System

Abstract

An object of a system according to an embodiment is to collectively and efficiently perform expenditure management and asset management of a user.SOLUTION: A system according to an embodiment includes an expense data collection part, an asset data collection part, an expense management proposal part, and an asset management proposal part. The expense data collection unit collects user expense data. The asset data collector collects asset data of a user. The expense management proposal unit analyzes the expense data collected by the expense data collection unit, and proposes an optimal expense management method. The asset management proposal part analyzes the asset data collected by the asset data collection part and proposes an optimum asset management plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to efficiently manage users' expenditures and assets in a unified manner.

[0005] The system according to the embodiment aims to efficiently manage a user's expenditures and assets in an integrated manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an expenditure data collection unit, an asset data collection unit, an expenditure management proposal unit, and an asset management proposal unit. The expenditure data collection unit collects expenditure data of a user. The asset data collection unit collects asset data of a user. The expenditure management proposal unit analyzes the expenditure data collected by the expenditure data collection unit and proposes an optimal expenditure management method. The asset management proposal unit analyzes the asset data collected by the asset data collection unit and proposes an optimal asset management plan. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage users' expenditures and assets in a unified manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The expense management and asset management integrated management system according to an embodiment of the present invention is a system in which a generation AI analyzes a user's expense data and asset data and proposes optimal expense management methods and asset management plans. As a result, the expense management and asset management integrated management system can efficiently manage a user's expenses and effectively manage their assets.

[0029] An expense management and asset management integrated management system according to an embodiment includes an expense data collection unit, an asset data collection unit, an expense management proposal unit, and an asset management proposal unit. The expense data collection unit collects a user's expense data. For example, it collects credit card usage history. The expense data collection unit can also collect bank account transaction history. The expense data collection unit can also collect electronic money usage history. The asset data collection unit collects a user's asset data. For example, it collects stock ownership status. The asset data collection unit can also collect real estate ownership status. The asset data collection unit can also collect deposit balances. The expense management proposal unit analyzes the expense data collected by the expense data collection unit and proposes an optimal expense management method. For example, it provides advice on reducing wasteful spending. The expense management proposal unit can also propose a method for setting a budget. The expense management proposal unit can also analyze spending trends and propose areas for improvement. The asset management proposal unit analyzes the asset data collected by the asset data collection unit and proposes an optimal asset management plan. For example, it suggests investment destinations based on risk tolerance. The asset management proposal unit can also suggest methods for diversifying asset investments. The asset management proposal unit can also analyze asset management performance and suggest areas for improvement. As a result, the expense management and asset management integrated management system according to the embodiment can efficiently manage a user's expenses and effectively manage their assets. For example, a user can improve their financial situation by reducing wasteful expenses and effectively managing their assets. Furthermore, a user can increase their assets while diversifying risk based on the suggestions of the generation AI.

[0030] The expenditure data collection unit monitors a user's purchasing behavior in real time and can immediately detect abnormal spending patterns. For example, the expenditure data collection unit monitors a user's credit card usage history in real time and immediately detects abnormal transactions that deviate from normal spending patterns. For example, it issues an alert when a high-value transaction that significantly exceeds the normal spending amount occurs. The expenditure data collection unit also analyzes bank account transaction history in real time to detect abnormal spending patterns. For example, it detects large withdrawals or transfers that are concentrated during a specific time period and notifies the user. The expenditure data collection unit also tracks a user's purchasing behavior in real time and develops algorithms to detect abnormal spending patterns. For example, it detects high-value expenditures at stores or categories that differ from normal purchasing patterns. This allows the system to immediately detect abnormal spending patterns and notify the user, thereby preventing fraudulent transactions and wasteful spending.

[0031] The expenditure data collection unit can generate an individually customized expenditure management plan taking into account the user's lifestyle and preferences. For example, the expenditure data collection unit analyzes the user's past expenditure data and generates an expenditure management plan based on the user's lifestyle and preferences. For example, a user who eats out frequently is provided with specific advice on reducing eating out expenses. The expenditure data collection unit also suggests customized budget settings based on the user's preferences and lifestyle. For example, a user who spends a lot on hobbies and entertainment is suggested with specific methods for reducing spending in those areas. The expenditure data collection unit also collects the user's lifestyle data and customizes the expenditure management plan based on that data. For example, a health-conscious user is provided with a plan that prioritizes managing expenditures related to health foods and fitness. This makes it possible to provide a more appropriate expenditure management plan based on the user's lifestyle and preferences.

