system

Abstract

The system according to this embodiment aims to integrate the user's financial information, understand their income and expenses, and suggest ways to save money and optimal investment options. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a voice operation unit, and a chat dialogue unit. The collection unit collects the user's financial information. The analysis unit analyzes the financial information collected by the collection unit and grasps the income and expenses. The proposal unit proposes saving points and optimal investment destinations based on the income and expenses grasped by the analysis unit. The voice operation unit accepts voice commands. The chat dialogue unit conducts dialogue via chat.

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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the financial information of users has not been sufficiently integrated to grasp income and expenditure, and to propose saving points and optimal investment destinations, leaving room for improvement.

[0005] The system according to the embodiment aims to integrate the financial information of users, grasp income and expenditure, and propose saving points and optimal investment destinations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a voice operation unit, and a chat dialogue unit. The data collection unit collects the user's financial information. The analysis unit analyzes the financial information collected by the data collection unit to understand the income and expenses. The proposal unit proposes saving points and optimal investment options based on the income and expenses understood by the analysis unit. The voice operation unit accepts voice commands. The chat dialogue unit conducts conversations via chat. [Effects of the Invention]

[0007] The system according to this embodiment can integrate the user's financial information, understand their income and expenses, and suggest saving points and optimal investment options. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​household budget assistant "MoneySmart" according to an embodiment of the present invention is a system that integrates information such as the user's bank accounts, credit cards, and electronic money, and automatically analyzes income and expenses. This system collects and integrates information such as the user's bank accounts, credit cards, and electronic money. Next, the AI ​​analyzes the collected information and automatically grasps income and expenses. This allows the system to understand the user's spending trends and suggest saving points and optimal investment options. It also allows voice operation and chat interaction, providing easy support for household budget management. For example, it collects and integrates information such as the user's bank accounts, credit cards, and electronic money. In this case, the user only needs to input various financial information. For example, the user inputs bank account information, credit card usage history, and electronic money balance. This information is collected and integrated by the AI. Next, the AI ​​analyzes the collected information and automatically grasps income and expenses. Based on the collected information, the AI ​​analyzes the user's income and expenses and grasps spending trends. For example, it analyzes monthly fixed costs, variable costs, and the proportion of spending in specific categories. This allows the system to grasp the user's spending trends. Furthermore, the AI ​​suggests saving points and optimal investment opportunities based on its identified spending trends. For example, it proposes saving techniques to reduce unnecessary expenses and investment plans tailored to the user's lifestyle. This allows users to efficiently manage their assets and build wealth. It also supports easy household budget management through voice control and chat. Users can ask questions and give instructions to the AI ​​via voice or chat, and the AI ​​will provide appropriate advice and actions accordingly. For example, the AI ​​can instantly answer questions such as "Tell me about this month's spending" or "Give me some saving advice." This system allows users to easily manage their household finances and gain financial security. In addition, the integration of communication and finance can propose new lifestyles. For example, it becomes possible to offer added value through point services provided by telecommunications carriers and integration with communication charges. As a result, the AI ​​household budget assistant "MoneySmart" can efficiently collect, analyze, suggest, operate, and interact with users' financial information.

[0029] The AI ​​household budgeting assistant "MoneySmart" according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a voice operation unit, and a chat dialogue unit. The collection unit collects the user's financial information. The collection unit can collect, for example, the user's bank account information, credit card information, and electronic money information. The collection unit can obtain bank account information, for example, using an API. The collection unit can also obtain credit card usage history. Furthermore, the collection unit can obtain electronic money balance information. For example, the collection unit can obtain account balances and transaction history through a bank's API. Credit card usage history can be obtained through a card company's API. Electronic money balance information can be obtained through an electronic money service's API. The analysis unit analyzes the financial information collected by the collection unit to understand income and expenses. For example, the analysis unit can analyze the user's income and expenses based on the collected information. For example, the analysis unit can analyze monthly income and expenses to understand the balance of income and expenses. Furthermore, the analysis unit can analyze the proportion of expenses in a specific category. Furthermore, the analysis unit can also analyze the fluctuation patterns of income and expenses. For example, the analysis unit calculates monthly income and expenditure balances based on income and expenditure data. The proportion of expenditures in specific categories can be analyzed by category, such as food expenses, transportation expenses, and entertainment expenses. Income and expenditure fluctuation patterns can be analyzed based on past data to predict future income and expenditures. The suggestion unit proposes saving points and optimal investment destinations based on the income and expenditures grasped by the analysis unit. For example, the suggestion unit can propose saving points based on the user's spending trends. For example, the suggestion unit can propose saving techniques to reduce unnecessary expenses. The suggestion unit can also propose investment plans tailored to the user's lifestyle. Furthermore, the suggestion unit can provide advice to improve the user's income and expenditure balance. For example, the suggestion unit can propose ways to save on food expenses or reduce transportation expenses. Investment plans can be proposed based on risk assessment and return predictions. Advice to improve the income and expenditure balance can propose ways to increase income or decrease expenses. The voice control unit accepts voice commands from the user.The voice control unit can, for example, recognize the user's voice commands and operate the system. For instance, the voice control unit can accept a voice command such as, "Tell me about this month's spending." It can also accept a voice command such as, "Give me some saving advice." Furthermore, it can accept a voice command such as, "Propose an investment plan." For example, the voice control unit uses speech recognition technology to analyze the user's voice commands and perform appropriate actions. Based on the voice commands, the system can display spending information or provide saving advice. The chat dialogue unit engages in chat dialogue with the user. For example, the chat dialogue unit can engage in text-based dialogue with the user. For instance, the chat dialogue unit can accept a chat message such as, "Tell me about this month's spending." It can also accept a chat message such as, "Give me some saving advice." Furthermore, it can accept a chat message such as, "Propose an investment plan." For example, the chat dialogue unit uses natural language processing technology to analyze the user's chat messages and provide appropriate responses. Based on the chat messages, the system can display spending information or provide saving advice. As a result, the AI ​​household budgeting assistant "MoneySmart" according to this embodiment can efficiently collect, analyze, suggest, manipulate, and interact with the user's financial information.

[0030] The data collection unit collects users' financial information. For example, it can collect users' bank account information, credit card information, and e-money information. Specifically, the unit obtains account balances and transaction history through bank APIs. Bank APIs can be securely accessed using the user's authentication information to obtain the latest account information. Credit card usage history can be obtained through card company APIs. Card company APIs provide users' card usage history in real time, and the data collection unit retrieves this periodically. E-money balance information can be obtained through e-money service APIs. E-money service APIs provide users' balances and transaction history, and the data collection unit uses this to obtain the latest information. The data collection unit uses these APIs to centrally collect users' financial information and store it in a database. The collected data is then prepared for use by the analysis and proposal units. The data collection unit can adjust the frequency and timing of data collection, enabling flexible data collection tailored to user needs. For example, settings can be configured to collect daily transaction information in real time or to collect it in weekly batches. This allows the data collection unit to efficiently and accurately collect users' financial information, thereby improving the overall performance of the system.

[0031] The analysis unit analyzes financial information collected by the data collection unit to understand income and expenses. For example, the analysis unit can analyze a user's income and expenses based on the collected information. Specifically, the analysis unit analyzes monthly income and expenses to understand the balance of income and expenses. Income is calculated based on data such as salary, side job income, and investment returns. Expenses are classified into categories such as food expenses, transportation expenses, entertainment expenses, and utility expenses, and the amount spent in each category is aggregated. Based on this data, the analysis unit calculates the monthly balance of income and expenses and provides it to the user. The analysis unit can also analyze the proportion of expenses in specific categories. For example, it can analyze what percentage of total expenses food expenses account for and present this to the user. Furthermore, the analysis unit can also analyze patterns of fluctuations in income and expenses. Based on past data, it analyzes trends in income and expenses and makes predictions about future income and expenses. For example, based on data from the past few months, it can predict the balance of income and expenses for the next month and present it to the user. The analysis unit can use AI technology to analyze data and make highly accurate predictions. This allows the analysis unit to accurately understand the user's financial situation and provide information for future planning.

