System

Abstract

To provide a system capable of proposing an optimal investment strategy and a saving method by comprehensively utilizing information such as an income, expense, goal, and risk tolerance of a customer.SOLUTION: A system according to an embodiment comprises a collection unit, a proposal unit, an analysis unit, a provision unit, and a countermeasure unit. The collection unit collects information such as an income, expense, goal, and risk tolerance of a customer. The proposal unit proposes an investment strategy and a saving method to the customer on the basis of the information collected by the collection unit. The analysis unit analyzes market trends or fluctuations. The provision unit provides the customer with real-time financial information on the basis of the analysis results by the analysis unit. The countermeasure unit proposes insurance, pension, or a tax strategy on the basis of the financial status or future goal of the customer.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately utilize information such as a customer's income, expenses, goals, and risk tolerance to propose optimal investment strategies and savings methods, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal investment strategies and savings methods by comprehensively utilizing information such as a client's income, expenditures, goals, and risk tolerance. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a proposal unit, an analysis unit, a provision unit, and a countermeasure unit. The collection unit collects information on a client's income, expenses, goals, and risk tolerance. The proposal unit proposes an investment strategy or a saving method to the client based on the information collected by the collection unit. The analysis unit analyzes market trends or fluctuations. The provision unit provides real-time financial information to the client based on the analysis results by the analysis unit. The countermeasure unit proposes insurance, pension, and tax countermeasures based on the client's asset situation or future goals. Effect of the Invention

[0007] The system according to the embodiment can comprehensively utilize information such as a client's income, expenditure, goals, and risk tolerance to propose optimal investment strategies and savings methods. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The financial plan proposal support AI assistant according to the embodiment of the present invention is a system that collects information such as a customer's income, expenditure, goal, and risk tolerance, and proposes an optimal investment strategy or savings method. The system collects information on the customer's income, expenditure, goal, and risk tolerance, and proposes an optimal investment strategy or savings method based on the collected information. In addition, the system analyzes market trends and fluctuations and provides real-time financial information. In addition, insurance, pension, and tax measures are proposed based on the customer's asset status and future goals. For example, when collecting information on the customer's income, expenditure, goal, and risk tolerance, the customer's bank account information and credit card usage information can be automatically obtained. In addition, it is also possible to collect health data from the customer's social media account information and fitness tracker and reflect it in the evaluation of risk tolerance. Next, an optimal investment strategy or savings method is proposed based on the collected information. At this time, an optimal investment strategy or savings method can be generated using a generation AI. For example, it is also possible to automatically reconstruct the customer's investment portfolio, monitor exchange rates in real time, and adjust the affected investment strategy. In addition, it is also possible to analyze the customer's psychological profile and propose an investment strategy that takes emotional biases into account. The system analyzes market trends and fluctuations and provides real-time financial information. At this time, data is collected to analyze market trends and fluctuations, and the AI ​​analyzes it. Based on the analysis results, real-time financial information is provided to the customer. Finally, insurance, pensions, and tax measures are proposed based on the customer's asset status and future goals. At this time, information on the customer's asset status and future goals is collected and analyzed by the AI. Based on the analysis results, optimal insurance, pensions, and tax measures are proposed. In this way, the financial plan proposal support AI assistant can collect information such as the customer's income, expenses, goals, and risk tolerance, and propose optimal investment strategies and savings methods. In this way, the financial plan proposal support AI assistant can support the customer's asset management and provide peace of mind for the future.

[0029] The financial plan proposal support AI assistant according to the embodiment includes a collection unit, a proposal unit, an analysis unit, a provision unit, and a countermeasure unit. The collection unit collects information on the customer's income, expenditure, goals, and risk tolerance. The collection unit can automatically acquire, for example, the customer's bank account information and credit card usage information. The collection unit can also collect the customer's social media account information and evaluate the risk tolerance based on the collected information. Furthermore, the collection unit can acquire health data from the customer's fitness tracker and reflect the acquired data in the evaluation of the risk tolerance. The proposal unit uses a generation AI to propose an optimal investment strategy or saving method based on the information collected by the collection unit. The proposal unit can, for example, automatically reconstruct the customer's investment portfolio. The proposal unit can also monitor exchange rates in real time and adjust the affected investment strategy. Furthermore, the proposal unit can analyze the customer's psychological profile and propose an investment strategy that takes emotional biases into account. The analysis unit analyzes market trends and fluctuations. The analysis unit, for example, collects data for analyzing market trends and fluctuations, and the AI ​​analyzes the data. The provision unit provides real-time financial information to the customer based on the analysis results by the analysis unit. The provision unit can provide real-time financial information on market trends and fluctuations, for example. The countermeasure unit proposes insurance, pension, and tax measures based on the customer's asset status and future goals. The countermeasure unit collects, for example, information on the customer's asset status and future goals, and the AI ​​analyzes the information. As a result, the financial plan proposal support AI assistant according to the embodiment can collect information such as the customer's income, expenses, goals, and risk tolerance, and propose optimal investment strategies and savings methods.

[0030] The collection unit collects information on the customer's income, expenditure, goals, and risk tolerance. For example, the collection unit can automatically obtain the customer's bank account information and credit card usage information. Specifically, to obtain bank account information, the bank cooperates with the bank through an API to obtain transaction history and balance information in real time. For credit card usage information, the card company's API is used to collect usage details and payment history. This allows for accurate understanding of detailed data on the customer's income and expenditure. The collection unit can also collect the customer's social media account information and evaluate the risk tolerance based on the collected information. Data such as the customer's lifestyle, consumption trends, and interests are collected from the social media accounts and used for risk assessment. In addition, the collection unit can also obtain health data from the customer's fitness tracker and reflect the obtained data in the evaluation of the risk tolerance. Health data such as heart rate, exercise volume, and sleep patterns are collected from the fitness tracker and incorporated into the customer's risk profile. This allows the collection unit to collect information from multiple data sources and comprehensively evaluate the customer's economic situation and risk tolerance. Furthermore, the collection department can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the proposal department and analysis department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The proposal unit uses the generation AI to propose an optimal investment strategy or savings method based on the information collected by the collection unit. The generation AI receives data such as the customer's income, expenses, goals, and risk tolerance as input, and analyzes these data to generate an optimal investment strategy. Specifically, the generation AI can automatically reconstruct the customer's investment portfolio. For example, it adjusts the allocation of asset classes such as stocks, bonds, and real estate according to the customer's risk tolerance and proposes an optimal portfolio. The proposal unit can also monitor exchange rates in real time and adjust the affected investment strategy. The generation AI predicts fluctuations in exchange rates and dynamically adjusts the investment strategy based on this. Furthermore, the proposal unit can analyze the customer's psychological profile and propose an investment strategy that takes emotional biases into account. The generation AI evaluates the customer's psychological characteristics based on the customer's past investment behavior and survey results, and customizes the investment strategy based on this. This allows the proposal unit to provide an optimal investment strategy according to the customer's individual needs and characteristics. Furthermore, the proposal unit can utilize the learning function of the generation AI to continuously improve the accuracy of the proposal. For example, by comparing past proposal results with actual investment results and adjusting the algorithm of the generating AI, more accurate proposals can be made. This allows the proposal department to always use the latest information and technology to continue providing optimal investment strategies to clients.

[0032] The analysis unit analyzes market trends and fluctuations. For example, the analysis unit collects data for analyzing market trends and fluctuations, and the AI ​​analyzes the data. Specifically, the analysis unit collects information from various data sources such as stock price data, exchange rates, economic indicators, and news articles, and the AI ​​analyzes the data. The AI ​​uses machine learning algorithms to extract patterns from past data and predict future trends. For example, the past movements of stock prices are analyzed to find trends in price fluctuations under certain conditions. In addition, exchange rate fluctuations are analyzed to evaluate the impact of certain economic indicators and news on exchange rates. In addition, the AI ​​performs text analysis of news articles to detect events and trends that may affect the market. This allows the analysis unit to grasp market trends and fluctuations in real time and provide appropriate information to customers. In addition, the analysis unit can use past data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past market data, risk fluctuations in specific asset classes and regions are predicted, and future investment strategies are developed. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0033] The provision unit provides real-time financial information to the customer based on the analysis results by the analysis unit. The provision unit can provide, for example, real-time financial information on market trends and fluctuations. Specifically, the provision unit updates market information related to the customer's portfolio in real time and notifies the customer. For example, in the event of a sudden fluctuation in stock prices or a large fluctuation in exchange rates, the provision unit immediately notifies the customer and urges the customer to take necessary measures. The provision unit also provides news and event information that may affect the customer's investment strategy. For example, in the event of an event that has a significant impact on the market, such as the announcement of important economic indicators or the announcement of corporate financial results, the provision unit notifies the customer of this and suggests a review of the investment strategy. Furthermore, the provision unit can collect customer feedback and continuously improve the accuracy and usefulness of the information provided. For example, the provision unit analyzes what actions the customer took in response to the information provided and optimizes the timing and content of the information provided. The provision unit can also reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the provider to provide financial information to clients quickly and reliably, and assist in optimizing investment strategies.

[0034] The countermeasures department proposes insurance, pension, and tax measures based on the customer's asset status and future goals. For example, the countermeasures department collects information on the customer's asset status and future goals, and AI analyzes it. Specifically, the countermeasures department grasps the customer's current asset status, income, expenses, and future goals (for example, purchasing a house, children's education expenses, living expenses after retirement, etc.) in detail. Based on this information, AI proposes optimal insurance products and pension plans. For example, it selects and proposes appropriate life insurance and medical insurance based on the customer's risk tolerance and future income forecast. The countermeasures department also proposes tax measures. For example, it proposes optimal tax saving measures according to the customer's income and asset status and shows how to reduce tax burdens. Furthermore, the countermeasures department can collect customer feedback and continuously improve the accuracy and usefulness of the proposal content. For example, it analyzes the results of the customer's actual use of the proposed insurance products and pension plans and optimizes the proposal content. In addition, the countermeasures department can reliably convey information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, emails, etc. to reliably deliver important information. This enables the department to quickly and reliably provide customers with information on insurance, pensions, and tax planning, helping them achieve their future goals.