[0032] The expenditure data collection unit can utilize IoT devices to collect comprehensive expenditure data, including household power consumption and water usage. The expenditure data collection unit, for example, collects household power consumption data through IoT devices and analyzes it as comprehensive expenditure data. For example, it identifies time periods with high power usage and provides specific advice for saving power. The expenditure data collection unit also collects water usage data using IoT devices and integrates it as expenditure data. For example, it analyzes time periods with high water usage and usage patterns and suggests specific methods for saving water. The expenditure data collection unit also integrates data collected from IoT devices in the home and analyzes it as comprehensive expenditure data. For example, it suggests energy-efficient lifestyles based on electricity and water usage. This allows for the collection of comprehensive expenditure data, including household power consumption and water usage, and enables more detailed expenditure management.

[0033] The expenditure data collection unit can add a function to anonymously compare the expenditure data analysis results with other users and use them as a benchmark. The expenditure data collection unit, for example, provides a function to anonymously compare the expenditure data analysis results with other users and use them as a benchmark. For example, the expenditure patterns of users in the same income bracket can be compared to identify areas for improvement. The expenditure data collection unit also anonymously compares the expenditure data with that of other users and uses it as a benchmark for expenditure management. For example, the expenditure data can be compared with that of users living in the same area to understand area-specific spending trends. The expenditure data collection unit also anonymously shares the expenditure data analysis results and compares them with other users to provide a benchmark for expenditure management. For example, the expenditure data can be compared with that of users in the same age bracket to suggest expenditure management methods according to life stages. In this way, anonymous comparisons with other users can be used as a benchmark for expenditure management to identify areas for improvement.

[0034] The asset data collection unit can analyze the liquidity and risk profile of the user's assets in detail and propose an optimal asset allocation. The asset data collection unit, for example, collects the user's asset data and analyzes the liquidity and risk profile in detail. For example, it evaluates the liquidity of assets such as cash, stocks, and real estate and proposes an optimal asset allocation. The asset data collection unit also analyzes the user's risk tolerance in detail when collecting asset data and proposes an asset allocation based on that. For example, it proposes a method of investing in different asset classes to diversify risk. The asset data collection unit also analyzes the liquidity and risk profile based on the user's asset data and proposes an optimal asset allocation. For example, it proposes an asset allocation that ensures short-term liquidity while aiming for long-term growth. In this way, the efficiency of asset management can be improved by analyzing the liquidity and risk profile of the user's assets in detail and proposing an optimal asset allocation.

[0035] The asset data collection unit can introduce an algorithm that predicts future market trends by referring to past market data and economic indicators. For example, when analyzing asset data, the asset data collection unit introduces an algorithm that predicts future market trends by referring to past market data and economic indicators. For example, future stock price trends are predicted based on past stock price data. The asset data collection unit also analyzes economic indicators and market data to develop an algorithm that predicts future market trends. For example, market trends are predicted based on economic indicators such as GDP growth rate and unemployment rate. The asset data collection unit also introduces an algorithm that predicts future market trends based on past market data and reflects the results in the asset management plan. For example, future real estate market trends are predicted based on past real estate price data. This makes it possible to predict future market trends based on past market data and economic indicators and reflect them in the asset management plan.

[0036] The asset data collection unit can utilize blockchain technology to improve the transparency and reliability of asset information. The asset data collection unit, for example, utilizes blockchain technology to collect asset data to improve the transparency and reliability of asset information. For example, asset transaction history is recorded on the blockchain to prevent fraudulent transactions. The asset data collection unit also uses blockchain technology to collect and manage asset data to ensure transparency and reliability. For example, asset ownership and transaction history are recorded on the blockchain to prevent tampering. The asset data collection unit also introduces blockchain technology to collect asset data to improve the transparency and reliability of asset information. For example, asset valuation and transaction history are recorded on the blockchain to provide highly reliable data. In this way, the transparency and reliability of asset information can be improved by utilizing blockchain technology.