[0032] The proposal department suggests saving points and optimal investment options based on the income and expenses analyzed by the analysis department. For example, the proposal department can suggest saving points based on the user's spending habits. Specifically, the proposal department suggests saving techniques to reduce unnecessary expenses. For example, as a way to save on food expenses, it suggests buying in bulk on weekends and reducing eating out while increasing home cooking. As a way to save on transportation expenses, it recommends using public transportation and suggests purchasing a commuter pass. The proposal department can also propose investment plans tailored to the user's lifestyle. For example, it suggests government bonds and corporate bonds as stable investment options with reduced risk. If the user wants to take on more risk and aim for higher returns, it suggests stocks and mutual funds. The proposal department proposes the optimal investment plan according to the user's risk tolerance and investment goals. Furthermore, the proposal department can also provide advice to improve the user's income and expense balance. For example, as a way to increase income, it suggests taking on a side job or obtaining qualifications to improve skills. As a way to reduce expenses, it suggests reviewing fixed costs and canceling unnecessary subscriptions. In this way, the proposal department can improve the user's income and expense balance and support their financial stability.

[0033] The voice control unit accepts user voice commands. For example, the voice control unit can recognize user voice commands and operate the system. Specifically, the voice control unit can accept voice commands such as "Tell me my spending for this month." It can also accept voice commands such as "Give me some saving advice." Furthermore, it can accept voice commands such as "Propose an investment plan." The voice control unit analyzes the user's voice commands using speech recognition technology and performs appropriate operations. For example, if the user says "Tell me my spending for this month," the voice control unit analyzes this command and displays the user's spending for the month based on data obtained from the analysis unit. In response to the command "Give me some saving advice," it provides the user with appropriate saving advice based on information from the proposal unit. The voice control unit can quickly and accurately analyze user voice commands and operate the entire system smoothly. As a result, the voice control unit can provide the convenience of users being able to operate the system without using their hands, improving the user experience.

[0034] The chat dialogue unit engages in chat-based conversations with users. For example, the chat dialogue unit can engage in text-based conversations with users. Specifically, the chat dialogue unit can receive chat messages such as "Tell me about my spending this month." It can also receive chat messages such as "Give me some saving advice." Furthermore, it can receive chat messages such as "Propose an investment plan." The chat dialogue unit uses natural language processing technology to analyze the user's chat messages and provide appropriate responses. For example, if a user sends the message "Tell me about my spending this month," the chat dialogue unit analyzes this message and displays the user's spending status for the month based on data obtained from the analysis unit. Similarly, in response to the message "Give me some saving advice," it provides the user with appropriate saving advice based on information from the proposal unit. The chat dialogue unit can quickly and accurately analyze the user's chat messages, enabling smooth operation of the entire system. As a result, the chat dialogue unit can provide users with the convenience of operating the system in a text-based manner, improving the user experience.

[0035] The data collection unit can collect information such as the user's bank account, credit card, and electronic money. For example, the data collection unit can obtain the user's bank account information using an API. For example, the data collection unit can obtain the credit card usage history using an API. For example, the data collection unit can obtain electronic money balance information using an API. For example, the data collection unit can obtain account balances and transaction history through the bank's API. Credit card usage history can be obtained through the card company's API. Electronic money balance information can be obtained through the electronic money service's API. This allows for the comprehensive collection of the user's financial information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input account balances and transaction history obtained through the bank's API into a generating AI, and the generating AI can integrate the data.

[0036] The analysis unit can analyze the user's income and expenses based on the collected information and understand their spending trends. For example, the analysis unit can analyze monthly income and expenses based on the collected information to understand the balance of income and expenses. For example, the analysis unit can analyze the proportion of expenses in a specific category. For example, the analysis unit can analyze the fluctuation patterns of income and expenses. For example, the analysis unit calculates the monthly balance of income and expenses based on income and expense data. The proportion of expenses in a specific category can be analyzed by category, such as food expenses, transportation expenses, and entertainment expenses. The fluctuation patterns of income and expenses can be analyzed based on past data to make predictions about future income and expenses. This allows for an accurate analysis of the user's income and expenses and an understanding of their spending trends. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, and the generating AI can perform the income and expense analysis.

[0037] The suggestion unit can propose saving points and optimal investment options based on the identified spending trends. For example, the suggestion unit can propose saving points based on the user's spending trends. For example, the suggestion unit can propose saving techniques to reduce unnecessary spending. For example, the suggestion unit can propose investment plans tailored to the user's lifestyle. For example, the suggestion unit can provide advice to improve the user's income and expenditure balance. For example, the suggestion unit can suggest ways to save on food expenses or reduce transportation expenses. Investment plans can be proposed based on risk assessment and return forecasts. Advice on improving the income and expenditure balance can suggest ways to increase income or decrease expenses. This allows the suggestion unit to propose effective saving points and investment options to the user. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the identified spending trends into a generating AI, and the generating AI can then propose saving points and investment options.

[0038] The voice control unit can receive voice commands from the user. For example, the voice control unit can recognize the user's voice commands and operate the system. For example, the voice control unit can accept a voice command such as, "Tell me about this month's spending." It can also accept a voice command such as, "Give me some saving advice." Furthermore, it can accept a voice command such as, "Suggest an investment plan." For example, the voice control unit can analyze the user's voice commands using voice recognition technology and perform appropriate operations. Based on the voice commands, the system can display spending information or provide saving advice. This allows the user to operate the system through voice commands. Some or all of the above-described processes in the voice control unit may be performed using AI, or not. For example, the voice control unit can input the user's voice commands into a generating AI, which can then analyze and operate the voice commands.

[0039] The chat dialogue unit can engage in chat dialogues with users. For example, the chat dialogue unit can engage in text-based dialogues with users. For example, the chat dialogue unit can receive chat messages such as, "Tell me about my spending this month." It can also receive chat messages such as, "Give me some saving advice." Furthermore, it can also receive chat messages such as, "Propose an investment plan." For example, the chat dialogue unit can analyze the user's chat messages using natural language processing technology and provide appropriate responses. Based on the chat messages, the system can display spending information or provide saving advice. This allows users to interact with the system through chat. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or not using AI. For example, the chat dialogue unit can input the user's chat messages into a generation AI, which can then analyze the chat messages and provide responses.

[0040] The data collection unit can analyze the user's past financial information collection history and select the optimal collection method. For example, the data collection unit can prioritize collection methods that the user has frequently used in the past (such as APIs or manual input). For example, the data collection unit can suggest the most efficient collection method based on the user's past collection history. For example, the data collection unit can analyze the user's past collection history and optimize the collection frequency. This allows the system to select the optimal collection method based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI, which can then select the optimal collection method.

[0041] The data collection unit can filter financial information based on the user's current economic situation and areas of interest. For example, the data collection unit can collect only important financial information based on the user's current economic situation. For example, the data collection unit can prioritize the collection of relevant financial information based on the user's areas of interest. For example, the data collection unit can filter out unnecessary information based on the user's economic situation and areas of interest. This allows for the priority collection of important information based on the user's economic situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's economic situation and areas of interest into a generating AI, and the generating AI can perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting financial information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of financial information related to that region. For example, the data collection unit can collect region-specific financial information based on the user's geographical location. For example, the data collection unit can suggest optimal financial information based on the user's current location. This allows for the priority collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI, and the generating AI can collect highly relevant information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information when collecting financial information. For example, the data collection unit can collect financial information of interest from the user's social media activity. For example, the data collection unit can suggest relevant financial information based on the user's social media posts. For example, the data collection unit can analyze the activity of the user's social media followers and friends and collect relevant financial information. This allows the data collection unit to collect relevant financial information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can collect relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on specific expenditure categories during income and expenditure analysis. For example, if the user is interested in a particular expenditure category, the analysis unit can provide a detailed analysis of that category. For example, the analysis unit can prioritize the analysis of high-importance categories based on the user's expenditure categories. For example, the analysis unit can adjust the level of detail based on the user's expenditure categories to provide an optimal analysis. This allows for the provision of a detailed analysis based on the expenditure categories of interest to the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's expenditure category data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms to the user's lifestyle during income and expenditure analysis. For example, if the user is frugal, the analysis unit can apply an analysis algorithm specialized for saving. If the user is investment-oriented, the analysis unit can apply an analysis algorithm specialized for investment. The analysis unit can select the optimal analysis algorithm according to the user's lifestyle. This allows the system to provide the most suitable analysis for the user's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI, which can then apply different analysis algorithms.

