system

Abstract

The system according to this embodiment aims to provide unified household budget management and investment advice based on the user's income and expenditure data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a display unit, a simulation unit, an advice unit, a forecast unit, and an analysis unit. The collection unit collects data on the user's income and expenses. The analysis unit analyzes the data collected by the collection unit and creates a household ledger. The display unit displays the household ledger created by the analysis unit. The simulation unit performs a simulation based on goals set by the user. The advice unit provides investment and insurance advice based on the results obtained by the simulation unit. The forecast unit forecasts cash flow based on the advice provided by the advice unit. The analysis unit analyzes market data in real time based on the results obtained by the forecast unit and formulates an investment plan.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, household management and investment advice based on data of users' income and expenditure are not provided unidimensionally, and there is room for improvement.

[0005] The system according to the embodiment aims to unidimensionally provide household management and investment advice based on data of users' income and expenditure.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a display unit, a simulation unit, an advice unit, a forecast unit, and an analysis unit. The data collection unit collects data on the user's income and expenses. The analysis unit analyzes the data collected by the data collection unit and creates a household ledger. The display unit displays the household ledger created by the analysis unit. The simulation unit performs a simulation based on goals set by the user. The advice unit provides investment and insurance advice based on the results obtained by the simulation unit. The forecast unit forecasts cash flow based on the advice provided by the advice unit. The analysis unit analyzes market data in real time based on the results obtained by the forecast unit and formulates an investment plan. [Effects of the Invention]

[0007] The system according to this embodiment can provide unified household budget management and investment advice based on the user's income and expenditure data. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The financial planning system according to an embodiment of the present invention is a system provided in the form of a monthly or annual subscription. This system provides the following services by having the user input the necessary information and having a generating AI analyze that information: automatic household budget management, goal setting and simulation, investment and insurance advice (which can also be considered as a premium plan), cash flow forecasting, real-time market analysis and investment plan formulation (which can also be considered as a premium plan). For example, if the user inputs their monthly income and expenses, the generating AI will automatically create a household budget and display the balance of income and expenses. If the user sets a goal of "saving 1 million yen in 5 years," the generating AI will calculate the monthly savings amount and perform a simulation to achieve the goal. If the user inputs "I want to invest with low risk," the generating AI will suggest appropriate investment products. If the user inputs "I'm worried about next month's cash flow," the generating AI will predict next month's income and expenses and provide cash flow advice. If the user inputs "I want to create an investment plan based on the current market situation," the generating AI will analyze market data in real time and suggest the optimal investment plan. This service allows users to gain a detailed understanding of their financial situation and create an optimal financial plan. In particular, by utilizing AI-generated data, sophisticated financial planning becomes possible even without specialized knowledge, enabling users to achieve financial freedom and peace of mind. As a result, the financial planning system can gain a detailed understanding of the user's financial situation and provide an optimal financial plan.

[0029] The financial planning system according to this embodiment comprises a data collection unit, an analysis unit, a display unit, a simulation unit, an advice unit, a forecasting unit, and an analysis unit. The data collection unit collects the user's income and expenditure data. For example, the data collection unit automatically collects income and expenditure data entered by the user. The data collection unit can also acquire transaction data from bank accounts and credit cards. Furthermore, the data collection unit can also collect data entered manually by the user. For example, the data collection unit provides a form for the user to enter their monthly income and expenditure and collects that data. The analysis unit analyzes the data collected by the data collection unit and creates a household budget. For example, the analysis unit calculates the monthly balance of income and expenditure based on the income and expenditure data. Furthermore, the analysis unit can classify the income and expenditure data by category and create a detailed household budget. Furthermore, the analysis unit can also predict future income and expenditure based on past data. For example, the analysis unit predicts next month's income and expenditure based on past income and expenditure data. The display unit displays the household budget created by the analysis unit. The display unit, for example, displays the household budget in a user-friendly format. The display unit can also visually display the household budget using graphs and charts. Furthermore, the display unit can display the household budget in a user-customized format. For example, the display unit can display income and expenses by categories selected by the user. The simulation unit performs simulations based on goals set by the user. For example, the simulation unit calculates the monthly savings amount based on the user's savings goal. The simulation unit can also perform investment simulations based on the user's investment goal. Furthermore, the simulation unit can perform expense reduction simulations based on the user's expense reduction goal. For example, if the user sets a goal of "saving 1 million yen in 5 years," the simulation unit calculates the monthly savings amount and performs a simulation to achieve that goal. The advice unit provides investment and insurance advice based on the results obtained by the simulation unit. For example, the advice unit suggests the optimal investment product based on the user's financial situation.The advisory unit can also suggest appropriate insurance products based on the user's risk tolerance. Furthermore, the advisory unit can provide specific advice based on the user's goals. For example, if the user inputs "I want to invest with reduced risk," the advisory unit will suggest appropriate investment products. The forecasting unit predicts cash flow based on the advice provided by the advisory unit. For example, the forecasting unit predicts future cash flow based on the user's income and expenditure data. The forecasting unit can also perform cash flow simulations based on the user's goals. Furthermore, the forecasting unit can assess cash flow risk based on the user's financial situation. For example, if the user inputs "I'm worried about cash flow next month," the forecasting unit will predict next month's income and expenses and provide cash flow advice. The analysis unit analyzes market data in real time based on the results obtained by the forecasting unit and formulates investment plans. For example, the analysis unit collects market data in real time and proposes the optimal investment plan. Furthermore, the analysis unit can customize investment plans based on the user's investment goals. Furthermore, the analysis unit can adjust investment plans based on the user's risk tolerance. For example, when a user inputs "I want to create an investment plan based on the current market situation," the analysis unit analyzes market data in real time and proposes the optimal investment plan. This allows the financial planning system according to this embodiment to gain a detailed understanding of the user's financial situation and provide the most suitable financial plan.

[0030] The data collection unit collects user income and expenditure data. For example, it automatically collects income and expenditure data entered by the user. The data collection unit can also retrieve bank account and credit card transaction data. Furthermore, the data collection unit can collect data entered manually by the user. For example, the data collection unit provides a form for the user to enter their monthly income and expenditure, and collects that data. The data collection unit centrally manages the data entered by the user and has functions to detect duplicates and errors to maintain data integrity. For example, the data collection unit displays a warning and prompts correction if the user enters the same transaction multiple times. The data collection unit can also update data in real time by using APIs to automatically retrieve bank account and credit card transaction data. This saves users the trouble of manually entering data. Furthermore, the data collection unit can also obtain additional information from external data sources to supplement the data entered manually by the user. For example, the data collection unit can automatically retrieve price information and store information for related products for the expenditure data entered by the user, and perform detailed expenditure analysis. This allows the data collection unit to efficiently and accurately collect user income and expenditure data, improving the overall data quality of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit and creates a household budget. For example, the analysis unit calculates the monthly balance of income and expenses based on income and expense data. The analysis unit can also categorize income and expense data to create a detailed household budget. Furthermore, the analysis unit can predict future income and expenses based on past data. For example, the analysis unit predicts next month's income and expenses based on past income and expense data. The analysis unit uses AI to analyze data and learn the user's income and expense patterns. Specifically, it uses machine learning algorithms to analyze the user's income and expense trends and detect abnormal patterns and irregular expenses. This allows the analysis unit to provide specific advice to the user to reduce unnecessary expenses. In addition, the analysis unit creates a dashboard to visually display the user's financial situation based on the collected data. The dashboard displays graphs and charts of income and expenses, the monthly balance of income and expenses, and the percentage of expenses by category, allowing the user to understand their financial situation at a glance. Furthermore, the analysis unit also has a function to evaluate the user's progress toward their goals and update the progress in real time. For example, the system displays the user's current savings amount and the remaining time until the savings goal is achieved, supporting the user in taking planned actions towards their goal. This allows the analytics unit to analyze the user's financial situation in detail and support effective household budget management.

[0032] The display unit shows the household budget created by the analysis unit. The display unit displays the budget in a user-friendly format, for example. It can also visually display the budget using graphs and charts. Furthermore, the display unit can display the budget in a user-customized format. For example, it can display income and expenses by categories selected by the user. The display unit utilizes the latest web technologies to create an intuitive and user-friendly user interface. Specifically, it employs responsive design to ensure comfortable viewing on different devices such as smartphones and tablets. The display unit also provides interactive graphs and charts, allowing users to click on data to view detailed information or filter by specific periods or categories. Additionally, the display unit offers the ability to customize the display format to the user's preferences. For example, users can choose to display income and expense data monthly, weekly, or daily, or highlight specific categories or periods. This allows users to gain a more detailed understanding of their financial situation and manage their household finances effectively. The display unit also includes a function to visually display the progress towards user-set goals. For example, it can display the current savings amount and progress towards a savings goal, supporting users in taking planned action towards their goals. This makes the display a powerful tool for users to intuitively understand their financial situation and manage their household finances effectively.

[0033] The simulation unit performs simulations based on goals set by the user. For example, the simulation unit calculates the monthly savings amount based on the user's savings goal. The simulation unit can also perform investment simulations based on the user's investment goal. Furthermore, the simulation unit can perform expense reduction simulations based on the user's expense reduction goal. For example, if the user sets "I want to save 1 million yen in 5 years," the simulation unit will calculate the monthly savings amount and perform a simulation to achieve that goal. The simulation unit uses AI to simulate multiple scenarios and propose the optimal plan. Specifically, it performs simulations that take into account different economic conditions and market conditions based on the user's income, expenses, savings, and investment data. For example, it considers economic growth rates, inflation rates, and stock market fluctuations to propose the optimal savings and investment plans for the goals set by the user. The simulation unit also provides a function to compare multiple scenarios for the goals set by the user. For example, it compares the simulation results when different savings and investment amounts are set, helping the user select the optimal plan. Furthermore, the simulation unit also features a function that updates the progress towards the user's set goals in real time and visually displays the path to achieving those goals. This allows users to act systematically towards their goals. The simulation unit is a powerful tool to support users in achieving their financial goals and plays a crucial role in enabling users to create effective financial plans.