[0037] The asset data collection unit can add a function to anonymously share the results of asset data analysis with other investors and propose an investment strategy utilizing collective wisdom. The asset data collection unit, for example, provides a function to anonymously share the results of asset data analysis with other investors and propose an investment strategy utilizing collective wisdom. For example, an investment strategy is proposed based on data of investors with the same risk profile. The asset data collection unit also anonymously compares the asset data of other investors and proposes an investment strategy utilizing collective wisdom. For example, an optimal investment strategy is proposed based on data of investors with the same investment goals. The asset data collection unit also anonymously shares the results of asset data analysis with other investors and compares it with other investors to provide an investment strategy utilizing collective wisdom. For example, an investment strategy is proposed based on data of investors investing in the same market. In this way, by anonymously sharing data with other investors, an optimal investment strategy utilizing collective wisdom can be proposed.

[0038] The expenditure management proposal unit can predict seasonal spending patterns based on the user's past spending history and propose an optimal budget. The expenditure management proposal unit, for example, analyzes the user's past spending history and predicts seasonal spending patterns. For example, if there is a tendency for air conditioning costs to increase in the summer, the expenditure management proposal unit proposes a budget that takes that into account. The expenditure management proposal unit also predicts seasonal spending patterns based on past spending data and proposes an optimal budget. For example, if there is a tendency for spending to increase during the year-end and New Year holidays, the expenditure management proposal unit sets a budget for that period. The expenditure management proposal unit also analyzes the user's spending history and develops an algorithm that predicts seasonal spending patterns. For example, it identifies a tendency for spending in a specific category to increase in a specific season and reflects that amount in the budget. In this way, by predicting seasonal spending patterns and proposing an optimal budget, the user's spending can be managed more effectively.

[0039] The expenditure management suggestion unit can provide expenditure advice to promote a healthy lifestyle by taking into account the user's health data. The expenditure management suggestion unit, for example, collects the user's health data and suggests an expenditure management method based on the data. For example, to promote a healthy diet, advice is provided to prioritize expenditures related to health foods and fitness. The expenditure management suggestion unit also provides expenditure advice to promote a healthy lifestyle by taking into account the health data. For example, regular exercise is recommended and gym membership fees and the purchase of exercise equipment are incorporated into the expenditure plan. The expenditure management suggestion unit also proposes an expenditure management plan to promote a healthy lifestyle based on the user's health data. For example, a budget is set for health checkups and medical expenses, and expenditures for maintaining health are planned. In this way, the expenditure advice to promote a healthy lifestyle by taking into account the user's health data can be provided, thereby supporting the user in maintaining their health.

[0040] The expenditure management proposal unit can propose an expenditure management plan for the entire family, taking into account the user's family structure and life stage. The expenditure management proposal unit, for example, proposes an expenditure management plan for the entire family, taking into account the user's family structure and life stage. For example, it sets budgets for children's education expenses and family trips. The expenditure management proposal unit also customizes the expenditure management plan based on the family structure and life stage. For example, it proposes a budget for a newlywed family that takes into account expenses necessary for their new life. The expenditure management proposal unit also collects the user's life stage data and proposes an expenditure management plan for the entire family based on that data. For example, it sets a budget that takes into account living expenses after retirement and medical expenses. In this way, by providing an expenditure management plan that takes into account the user's family structure and life stage, it is possible to effectively manage the expenditures of the entire family.

[0041] The expenditure management suggestion unit can provide a plan that reflects the user's hobbies and interests, allowing the user to manage their expenses while having fun. The expenditure management suggestion unit, for example, proposes an expenditure management plan that reflects the user's hobbies and interests. For example, for a user whose hobby is traveling, it proposes a method for systematically saving for travel expenses. The expenditure management suggestion unit also provides a plan that allows the user to manage their expenses while having fun, based on the user's hobbies and interests. For example, for a user who likes music, it sets a budget for concert tickets. The expenditure management suggestion unit also customizes the expenditure management plan taking the user's hobbies and interests into consideration. For example, for a user who likes sports, it incorporates sports event and gym membership fees into the spending plan. In this way, by providing an expenditure management plan that reflects the user's hobbies and interests, the user can manage their expenses while having fun.

