system

Abstract

The system according to this embodiment aims to enable individuals to clearly understand their own financial situation and to create long-term financial plans. [Solution] The system according to the embodiment comprises a collection unit, a planning unit, and an analysis unit. The collection unit clarifies the financial situation. The planning unit creates a long-term financial plan based on the information collected by the collection unit. The analysis unit periodically analyzes the progress of the plan created by the planning unit and modifies the plan as necessary.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for an individual to clearly understand their own financial situation and make a long-term financial plan, which may cause anxiety about getting married or having children.

[0005] The system according to the embodiment aims to enable an individual to clearly understand their own financial situation and make a long-term financial plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a planning unit, and an analysis unit. The data collection unit clarifies the financial situation. The planning unit creates a long-term financial plan based on the information collected by the data collection unit. The analysis unit periodically analyzes the progress of the plan created by the planning unit and modifies the plan as necessary. [Effects of the Invention]

[0007] The system according to this embodiment allows individuals to clearly understand their own financial situation and create long-term financial plans. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Financial Clarification Platform (FCP) according to an embodiment of the present invention is a system for solving the problems of low birth rates and population decline in Japan. This system uses AI to ask relevant questions to help users understand their current financial situation and clarify their financial status. The AI ​​then creates a long-term financial plan based on the user's current financial situation and future goals. This plan includes different investment options and how much to invest in each option. The AI ​​also predicts future wealth, motivating the user to begin investing. Furthermore, the AI ​​periodically analyzes the user's progress and makes suggestions if the plan needs to be revised. The FCP provides users with financial education, helping them understand how they are managing their funds. This allows users to gain confidence in building wealth while having a family. The FCP also shares how government support programs can help families. This platform allows users to gain financial security and reduce anxieties about marriage and starting a family. The FCP is designed as an independent system that can be integrated with cashless payment and external systems, enabling cashless payment asset management, bank account integration, and integration with matchmaking platforms. This allows the Financial Clarification Platform (FCP) to alleviate users' financial anxieties and provide them with confidence in getting married and starting a family.

[0029] The Financial Clarification Platform (FCP) according to this embodiment comprises a data collection unit, a planning unit, and an analysis unit. The data collection unit clarifies the user's financial situation. The data collection unit collects information such as the user's income, expenses, assets, and liabilities. The data collection unit can collect this information by asking the user relevant questions. For example, the data collection unit asks about the user's monthly income and expenses. The data collection unit can also ask for details of the user's assets and liabilities. Furthermore, the data collection unit can obtain data from the user's bank accounts and investment accounts in order to clarify the user's financial situation. The planning unit creates a long-term financial plan based on the information collected by the data collection unit. The planning unit creates investment and savings plans based on the user's current financial situation and future goals, for example. The planning unit can offer the user different investment options and indicate how much to invest in each option. For example, the planning unit can offer options such as equity investment, bond investment, and real estate investment. The planning unit can also suggest an optimal investment portfolio based on the user's risk tolerance. Furthermore, the planning department forecasts the user's future wealth and motivates the user to begin investing. The analysis department periodically analyzes the progress of the plan created by the planning department and modifies the plan as needed. For example, the analysis department monitors the user's investment performance and makes suggestions if plan modifications are necessary. The analysis department can also suggest rebalancing the user's investment portfolio. In addition, the analysis department can modify the plan in response to changes in the user's financial situation. For example, if the user's income increases, the analysis department can suggest additional investment options. Thus, the Financial Clarification Platform (FCP) according to this embodiment can alleviate the user's financial anxiety, improve the user's financial situation by creating a long-term financial plan and periodically analyzing its progress.

[0030] The data collection unit gathers information such as users' income, expenses, assets, and liabilities to clarify their financial situation. Specifically, the unit asks users a series of questions to inquire about the details of their monthly income and expenses. For example, it confirms the user's sources of income, such as salary, bonuses, and side income, and gains detailed information on expense items such as rent, utilities, food, and entertainment. The unit also inquires about the user's assets and liabilities. Assets include cash, deposits, stocks, bonds, and real estate, while liabilities include mortgages, car loans, and outstanding credit card balances. Furthermore, the unit can also obtain data directly from users' bank accounts and investment accounts. This allows for a more accurate understanding of the user's financial situation. With the user's consent, the unit connects with bank and securities company systems via APIs to obtain data in real time. This ensures that the user's financial data is always up-to-date. In addition, the unit uses encryption technology to protect the data in order to securely manage the user's financial data. This ensures that accurate financial information is collected while maintaining user privacy.

[0031] The Planning Department creates long-term financial plans based on information collected by the Data Collection Department. Specifically, the Planning Department considers the user's current financial situation and future goals to develop investment and savings plans. For example, if a user has goals such as purchasing a home in the future, paying for their children's education, or living expenses after retirement, the Planning Department outlines specific steps to achieve these goals. The Planning Department provides the user with different investment options and indicates how much to invest in each option. For example, it offers options such as stock investment, bond investment, and real estate investment, and explains the risks and returns of each. The Planning Department also proposes an optimal investment portfolio based on the user's risk tolerance. Risk tolerance is assessed considering the user's age, income, asset situation, and investment experience. Furthermore, the Planning Department forecasts the user's future wealth and motivates the user to start investing. For example, it simulates the future asset amount if the current investment plan is continued, showing the likelihood of achieving the goal. This allows the user to act systematically towards specific goals. The Planning Department can also flexibly adjust the plan according to the user's life stage and market fluctuations. This allows the user to always maintain an optimal financial strategy.

[0032] The analytics department regularly analyzes the progress of plans created by the planning department and modifies them as needed. Specifically, the analytics department monitors the user's investment performance and makes suggestions when plan modifications are necessary. For example, if a user's investments are performing better than expected, they will suggest additional investment options; conversely, if investments are underperforming, they will suggest rebalancing to diversify risk. The analytics department also modifies plans in response to changes in the user's financial situation. For example, if a user's income increases, they will suggest additional savings or investments; conversely, if income decreases, they will suggest reviewing expenses or shifting to lower-risk investments. The analytics department uses AI to analyze the user's financial data and make optimal suggestions. Based on historical data and market trends, the AI ​​predicts future risks and returns and derives the optimal strategy for the user. Furthermore, the analytics department collects user feedback and continuously improves the accuracy and effectiveness of the plans. For example, they analyze how users reacted to suggestions and incorporate that feedback into future suggestions. This allows the analytics department to respond flexibly to user needs and optimally manage the user's financial situation.

[0033] The system includes an Education Department. The Education Department provides financial education to users. For example, the Education Department provides investment education, savings education, risk management education, etc. The Education Department can teach users basic financial concepts. For example, the Education Department can explain the basic mechanisms of stocks and bonds. The Education Department can also explain the relationship between risk and return to users. Furthermore, the Education Department can teach users the importance of investment diversification. For example, the Education Department can explain how to mitigate risk by diversifying investments across different asset classes. This allows users to understand how their funds are being managed and to make smarter financial decisions. Some or all of the above processes in the Education Department may be performed using AI, for example, or not using AI. For example, the Education Department can input the user's learning history into AI and have the AI ​​generate optimal educational content.

