System

Abstract

An object of a system according to an embodiment is to generate an investment strategy customized based on financial data of a user and evaluate a future financial risk.SOLUTION: A system includes a financial data acquisition part, an investment strategy generation part, and a risk evaluation part. The financial data acquisition unit acquires financial data of a user. The investment strategy generation unit generates an investment strategy customized based on the financial data of the user acquired by the financial data acquisition unit. The risk evaluation unit evaluates a future financial risk based on the financial data of the user.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 technologies do not adequately generate customized investment strategies or assess future financial risks based on personal financial data, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a customized investment strategy based on a user's financial data and to assess future financial risk. [Means for solving the problem]

[0006] The system according to the embodiment includes a financial data acquisition unit, an investment strategy generation unit, and a risk assessment unit. The financial data acquisition unit acquires financial data of a user. The investment strategy generation unit generates a customized investment strategy based on the financial data of the user acquired by the financial data acquisition unit. The risk assessment unit assesses future financial risk based on the user's financial data. [Effects of the Invention]

[0007] An embodiment of the system can generate a customized investment strategy based on a user's financial data and assess future financial risk. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) An asset management support system according to an embodiment of the present invention is a system that automatically analyzes a user's financial data, and a generation AI proposes a customized investment strategy and evaluates future risks. As a result, the asset management support system can provide an optimal investment strategy based on the user's financial situation and support preparation for future risks.

[0029] The asset management support system according to the embodiment includes a financial data acquisition unit, an investment strategy generation unit, and a risk assessment unit. The financial data acquisition unit acquires a user's financial data. For example, it collects data such as income, expenses, assets, and liabilities. The financial data acquisition unit can also acquire transaction data for bank accounts and credit cards. For example, it acquires data from financial institutions via an API. The financial data acquisition unit can also acquire data manually entered by a user. For example, it collects income and expense data entered by a user into an application. The investment strategy generation unit generates a customized investment strategy based on the user's financial data acquired by the financial data acquisition unit. For example, the generation AI proposes an optimal portfolio based on the user's risk tolerance and investment goals. The generation AI can also analyze past investment performance and adjust future investment strategies. The generation AI can also analyze data from different markets and propose diversified investment strategies. For example, it integrates and analyzes data from the stock market, cryptocurrency market, and real estate market. The risk assessment unit assesses future financial risks based on the user's financial data. For example, the generation AI performs risk assessment taking into account economic fluctuations and market risks. The generation AI can also predict the user's life events (marriage, childbirth, retirement, etc.) and perform risk assessment based on the predictions. The generation AI can also analyze the user's health data and lifestyle data to assess personal life event risks. For example, it can calculate risk based on health status and lifestyle habits. As a result, the asset management support system according to the embodiment can generate a customized investment strategy based on the user's financial data and assess future financial risks. For example, the user can implement an optimal investment strategy based on their financial situation and prepare for future risks. The system can also provide the user with real-time alerts and advice to support rapid response. For example, it can suggest appropriate actions to the user based on the performance of their investment portfolio and market fluctuations.

[0030] The investment strategy generation unit analyzes the user's past investment behavior, learns the user's investment style and risk tolerance, and can propose a more accurate portfolio. For example, the generation AI collects the user's past investment behavior data and learns the user's investment style and risk tolerance. For example, it analyzes past trading history and investment patterns to understand the user's investment tendencies. The investment strategy generation unit can also analyze the user's successful and unsuccessful investment behavior and adjust the user's investment style and risk tolerance. For example, it can optimize the investment strategy based on past examples of high-profit investments and successful risk management. The investment strategy generation unit can also evaluate the user's risk tolerance based on the user's investment behavior data and propose an investment strategy. For example, it evaluates risk tolerance through a questionnaire survey or analysis of past investment behavior. This allows the unit to analyze the user's past investment behavior, learn the user's investment style and risk tolerance, and propose a more accurate portfolio. For example, the user can implement an optimal investment strategy that suits their investment style.

[0031] The investment strategy generation unit can predict a user's life events and dynamically adjust the investment strategy based on the predictions. For example, the investment strategy generation unit uses a generation AI to predict a user's life events and adjust the investment strategy based on the predictions. For example, the investment strategy generation unit can consider events such as marriage and childbirth and propose an investment strategy that increases the liquidity of funds. The investment strategy generation unit can also predict when a user's life events will occur and adjust the investment strategy based on the predictions. For example, the investment strategy generation unit can consider events such as retirement and job change and propose an investment strategy that reduces risk. The investment strategy generation unit can also evaluate the impact of a user's life events and adjust the investment strategy based on the evaluations. For example, the investment portfolio composition can be changed depending on the impact of a life event. This makes it possible to predict a user's life events and dynamically adjust the investment strategy based on the predictions. For example, the user can implement an optimal investment strategy in accordance with their life events.

[0032] The investment strategy generation unit can learn from the success stories of other users and propose an investment strategy based on them. In the investment strategy generation unit, for example, the generation AI collects and learns from the success stories of other users from a database. For example, it analyzes investment strategies and portfolio compositions that have been successful in the past. The investment strategy generation unit can also propose an investment strategy based on the success stories of other users. For example, it can refer to investment strategies that have produced high profits and propose an optimal investment strategy for the user. The investment strategy generation unit can also analyze the success stories of other users and propose risk management methods. For example, it can propose risk diversification methods and risk reduction measures. In this way, it can learn from the success stories of other users and propose an investment strategy based on them. For example, the user can refer to the success stories and implement an optimal investment strategy.