[0046] The analysis unit can weight the analysis based on the user's income sources during revenue and expenditure analysis. For example, the analysis unit can perform high-priority analyses based on the user's main income sources. For example, the analysis unit can weight analyses according to the proportion of income based on the user's income sources. For example, the analysis unit can provide optimal analyses based on the user's income sources. This allows important analyses to be performed preferentially based on the user's income sources. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's income source data into a generating AI, and the generating AI can weight the analysis.

[0047] The analysis unit can adjust the direction of the analysis based on the user's future goals when performing income and expenditure analysis. For example, if the user prioritizes saving as a future goal, the analysis unit can perform an analysis focused on savings. For example, if the user prioritizes investing as a future goal, the analysis unit can perform an analysis focused on investing. For example, the analysis unit can determine the optimal direction of the analysis based on the user's future goals. This allows the analysis unit to provide the optimal analysis based on the user's future goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's future goal data into a generating AI, and the generating AI can adjust the direction of the analysis.

[0048] The proposal unit can adjust the level of detail of its proposals based on the user's spending habits. For example, the proposal unit can prioritize proposals of high importance based on the user's spending habits. For example, the proposal unit can provide detailed proposals based on the user's spending habits. For example, the proposal unit can determine the optimal level of detail for a proposal based on the user's spending habits. This allows the proposal unit to provide optimal proposals based on the user's spending habits. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user spending habit data into a generating AI, and the generating AI can adjust the level of detail of the proposals.

[0049] The proposal unit can apply different proposal algorithms depending on the user's life stage when making a proposal. For example, if the user is young, the proposal unit can make proposals focused on future asset formation. For example, if the user is middle-aged, the proposal unit can make proposals focused on current asset management. The proposal unit can select the optimal proposal algorithm according to the user's life stage. This allows the system to provide optimal proposals tailored to the user's life stage. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's life stage data into a generating AI, and the generating AI can apply different proposal algorithms.

[0050] The proposal unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the proposal unit can suggest investment opportunities related to that region. For example, the proposal unit can suggest region-specific savings points based on the user's geographical location information. For example, the proposal unit can make optimal suggestions based on the user's current location. This allows the system to provide optimal suggestions based on the user's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's geographical location information into a generating AI, and the generating AI can make optimal suggestions.

[0051] The proposal unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the proposal unit can suggest investment opportunities of interest based on the user's social media activity. For example, the proposal unit can suggest relevant savings points based on the content of the user's social media posts. For example, the proposal unit can analyze the activity of the user's social media followers and friends and make relevant suggestions. This allows the proposal unit to provide relevant suggestions based on the user's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's social media activity data into a generating AI, and the generating AI can make relevant suggestions.

[0052] The voice control unit can provide the optimal response by referring to the user's past voice control history when voice commands are used. For example, the voice control unit prioritizes responding to voice commands that the user has frequently used in the past. For example, the voice control unit can suggest the optimal response method based on the user's past voice control history. For example, the voice control unit can analyze the user's past voice control history and provide the most efficient response. This allows the voice control unit to provide the optimal response based on the user's past voice control history. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's past voice control history data into a generating AI, and the generating AI can provide the optimal response.

[0053] The voice control unit can apply different response algorithms depending on the user's speech pattern during voice operation. For example, if the user speaks slowly, the voice control unit will provide a slow response. For example, if the user speaks quickly, the voice control unit can provide a rapid response. For example, the voice control unit can select the optimal response algorithm according to the user's speech pattern. This allows the unit to provide the optimal response according to the user's speech pattern. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's speech pattern data into a generating AI, and the generating AI can apply different response algorithms.

[0054] The voice control unit can provide the optimal response when a user is using voice commands, taking into account the user's device information. For example, if the user is using a smartphone, the voice control unit can provide a response that matches the screen size. For example, if the user is using a tablet, the voice control unit can provide a response optimized for a larger screen. For example, if the user is using a smartwatch, the voice control unit can provide a concise and easily readable response. This allows the system to provide the optimal response based on the user's device information. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's device information into a generating AI, which can then provide the optimal response.

[0055] The voice control unit can analyze the user's ambient sounds during voice operation and provide the optimal response. For example, if the user is in a noisy environment, the voice control unit can increase the volume when responding. For example, if the user is in a quiet environment, the voice control unit can respond at a normal volume. For example, the voice control unit can select the optimal response method according to the user's ambient sounds. This allows it to provide the optimal response according to the user's ambient sounds. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's ambient sound data into a generating AI, and the generating AI can provide the optimal response.

[0056] The chat dialogue unit can provide the optimal response during a chat conversation by referring to the user's past chat history. For example, the chat dialogue unit can prioritize responses to chat topics that the user has frequently discussed in the past. For example, the chat dialogue unit can suggest the optimal response method based on the user's past chat history. For example, the chat dialogue unit can analyze the user's past chat history and provide the most efficient response. This allows the system to provide the optimal response based on the user's past chat history. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input the user's past chat history data into a generating AI, which can then provide the optimal response.

[0057] The chat dialogue unit can apply different response algorithms depending on the user's dialogue style during a chat conversation. For example, if the user uses a polite dialogue style, the chat dialogue unit will provide a polite response. For example, if the user uses a casual dialogue style, the chat dialogue unit can provide a casual response. For example, the chat dialogue unit can select the optimal response algorithm depending on the user's dialogue style. This allows the system to provide the most appropriate response for the user's dialogue style. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input user dialogue style data into a generating AI, which can then apply different response algorithms.

[0058] The chat dialogue unit can provide the optimal response during a chat conversation by taking into account the user's device information. For example, if the user is using a smartphone, the chat dialogue unit can provide a response that is adapted to the screen size. For example, if the user is using a tablet, the chat dialogue unit can provide a response optimized for a larger screen. For example, if the user is using a smartwatch, the chat dialogue unit can provide a concise and highly visible response. This allows the system to provide the optimal response based on the user's device information. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input the user's device information into a generating AI, and the generating AI can provide the optimal response.

[0059] The chat dialogue unit can analyze the user's dialogue history during a chat conversation and provide the optimal response. For example, the chat dialogue unit can prioritize responses to topics of interest based on the user's past dialogue history. For example, the chat dialogue unit can analyze the user's dialogue history and suggest the optimal response method. For example, the chat dialogue unit can provide the most efficient response based on the user's dialogue history. This allows the system to provide the optimal response based on the user's dialogue history. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input the user's dialogue history data into a generating AI, which can then provide the optimal response.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze the user's past data collection history and select the optimal data collection method when collecting a user's financial information. For example, it can prioritize data collection methods that the user has frequently used in the past (such as APIs or manual input). It can also suggest the most efficient data collection method based on the user's past data collection history. Furthermore, it can analyze the user's past data collection history and optimize the data collection frequency. This allows the optimal data collection method to be selected based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal data collection method.

[0062] The analysis unit can weight the analysis based on the user's income sources during revenue and expenditure analysis. For example, it can perform analyses of high importance based on the user's main income sources. It can also weight analyses according to the proportion of income based on the user's income sources. Furthermore, it can provide optimal analyses based on the user's income sources. This allows important analyses to be prioritized based on the user's income sources. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's income source data into a generating AI, and the generating AI can perform the analysis weighting.

[0063] The proposal unit can apply different proposal algorithms depending on the user's life stage when making a proposal. For example, if the user is young, it can make proposals focused on future asset formation. If the user is middle-aged, it can make proposals focused on current asset management. Furthermore, it can select the optimal proposal algorithm according to the user's life stage. This allows the unit to provide optimal proposals tailored to the user's life stage. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's life stage data into a generating AI, and the generating AI can apply different proposal algorithms.

