and analyzes income and expenses in real time. For example, the financial management system collects transaction data from each financial account to grasp the overall picture of income and expenses. For example, it can analyze credit card usage history and bank account deposit and withdrawal history in real time to understand the user's income and expense situation. Next, the financial management system uses AI to automatically propose an optimal savings plan and investment / management plan based on the user's spending patterns and income. For example, it analyzes the user's past spending history and income fluctuations to predict future income and expenses. Based on this, it can propose an optimal savings plan and investment strategy to the user. For example, it proposes a low-risk savings plan to users who are expected to have a stable income, and a diversified investment plan to users with unstable income. Furthermore, the financial management system's AI agent tracks the user's income and expenses in real time and manages all financial accounts, including multi-currency accounts and overseas accounts. For example, if a user trades in multiple currencies, the AI ​​analyzes exchange rate fluctuations in real time and proposes an optimal asset allocation. For users with overseas accounts, it optimizes the costs of international remittances and foreign exchange transactions, supporting asset management from a global perspective. This system allows users to centrally manage multiple financial accounts and understand the overall picture of their income and expenses. Furthermore, the AI ​​automatically proposes optimal savings plans and investment strategies, enabling users to manage their assets effectively. In addition, specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback features can be added to provide users with more advanced financial advice. For example, specialized investment simulations allow users to simulate specific investment scenarios and develop investment strategies based on the results. AI chatbot support allows users to ask questions to the AI ​​in real time and receive immediate answers.Community advice allows users to share information and receive advice from other users. Sustainable investment options suggest investments that are environmentally and socially responsible. Personalized feedback provides individual feedback based on the user's income and expenses and investment performance. As a result, the financial management system can centrally manage a user's multiple financial accounts, understand the overall picture of income and expenses, suggest optimal savings and investment plans, and autonomously adjust the balance of income and expenses.

[0083] The financial management system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a management unit, and an adjustment unit. The collection unit collects information from the user's multiple credit cards and bank accounts. For example, the collection unit collects credit card usage history and bank account deposit and withdrawal history. For example, the collection unit can collect information such as transaction date and time, transaction amount, and store used as credit card usage history. The collection unit can also collect information such as deposit date and time, withdrawal date and time, and transaction details as bank account deposit and withdrawal history. The analysis unit analyzes the information collected by the collection unit to grasp the overall picture of income and expenses. For example, the analysis unit analyzes the collected transaction data to grasp the balance between income and expenses. For example, the analysis unit can create a monthly income and expense report and provide it to the user. The analysis unit can also perform category-based analysis of income and expenses in order to grasp the overall picture of income and expenses. The proposal unit proposes an optimal savings plan or investment plan based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes an optimal savings plan based on the user's spending patterns and income. The proposal unit can propose an optimal investment plan, for example, by considering the user's risk tolerance, target amount, and investment period. The management unit manages all financial accounts, including multi-currency accounts and overseas accounts. The management unit manages transactions in multiple currencies and analyzes exchange rate fluctuations, for example. The management unit can propose an optimal asset allocation, for example, by considering the types of currencies and methods for obtaining exchange rates. The adjustment unit autonomously adjusts the balance of income and expenses. The adjustment unit performs tasks such as automatic adjustment of income and expenses and budget setting. The adjustment unit can optimize the balance of income and expenses and create monthly income and expense reports, for example. As a result, the financial management system according to this embodiment can centrally manage a user's multiple financial accounts, grasp the overall picture of income and expenses, propose optimal savings and investment plans, and autonomously adjust the balance of income and expenses.

[0084] The data collection unit collects information from a user's multiple credit cards and bank accounts. Specifically, it collects credit card usage history and bank account deposit and withdrawal history. For credit card usage history, it can collect detailed information such as transaction date and time, transaction amount, and store where the transaction was made. This allows for an accurate understanding of how much a user spent at which stores. For bank account deposit and withdrawal history, it collects information such as deposit date and time, withdrawal date and time, and transaction details. This allows for a detailed understanding of the user's income sources and spending destinations. The data collection unit collects this information in real time and transmits it to a central database. Furthermore, with the user's permission, the data collection unit can automatically retrieve data using financial institution APIs. This eliminates the need for users to manually enter data. The data collection unit centrally manages the collected data and can link with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0085] The analysis unit analyzes the information collected by the data collection unit to understand the overall picture of income and expenses. Specifically, it analyzes the collected transaction data to understand the balance between income and expenses. For example, the analysis unit can create and provide monthly income and expense reports to users. These reports include total income and expenses, income and expenses by category, and monthly trends in income and expenses. This allows users to understand their own financial situation at a glance. The analysis unit can also perform category-based analysis of income and expenses to understand the overall picture of income and expenses. For example, it can classify expenses by category, such as food expenses, transportation expenses, and entertainment expenses, and calculate the percentage of expenses in each category. This allows users to understand which categories are concentrating the most spending and take measures to reduce unnecessary expenses. Furthermore, the analysis unit can also analyze long-term income and expense trends by utilizing historical data and statistical information. For example, based on income and expense data from the past few years, it can predict trends in increases and decreases in income and expenses and provide reference information for future financial planning. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal transactions, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term revenue and expenditure management and anomaly detection, thereby improving the reliability and security of the entire system.

[0086] The Proposal Department proposes optimal savings and investment plans based on the analysis results obtained by the Analysis Department. Specifically, it proposes the optimal savings plan based on the user's spending patterns and income. For example, it analyzes the user's monthly income and expenses and sets a savings target within a reasonable range. The Proposal Department can also propose the optimal investment plan considering the user's risk tolerance, target amount, and investment period. For example, for users who want stable investments with reduced risk, it proposes low-risk investment products such as bonds and time deposits, and for users who aim for high returns, it proposes high-risk, high-return investment products such as stocks and mutual funds. The Proposal Department presents these proposals to users in an easy-to-understand manner and provides concrete action plans. For example, it shows specific monthly savings and investment amounts and makes proposals in a way that is easy for users to implement. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it revises savings and investment plans based on feedback from users who have implemented the proposals, and makes more effective proposals. This allows the proposal department to provide users with optimal savings and investment plans tailored to their needs, thereby supporting their asset building.

[0087] The Management Department manages all financial accounts, including multi-currency accounts and overseas accounts. Specifically, it manages transactions in multiple currencies and analyzes exchange rate fluctuations. For example, if a user trades in multiple currencies, the Management Department centrally manages the transaction history for each currency and understands the impact of exchange rate fluctuations in real time. The Management Department can also propose optimal asset allocations, taking into account the types of currencies supported and how exchange rates are obtained. For example, to minimize exchange rate risk, it proposes a balanced asset allocation that is not biased towards any particular currency. Furthermore, the Management Department centrally manages all financial accounts, including overseas accounts, and can accurately understand how much assets a user holds in each account. This allows users to grasp their asset status at a glance and manage their assets efficiently. In addition, the Management Department securely manages users' financial information in accordance with the security policies of each financial institution. For example, it implements data encryption and multi-factor authentication to prevent unauthorized access and information leaks. This allows the Management Department to manage users' financial information safely and efficiently, improving the reliability of the entire system.