[0034] The advisory department provides investment and insurance advice based on the results obtained by the simulation department. For example, the advisory department can suggest the optimal investment product based on the user's financial situation. It can also suggest appropriate insurance products based on the user's risk tolerance. Furthermore, the advisory department can provide specific advice based on the user's goals. For example, if the user inputs "I want to invest with reduced risk," the advisory department will suggest appropriate investment products. The advisory department uses AI to analyze the user's financial data and provide advice best suited to individual needs. Specifically, it creates a risk profile based on the user's income, expenses, savings, and investment data, and suggests investment and insurance products according to the user's risk tolerance. The advisory department also has a function to provide specific action plans to help the user achieve their goals. For example, it can suggest specific monthly savings and investment amounts for a savings goal set by the user, clearly showing the steps to achieve the goal. Furthermore, the advisory department also provides a function to collect user feedback and continuously improve the advice. For example, if the user inputs feedback on the advice provided, the AI ​​learns from that feedback and reflects it in future advice. This allows the advisory department to provide personalized advice tailored to the user's needs and support them in creating effective financial plans. The advisory department plays a crucial role in helping users achieve their financial goals and serves as a powerful tool for users to manage their finances with confidence.

[0035] The forecasting unit predicts cash flow based on advice provided by the advisory unit. For example, the forecasting unit predicts future cash flow based on the user's income and expenditure data. The forecasting unit can also perform cash flow simulations based on the user's goals. Furthermore, the forecasting unit can assess cash flow risks based on the user's financial situation. For example, if the user inputs "I'm worried about next month's cash flow," the forecasting unit will predict next month's income and expenses and provide cash flow advice. The forecasting unit uses AI to analyze the user's income and expenditure data and predict future cash flow with high accuracy. Specifically, it uses machine learning algorithms to learn the user's income and expenditure patterns and predict future fluctuations in income and expenses. The forecasting unit also has the function to simulate different scenarios and propose the optimal cash flow plan. For example, it compares multiple scenarios for the user's set goals and helps the user select the best plan. Furthermore, the forecasting unit assesses cash flow risks based on the user's financial situation and provides specific advice for risk mitigation. For example, the forecasting section proposes savings plans to help users prepare for future income decreases or expenses increases, and suggests measures to minimize risk. In this way, the forecasting section supports users in systematically managing their future finances and managing their finances with peace of mind. The forecasting section plays a crucial role in helping users achieve their financial goals and is a powerful tool for effective financial management.

[0036] The analysis department analyzes market data in real time based on the results obtained by the forecasting department and formulates investment plans. For example, the analysis department collects market data in real time and proposes the optimal investment plan. The analysis department can also customize investment plans based on the user's investment goals. Furthermore, the analysis department can adjust investment plans based on the user's risk tolerance. For example, if a user inputs "I want to create an investment plan based on the current market situation," the analysis department will analyze market data in real time and propose the optimal investment plan. The analysis department uses AI to analyze market data and formulate the optimal plan for the user's investment goals. Specifically, it uses machine learning algorithms to analyze past market data and current market trends and predict future market trends. The analysis department also simulates different investment scenarios and proposes the optimal investment plan according to the user's risk tolerance. For example, if a user desires low-risk investments, the analysis department will construct a portfolio centered on low-risk investment products and optimize the balance between risk and return. If a user desires high-risk, high-return investments, the analysis department will select high-growth investment products to maximize returns. Furthermore, the analytics department also has the capability to monitor market data in real time and adjust investment plans. For example, it can quickly review investment plans in response to rapid market fluctuations or changes in economic conditions and provide users with appropriate advice. In this way, the analytics department supports users in flexibly responding to market fluctuations and executing optimal investment strategies. The analytics department plays a crucial role in supporting users in achieving their investment goals and is a powerful tool for users to develop effective investment plans.

[0037] The data collection unit can analyze a user's past income and expenditure patterns and select the optimal data collection method. For example, if the data collection unit analyzes a user's past income and expenditure data and finds that income is concentrated at the end of the month, it will concentrate data collection at the end of the month. The data collection unit can also analyze a user's past expenditure patterns and find that expenditures are high on specific days of the week, and collect data on those days. Furthermore, if the income is irregular, the data collection unit can analyze a user's past income and expenditure patterns and collect data at the time the income is generated. For example, if the data collection unit analyzes a user's past income and expenditure data and finds that income is concentrated at the end of the month, it will concentrate data collection at the end of the month. The data collection unit can also analyze a user's past expenditure patterns and find that expenditures are high on specific days of the week, and collect data on those days. The data collection unit can also analyze a user's past income and expenditure patterns and find that income is irregular, and collect data at the time the income is generated. By analyzing past income and expenditure patterns, the optimal data collection method can be selected, enabling efficient data collection. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past income and expenditure data into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter income and expenditure data based on the user's current living situation and areas of interest. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related expenditure data. It can also prioritize collecting expenditure data related to a new hobby if the user has started one. Furthermore, if the user is planning to move, the data collection unit can prioritize collecting moving-related expenditure data. This allows for the priority collection of highly relevant data by filtering the data based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting income and expenditure data. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of data related to the cost of living in that region. Furthermore, if the user is on a business trip, the data collection unit can prioritize the collection of expenditure data at the business trip location. Additionally, if the user is planning to move, the data collection unit can prioritize the collection of data related to the cost of living at the new location. This allows for the priority collection of highly relevant data by considering 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 information into a generating AI and have the generating AI collect highly relevant data.

[0040] The data collection unit can analyze a user's social media activity and collect relevant data when collecting income and expenditure data. For example, if a user indicates on social media that they will be attending a specific event, the data collection unit can collect expenditure data related to that event. It can also collect expenditure data related to a hobby if a user indicates on social media that they have started a new hobby. Furthermore, if a user indicates on social media that they will be traveling to a specific region, the data collection unit can collect travel-related expenditure data for that region. This allows for the efficient collection of relevant data by analyzing a 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 have the generating AI collect the relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit can perform a detailed analysis on important income and expenditure data and a concise analysis on other data. It can also perform a detailed analysis on data related to user-set goals and a concise analysis on other data. Furthermore, it can perform a detailed analysis on data related to the user's areas of interest and a concise analysis on other data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 importance of the collected data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the collected data. For example, the analysis unit can apply an income analysis algorithm to income data and an expenditure analysis algorithm to expenditure data. It can also apply a fixed cost analysis algorithm to fixed cost data and a variable cost analysis algorithm to variable cost data. Furthermore, it can apply an investment analysis algorithm to investment data and an insurance analysis algorithm to insurance data. For example, the analysis unit can apply an income analysis algorithm to income data and an expenditure analysis algorithm to expenditure data. It can also apply a fixed cost analysis algorithm to fixed cost data and a variable cost analysis algorithm to variable cost data. It can also apply an investment analysis algorithm to investment data and an insurance analysis algorithm to insurance data. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the categories of the collected data into the generating AI and have the generating AI execute different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the timing of data submission. For example, the analysis unit may prioritize the analysis of the most recent income and expenditure data, delaying the analysis of past data. Alternatively, the analysis unit may prioritize the analysis of data related to user-set goals, delaying the analysis of other data. Furthermore, the analysis unit may prioritize the analysis of data related to the user's areas of interest, delaying the analysis of other data. This enables efficient analysis by determining the priority of analysis based on the timing of data submission. 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 timing of data submission into a generating AI and have the generating AI determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relationships between the collected data. For example, the analysis unit can adjust the order of analysis considering the relationships between income data and expenditure data. It can also adjust the order of analysis considering the relationships between fixed cost data and variable cost data. Furthermore, it can adjust the order of analysis considering the relationships between investment data and insurance data. For example, the analysis unit can adjust the order of analysis considering the relationships between income data and expenditure data. It can also adjust the order of analysis considering the relationships between fixed cost data and variable cost data. It can also adjust the order of analysis considering the relationships between investment data and insurance data. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. 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 relationships between the collected data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0045] The display unit can select the optimal display method by referring to the user's past operation history when displaying the household account ledger. For example, the display unit can prioritize display methods previously used by the user. The display unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the display unit can automatically set the display method that the user has previously preferred. For example, the display unit can prioritize display methods previously used by the user. The display unit can also suggest the optimal display method based on the user's past operation history. The display unit can also automatically set the display method that the user has previously preferred. This allows the display unit to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0046] The display unit can select the optimal display method when displaying household accounts, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can also provide a display method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can also provide a display method optimized for larger screens if the user is using a tablet. The display unit can also provide a concise and highly visible display method if the user is using a smartwatch. This allows the display unit to provide the optimal display method by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0047] The display unit can provide a multilingual display according to the user's language settings when displaying the household account book. For example, the display unit can automatically set the language of the household account book based on the language settings of the user's device. The display unit can also provide a language switching function if the user uses multiple languages. Furthermore, the display unit can display the household account book in a language selected by the user. For example, the display unit can automatically set the language of the household account book based on the language settings of the user's device. The display unit can also provide a language switching function if the user uses multiple languages. The display unit can also display the household account book in a language selected by the user. This enables a household account book display tailored to the user by providing a multilingual display according to the user's language settings. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's language setting data into a generating AI and have the generating AI perform the multilingual display.

[0048] The simulation unit can adjust the level of detail of the simulation based on the importance of the objectives during the simulation. For example, the simulation unit can perform a detailed simulation for important objectives and a simplified simulation for other objectives. The simulation unit can also adjust the level of detail of the simulation based on the priority of objectives set by the user. Furthermore, the simulation unit can perform a detailed simulation for objectives related to the user's area of ​​interest and a simplified simulation for other objectives. For example, the simulation unit can perform a detailed simulation for important objectives and a simplified simulation for other objectives. The simulation unit can also adjust the level of detail of the simulation based on the priority of objectives set by the user. The simulation unit can also perform a detailed simulation for objectives related to the user's area of ​​interest and a simplified simulation for other objectives. This allows for efficient simulation by adjusting the level of detail of the simulation based on the importance of the objectives. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input objective importance data into a generating AI and have the generating AI adjust the level of detail of the simulation.