[0035] The collection unit can automatically acquire the bank account information and credit card usage information of the customer. For example, the collection unit automatically acquires the bank account information of the customer. The bank account information includes an account number, a transaction history, and the like. The collection unit also automatically acquires the credit card usage information of the customer. The credit card usage information includes a usage history, a usage limit, and the like. By automatically acquiring the bank account information and credit card usage information of the customer, the efficiency of collection is improved. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the bank account information and credit card usage information into AI, which can automatically acquire the information.

[0036] The suggestion unit can generate an investment strategy or a saving method using the generation AI. The suggestion unit can generate an optimal investment strategy using the generation AI, for example. The generation AI can generate an optimal investment strategy based on customer information using a machine learning model or a deep learning algorithm. The suggestion unit can also generate an optimal saving method using the generation AI. For example, the generation AI analyzes the customer's spending pattern and proposes saving methods such as reducing fixed costs and managing variable costs. As a result, the use of the generation AI improves the accuracy of proposing optimal investment strategies and saving methods. A part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs information on the customer's income, expenditure, goals, and risk tolerance into the generation AI, and the generation AI generates an optimal investment strategy or saving method.

[0037] The collection unit can collect social media account information of the customer and evaluate the risk tolerance based on the collected information. The collection unit, for example, collects social media account information of the customer. The social media account information includes the content of posts and the number of followers. The collection unit can evaluate the risk tolerance of the customer based on the collected social media account information. For example, the collection unit analyzes the content of posts of the customer and evaluates the risk tolerance. The collection unit can also evaluate the risk tolerance based on the number of followers of the customer and information on the accounts followed. This allows for more accurate risk evaluation by collecting social media account information and evaluating the risk tolerance. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media account information into AI, and the AI ​​can automatically collect information and evaluate the risk tolerance.

[0038] The proposal unit can reconstruct the customer's investment portfolio. The investment portfolio includes asset allocation and risk assessment. The proposal unit can optimize the investment strategy by reconstructing the customer's investment portfolio. For example, the proposal unit reviews the customer's asset allocation and diversifies risk. The proposal unit can also reconstruct the investment portfolio based on the customer's risk assessment. This makes it possible to optimize the investment strategy by automatically reconstructing the customer's investment portfolio. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information about the customer's investment portfolio into the generation AI, which then reconstructs the optimal portfolio.

[0039] The proposal unit can monitor exchange rates in real time and adjust the affected investment strategy. The proposal unit, for example, monitors exchange rates in real time. The exchange rates include real-time data and exchange risks. The proposal unit can reduce the investment risk by monitoring the exchange rate and adjusting the affected investment strategy. For example, the proposal unit monitors fluctuations in exchange rates in real time and adjusts the investment strategy. The proposal unit can also evaluate exchange risk and propose an investment strategy for reducing the risk. Thereby, the investment risk can be reduced by monitoring exchange rates in real time and adjusting the investment strategy. A part or all of the above-mentioned processing in the proposal unit is performed using the generation AI. For example, the proposal unit inputs real-time data of the exchange rate to the generation AI, and the generation AI adjusts the investment strategy.

[0040] The collection unit can acquire health data from the customer's fitness tracker and reflect the acquired data in the evaluation of the risk tolerance. The collection unit, for example, acquires health data from the customer's fitness tracker. The fitness tracker includes a pedometer, a heart rate monitor, and the like. The collection unit reflects the acquired health data in the evaluation of the risk tolerance. For example, the collection unit evaluates the risk tolerance based on the customer's step count data. The collection unit can also evaluate the risk tolerance based on the customer's heart rate data. This allows the health data to be reflected in the evaluation of the risk tolerance, thereby enabling a more accurate risk evaluation. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the data of the fitness tracker into AI, and the AI ​​can automatically acquire the data and evaluate the risk tolerance.

[0041] The proposal unit can analyze the psychological profile of the customer and propose an investment strategy that takes into account the emotional bias. The proposal unit, for example, analyzes the psychological profile of the customer. The psychological profile includes a personality diagnostic test and a behavioral analysis. The proposal unit proposes an investment strategy that takes into account the emotional bias based on the psychological profile of the customer. For example, the proposal unit evaluates the risk tolerance based on the result of the personality diagnostic test of the customer and proposes an appropriate investment strategy. The proposal unit can also propose an investment strategy that takes into account the emotional bias based on the result of the behavioral analysis of the customer. As a result, by proposing an investment strategy that takes into account the emotional bias, an investment strategy that is suitable for the psychological state of the customer can be provided. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information on the psychological profile of the customer into the generation AI, and the generation AI proposes an investment strategy that takes into account the emotional bias.

[0042] The proposal unit can propose region-specific investment opportunities using the geographical location information of the client. The proposal unit, for example, proposes region-specific investment opportunities using the geographical location information of the client. The geographical location information includes GPS data and address information. The proposal unit proposes region-specific investment opportunities based on the geographical location information of the client. For example, the proposal unit proposes investment opportunities related to urban areas to a client living in an urban area. The proposal unit can also propose investment opportunities related to rural areas to a client living in a rural area. This makes it possible to provide an investment strategy suited to a region by proposing region-specific investment opportunities. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs the geographical location information of the client to the generation AI, which then proposes region-specific investment opportunities.

[0043] The collection unit can analyze the past financial behavior of the customer and select the optimal information collection method. The collection unit, for example, analyzes the past financial behavior of the customer. The past financial behavior includes transaction history and investment history. The collection unit selects the optimal information collection method based on the past financial behavior of the customer. For example, the collection unit preferentially collects bank account information that the customer has used frequently in the past. In addition, if the customer has used many credit cards in the past, the collection unit can also preferentially collect the usage information. In this way, the optimal information collection method can be selected by analyzing the past financial behavior of the customer. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the customer's transaction history into AI, and the AI ​​selects the optimal information collection method.

[0044] The collection unit can perform filtering based on the customer's current living situation and areas of interest at the time of collection. For example, the collection unit performs filtering based on the customer's current living situation and areas of interest at the time of collection. The current living situation includes income situation and family structure. The collection unit preferentially collects highly relevant information based on the customer's current living situation. For example, if the customer wishes to review his / her household finances in his / her current living situation, the collection unit preferentially collects information about expenditures. In addition, if the customer is interested in health, the collection unit can also preferentially collect health data from a fitness tracker. This allows more relevant information to be collected by filtering based on the customer's current living situation and areas of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the customer's living situation data into AI, and the AI ​​performs filtering.

[0045] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the customer at the time of collection. The geographical location information includes GPS data and address information. The collection unit preferentially collects highly relevant information based on the geographical location information of the customer. For example, if the customer lives in an urban area, the collection unit preferentially collects financial information related to the urban area. In addition, if the customer lives in a rural area, the collection unit can also preferentially collect financial information related to the rural area. This makes it possible to collect information appropriate to the region by collecting information in consideration of the geographical location information of the customer. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs the geographical location information of the customer into AI, and the AI ​​preferentially collects highly relevant information.

[0046] The collection unit can analyze the social media activity of the customer at the time of collection and collect related information. For example, the collection unit analyzes the social media activity of the customer at the time of collection. The social media activity includes the content of posts and the number of followers. The collection unit collects related information based on the social media activity of the customer. For example, the collection unit collects information related to topics that the customer frequently mentions on social media. The collection unit can also collect information on accounts that the customer follows on social media. Furthermore, the collection unit can also collect information on groups that the customer participates in on social media. This makes it possible to collect more relevant information by analyzing the social media activity of the customer. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs the social media activity data of the customer into AI, and the AI ​​collects related information.

[0047] The proposal unit can adjust the level of detail of the proposal based on the importance of the investment strategy or the saving method when making a proposal. The importance of the investment strategy or the saving method includes risk evaluation and profitability evaluation. The proposal unit makes a proposal including a detailed explanation for an investment strategy with a high level of importance. The proposal unit can also make a concise proposal for a saving method with a low level of importance. Furthermore, the proposal unit can adjust the level of detail of the proposal according to the customer's interest. As a result, by adjusting the level of detail of the proposal based on the importance of the investment strategy or the saving method, more appropriate proposals can be made. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information on the customer's investment strategy or the saving method into the generation AI, and the generation AI adjusts the level of detail of the proposal based on the importance.

[0048] When making a proposal, the proposal unit can make a proposal taking into consideration the risk diversification of the customer's investment portfolio. Risk diversification includes asset allocation and risk assessment. The proposal unit proposes risk diversification when the customer's investment portfolio is risk concentrated. In addition, the proposal unit can also propose maintaining the status quo when the customer's investment portfolio is risk diversified. Furthermore, the proposal unit can also propose an optimal investment strategy depending on the risk diversification status of the customer's investment portfolio. In this way, a more appropriate investment strategy can be proposed by taking into consideration the risk diversification of the customer's investment portfolio. A part or all of the above-mentioned processing in the proposal unit is performed using the generation AI. For example, the proposal unit inputs information about the customer's investment portfolio into the generation AI, which then proposes an investment strategy taking into consideration risk diversification.

[0049] When making a proposal, the proposal unit can determine the priority of the proposal based on the investment history of the customer. The investment history includes a trading history and an investment performance. The proposal unit preferentially proposes an optimal investment strategy based on the investment history of the customer. For example, the proposal unit preferentially proposes an investment strategy that has been successful for the customer in the past. The proposal unit can also make a proposal that avoids an investment strategy that has failed for the customer in the past. This enables more appropriate proposals to be made by determining the priority of the proposal based on the investment history of the customer. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information on the customer's investment history into the generation AI, and the generation AI determines the priority of the proposal.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the customer when making a proposal. The relevance includes investment themes and areas of interest of the customer. The proposal unit makes proposals that are of high interest to the customer first. The proposal unit can also postpone proposals that are of low interest to the customer. Furthermore, the proposal unit can optimize the order of proposals based on the relevance of the customer. This enables more appropriate proposals to be made by adjusting the order of proposals based on the relevance of the customer. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information on the customer's areas of interest to the generation AI, and the generation AI adjusts the order of proposals.

[0051] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the markets during the analysis. For example, the analysis unit performs the analysis by taking into account the interrelationships of the markets during the analysis. The interrelationships of the markets include the relationship between stock prices and interest rates, and the relationship between exchange rates and trade balances. The analysis unit performs the analysis by taking into account the interrelationships of the stock market and the bond market. The analysis unit can also perform the analysis by taking into account the interrelationships of the foreign exchange market and the commodity market. Furthermore, the analysis unit can also perform the analysis by taking into account the interrelationships of the real estate market and the stock market. As a result, the accuracy of the analysis is improved by taking into account the interrelationships of the markets. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs market interrelationship data into an AI, and the AI ​​performs the analysis by taking into account the interrelationships.