[0033] The investment strategy generation unit can analyze different markets across the board and propose an optimal investment portfolio. For example, the generation AI collects data from different markets and analyzes it across the board. For example, it integrates and analyzes data from the stock market, cryptocurrency market, and real estate market. The investment strategy generation unit can also evaluate the risks and returns of different markets and propose an optimal investment portfolio. For example, it constructs a portfolio taking into account the balance of risk and return of each market. The investment strategy generation unit can also analyze trends in different markets and adjust investment strategies. For example, it optimizes investment strategies by taking into account the upward trend in the stock market and the volatility of the cryptocurrency market. This allows for a cross-sectional analysis of different markets and a proposal of an optimal investment portfolio. For example, a user can diversify their investments across multiple markets to maximize returns while reducing risk.

[0034] The risk assessment unit can combine the user's financial data and market data to simulate risk scenarios and perform risk assessment. In the risk assessment unit, for example, the generation AI collects the user's financial data and market data and simulates risk scenarios. For example, it generates scenarios that take economic fluctuations and market risks into account. The risk assessment unit can also have the generation AI perform risk assessment based on the risk scenarios. For example, it evaluates the risks and returns for each scenario and proposes risk management methods. The risk assessment unit can also have the generation AI combine the user's financial data and market data to dynamically adjust the risk scenarios. For example, it updates the scenarios in response to economic fluctuations and market risks. This allows the user's financial data and market data to be combined to simulate risk scenarios and perform risk assessment. For example, the user can prepare for future risks and implement risk management methods.

[0035] The risk assessment unit can analyze economic data from different regions and countries to perform global risk assessments. For example, the generation AI collects economic data from different regions and countries to perform global risk assessments. For example, it analyzes economic indicators and market data from each country. The risk assessment unit can also perform risk assessments based on economic data from different regions and countries. For example, it performs risk assessments based on each country's GDP, unemployment rate, and inflation rate. The risk assessment unit can also simulate global risk scenarios by combining economic data from different regions and countries. For example, it can generate scenarios for economic crises, natural disasters, political instability, and more. This allows the analysis of economic data from different regions and countries to perform global risk assessments. For example, users can manage risk from a global perspective and implement international investment strategies.

[0036] The risk assessment unit can analyze the user's occupational data and industry data to assess occupational risk and industry risk. In the risk assessment unit, for example, the generation AI collects the user's occupational data and assesses occupational risk. For example, it analyzes the risk factors and working environment for each occupation. The risk assessment unit can also collect the user's industry data and assess industry risk. For example, it analyzes the industry's growth rate, competitive situation, and regulatory environment. The risk assessment unit can also combine the user's occupational data and industry data to assess occupational risk and industry risk. For example, it performs risk assessment based on occupation type, work history, and salary data. This makes it possible to analyze the user's occupational data and industry data and assess occupational risk and industry risk. For example, the user can implement a risk management method based on their occupation and industry.

[0037] The real-time monitoring unit can analyze the user's financial data in real time and detect fraudulent transactions and abnormal expenditures using an anomaly detection algorithm. For example, the real-time monitoring unit uses a generation AI to collect the user's financial data in real time and detect fraudulent transactions using an anomaly detection algorithm. For example, it detects large transactions that are out of the ordinary. The real-time monitoring unit can also analyze the user's financial data in real time and detect abnormal expenditures. For example, it detects expenditures that deviate significantly from normal spending patterns. The real-time monitoring unit can also monitor fraudulent transactions and abnormal expenditures based on the user's financial data in real time using an anomaly detection algorithm. For example, it can detect fraud and money laundering. This allows the user's financial data to be analyzed in real time and fraudulent transactions and abnormal expenditures to be detected using an anomaly detection algorithm. For example, the user can quickly discover fraudulent transactions and abnormal expenditures and take appropriate measures.

[0038] The real-time monitoring unit can analyze market data in real time and provide alerts for sudden market fluctuations. In the real-time monitoring unit, for example, the generation AI collects market data in real time and detects sudden market fluctuations. For example, it analyzes sudden drops or rises in stock prices. The real-time monitoring unit can also analyze market data in real time and provide alerts for sudden market fluctuations. For example, it detects sudden changes in exchange rates and market volatility. The real-time monitoring unit can also provide alerts for sudden market fluctuations in real time based on the market data. For example, it can suggest appropriate actions to the user. This allows the system to analyze market data in real time and provide alerts for sudden market fluctuations. For example, the user can respond quickly to sudden market fluctuations and implement an appropriate investment strategy.

[0039] The real-time monitoring unit can integrate the user's financial data and health data to provide financial advice based on their health status. For example, the generation AI collects, integrates, and analyzes the user's financial and health data. For example, the real-time monitoring unit can provide financial advice based on their health status and medical expenses. The generation AI can also evaluate the user's health status and provide financial advice based on that evaluation. For example, the advice can take into account the presence or absence of chronic diseases and fitness level. The real-time monitoring unit can also combine the user's financial and health data to provide financial advice based on their health status. For example, the generation AI can predict medical expenses and suggest investment strategies for maintaining health. This allows the user's financial and health data to be integrated and financial advice based on their health status can be provided. For example, the user can manage their finances taking their health status into account and prepare for future medical expenses.