[0049] The simulation unit can apply different simulation algorithms depending on the category of the target during the simulation. For example, the simulation unit can apply a savings simulation algorithm to a savings target and an investment simulation algorithm to an investment target. Furthermore, the simulation unit can apply a short-term simulation algorithm to a short-term target and a long-term simulation algorithm to a long-term target. In addition, the simulation unit can apply a monetary simulation algorithm to a specific monetary target and an abstract simulation algorithm to an abstract target. For example, the simulation unit can apply a savings simulation algorithm to a savings target and an investment simulation algorithm to an investment target. The simulation unit can also apply a short-term simulation algorithm to a short-term target and a long-term simulation algorithm to a long-term target. The simulation unit can also apply a monetary simulation algorithm to a specific monetary target and an abstract simulation algorithm to an abstract target. This allows for highly accurate simulations by applying the appropriate simulation algorithm according to the category of the target. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input target category data into the generating AI and have the generating AI execute different simulation algorithms.

[0050] The simulation unit can determine the priority of simulations based on the timing of goal setting. For example, the simulation unit can prioritize simulations for immediate goals and postpone simulations for distant future goals. The simulation unit can also determine the priority of simulations based on the deadlines of goals set by the user. Furthermore, the simulation unit can prioritize simulations for goals related to the user's areas of interest and postpone simulations for other goals. For example, the simulation unit can prioritize simulations for immediate goals and postpone simulations for distant future goals. The simulation unit can also determine the priority of simulations based on the deadlines of goals set by the user. The simulation unit can also prioritize simulations for goals related to the user's areas of interest and postpone simulations for other goals. This enables efficient simulations by determining the priority of simulations based on the timing of goal setting. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input goal setting timing data into a generating AI and have the generating AI determine the priority of simulations.

[0051] The simulation unit can adjust the order of simulations based on the relationships between targets during the simulation. For example, the simulation unit can adjust the order of simulations considering the relationship between income targets and expenditure targets. It can also adjust the order of simulations considering the relationship between savings targets and investment targets. Furthermore, the simulation unit can adjust the order of simulations considering the relationship between short-term targets and long-term targets. For example, the simulation unit can adjust the order of simulations considering the relationship between income targets and expenditure targets. It can also adjust the order of simulations considering the relationship between savings targets and investment targets. It can also adjust the order of simulations considering the relationship between short-term targets and long-term targets. This allows for efficient simulation by adjusting the order of simulations based on the relationships between targets. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input target relationship data into a generating AI and have the generating AI perform the adjustment of the simulation order.

[0052] The advice unit can adjust the level of detail of its advice based on the importance of the simulation results when providing advice. For example, the advice unit can provide detailed advice for important simulation results and concise advice for other results. The advice unit can also adjust the level of detail of its advice based on the priority of goals set by the user. Furthermore, the advice unit can provide detailed advice for simulation results related to the user's area of ​​interest and concise advice for other results. For example, the advice unit can provide detailed advice for important simulation results and concise advice for other results. The advice unit can also adjust the level of detail of its advice based on the priority of goals set by the user. The advice unit can provide detailed advice for simulation results related to the user's area of ​​interest and concise advice for other results. This allows for efficient advice by adjusting the level of detail of the advice based on the importance of the simulation results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance data of the simulation results into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0053] The advisory unit can apply different advisory algorithms depending on the category of the simulation results when providing advice. For example, the advisory unit can apply an investment advisory algorithm to investment simulation results and an insurance advisory algorithm to insurance simulation results. It can also apply a short-term advisory algorithm to short-term simulation results and a long-term advisory algorithm to long-term simulation results. Furthermore, it can apply a monetary advisory algorithm to specific monetary simulation results and an abstract advisory algorithm to abstract simulation results. This allows for highly accurate advice by applying the appropriate advisory algorithm according to the category of the simulation results. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advice unit can input the categorical data of the simulation results into the generating AI and have the generating AI apply different advice algorithms.

[0054] The advice unit can prioritize advice based on the timing of simulation result submissions when providing advice. For example, the advice unit may prioritize advice on the most recent simulation results and postpone advice on past results. The advice unit can also prioritize advice based on the deadlines for goals set by the user. Furthermore, the advice unit may prioritize advice on simulation results related to the user's area of ​​interest and postpone advice on other results. For example, the advice unit may prioritize advice on the most recent simulation results and postpone advice on past results. The advice unit can also prioritize advice based on the deadlines for goals set by the user. The advice unit may also prioritize advice on simulation results related to the user's area of ​​interest and postpone advice on other results. This enables efficient advice by prioritizing advice based on the timing of simulation result submissions. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input simulation result submission timing data into a generating AI and have the generating AI determine the priority of advice.

[0055] The advice unit can adjust the order of advice based on the relevance of the simulation results when providing advice. For example, the advice unit can adjust the order of advice considering the relevance of income simulation results and expenditure simulation results. It can also adjust the order of advice considering the relevance of savings simulation results and investment simulation results. Furthermore, the advice unit can adjust the order of advice considering the relevance of short-term simulation results and long-term simulation results. For example, the advice unit can adjust the order of advice considering the relevance of income simulation results and expenditure simulation results. It can also adjust the order of advice considering the relevance of savings simulation results and investment simulation results. It can also adjust the order of advice considering the relevance of short-term simulation results and long-term simulation results. This allows for efficient advice by adjusting the order of advice based on the relevance of the simulation results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the relevance data of the simulation results into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0056] The forecasting unit can analyze the user's past income and expense data to select the optimal forecasting method when forecasting cash flow. For example, the forecasting unit can analyze the user's past income and expense data and, if income is irregular, make a forecast at the time income is generated. The forecasting unit can also analyze the user's past spending patterns and, if spending is high on a particular day of the week, make a forecast on that day. Furthermore, the forecasting unit can analyze the user's past income and expense data and, if income is concentrated at the end of the month, make a forecast at the end of the month. For example, the forecasting unit can analyze the user's past income and expense data and, if income is irregular, make a forecast at the time income is generated. The forecasting unit can also analyze the user's past spending patterns and, if spending is high on a particular day of the week, make a forecast on that day. The forecasting unit can also analyze the user's past income and expense data and, if income is concentrated at the end of the month, make a forecast at the end of the month. By analyzing the user's past income and expense data, the forecasting unit can select the optimal forecasting method and enable highly accurate forecasts. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's past income and expense data into the generating AI and have the generating AI select the optimal prediction method.

[0057] The forecasting unit can customize its forecasting methods based on the user's current living situation when forecasting cash flow. For example, if the user is traveling, the forecasting unit will consider travel-related expenses when making a forecast. The forecasting unit can also consider expenses related to a hobby if the user has started a new hobby. Furthermore, if the user is planning to move, the forecasting unit can also consider expenses related to moving when making a forecast. This allows for highly accurate forecasts by customizing the forecasting methods based on the user's current living situation. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or not. For example, the forecasting unit can input the user's current living situation data into a generating AI and have the generating AI customize the forecasting methods.

[0058] The forecasting unit can select the optimal forecasting method when forecasting cash flow, taking into account the user's geographical location. For example, if the user lives in a specific region, the forecasting unit will make a forecast considering the cost of living in that region. Furthermore, if the user is on a business trip, the forecasting unit can also make a forecast considering the cost of living at the destination. In addition, if the user is planning to move, the forecasting unit can also make a forecast considering the cost of living at the destination. This allows for the selection of the optimal forecasting method and enables highly accurate forecasts by considering the user's geographical location. Some or all of the above-described processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal forecasting method.

[0059] The forecasting unit can analyze a user's social media activity and propose forecasting methods when forecasting cash flow. For example, if the forecasting unit indicates on social media that a user will participate in a specific event, it will make a forecast considering the expenses related to that event. Similarly, if the forecasting unit indicates on social media that a user has started a new hobby, it can also make a forecast considering the expenses related to that hobby. Furthermore, if the forecasting unit indicates on social media that a user will travel to a specific region, it can also make a forecast considering the travel-related expenses for that region. This allows the forecasting unit to propose relevant forecasting methods and enable highly accurate forecasts by analyzing the user's social media activity. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user social media activity data into a generating AI and have the generating AI execute suggestions for prediction methods.

[0060] The analysis department can analyze users' past investment behavior to select the optimal analysis method when analyzing market data. For example, if the analysis department analyzes a user's past investment behavior and finds that they prefer low-risk investments, it can analyze market data based on that tendency. The analysis department can also analyze market data based on a user's past investment behavior and find that they prefer short-term investments. Furthermore, if the analysis department analyzes a user's past investment behavior and finds that they tend to invest in a particular industry, it can prioritize the analysis of market data for that industry. For example, if the analysis department analyzes a user's past investment behavior and finds that they prefer low-risk investments, it can analyze market data based on that tendency. The analysis department can also analyze market data based on a user's past investment behavior and find that they prefer short-term investments. The analysis department can also analyze market data based on a user's past investment behavior and find that they tend to invest in a particular industry, it can prioritize the analysis of market data for that industry. By analyzing users' past investment behavior, the analysis department can select the optimal analysis method and enable highly accurate analysis. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis unit can input the user's past investment behavior data into a generating AI and have the AI ​​select the optimal analysis method.

[0061] The analysis unit can customize its analysis methods based on the user's current investment situation when analyzing market data. For example, the analysis unit analyzes market data considering the performance of investment products the user currently holds. Furthermore, if the user is considering a new investment, the analysis unit can prioritize the analysis of market data for that investment product. Additionally, if the user has a specific risk tolerance, the analysis unit can analyze market data based on that risk level. This allows for highly accurate analysis by customizing the analysis methods based on the user's current investment situation. 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 current investment situation data into a generating AI and have the generating AI perform the customization of the analysis methods.