[0034] The system includes a provisioning unit. The provisioning unit shares government support programs with users. The provisioning unit provides programs such as mortgage assistance, education expense assistance, and medical expense assistance. The provisioning unit can explain the details of government support programs to users. For example, the provisioning unit can explain how to apply for and the conditions for mortgage assistance programs. It can also explain how to use education expense assistance programs. Furthermore, the provisioning unit can explain the scope of medical expense assistance programs. For example, the provisioning unit can explain whether specific medical expenses are covered. This allows users to reduce anxiety about having a family and gain financial security by receiving government support. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or not using AI. For example, the provisioning unit can input the user's financial situation into AI and have the AI ​​generate and execute the optimal support program.

[0035] The system includes an investment section. The investment section provides users with investment options. For example, the investment section offers options such as stock investments, bond investments, and real estate investments. The investment section can explain the details of each investment option to the user. For example, the investment section can explain the risks and returns of stock investments. It can also explain the advantages and disadvantages of bond investments. Furthermore, the investment section can explain the profitability of real estate investments. For example, the investment section can show the historical performance of a particular real estate investment. This makes it easier for users to execute their financial plans and understand their investment options. Some or all of the above processing in the investment section may be performed using AI, for example, or not using AI. For example, the investment section can input the user's risk tolerance into the AI ​​and have the AI ​​generate and execute the optimal investment option.

[0036] The system includes a prediction unit. The prediction unit predicts the user's future wealth. For example, the prediction unit predicts income, expenses, and asset values. The prediction unit can provide the user with predictions of their future wealth. For example, the prediction unit can show how the user's income will fluctuate in the future. The prediction unit can also show how the user's expenses will fluctuate in the future. Furthermore, the prediction unit can show how the user's asset values ​​will fluctuate in the future. For example, the prediction unit predicts the future performance of a particular investment option. This gives the user motivation to start investing and to plan for building future wealth. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input the user's financial data into an AI and have the AI ​​generate predictions of future wealth.

[0037] The data collection unit can analyze the user's past financial history and select the optimal data collection method. For example, the data collection unit may prioritize suggesting data collection methods the user has used in the past (manual input, import, etc.). The data collection unit can also select the most efficient data collection method based on the user's past financial history. Furthermore, the data collection unit can analyze the user's past financial history and customize the data collection method. For example, if the data collection unit has preferred manual input in the past, it will prioritize suggesting manual input. This allows the data collection unit to select the optimal data collection method by analyzing the user's past financial history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's past financial history data into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter the collected financial information based on the user's current living situation and areas of interest. For example, the data collection unit can collect relevant financial information based on the user's current living situation (single, married, etc.). The data collection unit can also filter the information to be collected based on the user's areas of interest (investment, savings, etc.). Furthermore, the data collection unit can determine the priority of the information to be collected based on the user's living situation and areas of interest. For example, if the user is married, the data collection unit will prioritize the collection of financial information related to family. This allows the data collection unit to collect highly relevant information by filtering the information 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 not using 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 information filtering.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting financial information. For example, the data collection unit can collect region-specific financial information based on the user's geographical location. The data collection unit can also collect relevant investment options by considering the user's geographical location. Furthermore, the data collection unit can collect information that reflects the local economic situation based on the user's geographical location. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of information related to the economic situation of that region. In this way, the data collection unit can prioritize the collection of region-specific information 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 perform the information collection.

[0040] The data collection unit can analyze the user's social media activity and collect relevant information when collecting financial information. For example, the data collection unit can identify areas of interest from the user's social media activity and collect relevant information. The data collection unit can also analyze the user's social media activity and collect information that may affect the financial situation. Furthermore, the data collection unit can determine the priority of information to collect based on the user's social media activity. For example, if the data collection unit has shown interest in a particular area of ​​investment, it will prioritize collecting information related to that area. In this way, the data collection unit can collect information of interest by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the information collection.

[0041] The planning department can adjust the level of detail in financial plans based on the user's important life events. For example, if the user is planning to get married, the planning department will incorporate wedding-related expenses in detail into the plan. If the user plans to have children, the planning department can also include children's education expenses in the plan. Furthermore, if the user is planning to retire, the planning department can reflect post-retirement living expenses in the plan. In this way, the planning department can provide a more realistic plan by adjusting the level of detail based on the user's life events. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input the user's life event data into a generating AI and have the generating AI perform the adjustment of the level of detail in the plan.

[0042] The planning unit can apply different planning algorithms to the user's occupation and income when creating financial plans. For example, if the user is in a high-income occupation, the planning unit can provide a plan that includes high-risk investment options. If the user is in a low-income occupation, the planning unit can also provide a plan that includes low-risk investment options. Furthermore, depending on the user's occupation, the planning unit can provide a plan that takes into account fluctuations in income. This allows the planning unit to provide a more appropriate plan by applying planning algorithms according to the user's occupation and income. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input the user's occupation and income data into a generating AI and have the generating AI execute the application of planning algorithms.

[0043] The planning department can prioritize financial plans based on the user's submission timing. For example, if a user needs a plan urgently, the planning department will prioritize including the most important items in the plan. If a user desires a long-term plan, the planning department can also include detailed items in the plan. Furthermore, the planning department can adjust the priority of plans based on the user's submission timing. This allows the planning department to provide more appropriate plans by prioritizing plans based on the user's submission timing. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input user submission timing data into a generating AI and have the generating AI perform the determination of plan priorities.

[0044] The planning department can adjust the order of plans based on user relevance when creating financial plans. For example, if a user is planning to get married, the planning department will include marriage-related items first in the plan. If a user is planning to have children, the planning department can also prioritize including child-related items in the plan. The planning department can also adjust the order of plans based on user relevance. This allows the planning department to provide a more appropriate plan by adjusting the order of plans based on user relevance. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input user relevance data into a generating AI and have the generating AI perform the adjustment of the order of plans.

[0045] The analysis unit can improve the accuracy of its analysis by considering the relationships between users. For example, the analysis unit can analyze the financial situation of the entire family by considering the user's family structure. The analysis unit can also analyze the possibility of co-investment by considering the user's friendships. Furthermore, the analysis unit can analyze financial support in the workplace by considering the user's work relationships. In this way, the analysis unit can perform more accurate analyses by considering the relationships between users. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user relationship data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0046] The analysis unit can perform analyses while considering user attribute information. For example, the analysis unit can analyze appropriate investment options by considering the user's age. The analysis unit can also analyze income fluctuations by considering the user's occupation. Furthermore, the analysis unit can analyze region-specific financial information by considering the user's place of residence. This allows the analysis unit to perform more appropriate analyses by considering user attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into a generating AI and have the generating AI perform the analysis.

