system

Abstract

The system according to this embodiment aims to centrally manage information from multiple credit cards and bank accounts, and to track and analyze income and expenses in real time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects information from credit cards and bank accounts. The analysis unit analyzes the information collected by the collection unit and tracks income and expenses in real time. The proposal unit proposes an optimal savings plan and investment / management plan based on the analysis results obtained by the analysis unit. The execution unit executes the plan proposed by the proposal unit.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to centrally manage information of a plurality of credit cards and bank accounts and track / analyze income and expenditure in real time.

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

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects information from credit cards and bank accounts. The analysis unit analyzes the information collected by the collection unit and tracks income and expenses in real time. The proposal unit proposes an optimal savings plan and investment / management plan based on the analysis results obtained by the analysis unit. The execution unit implements the plan proposed by the proposal unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The platform according to an embodiment of the present invention is a platform that uses AI to centrally manage information from multiple credit cards and bank accounts, and automatically tracks and analyzes income and expenses in real time. This platform automatically proposes an optimal savings plan and investment / management plan based on the user's spending patterns and income. Furthermore, an AI agent analyzes the user's personality, risk tolerance, long-term goals, etc., and proposes asset allocation and dynamic investment strategies based on individuality. It analyzes changes in market conditions and individual economic conditions in real time and autonomously adjusts and optimizes investment strategies and risk management. Specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback functions are also added. For example, the AI ​​collects and centrally manages information from the user's credit cards and bank accounts. In this process, data from each financial institution is automatically acquired and integrated. For example, if a user has multiple credit cards, the usage history and balance of each card can be centrally managed. Next, the AI ​​tracks and analyzes the user's income and expenses in real time based on the information it has collected. This allows the AI ​​to understand the user's spending patterns and income and automatically propose an optimal savings plan and investment / management plan. For example, it can analyze a user's monthly spending and income and provide advice on reducing unnecessary expenses. Furthermore, the AI ​​agent analyzes the user's personality, risk tolerance, and long-term goals to propose asset allocation and dynamic investment strategies based on their individuality. For instance, it can suggest an aggressive investment strategy to risk-seeking users and a conservative strategy to risk-averse users. It also analyzes market conditions and individual economic changes in real time and autonomously adjusts and optimizes investment strategies and risk management. For example, it can restructure portfolios in response to sudden fluctuations in the stock market to minimize risk. In addition, specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback features will be added. This will enable users to achieve more effective asset management and investment strategies.This allows the platform to centrally manage users' financial information, track and analyze income and expenses in real time, and propose and implement optimal savings and investment plans.

[0029] The platform according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects information from credit cards and bank accounts. For example, the collection unit can collect usage history and balance information from multiple credit cards owned by the user. The collection unit can also collect transaction history and balance information from bank accounts. Furthermore, the collection unit can automatically acquire and integrate data from each financial institution. For example, the collection unit can use APIs to acquire data from each financial institution and manage it in a unified format. The analysis unit analyzes the information collected by the collection unit and tracks income and expenses in real time. For example, the analysis unit can understand the user's spending patterns and income and provide advice to reduce unnecessary spending. The analysis unit can also monitor fluctuations in income and expenses in real time and detect abnormal transactions. Furthermore, the analysis unit can make future income and expense forecasts based on the user's income and expense data. The proposal unit proposes optimal savings plans and investment / management plans based on the analysis results obtained by the analysis unit. The proposal unit can analyze, for example, the user's personality, risk tolerance, and long-term goals, and propose asset allocation and dynamic investment strategies based on that individuality. The proposal unit can also analyze changes in market conditions and individual economic circumstances in real time, and autonomously adjust and optimize investment strategies and risk management. Furthermore, the proposal unit provides specialized investment simulations, allowing users to try various investment scenarios. The execution unit executes the proposals made by the proposal unit. For example, the execution unit can perform actual transactions based on the user's investment instructions. The execution unit can also provide AI chatbot support, answering user questions in real time. Additionally, the execution unit can provide community advice and receive feedback from other users. As a result, the platform according to this embodiment can centrally manage the user's financial information, track and analyze income and expenses in real time, and propose and execute optimal savings and investment / management plans.

[0030] The data collection unit collects information from credit cards and bank accounts. For example, it can collect transaction history and balance information from multiple credit cards owned by a user. Specifically, credit card transaction history includes detailed information such as the date and time of each transaction, the merchant, the amount, and the category. This allows for a detailed understanding of the user's spending patterns. The data collection unit can also collect transaction history and balance information from bank accounts. Bank account transaction history includes detailed information such as deposits, withdrawals, transfers, and fees, allowing for a comprehensive understanding of the user's income and expenses. Furthermore, the data collection unit can automatically acquire and integrate data from various financial institutions. For example, the data collection unit uses APIs to acquire data from each financial institution and manage it in a unified format. By using APIs, the data collection unit can regularly acquire the latest data and keep the user's financial information always up-to-date. This allows the data collection unit to centrally manage the user's financial information and enable real-time income and expense tracking. The data collection unit also takes data security into consideration; acquired data is encrypted and stored securely. This allows for efficient data collection and management while protecting user privacy.

[0031] The analytics department analyzes information collected by the data collection department and tracks income and expenses in real time. For example, the analytics department can understand users' spending patterns and income and provide advice to reduce unnecessary spending. Specifically, it uses AI to analyze users' transaction data and detect spending trends and unusual transactions. For example, if spending in a particular category suddenly increases, the analytics department can identify the cause and provide users with saving advice. The analytics department can also monitor fluctuations in income and expenses in real time and detect unusual transactions. For example, if fraudulent transactions or unexpected large expenses occur, the analytics department can immediately notify the user and take countermeasures. Furthermore, the analytics department can make future income and expense forecasts based on users' income and expense data. Using AI-based predictive models, it analyzes users' income and spending trends and predicts future income and expense balances. This makes it easier for users to plan their future finances and reduce unnecessary spending. In addition, the analytics department can utilize historical data and statistical information to understand long-term income and expense trends and provide users with more specific advice. This allows the analytics department to track users' income and expenses in real time, reduce unnecessary spending, and forecast future income and expenses, thereby supporting users' financial management.