[0062] The analysis department can select the optimal analysis method when analyzing market data, taking into account the user's geographical location. For example, if the user lives in a specific region, the analysis department will prioritize analyzing market data for that region. Furthermore, if the user is on a business trip, the analysis department can prioritize analyzing market data for their destination. In addition, if the user is planning to move, the analysis department can prioritize analyzing market data for their new destination. This allows for the selection of the optimal analysis method and enables highly accurate analysis by considering the user's geographical location. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the user's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0063] The analysis department can analyze users' social media activity and propose analytical methods when analyzing market data. For example, if a user shows interest in a particular industry on social media, the analysis department will prioritize analyzing market data for that industry. Similarly, if a user shows interest in a particular investment product on social media, the analysis department can prioritize analyzing market data for that investment product. Furthermore, if a user shows interest in a particular region on social media, the analysis department can prioritize analyzing market data for that region. This allows the analysis department to propose relevant analytical methods and enable highly accurate analysis by analyzing users' social media activity. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input user social media activity data into a generating AI and have the AI ​​propose methods for analysis.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The financial planning system can further collect user health data and adjust financial plans based on their health status. For example, the data collection unit can acquire health data from the user's fitness tracker or smartwatch, and the analysis unit can suggest high-risk investments if the user is in good health. Conversely, if the user's health is deteriorating, it can suggest a review of their insurance products. Furthermore, based on health data, it can predict future medical expenses and incorporate them into the financial plan. This allows the system to provide an optimal financial plan tailored to the user's health condition.

[0066] A financial planning system can further adjust financial plans by taking into account the user's life events. For example, the data collection unit can collect life events such as marriage, childbirth, and moving, and the analysis unit can predict increases or decreases in spending based on these events. Furthermore, the simulation unit can simulate future financial plans based on life events, and the advice unit can suggest appropriate investments and insurance products. This allows the system to provide an optimal financial plan tailored to the user's life events.

[0067] The financial planning system can further customize financial plans by taking into account the user's hobbies and interests. For example, the data collection unit can collect spending data related to the user's hobbies and interests, and the analysis unit can analyze spending trends based on this data. Furthermore, the simulation unit can simulate future spending based on hobbies and interests, and the advice unit can suggest appropriate saving methods and investment products. This allows the system to provide an optimal financial plan tailored to the user's hobbies and interests.

[0068] The financial planning system can further adjust the financial plan by taking into account the user's geographical location. For example, the data collection unit can collect living expense data for the user's area, and the analysis unit can analyze spending trends based on this data. Furthermore, the simulation unit can simulate future spending based on geographical location information, and the advice unit can suggest appropriate saving methods and investment products. This allows the system to provide an optimal financial plan tailored to the user's geographical location.

[0069] The financial planning system can further customize financial plans by analyzing the user's social media activity. For example, the data collection unit can collect data on the user's social media posts and activities, and the analysis unit can analyze spending trends based on this data. Furthermore, the simulation unit can simulate future spending based on social media activity, and the advice unit can suggest appropriate saving methods and investment products. This allows the system to provide an optimal financial plan tailored to the user's social media activity.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The collection unit collects user income and expenditure data. The collection unit automatically collects income and expenditure data entered by the user. It can also obtain bank account and credit card transaction data. Furthermore, it collects data entered manually by the user. Step 2: The analysis unit analyzes the data collected by the collection unit and creates a household budget. The analysis unit calculates the monthly balance of income and expenses based on the income and expense data, classifies it into categories, and creates a detailed household budget. Furthermore, it predicts future income and expenses based on past data. Step 3: The display unit displays the household account book created by the analysis unit. The display unit displays the household account book in a user-friendly format and can also display it visually using graphs and charts. Furthermore, it can display the household account book in a display format customized by the user. Step 4: The simulation unit performs simulations based on the goals set by the user. The simulation unit performs simulations based on savings goals, investment goals, and expense reduction goals, and provides a plan for achieving those goals. Step 5: The advisory department provides investment and insurance advice based on the results obtained by the simulation department. The advisory department proposes the most suitable investment and insurance products based on the user's financial situation and risk tolerance. Step 6: The forecasting unit forecasts cash flow based on the advice provided by the advisory unit. The forecasting unit forecasts future cash flow and assesses cash flow risks based on the user's income and expense data. Step 7: The Analysis Department analyzes market data in real time based on the results obtained by the Forecasting Department and formulates an investment plan. The Analysis Department collects market data in real time and proposes the optimal investment plan based on the user's investment goals and risk tolerance.

[0072] (Example of form 2) The financial planning system according to an embodiment of the present invention is a system provided in the form of a monthly or annual subscription. This system provides the following services by having the user input the necessary information and having a generating AI analyze that information: automatic household budget management, goal setting and simulation, investment and insurance advice (which can also be considered as a premium plan), cash flow forecasting, real-time market analysis and investment plan formulation (which can also be considered as a premium plan). For example, if the user inputs their monthly income and expenses, the generating AI will automatically create a household budget and display the balance of income and expenses. If the user sets a goal of "saving 1 million yen in 5 years," the generating AI will calculate the monthly savings amount and perform a simulation to achieve the goal. If the user inputs "I want to invest with low risk," the generating AI will suggest appropriate investment products. If the user inputs "I'm worried about next month's cash flow," the generating AI will predict next month's income and expenses and provide cash flow advice. If the user inputs "I want to create an investment plan based on the current market situation," the generating AI will analyze market data in real time and suggest the optimal investment plan. This service allows users to gain a detailed understanding of their financial situation and create an optimal financial plan. In particular, by utilizing AI-generated data, sophisticated financial planning becomes possible even without specialized knowledge, enabling users to achieve financial freedom and peace of mind. As a result, the financial planning system can gain a detailed understanding of the user's financial situation and provide an optimal financial plan.

[0073] The financial planning system according to this embodiment comprises a data collection unit, an analysis unit, a display unit, a simulation unit, an advice unit, a forecasting unit, and an analysis unit. The data collection unit collects the user's income and expenditure data. For example, the data collection unit automatically collects income and expenditure data entered by the user. The data collection unit can also acquire transaction data from bank accounts and credit cards. Furthermore, the data collection unit can also collect data entered manually by the user. For example, the data collection unit provides a form for the user to enter their monthly income and expenditure and collects that data. The analysis unit analyzes the data collected by the data collection unit and creates a household budget. For example, the analysis unit calculates the monthly balance of income and expenditure based on the income and expenditure data. Furthermore, the analysis unit can classify the income and expenditure data by category and create a detailed household budget. Furthermore, the analysis unit can also predict future income and expenditure based on past data. For example, the analysis unit predicts next month's income and expenditure based on past income and expenditure data. The display unit displays the household budget created by the analysis unit. The display unit, for example, displays the household budget in a user-friendly format. The display unit can also visually display the household budget using graphs and charts. Furthermore, the display unit can display the household budget in a user-customized format. For example, the display unit can display income and expenses by categories selected by the user. The simulation unit performs simulations based on goals set by the user. For example, the simulation unit calculates the monthly savings amount based on the user's savings goal. The simulation unit can also perform investment simulations based on the user's investment goal. Furthermore, the simulation unit can perform expense reduction simulations based on the user's expense reduction goal. For example, if the user sets a goal of "saving 1 million yen in 5 years," the simulation unit calculates the monthly savings amount and performs a simulation to achieve that goal. The advice unit provides investment and insurance advice based on the results obtained by the simulation unit. For example, the advice unit suggests the optimal investment product based on the user's financial situation.The advisory unit can also suggest appropriate insurance products based on the user's risk tolerance. Furthermore, the advisory unit can provide specific advice based on the user's goals. For example, if the user inputs "I want to invest with reduced risk," the advisory unit will suggest appropriate investment products. The forecasting unit predicts cash flow based on the advice provided by the advisory unit. For example, the forecasting unit predicts future cash flow based on the user's income and expenditure data. The forecasting unit can also perform cash flow simulations based on the user's goals. Furthermore, the forecasting unit can assess cash flow risk based on the user's financial situation. For example, if the user inputs "I'm worried about cash flow next month," the forecasting unit will predict next month's income and expenses and provide cash flow advice. The analysis unit analyzes market data in real time based on the results obtained by the forecasting unit and formulates investment plans. For example, the analysis unit collects market data in real time and proposes the optimal investment plan. Furthermore, the analysis unit can customize investment plans based on the user's investment goals. Furthermore, the analysis unit can adjust investment plans based on the user's risk tolerance. For example, when a user inputs "I want to create an investment plan based on the current market situation," the analysis unit analyzes market data in real time and proposes the optimal investment plan. This allows the financial planning system according to this embodiment to gain a detailed understanding of the user's financial situation and provide the most suitable financial plan.

[0074] The data collection unit collects user income and expenditure data. For example, it automatically collects income and expenditure data entered by the user. The data collection unit can also retrieve bank account and credit card transaction data. Furthermore, the data collection unit can collect data entered manually by the user. For example, the data collection unit provides a form for the user to enter their monthly income and expenditure, and collects that data. The data collection unit centrally manages the data entered by the user and has functions to detect duplicates and errors to maintain data integrity. For example, the data collection unit displays a warning and prompts correction if the user enters the same transaction multiple times. The data collection unit can also update data in real time by using APIs to automatically retrieve bank account and credit card transaction data. This saves users the trouble of manually entering data. Furthermore, the data collection unit can also obtain additional information from external data sources to supplement the data entered manually by the user. For example, the data collection unit can automatically retrieve price information and store information for related products for the expenditure data entered by the user, and perform detailed expenditure analysis. This allows the data collection unit to efficiently and accurately collect user income and expenditure data, improving the overall data quality of the system.

[0075] The analysis unit analyzes the data collected by the data collection unit and creates a household budget. For example, the analysis unit calculates the monthly balance of income and expenses based on income and expense data. The analysis unit can also categorize income and expense data to create a detailed household budget. Furthermore, the analysis unit can predict future income and expenses based on past data. For example, the analysis unit predicts next month's income and expenses based on past income and expense data. The analysis unit uses AI to analyze data and learn the user's income and expense patterns. Specifically, it uses machine learning algorithms to analyze the user's income and expense trends and detect abnormal patterns and irregular expenses. This allows the analysis unit to provide specific advice to the user to reduce unnecessary expenses. In addition, the analysis unit creates a dashboard to visually display the user's financial situation based on the collected data. The dashboard displays graphs and charts of income and expenses, the monthly balance of income and expenses, and the percentage of expenses by category, allowing the user to understand their financial situation at a glance. Furthermore, the analysis unit also has a function to evaluate the user's progress toward their goals and update the progress in real time. For example, the system displays the user's current savings amount and the remaining time until the savings goal is achieved, supporting the user in taking planned actions towards their goal. This allows the analytics unit to analyze the user's financial situation in detail and support effective household budget management.