[0064] The voice control unit can provide the optimal response when a user is using voice commands, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a response that matches the screen size. If the user is using a tablet, it can provide a response optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and easily readable response. This allows the unit to provide the optimal response based on the user's device information. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's device information into a generating AI, which can then provide the optimal response.

[0065] The chat dialogue unit can apply different response algorithms depending on the user's dialogue style during a chat conversation. For example, if the user uses a polite dialogue style, it can provide a polite response. If the user uses a casual dialogue style, it can provide a casual response. Furthermore, it can select the optimal response algorithm according to the user's dialogue style. This allows for the provision of the most appropriate response for the user's dialogue style. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input user dialogue style data into a generating AI, which can then apply different response algorithms.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects the user's financial information. The collection unit can collect, for example, the user's bank account information, credit card information, and e-money information. The collection unit can obtain bank account information using an API, for example. The collection unit can also obtain credit card usage history. Furthermore, the collection unit can obtain e-money balance information. For example, the collection unit can obtain account balances and transaction history through the bank's API. Credit card usage history can be obtained through the card company's API. E-money balance information can be obtained through the e-money service's API. Step 2: The analysis unit analyzes the financial information collected by the collection unit to understand income and expenses. For example, the analysis unit can analyze the user's income and expenses based on the collected information. For example, the analysis unit can analyze monthly income and expenses to understand the balance of income and expenses. The analysis unit can also analyze the proportion of expenses in specific categories. Furthermore, the analysis unit can analyze the patterns of fluctuations in income and expenses. For example, the analysis unit calculates the monthly balance of income and expenses based on income and expense data. The proportion of expenses in specific categories can be analyzed by category, such as food expenses, transportation expenses, and entertainment expenses. The patterns of fluctuations in income and expenses can be analyzed based on past data to make predictions about future income and expenses. Step 3: The proposal department suggests saving points and optimal investment options based on the income and expenses identified by the analysis department. For example, the proposal department can suggest saving points based on the user's spending habits. For example, the proposal department can suggest saving techniques to reduce unnecessary expenses. The proposal department can also suggest investment plans tailored to the user's lifestyle. Furthermore, the proposal department can provide advice to improve the user's income and expense balance. For example, the proposal department can suggest ways to save on food expenses or reduce transportation expenses. Investment plans can be proposed based on risk assessment and return forecasts. Advice to improve the income and expense balance can suggest ways to increase income or decrease expenses. Step 4: The voice control unit receives voice commands from the user. The voice control unit can, for example, recognize the user's voice commands and operate the system. For example, the voice control unit can receive a voice command such as, "Tell me my spending for this month." It can also receive a voice command such as, "Give me some saving advice." Furthermore, it can also receive a voice command such as, "Suggest an investment plan." For example, the voice control unit uses voice recognition technology to analyze the user's voice commands and perform the appropriate operation. Based on the voice commands, the system can display spending information or provide saving advice. Step 5: The chat dialogue unit engages in chat conversations with the user. The chat dialogue unit can, for example, engage in text-based conversations with the user. For example, the chat dialogue unit can receive chat messages such as, "Tell me about my spending this month." It can also receive chat messages such as, "Give me some saving advice." Furthermore, it can also receive chat messages such as, "Propose an investment plan." For example, the chat dialogue unit can analyze the user's chat messages using natural language processing technology and provide appropriate responses. Based on the chat messages, the system can display spending information or provide saving advice.

[0068] (Example of form 2) The AI ​​household budget assistant "MoneySmart" according to an embodiment of the present invention is a system that integrates information such as the user's bank accounts, credit cards, and electronic money, and automatically analyzes income and expenses. This system collects and integrates information such as the user's bank accounts, credit cards, and electronic money. Next, the AI ​​analyzes the collected information and automatically grasps income and expenses. This allows the system to understand the user's spending trends and suggest saving points and optimal investment options. It also allows voice operation and chat interaction, providing easy support for household budget management. For example, it collects and integrates information such as the user's bank accounts, credit cards, and electronic money. In this case, the user only needs to input various financial information. For example, the user inputs bank account information, credit card usage history, and electronic money balance. This information is collected and integrated by the AI. Next, the AI ​​analyzes the collected information and automatically grasps income and expenses. Based on the collected information, the AI ​​analyzes the user's income and expenses and grasps spending trends. For example, it analyzes monthly fixed costs, variable costs, and the proportion of spending in specific categories. This allows the system to grasp the user's spending trends. Furthermore, the AI ​​suggests saving points and optimal investment opportunities based on its identified spending trends. For example, it proposes saving techniques to reduce unnecessary expenses and investment plans tailored to the user's lifestyle. This allows users to efficiently manage their assets and build wealth. It also supports easy household budget management through voice control and chat. Users can ask questions and give instructions to the AI ​​via voice or chat, and the AI ​​will provide appropriate advice and actions accordingly. For example, the AI ​​can instantly answer questions such as "Tell me about this month's spending" or "Give me some saving advice." This system allows users to easily manage their household finances and gain financial security. In addition, the integration of communication and finance can propose new lifestyles. For example, it becomes possible to offer added value through point services provided by telecommunications carriers and integration with communication charges. As a result, the AI ​​household budget assistant "MoneySmart" can efficiently collect, analyze, suggest, operate, and interact with users' financial information.

[0069] The AI ​​household budgeting assistant "MoneySmart" according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a voice operation unit, and a chat dialogue unit. The collection unit collects the user's financial information. The collection unit can collect, for example, the user's bank account information, credit card information, and electronic money information. The collection unit can obtain bank account information, for example, using an API. The collection unit can also obtain credit card usage history. Furthermore, the collection unit can obtain electronic money balance information. For example, the collection unit can obtain account balances and transaction history through a bank's API. Credit card usage history can be obtained through a card company's API. Electronic money balance information can be obtained through an electronic money service's API. The analysis unit analyzes the financial information collected by the collection unit to understand income and expenses. For example, the analysis unit can analyze the user's income and expenses based on the collected information. For example, the analysis unit can analyze monthly income and expenses to understand the balance of income and expenses. Furthermore, the analysis unit can analyze the proportion of expenses in a specific category. Furthermore, the analysis unit can also analyze the fluctuation patterns of income and expenses. For example, the analysis unit calculates monthly income and expenditure balances based on income and expenditure data. The proportion of expenditures in specific categories can be analyzed by category, such as food expenses, transportation expenses, and entertainment expenses. Income and expenditure fluctuation patterns can be analyzed based on past data to predict future income and expenditures. The suggestion unit proposes saving points and optimal investment destinations based on the income and expenditures grasped by the analysis unit. For example, the suggestion unit can propose saving points based on the user's spending trends. For example, the suggestion unit can propose saving techniques to reduce unnecessary expenses. The suggestion unit can also propose investment plans tailored to the user's lifestyle. Furthermore, the suggestion unit can provide advice to improve the user's income and expenditure balance. For example, the suggestion unit can propose ways to save on food expenses or reduce transportation expenses. Investment plans can be proposed based on risk assessment and return predictions. Advice to improve the income and expenditure balance can propose ways to increase income or decrease expenses. The voice control unit accepts voice commands from the user.The voice control unit can, for example, recognize the user's voice commands and operate the system. For instance, the voice control unit can accept a voice command such as, "Tell me about this month's spending." It can also accept a voice command such as, "Give me some saving advice." Furthermore, it can accept a voice command such as, "Propose an investment plan." For example, the voice control unit uses speech recognition technology to analyze the user's voice commands and perform appropriate actions. Based on the voice commands, the system can display spending information or provide saving advice. The chat dialogue unit engages in chat dialogue with the user. For example, the chat dialogue unit can engage in text-based dialogue with the user. For instance, the chat dialogue unit can accept a chat message such as, "Tell me about this month's spending." It can also accept a chat message such as, "Give me some saving advice." Furthermore, it can accept a chat message such as, "Propose an investment plan." For example, the chat dialogue unit uses natural language processing technology to analyze the user's chat messages and provide appropriate responses. Based on the chat messages, the system can display spending information or provide saving advice. As a result, the AI ​​household budgeting assistant "MoneySmart" according to this embodiment can efficiently collect, analyze, suggest, manipulate, and interact with the user's financial information.