[0032] The Proposal Department proposes optimal savings and investment plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can analyze the user's personality, risk tolerance, and long-term goals to propose asset allocation and dynamic investment strategies tailored to their individual needs. Specifically, it creates a risk profile based on the user's past investment history and current asset status, and proposes investment products and savings plans accordingly. Furthermore, the Proposal Department can analyze changes in market conditions and individual economic circumstances in real time, autonomously adjusting and optimizing investment strategies and risk management. For example, it can rebalance portfolios in response to rapid fluctuations in the stock market or changes in interest rates to minimize risk. The Proposal Department also provides specialized investment simulations, allowing users to try various investment scenarios. The simulations set different investment strategies and market conditions and predict the results, providing users with reference information to make optimal investment decisions. Moreover, the Proposal Department continuously improves its proposals based on user feedback, providing more accurate advice. This allows the Proposal Department to propose optimal savings and investment plans tailored to the user's personality and market conditions, supporting their asset building.

[0033] The execution unit carries out the proposals made by the proposal unit. For example, the execution unit can execute actual transactions based on the user's investment instructions. Specifically, it can purchase proposed investment products or buy and sell to rebalance existing portfolios. The execution unit can also provide AI chatbot support, answering user questions in real time. The AI ​​chatbot answers user questions quickly and accurately, providing the necessary information. Furthermore, the execution unit can provide community advice and receive feedback from other users. Community advice allows users to share their experiences and knowledge, which can be used as a reference for investment decisions. In this way, the execution unit supports the user's investment activities and helps them achieve better investment results. The execution unit can also automatically make savings based on the proposed savings plan. For example, it can achieve planned savings by automatically transferring a fixed amount to a savings account each month. Furthermore, the execution unit can continuously improve its execution based on user feedback, providing more effective support. In this way, the execution unit can quickly and reliably execute the proposed plan and support the user's asset building.

[0034] The data collection unit can automatically acquire and integrate data from various financial institutions. For example, the data collection unit can use APIs to acquire data from each financial institution and manage it in a unified format. For example, the data collection unit can collect usage history and balance information for multiple credit cards owned by a user. It can also collect transaction history and balance information for bank accounts. Furthermore, the data collection unit can automatically acquire and integrate data from each financial institution. This allows for centralized management of the user's financial information by automatically acquiring and integrating data from each financial institution. 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 data acquired from each financial institution into a generating AI and have the generating AI perform the data integration.

[0035] The analytics department can understand users' spending patterns and income and provide advice to reduce unnecessary spending. For example, the analytics department can categorize users' spending patterns and understand the amount spent in each category. The analytics department can also identify users' income sources and monitor fluctuations in income. Furthermore, based on users' spending patterns and income, the analytics department can provide advice to reduce unnecessary spending. For example, the analytics department can analyze users' spending patterns and propose specific ways to save money to reduce unnecessary spending. The analytics department can also identify users' income sources and provide advice to increase income. In this way, by understanding users' spending patterns and income and providing advice to reduce unnecessary spending, users can manage their finances more efficiently. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input users' spending patterns and income data into a generating AI and have the generating AI generate advice to reduce unnecessary spending.

[0036] The proposal department can analyze the user's personality, risk tolerance, and long-term goals, and propose asset allocation and dynamic investment strategies based on their individuality. For example, the proposal department can analyze the user's personality from survey results and past behavioral history. It can also evaluate the user's risk tolerance and propose aggressive investment strategies for risk-loving users and stable investment strategies for risk-averse users. Furthermore, the proposal department can understand the user's long-term goals and propose asset allocation and investment strategies based on them. For example, the proposal department can analyze the user's personality and propose high-risk, high-return investment strategies for risk-loving users. It can also evaluate the user's risk tolerance and propose low-risk, stable investment strategies for risk-averse users. Furthermore, the proposal department can understand the user's long-term goals and propose asset allocation and investment strategies based on them. In this way, by analyzing the user's personality, risk tolerance, and long-term goals and proposing asset allocation and dynamic investment strategies based on their individuality, the proposal department can provide the user with the optimal investment strategy. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input user personality and risk tolerance data into a generating AI, which can then generate asset allocation and investment strategy suggestions based on that individuality.

[0037] The proposal unit can analyze changes in market conditions and individual economic conditions in real time and autonomously adjust and optimize investment strategies and risk management. For example, the proposal unit can collect and analyze market data in real time. It can also monitor changes in individual economic conditions and adjust investment strategies and risk management. Furthermore, the proposal unit can build a feedback loop to optimize investment strategies and risk management. For example, the proposal unit adjusts investment strategies by collecting and analyzing market data in real time. It can also monitor changes in individual economic conditions and adjust risk management. Furthermore, the proposal unit can build a feedback loop to optimize investment strategies and risk management. This allows the user's investment risk to be minimized by analyzing changes in market conditions and individual economic conditions in real time and autonomously adjusting and optimizing investment strategies and risk management. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input market data and economic condition data into a generating AI and have the generating AI perform the adjustment and optimization of investment strategies and risk management.

[0038] The execution unit can provide specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback functions. For example, the execution unit can provide specialized investment simulations, allowing users to try out various investment scenarios. It can also provide AI chatbot support, answering user questions in real time. Furthermore, it can provide community advice, allowing users to receive feedback from other users. In addition, it can provide sustainable investment options, enabling users to make environmentally and socially conscious investments. It can also provide personalized feedback functions, providing feedback based on the user's investment results. By providing specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback functions, the user's investment experience can be improved. Some or all of the above processing in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input the user's investment simulation data into a generating AI and have the generating AI execute the results of the investment simulation.