[0076] The display unit shows the household budget created by the analysis unit. The display unit displays the budget in a user-friendly format, for example. It can also visually display the budget using graphs and charts. Furthermore, the display unit can display the budget in a user-customized format. For example, it can display income and expenses by categories selected by the user. The display unit utilizes the latest web technologies to create an intuitive and user-friendly user interface. Specifically, it employs responsive design to ensure comfortable viewing on different devices such as smartphones and tablets. The display unit also provides interactive graphs and charts, allowing users to click on data to view detailed information or filter by specific periods or categories. Additionally, the display unit offers the ability to customize the display format to the user's preferences. For example, users can choose to display income and expense data monthly, weekly, or daily, or highlight specific categories or periods. This allows users to gain a more detailed understanding of their financial situation and manage their household finances effectively. The display unit also includes a function to visually display the progress towards user-set goals. For example, it can display the current savings amount and progress towards a savings goal, supporting users in taking planned action towards their goals. This makes the display a powerful tool for users to intuitively understand their financial situation and manage their household finances effectively.

[0077] The simulation unit performs simulations based on goals set by the user. For example, the simulation unit calculates the monthly savings amount based on the user's savings goal. The simulation unit can also perform investment simulations based on the user's investment goal. Furthermore, the simulation unit can perform expense reduction simulations based on the user's expense reduction goal. For example, if the user sets "I want to save 1 million yen in 5 years," the simulation unit will calculate the monthly savings amount and perform a simulation to achieve that goal. The simulation unit uses AI to simulate multiple scenarios and propose the optimal plan. Specifically, it performs simulations that take into account different economic conditions and market conditions based on the user's income, expenses, savings, and investment data. For example, it considers economic growth rates, inflation rates, and stock market fluctuations to propose the optimal savings and investment plans for the goals set by the user. The simulation unit also provides a function to compare multiple scenarios for the goals set by the user. For example, it compares the simulation results when different savings and investment amounts are set, helping the user select the optimal plan. Furthermore, the simulation unit also features a function that updates the progress towards the user's set goals in real time and visually displays the path to achieving those goals. This allows users to act systematically towards their goals. The simulation unit is a powerful tool to support users in achieving their financial goals and plays a crucial role in enabling users to create effective financial plans.

[0078] The advisory department provides investment and insurance advice based on the results obtained by the simulation department. For example, the advisory department can suggest the optimal investment product based on the user's financial situation. It can also suggest appropriate insurance products based on the user's risk tolerance. Furthermore, the advisory department can provide specific advice based on the user's goals. For example, if the user inputs "I want to invest with reduced risk," the advisory department will suggest appropriate investment products. The advisory department uses AI to analyze the user's financial data and provide advice best suited to individual needs. Specifically, it creates a risk profile based on the user's income, expenses, savings, and investment data, and suggests investment and insurance products according to the user's risk tolerance. The advisory department also has a function to provide specific action plans to help the user achieve their goals. For example, it can suggest specific monthly savings and investment amounts for a savings goal set by the user, clearly showing the steps to achieve the goal. Furthermore, the advisory department also provides a function to collect user feedback and continuously improve the advice. For example, if the user inputs feedback on the advice provided, the AI ​​learns from that feedback and reflects it in future advice. This allows the advisory department to provide personalized advice tailored to the user's needs and support them in creating effective financial plans. The advisory department plays a crucial role in helping users achieve their financial goals and serves as a powerful tool for users to manage their finances with confidence.

[0079] The forecasting unit predicts cash flow based on advice provided by the advisory unit. For example, the forecasting unit predicts future cash flow based on the user's income and expenditure data. The forecasting unit can also perform cash flow simulations based on the user's goals. Furthermore, the forecasting unit can assess cash flow risks based on the user's financial situation. For example, if the user inputs "I'm worried about next month's cash flow," the forecasting unit will predict next month's income and expenses and provide cash flow advice. The forecasting unit uses AI to analyze the user's income and expenditure data and predict future cash flow with high accuracy. Specifically, it uses machine learning algorithms to learn the user's income and expenditure patterns and predict future fluctuations in income and expenses. The forecasting unit also has the function to simulate different scenarios and propose the optimal cash flow plan. For example, it compares multiple scenarios for the user's set goals and helps the user select the best plan. Furthermore, the forecasting unit assesses cash flow risks based on the user's financial situation and provides specific advice for risk mitigation. For example, the forecasting section proposes savings plans to help users prepare for future income decreases or expenses increases, and suggests measures to minimize risk. In this way, the forecasting section supports users in systematically managing their future finances and managing their finances with peace of mind. The forecasting section plays a crucial role in helping users achieve their financial goals and is a powerful tool for effective financial management.

[0080] The analysis department analyzes market data in real time based on the results obtained by the forecasting department and formulates investment plans. For example, the analysis department collects market data in real time and proposes the optimal investment plan. The analysis department can also customize investment plans based on the user's investment goals. Furthermore, the analysis department can adjust investment plans based on the user's risk tolerance. For example, if a user inputs "I want to create an investment plan based on the current market situation," the analysis department will analyze market data in real time and propose the optimal investment plan. The analysis department uses AI to analyze market data and formulate the optimal plan for the user's investment goals. Specifically, it uses machine learning algorithms to analyze past market data and current market trends and predict future market trends. The analysis department also simulates different investment scenarios and proposes the optimal investment plan according to the user's risk tolerance. For example, if a user desires low-risk investments, the analysis department will construct a portfolio centered on low-risk investment products and optimize the balance between risk and return. If a user desires high-risk, high-return investments, the analysis department will select high-growth investment products to maximize returns. Furthermore, the analytics department also has the capability to monitor market data in real time and adjust investment plans. For example, it can quickly review investment plans in response to rapid market fluctuations or changes in economic conditions and provide users with appropriate advice. In this way, the analytics department supports users in flexibly responding to market fluctuations and executing optimal investment strategies. The analytics department plays a crucial role in supporting users in achieving their investment goals and is a powerful tool for users to develop effective investment plans.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of income and expenditure data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can automatically collect income and expenditure data, reducing the user's effort. The data collection unit can also encourage user involvement by offering the option to manually collect income and expenditure data when the user is relaxed. Furthermore, if the user is busy, the data collection unit can schedule income and expenditure data collection for evenings or weekends, reducing the user's burden. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0082] The data collection unit can analyze a user's past income and expenditure patterns and select the optimal data collection method. For example, if the data collection unit analyzes a user's past income and expenditure data and finds that income is concentrated at the end of the month, it will concentrate data collection at the end of the month. The data collection unit can also analyze a user's past expenditure patterns and find that expenditures are high on specific days of the week, and collect data on those days. Furthermore, if the income is irregular, the data collection unit can analyze a user's past income and expenditure patterns and collect data at the time the income is generated. For example, if the data collection unit analyzes a user's past income and expenditure data and finds that income is concentrated at the end of the month, it will concentrate data collection at the end of the month. The data collection unit can also analyze a user's past expenditure patterns and find that expenditures are high on specific days of the week, and collect data on those days. The data collection unit can also analyze a user's past income and expenditure patterns and find that income is irregular, and collect data at the time the income is generated. By analyzing past income and expenditure patterns, the optimal data collection method can be selected, enabling efficient data collection. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past income and expenditure data into a generating AI and have the generating AI select the optimal data collection method.

[0083] The data collection unit can filter income and expenditure data based on the user's current living situation and areas of interest. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related expenditure data. It can also prioritize collecting expenditure data related to a new hobby if the user has started one. Furthermore, if the user is planning to move, the data collection unit can prioritize collecting moving-related expenditure data. This allows for the priority collection of highly relevant data by filtering the data based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0084] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit will prioritize collecting important income and expenditure data, delaying the collection of more detailed data. Conversely, if the user is relaxed, the unit can collect more detailed income and expenditure data, encouraging user engagement. Furthermore, if the user is busy, the unit can prioritize collecting key income and expenditure data, reducing the user's burden. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting income and expenditure data. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of data related to the cost of living in that region. Furthermore, if the user is on a business trip, the data collection unit can prioritize the collection of expenditure data at the business trip location. Additionally, if the user is planning to move, the data collection unit can prioritize the collection of data related to the cost of living at the new location. This allows for the priority collection of highly relevant data by considering 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 information into a generating AI and have the generating AI collect highly relevant data.

[0086] The data collection unit can analyze a user's social media activity and collect relevant data when collecting income and expenditure data. For example, if a user indicates on social media that they will be attending a specific event, the data collection unit can collect expenditure data related to that event. It can also collect expenditure data related to a hobby if a user indicates on social media that they have started a new hobby. Furthermore, if a user indicates on social media that they will be traveling to a specific region, the data collection unit can collect travel-related expenditure data for that region. This allows for the efficient collection of relevant data by analyzing a 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 have the generating AI collect the relevant data.