[0042] The asset management proposal unit can analyze the risk tolerance in detail based on the user's past investment history and propose an optimal investment strategy. The asset management proposal unit, for example, analyzes the user's past investment history and evaluates the risk tolerance in detail. For example, it proposes an optimal investment strategy based on past investment performance and risk-taking tendencies. The asset management proposal unit also analyzes the user's risk tolerance in detail based on investment history data and proposes an investment strategy based on that. For example, it proposes a method of investing in different asset classes to diversify risk. The asset management proposal unit also analyzes the user's past investment history in detail and develops an algorithm to evaluate the risk tolerance. For example, it creates a risk profile based on past investment behavior and proposes an investment strategy based on that. In this way, the efficiency of asset management can be improved by analyzing the risk tolerance in detail based on the user's past investment history and proposing an optimal investment strategy.

[0043] The asset management proposal unit can take into account the user's long-term goals and provide a specific plan for achieving those goals. For example, the asset management proposal unit can take into account the user's long-term goals and propose an asset management plan based on those goals. For example, it can provide a specific investment strategy for securing living expenses after retirement. The asset management proposal unit can also customize the asset management plan based on the long-term goals. For example, it can propose an investment plan targeting children's education expenses and home purchase funds. The asset management proposal unit can also hear the user's long-term goals in detail and provide a specific asset management plan based on those goals. For example, it can propose a step-by-step investment strategy for achieving the goals. In this way, the efficiency of asset management can be improved by taking into account the user's long-term goals and providing a specific plan for achieving the goals.

[0044] The asset management proposal department can reflect the user's preferences for socially responsible investment (SRI) and environmental, social, and governance (ESG) investment. For example, the asset management proposal department proposes an asset management plan that reflects the user's preferences for socially responsible investment (SRI) and environmental, social, and governance (ESG) investment. For example, it recommends investments in environmentally conscious companies. The asset management proposal department also considers the user's preferences for SRI and ESG investment and proposes optimal investment destinations for the user. For example, it recommends investments in socially responsible companies and projects. The asset management proposal department also listens to the user's investment preferences in detail and provides an asset management plan that reflects SRI and ESG investment based on the interview. For example, it proposes an investment strategy that emphasizes environmental protection and social contribution. In this way, by providing an asset management plan that reflects the user's preferences for socially responsible investment (SRI) and environmental, social, and governance (ESG) investment, it is possible to support asset management that matches the user's investment preferences.

[0045] The asset management proposal unit can provide a locally tailored investment strategy taking into account the user's local economy and industrial trends. The asset management proposal unit, for example, proposes a locally tailored investment strategy taking into account the user's local economy and industrial trends. For example, it recommends investments in local growth industries and local companies. The asset management proposal unit also analyzes local economic data and provides the user with an optimal locally tailored investment strategy based on that data. For example, it selects investment destinations based on the local economic growth rate and industrial trends. The asset management proposal unit also analyzes local economic and industrial trends in detail to propose an investment strategy tailored to the user's region. For example, it recommends investments in local infrastructure projects and emerging companies. In this way, by providing a locally tailored investment strategy taking into account the user's local economy and industrial trends, it is possible to support the user's asset management while contributing to the development of the local economy.

[0046] The expenditure data collection unit and the asset data collection unit can monitor a user's overall financial situation in real time and immediately detect abnormal expenditure or investment patterns. The expenditure data collection unit and the asset data collection unit, for example, build a system that monitors a user's overall financial situation in real time and immediately detects abnormal expenditure or investment patterns. For example, they detect high-value transactions that deviate from normal expenditure patterns and issue alerts. The expenditure data collection unit and the asset data collection unit also integrate expenditure data and asset data to monitor the financial situation in real time. For example, they detect abnormal investment patterns or sudden asset fluctuations and notify the user. The expenditure data collection unit and the asset data collection unit also develop algorithms that analyze a user's financial situation in real time and immediately detect abnormal expenditure or investment patterns. For example, they detect transactions that deviate from normal expenditure or investment patterns and issue alerts. This makes it possible to monitor a user's overall financial situation in real time and immediately detect abnormal expenditure or investment patterns, enabling rapid response.

[0047] The expenditure management proposal unit and the asset management proposal unit can provide a financial plan appropriate to a user's life stage by taking into account the user's life events. For example, the expenditure management proposal unit and the asset management proposal unit can provide a comprehensive expenditure management and asset management plan based on the user's life events. For example, they can propose plans for expenses and asset management related to marriage and childbirth. The expenditure management proposal unit and the asset management proposal unit can also collect data on the user's life events and customize the expenditure management and asset management plan based on the data to provide a financial plan appropriate to the user's life stage. For example, they can provide a plan that takes into account living expenses and medical expenses after retirement. The expenditure management proposal unit and the asset management proposal unit can also hear the user's life events in detail and provide a financial plan appropriate to the user's life stage based on the data. For example, they can propose an expenditure management and asset management plan targeting children's education expenses and home purchase funds. This can support the user's life planning by providing a financial plan appropriate to the user's life stage by taking into account the user's life events.