[0039] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the data collection unit can prioritize collection methods that the user has frequently used in the past. Furthermore, the data collection unit can suggest the most efficient collection method based on the user's past collection history. In addition, the data collection unit can analyze the user's past collection history and customize the collection method. This enables efficient data collection by analyzing the user's past financial data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter financial data based on the user's current economic situation and areas of interest. For example, the data collection unit can prioritize the collection of important data based on the user's current economic situation. Furthermore, the data collection unit can filter and collect relevant data based on the user's areas of interest. In addition, the data collection unit can adjust the scope of data to be collected, taking into account the user's economic situation and areas of interest. This allows for the efficient collection of highly relevant data by filtering based on the user's current economic situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's economic situation and areas of interest data into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, the data collection unit can prioritize the collection of relevant financial data based on the user's current location. The data collection unit can also collect highly relevant data by referring to the user's past location information. Furthermore, the data collection unit can adjust the range of data to be collected by considering the user's geographical location information. This allows for the efficient collection of data that is beneficial to the user by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can collect relevant financial data from a user's social media activity. The data collection unit can also analyze a user's social media activity and adjust the scope of data to be collected. Furthermore, the data collection unit can determine the priority of data to be collected based on the user's social media activity. This enables data collection tailored to the user's interests by analyzing the user's social media activity and collecting relevant data. 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 user social media activity data into a generating AI and have the generating AI collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of each financial item. For example, the analysis unit can perform a detailed analysis on important financial items. It can also perform a simplified analysis on less important financial items. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of each financial item. This allows for a detailed analysis of important financial items by adjusting the level of detail based on their importance. 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 financial data into a generating AI and have the generating AI adjust the level of detail of the analysis based on importance.

[0044] The analysis unit can apply different analysis algorithms depending on the income and expense category during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the income and expense category. The analysis unit can also apply different analysis methods for each income and expense category. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the income and expense category. This allows for optimal analysis for each category by applying different analysis algorithms depending on the income and expense category. 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 income and expense category data into a generating AI and have the generating AI execute the application of analysis algorithms according to the category.

[0045] The analysis unit can determine the priority of analysis based on the timing of income and expenses during the analysis. For example, the analysis unit can prioritize the analysis of important items based on the timing of income and expenses. The analysis unit can also adjust the order of analysis considering the timing of income and expenses. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the timing of income and expenses. This allows for the priority analysis of important items based on the timing of income and expenses. 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 income and expense timing data into a generating AI and have the generating AI determine the priority of analysis based on the timing of occurrence.

[0046] The analysis unit can adjust the order of analysis based on the relevance of income and expenses during the analysis. For example, the analysis unit can prioritize the analysis of important items based on the relevance of income and expenses. The analysis unit can also adjust the order of analysis considering the relevance of income and expenses. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the relevance of income and expenses. This allows for the priority analysis of important items based on the relevance of income and expenses. 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 income and expense relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0047] The proposal unit can adjust the level of detail in its proposals based on the importance of savings and investment plans. For example, it can provide detailed proposals for important savings and investment plans, and simplified proposals for less important ones. Furthermore, the proposal unit can prioritize proposals based on the importance of savings and investment plans. This allows for detailed proposals for important items by adjusting the level of detail based on the importance of savings and investment plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input savings and investment plan data into a generating AI and have the generating AI adjust the level of detail of proposals based on importance.

[0048] The proposal unit can apply different proposal algorithms depending on the user's personality and risk tolerance when making a proposal. For example, the proposal unit can select the optimal proposal algorithm based on the user's personality. Furthermore, the proposal unit can apply different proposal methods based on the user's risk tolerance. In addition, the proposal unit can adjust the level of detail of the proposal based on the user's personality and risk tolerance. This allows the proposal unit to provide the user with the most suitable proposal by applying different proposal algorithms according to the user's personality and risk tolerance. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input user personality and risk tolerance data into a generating AI and have the generating AI apply the proposal algorithm.

[0049] The proposal unit can determine the priority of proposals based on the timing of savings and investment plans. For example, the proposal unit will prioritize proposals based on the timing of savings and investment plans. The proposal unit can also adjust the order of proposals, taking into account the timing of savings and investment plans. Furthermore, the proposal unit can adjust the level of detail of proposals based on the timing of savings and investment plans. This allows the proposal unit to prioritize proposals based on the timing of savings and investment plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data on the timing of savings and investment plans into a generating AI and have the generating AI determine the priority of proposals based on the timing.

[0050] The proposal unit can adjust the order of proposals based on the relevance of savings and investment plans. For example, the proposal unit can prioritize proposing important items based on the relevance of savings and investment plans. The proposal unit can also adjust the order of proposals considering the relevance of savings and investment plans. Furthermore, the proposal unit can adjust the level of detail of proposals based on the relevance of savings and investment plans. This allows the proposal unit to prioritize proposing important items based on the relevance of savings and investment plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the relevance of savings and investment plans into a generating AI and have the generating AI perform the adjustment of the order of proposals based on relevance.

[0051] The execution unit can analyze the user's past execution history and select the optimal execution method during execution. For example, the execution unit may prioritize execution methods that the user has successfully used in the past. Furthermore, the execution unit can suggest the most efficient execution method based on the user's past execution history. In addition, the execution unit can analyze the user's past execution history and customize the execution method. This enables efficient execution by analyzing the user's past execution history and selecting the optimal method. Some or all of the above processing in the execution unit may be performed using AI, or without AI. For example, the execution unit can input the user's past execution history data into a generating AI and have the generating AI select the optimal execution method.

[0052] The execution unit can customize the means of execution at runtime based on the user's current economic situation. For example, the execution unit can select the optimal means of execution based on the user's current economic situation. The execution unit can also customize the means of execution considering the user's economic situation. Furthermore, the execution unit can determine the priority of execution based on the user's economic situation. This allows the execution unit to provide the user with the optimal means of execution by customizing the means of execution based on the user's current economic situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's economic situation data into a generating AI and have the generating AI perform the customization of the means of execution.

[0053] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, the execution unit can select the optimal execution method based on the user's current location. Furthermore, the execution unit can suggest the optimal execution method by referring to the user's past location information. In addition, the execution unit can customize the execution method, taking into account the user's geographical location information. This allows the execution unit to provide a beneficial execution method to the user by selecting the optimal method while considering the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal execution method.