[0070] The data collection unit collects users' financial information. For example, it can collect users' bank account information, credit card information, and e-money information. Specifically, the unit obtains account balances and transaction history through bank APIs. Bank APIs can be securely accessed using the user's authentication information to obtain the latest account information. Credit card usage history can be obtained through card company APIs. Card company APIs provide users' card usage history in real time, and the data collection unit retrieves this periodically. E-money balance information can be obtained through e-money service APIs. E-money service APIs provide users' balances and transaction history, and the data collection unit uses this to obtain the latest information. The data collection unit uses these APIs to centrally collect users' financial information and store it in a database. The collected data is then prepared for use by the analysis and proposal units. The data collection unit can adjust the frequency and timing of data collection, enabling flexible data collection tailored to user needs. For example, settings can be configured to collect daily transaction information in real time or to collect it in weekly batches. This allows the data collection unit to efficiently and accurately collect users' financial information, thereby improving the overall performance of the system.

[0071] The analysis unit analyzes financial information collected by the data collection unit to understand income and expenses. For example, the analysis unit can analyze a user's income and expenses based on the collected information. Specifically, the analysis unit analyzes monthly income and expenses to understand the balance of income and expenses. Income is calculated based on data such as salary, side job income, and investment returns. Expenses are classified into categories such as food expenses, transportation expenses, entertainment expenses, and utility expenses, and the amount spent in each category is aggregated. Based on this data, the analysis unit calculates the monthly balance of income and expenses and provides it to the user. The analysis unit can also analyze the proportion of expenses in specific categories. For example, it can analyze what percentage of total expenses food expenses account for and present this to the user. Furthermore, the analysis unit can also analyze patterns of fluctuations in income and expenses. Based on past data, it analyzes trends in income and expenses and makes predictions about future income and expenses. For example, based on data from the past few months, it can predict the balance of income and expenses for the next month and present it to the user. The analysis unit can use AI technology to analyze data and make highly accurate predictions. This allows the analysis unit to accurately understand the user's financial situation and provide information for future planning.

[0072] The proposal department suggests saving points and optimal investment options based on the income and expenses analyzed by the analysis department. For example, the proposal department can suggest saving points based on the user's spending habits. Specifically, the proposal department suggests saving techniques to reduce unnecessary expenses. For example, as a way to save on food expenses, it suggests buying in bulk on weekends and reducing eating out while increasing home cooking. As a way to save on transportation expenses, it recommends using public transportation and suggests purchasing a commuter pass. The proposal department can also propose investment plans tailored to the user's lifestyle. For example, it suggests government bonds and corporate bonds as stable investment options with reduced risk. If the user wants to take on more risk and aim for higher returns, it suggests stocks and mutual funds. The proposal department proposes the optimal investment plan according to the user's risk tolerance and investment goals. Furthermore, the proposal department can also provide advice to improve the user's income and expense balance. For example, as a way to increase income, it suggests taking on a side job or obtaining qualifications to improve skills. As a way to reduce expenses, it suggests reviewing fixed costs and canceling unnecessary subscriptions. In this way, the proposal department can improve the user's income and expense balance and support their financial stability.

[0073] The voice control unit accepts user voice commands. For example, the voice control unit can recognize user voice commands and operate the system. Specifically, the voice control unit can accept voice commands such as "Tell me my spending for this month." It can also accept voice commands such as "Give me some saving advice." Furthermore, it can accept voice commands such as "Propose an investment plan." The voice control unit analyzes the user's voice commands using speech recognition technology and performs appropriate operations. For example, if the user says "Tell me my spending for this month," the voice control unit analyzes this command and displays the user's spending for the month based on data obtained from the analysis unit. In response to the command "Give me some saving advice," it provides the user with appropriate saving advice based on information from the proposal unit. The voice control unit can quickly and accurately analyze user voice commands and operate the entire system smoothly. As a result, the voice control unit can provide the convenience of users being able to operate the system without using their hands, improving the user experience.

[0074] The chat dialogue unit engages in chat-based conversations with users. For example, the chat dialogue unit can engage in text-based conversations with users. Specifically, the chat dialogue unit can receive chat messages such as "Tell me about my spending this month." It can also receive chat messages such as "Give me some saving advice." Furthermore, it can receive chat messages such as "Propose an investment plan." The chat dialogue unit uses natural language processing technology to analyze the user's chat messages and provide appropriate responses. For example, if a user sends the message "Tell me about my spending this month," the chat dialogue unit analyzes this message and displays the user's spending status for the month based on data obtained from the analysis unit. Similarly, in response to the message "Give me some saving advice," it provides the user with appropriate saving advice based on information from the proposal unit. The chat dialogue unit can quickly and accurately analyze the user's chat messages, enabling smooth operation of the entire system. As a result, the chat dialogue unit can provide users with the convenience of operating the system in a text-based manner, improving the user experience.

[0075] The data collection unit can collect information such as the user's bank account, credit card, and electronic money. For example, the data collection unit can obtain the user's bank account information using an API. For example, the data collection unit can obtain the credit card usage history using an API. For example, the data collection unit can obtain electronic money balance information using an API. For example, the data collection unit can obtain account balances and transaction history through the bank's API. Credit card usage history can be obtained through the card company's API. Electronic money balance information can be obtained through the electronic money service's API. This allows for the comprehensive collection of the user's financial information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input account balances and transaction history obtained through the bank's API into a generating AI, and the generating AI can integrate the data.

[0076] The analysis unit can analyze the user's income and expenses based on the collected information and understand their spending trends. For example, the analysis unit can analyze monthly income and expenses based on the collected information to understand the balance of income and expenses. For example, the analysis unit can analyze the proportion of expenses in a specific category. For example, the analysis unit can analyze the fluctuation patterns of income and expenses. For example, the analysis unit calculates the monthly balance of income and expenses based on income and expense data. The proportion of expenses in a specific category can be analyzed by category, such as food expenses, transportation expenses, and entertainment expenses. The fluctuation patterns of income and expenses can be analyzed based on past data to make predictions about future income and expenses. This allows for an accurate analysis of the user's income and expenses and an understanding of their spending trends. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, and the generating AI can perform the income and expense analysis.

[0077] The suggestion unit can propose saving points and optimal investment options based on the identified spending trends. For example, the suggestion unit can propose saving points based on the user's spending trends. For example, the suggestion unit can propose saving techniques to reduce unnecessary spending. For example, the suggestion unit can propose investment plans tailored to the user's lifestyle. For example, the suggestion unit can provide advice to improve the user's income and expenditure balance. For example, the suggestion unit can suggest ways to save on food expenses or reduce transportation expenses. Investment plans can be proposed based on risk assessment and return forecasts. Advice on improving the income and expenditure balance can suggest ways to increase income or decrease expenses. This allows the suggestion unit to propose effective saving points and investment options to the user. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the identified spending trends into a generating AI, and the generating AI can then propose saving points and investment options.

[0078] The voice control unit can receive voice commands from the user. For example, the voice control unit can recognize the user's voice commands and operate the system. For example, the voice control unit can accept a voice command such as, "Tell me about this month's spending." It can also accept a voice command such as, "Give me some saving advice." Furthermore, it can accept a voice command such as, "Suggest an investment plan." For example, the voice control unit can analyze the user's voice commands using voice recognition technology and perform appropriate operations. Based on the voice commands, the system can display spending information or provide saving advice. This allows the user to operate the system through voice commands. Some or all of the above-described processes in the voice control unit may be performed using AI, or not. For example, the voice control unit can input the user's voice commands into a generating AI, which can then analyze and operate the voice commands.