[0048] The expenditure management proposal unit and the asset management proposal unit can integrate the user's health data and provide a financial plan to promote a healthy lifestyle. For example, the expenditure management proposal unit and the asset management proposal unit integrate the user's health data and provide a comprehensive expenditure management and asset management plan based on the health data. For example, they provide advice that prioritizes healthy eating habits and fitness-related expenses. The expenditure management proposal unit and the asset management proposal unit also take the health data into consideration and provide a financial plan to promote a healthy lifestyle. For example, they recommend regular exercise and incorporate gym membership fees and the purchase of exercise equipment into the spending plan. The expenditure management proposal unit and the asset management proposal unit also propose an expenditure management and asset management plan to promote a healthy lifestyle based on the user's health data. For example, they set budgets for health checkups and medical expenses and plan expenditures for maintaining health. In this way, the system can support the user in maintaining their health by integrating the user's health data and providing a financial plan to promote a healthy lifestyle.

[0049] The expenditure management proposal unit and the asset management proposal unit can provide a plan that reflects the user's hobbies and interests, allowing the user to manage their finances while having fun. The expenditure management proposal unit and the asset management proposal unit, for example, propose a financial management plan that reflects the user's hobbies and interests. For example, for a user whose hobby is traveling, they propose a method for systematically saving money for travel expenses. The expenditure management proposal unit and the asset management proposal unit also provide a plan that allows the user to manage their finances while having fun, based on the user's hobbies and interests. For example, for a user who likes music, they set a budget for concert tickets. The expenditure management proposal unit and the asset management proposal unit also customize the financial management plan taking into account the user's hobbies and interests. For example, for a user who likes sports, they incorporate sports event and gym membership fees into the spending plan. In this way, by providing a financial management plan that reflects the user's hobbies and interests, the user can manage their finances while having fun.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The expenditure management suggestion unit can predict seasonal expenditure patterns based on the user's past expenditure history and propose an optimal budget. For example, if air conditioning costs tend to increase in the summer, the unit can propose a budget that takes that into account. Also, if expenditures tend to increase around the year-end and New Year holidays, the unit can set a budget for that time of year. Furthermore, it is possible to understand the tendency for expenditures in specific categories to increase during certain seasons and reflect that in the budget. In this way, by predicting seasonal expenditure patterns and proposing an optimal budget, the user can manage their expenditures more effectively.

[0052] The expenditure data collection unit can use IoT devices to collect comprehensive expenditure data, including household power consumption and water usage. For example, household power consumption data can be collected through IoT devices and analyzed as comprehensive expenditure data. It can identify time periods with high power usage and provide specific advice for saving energy. It can also collect water usage data using IoT devices and integrate it as expenditure data. It can analyze time periods with high water usage and usage patterns and suggest specific methods for saving water. This allows for the collection of comprehensive expenditure data, including household power consumption and water usage, enabling more detailed expenditure management.

[0053] The expenditure data collection unit can add a function that allows the results of expenditure data analysis to be anonymously compared with other users and used as a benchmark. For example, it is possible to compare spending patterns with users in the same income bracket and identify areas for improvement. It is also possible to compare expenditure data with other users anonymously and use it as a benchmark for expenditure management. It is possible to compare expenditure data with users living in the same area and understand spending trends specific to that area. Furthermore, it is possible to anonymously share the results of expenditure data analysis and compare it with other users to provide a benchmark for expenditure management. It is possible to compare expenditure data with users in the same age group and suggest expenditure management methods according to life stages. In this way, anonymous comparison with other users can be used as a benchmark for expenditure management and identify areas for improvement.