[0087] The analysis unit can estimate the user's emotions and adjust the household budget analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results to encourage user engagement. Furthermore, if the user is busy, the analysis unit can prioritize providing analysis results for major income and expenses. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results to encourage user engagement. If the user is busy, the analysis unit can also prioritize providing analysis results for major income and expenses. This allows the system to provide analysis results that are appropriate for the user by adjusting the analysis 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. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit can perform a detailed analysis on important income and expenditure data and a concise analysis on other data. It can also perform a detailed analysis on data related to user-set goals and a concise analysis on other data. Furthermore, it can perform a detailed analysis on data related to the user's areas of interest and a concise analysis on other data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 importance of the collected data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of the collected data. For example, the analysis unit can apply an income analysis algorithm to income data and an expenditure analysis algorithm to expenditure data. It can also apply a fixed cost analysis algorithm to fixed cost data and a variable cost analysis algorithm to variable cost data. Furthermore, it can apply an investment analysis algorithm to investment data and an insurance analysis algorithm to insurance data. For example, the analysis unit can apply an income analysis algorithm to income data and an expenditure analysis algorithm to expenditure data. It can also apply a fixed cost analysis algorithm to fixed cost data and a variable cost analysis algorithm to variable cost data. It can also apply an investment analysis algorithm to investment data and an insurance analysis algorithm to insurance data. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the categories of the collected data into the generating AI and have the generating AI execute different analysis algorithms.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Additionally, if the user is in a hurry, the analysis unit can provide a concise display method. This allows the system to provide analysis results tailored to the user by adjusting the display method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0091] The analysis unit can determine the priority of analysis based on the timing of data submission. For example, the analysis unit may prioritize the analysis of the most recent income and expenditure data, delaying the analysis of past data. Alternatively, the analysis unit may prioritize the analysis of data related to user-set goals, delaying the analysis of other data. Furthermore, the analysis unit may prioritize the analysis of data related to the user's areas of interest, delaying the analysis of other data. This enables efficient analysis by determining the priority of analysis based on the timing of data submission. 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 timing of data submission into a generating AI and have the generating AI determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on the relationships between the collected data. For example, the analysis unit can adjust the order of analysis considering the relationships between income data and expenditure data. It can also adjust the order of analysis considering the relationships between fixed cost data and variable cost data. Furthermore, it can adjust the order of analysis considering the relationships between investment data and insurance data. For example, the analysis unit can adjust the order of analysis considering the relationships between income data and expenditure data. It can also adjust the order of analysis considering the relationships between fixed cost data and variable cost data. It can also adjust the order of analysis considering the relationships between investment data and insurance data. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. 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 relationships between the collected data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0093] The display unit can estimate the user's emotions and adjust the display method of the household account book based on the estimated emotions. For example, if the user is stressed, the display unit can provide a simple and highly visible display method. The display unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, the display unit can provide a concise display method if the user is in a hurry. This allows for a household account book display tailored to the user by adjusting the display method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, or without AI. For example, the display unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The display unit can select the optimal display method by referring to the user's past operation history when displaying the household account ledger. For example, the display unit can prioritize display methods previously used by the user. The display unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the display unit can automatically set the display method that the user has previously preferred. For example, the display unit can prioritize display methods previously used by the user. The display unit can also suggest the optimal display method based on the user's past operation history. The display unit can also automatically set the display method that the user has previously preferred. This allows the display unit to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0095] The display unit can select the optimal display method when displaying household accounts, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can also provide a display method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can also provide a display method optimized for larger screens if the user is using a tablet. The display unit can also provide a concise and highly visible display method if the user is using a smartwatch. This allows the display unit to provide the optimal display method by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0096] The display unit can estimate the user's emotions and adjust the display order of the household account book based on the estimated emotions. For example, if the user is stressed, the display unit will show important information first and detailed information later. Similarly, if the user is relaxed, the display unit can also show detailed information first and important information later. Furthermore, if the user is in a hurry, the display unit can also show concise information first and detailed information later. This allows for a more user-friendly display of the household account book by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the display unit may be performed using AI, or not using AI. For example, the display unit can input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0097] The display unit can provide a multilingual display according to the user's language settings when displaying the household account book. For example, the display unit can automatically set the language of the household account book based on the language settings of the user's device. The display unit can also provide a language switching function if the user uses multiple languages. Furthermore, the display unit can display the household account book in a language selected by the user. For example, the display unit can automatically set the language of the household account book based on the language settings of the user's device. The display unit can also provide a language switching function if the user uses multiple languages. The display unit can also display the household account book in a language selected by the user. This enables a household account book display tailored to the user by providing a multilingual display according to the user's language settings. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's language setting data into a generating AI and have the generating AI perform the multilingual display.

[0098] The simulation unit can estimate the user's emotions and adjust the simulation's presentation based on the estimated emotions. For example, if the user is tense, the simulation unit provides a simple and easy-to-understand simulation result. If the user is relaxed, the simulation unit can also provide a detailed simulation result to encourage user engagement. Furthermore, if the user is in a hurry, the simulation unit can provide a concise simulation result. This allows for the provision of simulation results tailored to the user by adjusting the simulation's presentation according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the simulation unit may be performed using AI, or without AI. For example, the simulation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0099] The simulation unit can adjust the level of detail of the simulation based on the importance of the objectives during the simulation. For example, the simulation unit can perform a detailed simulation for important objectives and a simplified simulation for other objectives. The simulation unit can also adjust the level of detail of the simulation based on the priority of objectives set by the user. Furthermore, the simulation unit can perform a detailed simulation for objectives related to the user's area of ​​interest and a simplified simulation for other objectives. For example, the simulation unit can perform a detailed simulation for important objectives and a simplified simulation for other objectives. The simulation unit can also adjust the level of detail of the simulation based on the priority of objectives set by the user. The simulation unit can also perform a detailed simulation for objectives related to the user's area of ​​interest and a simplified simulation for other objectives. This allows for efficient simulation by adjusting the level of detail of the simulation based on the importance of the objectives. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input objective importance data into a generating AI and have the generating AI adjust the level of detail of the simulation.

[0100] The simulation unit can apply different simulation algorithms depending on the category of the target during the simulation. For example, the simulation unit can apply a savings simulation algorithm to a savings target and an investment simulation algorithm to an investment target. Furthermore, the simulation unit can apply a short-term simulation algorithm to a short-term target and a long-term simulation algorithm to a long-term target. In addition, the simulation unit can apply a monetary simulation algorithm to a specific monetary target and an abstract simulation algorithm to an abstract target. For example, the simulation unit can apply a savings simulation algorithm to a savings target and an investment simulation algorithm to an investment target. The simulation unit can also apply a short-term simulation algorithm to a short-term target and a long-term simulation algorithm to a long-term target. The simulation unit can also apply a monetary simulation algorithm to a specific monetary target and an abstract simulation algorithm to an abstract target. This allows for highly accurate simulations by applying the appropriate simulation algorithm according to the category of the target. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input target category data into the generating AI and have the generating AI execute different simulation algorithms.

[0101] The simulation unit can estimate the user's emotions and adjust the length of the simulation based on the estimated emotions. For example, if the user is in a hurry, the simulation unit can provide a short, concise simulation. If the user is relaxed, the simulation unit can also provide a longer simulation with detailed explanations. Furthermore, if the user is excited, the simulation unit can provide a simulation with visually stimulating effects. For example, if the user is in a hurry, the simulation unit can provide a short, concise simulation. If the user is relaxed, the simulation unit can also provide a longer simulation with detailed explanations. If the user is excited, the simulation unit can also provide a simulation with visually stimulating effects. By adjusting the length of the simulation according to the user's emotions, it is possible to provide simulation results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0102] The simulation unit can determine the priority of simulations based on the timing of goal setting. For example, the simulation unit can prioritize simulations for immediate goals and postpone simulations for distant future goals. The simulation unit can also determine the priority of simulations based on the deadlines of goals set by the user. Furthermore, the simulation unit can prioritize simulations for goals related to the user's areas of interest and postpone simulations for other goals. For example, the simulation unit can prioritize simulations for immediate goals and postpone simulations for distant future goals. The simulation unit can also determine the priority of simulations based on the deadlines of goals set by the user. The simulation unit can also prioritize simulations for goals related to the user's areas of interest and postpone simulations for other goals. This enables efficient simulations by determining the priority of simulations based on the timing of goal setting. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input goal setting timing data into a generating AI and have the generating AI determine the priority of simulations.

[0103] The simulation unit can adjust the order of simulations based on the relationships between targets during the simulation. For example, the simulation unit can adjust the order of simulations considering the relationship between income targets and expenditure targets. It can also adjust the order of simulations considering the relationship between savings targets and investment targets. Furthermore, the simulation unit can adjust the order of simulations considering the relationship between short-term targets and long-term targets. For example, the simulation unit can adjust the order of simulations considering the relationship between income targets and expenditure targets. It can also adjust the order of simulations considering the relationship between savings targets and investment targets. It can also adjust the order of simulations considering the relationship between short-term targets and long-term targets. This allows for efficient simulation by adjusting the order of simulations based on the relationships between targets. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input target relationship data into a generating AI and have the generating AI perform the adjustment of the simulation order.

[0104] The advice unit can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is nervous, the advice unit can provide simple and easy-to-understand advice. If the user is relaxed, the advice unit can also provide detailed advice to encourage user engagement. Furthermore, if the user is in a hurry, the advice unit can provide concise advice. For example, if the user is nervous, the advice unit can provide simple and easy-to-understand advice. If the user is relaxed, the advice unit can also provide detailed advice to encourage user engagement. If the user is in a hurry, the advice unit can also provide concise advice. This allows the advice unit to provide advice that is appropriate for the user by adjusting the way it presents advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0105] The advice unit can adjust the level of detail of its advice based on the importance of the simulation results when providing advice. For example, the advice unit can provide detailed advice for important simulation results and concise advice for other results. The advice unit can also adjust the level of detail of its advice based on the priority of goals set by the user. Furthermore, the advice unit can provide detailed advice for simulation results related to the user's area of ​​interest and concise advice for other results. For example, the advice unit can provide detailed advice for important simulation results and concise advice for other results. The advice unit can also adjust the level of detail of its advice based on the priority of goals set by the user. The advice unit can provide detailed advice for simulation results related to the user's area of ​​interest and concise advice for other results. This allows for efficient advice by adjusting the level of detail of the advice based on the importance of the simulation results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance data of the simulation results into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0106] The advisory unit can apply different advisory algorithms depending on the category of the simulation results when providing advice. For example, the advisory unit can apply an investment advisory algorithm to investment simulation results and an insurance advisory algorithm to insurance simulation results. It can also apply a short-term advisory algorithm to short-term simulation results and a long-term advisory algorithm to long-term simulation results. Furthermore, it can apply a monetary advisory algorithm to specific monetary simulation results and an abstract advisory algorithm to abstract simulation results. This allows for highly accurate advice by applying the appropriate advisory algorithm according to the category of the simulation results. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advice unit can input the categorical data of the simulation results into the generating AI and have the generating AI apply different advice algorithms.

