System

Abstract

An object of a system according to an embodiment is to detect a change in lifestyle on the basis of financial data of a customer and propose an optimal investment plan or risk management method according to the change.SOLUTION: A system includes a financial data collection part, a lifestyle change detection part, an investment plan generation part, a risk management proposal part, and an asset transition prediction part. The financial data collection unit collects financial data of a customer. The lifestyle change detection unit analyzes the financial data collected by the financial data collection unit and detects a change in the lifestyle of the customer. The investment plan generation unit generates an optimal investment plan on the basis of the change in lifestyle detected by the lifestyle change detection unit. The risk management proposal unit proposes a risk management method based on the investment plan generated by the investment plan generation unit. The asset transition forecast part forecasts a future asset transition based on the investment plan generated by the investment plan generation part.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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem of not being able to adequately detect changes in a customer's lifestyle based on their financial data and propose optimal investment plans and risk management methods accordingly.

[0005] The system according to the embodiment aims to detect changes in a customer's lifestyle based on their financial data and propose optimal investment plans and risk management methods accordingly. [Means for solving the problem]

[0006] The system according to the embodiment includes a financial data collection unit, a lifestyle change detection unit, an investment plan generation unit, a risk management proposal unit, and an asset trend prediction unit. The financial data collection unit collects financial data of clients. The lifestyle change detection unit analyzes the financial data collected by the financial data collection unit and detects changes in the client's lifestyle. The investment plan generation unit generates an optimal investment plan based on the lifestyle changes detected by the lifestyle change detection unit. The risk management proposal unit proposes a risk management method based on the investment plan generated by the investment plan generation unit. The asset trend prediction unit predicts future asset trends based on the investment plan generated by the investment plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect changes in a customer's lifestyle based on their financial data and propose optimal investment plans and risk management methods accordingly. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The asset management support system according to an embodiment of the present invention analyzes a customer's financial data, proposes ways of using money, investments, asset management, and risk management that are adapted to changes in lifestyle, and predicts future asset trends. As a result, the asset management support system can accurately grasp the customer's financial situation and provide optimal asset management that is adapted to changes in lifestyle.

[0029] An asset management support system according to an embodiment includes a financial data collection unit, a lifestyle change detection unit, an investment plan generation unit, a risk management proposal unit, and an asset trend prediction unit. The financial data collection unit collects financial data about a customer. For example, it analyzes the customer's bank account transaction history and credit card statements. The financial data collection unit can also collect data about the customer's income, expenses, assets, liabilities, and the like. The lifestyle change detection unit analyzes the financial data collected by the financial data collection unit to detect changes in the customer's lifestyle. For example, when a customer approaches a life event such as marriage or childbirth, it proposes an asset management plan appropriate for that event. The investment plan generation unit generates an optimal investment plan based on the lifestyle changes detected by the lifestyle change detection unit. For example, it proposes investment destinations such as stocks, bonds, and real estate based on the customer's risk tolerance and investment goals. The risk management proposal unit proposes risk management techniques based on the investment plan generated by the investment plan generation unit. For example, it proposes portfolio diversification and hedging techniques to minimize risk. The asset trend prediction unit predicts future asset trends based on the investment plan generated by the investment plan generation unit. For example, the asset management support system according to the embodiment can simulate the increase or decrease in assets if the customer implements the proposed investment plan and predict the future asset situation. This allows the asset management support system according to the embodiment to accurately grasp the customer's financial situation and perform optimal asset management that adapts to the customer's changing lifestyle.

[0030] The financial data collection unit can analyze the transaction history of a customer's bank account and credit card usage details. For example, the financial data collection unit analyzes the transaction history of a customer's bank account to understand the deposit and withdrawal history and type of transaction. The financial data collection unit also analyzes the credit card usage details to understand the amount used, the store used, and the date and time of use. This allows for a more detailed understanding of the customer's financial situation.

[0031] The lifestyle change detection unit can propose an asset management plan suited to a customer's life events, such as marriage or childbirth. The lifestyle change detection unit, for example, collects information about a customer's life events, such as marriage or childbirth, and proposes an optimal asset management plan based on that information. For example, it proposes an asset management plan that takes into account changes in expenses and income due to marriage. It also proposes an asset management plan that takes into account increases in education expenses and living expenses due to childbirth. This makes it possible to provide an optimal asset management plan tailored to the customer's life events.

[0032] The investment plan generation unit can suggest investments such as stocks, bonds, and real estate according to the customer's risk tolerance and investment goals. The investment plan generation unit, for example, evaluates the customer's risk tolerance and suggests optimal investments based on that evaluation. For example, for a customer with a high risk tolerance, it suggests high-risk, high-return investments such as stocks and real estate. On the other hand, for a customer with a low risk tolerance, it suggests low-risk, low-return investments such as bonds and fixed-term deposits. The investment plan generation unit also takes into account the customer's investment goals and suggests investments according to those goals. For example, it suggests stocks and short-term bonds for a customer aiming for short-term profits, and real estate and long-term bonds for a customer aiming for long-term asset formation. This makes it possible to provide optimal investments according to the customer's risk tolerance and investment goals.

[0033] The risk management proposal department can propose portfolio diversification investments and hedging techniques. For example, the risk management proposal department analyzes a client's investment portfolio and identifies risk factors. For example, it makes proposals to diversify investments that are biased toward a particular industry or region. The risk management proposal department also proposes hedging techniques and provides methods for minimizing risk. For example, it suggests the use of derivatives or insurance products. This makes it possible to provide methods for minimizing client risk.

[0034] The asset trend prediction unit can simulate the increase or decrease in assets if the client implements the proposed investment plan. The asset trend prediction unit simulates future asset trends, for example, based on the client's investment plan. For example, it predicts the increase or decrease in assets if the client implements the proposed investment plan. The asset trend prediction unit also simulates asset trends under different scenarios and selects the optimal investment plan. For example, it predicts asset trends based on scenarios of economic growth and recession. This makes it possible to predict the future asset situation if the client implements the proposed investment plan.

[0035] The financial data collection unit can analyze behavioral patterns based on a customer's financial data and predict future spending trends. For example, the financial data collection unit uses generative AI to analyze a customer's past spending data and extract specific spending patterns. For example, it classifies monthly fixed and variable expenses and predicts future spending trends. The financial data collection unit also analyzes the balance between a customer's income and expenses and predicts future income and expenditures. For example, it takes into account increases and decreases in income and fluctuations in expenses to simulate future financial situations. The financial data collection unit also analyzes seasonal spending trends based on the customer's spending data. For example, it identifies trends in specific expenses increasing in summer and winter and creates future spending plans. This makes it possible to predict a customer's future spending trends and create appropriate financial plans.

[0036] The financial data collection unit can integrate a customer's financial data and health data to analyze the impact of their health condition on their financial situation. For example, the financial data collection unit integrates the customer's health data (e.g., medical expenses and health check results) with the financial data to analyze the impact of their health condition on their financial situation. For example, it predicts an increase in medical expenses if their health condition worsens. The financial data collection unit also predicts future medical expenses and insurance premiums based on the health data. For example, it simulates future medical expenses taking into account specific medical history and health risks. The financial data collection unit also analyzes the impact of changes in their health condition on their income. For example, it predicts the impact of a decline in their ability to work due to illness or injury on their income and reflects this in their financial plan. This allows the impact of a customer's health condition on their financial situation to be understood and appropriate financial plans to be developed.