[0054] The asset data collection unit can perform a detailed analysis of the liquidity and risk profile of a user's assets and propose an optimal asset allocation. For example, it can collect a user's asset data and perform a detailed analysis of the liquidity and risk profile. It can evaluate the liquidity of assets such as cash, stocks, and real estate and propose an optimal asset allocation. It can also perform a detailed analysis of the user's risk tolerance when collecting asset data and propose an asset allocation based on that. It can propose a method of investing in different asset classes to diversify risk. Furthermore, it can analyze the liquidity and risk profile based on the user's asset data and propose an optimal asset allocation. It can propose an asset allocation that aims for long-term growth while ensuring short-term liquidity. This allows for a detailed analysis of the user's asset liquidity and risk profile and proposes an optimal asset allocation, thereby improving the efficiency of asset management.

[0055] The asset data collection unit can introduce an algorithm that refers to past market data and economic indicators and predicts future market trends. For example, in analyzing asset data, an algorithm can be introduced that refers to past market data and economic indicators and predicts future market trends. Future stock price trends can be predicted based on past stock price data. It is also possible to analyze economic indicators and market data and develop an algorithm that predicts future market trends. Market trends can be predicted based on economic indicators such as GDP growth rate and unemployment rate. Furthermore, an algorithm that predicts future market trends based on past market data can be introduced and reflected in asset management plans. Future real estate market trends can be predicted based on past real estate price data. This makes it possible to predict future market trends based on past market data and economic indicators and reflect them in asset management plans.

[0056] The asset data collection unit can utilize blockchain technology to improve the transparency and reliability of asset information. For example, blockchain technology can be utilized to collect asset data to improve the transparency and reliability of asset information. Asset transaction history can be recorded on the blockchain to prevent fraudulent transactions. Blockchain technology can also be used to collect and manage asset data to ensure transparency and reliability. Asset ownership and transaction history can be recorded on the blockchain to prevent tampering. Furthermore, blockchain technology can be introduced to collect asset data to improve the transparency and reliability of asset information. Asset valuation and transaction history can be recorded on the blockchain to provide highly reliable data. As a result, the transparency and reliability of asset information can be improved by utilizing blockchain technology.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The expenditure data collection unit collects expenditure data of the user, such as credit card usage history, bank account transaction history, and electronic money usage history. Step 2: The asset data collection unit collects asset data of the user, such as stock holdings, real estate ownership, and deposit balances. Step 3: The expenditure management proposal unit analyzes the expenditure data collected by the expenditure data collection unit and proposes optimal expenditure management methods, such as advice on reducing wasteful expenditures, how to set a budget, and analyzing expenditure trends to propose areas for improvement. Step 4: The Asset Management Proposal Department analyzes the asset data collected by the Asset Data Collection Department and proposes an optimal asset management plan. For example, it analyzes investment destinations based on risk tolerance, asset diversification methods, and asset management performance to propose areas for improvement.

[0059] (Example 2) The expense management and asset management integrated management system according to an embodiment of the present invention is a system in which a generation AI analyzes a user's expense data and asset data and proposes optimal expense management methods and asset management plans. As a result, the expense management and asset management integrated management system can efficiently manage a user's expenses and effectively manage their assets.

[0060] An expense management and asset management integrated management system according to an embodiment includes an expense data collection unit, an asset data collection unit, an expense management proposal unit, and an asset management proposal unit. The expense data collection unit collects a user's expense data. For example, it collects credit card usage history. The expense data collection unit can also collect bank account transaction history. The expense data collection unit can also collect electronic money usage history. The asset data collection unit collects a user's asset data. For example, it collects stock ownership status. The asset data collection unit can also collect real estate ownership status. The asset data collection unit can also collect deposit balances. The expense management proposal unit analyzes the expense data collected by the expense data collection unit and proposes an optimal expense management method. For example, it provides advice on reducing wasteful spending. The expense management proposal unit can also propose a method for setting a budget. The expense management proposal unit can also analyze spending trends and propose areas for improvement. The asset management proposal unit analyzes the asset data collected by the asset data collection unit and proposes an optimal asset management plan. For example, it suggests investment destinations based on risk tolerance. The asset management proposal unit can also suggest methods for diversifying asset investments. The asset management proposal unit can also analyze asset management performance and suggest areas for improvement. As a result, the expense management and asset management integrated management system according to the embodiment can efficiently manage a user's expenses and effectively manage their assets. For example, a user can improve their financial situation by reducing wasteful expenses and effectively managing their assets. Furthermore, a user can increase their assets while diversifying risk based on the suggestions of the generation AI.