[0054] The execution unit can analyze the user's social media activity during execution and propose a means of execution. For example, the execution unit can propose the optimal means of execution based on the user's social media activity. The execution unit can also analyze the user's social media activity and customize the means of execution. Furthermore, the execution unit can determine the priority of execution based on the user's social media activity. This makes it possible to perform actions that are tailored to the user's interests by analyzing the user's social media activity and proposing a means of execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of a means of execution.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, it can prioritize selecting collection methods that the user has frequently used in the past. Furthermore, the data collection unit can suggest the most efficient collection method based on the user's past collection history. In addition, the data collection unit can analyze the user's past collection history and customize the collection method. This enables efficient data collection by analyzing the user's past financial data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenses. For example, it can perform a detailed analysis on important income and expense items, and a simplified analysis on less important items. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the income and expenses. This allows for a detailed analysis of important income and expense items by adjusting the level of detail based on the importance of the income and expenses. 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 income and expense data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0058] The proposal unit can adjust the level of detail in its proposals based on the importance of the savings and investment plans. For example, it can provide detailed proposals for important savings and investment plans, and simplified proposals for less important ones. Furthermore, the proposal unit can determine the priority of proposals based on the importance of the savings and investment plans. This allows for detailed proposals for important items by adjusting the level of detail based on the importance of the savings and investment plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input savings and investment plan data into a generating AI and have the generating AI adjust the level of detail of the proposals based on importance.

[0059] The execution unit can analyze the user's past execution history and select the optimal execution method during execution. For example, it can prioritize selecting execution methods that the user has successfully used in the past. The execution unit can also suggest the most efficient execution method based on the user's past execution history. Furthermore, the execution unit can analyze the user's past execution history and customize the execution method. This enables efficient execution by analyzing the user's past execution history and selecting the optimal execution method. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's past execution history data into a generating AI and have the generating AI select the optimal execution method.

[0060] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, it can prioritize the collection of relevant financial data based on the user's current location. The data collection unit can also collect highly relevant data by referring to the user's past location information. Furthermore, the data collection unit can adjust the range of data to be collected by considering the user's geographical location information. This allows for the efficient collection of data that is beneficial to the user by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

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

[0062] Step 1: The collection unit collects credit card and bank account information. For example, it can collect usage history and balance information for multiple credit cards owned by the user, as well as transaction history and balance information for bank accounts. Furthermore, the collection unit can automatically acquire and integrate data from each financial institution. For example, it can use APIs to acquire data from each financial institution and manage it in a unified format. Step 2: The analysis unit analyzes the information collected by the data collection unit and tracks income and expenses in real time. For example, it can understand the user's spending patterns and income and provide advice to reduce unnecessary spending. It can also monitor fluctuations in income and expenses in real time and detect abnormal transactions. Furthermore, it can make future income and expense forecasts based on the user's income and expense data. Step 3: The proposal department proposes optimal savings and investment / management plans based on the analysis results obtained by the analysis department. For example, it can analyze the user's personality, risk tolerance, and long-term goals to propose asset allocation and dynamic investment strategies based on individual characteristics. It can also analyze changes in market conditions and individual economic conditions in real time and autonomously adjust and optimize investment strategies and risk management. Furthermore, it provides specialized investment simulations, allowing users to try out various investment scenarios. Step 4: The execution unit carries out the proposals made by the proposal unit. For example, it can take investment instructions from the user and execute actual trades. It can also provide AI chatbot support and answer user questions in real time. Furthermore, it can provide community advice and receive feedback from other users.

[0063] (Example of form 2) The platform according to an embodiment of the present invention is a platform that uses AI to centrally manage information from multiple credit cards and bank accounts, and automatically tracks and analyzes income and expenses in real time. This platform automatically proposes an optimal savings plan and investment / management plan based on the user's spending patterns and income. Furthermore, an AI agent analyzes the user's personality, risk tolerance, long-term goals, etc., and proposes asset allocation and dynamic investment strategies based on individuality. It analyzes changes in market conditions and individual economic conditions in real time and autonomously adjusts and optimizes investment strategies and risk management. Specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback functions are also added. For example, the AI ​​collects and centrally manages information from the user's credit cards and bank accounts. In this process, data from each financial institution is automatically acquired and integrated. For example, if a user has multiple credit cards, the usage history and balance of each card can be centrally managed. Next, the AI ​​tracks and analyzes the user's income and expenses in real time based on the information it has collected. This allows the AI ​​to understand the user's spending patterns and income and automatically propose an optimal savings plan and investment / management plan. For example, it can analyze a user's monthly spending and income and provide advice on reducing unnecessary expenses. Furthermore, the AI ​​agent analyzes the user's personality, risk tolerance, and long-term goals to propose asset allocation and dynamic investment strategies based on their individuality. For instance, it can suggest an aggressive investment strategy to risk-seeking users and a conservative strategy to risk-averse users. It also analyzes market conditions and individual economic changes in real time and autonomously adjusts and optimizes investment strategies and risk management. For example, it can restructure portfolios in response to sudden fluctuations in the stock market to minimize risk. In addition, specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback features will be added. This will enable users to achieve more effective asset management and investment strategies.This allows the platform to centrally manage users' financial information, track and analyze income and expenses in real time, and propose and implement optimal savings and investment plans.

[0064] The platform according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects information from credit cards and bank accounts. For example, the collection unit can collect usage history and balance information from multiple credit cards owned by the user. The collection unit can also collect transaction history and balance information from bank accounts. Furthermore, the collection unit can automatically acquire and integrate data from each financial institution. For example, the collection unit can use APIs to acquire data from each financial institution and manage it in a unified format. The analysis unit analyzes the information collected by the collection unit and tracks income and expenses in real time. For example, the analysis unit can understand the user's spending patterns and income and provide advice to reduce unnecessary spending. The analysis unit can also monitor fluctuations in income and expenses in real time and detect abnormal transactions. Furthermore, the analysis unit can make future income and expense forecasts based on the user's income and expense data. The proposal unit proposes optimal savings plans and investment / management plans based on the analysis results obtained by the analysis unit. The proposal unit can analyze, for example, the user's personality, risk tolerance, and long-term goals, and propose asset allocation and dynamic investment strategies based on that individuality. The proposal unit can also analyze changes in market conditions and individual economic circumstances in real time, and autonomously adjust and optimize investment strategies and risk management. Furthermore, the proposal unit provides specialized investment simulations, allowing users to try various investment scenarios. The execution unit executes the proposals made by the proposal unit. For example, the execution unit can perform actual transactions based on the user's investment instructions. The execution unit can also provide AI chatbot support, answering user questions in real time. Additionally, the execution unit can provide community advice and receive feedback from other users. As a result, the platform according to this embodiment can centrally manage the user's financial information, track and analyze income and expenses in real time, and propose and execute optimal savings and investment / management plans.