[0037] The financial data collection unit can provide financial advice specific to the region by comparing the customer's financial data with the economic conditions of each region. The financial data collection unit, for example, compares the customer's financial data with economic indicators of each region and provides financial advice specific to the region. For example, it proposes a spending plan that takes into account local prices and average income. The financial data collection unit also analyzes the economic conditions of each region and proposes optimal investments based on the customer's financial situation. For example, it provides investment advice based on trends in the local real estate market and stock market. The financial data collection unit also compares the customer's financial data with the economic conditions of the region and proposes risk management methods specific to the region. For example, it provides risk management measures that respond to local disaster risks and economic fluctuations. This makes it possible to provide financial advice that takes into account the economic conditions specific to the region.

[0038] The financial data collection unit can use the customer's financial data to perform comparative analysis with other customers of the same age or occupation and provide a benchmark. The financial data collection unit, for example, compares the customer's financial data with other customers of the same age or occupation and provides a benchmark. For example, the financial situation of the customer is evaluated by comparing with the average income and expenses of customers with the same occupation. The financial data collection unit also benchmarks the customer's financial situation based on the financial data of customers of the same age. For example, the financial plan of the customer is reviewed by comparing with the average savings and investment amounts of the same age. The financial data collection unit also analyzes the financial data of other customers with the same occupation and benchmarks the customer's financial situation. For example, a financial plan for the customer is proposed based on the average spending patterns and investment destinations of customers with the same occupation. This makes it possible to provide financial advice based on comparison with other customers of the same age or occupation.

[0039] The lifestyle change detection unit can use generation AI to analyze spending patterns that accompany changes in a customer's lifestyle and propose an optimal spending plan. The lifestyle change detection unit, for example, uses generation AI to analyze spending patterns that accompany changes in a customer's lifestyle and propose an optimal spending plan. For example, it creates a spending plan that corresponds to life events such as marriage or childbirth. The lifestyle change detection unit also takes changes in the customer's lifestyle into account and predicts future spending. For example, it proposes a spending plan that takes into account children's education expenses and mortgage payments. The lifestyle change detection unit also analyzes spending patterns that accompany changes in a customer's lifestyle and provides advice to reduce wasteful spending. For example, it proposes savings measures that correspond to life events. This makes it possible to provide an optimal spending plan that corresponds to changes in the customer's lifestyle.

[0040] The lifestyle change detection unit can propose an asset management plan that corresponds to changes in the customer's lifestyle by comparing it with past successful cases. For example, the lifestyle change detection unit proposes an asset management plan that corresponds to changes in the customer's lifestyle by comparing it with past successful cases. For example, it proposes an asset management plan for marriage or childbirth based on past successful cases. The lifestyle change detection unit also analyzes past successful cases and proposes an optimal asset management plan that corresponds to changes in the customer's lifestyle. For example, it proposes investment destinations and risk management measures according to life events. The lifestyle change detection unit also evaluates an asset management plan that corresponds to changes in the customer's lifestyle by comparing it with past successful cases. For example, it evaluates risks and returns based on past successful cases and selects an optimal plan. This makes it possible to provide an asset management plan that refers to past successful cases.

[0041] The lifestyle change detection unit can provide proposals in response to changes in the customer's lifestyle by comparing them with examples from different cultural spheres or countries. For example, the lifestyle change detection unit provides proposals in response to changes in the customer's lifestyle by comparing them with examples from different cultural spheres or countries. For example, it proposes asset management plans for marriage and childbirth based on examples from different cultural spheres. The lifestyle change detection unit also analyzes asset management plans in response to changes in lifestyle in different countries and makes optimal proposals to the customer. For example, it proposes investment destinations and risk management measures based on successful examples from different countries. The lifestyle change detection unit also evaluates proposals in response to changes in the customer's lifestyle by comparing them with examples from different cultural spheres or countries. For example, it evaluates risks and returns based on successful examples from different cultural spheres and selects the optimal plan. This makes it possible to provide proposals that refer to examples from different cultural spheres or countries.

[0042] The lifestyle change detection unit can analyze spending patterns accompanying changes in a customer's lifestyle by comparing them with different age groups and generations. For example, the lifestyle change detection unit analyzes spending patterns accompanying changes in a customer's lifestyle by comparing them with different age groups and generations. For example, it compares spending patterns between young people and middle-aged and elderly people and proposes an optimal spending plan. The lifestyle change detection unit also analyzes spending patterns in response to changes in a customer's lifestyle based on spending data from different generations. For example, it proposes a spending plan based on the spending trends of the same generation. The lifestyle change detection unit also evaluates spending patterns accompanying changes in a customer's lifestyle by comparing them with different age groups and generations. For example, it reviews spending plans based on success stories from different generations. This makes it possible to provide an analysis of spending patterns based on comparisons with different age groups and generations.

[0043] The investment plan generation unit can use generation AI to analyze a customer's investment history and propose an optimal investment strategy based on past successes and failures. For example, the investment plan generation unit uses generation AI to analyze a customer's past investment history and extract patterns of success and failure. For example, it proposes an optimal investment strategy based on past successes. The investment plan generation unit also analyzes the balance between risk and return based on the customer's investment history and proposes an optimal investment strategy. For example, it proposes a portfolio that combines high-risk and low-risk investments. The investment plan generation unit also analyzes past investment history to understand the customer's investment trends. For example, if there is a bias toward specific investment destinations or investment methods, it proposes an investment strategy that takes that tendency into account. This makes it possible to provide an optimal investment strategy based on past successes and failures.

[0044] The investment plan generation unit can propose an asset management plan that matches the customer's investment goals with a combination of different investment products. The investment plan generation unit, for example, proposes an asset management plan that matches the customer's investment goals with a combination of different investment products. For example, it proposes a portfolio that combines investment destinations such as stocks, bonds, and real estate. The investment plan generation unit also simulates combinations of different investment products and proposes a plan that is optimal for the customer's investment goals. For example, it proposes a combination of investment products that takes into account the balance between risk and return. The investment plan generation unit also evaluates asset management plans that match the customer's investment goals with different combinations of investment products and selects the optimal plan. For example, it proposes a plan that combines high-risk investment products and low-risk investment products. This makes it possible to provide an optimal asset management plan based on a combination of different investment products.

[0045] The investment plan generation unit can make proposals based on the customer's investment history, taking into account success stories of other customers with the same investment goals. The investment plan generation unit, for example, makes proposals based on the customer's investment history, taking into account success stories of other customers with the same investment goals. For example, it proposes investment destinations based on success stories of customers with the same risk tolerance. The investment plan generation unit also analyzes success stories of other customers with the same investment goals and proposes an optimal investment strategy for the customer. For example, it optimizes the balance between risk and return based on past success stories. The investment plan generation unit also evaluates success stories of other customers with the same investment goals, based on the customer's investment history, and proposes an optimal investment plan. For example, it proposes investment destinations and risk management measures based on success stories. This makes it possible to provide proposals based on success stories of other customers with the same investment goals.