[0047] The analysis unit can perform analyses while considering the geographical distribution of users. For example, the analysis unit can analyze region-specific economic conditions based on the user's place of residence. The analysis unit can also analyze investment options while considering the geographical distribution of users. Furthermore, the analysis unit can analyze financial conditions based on the user's geographical distribution. In this way, the analysis unit can perform analyses that reflect region-specific information by considering the geographical distribution of users. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0048] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to literature related to the user's areas of interest. The analysis unit can also analyze fluctuations in income by referring to literature related to the user's occupation. Furthermore, the analysis unit can analyze region-specific financial information by referring to literature related to the user's place of residence. This allows the analysis unit to perform more accurate analyses by referring to the user's relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0049] The Ministry of Education can select the optimal teaching method during education by referring to the user's past learning history. For example, the Ministry of Education can suggest the optimal teaching method based on what the user has learned in the past. The Ministry of Education can also select an effective teaching method from the user's past learning history. Furthermore, the Ministry of Education can analyze the user's learning history and provide a customized teaching method. In this way, the Ministry of Education can provide the optimal teaching method by referring to the user's past learning history. Some or all of the above processes in the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the user's learning history data into a generating AI and have the generating AI select the optimal teaching method.

[0050] The Ministry of Education can select the optimal teaching method during education, taking into account the user's device information. For example, if the user is using a smartphone, the Ministry of Education can provide a teaching method adapted to the screen size. If the user is using a tablet, the Ministry of Education can also provide a teaching method optimized for a larger screen. Furthermore, if the user is using a personal computer, the Ministry of Education can provide a teaching method that includes detailed information. In this way, the Ministry of Education can provide the optimal teaching method by taking into account the user's device information. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the user's device information into a generating AI and have the generating AI select the optimal teaching method.

[0051] The service provider can select the most relevant information by referring to the user's past usage history at the time of delivery. For example, the service provider can provide the most relevant information based on information the user has used in the past. The service provider can also select highly relevant information from the user's past usage history. Furthermore, the service provider can analyze the user's usage history and provide customized information. This allows the service provider to provide the most relevant information by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's usage history data into a generating AI and have the generating AI select the most relevant information.

[0052] The service provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, the service provider can provide region-specific information based on the user's geographical location. The service provider can also provide relevant investment options by considering the user's geographical location. Furthermore, the service provider can provide information that reflects the local economic situation based on the user's geographical location. In this way, the service provider can provide region-specific information by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal information.

[0053] The investment unit can select the optimal investment option by referring to the user's past investment history when making an investment. For example, the investment unit can provide the optimal investment option based on the options the user has invested in in the past. The investment unit can also select highly relevant investment options from the user's past investment history. Furthermore, the investment unit can analyze the user's investment history and provide customized investment options. In this way, the investment unit can provide the optimal investment option by referring to the user's past investment history. Some or all of the above processes in the investment unit may be performed using AI, for example, or not using AI. For example, the investment unit can input the user's investment history data into a generating AI and have the generating AI perform the selection of the optimal investment option.

[0054] The investment unit can provide optimal investment options when an investment is made, taking into account the user's geographical location. For example, the investment unit can provide region-specific investment options based on the user's geographical location. The investment unit can also provide relevant investment options, taking into account the user's geographical location. Furthermore, the investment unit can provide investment options that reflect the local economic conditions based on the user's geographical location. In this way, the investment unit can provide region-specific investment options by taking into account the user's geographical location. Some or all of the above processing in the investment unit may be performed using AI, for example, or without AI. For example, the investment unit can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal investment options.

[0055] The forecasting unit can select the optimal forecasting method by referring to the user's past financial history during the forecasting process. For example, the forecasting unit can provide the optimal forecasting method based on forecasting methods the user has used in the past. The forecasting unit can also select a highly relevant forecasting method from the user's past financial history. Furthermore, the forecasting unit can analyze the user's financial history and provide a customized forecasting method. In this way, the forecasting unit can provide the optimal forecasting method by referring to the user's past financial history. Some or all of the above 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 financial history data into a generating AI and have the generating AI select the optimal forecasting method.

[0056] The prediction unit can provide an optimal prediction method when making predictions, taking into account the user's geographical location information. For example, the prediction unit can provide region-specific predictions based on the user's geographical location information. The prediction unit can also provide relevant prediction methods, taking into account the user's geographical location information. Furthermore, the prediction unit can provide predictions that reflect the regional economic situation based on the user's geographical location information. In this way, the prediction unit can provide region-specific predictions by taking into account the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing an optimal prediction method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can also collect information about users' health and lifestyles to clarify their financial situation. For example, the unit can collect users' health checkup results and exercise habits, and use this information to analyze their financial situation. Furthermore, the unit can collect information about users' lifestyles (e.g., frequency of travel and hobbies) and customize financial plans based on this information. In addition, the unit can adjust the priority of the information it collects in accordance with changes in the user's health and lifestyle. This allows the unit to conduct a more comprehensive financial analysis based on the user's health and lifestyle.

[0059] The Ministry of Education can customize educational content according to the user's learning style. For example, if a user prefers visual learning, the Ministry of Education can provide educational materials that make extensive use of graphs and diagrams. If a user prefers auditory learning, the Ministry of Education can also provide audio guides or educational content in podcast format. Furthermore, if a user prefers practical learning, the Ministry of Education can provide interactive simulations or quiz-style educational content. This allows the Ministry of Education to provide the most suitable educational method for each user's learning style, thereby achieving more effective financial education.

[0060] The service provider can propose customized government support programs based on the user's financial situation. For example, the service provider can propose the most suitable mortgage support program based on the user's income and family structure. It can also propose the most suitable education expense support program based on information regarding the user's education costs. Furthermore, it can propose the most suitable medical expense support program based on information regarding the user's medical expenses. In this way, the service provider can enhance the user's financial security by proposing the most suitable government support program according to their financial situation.

[0061] The investment department can customize investment options based on the user's investment experience. For example, it can suggest low-risk investment options to novice users, and high-risk but high-return investment options to experienced users. Furthermore, the investment department can provide investment education content based on the user's investment experience. For example, it can provide content explaining basic investment concepts to novice users and content explaining more advanced investment strategies to experienced users. In this way, the investment department can increase the user's investment success rate by suggesting the most suitable investment options according to the user's investment experience.