[0079] The chat dialogue unit can engage in chat dialogues with users. For example, the chat dialogue unit can engage in text-based dialogues with users. For example, the chat dialogue unit can receive chat messages such as, "Tell me about my spending this month." It can also receive chat messages such as, "Give me some saving advice." Furthermore, it can also receive chat messages such as, "Propose an investment plan." For example, the chat dialogue unit can analyze the user's chat messages using natural language processing technology and provide appropriate responses. Based on the chat messages, the system can display spending information or provide saving advice. This allows users to interact with the system through chat. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or not using AI. For example, the chat dialogue unit can input the user's chat messages into a generation AI, which can then analyze the chat messages and provide responses.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of financial information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can immediately collect financial information and begin analysis. For example, if the user is in a hurry, the data collection unit can advance the collection timing to collect information quickly. This allows for the collection of financial information at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then use the generative AI to estimate emotions and adjust the collection timing.

[0081] The data collection unit can analyze the user's past financial information collection history and select the optimal collection method. For example, the data collection unit can prioritize collection methods that the user has frequently used in the past (such as APIs or manual input). For example, the data collection unit can suggest the most efficient collection method based on the user's past collection history. For example, the data collection unit can analyze the user's past collection history and optimize the collection frequency. This allows the system to select the optimal collection method based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI, which can then select the optimal collection method.

[0082] The data collection unit can filter financial information based on the user's current economic situation and areas of interest. For example, the data collection unit can collect only important financial information based on the user's current economic situation. For example, the data collection unit can prioritize the collection of relevant financial information based on the user's areas of interest. For example, the data collection unit can filter out unnecessary information based on the user's economic situation and areas of interest. This allows for the priority collection of important information based on the user's economic situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's economic situation and areas of interest into a generating AI, and the generating AI can perform the filtering.

[0083] 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 postpone collecting less important information. For example, if the user is relaxed, the data collection unit can collect all information equally. For example, if the user is in a hurry, the data collection unit can prioritize collecting highly important information. This allows for the prioritization of financial information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of financial information to collect.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting financial information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of financial information related to that region. For example, the data collection unit can collect region-specific financial information based on the user's geographical location. For example, the data collection unit can suggest optimal financial information based on the user's current location. This allows for the priority collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI, and the generating AI can collect highly relevant information.

[0085] The data collection unit can analyze the user's social media activity and collect relevant information when collecting financial information. For example, the data collection unit can collect financial information of interest from the user's social media activity. For example, the data collection unit can suggest relevant financial information based on the user's social media posts. For example, the data collection unit can analyze the activity of the user's social media followers and friends and collect relevant financial information. This allows the data collection unit to collect relevant financial information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can collect relevant information.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the financial analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and visually easy-to-understand presentation. For example, if the user is relaxed, the analysis unit can provide a detailed financial analysis. For example, if the user is in a hurry, the analysis unit can provide a concise financial analysis. This allows the analysis to be presented in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI, which can then use the generative AI to estimate emotions and adjust the presentation of the financial analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on specific expenditure categories during income and expenditure analysis. For example, if the user is interested in a particular expenditure category, the analysis unit can provide a detailed analysis of that category. For example, the analysis unit can prioritize the analysis of high-importance categories based on the user's expenditure categories. For example, the analysis unit can adjust the level of detail based on the user's expenditure categories to provide an optimal analysis. This allows for the provision of a detailed analysis based on the expenditure categories of interest to the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's expenditure category data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms to the user's lifestyle during income and expenditure analysis. For example, if the user is frugal, the analysis unit can apply an analysis algorithm specialized for saving. If the user is investment-oriented, the analysis unit can apply an analysis algorithm specialized for investment. The analysis unit can select the optimal analysis algorithm according to the user's lifestyle. This allows the system to provide the most suitable analysis for the user's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI, which can then apply different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and determine the priority of the financial analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will postpone less important analyses. For example, if the user is relaxed, the analysis unit can perform all analyses equally. For example, if the user is in a hurry, the analysis unit can prioritize highly important analyses. This allows the priority of the financial analysis to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can then use the generative AI to estimate emotions and determine the priority of the financial analysis.

[0090] The analysis unit can weight the analysis based on the user's income sources during revenue and expenditure analysis. For example, the analysis unit can perform high-priority analyses based on the user's main income sources. For example, the analysis unit can weight analyses according to the proportion of income based on the user's income sources. For example, the analysis unit can provide optimal analyses based on the user's income sources. This allows important analyses to be performed preferentially based on the user's income sources. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's income source data into a generating AI, and the generating AI can weight the analysis.

[0091] The analysis unit can adjust the direction of the analysis based on the user's future goals when performing income and expenditure analysis. For example, if the user prioritizes saving as a future goal, the analysis unit can perform an analysis focused on savings. For example, if the user prioritizes investing as a future goal, the analysis unit can perform an analysis focused on investing. For example, the analysis unit can determine the optimal direction of the analysis based on the user's future goals. This allows the analysis unit to provide the optimal analysis based on the user's future goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's future goal data into a generating AI, and the generating AI can adjust the direction of the analysis.

[0092] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and visually easy-to-understand suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. This allows the suggestion unit to provide suggestions in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then use the generative AI to estimate emotions and adjust the way suggestions are presented.

[0093] The proposal unit can adjust the level of detail of its proposals based on the user's spending habits. For example, the proposal unit can prioritize proposals of high importance based on the user's spending habits. For example, the proposal unit can provide detailed proposals based on the user's spending habits. For example, the proposal unit can determine the optimal level of detail for a proposal based on the user's spending habits. This allows the proposal unit to provide optimal proposals based on the user's spending habits. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user spending habit data into a generating AI, and the generating AI can adjust the level of detail of the proposals.

[0094] The proposal unit can apply different proposal algorithms depending on the user's life stage when making a proposal. For example, if the user is young, the proposal unit can make proposals focused on future asset formation. For example, if the user is middle-aged, the proposal unit can make proposals focused on current asset management. The proposal unit can select the optimal proposal algorithm according to the user's life stage. This allows the system to provide optimal proposals tailored to the user's life stage. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's life stage data into a generating AI, and the generating AI can apply different proposal algorithms.

[0095] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will postpone less important suggestions. If the user is relaxed, the suggestion unit can distribute all suggestions equally. If the user is in a hurry, the suggestion unit can prioritize more important suggestions. This allows the suggestion unit to determine the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then estimate emotions and determine the priority of suggestions.

[0096] The proposal unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the proposal unit can suggest investment opportunities related to that region. For example, the proposal unit can suggest region-specific savings points based on the user's geographical location information. For example, the proposal unit can make optimal suggestions based on the user's current location. This allows the system to provide optimal suggestions based on the user's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's geographical location information into a generating AI, and the generating AI can make optimal suggestions.

[0097] The proposal unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the proposal unit can suggest investment opportunities of interest based on the user's social media activity. For example, the proposal unit can suggest relevant savings points based on the content of the user's social media posts. For example, the proposal unit can analyze the activity of the user's social media followers and friends and make relevant suggestions. This allows the proposal unit to provide relevant suggestions based on the user's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's social media activity data into a generating AI, and the generating AI can make relevant suggestions.

[0098] The voice control unit can estimate the user's emotions and adjust the voice control response method based on the estimated emotions. For example, if the user is stressed, the voice control unit can respond in a calm voice. For example, if the user is relaxed, the voice control unit can respond in a cheerful voice. For example, if the user is in a hurry, the voice control unit can provide a quick and concise response. This allows the voice control unit to provide the optimal response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input user emotion data into the generative AI, which can then estimate the emotions and adjust the voice control response method.

[0099] The voice control unit can provide the optimal response by referring to the user's past voice control history when voice commands are used. For example, the voice control unit prioritizes responding to voice commands that the user has frequently used in the past. For example, the voice control unit can suggest the optimal response method based on the user's past voice control history. For example, the voice control unit can analyze the user's past voice control history and provide the most efficient response. This allows the voice control unit to provide the optimal response based on the user's past voice control history. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's past voice control history data into a generating AI, and the generating AI can provide the optimal response.