[0065] The data collection unit collects information from credit cards and bank accounts. For example, it can collect transaction history and balance information from multiple credit cards owned by a user. Specifically, credit card transaction history includes detailed information such as the date and time of each transaction, the merchant, the amount, and the category. This allows for a detailed understanding of the user's spending patterns. The data collection unit can also collect transaction history and balance information from bank accounts. Bank account transaction history includes detailed information such as deposits, withdrawals, transfers, and fees, allowing for a comprehensive understanding of the user's income and expenses. Furthermore, the data collection unit can automatically acquire and integrate data from various financial institutions. For example, the data collection unit uses APIs to acquire data from each financial institution and manage it in a unified format. By using APIs, the data collection unit can regularly acquire the latest data and keep the user's financial information always up-to-date. This allows the data collection unit to centrally manage the user's financial information and enable real-time income and expense tracking. The data collection unit also takes data security into consideration; acquired data is encrypted and stored securely. This allows for efficient data collection and management while protecting user privacy.

[0066] The analytics department analyzes information collected by the data collection department and tracks income and expenses in real time. For example, the analytics department can understand users' spending patterns and income and provide advice to reduce unnecessary spending. Specifically, it uses AI to analyze users' transaction data and detect spending trends and unusual transactions. For example, if spending in a particular category suddenly increases, the analytics department can identify the cause and provide users with saving advice. The analytics department can also monitor fluctuations in income and expenses in real time and detect unusual transactions. For example, if fraudulent transactions or unexpected large expenses occur, the analytics department can immediately notify the user and take countermeasures. Furthermore, the analytics department can make future income and expense forecasts based on users' income and expense data. Using AI-based predictive models, it analyzes users' income and spending trends and predicts future income and expense balances. This makes it easier for users to plan their future finances and reduce unnecessary spending. In addition, the analytics department can utilize historical data and statistical information to understand long-term income and expense trends and provide users with more specific advice. This allows the analytics department to track users' income and expenses in real time, reduce unnecessary spending, and forecast future income and expenses, thereby supporting users' financial management.

[0067] The Proposal Department proposes optimal savings and investment plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can analyze the user's personality, risk tolerance, and long-term goals to propose asset allocation and dynamic investment strategies tailored to their individual needs. Specifically, it creates a risk profile based on the user's past investment history and current asset status, and proposes investment products and savings plans accordingly. Furthermore, the Proposal Department can analyze changes in market conditions and individual economic circumstances in real time, autonomously adjusting and optimizing investment strategies and risk management. For example, it can rebalance portfolios in response to rapid fluctuations in the stock market or changes in interest rates to minimize risk. The Proposal Department also provides specialized investment simulations, allowing users to try various investment scenarios. The simulations set different investment strategies and market conditions and predict the results, providing users with reference information to make optimal investment decisions. Moreover, the Proposal Department continuously improves its proposals based on user feedback, providing more accurate advice. This allows the Proposal Department to propose optimal savings and investment plans tailored to the user's personality and market conditions, supporting their asset building.

[0068] The execution unit carries out the proposals made by the proposal unit. For example, the execution unit can execute actual transactions based on the user's investment instructions. Specifically, it can purchase proposed investment products or buy and sell to rebalance existing portfolios. The execution unit can also provide AI chatbot support, answering user questions in real time. The AI ​​chatbot answers user questions quickly and accurately, providing the necessary information. Furthermore, the execution unit can provide community advice and receive feedback from other users. Community advice allows users to share their experiences and knowledge, which can be used as a reference for investment decisions. In this way, the execution unit supports the user's investment activities and helps them achieve better investment results. The execution unit can also automatically make savings based on the proposed savings plan. For example, it can achieve planned savings by automatically transferring a fixed amount to a savings account each month. Furthermore, the execution unit can continuously improve its execution based on user feedback, providing more effective support. In this way, the execution unit can quickly and reliably execute the proposed plan and support the user's asset building.

[0069] The data collection unit can automatically acquire and integrate data from various financial institutions. For example, the data collection unit can use APIs to acquire data from each financial institution and manage it in a unified format. For example, the data collection unit can collect usage history and balance information for multiple credit cards owned by a user. It can also collect transaction history and balance information for bank accounts. Furthermore, the data collection unit can automatically acquire and integrate data from each financial institution. This allows for centralized management of the user's financial information by automatically acquiring and integrating data from each financial institution. 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 data acquired from each financial institution into a generating AI and have the generating AI perform the data integration.

[0070] The analytics department can understand users' spending patterns and income and provide advice to reduce unnecessary spending. For example, the analytics department can categorize users' spending patterns and understand the amount spent in each category. The analytics department can also identify users' income sources and monitor fluctuations in income. Furthermore, based on users' spending patterns and income, the analytics department can provide advice to reduce unnecessary spending. For example, the analytics department can analyze users' spending patterns and propose specific ways to save money to reduce unnecessary spending. The analytics department can also identify users' income sources and provide advice to increase income. In this way, by understanding users' spending patterns and income and providing advice to reduce unnecessary spending, users can manage their finances more efficiently. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input users' spending patterns and income data into a generating AI and have the generating AI generate advice to reduce unnecessary spending.

[0071] The proposal department can analyze the user's personality, risk tolerance, and long-term goals, and propose asset allocation and dynamic investment strategies based on their individuality. For example, the proposal department can analyze the user's personality from survey results and past behavioral history. It can also evaluate the user's risk tolerance and propose aggressive investment strategies for risk-loving users and stable investment strategies for risk-averse users. Furthermore, the proposal department can understand the user's long-term goals and propose asset allocation and investment strategies based on them. For example, the proposal department can analyze the user's personality and propose high-risk, high-return investment strategies for risk-loving users. It can also evaluate the user's risk tolerance and propose low-risk, stable investment strategies for risk-averse users. Furthermore, the proposal department can understand the user's long-term goals and propose asset allocation and investment strategies based on them. In this way, by analyzing the user's personality, risk tolerance, and long-term goals and proposing asset allocation and dynamic investment strategies based on their individuality, the proposal department can provide the user with the optimal investment strategy. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input user personality and risk tolerance data into a generating AI, which can then generate asset allocation and investment strategy suggestions based on that individuality.