[0062] The forecasting unit can make predictions based on the user's future life events. For example, if the user is planning to get married, the forecasting unit can predict the costs associated with marriage. It can also predict the cost of children's education if the user plans to have children. Furthermore, if the user is planning to retire, the forecasting unit can predict the cost of living after retirement. This allows the forecasting unit to provide more realistic financial forecasts based on the user's future life events.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit clarifies the user's financial situation. The data collection unit collects information such as the user's income, expenses, assets, and liabilities. The data collection unit can collect this information by asking the user relevant questions. For example, the data collection unit may ask about the user's monthly income and expenses. The data collection unit may also ask for details about the user's assets and liabilities. Furthermore, the data collection unit may obtain data from the user's bank accounts and investment accounts in order to clarify the user's financial situation. Step 2: The Planning Department creates a long-term financial plan based on the information collected by the Data Collection Department. For example, the Planning Department creates investment and savings plans based on the user's current financial situation and future goals. The Planning Department can offer the user different investment options and indicate how much to invest in each option. For example, the Planning Department can offer options such as stock investments, bond investments, and real estate investments. The Planning Department can also propose an optimal investment portfolio based on the user's risk tolerance. Furthermore, the Planning Department makes projections of the user's future wealth and motivates the user to start investing. Step 3: The analysis department periodically analyzes the progress of the plan created by the planning department and modifies the plan as needed. For example, the analysis department monitors the user's investment performance and makes suggestions if plan modifications are necessary. The analysis department can also suggest rebalancing the user's investment portfolio. Furthermore, the analysis department can modify the plan in response to changes in the user's financial situation. For example, if the user's income increases, the analysis department can suggest additional investment options.

[0065] (Example of form 2) The Financial Clarification Platform (FCP) according to an embodiment of the present invention is a system for solving the problems of low birth rates and population decline in Japan. This system uses AI to ask relevant questions to help users understand their current financial situation and clarify their financial status. The AI ​​then creates a long-term financial plan based on the user's current financial situation and future goals. This plan includes different investment options and how much to invest in each option. The AI ​​also predicts future wealth, motivating the user to begin investing. Furthermore, the AI ​​periodically analyzes the user's progress and makes suggestions if the plan needs to be revised. The FCP provides users with financial education, helping them understand how they are managing their funds. This allows users to gain confidence in building wealth while having a family. The FCP also shares how government support programs can help families. This platform allows users to gain financial security and reduce anxieties about marriage and starting a family. The FCP is designed as an independent system that can be integrated with cashless payment ecosystems and external systems, enabling cashless payment asset management, bank account integration, and integration with matchmaking platforms. This allows the Financial Clarification Platform (FCP) to alleviate users' financial anxieties and provide them with confidence in getting married and starting a family.

[0066] The Financial Clarification Platform (FCP) according to this embodiment comprises a data collection unit, a planning unit, and an analysis unit. The data collection unit clarifies the user's financial situation. The data collection unit collects information such as the user's income, expenses, assets, and liabilities. The data collection unit can collect this information by asking the user relevant questions. For example, the data collection unit asks about the user's monthly income and expenses. The data collection unit can also ask for details of the user's assets and liabilities. Furthermore, the data collection unit can obtain data from the user's bank accounts and investment accounts in order to clarify the user's financial situation. The planning unit creates a long-term financial plan based on the information collected by the data collection unit. The planning unit creates investment and savings plans based on the user's current financial situation and future goals, for example. The planning unit can offer the user different investment options and indicate how much to invest in each option. For example, the planning unit can offer options such as equity investment, bond investment, and real estate investment. The planning unit can also suggest an optimal investment portfolio based on the user's risk tolerance. Furthermore, the planning department forecasts the user's future wealth and motivates the user to begin investing. The analysis department periodically analyzes the progress of the plan created by the planning department and modifies the plan as needed. For example, the analysis department monitors the user's investment performance and makes suggestions if plan modifications are necessary. The analysis department can also suggest rebalancing the user's investment portfolio. In addition, the analysis department can modify the plan in response to changes in the user's financial situation. For example, if the user's income increases, the analysis department can suggest additional investment options. Thus, the Financial Clarification Platform (FCP) according to this embodiment can alleviate the user's financial anxiety, improve the user's financial situation by creating a long-term financial plan and periodically analyzing its progress.

[0067] The data collection unit gathers information such as users' income, expenses, assets, and liabilities to clarify their financial situation. Specifically, the unit asks users a series of questions to inquire about the details of their monthly income and expenses. For example, it confirms the user's sources of income, such as salary, bonuses, and side income, and gains detailed information on expense items such as rent, utilities, food, and entertainment. The unit also inquires about the user's assets and liabilities. Assets include cash, deposits, stocks, bonds, and real estate, while liabilities include mortgages, car loans, and outstanding credit card balances. Furthermore, the unit can also obtain data directly from users' bank accounts and investment accounts. This allows for a more accurate understanding of the user's financial situation. With the user's consent, the unit connects with bank and securities company systems via APIs to obtain data in real time. This ensures that the user's financial data is always up-to-date. In addition, the unit uses encryption technology to protect the data in order to securely manage the user's financial data. This ensures that accurate financial information is collected while maintaining user privacy.

[0068] The Planning Department creates long-term financial plans based on information collected by the Data Collection Department. Specifically, the Planning Department considers the user's current financial situation and future goals to develop investment and savings plans. For example, if a user has goals such as purchasing a home in the future, paying for their children's education, or living expenses after retirement, the Planning Department outlines specific steps to achieve these goals. The Planning Department provides the user with different investment options and indicates how much to invest in each option. For example, it offers options such as stock investment, bond investment, and real estate investment, and explains the risks and returns of each. The Planning Department also proposes an optimal investment portfolio based on the user's risk tolerance. Risk tolerance is assessed considering the user's age, income, asset situation, and investment experience. Furthermore, the Planning Department forecasts the user's future wealth and motivates the user to start investing. For example, it simulates the future asset amount if the current investment plan is continued, showing the likelihood of achieving the goal. This allows the user to act systematically towards specific goals. The Planning Department can also flexibly adjust the plan according to the user's life stage and market fluctuations. This allows the user to always maintain an optimal financial strategy.

[0069] The analytics department regularly analyzes the progress of plans created by the planning department and modifies them as needed. Specifically, the analytics department monitors the user's investment performance and makes suggestions when plan modifications are necessary. For example, if a user's investments are performing better than expected, they will suggest additional investment options; conversely, if investments are underperforming, they will suggest rebalancing to diversify risk. The analytics department also modifies plans in response to changes in the user's financial situation. For example, if a user's income increases, they will suggest additional savings or investments; conversely, if income decreases, they will suggest reviewing expenses or shifting to lower-risk investments. The analytics department uses AI to analyze the user's financial data and make optimal suggestions. Based on historical data and market trends, the AI ​​predicts future risks and returns and derives the optimal strategy for the user. Furthermore, the analytics department collects user feedback and continuously improves the accuracy and effectiveness of the plans. For example, they analyze how users reacted to suggestions and incorporate that feedback into future suggestions. This allows the analytics department to respond flexibly to user needs and optimally manage the user's financial situation.

[0070] The system includes an Education Department. The Education Department provides financial education to users. For example, the Education Department provides investment education, savings education, risk management education, etc. The Education Department can teach users basic financial concepts. For example, the Education Department can explain the basic mechanisms of stocks and bonds. The Education Department can also explain the relationship between risk and return to users. Furthermore, the Education Department can teach users the importance of investment diversification. For example, the Education Department can explain how to mitigate risk by diversifying investments across different asset classes. This allows users to understand how their funds are being managed and to make smarter financial decisions. Some or all of the above processes in the Education Department may be performed using AI, for example, or not using AI. For example, the Education Department can input the user's learning history into AI and have the AI ​​generate optimal educational content.