[0046] The risk management proposal department can use the generation AI to perform a detailed analysis of the risk factors in a customer's investment portfolio and propose risk reduction measures. The risk management proposal department, for example, uses the generation AI to perform a detailed analysis of the risk factors in a customer's investment portfolio and propose risk reduction measures. For example, it evaluates the risk of a specific investment and proposes diversification investments. The risk management proposal department also analyzes the customer's portfolio and identifies risk factors. For example, it proposes diversifying investments that are biased toward a specific industry or region. The risk management proposal department also uses the generation AI to analyze the balance between risk and return in the customer's portfolio and propose optimal risk management measures. For example, it offers measures to hedge high-risk investments. This makes it possible to perform a detailed analysis of the risk factors in a customer's investment portfolio and propose risk reduction measures.

[0047] The risk management proposal department can simulate risk management methods according to a customer's risk tolerance using different market scenarios and select the optimal method. For example, the risk management proposal department simulates risk management methods according to a customer's risk tolerance using different market scenarios and selects the optimal method. For example, it evaluates risk management measures based on scenarios of economic growth and recession. The risk management proposal department also simulates different market scenarios and proposes a risk management method that is optimal for the customer's risk tolerance. For example, it proposes risk management measures that take into account fluctuations in the stock market and interest rates. The risk management proposal department also evaluates risk management methods according to a customer's risk tolerance using different market scenarios and selects the optimal method. For example, it compares high-risk scenarios with low-risk scenarios and selects the optimal risk management measure. This makes it possible to provide optimal risk management methods based on different market scenarios.

[0048] The risk management proposal department can propose risk management methods according to the customer's risk tolerance using a combination of different investment products. For example, the risk management proposal department proposes risk management methods according to the customer's risk tolerance using a combination of different investment products. For example, it proposes risk management measures that combine investment destinations such as stocks, bonds, and real estate. The risk management proposal department also simulates combinations of different investment products and proposes a risk management method that is optimal for the customer's risk tolerance. For example, it proposes a combination of investment products that takes into account the balance between risk and return. The risk management proposal department also evaluates risk management methods according to the customer's risk tolerance using a combination of different investment products and selects the optimal method. For example, it proposes a risk management measure that combines high-risk investment products and low-risk investment products. This makes it possible to provide an optimal risk management method based on a combination of different investment products.

[0049] The risk management proposal unit can propose a risk management method for a customer by referring to success stories of other customers who have the same risk tolerance. The risk management proposal unit, for example, proposes a risk management method for a customer by referring to success stories of other customers who have the same risk tolerance. For example, it proposes risk management measures based on success stories of customers who have the same risk tolerance. The risk management proposal unit also analyzes success stories of other customers who have the same risk tolerance and proposes an optimal risk management method for the customer. For example, it optimizes the balance between risk and return based on past success stories. The risk management proposal unit also evaluates the customer's risk management method by evaluating success stories of other customers who have the same risk tolerance and proposes optimal risk management measures. For example, it provides measures to hedge risk factors based on success stories. This makes it possible to provide a risk management method that refers to success stories of other customers who have the same risk tolerance.

[0050] The asset trend prediction unit can use generation AI to simulate future asset trends in detail based on the customer's past financial data and investment history. The asset trend prediction unit, for example, uses generation AI to simulate future asset trends in detail based on the customer's past financial data and investment history. For example, it predicts future asset increases or decreases based on past income and expenditure data. The asset trend prediction unit also analyzes the customer's investment history and simulates future asset trends. For example, it predicts future returns based on past investment performance. The asset trend prediction unit also simulates future asset trends under different scenarios based on the customer's financial data and investment history. For example, it predicts asset trends based on scenarios of economic growth and recession. This makes it possible to simulate future asset trends in detail based on the customer's past financial data and investment history.

[0051] The asset trend prediction unit can simulate future asset trends according to a customer's investment plan using different economic scenarios and select the optimal plan. The asset trend prediction unit, for example, simulates future asset trends according to a customer's investment plan using different economic scenarios and selects the optimal plan. For example, the investment plan is evaluated based on scenarios of economic growth periods and recession periods. The asset trend prediction unit also simulates different economic scenarios and predicts the optimal asset trend for the customer's investment plan. For example, it proposes a plan that takes into account fluctuations in the stock market and interest rates. The asset trend prediction unit also evaluates future asset trends according to a customer's investment plan using different economic scenarios and selects the optimal plan. For example, it compares high-risk scenarios with low-risk scenarios and selects the optimal plan. This makes it possible to simulate optimal asset trends based on different economic scenarios.

[0052] The asset trend prediction unit can simulate the future asset trend of a customer using a combination of different investment products and propose an optimal combination. The asset trend prediction unit, for example, simulates the future asset trend of a customer using a combination of different investment products and proposes an optimal combination. For example, it predicts asset trends using a combination of investment destinations such as stocks, bonds, and real estate. The asset trend prediction unit also simulates combinations of different investment products and predicts the future asset trend of a customer. For example, it proposes a combination of investment products that takes into account the balance between risk and return. The asset trend prediction unit also evaluates the future asset trend of a customer using a combination of different investment products and selects the optimal combination. For example, it predicts asset trends using a combination of high-risk and low-risk investment products. This makes it possible to simulate an optimal asset trend based on a combination of different investment products.

[0053] The asset trend prediction unit can predict the future asset trend of a customer by referring to success stories of other customers who have the same investment goals. The asset trend prediction unit, for example, predicts the future asset trend of a customer by referring to success stories of other customers who have the same investment goals. For example, it predicts the asset trend based on success stories of customers who have the same risk tolerance. The asset trend prediction unit also analyzes success stories of other customers who have the same investment goals and predicts the customer's future asset trend. For example, it optimizes the balance between risk and return based on past success stories. The asset trend prediction unit also evaluates success stories of other customers who have the same investment goals and predicts the optimal asset trend. For example, it suggests investment destinations and risk management measures based on success stories. This makes it possible to provide asset trend predictions that refer to success stories of other customers who have the same investment goals.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The asset management support system may further include a health data analysis unit that collects health data from customers and analyzes the impact of their health condition on their financial situation. For example, it may collect data on customers' health checkup results and medical expenses and predict increases in medical expenses if their health condition worsens. The health data analysis unit may also analyze the impact of changes in their health condition on their income. For example, it may predict the impact of a decline in their ability to work due to illness or injury on their income and reflect this in their financial plan. This allows the impact of their health condition on their financial situation to be understood and appropriate financial plans to be developed.

[0056] The asset management support system can further include a behavior pattern analysis unit that analyzes a customer's behavior patterns and predicts future spending trends. For example, it analyzes a customer's past spending data and extracts specific spending patterns. For example, it classifies monthly fixed and variable expenses and predicts future spending trends. The behavior pattern analysis unit can also analyze the balance between a customer's income and expenses and predict future income and expenditures. For example, it can simulate future financial situations taking into account increases and decreases in income and fluctuations in expenses. This makes it possible to predict a customer's future spending trends and create appropriate financial plans.