[0107] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the advice unit will provide short, concise advice. If the user is relaxed, the advice unit can also provide longer advice with more detailed explanations. Furthermore, if the user is excited, the advice unit can provide advice with visually stimulating effects. For example, if the user is in a hurry, the advice unit will provide short, concise advice. If the user is relaxed, the advice unit can also provide longer advice with more detailed explanations. If the user is excited, the advice unit can also provide advice with visually stimulating effects. This allows the advice unit to provide advice that is appropriate for the user by adjusting the length of the advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0108] The advice unit can prioritize advice based on the timing of simulation result submissions when providing advice. For example, the advice unit may prioritize advice on the most recent simulation results and postpone advice on past results. The advice unit can also prioritize advice based on the deadlines for goals set by the user. Furthermore, the advice unit may prioritize advice on simulation results related to the user's area of ​​interest and postpone advice on other results. For example, the advice unit may prioritize advice on the most recent simulation results and postpone advice on past results. The advice unit can also prioritize advice based on the deadlines for goals set by the user. The advice unit may also prioritize advice on simulation results related to the user's area of ​​interest and postpone advice on other results. This enables efficient advice by prioritizing advice based on the timing of simulation result submissions. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input simulation result submission timing data into a generating AI and have the generating AI determine the priority of advice.

[0109] The advice unit can adjust the order of advice based on the relevance of the simulation results when providing advice. For example, the advice unit can adjust the order of advice considering the relevance of income simulation results and expenditure simulation results. It can also adjust the order of advice considering the relevance of savings simulation results and investment simulation results. Furthermore, the advice unit can adjust the order of advice considering the relevance of short-term simulation results and long-term simulation results. For example, the advice unit can adjust the order of advice considering the relevance of income simulation results and expenditure simulation results. It can also adjust the order of advice considering the relevance of savings simulation results and investment simulation results. It can also adjust the order of advice considering the relevance of short-term simulation results and long-term simulation results. This allows for efficient advice by adjusting the order of advice based on the relevance of the simulation results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the relevance data of the simulation results into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0110] The prediction unit can estimate the user's emotions and adjust the cash flow forecasting method based on the estimated emotions. For example, if the user is stressed, the prediction unit can provide a simple and easy-to-understand cash flow forecast. If the user is relaxed, the prediction unit can also provide a detailed cash flow forecast to encourage user engagement. Furthermore, if the user is in a hurry, the prediction unit can provide a concise cash flow forecast. For example, if the user is stressed, the prediction unit can provide a simple and easy-to-understand cash flow forecast. If the user is relaxed, the prediction unit can also provide a detailed cash flow forecast to encourage user engagement. If the user is in a hurry, the prediction unit can also provide a concise cash flow forecast. This allows the system to provide forecast results that are appropriate for the user by adjusting the cash flow forecasting 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. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0111] The forecasting unit can analyze the user's past income and expense data to select the optimal forecasting method when forecasting cash flow. For example, the forecasting unit can analyze the user's past income and expense data and, if income is irregular, make a forecast at the time income is generated. The forecasting unit can also analyze the user's past spending patterns and, if spending is high on a particular day of the week, make a forecast on that day. Furthermore, the forecasting unit can analyze the user's past income and expense data and, if income is concentrated at the end of the month, make a forecast at the end of the month. For example, the forecasting unit can analyze the user's past income and expense data and, if income is irregular, make a forecast at the time income is generated. The forecasting unit can also analyze the user's past spending patterns and, if spending is high on a particular day of the week, make a forecast on that day. The forecasting unit can also analyze the user's past income and expense data and, if income is concentrated at the end of the month, make a forecast at the end of the month. By analyzing the user's past income and expense data, the forecasting unit can select the optimal forecasting method and enable highly accurate forecasts. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's past income and expense data into the generating AI and have the generating AI select the optimal prediction method.

[0112] The forecasting unit can customize its forecasting methods based on the user's current living situation when forecasting cash flow. For example, if the user is traveling, the forecasting unit will consider travel-related expenses when making a forecast. The forecasting unit can also consider expenses related to a hobby if the user has started a new hobby. Furthermore, if the user is planning to move, the forecasting unit can also consider expenses related to moving when making a forecast. This allows for highly accurate forecasts by customizing the forecasting methods based on the user's current living situation. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or not. For example, the forecasting unit can input the user's current living situation data into a generating AI and have the generating AI customize the forecasting methods.

[0113] The prediction unit can estimate the user's emotions and prioritize cash flow forecasts based on those emotions. For example, if the user is stressed, the prediction unit will prioritize important cash flow forecasts and postpone detailed forecasts. Conversely, if the user is relaxed, the prediction unit can provide detailed cash flow forecasts to encourage user engagement. Furthermore, if the user is in a hurry, the prediction unit can prioritize key cash flow forecasts and postpone detailed forecasts. This enables efficient forecasting by prioritizing cash flow forecasts according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input user sentiment data into the generative AI and have the generative AI perform sentiment estimation.

[0114] The forecasting unit can select the optimal forecasting method when forecasting cash flow, taking into account the user's geographical location. For example, if the user lives in a specific region, the forecasting unit will make a forecast considering the cost of living in that region. Furthermore, if the user is on a business trip, the forecasting unit can also make a forecast considering the cost of living at the destination. In addition, if the user is planning to move, the forecasting unit can also make a forecast considering the cost of living at the destination. This allows for the selection of the optimal forecasting method and enables highly accurate forecasts by considering the user's geographical location. Some or all of the above-described processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal forecasting method.

[0115] The forecasting unit can analyze a user's social media activity and propose forecasting methods when forecasting cash flow. For example, if the forecasting unit indicates on social media that a user will participate in a specific event, it will make a forecast considering the expenses related to that event. Similarly, if the forecasting unit indicates on social media that a user has started a new hobby, it can also make a forecast considering the expenses related to that hobby. Furthermore, if the forecasting unit indicates on social media that a user will travel to a specific region, it can also make a forecast considering the travel-related expenses for that region. This allows the forecasting unit to propose relevant forecasting methods and enable highly accurate forecasts by analyzing the user's social media activity. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user social media activity data into a generating AI and have the generating AI execute suggestions for prediction methods.

[0116] The analysis unit can estimate the user's emotions and adjust the market data analysis method based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand market data analysis. If the user is relaxed, the analysis unit can also provide a detailed market data analysis to encourage user engagement. Furthermore, if the user is in a hurry, the analysis unit can provide a concise market data analysis. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand market data analysis. If the user is relaxed, the analysis unit can also provide a detailed market data analysis to encourage user engagement. If the user is in a hurry, the analysis unit can also provide a concise market data analysis. This allows for the provision of analysis results tailored to the user by adjusting the market data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The analysis department can analyze users' past investment behavior to select the optimal analysis method when analyzing market data. For example, if the analysis department analyzes a user's past investment behavior and finds that they prefer low-risk investments, it can analyze market data based on that tendency. The analysis department can also analyze market data based on a user's past investment behavior and find that they prefer short-term investments. Furthermore, if the analysis department analyzes a user's past investment behavior and finds that they tend to invest in a particular industry, it can prioritize the analysis of market data for that industry. For example, if the analysis department analyzes a user's past investment behavior and finds that they prefer low-risk investments, it can analyze market data based on that tendency. The analysis department can also analyze market data based on a user's past investment behavior and find that they prefer short-term investments. The analysis department can also analyze market data based on a user's past investment behavior and find that they tend to invest in a particular industry, it can prioritize the analysis of market data for that industry. By analyzing users' past investment behavior, the analysis department can select the optimal analysis method and enable highly accurate analysis. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis unit can input the user's past investment behavior data into a generating AI and have the AI ​​select the optimal analysis method.

[0118] The analysis unit can customize its analysis methods based on the user's current investment situation when analyzing market data. For example, the analysis unit analyzes market data considering the performance of investment products the user currently holds. Furthermore, if the user is considering a new investment, the analysis unit can prioritize the analysis of market data for that investment product. Additionally, if the user has a specific risk tolerance, the analysis unit can analyze market data based on that risk level. This allows for highly accurate analysis by customizing the analysis methods based on the user's current investment situation. 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 current investment situation data into a generating AI and have the generating AI perform the customization of the analysis methods.

[0119] The analysis unit can estimate the user's emotions and adjust the display method of the market data analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, it can provide a concise display method if the user is in a hurry. This allows for a user-friendly display by adjusting the display method of the market data analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, 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 and have the generative AI perform emotion estimation.

[0120] The analysis department can select the optimal analysis method when analyzing market data, taking into account the user's geographical location. For example, if the user lives in a specific region, the analysis department will prioritize analyzing market data for that region. Furthermore, if the user is on a business trip, the analysis department can prioritize analyzing market data for their destination. In addition, if the user is planning to move, the analysis department can prioritize analyzing market data for their new destination. This allows for the selection of the optimal analysis method and enables highly accurate analysis by considering the user's geographical location. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the user's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0121] The analysis department can analyze users' social media activity and propose analytical methods when analyzing market data. For example, if a user shows interest in a particular industry on social media, the analysis department will prioritize analyzing market data for that industry. Similarly, if a user shows interest in a particular investment product on social media, the analysis department can prioritize analyzing market data for that investment product. Furthermore, if a user shows interest in a particular region on social media, the analysis department can prioritize analyzing market data for that region. This allows the analysis department to propose relevant analytical methods and enable highly accurate analysis by analyzing users' social media activity. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input user social media activity data into a generating AI and have the AI ​​propose methods for analysis.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The financial planning system can further collect user health data and adjust financial plans based on their health status. For example, the data collection unit can acquire health data from the user's fitness tracker or smartwatch, and the analysis unit can suggest high-risk investments if the user is in good health. Conversely, if the user's health is deteriorating, it can suggest a review of their insurance products. Furthermore, based on health data, it can predict future medical expenses and incorporate them into the financial plan. This allows the system to provide an optimal financial plan tailored to the user's health condition.

[0124] The financial planning system can further estimate the user's emotions and adjust investment risk based on those emotions. For example, if the user is stressed, the analysis unit can suggest low-risk investment products. Conversely, if the user is relaxed, it can suggest high-risk investment products. Furthermore, if the user is excited, it can suggest diversifying investments to spread risk. This allows for adjustment of investment risk according to the user's emotions, enabling the provision of more appropriate investment advice.