[0071] The system includes a provisioning unit. The provisioning unit shares government support programs with users. The provisioning unit provides programs such as mortgage assistance, education expense assistance, and medical expense assistance. The provisioning unit can explain the details of government support programs to users. For example, the provisioning unit can explain how to apply for and the conditions for mortgage assistance programs. It can also explain how to use education expense assistance programs. Furthermore, the provisioning unit can explain the scope of medical expense assistance programs. For example, the provisioning unit can explain whether specific medical expenses are covered. This allows users to reduce anxiety about having a family and gain financial security by receiving government support. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or not using AI. For example, the provisioning unit can input the user's financial situation into AI and have the AI ​​generate and execute the optimal support program.

[0072] The system includes an investment section. The investment section provides users with investment options. For example, the investment section offers options such as stock investments, bond investments, and real estate investments. The investment section can explain the details of each investment option to the user. For example, the investment section can explain the risks and returns of stock investments. It can also explain the advantages and disadvantages of bond investments. Furthermore, the investment section can explain the profitability of real estate investments. For example, the investment section can show the historical performance of a particular real estate investment. This makes it easier for users to execute their financial plans and understand their investment options. Some or all of the above processing in the investment section may be performed using AI, for example, or not using AI. For example, the investment section can input the user's risk tolerance into the AI ​​and have the AI ​​generate and execute the optimal investment option.

[0073] The system includes a prediction unit. The prediction unit predicts the user's future wealth. For example, the prediction unit predicts income, expenses, and asset values. The prediction unit can provide the user with predictions of their future wealth. For example, the prediction unit can show how the user's income will fluctuate in the future. The prediction unit can also show how the user's expenses will fluctuate in the future. Furthermore, the prediction unit can show how the user's asset values ​​will fluctuate in the future. For example, the prediction unit predicts the future performance of a particular investment option. This gives the user motivation to start investing and to plan for building future wealth. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input the user's financial data into an AI and have the AI ​​generate predictions of future wealth.

[0074] The data collection unit estimates the user's emotions and adjusts the timing of financial data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit delays the collection timing to collect information when the user is relaxed. If the user is relaxed, the data collection unit can collect information immediately to quickly clarify the financial situation. Furthermore, if the user is in a hurry, the data collection unit can speed up the collection timing to collect the necessary information in a shorter time. This allows the data collection unit to collect information at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0075] The data collection unit can analyze the user's past financial history and select the optimal data collection method. For example, the data collection unit may prioritize suggesting data collection methods the user has used in the past (manual input, import, etc.). The data collection unit can also select the most efficient data collection method based on the user's past financial history. Furthermore, the data collection unit can analyze the user's past financial history and customize the data collection method. For example, if the data collection unit has preferred manual input in the past, it will prioritize suggesting manual input. This allows the data collection unit to select the optimal data collection method by analyzing the user's past financial history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's past financial history data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter the collected financial information based on the user's current living situation and areas of interest. For example, the data collection unit can collect relevant financial information based on the user's current living situation (single, married, etc.). The data collection unit can also filter the information to be collected based on the user's areas of interest (investment, savings, etc.). Furthermore, the data collection unit can determine the priority of the information to be collected based on the user's living situation and areas of interest. For example, if the user is married, the data collection unit will prioritize the collection of financial information related to family. This allows the data collection unit to collect highly relevant information by filtering the information 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 not using 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 information filtering.

[0077] The data collection unit can estimate the user's emotions and prioritize the financial information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information and postpone collecting detailed information. If the user is relaxed, the data collection unit may also prioritize collecting detailed information. Furthermore, if the user is in a hurry, the data collection unit can quickly collect the most important information. In this way, the data collection unit can prioritize the collection of important information by prioritizing the information to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting financial information. For example, the data collection unit can collect region-specific financial information based on the user's geographical location. The data collection unit can also collect relevant investment options by considering the user's geographical location. Furthermore, the data collection unit can collect information that reflects the local economic situation based on the user's geographical location. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of information related to the economic situation of that region. In this way, the data collection unit can prioritize the collection of region-specific information 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 perform the information collection.

[0079] The data collection unit can analyze the user's social media activity and collect relevant information when collecting financial information. For example, the data collection unit can identify areas of interest from the user's social media activity and collect relevant information. The data collection unit can also analyze the user's social media activity and collect information that may affect the financial situation. Furthermore, the data collection unit can determine the priority of information to collect based on the user's social media activity. For example, if the data collection unit has shown interest in a particular area of ​​investment, it will prioritize collecting information related to that area. In this way, the data collection unit can collect information of interest by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the information collection.

[0080] The planning unit can estimate the user's emotions and adjust the presentation of the financial plan based on those emotions. For example, if the user is stressed, the planning unit can provide a simple and visually easy-to-understand plan. If the user is relaxed, the planning unit can also provide a plan with detailed explanations. If the user is in a hurry, the planning unit can provide a concise plan that gets straight to the point. In this way, the planning unit can provide a more understandable plan by adjusting the presentation of the plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the plan.

[0081] The planning department can adjust the level of detail in financial plans based on the user's important life events. For example, if the user is planning to get married, the planning department will incorporate wedding-related expenses in detail into the plan. If the user plans to have children, the planning department can also include children's education expenses in the plan. Furthermore, if the user is planning to retire, the planning department can reflect post-retirement living expenses in the plan. In this way, the planning department can provide a more realistic plan by adjusting the level of detail based on the user's life events. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input the user's life event data into a generating AI and have the generating AI perform the adjustment of the level of detail in the plan.

[0082] The planning unit can apply different planning algorithms to the user's occupation and income when creating financial plans. For example, if the user is in a high-income occupation, the planning unit can provide a plan that includes high-risk investment options. If the user is in a low-income occupation, the planning unit can also provide a plan that includes low-risk investment options. Furthermore, depending on the user's occupation, the planning unit can provide a plan that takes into account fluctuations in income. This allows the planning unit to provide a more appropriate plan by applying planning algorithms according to the user's occupation and income. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input the user's occupation and income data into a generating AI and have the generating AI execute the application of planning algorithms.

[0083] The planning unit can estimate the user's emotions and adjust the length of the plan based on the estimated emotions. For example, if the user is stressed, the planning unit can provide a short-term plan. If the user is relaxed, the planning unit can also provide a long-term plan. Furthermore, if the user is in a hurry, the planning unit can provide a short-term, achievable plan. In this way, the planning unit can provide a more appropriate plan by adjusting the length of the plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI adjust the length of the plan.