[0052] During the analysis, the analysis unit can predict the current trend by referring to past market data. The past market data includes past stock price data and the history of economic indicators. The analysis unit predicts the current stock price trend by referring to past stock price data. The analysis unit can also predict the current exchange rate trend by referring to past exchange rate data. Furthermore, the analysis unit can also predict the current real estate trend by referring to past real estate price data. As a result, the accuracy of predicting the current trend is improved by referring to the past market data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the past market data into AI, and the AI ​​predicts the current trend.

[0053] The analysis unit can perform the analysis taking into account the geographical distribution of the market. The geographical distribution includes regional economic data and geographical market trends. The analysis unit performs the analysis taking into account stock price data for each region. The analysis unit can also perform the analysis taking into account exchange rate data for each region. Furthermore, the analysis unit can also perform the analysis taking into account real estate price data for each region. This allows for more accurate analysis by taking into account the geographical distribution of the market. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs geographical distribution data into AI, which performs the analysis taking into account the geographical distribution.

[0054] The analysis unit can improve the accuracy of the analysis by referring to market-related literature during the analysis. The related literature includes academic papers and industry reports. The analysis unit performs the analysis by referring to the latest research papers on the stock market. The analysis unit can also perform the analysis by referring to the latest research papers on the foreign exchange market. Furthermore, the analysis unit can also perform the analysis by referring to the latest research papers on the real estate market. As a result, the accuracy of the analysis is improved by referring to market-related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs related literature data into AI, and the AI ​​performs the analysis by referring to the related literature.

[0055] The providing unit can provide optimal information by referring to the past financial behavior of the customer at the time of providing. Past financial behavior includes transaction history and investment history. The providing unit provides optimal information based on the past financial behavior of the customer. For example, the providing unit preferentially provides financial information that the customer has frequently used in the past. In addition, if the customer has used many credit cards in the past, the providing unit can also preferentially provide the usage information. Furthermore, if the customer has been active in investing in the past, the providing unit can also preferentially provide investment-related information. In this way, more appropriate information can be provided by referring to the past financial behavior of the customer. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the past financial behavior data of the customer into AI, and the AI ​​provides optimal information.

[0056] The providing unit can customize the information based on the customer's current living situation at the time of providing. The current living situation includes income situation, family structure, and the like. The providing unit preferentially provides highly relevant information based on the customer's current living situation. For example, if the customer wishes to review his / her household finances in his / her current living situation, the providing unit preferentially provides information related to expenditures. In addition, if the customer is interested in health, the providing unit can also preferentially provide health data from a fitness tracker. Furthermore, if the customer is interested in education, the providing unit can also preferentially provide education-related expenditure information. This allows more appropriate information to be provided by customizing information based on the customer's current living situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the customer's living situation data into AI, and the AI ​​customizes the information.

[0057] The providing unit can provide optimal information by considering the geographical location information of the customer at the time of providing. The geographical location information includes GPS data and address information. The providing unit preferentially provides highly relevant information based on the geographical location information of the customer. For example, if the customer lives in an urban area, the providing unit preferentially provides financial information related to the urban area. In addition, if the customer lives in a rural area, the providing unit can also preferentially provide financial information related to the rural area. Furthermore, if the customer lives overseas, the providing unit can also preferentially provide overseas financial information. This makes it possible to provide more appropriate information by considering the geographical location information of the customer. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the geographical location information of the customer into AI, and the AI ​​provides optimal information.

[0058] The providing unit can adjust the way information is provided by analyzing the social media activity of the customer at the time of providing. For example, the providing unit analyzes the social media activity of the customer at the time of providing. The social media activity includes the content of posts and the number of followers. The providing unit provides related information based on the social media activity of the customer. For example, the providing unit provides information related to topics that the customer frequently mentions on social media. The providing unit can also provide information on accounts that the customer follows on social media. Furthermore, the providing unit can also provide information on groups that the customer participates in on social media. This makes it possible to provide more appropriate information by analyzing the social media activity of the customer. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the social media activity data of the customer into AI, and the AI ​​adjusts the way information is provided.

[0059] The countermeasure department can select the optimal countermeasure method by analyzing the customer's past financial behavior at the time of countermeasure. For example, the countermeasure department analyzes the customer's past financial behavior at the time of countermeasure. The past financial behavior includes a transaction history and an investment history. The countermeasure department selects the optimal countermeasure method based on the customer's past financial behavior. For example, the countermeasure department preferentially proposes countermeasure methods that have been successful for the customer in the past. The countermeasure department can also make proposals to avoid countermeasure methods that have failed for the customer in the past. Furthermore, the countermeasure department can also select the optimal countermeasure method based on the customer's past financial behavior. In this way, a more appropriate countermeasure method can be selected by analyzing the customer's past financial behavior. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the customer's past financial behavior data into AI, and the AI ​​selects the optimal countermeasure method.

[0060] The countermeasure department can customize the countermeasure measures based on the customer's current living situation at the time of countermeasure. The current living situation includes income situation, family structure, etc. The countermeasure department preferentially proposes highly relevant countermeasure measures based on the customer's current living situation. For example, if the customer wishes to review his / her household finances in his / her current living situation, the countermeasure department proposes measures to reduce expenses. In addition, if the customer is interested in health, the countermeasure department can also propose a review of health insurance. Furthermore, if the customer is interested in education, the countermeasure department can also propose measures for education funds. This enables more appropriate countermeasures by customizing the countermeasure measures based on the customer's current living situation. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the customer's living situation data into AI, and the AI ​​customizes the countermeasure measures.

[0061] When taking countermeasures, the countermeasure department can select the optimal countermeasure method by taking into consideration the geographical location information of the customer. The geographical location information includes GPS data and address information. The countermeasure department preferentially proposes highly relevant countermeasure methods based on the geographical location information of the customer. For example, if the customer lives in an urban area, the countermeasure department proposes countermeasure methods related to urban areas. In addition, if the customer lives in a rural area, the countermeasure department can also propose countermeasure methods related to rural areas. Furthermore, if the customer lives overseas, the countermeasure department can also propose overseas countermeasure methods. In this way, a more appropriate countermeasure method can be selected by taking into consideration the geographical location information of the customer. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the geographical location information of the customer into AI, which selects the optimal countermeasure method.

[0062] The countermeasure department can analyze the social media activity of the customer and propose countermeasures at the time of countermeasures. For example, the countermeasure department analyzes the social media activity of the customer at the time of countermeasures. The social media activity includes the content of posts and the number of followers. The countermeasure department proposes related countermeasures based on the social media activity of the customer. For example, the countermeasure department proposes countermeasures related to topics that the customer frequently mentions on social media. The countermeasure department can also propose countermeasures based on information on accounts that the customer follows on social media. Furthermore, the countermeasure department can also propose countermeasures based on information on groups that the customer participates in on social media. In this way, by analyzing the social media activity of the customer, more appropriate countermeasures can be proposed. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the social media activity data of the customer into AI, and the AI ​​proposes countermeasures.

[0063] When taking countermeasures, the countermeasure department can select the optimal countermeasure method by taking into consideration the health data of the customer. The health data includes fitness tracker data and medical records. The countermeasure department preferentially proposes highly relevant countermeasure methods based on the health data of the customer. For example, if the customer is interested in health, the countermeasure department can propose a review of health insurance. In addition, if the customer is not getting enough exercise, the countermeasure department can also propose a fitness plan. Furthermore, if the customer is living a healthy lifestyle, the countermeasure department can also propose measures to maintain health. In this way, by taking into consideration the health data of the customer, a more appropriate countermeasure method can be selected. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the customer's health data into AI, which selects the optimal countermeasure method.

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

[0065] The collection unit can also analyze the purchase history of the customer and determine the priority of the information to be collected. For example, the collection unit can prioritize collecting income and expenditure information based on data on products purchased in the past by the customer. The collection unit can also analyze product categories frequently purchased by the customer and prioritize collecting related expenditure information. Furthermore, the collection unit can identify expenditure patterns related to specific seasons or events from the customer's purchase history and prioritize collecting that information. This makes it possible to collect more relevant information by taking into account the customer's purchase history.

[0066] The proposal department can also customize investment strategies based on the hobbies and interests of the customer. For example, if the customer is interested in sports, the proposal department can suggest sports-related investment opportunities. In addition, if the customer is interested in environmental protection, the proposal department can suggest environmentally friendly investment strategies. In addition, if the customer is interested in technology, the proposal department can suggest technology-related investment opportunities. This allows for more personalized proposals by customizing investment strategies based on the hobbies and interests of the customer.

[0067] The analysis department can also predict future investment risks based on the client's past investment performance. For example, the analysis department can analyze the client's past investment performance data and identify high-risk investment patterns. The analysis department can also suggest low-risk investment strategies based on the client's past investment performance. Furthermore, the analysis department can assess risks to future market fluctuations based on the client's past investment performance. This allows for more accurate risk predictions by taking into account the client's past investment performance.

[0068] The provision department can also collect customer feedback and improve the quality of the information to be provided. For example, the provision department collects feedback on how the customer felt about the provided information. The provision department can also improve the content and format of the information to be provided based on the customer feedback. Furthermore, the provision department can analyze the customer feedback and adjust the timing and frequency of providing information. In this way, by utilizing the customer feedback, it becomes possible to provide higher quality information.

[0069] The countermeasures department can also propose countermeasures based on the life events of the customer. For example, if the customer is planning to get married, the countermeasures department can propose a financial plan related to the marriage. In addition, if the customer is considering his / her children's education, the countermeasures department can also propose countermeasures for education funds. Furthermore, if the customer is planning to retire, the countermeasures department can also propose a financial plan for post-retirement life. In this way, more appropriate support can be provided by proposing countermeasures based on the life events of the customer.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The collection unit collects information on the customer's income, expenses, goals, and risk tolerance. For example, the collection unit can automatically obtain the customer's bank account information and credit card usage information. The collection unit can also collect the customer's social media account information and evaluate the risk tolerance based on the collected information. In addition, the collection unit can also obtain health data from the customer's fitness tracker and reflect the obtained data in the risk tolerance evaluation. Step 2: The suggestion unit uses the generative AI to suggest optimal investment strategies or savings methods based on the information collected by the collection unit. The suggestion unit can, for example, automatically reconfigure the customer's investment portfolio. The suggestion unit can also monitor exchange rates in real time and adjust affected investment strategies. Furthermore, the suggestion unit can analyze the customer's psychological profile and suggest investment strategies that take emotional biases into account. Step 3: The analysis unit analyzes market trends and fluctuations. For example, the analysis unit collects data to analyze market trends and fluctuations, and the AI ​​analyzes the data. Step 4: The providing unit provides the client with real-time financial information based on the analysis results by the analysis unit. The providing unit can provide real-time financial information regarding, for example, market trends and fluctuations. Step 5: The countermeasures department proposes insurance, pension, and tax measures based on the customer's asset status and future goals. For example, the countermeasures department collects information on the customer's asset status and future goals, and the AI ​​analyzes it.