[0100] The voice control unit can apply different response algorithms depending on the user's speech pattern during voice operation. For example, if the user speaks slowly, the voice control unit will provide a slow response. For example, if the user speaks quickly, the voice control unit can provide a rapid response. For example, the voice control unit can select the optimal response algorithm according to the user's speech pattern. This allows the unit to provide the optimal response according to the user's speech pattern. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's speech pattern data into a generating AI, and the generating AI can apply different response algorithms.

[0101] The voice control unit can estimate the user's emotions and determine the priority of voice operations based on the estimated emotions. For example, if the user is stressed, the voice control unit will postpone less important operations. For example, if the user is relaxed, the voice control unit can perform all operations equally. For example, if the user is in a hurry, the voice control unit can prioritize high-priority operations. This allows the priority of voice operations to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice control unit may be performed using AI, or not using AI. For example, the voice control unit can input user emotion data into a generative AI, which can then estimate emotions and determine the priority of voice operations.

[0102] The voice control unit can provide the optimal response when a user is using voice commands, taking into account the user's device information. For example, if the user is using a smartphone, the voice control unit can provide a response that matches the screen size. For example, if the user is using a tablet, the voice control unit can provide a response optimized for a larger screen. For example, if the user is using a smartwatch, the voice control unit can provide a concise and easily readable response. This allows the system to provide the optimal response based on the user's device information. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's device information into a generating AI, which can then provide the optimal response.

[0103] The voice control unit can analyze the user's ambient sounds during voice operation and provide the optimal response. For example, if the user is in a noisy environment, the voice control unit can increase the volume when responding. For example, if the user is in a quiet environment, the voice control unit can respond at a normal volume. For example, the voice control unit can select the optimal response method according to the user's ambient sounds. This allows it to provide the optimal response according to the user's ambient sounds. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's ambient sound data into a generating AI, and the generating AI can provide the optimal response.

[0104] The chat dialogue unit can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is stressed, the chat dialogue unit can respond in a calm tone. If the user is relaxed, the chat dialogue unit can respond in a cheerful tone. If the user is in a hurry, the chat dialogue unit can provide a quick and concise response. This allows the chat dialogue to be provided with the most appropriate response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 chat dialogue unit may be performed using AI, or not using AI. For example, the chat dialogue unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the response method of the chat dialogue.

[0105] The chat dialogue unit can provide the optimal response during a chat conversation by referring to the user's past chat history. For example, the chat dialogue unit can prioritize responses to chat topics that the user has frequently discussed in the past. For example, the chat dialogue unit can suggest the optimal response method based on the user's past chat history. For example, the chat dialogue unit can analyze the user's past chat history and provide the most efficient response. This allows the system to provide the optimal response based on the user's past chat history. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input the user's past chat history data into a generating AI, which can then provide the optimal response.

[0106] The chat dialogue unit can apply different response algorithms depending on the user's dialogue style during a chat conversation. For example, if the user uses a polite dialogue style, the chat dialogue unit will provide a polite response. For example, if the user uses a casual dialogue style, the chat dialogue unit can provide a casual response. For example, the chat dialogue unit can select the optimal response algorithm depending on the user's dialogue style. This allows the system to provide the most appropriate response for the user's dialogue style. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input user dialogue style data into a generating AI, which can then apply different response algorithms.

[0107] The chat dialogue unit can estimate the user's emotions and determine the priority of chat conversations based on the estimated emotions. For example, if the user is stressed, the chat dialogue unit will postpone less important conversations. For example, if the user is relaxed, the chat dialogue unit can handle all conversations equally. For example, if the user is in a hurry, the chat dialogue unit can prioritize highly important conversations. This allows the chat dialogue priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the chat dialogue unit may be performed using AI, or not using AI. For example, the chat dialogue unit can input user emotion data into a generative AI, which can then estimate emotions and determine the priority of chat conversations.

[0108] The chat dialogue unit can provide the optimal response during a chat conversation by taking into account the user's device information. For example, if the user is using a smartphone, the chat dialogue unit can provide a response that is adapted to the screen size. For example, if the user is using a tablet, the chat dialogue unit can provide a response optimized for a larger screen. For example, if the user is using a smartwatch, the chat dialogue unit can provide a concise and highly visible response. This allows the system to provide the optimal response based on the user's device information. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input the user's device information into a generating AI, and the generating AI can provide the optimal response.

[0109] The chat dialogue unit can analyze the user's dialogue history during a chat conversation and provide the optimal response. For example, the chat dialogue unit can prioritize responses to topics of interest based on the user's past dialogue history. For example, the chat dialogue unit can analyze the user's dialogue history and suggest the optimal response method. For example, the chat dialogue unit can provide the most efficient response based on the user's dialogue history. This allows the system to provide the optimal response based on the user's dialogue history. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input the user's dialogue history data into a generating AI, which can then provide the optimal response.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can analyze the user's past data collection history and select the optimal data collection method when collecting a user's financial information. For example, it can prioritize data collection methods that the user has frequently used in the past (such as APIs or manual input). It can also suggest the most efficient data collection method based on the user's past data collection history. Furthermore, it can analyze the user's past data collection history and optimize the data collection frequency. This allows the optimal data collection method to be selected based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal data collection method.

[0112] The analysis unit can weight the analysis based on the user's income sources during revenue and expenditure analysis. For example, it can perform analyses of high importance based on the user's main income sources. It can also weight analyses according to the proportion of income based on the user's income sources. Furthermore, it can provide optimal analyses based on the user's income sources. This allows important analyses to be prioritized based on the user's income sources. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's income source data into a generating AI, and the generating AI can perform the analysis weighting.

[0113] The proposal unit can apply different proposal algorithms depending on the user's life stage when making a proposal. For example, if the user is young, it can make proposals focused on future asset formation. If the user is middle-aged, it can make proposals focused on current asset management. Furthermore, it can select the optimal proposal algorithm according to the user's life stage. This allows the unit to provide optimal proposals tailored to the user's life stage. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's life stage data into a generating AI, and the generating AI can apply different proposal algorithms.

[0114] The voice control unit can provide the optimal response when a user is using voice commands, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a response that matches the screen size. If the user is using a tablet, it can provide a response optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and easily readable response. This allows the unit to provide the optimal response based on the user's device information. Some or all of the above processing in the voice control unit may be performed using AI, for example, or without AI. For example, the voice control unit can input the user's device information into a generating AI, which can then provide the optimal response.

[0115] The chat dialogue unit can apply different response algorithms depending on the user's dialogue style during a chat conversation. For example, if the user uses a polite dialogue style, it can provide a polite response. If the user uses a casual dialogue style, it can provide a casual response. Furthermore, it can select the optimal response algorithm according to the user's dialogue style. This allows for the provision of the most appropriate response for the user's dialogue style. Some or all of the above processing in the chat dialogue unit may be performed using AI, for example, or without AI. For example, the chat dialogue unit can input user dialogue style data into a generating AI, which can then apply different response algorithms.

[0116] The data collection unit can estimate the user's emotions and adjust the timing of financial information collection based on the estimated emotions. For example, if the user is stressed, the collection timing can be delayed to collect information when the user is relaxed. Also, if the user is relaxed, financial information can be collected and analysis can begin immediately. Furthermore, if the user is in a hurry, the collection timing can be advanced to collect information quickly. This allows for the collection of financial information at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then use the generative AI to estimate emotions and adjust the collection timing.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation of the financial analysis based on the estimated emotions. For example, if the user is stressed, a simple and visually easy-to-understand presentation can be provided. If the user is relaxed, a detailed financial analysis can be provided. Furthermore, if the user is in a hurry, a concise financial analysis can be provided. This allows the financial analysis to be presented in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then use the generative AI to estimate emotions and adjust the presentation of the financial analysis.

[0118] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can provide simple and visually easy-to-understand suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. This allows the suggestion unit to provide suggestions in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then use the generative AI to estimate emotions and adjust the way suggestions are presented.