[0084] The planning department can prioritize financial plans based on the user's submission timing. For example, if a user needs a plan urgently, the planning department will prioritize including the most important items in the plan. If a user desires a long-term plan, the planning department can also include detailed items in the plan. Furthermore, the planning department can adjust the priority of plans based on the user's submission timing. This allows the planning department to provide more appropriate plans by prioritizing plans based on the user's submission timing. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input user submission timing data into a generating AI and have the generating AI perform the determination of plan priorities.

[0085] The planning department can adjust the order of plans based on user relevance when creating financial plans. For example, if a user is planning to get married, the planning department will include marriage-related items first in the plan. If a user is planning to have children, the planning department can also prioritize including child-related items in the plan. The planning department can also adjust the order of plans based on user relevance. This allows the planning department to provide a more appropriate plan by adjusting the order of plans based on user relevance. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input user relevance data into a generating AI and have the generating AI perform the adjustment of the order of plans.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform an analysis using simple criteria. If the user is relaxed, the analysis unit can also perform an analysis using detailed criteria. Furthermore, if the user is in a hurry, the analysis unit can set criteria for a rapid analysis. This allows the analysis unit to perform a more appropriate analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis criteria.

[0087] The analysis unit can improve the accuracy of its analysis by considering the relationships between users. For example, the analysis unit can analyze the financial situation of the entire family by considering the user's family structure. The analysis unit can also analyze the possibility of co-investment by considering the user's friendships. Furthermore, the analysis unit can analyze financial support in the workplace by considering the user's work relationships. In this way, the analysis unit can perform more accurate analyses by considering the relationships between users. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user relationship data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0088] The analysis unit can perform analyses while considering user attribute information. For example, the analysis unit can analyze appropriate investment options by considering the user's age. The analysis unit can also analyze income fluctuations by considering the user's occupation. Furthermore, the analysis unit can analyze region-specific financial information by considering the user's place of residence. This allows the analysis unit to perform more appropriate analyses by considering user attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into a generating AI and have the generating AI perform the analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit will display the most important results first. If the user is relaxed, the analysis unit can also display detailed results sequentially. Furthermore, if the user is in a hurry, the analysis unit can display the most important results first. In this way, the analysis unit can provide more easily understandable results by adjusting the display order of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display order.

[0090] The analysis unit can perform analyses while considering the geographical distribution of users. For example, the analysis unit can analyze region-specific economic conditions based on the user's place of residence. The analysis unit can also analyze investment options while considering the geographical distribution of users. Furthermore, the analysis unit can analyze financial conditions based on the user's geographical distribution. In this way, the analysis unit can perform analyses that reflect region-specific information by considering the geographical distribution of users. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0091] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to literature related to the user's areas of interest. The analysis unit can also analyze fluctuations in income by referring to literature related to the user's occupation. Furthermore, the analysis unit can analyze region-specific financial information by referring to literature related to the user's place of residence. This allows the analysis unit to perform more accurate analyses by referring to the user's relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0092] The Ministry of Education can estimate a user's emotions and adjust the presentation of educational content based on those emotions. For example, if a user is stressed, the Ministry of Education can provide simple and visually easy-to-understand educational content. If a user is relaxed, the Ministry of Education can also provide educational content that includes detailed explanations. Furthermore, if a user is in a hurry, the Ministry of Education can provide concise educational content that gets straight to the point. In this way, the Ministry of Education can provide more easily understood education by adjusting the presentation of educational content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input user emotion data into a generative AI and have the generative AI adjust the presentation of educational content.

[0093] The Ministry of Education can select the optimal teaching method during education by referring to the user's past learning history. For example, the Ministry of Education can suggest the optimal teaching method based on what the user has learned in the past. The Ministry of Education can also select an effective teaching method from the user's past learning history. Furthermore, the Ministry of Education can analyze the user's learning history and provide a customized teaching method. In this way, the Ministry of Education can provide the optimal teaching method by referring to the user's past learning history. Some or all of the above processes in the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the user's learning history data into a generating AI and have the generating AI select the optimal teaching method.

[0094] The Ministry of Education can estimate the user's emotions and prioritize educational content based on those emotions. For example, if the user is stressed, the Ministry of Education will prioritize teaching important content. If the user is relaxed, the Ministry of Education may also prioritize teaching detailed content. Furthermore, if the user is in a hurry, the Ministry of Education may prioritize teaching concise content. In this way, the Ministry of Education can prioritize important content by prioritizing educational content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input user emotion data into a generative AI and have the generative AI determine the priority of educational content.

[0095] The Ministry of Education can select the optimal teaching method during education, taking into account the user's device information. For example, if the user is using a smartphone, the Ministry of Education can provide a teaching method adapted to the screen size. If the user is using a tablet, the Ministry of Education can also provide a teaching method optimized for a larger screen. Furthermore, if the user is using a personal computer, the Ministry of Education can provide a teaching method that includes detailed information. In this way, the Ministry of Education can provide the optimal teaching method by taking into account the user's device information. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the user's device information into a generating AI and have the generating AI select the optimal teaching method.

[0096] The service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and visually easy-to-understand information. If the user is relaxed, the service provider can also provide information that includes detailed explanations. Furthermore, if the user is in a hurry, the service provider can provide concise information that gets straight to the point. In this way, the service provider can provide more easily understood information by adjusting the way the information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the information is presented.

[0097] The service provider can select the most relevant information by referring to the user's past usage history at the time of delivery. For example, the service provider can provide the most relevant information based on information the user has used in the past. The service provider can also select highly relevant information from the user's past usage history. Furthermore, the service provider can analyze the user's usage history and provide customized information. This allows the service provider to provide the most relevant information by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's usage history data into a generating AI and have the generating AI select the most relevant information.

[0098] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the information provider will prioritize providing important information. If the user is relaxed, the information provider may also prioritize providing detailed information. Furthermore, if the user is in a hurry, the information provider may prioritize providing concise information. In this way, the information provider can prioritize providing important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0099] The service provider can provide optimal information by considering the user's geographical location at the time of delivery. For example, the service provider can provide region-specific information based on the user's geographical location. The service provider can also provide relevant investment options by considering the user's geographical location. Furthermore, the service provider can provide information that reflects the local economic situation based on the user's geographical location. In this way, the service provider can provide region-specific information by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal information.

[0100] The investment unit can estimate the user's emotions and adjust the presentation of investment options based on the estimated emotions. For example, if the user is stressed, the investment unit can provide simple and visually easy-to-understand investment options. If the user is relaxed, the investment unit can also provide investment options with detailed explanations. Furthermore, if the user is in a hurry, the investment unit can provide concise investment options that get straight to the point. In this way, the investment unit can provide more easily understandable investment options by adjusting the presentation of investment options according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the investment unit may be performed using AI, for example, or not using AI. For example, the investment unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of investment options.

[0101] The investment unit can select the optimal investment option by referring to the user's past investment history when making an investment. For example, the investment unit can provide the optimal investment option based on the options the user has invested in in the past. The investment unit can also select highly relevant investment options from the user's past investment history. Furthermore, the investment unit can analyze the user's investment history and provide customized investment options. In this way, the investment unit can provide the optimal investment option by referring to the user's past investment history. Some or all of the above processes in the investment unit may be performed using AI, for example, or not using AI. For example, the investment unit can input the user's investment history data into a generating AI and have the generating AI perform the selection of the optimal investment option.