[0072] (Example 2) The financial plan proposal support AI assistant according to the embodiment of the present invention is a system that collects information such as a customer's income, expenditure, goal, and risk tolerance, and proposes an optimal investment strategy or savings method. The system collects information on the customer's income, expenditure, goal, and risk tolerance, and proposes an optimal investment strategy or savings method based on the collected information. In addition, the system analyzes market trends and fluctuations and provides real-time financial information. In addition, insurance, pension, and tax measures are proposed based on the customer's asset status and future goals. For example, when collecting information on the customer's income, expenditure, goal, and risk tolerance, the customer's bank account information and credit card usage information can be automatically obtained. In addition, it is also possible to collect health data from the customer's social media account information and fitness tracker and reflect it in the evaluation of risk tolerance. Next, an optimal investment strategy or savings method is proposed based on the collected information. At this time, an optimal investment strategy or savings method can be generated using a generation AI. For example, it is also possible to automatically reconstruct the customer's investment portfolio, monitor exchange rates in real time, and adjust the affected investment strategy. In addition, it is also possible to analyze the customer's psychological profile and propose an investment strategy that takes emotional biases into account. The system analyzes market trends and fluctuations and provides real-time financial information. At this time, data is collected to analyze market trends and fluctuations, and the AI ​​analyzes it. Based on the analysis results, real-time financial information is provided to the customer. Finally, insurance, pensions, and tax measures are proposed based on the customer's asset status and future goals. At this time, information on the customer's asset status and future goals is collected and analyzed by the AI. Based on the analysis results, optimal insurance, pensions, and tax measures are proposed. In this way, the financial plan proposal support AI assistant can collect information such as the customer's income, expenses, goals, and risk tolerance, and propose optimal investment strategies and savings methods. In this way, the financial plan proposal support AI assistant can support the customer's asset management and provide peace of mind for the future.

[0073] The financial plan proposal support AI assistant according to the embodiment includes a collection unit, a proposal unit, an analysis unit, a provision unit, and a countermeasure unit. The collection unit collects information on the customer's income, expenditure, goals, and risk tolerance. The collection unit can automatically acquire, for example, the customer's bank account information and credit card usage information. The collection unit can also collect the customer's social media account information and evaluate the risk tolerance based on the collected information. Furthermore, the collection unit can acquire health data from the customer's fitness tracker and reflect the acquired data in the evaluation of the risk tolerance. The proposal unit uses a generation AI to propose an optimal investment strategy or saving method based on the information collected by the collection unit. The proposal unit can, for example, automatically reconstruct the customer's investment portfolio. The proposal unit can also monitor exchange rates in real time and adjust the affected investment strategy. Furthermore, the proposal unit can analyze the customer's psychological profile and propose an investment strategy that takes emotional biases into account. The analysis unit analyzes market trends and fluctuations. The analysis unit, for example, collects data for analyzing market trends and fluctuations, and the AI ​​analyzes the data. The provision unit provides real-time financial information to the customer based on the analysis results by the analysis unit. The provision unit can provide real-time financial information on market trends and fluctuations, for example. The countermeasure unit proposes insurance, pension, and tax measures based on the customer's asset status and future goals. The countermeasure unit collects, for example, information on the customer's asset status and future goals, and the AI ​​analyzes the information. As a result, the financial plan proposal support AI assistant according to the embodiment can collect information such as the customer's income, expenses, goals, and risk tolerance, and propose optimal investment strategies and savings methods.

[0074] The collection unit collects information on the customer's income, expenditure, goals, and risk tolerance. For example, the collection unit can automatically obtain the customer's bank account information and credit card usage information. Specifically, to obtain bank account information, the bank cooperates with the bank through an API to obtain transaction history and balance information in real time. For credit card usage information, the card company's API is used to collect usage details and payment history. This allows for accurate understanding of detailed data on the customer's income and expenditure. The collection unit can also collect the customer's social media account information and evaluate the risk tolerance based on the collected information. Data such as the customer's lifestyle, consumption trends, and interests are collected from the social media accounts and used for risk assessment. In addition, the collection unit can also obtain health data from the customer's fitness tracker and reflect the obtained data in the evaluation of the risk tolerance. Health data such as heart rate, exercise volume, and sleep patterns are collected from the fitness tracker and incorporated into the customer's risk profile. This allows the collection unit to collect information from multiple data sources and comprehensively evaluate the customer's economic situation and risk tolerance. Furthermore, the collection department can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the proposal department and analysis department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0075] The proposal unit uses the generation AI to propose an optimal investment strategy or savings method based on the information collected by the collection unit. The generation AI receives data such as the customer's income, expenses, goals, and risk tolerance as input, and analyzes these data to generate an optimal investment strategy. Specifically, the generation AI can automatically reconstruct the customer's investment portfolio. For example, it adjusts the allocation of asset classes such as stocks, bonds, and real estate according to the customer's risk tolerance and proposes an optimal portfolio. The proposal unit can also monitor exchange rates in real time and adjust the affected investment strategy. The generation AI predicts fluctuations in exchange rates and dynamically adjusts the investment strategy based on this. Furthermore, the proposal unit can analyze the customer's psychological profile and propose an investment strategy that takes emotional biases into account. The generation AI evaluates the customer's psychological characteristics based on the customer's past investment behavior and survey results, and customizes the investment strategy based on this. This allows the proposal unit to provide an optimal investment strategy according to the customer's individual needs and characteristics. Furthermore, the proposal unit can utilize the learning function of the generation AI to continuously improve the accuracy of the proposal. For example, by comparing past proposal results with actual investment results and adjusting the algorithm of the generating AI, more accurate proposals can be made. This allows the proposal department to always use the latest information and technology to continue providing optimal investment strategies to clients.

[0076] The analysis unit analyzes market trends and fluctuations. For example, the analysis unit collects data for analyzing market trends and fluctuations, and the AI ​​analyzes the data. Specifically, the analysis unit collects information from various data sources such as stock price data, exchange rates, economic indicators, and news articles, and the AI ​​analyzes the data. The AI ​​uses machine learning algorithms to extract patterns from past data and predict future trends. For example, the past movements of stock prices are analyzed to find trends in price fluctuations under certain conditions. In addition, exchange rate fluctuations are analyzed to evaluate the impact of certain economic indicators and news on exchange rates. In addition, the AI ​​performs text analysis of news articles to detect events and trends that may affect the market. This allows the analysis unit to grasp market trends and fluctuations in real time and provide appropriate information to customers. In addition, the analysis unit can use past data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past market data, risk fluctuations in specific asset classes and regions are predicted, and future investment strategies are developed. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0077] The provision unit provides real-time financial information to the customer based on the analysis results by the analysis unit. The provision unit can provide, for example, real-time financial information on market trends and fluctuations. Specifically, the provision unit updates market information related to the customer's portfolio in real time and notifies the customer. For example, in the event of a sudden fluctuation in stock prices or a large fluctuation in exchange rates, the provision unit immediately notifies the customer and urges the customer to take necessary measures. The provision unit also provides news and event information that may affect the customer's investment strategy. For example, in the event of an event that has a significant impact on the market, such as the announcement of important economic indicators or the announcement of corporate financial results, the provision unit notifies the customer of this and suggests a review of the investment strategy. Furthermore, the provision unit can collect customer feedback and continuously improve the accuracy and usefulness of the information provided. For example, the provision unit analyzes what actions the customer took in response to the information provided and optimizes the timing and content of the information provided. The provision unit can also reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the provider to provide financial information to clients quickly and reliably, and assist in optimizing investment strategies.

[0078] The countermeasures department proposes insurance, pension, and tax measures based on the customer's asset status and future goals. For example, the countermeasures department collects information on the customer's asset status and future goals, and AI analyzes it. Specifically, the countermeasures department grasps the customer's current asset status, income, expenses, and future goals (for example, purchasing a house, children's education expenses, living expenses after retirement, etc.) in detail. Based on this information, AI proposes optimal insurance products and pension plans. For example, it selects and proposes appropriate life insurance and medical insurance based on the customer's risk tolerance and future income forecast. The countermeasures department also proposes tax measures. For example, it proposes optimal tax saving measures according to the customer's income and asset status and shows how to reduce tax burdens. Furthermore, the countermeasures department can collect customer feedback and continuously improve the accuracy and usefulness of the proposal content. For example, it analyzes the results of the customer's actual use of the proposed insurance products and pension plans and optimizes the proposal content. In addition, the countermeasures department can reliably convey information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, emails, etc. to reliably deliver important information. This enables the department to quickly and reliably provide customers with information on insurance, pensions, and tax planning, helping them achieve their future goals.

[0079] The collection unit can automatically acquire the bank account information and credit card usage information of the customer. For example, the collection unit automatically acquires the bank account information of the customer. The bank account information includes an account number, a transaction history, and the like. The collection unit also automatically acquires the credit card usage information of the customer. The credit card usage information includes a usage history, a usage limit, and the like. By automatically acquiring the bank account information and credit card usage information of the customer, the efficiency of collection is improved. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the bank account information and credit card usage information into AI, which can automatically acquire the information.

[0080] The suggestion unit can generate an investment strategy or a saving method using the generation AI. The suggestion unit can generate an optimal investment strategy using the generation AI, for example. The generation AI can generate an optimal investment strategy based on customer information using a machine learning model or a deep learning algorithm. The suggestion unit can also generate an optimal saving method using the generation AI. For example, the generation AI analyzes the customer's spending pattern and proposes saving methods such as reducing fixed costs and managing variable costs. As a result, the use of the generation AI improves the accuracy of proposing optimal investment strategies and saving methods. A part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs information on the customer's income, expenditure, goals, and risk tolerance into the generation AI, and the generation AI generates an optimal investment strategy or saving method.