[0057] The asset management support system can further include a regional economic analysis unit that compares the customer's financial data with regional economic conditions and provides regionally specific financial advice. For example, the system can compare the customer's financial data with regional economic indicators and propose a regionally specific spending plan. The regional economic analysis unit can also provide investment advice based on trends in the local real estate market and stock market. For example, the system can propose a spending plan that takes into account regional prices and average income. This allows the system to provide financial advice that takes into account regional economic conditions.

[0058] The asset management support system may further include a comparison analysis unit that uses the client's financial data to perform comparative analysis with other clients of the same age or occupation and provide a benchmark. For example, the client's financial data may be compared with other clients of the same age or occupation to provide a benchmark. For example, the client's financial situation may be evaluated by comparing with the average income and expenses of clients of the same occupation. The comparison analysis unit may also benchmark the client's financial situation based on the financial data of clients of the same age. This makes it possible to provide financial advice based on a comparison with other clients of the same age or occupation.

[0059] The asset management support system can further include a cultural comparison unit that provides proposals in response to changes in a customer's lifestyle by comparing them with examples from different cultural spheres or countries. For example, proposals in response to changes in a customer's lifestyle can be provided by comparing them with examples from different cultural spheres or countries. For example, an asset management plan for marriage or childbirth can be proposed based on examples from different cultural spheres. The cultural comparison unit can also analyze asset management plans in response to changes in lifestyle in different countries and provide optimal proposals to the customer. This makes it possible to provide proposals that take into account examples from different cultural spheres or countries.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The financial data collection department collects the customer's financial data. For example, it analyzes the customer's bank account transaction history and credit card statements. It also collects data on the customer's income, expenses, assets, and liabilities. Step 2: The lifestyle change detection unit analyzes the financial data collected by the financial data collection unit and detects changes in the customer's lifestyle. For example, when a customer approaches a life event such as marriage or childbirth, the unit proposes an asset management plan appropriate for that event. Step 3: The investment plan generator generates an optimal investment plan based on the lifestyle changes detected by the lifestyle change detector. For example, it suggests investment options such as stocks, bonds, and real estate depending on the customer's risk tolerance and investment goals. Step 4: The risk management proposal unit proposes risk management techniques based on the investment plan generated by the investment plan generation unit. For example, it proposes portfolio diversification and hedging techniques to minimize risk. Step 5: The asset trend prediction unit predicts future asset trends based on the investment plan generated by the investment plan generation unit. For example, it simulates the increase or decrease in assets if the client implements the proposed investment plan, and predicts the future asset situation.

[0062] (Example 2) The asset management support system according to an embodiment of the present invention analyzes a customer's financial data, proposes ways of using money, investments, asset management, and risk management that are adapted to changes in lifestyle, and predicts future asset trends. As a result, the asset management support system can accurately grasp the customer's financial situation and provide optimal asset management that is adapted to changes in lifestyle.

[0063] An asset management support system according to an embodiment includes a financial data collection unit, a lifestyle change detection unit, an investment plan generation unit, a risk management proposal unit, and an asset trend prediction unit. The financial data collection unit collects financial data about a customer. For example, it analyzes the customer's bank account transaction history and credit card statements. The financial data collection unit can also collect data about the customer's income, expenses, assets, liabilities, and the like. The lifestyle change detection unit analyzes the financial data collected by the financial data collection unit to detect changes in the customer's lifestyle. For example, when a customer approaches a life event such as marriage or childbirth, it proposes an asset management plan appropriate for that event. The investment plan generation unit generates an optimal investment plan based on the lifestyle changes detected by the lifestyle change detection unit. For example, it proposes investment destinations such as stocks, bonds, and real estate based on the customer's risk tolerance and investment goals. The risk management proposal unit proposes risk management techniques based on the investment plan generated by the investment plan generation unit. For example, it proposes portfolio diversification and hedging techniques to minimize risk. The asset trend prediction unit predicts future asset trends based on the investment plan generated by the investment plan generation unit. For example, the asset management support system according to the embodiment can simulate the increase or decrease in assets if the customer implements the proposed investment plan and predict the future asset situation. This allows the asset management support system according to the embodiment to accurately grasp the customer's financial situation and perform optimal asset management that adapts to the customer's changing lifestyle.

[0064] The financial data collection unit can analyze the transaction history of a customer's bank account and credit card usage details. For example, the financial data collection unit analyzes the transaction history of a customer's bank account to understand the deposit and withdrawal history and type of transaction. The financial data collection unit also analyzes the credit card usage details to understand the amount used, the store used, and the date and time of use. This allows for a more detailed understanding of the customer's financial situation.

[0065] The lifestyle change detection unit can propose an asset management plan suited to a customer's life events, such as marriage or childbirth. The lifestyle change detection unit, for example, collects information about a customer's life events, such as marriage or childbirth, and proposes an optimal asset management plan based on that information. For example, it proposes an asset management plan that takes into account changes in expenses and income due to marriage. It also proposes an asset management plan that takes into account increases in education expenses and living expenses due to childbirth. This makes it possible to provide an optimal asset management plan tailored to the customer's life events.

[0066] The investment plan generation unit can suggest investments such as stocks, bonds, and real estate according to the customer's risk tolerance and investment goals. The investment plan generation unit, for example, evaluates the customer's risk tolerance and suggests optimal investments based on that evaluation. For example, for a customer with a high risk tolerance, it suggests high-risk, high-return investments such as stocks and real estate. On the other hand, for a customer with a low risk tolerance, it suggests low-risk, low-return investments such as bonds and fixed-term deposits. The investment plan generation unit also takes into account the customer's investment goals and suggests investments according to those goals. For example, it suggests stocks and short-term bonds for a customer aiming for short-term profits, and real estate and long-term bonds for a customer aiming for long-term asset formation. This makes it possible to provide optimal investments according to the customer's risk tolerance and investment goals.

[0067] The risk management proposal department can propose portfolio diversification investments and hedging techniques. For example, the risk management proposal department analyzes a client's investment portfolio and identifies risk factors. For example, it makes proposals to diversify investments that are biased toward a particular industry or region. The risk management proposal department also proposes hedging techniques and provides methods for minimizing risk. For example, it suggests the use of derivatives or insurance products. This makes it possible to provide methods for minimizing client risk.

[0068] The asset trend prediction unit can simulate the increase or decrease in assets if the client implements the proposed investment plan. The asset trend prediction unit simulates future asset trends, for example, based on the client's investment plan. For example, it predicts the increase or decrease in assets if the client implements the proposed investment plan. The asset trend prediction unit also simulates asset trends under different scenarios and selects the optimal investment plan. For example, it predicts asset trends based on scenarios of economic growth and recession. This makes it possible to predict the future asset situation if the client implements the proposed investment plan.

[0069] The financial data collection unit can analyze behavioral patterns based on a customer's financial data and predict future spending trends. For example, the financial data collection unit uses generative AI to analyze a customer's past spending data and extract specific spending patterns. For example, it classifies monthly fixed and variable expenses and predicts future spending trends. The financial data collection unit also analyzes the balance between a customer's income and expenses and predicts future income and expenditures. For example, it takes into account increases and decreases in income and fluctuations in expenses to simulate future financial situations. The financial data collection unit also analyzes seasonal spending trends based on the customer's spending data. For example, it identifies trends in specific expenses increasing in summer and winter and creates future spending plans. This makes it possible to predict a customer's future spending trends and create appropriate financial plans.