[0088] The adjustment unit autonomously adjusts the balance of income and expenses. Specifically, it automatically adjusts income and expenses and sets budgets. For example, it automatically sets a monthly budget based on the user's income and expense data to optimize the balance of income and expenses. The adjustment unit monitors the balance of income and expenses in real time and issues a warning if the budget is exceeded. Furthermore, the adjustment unit can flexibly adjust the budget in response to fluctuations in the user's income and expenses. For example, it can increase savings if income increases and reduce other expenses if expenses increase. In addition, the adjustment unit can create income and expense plans that are aligned with the user's long-term goals. For example, it can set a monthly savings amount to prepare for large future expenses and systematically accumulate funds. The adjustment unit can also collect user feedback and continuously improve the accuracy and effectiveness of income and expense adjustments. For example, it can survey user satisfaction with budget settings and the results of income and expense adjustments and review the adjustment methods as needed. In this way, the adjustment unit can optimize the user's balance of income and expenses and support efficient asset management.

[0089] The Simulation Unit performs specialized investment simulations. For example, the Simulation Unit can simulate a specific investment scenario and develop an investment strategy based on the results. For example, the Simulation Unit can propose an optimal investment strategy by considering the investment products and scenario settings to be simulated. For example, the Simulation Unit can provide the user with an optimal investment scenario by considering the balance between risk and return. In this way, by performing specialized investment simulations, users can simulate specific investment scenarios and develop investment strategies based on the results. Some or all of the above-described processes in the Simulation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Simulation Unit inputs a specific investment scenario into a generative AI, and the generative AI outputs the simulation results.

[0090] The Support Department provides AI chatbot support. For example, the Support Department allows users to ask questions to the AI ​​in real time and receive immediate answers. The Support Department can provide optimal support by considering the type of question and the accuracy of the answer. The Support Department can provide quick and accurate answers to user questions. Thus, by providing AI chatbot support, users can ask questions to the AI ​​in real time and receive immediate answers. Some or all of the above-described processes in the Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Support Department inputs the user's question into a generative AI, and the generative AI outputs an answer.

[0091] The Advice Unit provides community advice. For example, the Advice Unit allows users to share information with other users and receive advice. The Advice Unit can provide optimal advice by considering, for example, the selection criteria for users providing advice and the format of the advice. The Advice Unit allows users to share information with each other and provide advice to one another. In this way, by providing community advice, users can share information with other users and receive advice. Some or all of the above processing in the Advice Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Advice Unit inputs user information into a generative AI, and the generative AI outputs advice.

[0092] The Options Unit proposes sustainable investment options. For example, the Options Unit proposes investment destinations that are environmentally and socially conscious. For example, the Options Unit can propose the optimal investment destination by considering the specific content and criteria of sustainable investment options. For example, the Options Unit can propose socially responsible investments (SRI) or environmentally conscious investment destinations. In this way, by proposing sustainable investment options, users can choose investment destinations that are environmentally and socially conscious. Some or all of the above processing in the Options Unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the Options Unit inputs information on sustainable investment options into a generating AI, and the generating AI outputs the optimal investment destination.

[0093] The Feedback Unit provides personalized feedback. The Feedback Unit provides individual feedback based, for example, on the user's financial situation and investment results. The Feedback Unit can provide optimal feedback by considering, for example, the criteria for feedback and the content of individual advice. The Feedback Unit can analyze the user's financial situation and investment results and provide individual feedback. By providing personalized feedback, users can receive individual feedback based on their financial situation and investment results. Some or all of the above processing in the Feedback Unit may be performed using, for example, a generating AI, or without a generating AI. For example, the Feedback Unit inputs data on the user's financial situation and investment results into a generating AI, and the generating AI outputs individual feedback.

[0094] The data collection unit can collect credit card usage history and bank account deposit and withdrawal history. For example, the data collection unit collects information such as transaction date and time, transaction amount, and store used as part of the credit card usage history. The data collection unit can also collect information such as deposit date and time, withdrawal date and time, and transaction details as part of the bank account deposit and withdrawal history. By collecting credit card usage history and bank account deposit and withdrawal history, it is possible to accurately understand the user's financial transaction information. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs data on credit card usage history and bank account deposit and withdrawal history into a generating AI, and the generating AI collects the information.

[0095] The analysis unit can analyze the collected transaction data to grasp the overall picture of income and expenses. For example, the analysis unit can analyze the collected transaction data to understand the balance between income and expenses. For example, the analysis unit can create and provide a monthly income and expense report to the user. For example, the analysis unit can also perform a categorized analysis of income and expenses to grasp the overall picture of income and expenses. In this way, by analyzing the collected transaction data, the overall picture of the user's income and expenses can be grasped. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit inputs the collected transaction data into a generating AI, and the generating AI grasps the overall picture of income and expenses.

[0096] The suggestion unit can propose optimal savings and investment plans based on the user's spending patterns and income. For example, the suggestion unit can propose an optimal savings plan based on the user's spending patterns and income. For example, the suggestion unit can propose an optimal investment plan considering the user's risk tolerance, target amount, investment period, etc. This allows the user to effectively manage their assets by proposing optimal savings and investment plans based on their spending patterns and income. Some or all of the above processing in the suggestion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the suggestion unit inputs data on the user's spending patterns and income into a generating AI, and the generating AI proposes an optimal savings and investment plan.

[0097] The management unit can manage transactions in multiple currencies and analyze fluctuations in exchange rates. For example, the management unit can manage transactions in multiple currencies and analyze fluctuations in exchange rates. The management unit can, for example, propose an optimal asset allocation considering the types of currencies and methods for obtaining exchange rates. This allows users to manage their assets from a global perspective by managing transactions in multiple currencies and analyzing fluctuations in exchange rates. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit inputs transaction data in multiple currencies into a generative AI, and the generative AI analyzes fluctuations in exchange rates.

[0098] The adjustment unit can autonomously adjust the balance of income and expenses. For example, the adjustment unit can automatically adjust income and expenses and set budgets. For example, the adjustment unit can optimize the balance of income and expenses and create a monthly income and expense report. In this way, by autonomously adjusting the balance of income and expenses, the user can optimize their monthly income and expense balance. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the adjustment unit inputs income and expense data into the generating AI, and the generating AI autonomously adjusts the balance of income and expenses.