[0125] A financial planning system can further adjust financial plans by taking into account the user's life events. For example, the data collection unit can collect life events such as marriage, childbirth, and moving, and the analysis unit can predict increases or decreases in spending based on these events. Furthermore, the simulation unit can simulate future financial plans based on life events, and the advice unit can suggest appropriate investments and insurance products. This allows the system to provide an optimal financial plan tailored to the user's life events.

[0126] The financial planning system can further estimate the user's emotions and adjust the display of the household budget based on those emotions. For example, if the user is stressed, the display can provide a simple and highly visible format. If the user is relaxed, it can provide a format that includes detailed information. Furthermore, if the user is in a hurry, it can provide a format that focuses on the essentials. By adjusting the display of the household budget according to the user's emotions, it becomes possible to display the household budget in a way that is suitable for the user.

[0127] The financial planning system can further customize financial plans by taking into account the user's hobbies and interests. For example, the data collection unit can collect spending data related to the user's hobbies and interests, and the analysis unit can analyze spending trends based on this data. Furthermore, the simulation unit can simulate future spending based on hobbies and interests, and the advice unit can suggest appropriate saving methods and investment products. This allows the system to provide an optimal financial plan tailored to the user's hobbies and interests.

[0128] The financial planning system can further estimate the user's emotions and adjust the simulation's presentation based on those emotions. For example, if the user is stressed, the simulation unit can provide simple and easy-to-understand simulation results. If the user is relaxed, it can provide detailed simulation results to encourage user engagement. Furthermore, if the user is in a hurry, it can provide concise simulation results. In this way, by adjusting the simulation's presentation according to the user's emotions, the system can provide simulation results that are appropriate for the user.

[0129] The financial planning system can further adjust the financial plan by taking into account the user's geographical location. For example, the data collection unit can collect living expense data for the user's area, and the analysis unit can analyze spending trends based on this data. Furthermore, the simulation unit can simulate future spending based on geographical location information, and the advice unit can suggest appropriate saving methods and investment products. This allows the system to provide an optimal financial plan tailored to the user's geographical location.

[0130] The financial planning system can further estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is stressed, the advice section can provide simple, easy-to-understand advice. If the user is relaxed, it can provide more detailed advice to encourage user engagement. Furthermore, if the user is in a hurry, it can provide concise advice. In this way, by adjusting the way advice is presented according to the user's emotions, the system can provide advice that is appropriate for the user.

[0131] The financial planning system can further customize financial plans by analyzing the user's social media activity. For example, the data collection unit can collect data on the user's social media posts and activities, and the analysis unit can analyze spending trends based on this data. Furthermore, the simulation unit can simulate future spending based on social media activity, and the advice unit can suggest appropriate saving methods and investment products. This allows the system to provide an optimal financial plan tailored to the user's social media activity.

[0132] The financial planning system can further estimate the user's emotions and adjust the cash flow forecasting method based on those emotions. For example, if the user is stressed, the forecasting unit can provide a simple and easy-to-understand cash flow forecast. If the user is relaxed, it can provide a detailed cash flow forecast to encourage user engagement. Furthermore, if the user is in a hurry, it can provide a concise cash flow forecast. In this way, by adjusting the cash flow forecasting method according to the user's emotions, the system can provide forecast results that are appropriate for the user.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The collection unit collects user income and expenditure data. The collection unit automatically collects income and expenditure data entered by the user. It can also obtain bank account and credit card transaction data. Furthermore, it collects data entered manually by the user. Step 2: The analysis unit analyzes the data collected by the collection unit and creates a household budget. The analysis unit calculates the monthly balance of income and expenses based on the income and expense data, classifies it into categories, and creates a detailed household budget. Furthermore, it predicts future income and expenses based on past data. Step 3: The display unit displays the household account book created by the analysis unit. The display unit displays the household account book in a user-friendly format and can also display it visually using graphs and charts. Furthermore, it can display the household account book in a display format customized by the user. Step 4: The simulation unit performs simulations based on the goals set by the user. The simulation unit performs simulations based on savings goals, investment goals, and expense reduction goals, and provides a plan for achieving those goals. Step 5: The advisory department provides investment and insurance advice based on the results obtained by the simulation department. The advisory department proposes the most suitable investment and insurance products based on the user's financial situation and risk tolerance. Step 6: The forecasting unit forecasts cash flow based on the advice provided by the advisory unit. The forecasting unit forecasts future cash flow and assesses cash flow risks based on the user's income and expense data. Step 7: The Analysis Department analyzes market data in real time based on the results obtained by the Forecasting Department and formulates an investment plan. The Analysis Department collects market data in real time and proposes the optimal investment plan based on the user's investment goals and risk tolerance.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, display unit, simulation unit, advice unit, forecasting unit, and analysis unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's income and expenditure data by the control unit 46A of the smart device 14 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and creates a household budget. The display unit displays the analysis results on the display 40A of the smart device 14. The simulation unit performs a simulation based on the user's goals by the specific processing unit 290 of the data processing unit 12. The advice unit provides investment and insurance advice by the specific processing unit 290 of the data processing unit 12. The forecasting unit predicts cash flow by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes market data in real time by the specific processing unit 290 of the data processing unit 12 and formulates an investment plan. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, display unit, simulation unit, advice unit, forecasting unit, and analysis unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's income and expenditure data by the control unit 46A of the smart glasses 214 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and creates a household budget. The display unit displays the analysis results on the display of the smart glasses 214. The simulation unit performs a simulation based on the user's goals by the specific processing unit 290 of the data processing unit 12. The advice unit provides investment and insurance advice by the specific processing unit 290 of the data processing unit 12. The forecasting unit predicts cash flow by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes market data in real time by the specific processing unit 290 of the data processing unit 12 and formulates an investment plan. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the collection unit, analysis unit, display unit, simulation unit, advice unit, forecasting unit, and analysis unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's income and expenditure data by the control unit 46A of the headset terminal 314 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and creates a household account book. The display unit displays the analysis results on the display 343 of the headset terminal 314. The simulation unit performs a simulation based on the user's goals by the specific processing unit 290 of the data processing unit 12. The advice unit provides investment and insurance advice by the specific processing unit 290 of the data processing unit 12. The forecasting unit predicts cash flow by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes market data in real time by the specific processing unit 290 of the data processing unit 12 and formulates an investment plan. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the data collection unit, analysis unit, display unit, simulation unit, advice unit, forecasting unit, and analysis unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's income and expenditure data by the control unit 46A of the robot 414 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and creates a household budget. The display unit displays the analysis results on the display of the robot 414. The simulation unit performs a simulation based on the user's goals by the specific processing unit 290 of the data processing unit 12. The advice unit provides investment and insurance advice by the specific processing unit 290 of the data processing unit 12. The forecasting unit predicts cash flow by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes market data in real time by the specific processing unit 290 of the data processing unit 12 and formulates an investment plan. 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A data collection unit that collects user income and expenditure data, An analysis unit analyzes the data collected by the aforementioned collection unit and creates a household account book, A display unit that displays the household account book created by the analysis unit, A simulation unit that performs simulations based on goals set by the user, Based on the results obtained by the aforementioned simulation unit, an advisory unit provides investment and insurance advice. A forecasting unit that forecasts cash flow based on the advice provided by the aforementioned advisory unit, The system includes an analysis unit that analyzes market data in real time based on the results obtained by the forecasting unit and formulates an investment plan. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate user sentiment and adjust the timing of income and expenditure data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past income and spending patterns to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting income and expenditure data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting income and expenditure data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting income and expenditure data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the household budget analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Apply different analysis algorithms depending on the category of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Prioritize analysis based on the submission date of collected data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned display unit is The system estimates the user's emotions and adjusts how the household budget is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned display unit is When displaying the household budget, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is When displaying household budget information, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is It estimates the user's emotions and adjusts the display order of the household budget based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is When displaying the household budget, the system provides multilingual support according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, The system estimates the user's emotions and adjusts the simulation's representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, During the simulation, adjust the level of detail based on the importance of the objectives. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During the simulation, different simulation algorithms are applied depending on the target category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, During simulation, prioritize simulations based on the target setting time. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, During simulation, adjust the order of simulations based on the relevance of the objectives. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the simulation results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of the simulation results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, we prioritize the advice based on when the simulation results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, adjust the order of advice based on the relevance of the simulation results. The system described in Appendix 1, characterized by the features described herein. (Note 31) The prediction unit, We estimate user sentiment and adjust the cash flow forecasting method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The prediction unit, When forecasting cash flow, the system analyzes the user's past income and expense data to select the optimal forecasting method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The prediction unit, When forecasting cash flow, the forecasting method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The prediction unit, It estimates user sentiment and prioritizes cash flow forecasts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The prediction unit, When forecasting cash flow, the optimal forecasting method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The prediction unit, When forecasting cash flow, we propose methods for forecasting by analyzing users' social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned analysis unit is We estimate user sentiment and adjust the market data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned analysis unit is When analyzing market data, we analyze users' past investment behavior to select the most suitable analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned analysis unit is When analyzing market data, customize the analysis methods based on the user's current investment status. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned analysis unit is It estimates user sentiment and adjusts how market data analysis results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned analysis unit is When analyzing market data, the optimal analysis method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned analysis unit is When analyzing market data, we propose analytical methods by analyzing users' social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0207] 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 data collection unit that collects user income and expenditure data, An analysis unit analyzes the data collected by the aforementioned collection unit and creates a household account book, A display unit that displays the household account book created by the analysis unit, A simulation unit that performs simulations based on goals set by the user, Based on the results obtained by the aforementioned simulation unit, an advisory unit provides investment and insurance advice. A forecasting unit that forecasts cash flow based on the advice provided by the aforementioned advisory unit, The system comprises an analysis unit that analyzes market data in real time based on the results obtained by the forecasting unit and formulates an investment plan. A system characterized by the following features.

2. The aforementioned collection unit is We estimate user sentiment and adjust the timing of income and expenditure data collection based on the estimated user sentiment. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past income and spending patterns to select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting income and expenditure data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting income and expenditure data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting income and expenditure data, analyze users' social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the household budget analysis method based on those estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the collected data. The system according to feature 1.

10. The aforementioned analysis unit, Apply different analysis algorithms depending on the category of the collected data. The system according to feature 1.

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