[0102] The investment unit can estimate the user's emotions and prioritize investment options based on those emotions. For example, if the user is stressed, the investment unit will prioritize important investment options. If the user is relaxed, the investment unit may also prioritize detailed investment options. Furthermore, if the user is in a hurry, the investment unit may prioritize concise investment options. In this way, the investment unit can prioritize important investment options by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the investment unit may be performed using AI or not using AI. For example, the investment unit can input user emotion data into a generative AI and have the generative AI determine the priority of investment options.

[0103] The investment unit can provide optimal investment options when an investment is made, taking into account the user's geographical location. For example, the investment unit can provide region-specific investment options based on the user's geographical location. The investment unit can also provide relevant investment options, taking into account the user's geographical location. Furthermore, the investment unit can provide investment options that reflect the local economic conditions based on the user's geographical location. In this way, the investment unit can provide region-specific investment options by taking into account the user's geographical location. Some or all of the above processing in the investment unit may be performed using AI, for example, or without AI. For example, the investment unit can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal investment options.

[0104] The prediction unit can estimate the user's emotions and adjust the way the prediction is presented based on the estimated emotions. For example, if the user is stressed, the prediction unit provides a simple and visually easy-to-understand prediction. If the user is relaxed, the prediction unit can also provide a prediction that includes a detailed explanation. Furthermore, if the user is in a hurry, the prediction unit can provide a concise prediction that gets straight to the point. In this way, the prediction unit can provide a more understandable prediction by adjusting the way the prediction is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the way the prediction is presented.

[0105] The forecasting unit can select the optimal forecasting method by referring to the user's past financial history during the forecasting process. For example, the forecasting unit can provide the optimal forecasting method based on forecasting methods the user has used in the past. The forecasting unit can also select a highly relevant forecasting method from the user's past financial history. Furthermore, the forecasting unit can analyze the user's financial history and provide a customized forecasting method. In this way, the forecasting unit can provide the optimal forecasting method by referring to the user's past financial history. Some or all of the above 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 financial history data into a generating AI and have the generating AI select the optimal forecasting method.

[0106] The prediction unit can estimate the user's emotions and determine the priority of predictions based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize important predictions. If the user is relaxed, the prediction unit may also prioritize detailed predictions. Furthermore, if the user is in a hurry, the prediction unit may prioritize concise predictions. In this way, the prediction unit can prioritize important predictions by determining the priority of predictions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI determine the priority of predictions.

[0107] The prediction unit can provide an optimal prediction method when making predictions, taking into account the user's geographical location information. For example, the prediction unit can provide region-specific predictions based on the user's geographical location information. The prediction unit can also provide relevant prediction methods, taking into account the user's geographical location information. Furthermore, the prediction unit can provide predictions that reflect the regional economic situation based on the user's geographical location information. In this way, the prediction unit can provide region-specific predictions by taking into account the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing an optimal prediction method.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The data collection unit can also collect information about users' health and lifestyles to clarify their financial situation. For example, the unit can collect users' health checkup results and exercise habits, and use this information to analyze their financial situation. Furthermore, the unit can collect information about users' lifestyles (e.g., frequency of travel and hobbies) and customize financial plans based on this information. In addition, the unit can adjust the priority of the information it collects in accordance with changes in the user's health and lifestyle. This allows the unit to conduct a more comprehensive financial analysis based on the user's health and lifestyle.

[0110] The Ministry of Education can customize educational content according to the user's learning style. For example, if a user prefers visual learning, the Ministry of Education can provide educational materials that make extensive use of graphs and diagrams. If a user prefers auditory learning, the Ministry of Education can also provide audio guides or educational content in podcast format. Furthermore, if a user prefers practical learning, the Ministry of Education can provide interactive simulations or quiz-style educational content. This allows the Ministry of Education to provide the most suitable educational method for each user's learning style, thereby achieving more effective financial education.

[0111] The service provider can propose customized government support programs based on the user's financial situation. For example, the service provider can propose the most suitable mortgage support program based on the user's income and family structure. It can also propose the most suitable education expense support program based on information regarding the user's education costs. Furthermore, it can propose the most suitable medical expense support program based on information regarding the user's medical expenses. In this way, the service provider can enhance the user's financial security by proposing the most suitable government support program according to their financial situation.

[0112] The investment department can customize investment options based on the user's investment experience. For example, it can suggest low-risk investment options to novice users, and high-risk but high-return investment options to experienced users. Furthermore, the investment department can provide investment education content based on the user's investment experience. For example, it can provide content explaining basic investment concepts to novice users and content explaining more advanced investment strategies to experienced users. In this way, the investment department can increase the user's investment success rate by suggesting the most suitable investment options according to the user's investment experience.

[0113] The forecasting unit can make predictions based on the user's future life events. For example, if the user is planning to get married, the forecasting unit can predict the costs associated with marriage. It can also predict the cost of children's education if the user plans to have children. Furthermore, if the user is planning to retire, the forecasting unit can predict the cost of living after retirement. This allows the forecasting unit to provide more realistic financial forecasts based on the user's future life events.

[0114] The data collection unit can estimate the user's emotions and adjust the depth of information it collects based on those emotions. For example, if the user is stressed, the unit will collect only basic information, postponing detailed information. Conversely, if the user is relaxed, the unit can prioritize collecting detailed information. Furthermore, if the user is in a hurry, the unit can quickly collect the most important information. In this way, the data collection unit can optimize information gathering for the user by adjusting the depth of information collected according to the user's emotions.

[0115] The planning department can estimate the user's emotions and adjust the flexibility of the financial plan based on those emotions. For example, if the user is stressed, the planning department can provide a highly flexible plan that allows the user to proceed at their own pace. If the user is relaxed, the planning department can also provide a detailed, step-by-step plan. Furthermore, if the user is in a hurry, the planning department can provide a plan that can be achieved in a short period of time. In this way, the planning department can provide the optimal financial plan for the user by adjusting the flexibility of the plan according to the user's emotions.

[0116] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on those emotions. For example, if the user is stressed, the analysis unit will provide concise and to-the-point analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide summarized analysis results that can be quickly understood. In this way, the analysis unit can provide the user with the most optimal analysis results by adjusting the level of detail in the analysis results according to the user's emotions.

[0117] The Ministry of Education can estimate the user's emotions and adjust the pace of the educational content based on those emotions. For example, if the user is stressed, the Ministry of Education will proceed at a slower pace. Conversely, if the user is relaxed, the Ministry of Education can proceed at a normal pace. Furthermore, if the user is in a hurry, the Ministry of Education can provide quick, concise educational content. In this way, the Ministry of Education can provide the optimal education for the user by adjusting the pace of the educational content according to the user's emotions.