[0040] The educational content generation unit can analyze the user's learning history and provide educational content optimized for each individual learning style. In the educational content generation unit, for example, a generation AI collects the user's learning history and analyzes their learning style. For example, it analyzes past learning content and learning methods. The educational content generation unit can also provide educational content optimized for each individual learning style based on the user's learning history. For example, it adjusts content according to the user's learning pace and level of understanding. The educational content generation unit can also provide feedback optimized for the learning style based on the user's learning history. For example, it provides advice according to the user's learning progress and level of understanding. This makes it possible to analyze the user's learning history and provide educational content optimized for each individual learning style. For example, a user can use the optimal educational content that suits their learning style and effectively advance their studies.

[0041] The educational content generation unit can provide simulation learning using actual data based on the user's financial data. In the educational content generation unit, for example, a generation AI collects the user's financial data and provides simulation learning using actual data. For example, a simulation is performed based on past transaction history and expenditure data. The educational content generation unit can also provide simulations of virtual transactions and risk scenarios based on the user's financial data. For example, a virtual investment environment can be created, allowing the user to try out investment strategies using actual data. The educational content generation unit can also evaluate the results of the simulation learning based on the user's financial data and provide feedback. For example, advice can be provided based on the results of the simulation. This makes it possible to provide simulation learning using actual data based on the user's financial data. For example, a user can perform simulations using actual data to improve their practical financial management skills.

[0042] The educational content generation unit can provide educational content that corresponds to different languages ​​and cultures, thereby supporting the improvement of global financial literacy. For example, the generation AI of the educational content generation unit provides educational content that corresponds to different languages ​​and cultures. For example, content translated into multiple languages, such as English, French, and Chinese, is provided. The educational content generation unit can also provide educational content that corresponds to different cultural backgrounds. For example, financial educational content that corresponds to the culture and economic situation of each country is provided. The educational content generation unit can also support the improvement of global financial literacy based on educational content that corresponds to different languages ​​and cultures of the generation AI. For example, international investment strategies and risk management methods are introduced. This makes it possible to provide educational content that corresponds to different languages ​​and cultures and support the improvement of global financial literacy. For example, users can use educational content that matches their own language and culture to improve their international financial literacy.

[0043] The educational content generation unit can provide specialized financial education content tailored to the user's occupation or industry. For example, the generation AI in the educational content generation unit collects the user's occupation data and provides occupation-specific financial education content. For example, the generation AI can introduce investment strategies and risk management methods for medical professionals. The educational content generation unit can also collect the user's industry data and provide industry-specific financial education content. For example, the generation AI can introduce investment strategies and risk management methods tailored to the IT industry or the education industry. The educational content generation unit can also combine the user's occupation data with industry data to provide specialized financial education content. For example, the generation AI can introduce risk factors and investment opportunities for each occupation or industry. This makes it possible to provide specialized financial education content tailored to the user's occupation or industry. For example, the user can use financial education content tailored to their occupation or industry to improve their specialized financial knowledge.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The asset management support system can further analyze the user's social network data and reflect it in the investment strategy. For example, it can refer to the investment behavior of the user's friends and family and suggest a similar investment strategy. It can also analyze the content of the user's social media posts to understand investment interests and trends. It can also evaluate the influence of the user within the network and refer to the investment strategies of influential people. In this way, it can utilize the user's social network to provide a more personalized investment strategy.

[0046] The asset management support system can further analyze the user's health data and propose investment strategies based on their health condition. For example, it can propose investment strategies that take health risks into account based on the user's fitness data and medical records. It can also provide predictions of medical expenses and investment strategies for maintaining health in case the user's health condition deteriorates. It can also provide advice on lifestyle improvements to maintain health based on the user's health data. This allows it to propose investment strategies that take into account the user's health condition and prepare for future medical expenses.

[0047] The asset management support system can also suggest investment strategies based on the user's hobbies and interests. For example, if a user is interested in a particular sport or art, it can suggest investment opportunities related to that field. It can also adjust the user's risk tolerance and investment goals based on the user's hobbies and interests. It can also monitor events and news related to the user's hobbies and interests and adjust the investment strategy based on that. This allows the system to provide an investment strategy that reflects the user's hobbies and interests, making investing more enjoyable.

[0048] The asset management support system can further analyze the user's environmental data and propose environmentally friendly investment strategies. For example, it can propose environmentally friendly investment opportunities based on the user's living environment and energy consumption data. It can also evaluate the user's environmental awareness and interest in sustainability and adjust the investment strategy based on that. It can also recommend investments in eco-friendly companies and projects based on the environmental data. This can provide an investment strategy that reflects the user's environmental awareness and contribute to the realization of a sustainable society.