[0070] The financial data collection unit can integrate a customer's financial data and health data to analyze the impact of their health condition on their financial situation. For example, the financial data collection unit integrates the customer's health data (e.g., medical expenses and health check results) with the financial data to analyze the impact of their health condition on their financial situation. For example, it predicts an increase in medical expenses if their health condition worsens. The financial data collection unit also predicts future medical expenses and insurance premiums based on the health data. For example, it simulates future medical expenses taking into account specific medical history and health risks. The financial data collection unit also analyzes the impact of changes in their health condition on their income. For example, it predicts the impact of a decline in their ability to work due to illness or injury on their income and reflects this in their financial plan. This allows the impact of a customer's health condition on their financial situation to be understood and appropriate financial plans to be developed.

[0071] The financial data collection unit uses the emotion estimation function to analyze emotional fluctuations based on the customer's financial data and understand emotional spending trends. The financial data collection unit, for example, integrates the customer's spending data and emotion data to analyze the impact of emotional fluctuations on spending. For example, it understands the impact of stress and joy on spending. The financial data collection unit also uses the emotion estimation function to monitor the customer's emotional state in real time and predict spending trends based on emotional fluctuations. For example, it predicts increased spending during events that heighten emotions. The financial data collection unit also analyzes the impact of specific emotional states on spending based on the customer's emotion data. For example, it understands the tendency for negative emotions to curb spending and reflects this in financial planning. This makes it possible to understand the impact of customer emotional fluctuations on spending and create appropriate financial planning.

[0072] The financial data collection unit can provide financial advice specific to the region by comparing the customer's financial data with the economic conditions of each region. The financial data collection unit, for example, compares the customer's financial data with economic indicators of each region and provides financial advice specific to the region. For example, it proposes a spending plan that takes into account local prices and average income. The financial data collection unit also analyzes the economic conditions of each region and proposes optimal investments based on the customer's financial situation. For example, it provides investment advice based on trends in the local real estate market and stock market. The financial data collection unit also compares the customer's financial data with the economic conditions of the region and proposes risk management methods specific to the region. For example, it provides risk management measures that respond to local disaster risks and economic fluctuations. This makes it possible to provide financial advice that takes into account the economic conditions specific to the region.

[0073] The financial data collection unit can use the customer's financial data to perform comparative analysis with other customers of the same age or occupation and provide a benchmark. The financial data collection unit, for example, compares the customer's financial data with other customers of the same age or occupation and provides a benchmark. For example, the financial situation of the customer is evaluated by comparing with the average income and expenses of customers with the same occupation. The financial data collection unit also benchmarks the customer's financial situation based on the financial data of customers of the same age. For example, the financial plan of the customer is reviewed by comparing with the average savings and investment amounts of the same age. The financial data collection unit also analyzes the financial data of other customers with the same occupation and benchmarks the customer's financial situation. For example, a financial plan for the customer is proposed based on the average spending patterns and investment destinations of customers with the same occupation. This makes it possible to provide financial advice based on comparison with other customers of the same age or occupation.

[0074] The financial data collection unit can use the emotion estimation function to monitor emotional fluctuations based on the customer's financial data in real time and provide financial advice according to the emotions. The financial data collection unit, for example, uses the emotion estimation function to monitor the customer's emotional state in real time and provide financial advice according to the emotions. For example, advice to reduce spending when stress increases is provided. The financial data collection unit also provides investment advice according to emotional fluctuations based on the customer's emotional data. For example, high-risk investments are suggested when positive emotions increase. The financial data collection unit also uses the emotion estimation function to analyze the customer's emotional state in real time and suggest risk management methods based on emotions. For example, measures to reduce risk are suggested when negative emotions increase. This makes it possible to provide financial advice according to the customer's emotional fluctuations in real time.

[0075] The lifestyle change detection unit can use generation AI to analyze spending patterns that accompany changes in a customer's lifestyle and propose an optimal spending plan. The lifestyle change detection unit, for example, uses generation AI to analyze spending patterns that accompany changes in a customer's lifestyle and propose an optimal spending plan. For example, it creates a spending plan that corresponds to life events such as marriage or childbirth. The lifestyle change detection unit also takes changes in the customer's lifestyle into account and predicts future spending. For example, it proposes a spending plan that takes into account children's education expenses and mortgage payments. The lifestyle change detection unit also analyzes spending patterns that accompany changes in a customer's lifestyle and provides advice to reduce wasteful spending. For example, it proposes savings measures that correspond to life events. This makes it possible to provide an optimal spending plan that corresponds to changes in the customer's lifestyle.

[0076] The lifestyle change detection unit can propose an asset management plan that corresponds to changes in the customer's lifestyle by comparing it with past successful cases. For example, the lifestyle change detection unit proposes an asset management plan that corresponds to changes in the customer's lifestyle by comparing it with past successful cases. For example, it proposes an asset management plan for marriage or childbirth based on past successful cases. The lifestyle change detection unit also analyzes past successful cases and proposes an optimal asset management plan that corresponds to changes in the customer's lifestyle. For example, it proposes investment destinations and risk management measures according to life events. The lifestyle change detection unit also evaluates an asset management plan that corresponds to changes in the customer's lifestyle by comparing it with past successful cases. For example, it evaluates risks and returns based on past successful cases and selects an optimal plan. This makes it possible to provide an asset management plan that refers to past successful cases.

[0077] The lifestyle change detection unit can use the emotion estimation function to analyze the customer's emotions regarding life events and propose an asset management plan based on the emotions. The lifestyle change detection unit, for example, uses the emotion estimation function to analyze the customer's emotions regarding life events and propose an asset management plan based on the emotions. For example, it proposes a plan that takes into account positive emotions regarding marriage or childbirth. The lifestyle change detection unit also proposes an optimal asset management plan based on the customer's emotion data in accordance with the life event. For example, it advises the customer to refrain from high-risk investments during events that heighten emotions. The lifestyle change detection unit also uses the emotion estimation function to monitor the customer's emotions regarding life events in real time and proposes an asset management plan based on the emotions. For example, it suggests investment destinations according to emotional fluctuations. This makes it possible to provide an asset management plan based on the customer's emotions.

[0078] The lifestyle change detection unit can provide proposals in response to changes in the customer's lifestyle by comparing them with examples from different cultural spheres or countries. For example, the lifestyle change detection unit provides proposals in response to changes in the customer's lifestyle by comparing them with examples from different cultural spheres or countries. For example, it proposes asset management plans for marriage and childbirth based on examples from different cultural spheres. The lifestyle change detection unit also analyzes asset management plans in response to changes in lifestyle in different countries and makes optimal proposals to the customer. For example, it proposes investment destinations and risk management measures based on successful examples from different countries. The lifestyle change detection unit also evaluates proposals in response to changes in the customer's lifestyle by comparing them with examples from different cultural spheres or countries. For example, it evaluates risks and returns based on successful examples from different cultural spheres and selects the optimal plan. This makes it possible to provide proposals that refer to examples from different cultural spheres or countries.