[0099] The data collection unit can estimate the user's emotions and determine the priority of financial information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important financial information and postpone collecting detailed information. For example, if the user is relaxed, the data collection unit can collect all financial information equally and perform detailed analysis. For example, if the user is in a hurry, the data collection unit can quickly collect only the most important financial information. This enables appropriate information collection tailored to the user's situation by prioritizing financial information based on 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, for example, a generative AI, or not using a generative AI. For example, the data collection unit inputs the user's emotion data into a generative AI, and the generative AI determines the priority of financial information.

[0100] 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 may prioritize collecting information on credit cards and bank accounts that the user has frequently used in the past. For example, the data collection unit may collect information from the user's past transaction history with a focus on a specific period. For example, the data collection unit can analyze the user's transaction patterns and select the most efficient data collection method. This enables efficient information collection by selecting the optimal data collection method through analysis of the user's past financial transaction history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs the user's past financial transaction history into a generative AI, which then selects the optimal data collection method.

[0101] 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 information on investment areas that the user is currently interested in. For example, the data collection unit can filter and collect necessary financial information according to the user's living situation. For example, the data collection unit can collect relevant financial information based on the user's areas of interest. This allows for efficient collection of necessary financial information by filtering based on 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, for example, a generating AI, or without a generating AI. For example, the data collection unit inputs data on the user's current living situation and areas of interest into a generating AI, and the generating AI performs the filtering.

[0102] The data collection unit can estimate the user's emotions and adjust the timing of acquiring financial information based on the estimated emotions. For example, if the user is stressed, the data collection unit can quickly collect important financial information and postpone detailed information. For example, if the user is relaxed, the data collection unit can collect all financial information evenly and perform detailed analysis. For example, if the user is in a hurry, the data collection unit can quickly collect only the most important financial information. By adjusting the timing of acquiring financial information based on the user's emotions, it becomes possible to collect information at an appropriate time according to the user's situation. 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 data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the timing of acquiring financial information.

[0103] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during collection. For example, the data collection unit can prioritize the collection of financial information for the area where the user is currently located. For example, the data collection unit can collect relevant financial information based on the user's geographical location information. For example, the data collection unit can prioritize the collection of financial information for areas that the user frequently visits. By collecting information while considering the user's geographical location information, the data collection unit can prioritize the collection of information that is highly relevant to the user. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit inputs the user's geographical location information into a generating AI, and the generating AI prioritizes the collection of highly relevant information.

[0104] The data collection unit can analyze the user's social media activity and collect relevant financial information during collection. For example, the data collection unit can prioritize collecting financial information that the user has shown interest in on social media. The data collection unit can analyze the user's social media activity and collect relevant financial information. For example, the data collection unit can prioritize collecting information about financial experts that the user follows. This allows for the collection of financial information based on the user's interests by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit inputs data on the user's social media activity into a generative AI, and the generative AI collects relevant financial information.

[0105] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the display method of the analysis results.

[0106] The analysis unit can adjust the level of detail of its analysis based on the importance of the financial transactions. For example, the analysis unit can perform a detailed analysis for important financial transactions and a simplified analysis for less important transactions. The analysis unit can also adjust the level of detail of its analysis based on the amount of the financial transactions. The analysis unit can also adjust the level of detail of its analysis based on the frequency of the financial transactions. This allows for detailed analysis of important transactions by adjusting the level of detail based on the importance of the financial transactions. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit inputs data on the importance of financial transactions into a generating AI, and the generating AI adjusts the level of detail of the analysis.

[0107] The analysis unit can apply different analysis algorithms depending on the category of financial transaction during analysis. For example, the analysis unit can apply a risk analysis algorithm to investment transactions and an expenditure pattern analysis algorithm to daily expenditures. The analysis unit can apply different analysis algorithms depending on the category of financial transaction. For example, the analysis unit can select the optimal analysis algorithm depending on the type of financial transaction. By applying different analysis algorithms depending on the category of financial transaction, the optimal analysis can be performed according to the type of transaction. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit inputs data on the category of financial transactions into a generation AI, and the generation AI applies different analysis algorithms.

[0108] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit can display all analysis results equally. For example, if the user is in a hurry, the analysis unit can quickly display only the most important analysis results. In this way, by prioritizing analysis results based on the user's emotions, important analysis results appropriate to the user's situation can be displayed preferentially. 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 a generative AI, or not using a generative AI. For example, the analysis unit inputs the user's emotion data into a generative AI, and the generative AI determines the priority of analysis results.

[0109] The analysis unit can determine the priority of analysis based on the submission date of financial transactions during the analysis process. For example, the analysis unit may prioritize the analysis of recent financial transactions and postpone the analysis of past transactions. The analysis unit can determine the priority of analysis based on the submission date of financial transactions. For example, the analysis unit may prioritize the analysis of financial transactions that are of high urgency. This allows for the prioritization of analysis based on the submission date of financial transactions, thereby prioritizing the analysis of transactions that are of high urgency. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit inputs data on the submission date of financial transactions into a generating AI, and the generating AI determines the priority of analysis.

[0110] The analysis unit can adjust the order of analysis based on the relevance of financial transactions during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant financial transactions and postpone the analysis of less relevant transactions. The analysis unit can adjust the order of analysis based on the relevance of financial transactions. For example, the analysis unit can group highly relevant transactions for analysis. This allows for the prioritization of highly relevant transactions by adjusting the order of analysis based on the relevance of financial transactions. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit inputs data on the relevance of financial transactions into a generating AI, and the generating AI adjusts the order of analysis.

[0111] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and easily visible suggestions. If the user is relaxed, the suggestion unit can present suggestions that include detailed information. If the user is in a hurry, the suggestion unit can present suggestions that get straight to the point. By adjusting the way it presents suggestions based on the user's emotions, it is possible to present suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the way it presents suggestions.

[0112] The proposal unit can adjust the level of detail of its proposals based on the importance of the financial instruments. For example, it can provide detailed proposals for important financial instruments and simplified proposals for less important ones. The proposal unit can also adjust the level of detail of its proposals based on the monetary value of the financial instruments. For example, it can adjust the level of detail of its proposals based on the risk of the financial instruments. This allows for detailed proposals for important instruments by adjusting the level of detail based on the importance of the financial instruments. Some or all of the above processing in the proposal unit may be performed using, for example, a generating AI, or without a generating AI. For example, the proposal unit inputs data on the importance of financial instruments into a generating AI, and the generating AI adjusts the level of detail of the proposals.

[0113] The proposal unit can apply different proposal algorithms depending on the category of financial product when making a proposal. For example, the proposal unit can apply a risk analysis algorithm to investment products and a stability analysis algorithm to savings products. The proposal unit can apply different proposal algorithms depending on the category of financial product. For example, the proposal unit can select the optimal proposal algorithm depending on the type of financial product. By applying different proposal algorithms depending on the category of financial product, it is possible to make optimal proposals according to the type of product. Some or all of the above processing in the proposal unit may be performed using a generative AI, for example, or without a generative AI. For example, the proposal unit inputs data on the category of financial products into a generative AI, and the generative AI applies different proposal algorithms.