[0081] The collection unit can collect social media account information of the customer and evaluate the risk tolerance based on the collected information. The collection unit, for example, collects social media account information of the customer. The social media account information includes the content of posts and the number of followers. The collection unit can evaluate the risk tolerance of the customer based on the collected social media account information. For example, the collection unit analyzes the content of posts of the customer and evaluates the risk tolerance. The collection unit can also evaluate the risk tolerance based on the number of followers of the customer and information on the accounts followed. This allows for more accurate risk evaluation by collecting social media account information and evaluating the risk tolerance. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media account information into AI, and the AI ​​can automatically collect information and evaluate the risk tolerance.

[0082] The proposal unit can reconstruct the customer's investment portfolio. The investment portfolio includes asset allocation and risk assessment. The proposal unit can optimize the investment strategy by reconstructing the customer's investment portfolio. For example, the proposal unit reviews the customer's asset allocation and diversifies risk. The proposal unit can also reconstruct the investment portfolio based on the customer's risk assessment. This makes it possible to optimize the investment strategy by automatically reconstructing the customer's investment portfolio. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information about the customer's investment portfolio into the generation AI, which then reconstructs the optimal portfolio.

[0083] The proposal unit can monitor exchange rates in real time and adjust the affected investment strategy. The proposal unit, for example, monitors exchange rates in real time. The exchange rates include real-time data and exchange risks. The proposal unit can reduce the investment risk by monitoring the exchange rate and adjusting the affected investment strategy. For example, the proposal unit monitors fluctuations in exchange rates in real time and adjusts the investment strategy. The proposal unit can also evaluate exchange risk and propose an investment strategy for reducing the risk. Thereby, the investment risk can be reduced by monitoring exchange rates in real time and adjusting the investment strategy. A part or all of the above-mentioned processing in the proposal unit is performed using the generation AI. For example, the proposal unit inputs real-time data of the exchange rate to the generation AI, and the generation AI adjusts the investment strategy.

[0084] The collection unit can acquire health data from the customer's fitness tracker and reflect the acquired data in the evaluation of the risk tolerance. The collection unit, for example, acquires health data from the customer's fitness tracker. The fitness tracker includes a pedometer, a heart rate monitor, and the like. The collection unit reflects the acquired health data in the evaluation of the risk tolerance. For example, the collection unit evaluates the risk tolerance based on the customer's step count data. The collection unit can also evaluate the risk tolerance based on the customer's heart rate data. This allows the health data to be reflected in the evaluation of the risk tolerance, thereby enabling a more accurate risk evaluation. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the data of the fitness tracker into AI, and the AI ​​can automatically acquire the data and evaluate the risk tolerance.

[0085] The proposal unit can analyze the psychological profile of the customer and propose an investment strategy that takes into account the emotional bias. The proposal unit, for example, analyzes the psychological profile of the customer. The psychological profile includes a personality diagnostic test and a behavioral analysis. The proposal unit proposes an investment strategy that takes into account the emotional bias based on the psychological profile of the customer. For example, the proposal unit evaluates the risk tolerance based on the result of the personality diagnostic test of the customer and proposes an appropriate investment strategy. The proposal unit can also propose an investment strategy that takes into account the emotional bias based on the result of the behavioral analysis of the customer. As a result, by proposing an investment strategy that takes into account the emotional bias, an investment strategy that is suitable for the psychological state of the customer can be provided. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information on the psychological profile of the customer into the generation AI, and the generation AI proposes an investment strategy that takes into account the emotional bias.

[0086] The proposal unit can propose region-specific investment opportunities using the geographical location information of the client. The proposal unit, for example, proposes region-specific investment opportunities using the geographical location information of the client. The geographical location information includes GPS data and address information. The proposal unit proposes region-specific investment opportunities based on the geographical location information of the client. For example, the proposal unit proposes investment opportunities related to urban areas to a client living in an urban area. The proposal unit can also propose investment opportunities related to rural areas to a client living in a rural area. This makes it possible to provide an investment strategy suited to a region by proposing region-specific investment opportunities. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs the geographical location information of the client to the generation AI, which then proposes region-specific investment opportunities.

[0087] The collection unit can estimate the customer's emotion and determine the priority of information to be collected based on the estimated customer's emotion. The collection unit, for example, estimates the customer's emotion. The estimation of emotion includes facial expression recognition and voice analysis. The collection unit determines the priority of information to be collected based on the estimated customer's emotion. For example, when the customer is stressed, the collection unit prioritizes collecting information on income and expenditure, and postpones information on risk tolerance. In addition, when the customer is relaxed, the collection unit can also prioritize collecting information on goals and future plans. This enables more appropriate information collection by determining the priority of information based on the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit inputs facial expression data of the customer to the generation AI, which estimates the emotion and determines the priority of information.

[0088] The collection unit can analyze the past financial behavior of the customer and select the optimal information collection method. The collection unit, for example, analyzes the past financial behavior of the customer. The past financial behavior includes transaction history and investment history. The collection unit selects the optimal information collection method based on the past financial behavior of the customer. For example, the collection unit preferentially collects bank account information that the customer has used frequently in the past. In addition, if the customer has used many credit cards in the past, the collection unit can also preferentially collect the usage information. In this way, the optimal information collection method can be selected by analyzing the past financial behavior of the customer. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the customer's transaction history into AI, and the AI ​​selects the optimal information collection method.

[0089] The collection unit can perform filtering based on the customer's current living situation and areas of interest at the time of collection. For example, the collection unit performs filtering based on the customer's current living situation and areas of interest at the time of collection. The current living situation includes income situation and family structure. The collection unit preferentially collects highly relevant information based on the customer's current living situation. For example, if the customer wishes to review his / her household finances in his / her current living situation, the collection unit preferentially collects information about expenditures. In addition, if the customer is interested in health, the collection unit can also preferentially collect health data from a fitness tracker. This allows more relevant information to be collected by filtering based on the customer's current living situation and areas of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the customer's living situation data into AI, and the AI ​​performs filtering.

[0090] The collection unit can estimate the customer's emotion and adjust the timing of information collection based on the estimated customer's emotion. The collection unit, for example, estimates the customer's emotion. The emotion estimation includes facial expression recognition and voice analysis. The collection unit adjusts the timing of information collection based on the estimated customer's emotion. For example, the collection unit delays the timing of information collection when the customer is stressed. Also, the collection unit can advance the timing of information collection when the customer is relaxed. In this way, by adjusting the timing of information collection based on the customer's emotion, information can be collected at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit inputs the customer's facial expression data to the generation AI, which estimates the emotion and adjusts the timing of information collection.

[0091] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the customer at the time of collection. The geographical location information includes GPS data and address information. The collection unit preferentially collects highly relevant information based on the geographical location information of the customer. For example, if the customer lives in an urban area, the collection unit preferentially collects financial information related to the urban area. In addition, if the customer lives in a rural area, the collection unit can also preferentially collect financial information related to the rural area. This makes it possible to collect information appropriate to the region by collecting information in consideration of the geographical location information of the customer. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs the geographical location information of the customer into AI, and the AI ​​preferentially collects highly relevant information.

[0092] The collection unit can analyze the social media activity of the customer at the time of collection and collect related information. For example, the collection unit analyzes the social media activity of the customer at the time of collection. The social media activity includes the content of posts and the number of followers. The collection unit collects related information based on the social media activity of the customer. For example, the collection unit collects information related to topics that the customer frequently mentions on social media. The collection unit can also collect information on accounts that the customer follows on social media. Furthermore, the collection unit can also collect information on groups that the customer participates in on social media. This makes it possible to collect more relevant information by analyzing the social media activity of the customer. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs the social media activity data of the customer into AI, and the AI ​​collects related information.

[0093] The suggestion unit can estimate the customer's emotion and adjust the way of expressing the proposal based on the estimated emotion of the customer. The suggestion unit, for example, estimates the emotion of the customer. The estimation of the emotion includes facial expression recognition and voice analysis. The suggestion unit adjusts the way of expressing the proposal based on the estimated emotion of the customer. For example, the suggestion unit uses a simple and easy-to-understand expression method when the customer is stressed. In addition, the suggestion unit can also use an expression method including detailed information when the customer is relaxed. Furthermore, the suggestion unit can also use a visually attractive expression method when the customer is excited. This enables a more appropriate suggestion by adjusting the way of expressing the proposal based on the emotion of the customer. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the facial expression data of the customer to the generation AI, which estimates the emotion and adjusts the way of expressing the proposal.

[0094] The proposal unit can adjust the level of detail of the proposal based on the importance of the investment strategy or the saving method when making a proposal. The importance of the investment strategy or the saving method includes risk evaluation and profitability evaluation. The proposal unit makes a proposal including a detailed explanation for an investment strategy with a high level of importance. The proposal unit can also make a concise proposal for a saving method with a low level of importance. Furthermore, the proposal unit can adjust the level of detail of the proposal according to the customer's interest. As a result, by adjusting the level of detail of the proposal based on the importance of the investment strategy or the saving method, more appropriate proposals can be made. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information on the customer's investment strategy or the saving method into the generation AI, and the generation AI adjusts the level of detail of the proposal based on the importance.

[0095] When making a proposal, the proposal unit can make a proposal taking into consideration the risk diversification of the customer's investment portfolio. Risk diversification includes asset allocation and risk assessment. The proposal unit proposes risk diversification when the customer's investment portfolio is risk concentrated. In addition, the proposal unit can also propose maintaining the status quo when the customer's investment portfolio is risk diversified. Furthermore, the proposal unit can also propose an optimal investment strategy depending on the risk diversification status of the customer's investment portfolio. In this way, a more appropriate investment strategy can be proposed by taking into consideration the risk diversification of the customer's investment portfolio. A part or all of the above-mentioned processing in the proposal unit is performed using the generation AI. For example, the proposal unit inputs information about the customer's investment portfolio into the generation AI, which then proposes an investment strategy taking into consideration risk diversification.