[0049] The asset management support system can further analyze the user's travel data and propose investment strategies based on the economic conditions of the travel destination. For example, it can propose investment opportunities based on economic data of countries and regions frequently visited by the user. It can also evaluate market trends and risks of travel destinations based on the user's travel history. It can also adjust investment strategies according to the economic conditions of travel destinations based on the user's travel plans. This makes it possible to utilize the user's travel data to provide investment strategies from a global perspective.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The financial data acquisition unit acquires the user's financial data. Specifically, it collects data such as income, expenses, assets, and liabilities. It can also acquire bank account and credit card transaction data via API. It also acquires data manually entered by the user. For example, it collects income and expense data entered by the user into the application. Step 2: The investment strategy generation unit generates a customized investment strategy based on the user's financial data acquired by the financial data acquisition unit. Specifically, the generation AI proposes an optimal portfolio based on the user's risk tolerance and investment goals. It can also analyze past investment performance and adjust future investment strategies. It also analyzes data from different markets and proposes a diversified investment strategy. For example, it integrates and analyzes data from the stock market, cryptocurrency market, and real estate market. Step 3: The risk assessment unit evaluates future financial risks based on the user's financial data. Specifically, the generation AI performs risk assessment taking into account economic fluctuations and market risks. It can also predict the user's life events (marriage, childbirth, retirement, etc.) and perform risk assessment based on these. Furthermore, it analyzes the user's health and lifestyle data to evaluate personal life event risks. For example, it calculates risk based on health status and lifestyle habits.

[0052] (Example 2) An asset management support system according to an embodiment of the present invention is a system that automatically analyzes a user's financial data, and a generation AI proposes a customized investment strategy and evaluates future risks. As a result, the asset management support system can provide an optimal investment strategy based on the user's financial situation and support preparation for future risks.

[0053] The asset management support system according to the embodiment includes a financial data acquisition unit, an investment strategy generation unit, and a risk assessment unit. The financial data acquisition unit acquires a user's financial data. For example, it collects data such as income, expenses, assets, and liabilities. The financial data acquisition unit can also acquire transaction data for bank accounts and credit cards. For example, it acquires data from financial institutions via an API. The financial data acquisition unit can also acquire data manually entered by a user. For example, it collects income and expense data entered by a user into an application. The investment strategy generation unit generates a customized investment strategy based on the user's financial data acquired by the financial data acquisition unit. For example, the generation AI proposes an optimal portfolio based on the user's risk tolerance and investment goals. The generation AI can also analyze past investment performance and adjust future investment strategies. The generation AI can also analyze data from different markets and propose diversified investment strategies. For example, it integrates and analyzes data from the stock market, cryptocurrency market, and real estate market. The risk assessment unit assesses future financial risks based on the user's financial data. For example, the generation AI performs risk assessment taking into account economic fluctuations and market risks. The generation AI can also predict the user's life events (marriage, childbirth, retirement, etc.) and perform risk assessment based on the predictions. The generation AI can also analyze the user's health data and lifestyle data to assess personal life event risks. For example, it can calculate risk based on health status and lifestyle habits. As a result, the asset management support system according to the embodiment can generate a customized investment strategy based on the user's financial data and assess future financial risks. For example, the user can implement an optimal investment strategy based on their financial situation and prepare for future risks. The system can also provide the user with real-time alerts and advice to support rapid response. For example, it can suggest appropriate actions to the user based on the performance of their investment portfolio and market fluctuations.

[0054] The investment strategy generation unit analyzes the user's past investment behavior, learns the user's investment style and risk tolerance, and can propose a more accurate portfolio. For example, the generation AI collects the user's past investment behavior data and learns the user's investment style and risk tolerance. For example, it analyzes past trading history and investment patterns to understand the user's investment tendencies. The investment strategy generation unit can also analyze the user's successful and unsuccessful investment behavior and adjust the user's investment style and risk tolerance. For example, it can optimize the investment strategy based on past examples of high-profit investments and successful risk management. The investment strategy generation unit can also evaluate the user's risk tolerance based on the user's investment behavior data and propose an investment strategy. For example, it evaluates risk tolerance through a questionnaire survey or analysis of past investment behavior. This allows the unit to analyze the user's past investment behavior, learn the user's investment style and risk tolerance, and propose a more accurate portfolio. For example, the user can implement an optimal investment strategy that suits their investment style.

[0055] The investment strategy generation unit can predict a user's life events and dynamically adjust the investment strategy based on the predictions. For example, the investment strategy generation unit uses a generation AI to predict a user's life events and adjust the investment strategy based on the predictions. For example, the investment strategy generation unit can consider events such as marriage and childbirth and propose an investment strategy that increases the liquidity of funds. The investment strategy generation unit can also predict when a user's life events will occur and adjust the investment strategy based on the predictions. For example, the investment strategy generation unit can consider events such as retirement and job change and propose an investment strategy that reduces risk. The investment strategy generation unit can also evaluate the impact of a user's life events and adjust the investment strategy based on the evaluations. For example, the investment portfolio composition can be changed depending on the impact of a life event. This makes it possible to predict a user's life events and dynamically adjust the investment strategy based on the predictions. For example, the user can implement an optimal investment strategy in accordance with their life events.

[0056] The investment strategy generation unit can use the emotion estimation function to analyze the user's emotions regarding investment and propose an investment strategy that provides emotional peace of mind. The investment strategy generation unit, for example, uses the emotion estimation function to analyze the user's emotions regarding investment in real time. For example, the investment strategy generation unit may analyze the user's facial expressions and voice at the time of investment decision and calculate an emotion score. The investment strategy generation unit can also use the emotion estimation function to evaluate the user's emotions regarding investment and propose an investment strategy based on the evaluation. For example, an investment strategy that emphasizes risk reduction and stable profits may be proposed. The investment strategy generation unit can also use the emotion estimation function to monitor the user's emotions regarding investment and provide an investment strategy that provides emotional peace of mind. For example, advice may be provided to reduce investment anxiety. This makes it possible to analyze the user's emotions regarding investment and propose an investment strategy that provides emotional peace of mind. For example, the user's anxiety regarding investment may be reduced and they may invest with peace of mind.