[0114] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion based on the user's emotions, the suggestion unit can provide a suggestion of appropriate length according to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI or not. For example, the suggestion unit inputs user emotion data into a generative AI, and the generative AI adjusts the length of the suggestion.

[0115] The proposal department can determine the priority of proposals based on the submission timing of financial products at the time of proposal. For example, the proposal department may prioritize recent financial products and postpone older products. The proposal department can determine the priority of proposals based on the submission timing of financial products. For example, the proposal department may prioritize financial products that are urgent. This allows for prioritizing urgent products by determining the priority of proposals based on the submission timing of financial products. Some or all of the above processing in the proposal department may be performed using, for example, a generating AI, or not using a generating AI. For example, the proposal department inputs data on the submission timing of financial products into a generating AI, and the generating AI determines the priority of proposals.

[0116] The proposal unit can adjust the order of proposals based on the relevance of financial products when making a proposal. For example, the proposal unit may prioritize proposing highly relevant financial products and postpone less relevant products. The proposal unit can adjust the order of proposals based on the relevance of financial products. For example, the proposal unit can group highly relevant products together and propose them. This allows the proposal unit to prioritize the proposition of highly relevant products by adjusting the order of proposals based on the relevance of financial products. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit inputs data on the relevance of financial products into a generative AI, and the generative AI adjusts the order of proposals.

[0117] The management unit can estimate the user's emotions and determine the priority of financial accounts to manage based on the estimated emotions. For example, if the user is stressed, the management unit can prioritize managing important financial accounts. For example, if the user is relaxed, the management unit can manage all financial accounts equally. For example, if the user is in a hurry, the management unit can quickly manage only the most important financial accounts. In this way, by prioritizing financial accounts based on the user's emotions, it is possible to prioritize important accounts according to the user's situation. 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 management unit may be performed using or without generative AI. For example, the management unit inputs user emotion data into a generative AI, and the generative AI determines the priority of financial accounts.

[0118] The management department can adjust the level of detail in management based on the importance of the financial accounts. For example, the management department can perform detailed management for important financial accounts and simplified management for less important accounts. For example, the management department can adjust the level of detail in management based on the amount of the financial accounts. For example, the management department can adjust the level of detail in management based on the transaction frequency of the financial accounts. This allows for detailed management of important accounts by adjusting the level of detail in management based on the importance of the financial accounts. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department inputs data on the importance of the financial accounts into a generative AI, and the generative AI adjusts the level of detail in management.

[0119] The management department can apply different management algorithms depending on the category of the financial account during management. For example, the management department can apply a risk management algorithm to investment accounts and a stability management algorithm to savings accounts. The management department can apply different management algorithms depending on the category of the financial account. For example, the management department can select the optimal management algorithm depending on the type of financial account. By applying different management algorithms depending on the category of the financial account, optimal management can be performed according to the type of account. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or without using a generative AI. For example, the management department inputs data on the category of financial accounts into a generative AI, and the generative AI applies different management algorithms.

[0120] The management unit can estimate the user's emotions and adjust how financial accounts are displayed based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple and highly visible display. For example, if the user is relaxed, the management unit can provide a display that includes detailed information. For example, if the user is in a hurry, the management unit can provide a display that gets straight to the point. By adjusting how financial accounts are displayed based on the user's emotions, a display that is easy for the user to understand can be provided. 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 management unit may be performed using or without a generative AI. For example, the management unit inputs user emotion data into a generative AI, and the generative AI adjusts how financial accounts are displayed.

[0121] The management department can determine the priority of management based on the submission date of financial accounts during management. For example, the management department may prioritize the management of recent financial accounts and postpone older accounts. The management department can determine the priority of management based on the submission date of financial accounts. For example, the management department may prioritize the management of financial accounts with high urgency. This allows for the prioritization of urgent accounts by determining the priority of management based on the submission date of financial accounts. Some or all of the above processes in the management department may be performed using, for example, a generating AI, or not using a generating AI. For example, the management department inputs data on the submission date of financial accounts into a generating AI, and the generating AI determines the priority of management.

[0122] The management department can adjust the order of management based on the relevance of financial accounts during management. For example, the management department can prioritize the management of highly relevant financial accounts and postpone the management of less relevant accounts. The management department can adjust the order of management based on the relevance of financial accounts. For example, the management department can group and manage highly relevant accounts. This allows for priority management of highly relevant accounts by adjusting the order of management based on the relevance of financial accounts. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department inputs data on the relevance of financial accounts into a generative AI, and the generative AI adjusts the order of management.

[0123] The adjustment unit can estimate the user's emotions and determine how to adjust the balance of income and expenses based on the estimated emotions. For example, if the user is stressed, the adjustment unit can quickly adjust the balance of income and expenses, postponing detailed adjustments. For example, if the user is relaxed, the adjustment unit can adjust all balances of income and expenses evenly. For example, if the user is in a hurry, the adjustment unit can quickly adjust only the most important balance of income and expenses. In this way, by determining how to adjust the balance of income and expenses based on the user's emotions, appropriate adjustments can be made according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or not using a generative AI. For example, the adjustment unit inputs the user's emotion data into a generative AI, and the generative AI determines how to adjust the balance of income and expenses.

[0124] The adjustment unit can adjust the level of detail of the adjustment based on the importance of the balance of income and expenses during the adjustment process. For example, the adjustment unit can perform detailed adjustments for important balances of income and expenses and simplified adjustments for less important balances. For example, the adjustment unit can adjust the level of detail of the adjustment based on the amount of the balance of income and expenses. For example, the adjustment unit can adjust the level of detail of the adjustment based on the frequency of the balance of income and expenses. This allows for detailed adjustments to be made to important balances by adjusting the level of detail of the adjustment based on the importance of the balance of income and expenses. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit inputs data on the importance of the balance of income and expenses into the generating AI, and the generating AI adjusts the level of detail of the adjustment.

[0125] The adjustment unit can apply different adjustment algorithms depending on the category of the income and expenditure balance during the adjustment process. For example, the adjustment unit can apply a risk adjustment algorithm to investment income and expenditure balances and a stability adjustment algorithm to daily expenditure income and expenditure balances. The adjustment unit can apply different adjustment algorithms depending on the category of the income and expenditure balance. For example, the adjustment unit can select the optimal adjustment algorithm depending on the type of income and expenditure balance. By applying different adjustment algorithms depending on the category of the income and expenditure balance, the optimal adjustment can be made according to the type of balance. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit inputs data on the categories of income and expenditure balances into a generating AI, and the generating AI applies different adjustment algorithms.