[0096] The suggestion unit can estimate the customer's emotion and adjust the length of the proposal based on the estimated customer's emotion. The suggestion unit, for example, estimates the customer's emotion. The estimation of emotion includes facial expression recognition and voice analysis. The suggestion unit adjusts the length of the proposal based on the estimated customer's emotion. For example, the suggestion unit makes a short and to the point proposal when the customer is stressed. Also, the suggestion unit can make a longer proposal including detailed explanations when the customer is relaxed. Furthermore, the suggestion unit can make a visually attractive proposal when the customer is excited. This allows for a more appropriate proposal by adjusting the length of the proposal based on the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the customer's facial expression data into the generation AI, which estimates the emotion and adjusts the length of the proposal.

[0097] When making a proposal, the proposal unit can determine the priority of the proposal based on the investment history of the customer. The investment history includes a trading history and an investment performance. The proposal unit preferentially proposes an optimal investment strategy based on the investment history of the customer. For example, the proposal unit preferentially proposes an investment strategy that has been successful for the customer in the past. The proposal unit can also make a proposal that avoids an investment strategy that has failed for the customer in the past. This enables more appropriate proposals to be made by determining the priority of the proposal based on the investment history of the customer. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information on the customer's investment history into the generation AI, and the generation AI determines the priority of the proposal.

[0098] The proposal unit can adjust the order of proposals based on the relevance of the customer when making a proposal. The relevance includes investment themes and areas of interest of the customer. The proposal unit makes proposals that are of high interest to the customer first. The proposal unit can also postpone proposals that are of low interest to the customer. Furthermore, the proposal unit can optimize the order of proposals based on the relevance of the customer. This enables more appropriate proposals to be made by adjusting the order of proposals based on the relevance of the customer. A part or all of the above-mentioned processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs information on the customer's areas of interest to the generation AI, and the generation AI adjusts the order of proposals.

[0099] The analysis unit can estimate the customer's emotion and adjust the analysis criteria based on the estimated customer's emotion. The analysis unit, for example, estimates the customer's emotion. The emotion estimation includes facial expression recognition and voice analysis. The analysis unit adjusts the analysis criteria based on the estimated customer's emotion. For example, the analysis unit uses a criterion for estimating the risk low when the customer is stressed. The analysis unit can also use a criterion for estimating the risk high when the customer is relaxed. Furthermore, the analysis unit can also use a criterion for estimating the risk neutrally when the customer is excited. This enables more appropriate analysis by adjusting the analysis criteria based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the customer's facial expression data into the generation AI, which estimates the emotion and adjusts the analysis criteria.

[0100] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the markets during the analysis. For example, the analysis unit performs the analysis by taking into account the interrelationships of the markets during the analysis. The interrelationships of the markets include the relationship between stock prices and interest rates, and the relationship between exchange rates and trade balances. The analysis unit performs the analysis by taking into account the interrelationships of the stock market and the bond market. The analysis unit can also perform the analysis by taking into account the interrelationships of the foreign exchange market and the commodity market. Furthermore, the analysis unit can also perform the analysis by taking into account the interrelationships of the real estate market and the stock market. As a result, the accuracy of the analysis is improved by taking into account the interrelationships of the markets. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs market interrelationship data into an AI, and the AI ​​performs the analysis by taking into account the interrelationships.

[0101] During the analysis, the analysis unit can predict the current trend by referring to past market data. The past market data includes past stock price data and the history of economic indicators. The analysis unit predicts the current stock price trend by referring to past stock price data. The analysis unit can also predict the current exchange rate trend by referring to past exchange rate data. Furthermore, the analysis unit can also predict the current real estate trend by referring to past real estate price data. As a result, the accuracy of predicting the current trend is improved by referring to the past market data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the past market data into AI, and the AI ​​predicts the current trend.

[0102] The analysis unit can estimate the customer's emotion and adjust the display method of the analysis result based on the estimated customer's emotion. The analysis unit, for example, estimates the customer's emotion. The emotion estimation includes facial expression recognition and voice analysis. The analysis unit adjusts the display method of the analysis result based on the estimated customer's emotion. For example, the analysis unit uses a simple and highly visible display method when the customer is stressed. In addition, the analysis unit can also use a display method including detailed information when the customer is relaxed. Furthermore, the analysis unit can also use a visually attractive display method when the customer is excited. This enables more appropriate display by adjusting the display method of the analysis result based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the customer's facial expression data into the generation AI, which estimates the emotion and adjusts the display method of the analysis result.

[0103] The analysis unit can perform the analysis taking into account the geographical distribution of the market. The geographical distribution includes regional economic data and geographical market trends. The analysis unit performs the analysis taking into account stock price data for each region. The analysis unit can also perform the analysis taking into account exchange rate data for each region. Furthermore, the analysis unit can also perform the analysis taking into account real estate price data for each region. This allows for more accurate analysis by taking into account the geographical distribution of the market. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs geographical distribution data into AI, which performs the analysis taking into account the geographical distribution.

[0104] The analysis unit can improve the accuracy of the analysis by referring to market-related literature during the analysis. The related literature includes academic papers and industry reports. The analysis unit performs the analysis by referring to the latest research papers on the stock market. The analysis unit can also perform the analysis by referring to the latest research papers on the foreign exchange market. Furthermore, the analysis unit can also perform the analysis by referring to the latest research papers on the real estate market. As a result, the accuracy of the analysis is improved by referring to market-related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs related literature data into AI, and the AI ​​performs the analysis by referring to the related literature.

[0105] The providing unit can estimate the customer's emotion and adjust the display method of the financial information based on the estimated customer's emotion. The providing unit, for example, estimates the customer's emotion. The estimation of emotion includes facial expression recognition and voice analysis. The providing unit adjusts the display method of the financial information based on the estimated customer's emotion. For example, when the customer is stressed, the providing unit uses a display method that is simple and highly visible. In addition, when the customer is relaxed, the providing unit can also use a display method that includes detailed information. Furthermore, when the customer is excited, the providing unit can also use a visually attractive display method. This enables more appropriate display by adjusting the display method of the financial information based on the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit inputs the customer's facial expression data into the generation AI, which then estimates the emotion and adjusts the display method of the financial information.

[0106] The providing unit can provide optimal information by referring to the past financial behavior of the customer at the time of providing. Past financial behavior includes transaction history and investment history. The providing unit provides optimal information based on the past financial behavior of the customer. For example, the providing unit preferentially provides financial information that the customer has frequently used in the past. In addition, if the customer has used many credit cards in the past, the providing unit can also preferentially provide the usage information. Furthermore, if the customer has been active in investing in the past, the providing unit can also preferentially provide investment-related information. In this way, more appropriate information can be provided by referring to the past financial behavior of the customer. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the past financial behavior data of the customer into AI, and the AI ​​provides optimal information.

[0107] The providing unit can customize the information based on the customer's current living situation at the time of providing. The current living situation includes income situation, family structure, and the like. The providing unit preferentially provides highly relevant information based on the customer's current living situation. For example, if the customer wishes to review his / her household finances in his / her current living situation, the providing unit preferentially provides information related to expenditures. In addition, if the customer is interested in health, the providing unit can also preferentially provide health data from a fitness tracker. Furthermore, if the customer is interested in education, the providing unit can also preferentially provide education-related expenditure information. This allows more appropriate information to be provided by customizing information based on the customer's current living situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the customer's living situation data into AI, and the AI ​​customizes the information.

[0108] The providing unit can estimate the emotion of the customer and determine the priority of information based on the estimated emotion of the customer. The providing unit, for example, estimates the emotion of the customer. The estimation of emotion includes facial expression recognition and voice analysis. The providing unit determines the priority of information based on the estimated emotion of the customer. For example, when the customer is stressed, the providing unit provides information on income and expenditures with priority, and postpones information on risk tolerance. In addition, when the customer is relaxed, the providing unit can also provide information on goals and future plans with priority. Furthermore, when the customer is excited, the providing unit can also provide information on investments with priority, and postpone information on expenditures. This enables more appropriate information to be provided by determining the priority of information based on the emotion of the customer. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the provision department inputs the customer's facial expression data into the generation AI, which then infers the emotions and determines the priority of the information.

[0109] The providing unit can provide optimal information by considering the geographical location information of the customer at the time of providing. The geographical location information includes GPS data and address information. The providing unit preferentially provides highly relevant information based on the geographical location information of the customer. For example, if the customer lives in an urban area, the providing unit preferentially provides financial information related to the urban area. In addition, if the customer lives in a rural area, the providing unit can also preferentially provide financial information related to the rural area. Furthermore, if the customer lives overseas, the providing unit can also preferentially provide overseas financial information. This makes it possible to provide more appropriate information by considering the geographical location information of the customer. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the geographical location information of the customer into AI, and the AI ​​provides optimal information.

[0110] The providing unit can adjust the way information is provided by analyzing the social media activity of the customer at the time of providing. For example, the providing unit analyzes the social media activity of the customer at the time of providing. The social media activity includes the content of posts and the number of followers. The providing unit provides related information based on the social media activity of the customer. For example, the providing unit provides information related to topics that the customer frequently mentions on social media. The providing unit can also provide information on accounts that the customer follows on social media. Furthermore, the providing unit can also provide information on groups that the customer participates in on social media. This makes it possible to provide more appropriate information by analyzing the social media activity of the customer. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the social media activity data of the customer into AI, and the AI ​​adjusts the way information is provided.

[0111] The countermeasure unit can estimate the customer's emotion and adjust the countermeasure method based on the estimated customer's emotion. The countermeasure unit, for example, estimates the customer's emotion. The estimation of emotion includes facial expression recognition and voice analysis. The countermeasure unit adjusts the countermeasure method based on the estimated customer's emotion. For example, the countermeasure unit proposes a low-risk countermeasure method when the customer is stressed. In addition, the countermeasure unit can also propose a high-risk countermeasure method when the customer is relaxed. Furthermore, the countermeasure unit can also propose a risk-neutral countermeasure method when the customer is excited. This enables more appropriate countermeasures by adjusting the countermeasure method based on the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the countermeasure unit is performed using the generation AI. For example, the countermeasure unit inputs the customer's facial expression data into the generation AI, which estimates the emotion and adjusts the countermeasure method.