[0118] The information provider can estimate the user's emotions and adjust the amount of information provided based on those emotions. For example, if the user is stressed, the provider will provide only the minimum necessary information. Conversely, if the user is relaxed, the provider can provide detailed information. Furthermore, if the user is in a hurry, the provider can provide concise information that gets straight to the point. In this way, the provider can provide optimal information to the user by adjusting the amount of information provided according to the user's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit clarifies the user's financial situation. The data collection unit collects information such as the user's income, expenses, assets, and liabilities. The data collection unit can collect this information by asking the user relevant questions. For example, the data collection unit may ask about the user's monthly income and expenses. The data collection unit may also ask for details about the user's assets and liabilities. Furthermore, the data collection unit may obtain data from the user's bank accounts and investment accounts in order to clarify the user's financial situation. Step 2: The Planning Department creates a long-term financial plan based on the information collected by the Data Collection Department. For example, the Planning Department creates investment and savings plans based on the user's current financial situation and future goals. The Planning Department can offer the user different investment options and indicate how much to invest in each option. For example, the Planning Department can offer options such as stock investments, bond investments, and real estate investments. The Planning Department can also propose an optimal investment portfolio based on the user's risk tolerance. Furthermore, the Planning Department makes projections of the user's future wealth and motivates the user to start investing. Step 3: The analysis department periodically analyzes the progress of the plan created by the planning department and modifies the plan as needed. For example, the analysis department monitors the user's investment performance and makes suggestions if plan modifications are necessary. The analysis department can also suggest rebalancing the user's investment portfolio. Furthermore, the analysis department can modify the plan in response to changes in the user's financial situation. For example, if the user's income increases, the analysis department can suggest additional investment options.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the above-mentioned components, including the data collection unit, planning unit, analysis unit, education unit, provision unit, investment unit, forecasting unit, and sentiment estimation function, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the user's financial information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a long-term financial plan based on the collected information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and periodically analyzes the progress of the plan and modifies the plan as necessary. The education unit is implemented by the control unit 46A of the smart device 14 and provides financial education to the user. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares government support programs with the user. The investment unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides investment options to the user. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts the user's future assets. The emotion estimation function is implemented by the control unit 46A of the smart device 14 and estimates the user's emotions and adjusts the data collection timing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the data collection unit, planning unit, analysis unit, education unit, provision unit, investment unit, forecasting unit, and sentiment estimation function, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's financial information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a long-term financial plan based on the collected information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and periodically analyzes the progress of the plan and modifies the plan as necessary. The education unit is implemented by the control unit 46A of the smart glasses 214 and provides financial education to the user. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares government support programs with the user. The investment unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides investment options to the user. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts the user's future wealth. The emotion estimation function is implemented by the control unit 46A of the smart glasses 214 and estimates the user's emotions and adjusts the data collection timing. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the above-mentioned components, including the data collection unit, planning unit, analysis unit, education unit, provision unit, investment unit, forecasting unit, and sentiment estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's financial information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a long-term financial plan based on the collected information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and periodically analyzes the progress of the plan and modifies the plan as necessary. The education unit is implemented by the control unit 46A of the headset terminal 314 and provides financial education to the user. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares government support programs with the user. The investment unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides investment options to the user. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts the user's future assets. The emotion estimation function is implemented by the control unit 46A of the headset terminal 314 and estimates the user's emotions and adjusts the data collection timing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the above-mentioned elements, including the data collection unit, planning unit, analysis unit, education unit, provision unit, investment unit, forecasting unit, and sentiment estimation function, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's financial information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a long-term financial plan based on the collected information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and periodically analyzes the progress of the plan and modifies the plan as necessary. The education unit is implemented by the control unit 46A of the robot 414 and provides financial education to the user. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares government support programs with the user. The investment unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides investment options to the user. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts the user's future assets. The emotion estimation function is implemented by the control unit 46A of the robot 414 and estimates the user's emotions and adjusts the data collection timing. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The collection department clarifies the financial situation, A planning unit that creates a long-term financial plan based on the information collected by the aforementioned collection unit, The system includes an analysis unit that periodically analyzes the progress of the plan created by the planning unit and modifies the plan as necessary. A system characterized by the following features. (Note 2) It has an education department that provides financial education. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a provision department that shares government support programs. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has an investment department that offers investment options. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a forecasting unit that predicts future assets. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past financial history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting financial information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates user sentiment and prioritizes the financial information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting financial information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting financial information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned planning department, We estimate user sentiment and adjust the way financial plans are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned planning department, When creating financial plans, adjust the level of detail based on the user's important life events. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned planning department, When creating financial plans, different planning algorithms are applied depending on the user's occupation and income. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned planning department, The system estimates the user's emotions and adjusts the length of the plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned planning department, When creating financial plans, prioritize the plans based on when users submit them. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned planning department, When creating a financial plan, adjust the order of the plan based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is When performing analysis, consider the relationships between users to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When performing analysis, user attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is It estimates the user's emotions and adjusts the order in which the analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is When performing analysis, the geographical distribution of users should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During analysis, we refer to relevant user literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Ministry of Education, The system estimates the user's emotions and adjusts the presentation of educational content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned Ministry of Education, During training, the system selects the optimal teaching method by referring to the user's past learning history. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned Ministry of Education, It estimates user emotions and prioritizes educational content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned Ministry of Education, During training, the optimal training method is selected by considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, the system selects the most suitable information by referring to the user's past usage history. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most suitable information. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned investment department, It estimates user sentiment and adjusts how investment options are presented based on that estimated sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned investment department, When making an investment, the system selects the optimal investment option by referring to the user's past investment history. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned investment department, It estimates user sentiment and prioritizes investment options based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned investment department, When making an investment, we provide the optimal investment options by taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 36) The prediction unit, It estimates the user's emotions and adjusts how predictions are expressed based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 37) The prediction unit, During the forecasting process, the system selects the optimal forecasting method by referencing the user's past financial history. The system described in Appendix 5, characterized by the features described herein. (Note 38) The prediction unit, It estimates the user's emotions and determines the priority of predictions based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 39) The prediction unit, When making predictions, the system provides the optimal prediction method by taking into account the user's geographical location. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department clarifies the financial situation, A planning unit that creates a long-term financial plan based on the information collected by the aforementioned collection unit, The system includes an analysis unit that periodically analyzes the progress of the plan created by the planning unit and modifies the plan as necessary. A system characterized by the following features.

2. It has an education department that provides financial education. The system according to feature 1.

3. It has a provision department that shares government support programs. The system according to feature 1.

4. It has an investment department that offers investment options. The system according to feature 1.

5. It includes a forecasting unit that predicts future assets. The system according to feature 1.

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

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

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

9. The aforementioned collection unit is It estimates user sentiment and prioritizes the financial information to collect based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned collection unit is When collecting financial information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

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