[0126] The adjustment unit can estimate the user's emotions and determine the timing of adjustments to the balance of income and expenses based on the estimated emotions. For example, if the user is stressed, the adjustment unit can quickly adjust important balances and postpone detailed adjustments. For example, if the user is relaxed, the adjustment unit can adjust all balances evenly. For example, if the user is in a hurry, the adjustment unit can quickly adjust only the most important balances. This allows adjustments to be made at the appropriate time according to the user's situation by determining the timing of balance adjustments based on 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 adjustment unit may be performed using a generative AI, or not using a generative AI. For example, the adjustment unit inputs the user's emotion data into a generative AI, and the generative AI determines the timing of balance adjustments.

[0127] The adjustment unit can determine the priority of adjustments based on the submission timing of the income and expenditure balances. For example, the adjustment unit may prioritize adjusting recent income and expenditure balances and postpone past balances. The adjustment unit can determine the priority of adjustments based on the submission timing of the income and expenditure balances. For example, the adjustment unit may prioritize adjusting income and expenditure balances that require urgent attention. This allows for prioritizing adjustments of urgent balances by determining the priority of adjustments based on the submission timing of the income and expenditure balances. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit inputs data on the submission timing of income and expenditure balances into the generating AI, and the generating AI determines the priority of adjustments.

[0128] The adjustment unit can adjust the order of adjustments based on the relationships between income and expenditure balances during the adjustment process. For example, the adjustment unit may prioritize adjusting highly related income and expenditure balances and postpone adjusting less related balances. The adjustment unit can adjust the order of adjustments based on the relationships between income and expenditure balances. For example, the adjustment unit can group highly related balances and adjust them together. This allows for prioritizing the adjustment of highly related balances by adjusting the order of adjustments based on the relationships between income and expenditure balances. Some or all of the above processing in the adjustment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the adjustment unit inputs data on the relationships between income and expenditure balances into a generating AI, and the generating AI adjusts the order of adjustments.

[0129] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on the estimated emotions. For example, if the user is relaxed, the simulation unit can provide a simulation scenario that proceeds at a leisurely pace. For example, if the user is in a hurry, the simulation unit can provide a simulation scenario that emphasizes the shortest route. For example, if the user is excited, the simulation unit can provide a simulation scenario with visually stimulating effects. In this way, by adjusting the simulation scenario based on the user's emotions, an appropriate scenario can be provided according to the user's situation. 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 simulation unit may be performed using a generative AI, or not using a generative AI. For example, the simulation unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the simulation scenario.

[0130] The simulation unit can adjust the level of detail of the simulation based on the importance of the financial instruments during the simulation. For example, the simulation unit can perform detailed simulations for important financial instruments and simplified simulations for less important instruments. The simulation unit can adjust the level of detail of the simulation based on the amount of the financial instruments, for example. The simulation unit can adjust the level of detail of the simulation based on the risk of the financial instruments, for example. This allows for detailed simulations of important instruments by adjusting the level of detail of the simulation based on the importance of the financial instruments. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit inputs data on the importance of financial instruments into the generative AI, and the generative AI adjusts the level of detail of the simulation.

[0131] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is stressed, the simulation unit will prioritize important simulations and postpone detailed simulations. For example, if the user is relaxed, the simulation unit can perform all simulations equally. For example, if the user is in a hurry, the simulation unit can quickly perform only the most important simulations. In this way, by determining the priority of simulations based on the user's emotions, important simulations appropriate to the user's situation can be prioritized. 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 simulation unit may be performed using a generative AI, or not using a generative AI. For example, the simulation unit inputs user emotion data into a generative AI, and the generative AI determines the priority of simulations.

[0132] The simulation unit can weight simulations based on the filing date of financial products. For example, the simulation unit may prioritize simulating recent financial products and postpone simulating older products. The simulation unit can weight simulations based on the filing date of financial products. For example, the simulation unit can prioritize simulating financial products with high urgency. This allows for prioritizing the simulation of products with high urgency by weighting simulations based on the filing date of financial products. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit inputs data on the filing date of financial products into a generative AI, and the generative AI performs the simulation weighting.

[0133] The support unit can estimate the user's emotions and adjust the method of providing support based on the estimated emotions. For example, if the user is nervous, the support unit can provide support in a calm voice. For example, if the user is relaxed, the support unit can provide support in a cheerful voice. For example, if the user is in a hurry, the support unit can provide quick and concise support. In this way, by adjusting the method of providing support based on the user's emotions, appropriate support can be provided according to the user's situation. 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 support unit may be performed using a generative AI, or not using a generative AI. For example, the support unit inputs user emotion data into a generative AI, and the generative AI adjusts the method of providing support.

[0134] The support department can provide optimal support by referring to the user's past inquiry history during support. For example, the support department can provide optimal support based on the content of past inquiries the user has made. For example, the support department can provide relevant support information from the user's past inquiry history. For example, the support department can analyze the user's inquiry patterns and provide the most efficient support. In this way, by referring to the user's past inquiry history, the support department can provide the optimal support for the user. Some or all of the above processes in the support department may be performed using, for example, a generation AI, or not using a generation AI. For example, the support department inputs data from the user's past inquiry history into a generation AI, and the generation AI provides optimal support.

[0135] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize providing important support. For example, if the user is relaxed, the support unit can provide all support equally. For example, if the user is in a hurry, the support unit can quickly provide only the most important support. In this way, by determining the priority of support based on the user's emotions, important support appropriate to the user's situation can be provided preferentially. 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 support unit may be performed using a generative AI, or not using a generative AI. For example, the support unit inputs the user's emotion data into a generative AI, and the generative AI determines the priority of support.

[0136] The support unit can provide optimal support by considering the user's device information during support. For example, if the user is using a smartphone, the support unit can provide support tailored to the screen size. For example, if the user is using a tablet, the support unit can provide support optimized for a larger screen. For example, if the user is using a smartwatch, the support unit can provide concise and highly visible support. In this way, by considering the user's device information, it is possible to provide support optimized for the device the user is using. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit inputs the user's device information data into a generative AI, and the generative AI provides optimal support.

[0137] The advice unit can estimate the user's emotions and adjust the way advice is delivered based on the estimated emotions. For example, if the user is nervous, the advice unit can provide advice in a calm voice. For example, if the user is relaxed, the advice unit can provide advice in a cheerful voice. For example, if the user is in a hurry, the advice unit can provide quick and concise advice. In this way, by adjusting the way advice is delivered based on the user's emotions, appropriate advice can be provided according to the user's situation. 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 advice unit may be performed using a generative AI, or not using a generative AI. For example, the advice unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the way advice is delivered.