[0079] The lifestyle change detection unit can analyze spending patterns accompanying changes in a customer's lifestyle by comparing them with different age groups and generations. For example, the lifestyle change detection unit analyzes spending patterns accompanying changes in a customer's lifestyle by comparing them with different age groups and generations. For example, it compares spending patterns between young people and middle-aged and elderly people and proposes an optimal spending plan. The lifestyle change detection unit also analyzes spending patterns in response to changes in a customer's lifestyle based on spending data from different generations. For example, it proposes a spending plan based on the spending trends of the same generation. The lifestyle change detection unit also evaluates spending patterns accompanying changes in a customer's lifestyle by comparing them with different age groups and generations. For example, it reviews spending plans based on success stories from different generations. This makes it possible to provide an analysis of spending patterns based on comparisons with different age groups and generations.

[0080] The lifestyle change detection unit can use the emotion estimation function to monitor a customer's emotions regarding life events in real time and provide proposals based on the emotions. The lifestyle change detection unit can, for example, use the emotion estimation function to monitor a customer's emotions regarding life events in real time and provide proposals based on the emotions. For example, it can propose an asset management plan that takes into account emotional fluctuations regarding marriage and childbirth. The lifestyle change detection unit can also make optimal proposals based on life events based on the customer's emotional data. For example, it can advise the customer to refrain from high-risk investments during events that increase emotions. The lifestyle change detection unit can also use the emotion estimation function to analyze a customer's emotions regarding life events in real time and propose a spending plan based on the emotions. For example, it can provide savings measures based on emotional fluctuations. This makes it possible to provide proposals based on the customer's emotions in real time.

[0081] The investment plan generation unit can use generation AI to analyze a customer's investment history and propose an optimal investment strategy based on past successes and failures. For example, the investment plan generation unit uses generation AI to analyze a customer's past investment history and extract patterns of success and failure. For example, it proposes an optimal investment strategy based on past successes. The investment plan generation unit also analyzes the balance between risk and return based on the customer's investment history and proposes an optimal investment strategy. For example, it proposes a portfolio that combines high-risk and low-risk investments. The investment plan generation unit also analyzes past investment history to understand the customer's investment trends. For example, if there is a bias toward specific investment destinations or investment methods, it proposes an investment strategy that takes that tendency into account. This makes it possible to provide an optimal investment strategy based on past successes and failures.

[0082] The investment plan generation unit can use the emotion estimation function to analyze a customer's emotions regarding investment and propose an investment strategy based on those emotions. The investment plan generation unit, for example, uses the emotion estimation function to analyze a customer's emotions regarding investment and propose an investment strategy based on those emotions. For example, it proposes investment destinations that increase positive emotions. The investment plan generation unit also analyzes fluctuations in emotions regarding investment based on customer emotion data and proposes risk management measures based on those emotions. For example, it provides measures to reduce risk when negative emotions increase. The investment plan generation unit also uses the emotion estimation function to monitor a customer's emotions regarding investment in real time and proposes an investment strategy based on those emotions. For example, it proposes investment destinations that correspond to emotional fluctuations. This makes it possible to provide an investment strategy based on a customer's emotions.

[0083] The investment plan generation unit can propose an asset management plan that matches the customer's investment goals with a combination of different investment products. The investment plan generation unit, for example, proposes an asset management plan that matches the customer's investment goals with a combination of different investment products. For example, it proposes a portfolio that combines investment destinations such as stocks, bonds, and real estate. The investment plan generation unit also simulates combinations of different investment products and proposes a plan that is optimal for the customer's investment goals. For example, it proposes a combination of investment products that takes into account the balance between risk and return. The investment plan generation unit also evaluates asset management plans that match the customer's investment goals with different combinations of investment products and selects the optimal plan. For example, it proposes a plan that combines high-risk investment products and low-risk investment products. This makes it possible to provide an optimal asset management plan based on a combination of different investment products.

[0084] The investment plan generation unit can make proposals based on the customer's investment history, taking into account success stories of other customers with the same investment goals. The investment plan generation unit, for example, makes proposals based on the customer's investment history, taking into account success stories of other customers with the same investment goals. For example, it proposes investment destinations based on success stories of customers with the same risk tolerance. The investment plan generation unit also analyzes success stories of other customers with the same investment goals and proposes an optimal investment strategy for the customer. For example, it optimizes the balance between risk and return based on past success stories. The investment plan generation unit also evaluates success stories of other customers with the same investment goals, based on the customer's investment history, and proposes an optimal investment plan. For example, it proposes investment destinations and risk management measures based on success stories. This makes it possible to provide proposals based on success stories of other customers with the same investment goals.

[0085] The investment plan generation unit can use the emotion estimation function to monitor a customer's emotions regarding investments in real time and provide investment advice according to the emotions. The investment plan generation unit, for example, uses the emotion estimation function to monitor a customer's emotions regarding investments in real time and provide investment advice according to the emotions. For example, it suggests investment destinations that will increase emotions. The investment plan generation unit also analyzes fluctuations in emotions regarding investments based on the customer's emotion data and proposes risk management measures according to the emotions. For example, it provides measures to reduce risk when negative emotions increase. The investment plan generation unit also uses the emotion estimation function to analyze a customer's emotions regarding investments in real time and proposes an investment strategy based on emotions. For example, it suggests investment destinations according to emotional fluctuations. This makes it possible to provide investment advice according to the customer's emotions in real time.

[0086] The risk management proposal department can use the generation AI to perform a detailed analysis of the risk factors in a customer's investment portfolio and propose risk reduction measures. The risk management proposal department, for example, uses the generation AI to perform a detailed analysis of the risk factors in a customer's investment portfolio and propose risk reduction measures. For example, it evaluates the risk of a specific investment and proposes diversification investments. The risk management proposal department also analyzes the customer's portfolio and identifies risk factors. For example, it proposes diversifying investments that are biased toward a specific industry or region. The risk management proposal department also uses the generation AI to analyze the balance between risk and return in the customer's portfolio and propose optimal risk management measures. For example, it offers measures to hedge high-risk investments. This makes it possible to perform a detailed analysis of the risk factors in a customer's investment portfolio and propose risk reduction measures.

[0087] The risk management proposal department can simulate risk management methods according to a customer's risk tolerance using different market scenarios and select the optimal method. For example, the risk management proposal department simulates risk management methods according to a customer's risk tolerance using different market scenarios and selects the optimal method. For example, it evaluates risk management measures based on scenarios of economic growth and recession. The risk management proposal department also simulates different market scenarios and proposes a risk management method that is optimal for the customer's risk tolerance. For example, it proposes risk management measures that take into account fluctuations in the stock market and interest rates. The risk management proposal department also evaluates risk management methods according to a customer's risk tolerance using different market scenarios and selects the optimal method. For example, it compares high-risk scenarios with low-risk scenarios and selects the optimal risk management measure. This makes it possible to provide optimal risk management methods based on different market scenarios.