[0072] The proposal unit can analyze changes in market conditions and individual economic conditions in real time and autonomously adjust and optimize investment strategies and risk management. For example, the proposal unit can collect and analyze market data in real time. It can also monitor changes in individual economic conditions and adjust investment strategies and risk management. Furthermore, the proposal unit can build a feedback loop to optimize investment strategies and risk management. For example, the proposal unit adjusts investment strategies by collecting and analyzing market data in real time. It can also monitor changes in individual economic conditions and adjust risk management. Furthermore, the proposal unit can build a feedback loop to optimize investment strategies and risk management. This allows the user's investment risk to be minimized by analyzing changes in market conditions and individual economic conditions in real time and autonomously adjusting and optimizing investment strategies and risk management. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input market data and economic condition data into a generating AI and have the generating AI perform the adjustment and optimization of investment strategies and risk management.

[0073] The execution unit can provide specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback functions. For example, the execution unit can provide specialized investment simulations, allowing users to try out various investment scenarios. It can also provide AI chatbot support, answering user questions in real time. Furthermore, it can provide community advice, allowing users to receive feedback from other users. In addition, it can provide sustainable investment options, enabling users to make environmentally and socially conscious investments. It can also provide personalized feedback functions, providing feedback based on the user's investment results. By providing specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback functions, the user's investment experience can be improved. Some or all of the above processing in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input the user's investment simulation data into a generating AI and have the generating AI execute the results of the investment simulation.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of financial data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. Conversely, if the user is relaxed, the data collection unit can advance the collection timing to collect data quickly. Furthermore, if the user is in a hurry, the data collection unit can adjust the collection timing to suit the user's convenience. By adjusting the timing of financial data collection based on the user's emotions, it is possible to reduce user stress and enable efficient data collection. 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, for example, or not using AI. 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 data collection history and select the optimal collection method. For example, the data collection unit can prioritize collection methods that the user has frequently used in the past. Furthermore, the data collection unit can suggest the most efficient collection method based on the user's past collection history. In addition, the data collection unit can analyze the user's past collection history and customize the collection method. This enables efficient data collection by analyzing the user's past financial data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0076] The data collection unit can filter financial data based on the user's current economic situation and areas of interest. For example, the data collection unit can prioritize the collection of important data based on the user's current economic situation. Furthermore, the data collection unit can filter and collect relevant data based on the user's areas of interest. In addition, the data collection unit can adjust the scope of data to be collected, taking into account the user's economic situation and areas of interest. This allows for the efficient collection of highly relevant data by filtering based on the user's current economic situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's economic situation and areas of interest data into a generating AI and have the generating AI perform data filtering.

[0077] The data collection unit can estimate the user's emotions and prioritize the financial data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for data collection tailored to the user's needs by prioritizing the financial data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data to be collected.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, the data collection unit can prioritize the collection of relevant financial data based on the user's current location. The data collection unit can also collect highly relevant data by referring to the user's past location information. Furthermore, the data collection unit can adjust the range of data to be collected by considering the user's geographical location information. This allows for the efficient collection of data that is beneficial to the user by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can collect relevant financial data from a user's social media activity. The data collection unit can also analyze a user's social media activity and adjust the scope of data to be collected. Furthermore, the data collection unit can determine the priority of data to be collected based on the user's social media activity. This enables data collection tailored to the user's interests by analyzing the user's social media activity and collecting relevant data. 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 user social media activity data into a generating AI and have the generating AI collect relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple presentation. If the user is relaxed, the analysis unit can provide a detailed presentation. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation. By adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided in a way that is easy for the user to understand. 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 presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of each financial item. For example, the analysis unit can perform a detailed analysis on important financial items. It can also perform a simplified analysis on less important financial items. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of each financial item. This allows for a detailed analysis of important financial items by adjusting the level of detail based on their importance. 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 financial data into a generating AI and have the generating AI adjust the level of detail of the analysis based on importance.

[0082] The analysis unit can apply different analysis algorithms depending on the income and expense category during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the income and expense category. The analysis unit can also apply different analysis methods for each income and expense category. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the income and expense category. This allows for optimal analysis for each category by applying different analysis algorithms depending on the income and expense category. 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 income and expense category data into a generating AI and have the generating AI execute the application of analysis algorithms according to the category.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can provide the results quickly. By adjusting the length of the analysis based on the user's emotions, the analysis results can be tailored to the user's needs. 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the timing of income and expenses during the analysis. For example, the analysis unit can prioritize the analysis of important items based on the timing of income and expenses. The analysis unit can also adjust the order of analysis considering the timing of income and expenses. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the timing of income and expenses. This allows for the priority analysis of important items based on the timing of income and expenses. 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 income and expense timing data into a generating AI and have the generating AI determine the priority of analysis based on the timing of occurrence.

[0085] The analysis unit can adjust the order of analysis based on the relevance of income and expenses during the analysis. For example, the analysis unit can prioritize the analysis of important items based on the relevance of income and expenses. The analysis unit can also adjust the order of analysis considering the relevance of income and expenses. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the relevance of income and expenses. This allows for the priority analysis of important items based on the relevance of income and expenses. 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 income and expense relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0086] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a simple expression. If the user is relaxed, the suggestion unit can provide a detailed expression. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise expression. In this way, by adjusting the expression of the suggestion based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way the suggestion is expressed.

[0087] The proposal unit can adjust the level of detail in its proposals based on the importance of savings and investment plans. For example, it can provide detailed proposals for important savings and investment plans, and simplified proposals for less important ones. Furthermore, the proposal unit can prioritize proposals based on the importance of savings and investment plans. This allows for detailed proposals for important items by adjusting the level of detail based on the importance of savings and investment plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input savings and investment plan data into a generating AI and have the generating AI adjust the level of detail of proposals based on importance.