[0138] The advice unit can provide optimal advice by referring to the user's past advice history when providing advice. For example, the advice unit can provide optimal advice based on advice the user has received in the past. For example, the advice unit can provide relevant advice information from the user's past advice history. For example, the advice unit can analyze the user's advice patterns and provide the most efficient advice. In this way, by referring to the user's past advice history, it is possible to provide the best possible advice for the user. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the advice unit inputs data from the user's past advice history into a generative AI, and the generative AI provides optimal advice.

[0139] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is stressed, the advice unit will prioritize important advice. For example, if the user is relaxed, the advice unit can provide all advice equally. For example, if the user is in a hurry, the advice unit can quickly provide only the most important advice. In this way, by prioritizing advice based on the user's emotions, important advice appropriate to the user's situation can be prioritized. 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 advice unit may be performed using a generative AI, or not using a generative AI. For example, the advice unit inputs the user's emotion data into a generative AI, and the generative AI determines the priority of advice.

[0140] The advice unit can provide optimal advice by considering the user's device information when providing advice. For example, if the user is using a smartphone, the advice unit can provide advice tailored to the screen size. For example, if the user is using a tablet, the advice unit can provide advice optimized for a larger screen. For example, if the user is using a smartwatch, the advice unit can provide concise and easy-to-read advice. In this way, by considering the user's device information, it is possible to provide advice optimized for the device the user is using. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit inputs the user's device information data into a generative AI, and the generative AI provides optimal advice.

[0141] The options unit can estimate the user's emotions and adjust how investment options are presented based on the estimated emotions. For example, if the user is stressed, the options unit may prioritize offering low-risk investment options. If the user is relaxed, the options unit may offer investment options with a balanced risk-return ratio. If the user is in a hurry, the options unit may offer investment options that can be executed quickly. By adjusting how investment options are presented based on the user's emotions, appropriate investment options can be provided according to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the options unit may be performed using a generative AI or not. For example, the options unit inputs user emotion data into a generative AI, and the generative AI adjusts how investment options are presented.

[0142] The options unit can provide the optimal option by referring to the user's past investment history when offering investment options. For example, the options unit can provide the optimal option based on the user's past successful investment options. For example, the options unit can provide relevant investment options from the user's past investment history. For example, the options unit can analyze the user's investment patterns and provide the most efficient option. This allows the options unit to provide the optimal investment option for the user by referring to the user's past investment history. Some or all of the above processing in the options unit can be performed, for example, by collecting financial information using a generative AI. This allows the options unit to provide investment options that are more relevant to the user by considering the user's geographical location. Some or all of the above processing in the options unit may be performed, for example, using a generative AI or not. For example, the options unit inputs the user's geographical location data into a generative AI, and the generative AI quickly adjusts only the most important balance of income and expenses.

[0143] The options unit can estimate the user's emotions and prioritize investment options based on those emotions. For example, if the user is stressed, the options unit may prioritize offering low-risk investment options. If the user is relaxed, the options unit may offer investment options with a balanced risk-return ratio. If the user is in a hurry, the options unit may offer investment options that can be executed quickly. By prioritizing investment options based on the user's emotions, the system can prioritize offering important investment options that are relevant to the user's situation. 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 processing described above in the options unit may be performed using a generative AI, or not. For example, the options unit inputs user emotion data into a generative AI, which then determines the priority of investment options.

[0144] The options unit can provide the most suitable investment options by considering the user's geographical location when offering investment options. For example, the options unit can prioritize providing investment options for the region where the user is currently located. For example, the options unit can provide relevant investment options based on the user's geographical location. For example, the options unit can prioritize providing investment options for regions that the user frequently visits. This allows the options unit to provide investment options that are highly relevant to the user by considering the user's geographical location. Some or all of the above processing in the options unit may be performed using, for example, a generative AI, or without a generative AI. For example, the options unit inputs the user's geographical location data into a generative AI, and the generative AI provides the most suitable investment options.

[0145] The feedback unit can estimate the user's emotions and adjust the way feedback is provided based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide feedback in a calm voice. For example, if the user is relaxed, the feedback unit can provide feedback in a cheerful voice. For example, if the user is in a hurry, the feedback unit can provide quick and concise feedback. In this way, by adjusting the way feedback is provided based on the user's emotions, appropriate feedback can be provided according to the user's situation. 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 feedback unit may be performed using a generative AI, or not using a generative AI. For example, the feedback unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the way feedback is provided.

[0146] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit can provide optimal feedback based on feedback the user has received in the past. For example, the feedback unit can provide relevant feedback information from the user's past feedback history. For example, the feedback unit can analyze the user's feedback patterns and provide the most efficient feedback. In this way, by referring to the user's past feedback history, it is possible to provide the optimal feedback for the user. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback unit inputs data from the user's past feedback history into a generative AI, and the generative AI provides optimal feedback.

[0147] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will prioritize providing important feedback. For example, if the user is relaxed, the feedback unit can provide all feedback equally. For example, if the user is in a hurry, the feedback unit can quickly provide only the most important feedback. In this way, by prioritizing feedback based on the user's emotions, important feedback appropriate to the user's situation can be provided preferentially. 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 feedback unit may be performed using a generative AI, or not using a generative AI. For example, the feedback unit inputs the user's emotion data into a generative AI, and the generative AI determines the priority of feedback.

[0148] The feedback unit can provide optimal feedback by considering the user's device information when providing feedback. For example, if the user is using a smartphone, the feedback unit can provide feedback that is adapted to the screen size. For example, if the user is using a tablet, the feedback unit can provide feedback optimized for a larger screen. For example, if the user is using a smartwatch, the feedback unit can provide concise and highly visible feedback. In this way, by considering the user's device information, it is possible to provide feedback optimized for the device the user is using. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs user device information data into a generative AI, and the generative AI provides optimal feedback.

[0149] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0150] Financial management systems can also collect users' health data and provide financial advice based on their health status. For example, they can monitor users' exercise levels and sleep duration, suggesting high-risk investments if their health is good, and low-risk savings plans if their health is deteriorating. They can also analyze users' health data and provide financial plans for improving their health. For instance, they can recommend gym memberships or the purchase of health foods to maintain health. Furthermore, they can review existing insurance policies or suggest new insurance products based on the user's health status.

[0151] Financial management systems can also provide customized financial advice based on the user's hobbies and interests. For example, if a user enjoys traveling, the system can suggest savings plans for travel funds and travel insurance. It can also suggest investment products related to the user's hobbies. For instance, if a user is interested in art, it can suggest art-related investment products. Furthermore, it can provide information on specific events and seminars based on the user's hobbies and interests. For example, if a user is interested in fitness, it can provide information on fitness-related events and seminars.

[0152] Financial management systems can also provide financial advice tailored to the user's life stage. For example, if a user is about to get married, it can offer advice on saving for wedding expenses and managing household finances after marriage. If a user plans to have children, it can offer advice on saving for education expenses and suggest insurance products for children. Furthermore, if a user is about to retire, it can offer advice on planning for post-retirement living expenses and optimizing pensions. For example, it can provide estimates of living expenses after retirement and suggest pension payment methods.