[0119] The voice control unit can estimate the user's emotions and adjust the voice control response method based on the estimated emotions. For example, if the user is stressed, it can respond in a calm voice. If the user is relaxed, it can respond in a cheerful voice. Furthermore, if the user is in a hurry, it can provide a quick and concise response. This allows the voice control to be provided with the optimal response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 voice control unit may be performed using AI, or not using AI. For example, the voice control unit can input user emotion data into the generative AI, which can then estimate the emotions and adjust the voice control response method.

[0120] The chat dialogue unit can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is stressed, it can respond in a calm tone. If the user is relaxed, it can respond in a cheerful tone. Furthermore, if the user is in a hurry, it can provide a quick and concise response. This allows the chat dialogue to be provided with the most appropriate response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 chat dialogue unit may be performed using AI, or not using AI. For example, the chat dialogue unit can input user emotion data into a generative AI, which can then estimate emotions and adjust the chat dialogue response method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects the user's financial information. The collection unit can collect, for example, the user's bank account information, credit card information, and e-money information. The collection unit can obtain bank account information using an API, for example. The collection unit can also obtain credit card usage history. Furthermore, the collection unit can obtain e-money balance information. For example, the collection unit can obtain account balances and transaction history through the bank's API. Credit card usage history can be obtained through the card company's API. E-money balance information can be obtained through the e-money service's API. Step 2: The analysis unit analyzes the financial information collected by the collection unit to understand income and expenses. For example, the analysis unit can analyze the user's income and expenses based on the collected information. For example, the analysis unit can analyze monthly income and expenses to understand the balance of income and expenses. The analysis unit can also analyze the proportion of expenses in specific categories. Furthermore, the analysis unit can analyze the patterns of fluctuations in income and expenses. For example, the analysis unit calculates the monthly balance of income and expenses based on income and expense data. The proportion of expenses in specific categories can be analyzed by category, such as food expenses, transportation expenses, and entertainment expenses. The patterns of fluctuations in income and expenses can be analyzed based on past data to make predictions about future income and expenses. Step 3: The proposal department suggests saving points and optimal investment options based on the income and expenses identified by the analysis department. For example, the proposal department can suggest saving points based on the user's spending habits. For example, the proposal department can suggest saving techniques to reduce unnecessary expenses. The proposal department can also suggest investment plans tailored to the user's lifestyle. Furthermore, the proposal department can provide advice to improve the user's income and expense balance. For example, the proposal department can suggest ways to save on food expenses or reduce transportation expenses. Investment plans can be proposed based on risk assessment and return forecasts. Advice to improve the income and expense balance can suggest ways to increase income or decrease expenses. Step 4: The voice control unit receives voice commands from the user. The voice control unit can, for example, recognize the user's voice commands and operate the system. For example, the voice control unit can receive a voice command such as, "Tell me my spending for this month." It can also receive a voice command such as, "Give me some saving advice." Furthermore, it can also receive a voice command such as, "Suggest an investment plan." For example, the voice control unit uses voice recognition technology to analyze the user's voice commands and perform the appropriate operation. Based on the voice commands, the system can display spending information or provide saving advice. Step 5: The chat dialogue unit engages in chat conversations with the user. The chat dialogue unit can, for example, engage in text-based conversations with the user. For example, the chat dialogue unit can receive chat messages such as, "Tell me about my spending this month." It can also receive chat messages such as, "Give me some saving advice." Furthermore, it can also receive chat messages such as, "Propose an investment plan." For example, the chat dialogue unit can analyze the user's chat messages using natural language processing technology and provide appropriate responses. Based on the chat messages, the system can display spending information or provide saving advice.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, voice operation unit, and chat dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's bank account information, credit card information, electronic money information, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected financial information to understand income and expenses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes saving points and optimal investment destinations based on the analysis results. The voice operation unit is implemented by the control unit 46A of the smart device 14 and accepts the user's voice commands. The chat dialogue unit is implemented by the control unit 46A of the smart device 14 and conducts text-based dialogue with the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, voice operation unit, and chat dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's bank account information, credit card information, electronic money information, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected financial information to understand income and expenses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes saving points and optimal investment destinations based on the analysis results. The voice operation unit is implemented by the control unit 46A of the smart glasses 214 and accepts the user's voice commands. The chat dialogue unit is implemented by the control unit 46A of the smart glasses 214 and conducts text-based dialogue with the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, voice operation unit, and chat dialogue unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's bank account information, credit card information, electronic money information, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected financial information to understand income and expenses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes saving points and optimal investment destinations based on the analysis results. The voice operation unit is implemented by the control unit 46A of the headset terminal 314 and accepts the user's voice commands. The chat dialogue unit is implemented by the control unit 46A of the headset terminal 314 and conducts text-based dialogue with the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, voice operation unit, and chat dialogue unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects the user's bank account information, credit card information, electronic money information, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected financial information to understand income and expenses. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes saving points and optimal investment destinations based on the analysis results. The voice operation unit is implemented by the control unit 46A of the robot 414 and accepts the user's voice commands. The chat dialogue unit is implemented by the control unit 46A of the robot 414 and conducts text-based dialogue with the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection unit that collects users' financial information, The analysis unit analyzes the financial information collected by the aforementioned collection unit to understand the income and expenses, Based on the income and expenses grasped by the aforementioned analysis unit, the proposal unit proposes points for saving and optimal investment destinations. A voice control unit that accepts voice commands, It includes a chat dialogue unit for conducting conversations via chat. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects user information such as bank accounts, credit cards, and e-money. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected information, we analyze the user's income and expenses to understand their spending trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the identified spending patterns, we propose ways to save money and optimal investment opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned voice control unit is Accepts user voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned chat dialogue section is Conduct chat conversations with users The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of financial information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze the user's past financial information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting financial information, filtering is performed based on the user's current economic situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is When collecting financial information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting financial information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the representation of the revenue and expenditure analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When performing income and expenditure analysis, adjust the level of detail of the analysis based on specific expenditure categories. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing income and expenses, different analysis algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates user sentiment and determines the priority of revenue and expenditure analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During revenue and expenditure analysis, the analysis is weighted based on the user's revenue sources. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the revenue and expenditure analysis, the direction of the analysis is adjusted based on the user's future goals. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's spending habits. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's life stage. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned voice control unit is It estimates the user's emotions and adjusts the voice response method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned voice control unit is When using voice commands, the system provides the optimal response by referring to the user's past voice command history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned voice control unit is When using voice commands, different response algorithms are applied depending on the user's speech patterns. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned voice control unit is It estimates the user's emotions and determines the priority of voice commands based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned voice control unit is When using voice commands, the system provides the optimal response by taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned voice control unit is When voice commands are used, the system analyzes the user's ambient sounds and provides the optimal response. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned chat dialogue section is It estimates the user's emotions and adjusts the chat response method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned chat dialogue section is During chat conversations, the system provides the most appropriate response by referring to the user's past chat history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned chat dialogue section is During chat conversations, different response algorithms are applied depending on the user's conversation style. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned chat dialogue section is It estimates the user's emotions and prioritizes chat conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned chat dialogue section is During chat conversations, the system takes the user's device information into consideration to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned chat dialogue section is During chat conversations, the system analyzes the user's conversation history and provides the most appropriate response. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 users' financial information, The analysis unit analyzes the financial information collected by the aforementioned collection unit to understand the income and expenses, Based on the income and expenses grasped by the aforementioned analysis unit, the proposal unit proposes points for saving and optimal investment destinations. A voice control unit that accepts voice commands, It includes a chat dialogue unit for conducting conversations via chat. A system characterized by the following features.

2. The aforementioned collection unit is Collects user information such as bank accounts, credit cards, and e-money. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected information, we analyze the user's income and expenses to understand their spending trends. The system according to feature 1.

4. The aforementioned proposal section is, Based on the identified spending patterns, we propose ways to save money and optimal investment opportunities. The system according to feature 1.

5. The aforementioned voice control unit is Accepts user voice commands. The system according to feature 1.

6. The aforementioned chat dialogue section is Conduct chat conversations with users The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of financial information collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is We analyze the user's past financial information gathering history and select the optimal collection method. The system according to feature 1.

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