[0088] The proposal unit can apply different proposal algorithms depending on the user's personality and risk tolerance when making a proposal. For example, the proposal unit can select the optimal proposal algorithm based on the user's personality. Furthermore, the proposal unit can apply different proposal methods based on the user's risk tolerance. In addition, the proposal unit can adjust the level of detail of the proposal based on the user's personality and risk tolerance. This allows the proposal unit to provide the user with the most suitable proposal by applying different proposal algorithms according to the user's personality and risk tolerance. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input user personality and risk tolerance data into a generating AI and have the generating AI apply the proposal algorithm.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions quickly. In this way, by adjusting the length of suggestions based on the user's emotions, suggestions tailored to the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0090] The proposal unit can determine the priority of proposals based on the timing of savings and investment plans. For example, the proposal unit will prioritize proposals based on the timing of savings and investment plans. The proposal unit can also adjust the order of proposals, taking into account the timing of savings and investment plans. Furthermore, the proposal unit can adjust the level of detail of proposals based on the timing of savings and investment plans. This allows the proposal unit to prioritize proposals based on the timing of savings and investment plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data on the timing of savings and investment plans into a generating AI and have the generating AI determine the priority of proposals based on the timing.

[0091] The proposal unit can adjust the order of proposals based on the relevance of savings and investment plans. For example, the proposal unit can prioritize proposing important items based on the relevance of savings and investment plans. The proposal unit can also adjust the order of proposals considering the relevance of savings and investment plans. Furthermore, the proposal unit can adjust the level of detail of proposals based on the relevance of savings and investment plans. This allows the proposal unit to prioritize proposing important items based on the relevance of savings and investment plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the relevance of savings and investment plans into a generating AI and have the generating AI perform the adjustment of the order of proposals based on relevance.

[0092] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated emotions. For example, if the user is stressed, the execution unit can provide a simple execution method. If the user is relaxed, the execution unit can provide a more detailed execution method. Furthermore, if the user is in a hurry, the execution unit can provide a method that allows for quick execution. In this way, by adjusting the execution method based on the user's emotions, the optimal execution method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input user emotion data into the generative AI and have the generative AI adjust the execution method.

[0093] The execution unit can analyze the user's past execution history and select the optimal execution method during execution. For example, the execution unit may prioritize execution methods that the user has successfully used in the past. Furthermore, the execution unit can suggest the most efficient execution method based on the user's past execution history. In addition, the execution unit can analyze the user's past execution history and customize the execution method. This enables efficient execution by analyzing the user's past execution history and selecting the optimal method. Some or all of the above processing in the execution unit may be performed using AI, or without AI. For example, the execution unit can input the user's past execution history data into a generating AI and have the generating AI select the optimal execution method.

[0094] The execution unit can customize the means of execution at runtime based on the user's current economic situation. For example, the execution unit can select the optimal means of execution based on the user's current economic situation. The execution unit can also customize the means of execution considering the user's economic situation. Furthermore, the execution unit can determine the priority of execution based on the user's economic situation. This allows the execution unit to provide the user with the optimal means of execution by customizing the means of execution based on the user's current economic situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's economic situation data into a generating AI and have the generating AI perform the customization of the means of execution.

[0095] The execution unit can estimate the user's emotions and determine the priority of actions based on the estimated emotions. For example, if the user is stressed, the execution unit will prioritize important actions. If the user is relaxed, the execution unit can prioritize detailed actions. Furthermore, if the user is in a hurry, the execution unit can prioritize actions that can be performed quickly. In this way, by determining the priority of actions based on the user's emotions, the execution unit can prioritize actions that are important to the user. 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 execution unit may be performed using AI, or not using AI. For example, the execution unit can input user emotion data into a generative AI and have the generative AI determine the priority of actions.

[0096] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, the execution unit can select the optimal execution method based on the user's current location. Furthermore, the execution unit can suggest the optimal execution method by referring to the user's past location information. In addition, the execution unit can customize the execution method, taking into account the user's geographical location information. This allows the execution unit to provide a beneficial execution method to the user by selecting the optimal method while considering the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal execution method.

[0097] The execution unit can analyze the user's social media activity during execution and propose a means of execution. For example, the execution unit can propose the optimal means of execution based on the user's social media activity. The execution unit can also analyze the user's social media activity and customize the means of execution. Furthermore, the execution unit can determine the priority of execution based on the user's social media activity. This makes it possible to perform actions that are tailored to the user's interests by analyzing the user's social media activity and proposing a means of execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of a means of execution.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can estimate the user's emotions and adjust the method of collecting financial data based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection, and if the user is relaxed, it can increase the frequency. Also, if the user is in a hurry, the data collection unit can collect data quickly, and if the user has time, it can collect detailed data. By adjusting the collection method based on the user's emotions, the burden on the user is reduced and efficient data collection becomes possible. Emotion estimation is achieved using, for example, 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 the user's emotion data into a generative AI and have the generative AI adjust the collection method.

[0100] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can delay the timing of the analysis, and if the user is relaxed, it can speed up the timing. Also, if the user is in a hurry, the analysis unit can perform a rapid analysis, and if the user has time, it can perform a detailed analysis. By adjusting the timing of the analysis based on the user's emotions, the burden on the user is reduced and efficient analysis becomes possible. Emotion estimation is achieved using, for example, 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, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of the analysis.

[0101] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can offer simple and easy-to-implement suggestions; if the user is relaxed, it can offer detailed and complex suggestions. Similarly, if the user is in a hurry, the suggestion unit can offer suggestions that can be implemented quickly; if the user has ample time, it can offer long-term suggestions. By adjusting the content of suggestions based on the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of its suggestions.

[0102] The execution unit can estimate the user's emotions and adjust the timing of execution based on the estimated emotions. For example, if the user is stressed, the execution unit can delay the timing of execution, and if the user is relaxed, it can speed up the timing. Also, if the user is in a hurry, the execution unit can perform the execution quickly, and if the user has ample time, it can perform a detailed execution. By adjusting the timing of execution based on the user's emotions, the burden on the user is reduced and efficient execution becomes possible. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, or not using AI. For example, the execution unit can input user emotion data into a generative AI and have the generative AI adjust the timing of execution.