[0153] Financial management systems can also provide local financial services and promotions based on a user's geographical location. For example, if a user is in a specific region, it can provide information on special offers from banks and financial institutions in that region. If a user is traveling, it can also provide information on financial services and exchange rates in their destination. Furthermore, it can guide users to nearby ATMs and bank branches based on their location. For instance, if a user needs to withdraw cash, it can guide them to the nearest ATM.

[0154] Financial management systems can also analyze users' social media activity and provide relevant financial advice. For example, if a user shows interest in a particular brand or product on social media, it can provide financial advice related to that brand or product. It can also provide appropriate financial advice based on life events shared by users on social media. For instance, if a user starts a new job, it can suggest savings plans and investment strategies related to that job. Furthermore, it can analyze users' social media activity and suggest financial products based on their interests.

[0155] Financial management systems can also estimate a user's emotions and adjust the timing of financial advice based on those emotions. For example, if a user is stressed, important financial advice can be postponed and delivered when they are relaxed. Similarly, if a user is agitated, high-risk investment suggestions can be avoided, and a stable savings plan can be proposed instead. Furthermore, if a user is in a hurry, concise and to-the-point advice can be provided. This allows for the delivery of financial advice at the appropriate time, tailored to the user's emotions.

[0156] Financial management systems can also estimate a user's emotions and adjust the content of financial advice based on those emotions. For example, if a user is feeling anxious, it can offer low-risk investment suggestions to provide reassurance. Conversely, if a user is confident, it can offer high-risk investment suggestions and propose a more challenging approach. Furthermore, if a user is feeling down, it can provide advice that includes positive feedback to boost their motivation. This allows for the provision of financial advice that is appropriate to the user's emotions.

[0157] Financial management systems can also estimate a user's emotions and adjust the way financial advice is presented based on those emotions. For example, if a user is stressed, simple and easy-to-understand advice can be provided. If a user is relaxed, advice with more detailed information can be provided. Furthermore, if a user is in a hurry, concise and to-the-point advice can be provided. This allows financial advice to be delivered in an appropriate manner according to the user's emotions.

[0158] Financial management systems can also estimate a user's emotions and prioritize financial advice based on those emotions. For example, if a user is stressed, important advice can be prioritized, while detailed advice can be postponed. If a user is relaxed, all advice can be provided equally. Furthermore, if a user is in a hurry, only the most important advice can be provided quickly. This allows for the prioritization of important advice in accordance with the user's emotions.

[0159] Financial management systems can also estimate a user's emotions and adjust the feedback method of financial advice based on those estimated emotions. For example, if a user is stressed, feedback can be provided in a calm voice. If the user is relaxed, feedback can be provided in a cheerful voice. Furthermore, if the user is in a hurry, quick and concise feedback can be provided. This allows for the delivery of financial advice using an appropriate feedback method tailored to the user's emotions.

[0160] The following briefly describes the processing flow for example form 2.

[0161] Step 1: The data collection unit collects information from the user's multiple credit cards and bank accounts. For example, it can collect credit card transaction history and bank account deposit and withdrawal history, and collect information such as transaction date and time, transaction amount, store used, deposit date and time, withdrawal date and time, and transaction details. Step 2: The analysis unit analyzes the information collected by the collection unit to grasp the overall picture of income and expenses. For example, it can analyze the collected transaction data to understand the balance between income and expenses, create monthly income and expense reports, and perform a categorized analysis of income and expenses. Step 3: The proposal unit proposes optimal savings and investment plans based on the analysis results obtained by the analysis unit. For example, it can propose an optimal savings plan based on the user's spending patterns and income, and an optimal investment plan considering the user's risk tolerance, target amount, investment period, etc. Step 4: The management department manages all financial accounts, including multi-currency accounts and overseas accounts. For example, it can manage transactions in multiple currencies, analyze exchange rate fluctuations, and propose optimal asset allocations considering the types of currencies and methods for obtaining exchange rates. Step 5: The adjustment unit autonomously adjusts the balance of income and expenses. For example, it can automatically adjust income and expenses, set budgets, optimize the balance of income and expenses, and generate monthly income and expense reports.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, management unit, adjustment unit, simulation unit, support unit, advice unit, options unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects credit card usage history and bank account deposit and withdrawal history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected transaction data to grasp the overall picture of income and expenses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal savings plan or investment plan based on the user's spending patterns and income. The management unit is implemented by the control unit 46A of the smart device 14 and manages all financial accounts, including multi-currency support and overseas accounts. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and autonomously adjusts the balance of income and expenses. The simulation unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and simulates specific investment scenarios and develops investment strategies based on the results. The support unit, for example, is implemented by the control unit 46A of the smart device 14, and allows users to ask questions to the AI ​​in real time and receive immediate answers. The advice unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and allows users to share information with other users and receive advice. The options unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and proposes investment options that take the environment and society into consideration. The feedback unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and provides individual feedback based on the user's financial situation and investment results. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0166] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In 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.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 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.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, management unit, adjustment unit, simulation unit, support unit, advice unit, options unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects credit card usage history and bank account deposit and withdrawal history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected transaction data to grasp the overall picture of income and expenses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal savings plan or investment plan based on the user's spending patterns and income. The management unit is implemented by the control unit 46A of the smart glasses 214 and manages all financial accounts, including multi-currency support and overseas accounts. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and autonomously adjusts the balance of income and expenses. The simulation unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and simulates a specific investment scenario and develops an investment strategy based on the results. The support unit, for example, is implemented by the control unit 46A of the smart glasses 214, and allows the user to ask questions to the AI ​​in real time and receive immediate answers. The advice unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and allows the user to share information with other users and receive advice. The options unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and proposes investment options that take the environment and society into consideration. The feedback unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and provides individual feedback based on the user's financial situation and investment results. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0182] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, management unit, adjustment unit, simulation unit, support unit, advice unit, options unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects credit card usage history and bank account deposit and withdrawal history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected transaction data to grasp the overall picture of income and expenses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal savings plan or investment plan based on the user's spending patterns and income. The management unit is implemented by the control unit 46A of the headset terminal 314 and manages all financial accounts, including multi-currency support and overseas accounts. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and autonomously adjusts the balance of income and expenses. The simulation unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and simulates a specific investment scenario and develops an investment strategy based on the results. The support unit, for example, is implemented by the control unit 46A of the headset terminal 314, and allows the user to ask questions to the AI ​​in real time and receive immediate answers. The advice unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and allows the user to share information with other users and receive advice. The options unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and proposes investment options that take the environment and society into consideration. The feedback unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and provides individual feedback based on the user's financial situation and investment results. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0198] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, management unit, adjustment unit, simulation unit, support unit, advice unit, options unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects credit card usage history and bank account deposit and withdrawal history. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected transaction data to grasp the overall picture of income and expenses. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal savings plan or investment plan based on the user's spending patterns and income. The management unit is implemented by, for example, the control unit 46A of the robot 414 and manages all financial accounts, including multi-currency support and overseas accounts. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and autonomously adjusts the balance of income and expenses. The simulation unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and simulates a specific investment scenario and develops an investment strategy based on the results. The support unit, for example, is implemented by the control unit 46A of the robot 414, and allows the user to ask questions to the AI ​​in real time and receive immediate answers. The advice unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and allows the user to share information with other users and receive advice. The options unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and proposes investment options that take the environment and society into consideration. The feedback unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and provides individual feedback based on the user's financial situation and investment results. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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."