[0088] The risk management proposal unit can use the emotion estimation function to analyze customer emotions toward risk and propose emotion-based risk management methods. The risk management proposal unit, for example, uses the emotion estimation function to analyze customer emotions toward risk and propose emotion-based risk management methods. For example, it provides measures to reduce risk when negative emotions increase. The risk management proposal unit also analyzes fluctuations in emotions toward risk based on customer emotion data and proposes emotion-based risk management measures. For example, it provides measures to hedge against risk factors that increase emotions. The risk management proposal unit also uses the emotion estimation function to monitor customer emotions toward risk in real time and propose emotion-based risk management methods. For example, it proposes risk management measures according to emotional fluctuations. This makes it possible to provide risk management methods based on customer emotions.

[0089] The risk management proposal department can propose risk management methods according to the customer's risk tolerance using a combination of different investment products. For example, the risk management proposal department proposes risk management methods according to the customer's risk tolerance using a combination of different investment products. For example, it proposes risk management measures that combine investment destinations such as stocks, bonds, and real estate. The risk management proposal department also simulates combinations of different investment products and proposes a risk management method that is optimal for the customer's risk tolerance. For example, it proposes a combination of investment products that takes into account the balance between risk and return. The risk management proposal department also evaluates risk management methods according to the customer's risk tolerance using a combination of different investment products and selects the optimal method. For example, it proposes a risk management measure that combines high-risk investment products and low-risk investment products. This makes it possible to provide an optimal risk management method based on a combination of different investment products.

[0090] The risk management proposal unit can propose a risk management method for a customer by referring to success stories of other customers who have the same risk tolerance. The risk management proposal unit, for example, proposes a risk management method for a customer by referring to success stories of other customers who have the same risk tolerance. For example, it proposes risk management measures based on success stories of customers who have the same risk tolerance. The risk management proposal unit also analyzes success stories of other customers who have the same risk tolerance and proposes an optimal risk management method for the customer. For example, it optimizes the balance between risk and return based on past success stories. The risk management proposal unit also evaluates the customer's risk management method by evaluating success stories of other customers who have the same risk tolerance and proposes optimal risk management measures. For example, it provides measures to hedge risk factors based on success stories. This makes it possible to provide a risk management method that refers to success stories of other customers who have the same risk tolerance.

[0091] The asset trend prediction unit can use generation AI to simulate future asset trends in detail based on the customer's past financial data and investment history. The asset trend prediction unit, for example, uses generation AI to simulate future asset trends in detail based on the customer's past financial data and investment history. For example, it predicts future asset increases or decreases based on past income and expenditure data. The asset trend prediction unit also analyzes the customer's investment history and simulates future asset trends. For example, it predicts future returns based on past investment performance. The asset trend prediction unit also simulates future asset trends under different scenarios based on the customer's financial data and investment history. For example, it predicts asset trends based on scenarios of economic growth and recession. This makes it possible to simulate future asset trends in detail based on the customer's past financial data and investment history.

[0092] The asset trend prediction unit can simulate future asset trends according to a customer's investment plan using different economic scenarios and select the optimal plan. The asset trend prediction unit, for example, simulates future asset trends according to a customer's investment plan using different economic scenarios and selects the optimal plan. For example, the investment plan is evaluated based on scenarios of economic growth periods and recession periods. The asset trend prediction unit also simulates different economic scenarios and predicts the optimal asset trend for the customer's investment plan. For example, it proposes a plan that takes into account fluctuations in the stock market and interest rates. The asset trend prediction unit also evaluates future asset trends according to a customer's investment plan using different economic scenarios and selects the optimal plan. For example, it compares high-risk scenarios with low-risk scenarios and selects the optimal plan. This makes it possible to simulate optimal asset trends based on different economic scenarios.

[0093] The asset trend prediction unit can use the emotion estimation function to analyze the customer's emotions regarding future asset trends and provide an asset trend prediction based on emotions. The asset trend prediction unit, for example, uses the emotion estimation function to analyze the customer's emotions regarding future asset trends and provide an asset trend prediction based on emotions. For example, the asset trend prediction unit predicts asset trends based on scenarios in which positive emotions increase. The asset trend prediction unit also analyzes emotional fluctuations regarding future asset trends based on customer emotion data and proposes risk management measures based on emotions. For example, it provides measures to reduce risk when negative emotions increase. The asset trend prediction unit also uses the emotion estimation function to monitor the customer's emotions regarding future asset trends in real time and provide an asset trend prediction based on emotions. For example, it predicts asset trends according to emotional fluctuations. This makes it possible to provide an asset trend prediction based on the customer's emotions.

[0094] The asset trend prediction unit can simulate the future asset trend of a customer using a combination of different investment products and propose an optimal combination. The asset trend prediction unit, for example, simulates the future asset trend of a customer using a combination of different investment products and proposes an optimal combination. For example, it predicts asset trends using a combination of investment destinations such as stocks, bonds, and real estate. The asset trend prediction unit also simulates combinations of different investment products and predicts the future asset trend of a customer. For example, it proposes a combination of investment products that takes into account the balance between risk and return. The asset trend prediction unit also evaluates the future asset trend of a customer using a combination of different investment products and selects the optimal combination. For example, it predicts asset trends using a combination of high-risk and low-risk investment products. This makes it possible to simulate an optimal asset trend based on a combination of different investment products.

[0095] The asset trend prediction unit can predict the future asset trend of a customer by referring to success stories of other customers who have the same investment goals. The asset trend prediction unit, for example, predicts the future asset trend of a customer by referring to success stories of other customers who have the same investment goals. For example, it predicts the asset trend based on success stories of customers who have the same risk tolerance. The asset trend prediction unit also analyzes success stories of other customers who have the same investment goals and predicts the customer's future asset trend. For example, it optimizes the balance between risk and return based on past success stories. The asset trend prediction unit also evaluates success stories of other customers who have the same investment goals and predicts the optimal asset trend. For example, it suggests investment destinations and risk management measures based on success stories. This makes it possible to provide asset trend predictions that refer to success stories of other customers who have the same investment goals.

[0096] The asset trend prediction unit can use the emotion estimation function to monitor the customer's emotions regarding future asset trends in real time and provide an asset trend prediction based on the emotions. The asset trend prediction unit, for example, uses the emotion estimation function to monitor the customer's emotions regarding future asset trends in real time and provide an asset trend prediction based on the emotions. For example, the asset trend prediction unit predicts asset trends based on scenarios in which emotions increase. The asset trend prediction unit also analyzes emotional fluctuations regarding future asset trends based on customer emotion data and proposes risk management measures based on emotions. For example, it provides measures to reduce risk when negative emotions increase. The asset trend prediction unit also uses the emotion estimation function to analyze the customer's emotions regarding future asset trends in real time and provide an asset trend prediction based on emotions. For example, it predicts asset trends based on emotional fluctuations. This makes it possible to provide asset trend predictions based on the customer's emotions in real time.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The asset management support system may further include a health data analysis unit that collects health data from customers and analyzes the impact of their health condition on their financial situation. For example, it may collect data on customers' health checkup results and medical expenses and predict increases in medical expenses if their health condition worsens. The health data analysis unit may also analyze the impact of changes in their health condition on their income. For example, it may predict the impact of a decline in their ability to work due to illness or injury on their income and reflect this in their financial plan. This allows the impact of their health condition on their financial situation to be understood and appropriate financial plans to be developed.