[0057] The investment strategy generation unit can learn from the success stories of other users and propose an investment strategy based on them. In the investment strategy generation unit, for example, the generation AI collects and learns from the success stories of other users from a database. For example, it analyzes investment strategies and portfolio compositions that have been successful in the past. The investment strategy generation unit can also propose an investment strategy based on the success stories of other users. For example, it can refer to investment strategies that have produced high profits and propose an optimal investment strategy for the user. The investment strategy generation unit can also analyze the success stories of other users and propose risk management methods. For example, it can propose risk diversification methods and risk reduction measures. In this way, it can learn from the success stories of other users and propose an investment strategy based on them. For example, the user can refer to the success stories and implement an optimal investment strategy.

[0058] The investment strategy generation unit can analyze different markets across the board and propose an optimal investment portfolio. For example, the generation AI collects data from different markets and analyzes it across the board. For example, it integrates and analyzes data from the stock market, cryptocurrency market, and real estate market. The investment strategy generation unit can also evaluate the risks and returns of different markets and propose an optimal investment portfolio. For example, it constructs a portfolio taking into account the balance of risk and return of each market. The investment strategy generation unit can also analyze trends in different markets and adjust investment strategies. For example, it optimizes investment strategies by taking into account the upward trend in the stock market and the volatility of the cryptocurrency market. This allows for a cross-sectional analysis of different markets and a proposal of an optimal investment portfolio. For example, a user can diversify their investments across multiple markets to maximize returns while reducing risk.

[0059] The investment strategy generation unit can use the emotion estimation function to provide investment educational content so that the user will have positive emotions toward investing. The investment strategy generation unit can, for example, use the emotion estimation function to provide investment educational content so that the user will have positive emotions toward investing. For example, success stories and positive investment experiences can be introduced. The investment strategy generation unit can also use the emotion estimation function to evaluate the user's emotions toward investing and provide educational content based on the evaluation. For example, risk management methods and basic knowledge about investing can be provided. The investment strategy generation unit can also use the emotion estimation function to monitor the user's emotions toward investing and provide feedback so that the user will have positive emotions. For example, successful investment experiences can be highlighted and displayed. This makes it possible to provide investment educational content so that the user will have positive emotions toward investing. For example, the user can deepen their understanding of investing and invest with positive emotions.

[0060] The risk assessment unit can combine the user's financial data and market data to simulate risk scenarios and perform risk assessment. In the risk assessment unit, for example, the generation AI collects the user's financial data and market data and simulates risk scenarios. For example, it generates scenarios that take economic fluctuations and market risks into account. The risk assessment unit can also have the generation AI perform risk assessment based on the risk scenarios. For example, it evaluates the risks and returns for each scenario and proposes risk management methods. The risk assessment unit can also have the generation AI combine the user's financial data and market data to dynamically adjust the risk scenarios. For example, it updates the scenarios in response to economic fluctuations and market risks. This allows the user's financial data and market data to be combined to simulate risk scenarios and perform risk assessment. For example, the user can prepare for future risks and implement risk management methods.

[0061] The risk assessment unit can use the emotion estimation function to analyze how the user feels about risk and provide a risk assessment that gives emotional peace of mind. The risk assessment unit, for example, uses the emotion estimation function to analyze how the user feels about risk in real time. For example, it analyzes facial expressions and voice during the risk assessment and calculates an emotion score. The risk assessment unit can also use the emotion estimation function to evaluate the user's emotions about risk and provide a risk assessment based on the evaluation. For example, it can provide a risk assessment that emphasizes risk reduction and a sense of security. The risk assessment unit can also use the emotion estimation function to monitor the user's emotions about risk and provide a risk assessment that gives emotional peace of mind. For example, it can provide advice to reduce risk anxiety. This makes it possible to analyze how the user feels about risk and provide a risk assessment that gives emotional peace of mind. For example, the user can reduce their anxiety about risk and manage risks with peace of mind.

[0062] The risk assessment unit can analyze economic data from different regions and countries to perform global risk assessments. For example, the generation AI collects economic data from different regions and countries to perform global risk assessments. For example, it analyzes economic indicators and market data from each country. The risk assessment unit can also perform risk assessments based on economic data from different regions and countries. For example, it performs risk assessments based on each country's GDP, unemployment rate, and inflation rate. The risk assessment unit can also simulate global risk scenarios by combining economic data from different regions and countries. For example, it can generate scenarios for economic crises, natural disasters, political instability, and more. This allows the analysis of economic data from different regions and countries to perform global risk assessments. For example, users can manage risk from a global perspective and implement international investment strategies.

[0063] The risk assessment unit can analyze the user's occupational data and industry data to assess occupational risk and industry risk. In the risk assessment unit, for example, the generation AI collects the user's occupational data and assesses occupational risk. For example, it analyzes the risk factors and working environment for each occupation. The risk assessment unit can also collect the user's industry data and assess industry risk. For example, it analyzes the industry's growth rate, competitive situation, and regulatory environment. The risk assessment unit can also combine the user's occupational data and industry data to assess occupational risk and industry risk. For example, it performs risk assessment based on occupation type, work history, and salary data. This makes it possible to analyze the user's occupational data and industry data and assess occupational risk and industry risk. For example, the user can implement a risk management method based on their occupation and industry.