[0112] The countermeasure department can select the optimal countermeasure method by analyzing the customer's past financial behavior at the time of countermeasure. For example, the countermeasure department analyzes the customer's past financial behavior at the time of countermeasure. The past financial behavior includes a transaction history and an investment history. The countermeasure department selects the optimal countermeasure method based on the customer's past financial behavior. For example, the countermeasure department preferentially proposes countermeasure methods that have been successful for the customer in the past. The countermeasure department can also make proposals to avoid countermeasure methods that have failed for the customer in the past. Furthermore, the countermeasure department can also select the optimal countermeasure method based on the customer's past financial behavior. In this way, a more appropriate countermeasure method can be selected by analyzing the customer's past financial behavior. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the customer's past financial behavior data into AI, and the AI ​​selects the optimal countermeasure method.

[0113] The countermeasure department can customize the countermeasure measures based on the customer's current living situation at the time of countermeasure. The current living situation includes income situation, family structure, etc. The countermeasure department preferentially proposes highly relevant countermeasure measures based on the customer's current living situation. For example, if the customer wishes to review his / her household finances in his / her current living situation, the countermeasure department proposes measures to reduce expenses. In addition, if the customer is interested in health, the countermeasure department can also propose a review of health insurance. Furthermore, if the customer is interested in education, the countermeasure department can also propose measures for education funds. This enables more appropriate countermeasures by customizing the countermeasure measures based on the customer's current living situation. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the customer's living situation data into AI, and the AI ​​customizes the countermeasure measures.

[0114] The countermeasure unit can estimate the customer's emotion and determine the priority of countermeasures based on the estimated customer's emotion. The countermeasure unit, for example, estimates the customer's emotion. The estimation of emotion includes facial expression recognition and voice analysis. The countermeasure unit determines the priority of countermeasures based on the estimated customer's emotion. For example, the countermeasure unit preferentially proposes low-risk countermeasures when the customer is stressed. In addition, the countermeasure unit can also preferentially propose high-risk countermeasures when the customer is relaxed. Furthermore, the countermeasure unit can also preferentially propose risk-neutral countermeasures when the customer is excited. This enables more appropriate countermeasures by determining the priority of countermeasures based on the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the countermeasure unit is performed using the generation AI. For example, the countermeasure unit inputs facial expression data of the customer into the generation AI, which then estimates the emotion and determines the priority of the countermeasures.

[0115] When taking countermeasures, the countermeasure department can select the optimal countermeasure method by taking into consideration the geographical location information of the customer. The geographical location information includes GPS data and address information. The countermeasure department preferentially proposes highly relevant countermeasure methods based on the geographical location information of the customer. For example, if the customer lives in an urban area, the countermeasure department proposes countermeasure methods related to urban areas. In addition, if the customer lives in a rural area, the countermeasure department can also propose countermeasure methods related to rural areas. Furthermore, if the customer lives overseas, the countermeasure department can also propose overseas countermeasure methods. In this way, a more appropriate countermeasure method can be selected by taking into consideration the geographical location information of the customer. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the geographical location information of the customer into AI, which selects the optimal countermeasure method.

[0116] The countermeasure department can analyze the social media activity of the customer and propose countermeasures at the time of countermeasures. For example, the countermeasure department analyzes the social media activity of the customer at the time of countermeasures. The social media activity includes the content of posts and the number of followers. The countermeasure department proposes related countermeasures based on the social media activity of the customer. For example, the countermeasure department proposes countermeasures related to topics that the customer frequently mentions on social media. The countermeasure department can also propose countermeasures based on information on accounts that the customer follows on social media. Furthermore, the countermeasure department can also propose countermeasures based on information on groups that the customer participates in on social media. In this way, by analyzing the social media activity of the customer, more appropriate countermeasures can be proposed. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the social media activity data of the customer into AI, and the AI ​​proposes countermeasures.

[0117] When taking countermeasures, the countermeasure department can select the optimal countermeasure method by taking into consideration the health data of the customer. The health data includes fitness tracker data and medical records. The countermeasure department preferentially proposes highly relevant countermeasure methods based on the health data of the customer. For example, if the customer is interested in health, the countermeasure department can propose a review of health insurance. In addition, if the customer is not getting enough exercise, the countermeasure department can also propose a fitness plan. Furthermore, if the customer is living a healthy lifestyle, the countermeasure department can also propose measures to maintain health. In this way, by taking into consideration the health data of the customer, a more appropriate countermeasure method can be selected. Part or all of the above-mentioned processing in the countermeasure department may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure department inputs the customer's health data into AI, which selects the optimal countermeasure method.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The collection unit can also analyze the purchase history of the customer and determine the priority of the information to be collected. For example, the collection unit can prioritize collecting income and expenditure information based on data on products purchased in the past by the customer. The collection unit can also analyze product categories frequently purchased by the customer and prioritize collecting related expenditure information. Furthermore, the collection unit can identify expenditure patterns related to specific seasons or events from the customer's purchase history and prioritize collecting that information. This makes it possible to collect more relevant information by taking into account the customer's purchase history.

[0120] The proposal department can also customize investment strategies based on the hobbies and interests of the customer. For example, if the customer is interested in sports, the proposal department can suggest sports-related investment opportunities. In addition, if the customer is interested in environmental protection, the proposal department can suggest environmentally friendly investment strategies. In addition, if the customer is interested in technology, the proposal department can suggest technology-related investment opportunities. This allows for more personalized proposals by customizing investment strategies based on the hobbies and interests of the customer.

[0121] The analysis department can also predict future investment risks based on the client's past investment performance. For example, the analysis department can analyze the client's past investment performance data and identify high-risk investment patterns. The analysis department can also suggest low-risk investment strategies based on the client's past investment performance. Furthermore, the analysis department can assess risks to future market fluctuations based on the client's past investment performance. This allows for more accurate risk predictions by taking into account the client's past investment performance.

[0122] The provision department can also collect customer feedback and improve the quality of the information to be provided. For example, the provision department collects feedback on how the customer felt about the provided information. The provision department can also improve the content and format of the information to be provided based on the customer feedback. Furthermore, the provision department can analyze the customer feedback and adjust the timing and frequency of providing information. In this way, by utilizing the customer feedback, it becomes possible to provide higher quality information.

[0123] The countermeasures department can also propose countermeasures based on the life events of the customer. For example, if the customer is planning to get married, the countermeasures department can propose a financial plan related to the marriage. In addition, if the customer is considering his / her children's education, the countermeasures department can also propose countermeasures for education funds. Furthermore, if the customer is planning to retire, the countermeasures department can also propose a financial plan for post-retirement life. In this way, more appropriate support can be provided by proposing countermeasures based on the life events of the customer.

[0124] The proposal unit can also estimate the customer's emotions and adjust the method of explaining investment risks based on the estimated customer's emotions. For example, if the customer feels anxious, the proposal unit preferentially proposes a low-risk investment strategy. In addition, if the customer feels excited, the proposal unit can also propose a high-risk investment strategy. Furthermore, if the customer feels relaxed, the proposal unit can also propose an investment strategy that takes into account the balance between risk and return. This makes it possible to make more appropriate proposals by adjusting the method of explaining investment risks based on the customer's emotions.

[0125] The collection unit can also estimate the customer's emotions and adjust the accuracy of the information to be collected based on the estimated customer's emotions. For example, when the customer is feeling stressed, the collection unit can provide more reliable data by increasing the accuracy of the information to be collected. Also, when the customer is relaxed, the collection unit can adjust the accuracy of the information to be collected and collect only necessary information. Furthermore, when the customer is excited, the collection unit can provide balanced data by keeping the accuracy of the information to be collected neutral. This makes it possible to collect more appropriate information by adjusting the accuracy of the information based on the customer's emotions.

[0126] The analysis unit can also estimate the customer's emotions and adjust the method of interpretation of the analysis results based on the estimated customer's emotions. For example, if the customer feels anxious, the analysis unit uses an interpretation method that estimates the risk low. Also, if the customer feels relaxed, the analysis unit can use an interpretation method that estimates the risk high. Furthermore, if the customer is excited, the analysis unit can use an interpretation method that estimates the risk neutrally. In this way, by adjusting the method of interpretation of the analysis results based on the customer's emotions, more appropriate analysis is possible.

[0127] The providing unit can also estimate the customer's emotions and adjust the timing of providing information based on the estimated customer's emotions. For example, if the customer is feeling stressed, the providing unit can delay the timing of providing information to reduce the burden on the customer. Also, if the customer is relaxed, the providing unit can speed up the timing of providing information to provide a quick response. Furthermore, if the customer is excited, the providing unit can adjust the timing of providing information and provide information at an appropriate timing. This makes it possible to provide more appropriate information by adjusting the timing of providing information based on the customer's emotions.

[0128] The countermeasures department can also estimate the emotions of the customer and adjust the method of explaining the countermeasures based on the estimated emotions of the customer. For example, if the customer feels anxious, the countermeasures department uses a simple and easy-to-understand explanation method. In addition, if the customer feels relaxed, the countermeasures department can use an explanation method including detailed information. Furthermore, if the customer feels excited, the countermeasures department can use a visually appealing explanation method. In this way, by adjusting the explanation method of the countermeasures based on the emotions of the customer, more appropriate countermeasures can be taken.

[0129] The process flow of the second embodiment will be briefly described below.