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] (Note 1) A collection unit that collects information on multiple credit cards and bank accounts of users, The analysis unit analyzes the information collected by the aforementioned collection unit to grasp the overall picture of income and expenses, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an optimal savings plan or investment plan. The management department manages all financial accounts, including multi-currency accounts and overseas accounts, It includes an adjustment unit that autonomously adjusts the balance of income and expenses. A system characterized by the following features. (Note 2) It has a simulation department that performs specialized investment simulations. The system described in Appendix 1, characterized by the features described herein. (Note 3) Support Department provides AI chatbot support. The system described in Appendix 1, characterized by the features described herein. (Note 4) A community advice department is provided to offer community advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) Features an options section that proposes sustainable investment options. The system described in Appendix 1, characterized by the features described herein. (Note 6) Equipped with a feedback section that provides personalized feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Collect credit card transaction history and bank account deposit and withdrawal history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Analyze the collected transaction data to understand the overall picture of income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, We propose optimal savings and investment plans based on the user's spending patterns and income. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, Manage transactions in multiple currencies and analyze exchange rate fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The adjustment unit is, Autonomously adjust the balance of income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates user sentiment and determines the priority of financial information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) 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 15) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of acquiring financial information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned collection unit is During data collection, the user's social media activity is analyzed, and relevant financial information is collected. 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 how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the financial transactions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of financial transaction. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the timing of financial transaction submissions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of financial transactions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the financial product. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of financial product. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When submitting proposals, the priority of proposals will be determined based on the timing of the submission of financial products. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, adjust the order of proposals based on the relevance of the financial products. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, It estimates user sentiment and determines the priority of financial accounts to manage based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, During management, adjust the level of detail based on the importance of the financial account. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, During management, different management algorithms are applied depending on the category of the financial account. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, We estimate user sentiment and adjust how financial accounts are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, When managing, determine the priority of management based on the submission time of the financial account. The system according to Appendix 1, characterized by this. (Appendix 35) The management department When managing, adjust the order of management based on the relevance of the financial account. The system according to Appendix 1, characterized by this. (Appendix 36) The adjustment department Estimate the user's emotion and determine the adjustment method of the income and expenditure balance based on the estimated user's emotion. The system according to Appendix 1, characterized by this. (Appendix 37) The adjustment department When adjusting, adjust the detail level of the adjustment based on the importance of the income and expenditure balance. The system according to Appendix 1, characterized by this. (Appendix 38) The adjustment department When adjusting, apply different adjustment algorithms according to the category of the income and expenditure balance. The system according to Appendix 1, characterized by this. (Appendix 39) The adjustment department Estimate the user's emotion and determine the adjustment timing of the income and expenditure balance based on the estimated user's emotion. The system according to Appendix 1, characterized by this. (Appendix 40) The adjustment department When adjusting, determine the priority of the adjustment based on the submission time of the income and expenditure balance. The system according to Appendix 1, characterized by this. <000091​​​​​​​​​​​It estimates the user's emotions and adjusts the simulation scenario based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned simulation unit, During the simulation, adjust the level of detail based on the importance of the financial instruments. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned simulation unit, During the simulation, the simulation is weighted based on the timing of the submission of financial products. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned support unit is We estimate the user's emotions and adjust how support is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned support unit is When providing support, we refer to the user's past inquiry history to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned support unit is When providing support, we take the user's device information into consideration to provide the best possible support. The system described in Appendix 1, characterized by the features described herein. (Note 50) The advice unit estimates the user's emotion and adjusts the advice providing method based on the estimated user emotion The system according to Supplementary Note 1, characterized in that (Supplementary Note 51) The advice unit provides optimal advice by referring to the user's past advice history when giving advice The system according to Supplementary Note 1, characterized in that (Supplementary Note 52) The advice unit estimates the user's emotion and determines the priority order of advice based on the estimated user emotion The system according to Supplementary Note 1, characterized in that (Supplementary Note 53) The advice unit provides optimal advice by considering the user's device information when giving advice The system according to Supplementary Note 1, characterized in that (Supplementary Note 54) The option unit estimates the user's emotion and adjusts the investment option providing method based on the estimated user emotion The system according to Supplementary Note 1, characterized in that (Supplementary Note 55) The option unit provides optimal options by referring to the user's past investment history when providing investment options The system according to Supplementary Note 1, characterized in that (Supplementary Note 56) The option unit estimates the user's emotion and determines the priority order of investment options based on the estimated user emotion The system according to Supplementary Note 1, characterized in that (Supplementary Note 57) The option unit provides optimal options by considering the user's geographical location information when providing investment options The system described in Appendix 1, characterized by the features described herein. (Note 58) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 59) The aforementioned feedback unit is When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 60) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 61) The aforementioned feedback unit is When providing feedback, we take the user's device information into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0234] 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 information on multiple credit cards and bank accounts of users, The analysis unit analyzes the information collected by the aforementioned collection unit to grasp the overall picture of income and expenses, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an optimal savings plan or investment plan. The management department manages all financial accounts, including multi-currency accounts and overseas accounts, It includes an adjustment unit that autonomously adjusts the balance of income and expenses. A system characterized by the following features.

2. It has a simulation department that performs specialized investment simulations. The system according to feature 1.

3. It has a support department that provides AI chatbot support. The system according to feature 1.

4. A community advice department is provided to offer community advice. The system according to feature 1.

5. Features an options section that proposes sustainable investment options. The system according to feature 1.

6. Equipped with a feedback section that provides personalized feedback. The system according to feature 1.

7. The aforementioned collection unit is Collect credit card transaction history and bank account deposit and withdrawal history. The system according to feature 1.

8. The aforementioned analysis unit, Analyze the collected transaction data to understand the overall picture of income and expenses. The system according to feature 1.

9. The aforementioned proposal section is, We propose optimal savings and investment plans based on the user's spending patterns and income. The system according to feature 1.

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