[0064] The risk assessment unit can use the emotion estimation function to provide risk management advice so that the user will have positive feelings about the risk assessment results. The risk assessment unit, for example, uses the emotion estimation function to provide risk management advice so that the user will have positive feelings about the risk assessment results. For example, it can introduce risk reduction measures and success stories. The risk assessment unit can also use the emotion estimation function to evaluate the user's feelings about the risk assessment results and provide advice based on that. For example, it can suggest risk diversification methods and the use of insurance. The risk assessment unit can also use the emotion estimation function to monitor the user's feelings about the risk assessment results and provide feedback so that the user will have positive feelings. For example, it can highlight successful risk management experiences. This makes it possible to provide risk management advice so that the user will have positive feelings about the risk assessment results. For example, the user can deepen their understanding of risk management and perform risk management with positive feelings.

[0065] The real-time monitoring unit can analyze the user's financial data in real time and detect fraudulent transactions and abnormal expenditures using an anomaly detection algorithm. For example, the real-time monitoring unit uses a generation AI to collect the user's financial data in real time and detect fraudulent transactions using an anomaly detection algorithm. For example, it detects large transactions that are out of the ordinary. The real-time monitoring unit can also analyze the user's financial data in real time and detect abnormal expenditures. For example, it detects expenditures that deviate significantly from normal spending patterns. The real-time monitoring unit can also monitor fraudulent transactions and abnormal expenditures based on the user's financial data in real time using an anomaly detection algorithm. For example, it can detect fraud and money laundering. This allows the user's financial data to be analyzed in real time and fraudulent transactions and abnormal expenditures to be detected using an anomaly detection algorithm. For example, the user can quickly discover fraudulent transactions and abnormal expenditures and take appropriate measures.

[0066] The real-time monitoring unit can analyze market data in real time and provide alerts for sudden market fluctuations. In the real-time monitoring unit, for example, the generation AI collects market data in real time and detects sudden market fluctuations. For example, it analyzes sudden drops or rises in stock prices. The real-time monitoring unit can also analyze market data in real time and provide alerts for sudden market fluctuations. For example, it detects sudden changes in exchange rates and market volatility. The real-time monitoring unit can also provide alerts for sudden market fluctuations in real time based on the market data. For example, it can suggest appropriate actions to the user. This allows the system to analyze market data in real time and provide alerts for sudden market fluctuations. For example, the user can respond quickly to sudden market fluctuations and implement an appropriate investment strategy.

[0067] The real-time monitoring unit can use the emotion estimation function to monitor the user's emotional state in real time and provide appropriate advice during emotionally unstable periods. The real-time monitoring unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The real-time monitoring unit can also use the emotion estimation function to evaluate the user's emotional state and provide advice based on the evaluation. For example, it can evaluate stress levels and happiness levels and provide appropriate advice. The real-time monitoring unit can also use the emotion estimation function to monitor the user's emotional state and provide appropriate advice during emotionally unstable periods. For example, it can suggest relaxation methods and stress management methods. This makes it possible to monitor the user's emotional state in real time and provide appropriate advice during emotionally unstable periods. For example, the user can invest or manage their finances in an emotionally stable state.

[0068] The real-time monitoring unit can integrate the user's financial data and health data to provide financial advice based on their health status. For example, the generation AI collects, integrates, and analyzes the user's financial and health data. For example, the real-time monitoring unit can provide financial advice based on their health status and medical expenses. The generation AI can also evaluate the user's health status and provide financial advice based on that evaluation. For example, the advice can take into account the presence or absence of chronic diseases and fitness level. The real-time monitoring unit can also combine the user's financial and health data to provide financial advice based on their health status. For example, the generation AI can predict medical expenses and suggest investment strategies for maintaining health. This allows the user's financial and health data to be integrated and financial advice based on their health status can be provided. For example, the user can manage their finances taking their health status into account and prepare for future medical expenses.

[0069] The real-time monitoring unit can use the emotion estimation function to provide feedback to encourage the user to have positive emotions about the real-time monitoring results. The real-time monitoring unit, for example, uses the emotion estimation function to provide feedback to encourage the user to have positive emotions about the real-time monitoring results. For example, positive results can be highlighted and displayed. The real-time monitoring unit can also use the emotion estimation function to evaluate the user's emotions about the real-time monitoring results and provide feedback based on the evaluation. For example, it can evaluate anomaly detection results or changes in the emotional state and provide appropriate feedback. The real-time monitoring unit can also use the emotion estimation function to monitor the user's emotions about the real-time monitoring results and provide feedback to encourage the user to have positive emotions. For example, positive results can be highlighted and displayed to give the user a sense of security. This makes it possible to provide feedback to encourage the user to have positive emotions about the real-time monitoring results. For example, the user can have positive emotions about the real-time monitoring results and manage their finances with peace of mind.

[0070] The educational content generation unit can analyze the user's learning history and provide educational content optimized for each individual learning style. In the educational content generation unit, for example, a generation AI collects the user's learning history and analyzes their learning style. For example, it analyzes past learning content and learning methods. The educational content generation unit can also provide educational content optimized for each individual learning style based on the user's learning history. For example, it adjusts content according to the user's learning pace and level of understanding. The educational content generation unit can also provide feedback optimized for the learning style based on the user's learning history. For example, it provides advice according to the user's learning progress and level of understanding. This makes it possible to analyze the user's learning history and provide educational content optimized for each individual learning style. For example, a user can use the optimal educational content that suits their learning style and effectively advance their studies.