[0130] Step 1: The collection unit collects information on the customer's income, expenses, goals, and risk tolerance. For example, the collection unit can automatically obtain the customer's bank account information and credit card usage information. The collection unit can also collect the customer's social media account information and evaluate the risk tolerance based on the collected information. In addition, the collection unit can also obtain health data from the customer's fitness tracker and reflect the obtained data in the risk tolerance evaluation. Step 2: The suggestion unit uses the generative AI to suggest optimal investment strategies or savings methods based on the information collected by the collection unit. The suggestion unit can, for example, automatically reconfigure the customer's investment portfolio. The suggestion unit can also monitor exchange rates in real time and adjust affected investment strategies. Furthermore, the suggestion unit can analyze the customer's psychological profile and suggest investment strategies that take emotional biases into account. Step 3: The analysis unit analyzes market trends and fluctuations. For example, the analysis unit collects data to analyze market trends and fluctuations, and the AI ​​analyzes the data. Step 4: The providing unit provides the client with real-time financial information based on the analysis results by the analysis unit. The providing unit can provide real-time financial information regarding, for example, market trends and fluctuations. Step 5: The countermeasures department proposes insurance, pension, and tax measures based on the customer's asset status and future goals. For example, the countermeasures department collects information on the customer's asset status and future goals, and the AI ​​analyzes it.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0133] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] For example, the collection unit can collect information on the customer's income, expenditure, goals, and risk tolerance using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized, for example, by the specific processing unit 290 of the data processing device 12, and can automatically acquire the customer's bank account information and credit card usage information. The proposal unit can be realized, for example, by the control unit 46A of the smart device 14, and can propose optimal investment strategies and savings methods using generation AI. The proposal unit can also be realized, for example, by the specific processing unit 290 of the data processing device 12, and can automatically reconstruct the customer's investment portfolio. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and can analyze market trends and fluctuations. The provision unit can be realized, for example, by the control unit 46A of the smart device 14, and can provide real-time financial information to the customer. The countermeasure unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and can propose insurance, pension, and tax countermeasures based on the customer's asset situation and future goals. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] For example, the collection unit can collect information on the customer's income, expenditure, goals, and risk tolerance using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized, for example, by the specific processing unit 290 of the data processing device 12, and can automatically acquire the customer's bank account information and credit card usage information. The proposal unit can also be realized, for example, by the control unit 46A of the smart glasses 214, and can propose optimal investment strategies and savings methods using generation AI. The proposal unit can also be realized, for example, by the specific processing unit 290 of the data processing device 12, and can automatically reconstruct the customer's investment portfolio. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and can analyze market trends and fluctuations. The provision unit can be realized, for example, by the control unit 46A of the smart glasses 214, and can provide real-time financial information to the customer. The countermeasure unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and can propose insurance, pension, and tax countermeasures based on the customer's asset situation and future goals. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0153] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0166] For example, the collection unit can collect information on the customer's income, expenditure, goals, and risk tolerance using the camera 42 and microphone 238 of the headset terminal 314. The collection unit can also be realized by, for example, the specific processing unit 290 of the data processing device 12, and can automatically acquire the customer's bank account information and credit card usage information. The proposal unit can also be realized by, for example, the control unit 46A of the headset terminal 314, and can propose optimal investment strategies and savings methods using generation AI. The proposal unit can also be realized by, for example, the specific processing unit 290 of the data processing device 12, and can automatically reconstruct the customer's investment portfolio. The analysis unit can be realized by, for example, the specific processing unit 290 of the data processing device 12, and can analyze market trends and fluctuations. The provision unit can be realized by, for example, the control unit 46A of the headset terminal 314, and can provide real-time financial information to the customer. The countermeasure unit can be realized by, for example, the specific processing unit 290 of the data processing device 12, and can propose insurance, pension, and tax countermeasures based on the customer's asset situation and future goals. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a 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.

[0169] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0178] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0180] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0182] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] For example, the collection unit can collect information on the customer's income, expenditure, goals, and risk tolerance using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized, for example, by the specific processing unit 290 of the data processing device 12, and can automatically acquire the customer's bank account information and credit card usage information. The proposal unit can be realized, for example, by the control unit 46A of the robot 414, and can propose optimal investment strategies and savings methods using generation AI. The proposal unit can also be realized, for example, by the specific processing unit 290 of the data processing device 12, and can automatically reconstruct the customer's investment portfolio. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and can analyze market trends and fluctuations. The provision unit can be realized, for example, by the control unit 46A of the robot 414, and can provide real-time financial information to the customer. The countermeasure unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and can propose insurance, pension, and tax countermeasures based on the customer's asset situation and future goals. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0185] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0189] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0192] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0193] In addition, 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 upon request from the data processing device 12.

[0194] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0195] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0196] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0197] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0198] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0200] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0202] (Appendix 1) a collection department for collecting information on clients' income, expenses, goals, and risk tolerance; A suggestion unit that suggests an investment strategy or a saving method to the customer based on the information collected by the collection unit; an analysis department for analyzing market trends or fluctuations; a providing unit that provides real-time financial information to the customer based on the analysis result by the analysis unit; A countermeasure section that proposes insurance, pension, and tax countermeasures based on the customer's asset status or future goals. A system characterized by: (Appendix 2) The collecting unit includes: Automatically capture customer bank account and credit card information 2. The system of claim 1, (Appendix 3) The suggestion unit, Generate investment strategies or savings methods using generative AI 2. The system of claim 1, (Appendix 4) The collecting unit includes: Collecting customer social media account information and assessing risk tolerance based on the collected information 2. The system of claim 1, (Appendix 5) The suggestion unit, Reconfiguring clients' investment portfolios 2. The system of claim 1, (Appendix 6) The suggestion unit, Monitor exchange rates and adjust your investment strategies accordingly 2. The system of claim 1, (Appendix 7) The collecting unit includes: Collect health data from customers' fitness trackers and use that data to inform risk tolerance assessments 2. The system of claim 1, (Appendix 8) The suggestion unit, Analyze customer psychological profiles and suggest investment strategies that take biases into account 2. The system of claim 1, (Appendix 9) The suggestion unit, Use clients' geographic location to suggest local investment opportunities 2. The system of claim 1, (Appendix 10) The collecting unit includes: Use a specific method to estimate customer sentiment and prioritize the information to be collected based on the estimated customer sentiment. 2. The system of claim 1, (Appendix 11) The collecting unit includes: Analyze customers' past financial behavior and select the most appropriate method of collecting information 2. The system of claim 1, (Appendix 12) The collecting unit includes: At the time of collection, filtering is done based on the customer's current life situation and interests 2. The system of claim 1, (Appendix 13) The collecting unit includes: Use a specific method for estimating customer sentiment and adjust the timing of information collection based on the estimated customer sentiment. 2. The system of claim 1, (Appendix 14) The collecting unit includes: At the time of collection, take into account the customer's geographic location to prioritize collection of relevant information 2. The system of claim 1, (Appendix 15) The collecting unit includes: At the time of collection, we analyze our customers' social media activity and collect relevant information. 2. The system of claim 1, (Appendix 16) The suggestion unit, Use a specific method to estimate customer sentiment and tailor the wording of offers based on the estimated customer sentiment 2. The system of claim 1, (Appendix 17) The suggestion unit, When making a proposal, adjust the level of detail based on the importance of the investment strategy or savings method 2. The system of claim 1, (Appendix 18) The suggestion unit, When making proposals, consider the risk diversification of the client's investment portfolio. 2. The system of claim 1, (Appendix 19) The suggestion unit, Use a specific method to estimate customer sentiment and adjust the length of the offer based on the estimated customer sentiment 2. The system of claim 1, (Appendix 20) The suggestion unit, When making proposals, prioritize proposals based on the customer's investment history 2. The system of claim 1, (Appendix 21) The suggestion unit, During suggestions, adjust the order of suggestions based on customer relevance 2. The system of claim 1, (Appendix 22) The analysis unit includes: Use specific methods to estimate customer sentiment and then adjust your analysis criteria based on the estimated customer sentiment 2. The system of claim 1, (Appendix 23) The analysis unit includes: During analysis, market correlations are taken into account to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 24) The analysis unit includes: During analysis, look at historical market data to predict current trends 2. The system of claim 1, (Appendix 25) The analysis unit includes: Use a specific method for estimating customer sentiment and tailor the presentation of analytics results based on the estimated customer sentiment 2. The system of claim 1, (Appendix 26) The analysis unit includes: During the analysis, the geographical distribution of the market is taken into consideration. 2. The system of claim 1, (Appendix 27) The analysis unit includes: During the analysis, refer to relevant market literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 28) The providing unit is Using a specific method for estimating customer sentiment, and adjusting the presentation of financial information based on the estimated customer sentiment 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, the most appropriate information is provided by referring to the customer's past financial behavior. 2. The system of claim 1, (Appendix 30) The providing unit is At the time of delivery, personalize information based on the customer's current life situation 2. The system of claim 1, (Appendix 31) The providing unit is Use a specific method to estimate customer sentiment and prioritize information based on the estimated customer sentiment 2. The system of claim 1, (Appendix 32) The providing unit is When providing information, provide optimal information by taking into account the customer's geographic location 2. The system of claim 1, (Appendix 33) The providing unit is When providing information, analyze your social media activity to tailor how information is provided to you 2. The system of claim 1, (Appendix 34) The countermeasure unit includes: Use a specific method to estimate customer sentiment and adjust countermeasures based on the estimated customer sentiment 2. The system of claim 1, (Appendix 35) The countermeasure unit includes: When taking measures, the most appropriate countermeasures are selected by analyzing the customer's past financial behavior. 2. The system of claim 1, (Appendix 36) The countermeasure unit includes: When responding, customize the response measures based on the customer's current living situation 2. The system of claim 1, (Appendix 37) The countermeasure unit includes: Use a specific method to estimate customer sentiment and prioritize countermeasures based on the estimated customer sentiment 2. The system of claim 1, (Appendix 38) The countermeasure unit includes: When implementing countermeasures, the most appropriate countermeasure method is selected taking into account the geographic location information of the customer. 2. The system of claim 1, (Appendix 39) The countermeasure unit includes: When taking countermeasures, analyze the customer's social media activity and suggest countermeasures 2. The system of claim 1, (Appendix 40) The countermeasure unit includes: Use a specific method to estimate customer sentiment and adjust countermeasures based on the estimated customer sentiment 2. The system of claim 1, [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department for collecting information on clients' income, expenses, goals, and risk tolerance; A suggestion unit that suggests an investment strategy or a saving method to the customer based on the information collected by the collection unit; an analysis department for analyzing market trends or fluctuations; a providing unit that provides real-time financial information to the customer based on the analysis result by the analysis unit; A countermeasure section that proposes insurance, pension, and tax countermeasures based on the customer's asset status or future goals. A system characterized by:

2. The collecting unit includes: Automatically obtain the customer's bank account information and credit card usage information 2. The system of claim 1 .

3. The suggestion unit, Generate investment strategies or savings methods using generative AI 2. The system of claim 1 .

4. The collecting unit includes: Collecting social media account information of said customers and assessing their risk tolerance based on the collected information.

2. The system of claim 1 .

5. The suggestion unit, Reconfiguring the client's investment portfolio 2. The system of claim 1 .

6. The suggestion unit, Monitor exchange rates and adjust your investment strategies accordingly 2. The system of claim 1 .

7. The collecting unit includes: Obtaining health data from the customer's fitness tracker and incorporating the obtained data into an assessment of risk tolerance.

2. The system of claim 1 .

8. The suggestion unit, Analyze the psychological profile of said customers and propose investment strategies that take biases into account 2. The system of claim 1 .

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