[0099] The asset management support system can further include a behavior pattern analysis unit that analyzes a customer's behavior patterns and predicts future spending trends. For example, it analyzes a customer's past spending data and extracts specific spending patterns. For example, it classifies monthly fixed and variable expenses and predicts future spending trends. The behavior pattern analysis unit can also analyze the balance between a customer's income and expenses and predict future income and expenditures. For example, it can simulate future financial situations taking into account increases and decreases in income and fluctuations in expenses. This makes it possible to predict a customer's future spending trends and create appropriate financial plans.

[0100] The asset management support system can further include a regional economic analysis unit that compares the customer's financial data with regional economic conditions and provides regionally specific financial advice. For example, the system can compare the customer's financial data with regional economic indicators and propose a regionally specific spending plan. The regional economic analysis unit can also provide investment advice based on trends in the local real estate market and stock market. For example, the system can propose a spending plan that takes into account regional prices and average income. This allows the system to provide financial advice that takes into account regional economic conditions.

[0101] The asset management support system may further include a comparison analysis unit that uses the client's financial data to perform comparative analysis with other clients of the same age or occupation and provide a benchmark. For example, the client's financial data may be compared with other clients of the same age or occupation to provide a benchmark. For example, the client's financial situation may be evaluated by comparing with the average income and expenses of clients of the same occupation. The comparison analysis unit may also benchmark the client's financial situation based on the financial data of clients of the same age. This makes it possible to provide financial advice based on a comparison with other clients of the same age or occupation.

[0102] The asset management support system can further include a cultural comparison unit that provides proposals in response to changes in a customer's lifestyle by comparing them with examples from different cultural spheres or countries. For example, proposals in response to changes in a customer's lifestyle can be provided by comparing them with examples from different cultural spheres or countries. For example, an asset management plan for marriage or childbirth can be proposed based on examples from different cultural spheres. The cultural comparison unit can also analyze asset management plans in response to changes in lifestyle in different countries and provide optimal proposals to the customer. This makes it possible to provide proposals that take into account examples from different cultural spheres or countries.

[0103] The asset management support system can further use the emotion estimation function to analyze emotional fluctuations based on the customer's financial data and understand emotional spending trends. For example, the system can integrate the customer's spending data with their emotional data to analyze the impact of emotional fluctuations on spending. For example, it can understand the impact of stress or joy on spending. The emotion estimation function can also be used to monitor the customer's emotional state in real time and predict spending trends based on emotional fluctuations. This allows the system to understand the impact of customer emotional fluctuations on spending and develop appropriate financial plans.

[0104] The asset management support system can also use the emotion estimation function to monitor the emotional state of the customer in real time and provide financial advice according to the customer's emotions. For example, advice on reducing spending when stress levels rise can be provided. The emotion estimation function can also be used to provide investment advice according to emotional fluctuations based on the customer's emotional data. This makes it possible to provide financial advice according to the customer's emotional fluctuations in real time.

[0105] The asset management support system can further use the emotion estimation function to analyze the customer's emotions regarding life events and propose an asset management plan based on those emotions. For example, it can propose a plan that takes into account the positive emotions toward marriage or childbirth. The emotion estimation function can also be used to propose the optimal asset management plan according to life events based on the customer's emotional data. This makes it possible to provide an asset management plan based on the customer's emotions.

[0106] The asset management support system can further use the emotion estimation function to analyze customers' emotions regarding investments and propose investment strategies based on those emotions. For example, it can propose investments that increase positive emotions. The emotion estimation function can also be used to analyze fluctuations in customers' emotions regarding investments based on their emotional data and propose risk management measures based on those emotions. This makes it possible to provide investment strategies based on customers' emotions.

[0107] The asset management support system can further use the emotion estimation function to analyze the customer's emotions regarding future asset trends and provide asset trend forecasts based on emotions. For example, asset trends can be predicted based on scenarios in which positive emotions increase. The emotion estimation function can also be used to analyze emotional fluctuations regarding future asset trends based on customer emotion data and propose emotion-based risk management measures. This makes it possible to provide asset trend forecasts based on the customer's emotions.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The financial data collection department collects the customer's financial data. For example, it analyzes the customer's bank account transaction history and credit card statements. It also collects data on the customer's income, expenses, assets, and liabilities. Step 2: The lifestyle change detection unit analyzes the financial data collected by the financial data collection unit and detects changes in the customer's lifestyle. For example, when a customer approaches a life event such as marriage or childbirth, the unit proposes an asset management plan appropriate for that event. Step 3: The investment plan generator generates an optimal investment plan based on the lifestyle changes detected by the lifestyle change detector. For example, it suggests investment options such as stocks, bonds, and real estate depending on the customer's risk tolerance and investment goals. Step 4: The risk management proposal unit proposes risk management techniques based on the investment plan generated by the investment plan generation unit. For example, it proposes portfolio diversification and hedging techniques to minimize risk. Step 5: The asset trend prediction unit predicts future asset trends based on the investment plan generated by the investment plan generation unit. For example, it simulates the increase or decrease in assets if the client implements the proposed investment plan, and predicts the future asset situation.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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, RAM 30, and 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.

[0116] 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.

[0117] 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 audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] 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 carried out in a secure state.

[0120] Fig. 4 shows an example of the 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.

[0121] 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.

[0122] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0126] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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, RAM 30, and 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.

[0131] 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.

[0132] 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 audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] 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 carried out in a secure state.

[0135] 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.

[0136] 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.

[0137] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 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 specific processing unit 290 using these models.

[0139] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0141] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] 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, RAM 30, and 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.

[0146] 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.

[0147] 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 audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] 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 carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0151] 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.

[0152] 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.

[0153] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. 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.

[0155] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] 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 voice indicating a user input regarding 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.

[0157] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] 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.

[0167] 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 USB (Universal Serial Bus) 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.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] 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.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0172] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0177] 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 financial data collection unit that collects financial data of customers; a lifestyle change detection unit that analyzes the financial data collected by the financial data collection unit and detects changes in the customer's lifestyle; an investment plan generation unit that generates an optimal investment plan based on the lifestyle change detected by the lifestyle change detection unit; a risk management proposal unit that proposes a risk management method based on the investment plan generated by the investment plan generation unit; an asset trend prediction unit that predicts future asset trends based on the investment plan generated by the investment plan generation unit. A system characterized by:

2. The financial data collection unit Analyzing customer bank account transaction histories and credit card statements 2. The system of claim 1.

3. The lifestyle change detection unit Propose asset management plans suited to customers' life events such as marriage and childbirth 2. The system of claim 1.

4. The investment plan generation unit: Propose investment options such as stocks, bonds, and real estate based on the client's risk tolerance and investment goals.

2. The system of claim 1.

5. The risk management proposal unit Proposing portfolio diversification and hedging methods 2. The system of claim 1.

6. The asset transition prediction unit Simulate the increase or decrease in assets if the client implements the proposed investment plan 2. The system of claim 1.

7. The financial data collection unit Analyzing behavioral patterns based on customer financial data to predict future spending trends 2. The system of claim 1.

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