[0071] The educational content generation unit can provide simulation learning using actual data based on the user's financial data. In the educational content generation unit, for example, a generation AI collects the user's financial data and provides simulation learning using actual data. For example, a simulation is performed based on past transaction history and expenditure data. The educational content generation unit can also provide simulations of virtual transactions and risk scenarios based on the user's financial data. For example, a virtual investment environment can be created, allowing the user to try out investment strategies using actual data. The educational content generation unit can also evaluate the results of the simulation learning based on the user's financial data and provide feedback. For example, advice can be provided based on the results of the simulation. This makes it possible to provide simulation learning using actual data based on the user's financial data. For example, a user can perform simulations using actual data to improve their practical financial management skills.

[0072] The educational content generation unit can use the emotion estimation function to provide relaxation content to reduce stress and anxiety felt by the user while studying. The educational content generation unit, for example, uses the emotion estimation function to analyze the stress and anxiety felt by the user while studying in real time. For example, the emotion estimation function can analyze facial expressions and voices during studying and calculate an emotion score. The educational content generation unit can also use the emotion estimation function to evaluate the user's emotions during studying and provide relaxation content based on the evaluation. For example, the educational content generation unit can provide relaxation music or meditation guides. The educational content generation unit can also use the emotion estimation function to monitor the user's emotions during studying and provide advice to reduce stress and anxiety. For example, the educational content generation unit can suggest timing for breaks and relaxation methods. This makes it possible to provide relaxation content to reduce stress and anxiety felt by the user while studying. For example, the user can study in a relaxed state, thereby improving learning effectiveness.

[0073] The educational content generation unit can provide educational content that corresponds to different languages ​​and cultures, thereby supporting the improvement of global financial literacy. For example, the generation AI of the educational content generation unit provides educational content that corresponds to different languages ​​and cultures. For example, content translated into multiple languages, such as English, French, and Chinese, is provided. The educational content generation unit can also provide educational content that corresponds to different cultural backgrounds. For example, financial educational content that corresponds to the culture and economic situation of each country is provided. The educational content generation unit can also support the improvement of global financial literacy based on educational content that corresponds to different languages ​​and cultures of the generation AI. For example, international investment strategies and risk management methods are introduced. This makes it possible to provide educational content that corresponds to different languages ​​and cultures and support the improvement of global financial literacy. For example, users can use educational content that matches their own language and culture to improve their international financial literacy.

[0074] The educational content generation unit can provide specialized financial education content tailored to the user's occupation or industry. For example, the generation AI in the educational content generation unit collects the user's occupation data and provides occupation-specific financial education content. For example, the generation AI can introduce investment strategies and risk management methods for medical professionals. The educational content generation unit can also collect the user's industry data and provide industry-specific financial education content. For example, the generation AI can introduce investment strategies and risk management methods tailored to the IT industry or the education industry. The educational content generation unit can also combine the user's occupation data with industry data to provide specialized financial education content. For example, the generation AI can introduce risk factors and investment opportunities for each occupation or industry. This makes it possible to provide specialized financial education content tailored to the user's occupation or industry. For example, the user can use financial education content tailored to their occupation or industry to improve their specialized financial knowledge.

[0075] The educational content generation unit can use the emotion estimation function to provide content that increases motivation so that the user has positive emotions toward learning. The educational content generation unit, for example, uses the emotion estimation function to provide content that increases motivation so that the user has positive emotions toward learning. For example, it can introduce success stories and positive learning experiences. The educational content generation unit can also use the emotion estimation function to evaluate the user's emotions toward learning and provide content that increases motivation based on the evaluation. For example, it can provide encouraging messages and advice for achieving goals. The educational content generation unit can also use the emotion estimation function to monitor the user's emotions toward learning and provide feedback to increase positive emotions. For example, it can highlight and display learning progress and results. This makes it possible to provide content that increases motivation so that the user has positive emotions toward learning. For example, the user can increase their motivation toward learning and progress effectively.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The asset management support system can further analyze the user's social network data and reflect it in the investment strategy. For example, it can refer to the investment behavior of the user's friends and family and suggest a similar investment strategy. It can also analyze the content of the user's social media posts to understand investment interests and trends. It can also evaluate the influence of the user within the network and refer to the investment strategies of influential people. In this way, it can utilize the user's social network to provide a more personalized investment strategy.

[0078] The asset management support system can further analyze the user's health data and propose investment strategies based on their health condition. For example, it can propose investment strategies that take health risks into account based on the user's fitness data and medical records. It can also provide predictions of medical expenses and investment strategies for maintaining health in case the user's health condition deteriorates. It can also provide advice on lifestyle improvements to maintain health based on the user's health data. This allows it to propose investment strategies that take into account the user's health condition and prepare for future medical expenses.

[0079] The asset management support system can also suggest investment strategies based on the user's hobbies and interests. For example, if a user is interested in a particular sport or art, it can suggest investment opportunities related to that field. It can also adjust the user's risk tolerance and investment goals based on the user's hobbies and interests. It can also monitor events and news related to the user's hobbies and interests and adjust the investment strategy based on that. This allows the system to provide an investment strategy that reflects the user's hobbies and interests, making investing more enjoyable.