[0103] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect only important data, while if the user is relaxed, it can collect detailed data. Also, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly, while if the user has time, it can collect more data. By adjusting the type of data collected based on the user's emotions, the burden on the user is reduced and efficient data collection becomes possible. Emotion estimation is achieved using, for example, 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 the user's emotion data into a generative AI and have the generative AI adjust the type of data to be collected.

[0104] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, it can prioritize selecting collection methods that the user has frequently used in the past. Furthermore, the data collection unit can suggest the most efficient collection method based on the user's past collection history. In addition, the data collection unit can analyze the user's past collection history and customize the collection method. This enables efficient data collection by analyzing the user's past financial data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0105] The analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenses. For example, it can perform a detailed analysis on important income and expense items, and a simplified analysis on less important items. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the income and expenses. This allows for a detailed analysis of important income and expense items by adjusting the level of detail based on the importance of the income and expenses. 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 income and expense data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0106] The proposal unit can adjust the level of detail in its proposals based on the importance of the savings and investment plans. For example, it can provide detailed proposals for important savings and investment plans, and simplified proposals for less important ones. Furthermore, the proposal unit can determine the priority of proposals based on the importance of the savings and investment plans. This allows for detailed proposals for important items by adjusting the level of detail based on the importance of the savings and investment plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input savings and investment plan data into a generating AI and have the generating AI adjust the level of detail of the proposals based on importance.

[0107] The execution unit can analyze the user's past execution history and select the optimal execution method during execution. For example, it can prioritize selecting execution methods that the user has successfully used in the past. The execution unit can also suggest the most efficient execution method based on the user's past execution history. Furthermore, the execution unit can analyze the user's past execution history and customize the execution method. This enables efficient execution by analyzing the user's past execution history and selecting the optimal execution method. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's past execution history data into a generating AI and have the generating AI select the optimal execution method.

[0108] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, it can prioritize the collection of relevant financial data based on the user's current location. The data collection unit can also collect highly relevant data by referring to the user's past location information. Furthermore, the data collection unit can adjust the range of data to be collected by considering the user's geographical location information. This allows for the efficient collection of data that is beneficial to the user by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects credit card and bank account information. For example, it can collect usage history and balance information for multiple credit cards owned by the user, as well as transaction history and balance information for bank accounts. Furthermore, the collection unit can automatically acquire and integrate data from each financial institution. For example, it can use APIs to acquire data from each financial institution and manage it in a unified format. Step 2: The analysis unit analyzes the information collected by the data collection unit and tracks income and expenses in real time. For example, it can understand the user's spending patterns and income and provide advice to reduce unnecessary spending. It can also monitor fluctuations in income and expenses in real time and detect abnormal transactions. Furthermore, it can make future income and expense forecasts based on the user's income and expense data. Step 3: The proposal department proposes optimal savings and investment / management plans based on the analysis results obtained by the analysis department. For example, it can analyze the user's personality, risk tolerance, and long-term goals to propose asset allocation and dynamic investment strategies based on individual characteristics. It can also analyze changes in market conditions and individual economic conditions in real time and autonomously adjust and optimize investment strategies and risk management. Furthermore, it provides specialized investment simulations, allowing users to try out various investment scenarios. Step 4: The execution unit carries out the proposals made by the proposal unit. For example, it can take investment instructions from the user and execute actual trades. It can also provide AI chatbot support and answer user questions in real time. Furthermore, it can provide community advice and receive feedback from other users.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects credit card and bank account information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks and analyzes income and expenses in real time based on the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal savings plan and investment / operation plan. The execution unit is implemented by the control unit 46A of the smart device 14 and executes the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information on credit cards and bank accounts. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks and analyzes income and expenses in real time based on the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal savings plan and investment / operation plan. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects credit card and bank account information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks and analyzes income and expenses in real time based on the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal savings plan and investment / operation plan. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects credit card and bank account information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and tracks and analyzes income and expenses in real time based on the collected information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal savings plan and investment / operation plan. The execution unit is implemented by, for example, the control unit 46A of the robot 414 and executes the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection department that collects credit card and bank account information, The analysis unit analyzes the information collected by the aforementioned collection unit and tracks income and expenses in real time. Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal savings plan and investment / management plan. The system comprises an execution unit that executes the content proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Automatically acquires and integrates data from various financial institutions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We understand users' spending patterns and income, and provide advice to reduce unnecessary spending. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We analyze the user's personality, risk tolerance, and long-term goals to propose asset allocation and dynamic investment strategies based on their individual characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It analyzes market conditions and individual economic changes in real time, and autonomously adjusts and optimizes investment strategies and risk management. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, It offers specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback features. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned collection unit is Analyze the user's past financial data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting financial data, filtering is performed based on the user's current economic situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and prioritizes the financial data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting financial data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting financial data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, different analytical algorithms are applied depending on the income and expenditure category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the income and expenses occurred. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relationship between income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of your savings and investment plans. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the user's personality and risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, prioritize them based on when savings and investment plans are due. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to savings and investment plans. The system described in Appendix 1, characterized by the features described herein. (Note 25) The execution unit is, It estimates the user's emotions and adjusts the execution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The execution unit is, During execution, the system analyzes the user's past execution history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, At runtime, the execution method is customized based on the user's current financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, During execution, the system selects the optimal execution method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, During execution, the system analyzes the user's social media activity and suggests implementation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects credit card and bank account information, The analysis unit analyzes the information collected by the aforementioned collection unit and tracks income and expenses in real time. Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal savings plan and investment / management plan. The system comprises an execution unit that executes the content proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Automatically acquires and integrates data from various financial institutions. The system according to feature 1.

3. The aforementioned analysis unit is We understand users' spending patterns and income, and provide advice to reduce unnecessary spending. The system according to feature 1.

4. The aforementioned proposal section is, We analyze the user's personality, risk tolerance, and long-term goals to propose asset allocation and dynamic investment strategies based on their individual characteristics. The system according to feature 1.

5. The aforementioned proposal section is, It analyzes market conditions and individual economic changes in real time, and autonomously adjusts and optimizes investment strategies and risk management. The system according to feature 1.

6. The execution unit is, It offers specialized investment simulations, AI chatbot support, community advice, sustainable investment options, and personalized feedback features. The system according to feature 1.

7. 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.

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

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