[0080] The asset management support system can further analyze the user's environmental data and propose environmentally friendly investment strategies. For example, it can propose environmentally friendly investment opportunities based on the user's living environment and energy consumption data. It can also evaluate the user's environmental awareness and interest in sustainability and adjust the investment strategy based on that. It can also recommend investments in eco-friendly companies and projects based on the environmental data. This can provide an investment strategy that reflects the user's environmental awareness and contribute to the realization of a sustainable society.

[0081] The asset management support system can further analyze the user's travel data and propose investment strategies based on the economic conditions of the travel destination. For example, it can propose investment opportunities based on economic data of countries and regions frequently visited by the user. It can also evaluate market trends and risks of travel destinations based on the user's travel history. It can also adjust investment strategies according to the economic conditions of travel destinations based on the user's travel plans. This makes it possible to utilize the user's travel data to provide investment strategies from a global perspective.

[0082] The asset management support system can also estimate the user's emotions and manage investment risk based on those emotions. For example, if the user feels anxious about investing, it can suggest a low-risk investment strategy. On the other hand, if the user has positive emotions, it can suggest a riskier investment strategy. Furthermore, it can monitor the user's emotional state in real time and adjust the investment strategy according to changes in emotions. This allows investment risk management based on the user's emotions, allowing them to invest with peace of mind.

[0083] The asset management support system can also estimate the user's emotions and provide investment education content based on those emotions. For example, if the user is feeling anxious about investing, it can introduce risk management methods and success stories. If the user has positive emotions, it can provide basic investment knowledge and advanced investment strategies. Furthermore, it can monitor the user's emotional state in real time and adjust the educational content according to changes in emotions. This allows it to provide investment education content based on the user's emotions and deepen their understanding of investing.

[0084] The asset management support system can also estimate the user's emotions and provide investment advice based on those emotions. For example, if the user is feeling anxious about investing, it can suggest low-risk investments or investments that are expected to produce stable returns. On the other hand, if the user has positive emotions, it can suggest riskier investments or investments that are expected to produce high returns. Furthermore, it can monitor the user's emotional state in real time and adjust investment advice according to changes in emotions. This allows the system to provide investment advice based on the user's emotions, allowing them to invest with peace of mind.

[0085] The asset management support system can also estimate the user's emotions and perform risk assessment based on those emotions. For example, if the user feels anxious about risk, it can suggest a low-risk investment strategy. On the other hand, if the user has positive emotions, it can also suggest a riskier investment strategy. Furthermore, it can monitor the user's emotional state in real time and adjust risk assessment according to changes in emotion. This allows users to perform risk assessment based on their emotions and make investments with peace of mind.

[0086] The asset management support system can also estimate the user's emotions and adjust the investment portfolio based on those emotions. For example, if the user feels anxious about investing, it can shift to lower-risk assets. On the other hand, if the user has positive emotions, it can shift to riskier assets. Furthermore, it can monitor the user's emotional state in real time and adjust the investment portfolio according to changes in emotions. This allows the investment portfolio to be adjusted based on the user's emotions, allowing them to invest with peace of mind.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The financial data acquisition unit acquires the user's financial data. Specifically, it collects data such as income, expenses, assets, and liabilities. It can also acquire bank account and credit card transaction data via API. It also acquires data manually entered by the user. For example, it collects income and expense data entered by the user into the application. Step 2: The investment strategy generation unit generates a customized investment strategy based on the user's financial data acquired by the financial data acquisition unit. Specifically, the generation AI proposes an optimal portfolio based on the user's risk tolerance and investment goals. It can also analyze past investment performance and adjust future investment strategies. It also analyzes data from different markets and proposes a diversified investment strategy. For example, it integrates and analyzes data from the stock market, cryptocurrency market, and real estate market. Step 3: The risk assessment unit evaluates future financial risks based on the user's financial data. Specifically, the generation AI performs risk assessment taking into account economic fluctuations and market risks. It can also predict the user's life events (marriage, childbirth, retirement, etc.) and perform risk assessment based on these. Furthermore, it analyzes the user's health and lifestyle data to evaluate personal life event risks. For example, it calculates risk based on health status and lifestyle habits.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 acquisition unit that acquires financial data of a user; an investment strategy generation unit that generates a customized investment strategy based on the user's financial data acquired by the financial data acquisition unit; a risk assessment unit that assesses future financial risks based on the financial data of the user. A system characterized by:

2. The investment strategy generation unit Analyze the user's past investment behavior, learn their investment style and risk tolerance, and propose a more accurate portfolio.

2. The system of claim 1.

3. The investment strategy generation unit Learn from other users' success stories and propose the above investment strategies based on them 2. The system of claim 1.

4. The risk assessment unit Combining the financial data of the user with market data to simulate risk scenarios and perform risk assessments.

2. The system of claim 1.

5. The real-time monitoring section Analyzing the user's financial data in real time and using anomaly detection algorithms to detect fraudulent transactions and unusual spending 2. The system of claim 1.

6. The investment strategy generation unit Using an emotion estimation function, the user's emotions regarding investment are analyzed, and an investment strategy that provides emotional comfort is proposed.

2. The system of claim 1.

7. The risk assessment unit Using emotion estimation function, the user's emotions regarding risk are analyzed and an emotionally reassuring risk assessment is provided.

2. The system of claim 1.

8. The real-time monitoring section Using emotion estimation capabilities, the emotional state of the user is monitored in real time, and appropriate advice is provided during emotionally unstable times.

2. The system of claim 1.

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