[0061] The expenditure data collection unit monitors a user's purchasing behavior in real time and can immediately detect abnormal spending patterns. For example, the expenditure data collection unit monitors a user's credit card usage history in real time and immediately detects abnormal transactions that deviate from normal spending patterns. For example, it issues an alert when a high-value transaction that significantly exceeds the normal spending amount occurs. The expenditure data collection unit also analyzes bank account transaction history in real time to detect abnormal spending patterns. For example, it detects large withdrawals or transfers that are concentrated during a specific time period and notifies the user. The expenditure data collection unit also tracks a user's purchasing behavior in real time and develops algorithms to detect abnormal spending patterns. For example, it detects high-value expenditures at stores or categories that differ from normal purchasing patterns. This allows the system to immediately detect abnormal spending patterns and notify the user, thereby preventing fraudulent transactions and wasteful spending.

[0062] The expenditure data collection unit can generate an individually customized expenditure management plan taking into account the user's lifestyle and preferences. For example, the expenditure data collection unit analyzes the user's past expenditure data and generates an expenditure management plan based on the user's lifestyle and preferences. For example, a user who eats out frequently is provided with specific advice on reducing eating out expenses. The expenditure data collection unit also suggests customized budget settings based on the user's preferences and lifestyle. For example, a user who spends a lot on hobbies and entertainment is suggested with specific methods for reducing spending in those areas. The expenditure data collection unit also collects the user's lifestyle data and customizes the expenditure management plan based on that data. For example, a health-conscious user is provided with a plan that prioritizes managing expenditures related to health foods and fitness. This makes it possible to provide a more appropriate expenditure management plan based on the user's lifestyle and preferences.

[0063] The expenditure data collection unit can use the emotion estimation function to analyze a user's emotions when making a purchase and provide advice to curb emotional spending. The expenditure data collection unit, for example, analyzes a user's emotions when making a purchase and provides advice to curb emotional spending. For example, for a user who tends to make expensive purchases during times of high stress, the expenditure data collection unit suggests alternative ways to relax. The expenditure data collection unit also collects emotional data when making a purchase and provides specific advice to curb emotional spending. For example, the expenditure data collection unit suggests taking some time to calm down before making a purchase to prevent emotional impulse buying. The expenditure data collection unit also uses the emotion estimation function to analyze a user's emotions when making a purchase in real time and issue an alert to curb emotional spending. For example, a warning is displayed if a user is about to make a large expenditure during an emotionally unstable period. This allows the user to curb emotional spending and achieve more planned spending management.

[0064] The expenditure data collection unit can utilize IoT devices to collect comprehensive expenditure data, including household power consumption and water usage. The expenditure data collection unit, for example, collects household power consumption data through IoT devices and analyzes it as comprehensive expenditure data. For example, it identifies time periods with high power usage and provides specific advice for saving power. The expenditure data collection unit also collects water usage data using IoT devices and integrates it as expenditure data. For example, it analyzes time periods with high water usage and usage patterns and suggests specific methods for saving water. The expenditure data collection unit also integrates data collected from IoT devices in the home and analyzes it as comprehensive expenditure data. For example, it suggests energy-efficient lifestyles based on electricity and water usage. This allows for the collection of comprehensive expenditure data, including household power consumption and water usage, and enables more detailed expenditure management.

[0065] The expenditure data collection unit can add a function to anonymously compare the expenditure data analysis results with other users and use them as a benchmark. The expenditure data collection unit, for example, provides a function to anonymously compare the expenditure data analysis results with other users and use them as a benchmark. For example, the expenditure patterns of users in the same income bracket can be compared to identify areas for improvement. The expenditure data collection unit also anonymously compares the expenditure data with that of other users and uses it as a benchmark for expenditure management. For example, the expenditure data can be compared with that of users living in the same area to understand area-specific spending trends. The expenditure data collection unit also anonymously shares the expenditure data analysis results and compares them with other users to provide a benchmark for expenditure management. For example, the expenditure data can be compared with that of users in the same age bracket to suggest expenditure management methods according to life stages. In this way, anonymous comparisons with other users can be used as a benchmark for expenditure management to identify areas for improvement.

[0066] The expenditure data collection unit can use the emotion estimation function to estimate the user's emotion in real time when entering expenditure data and make suggestions to elicit positive emotions. For example, the expenditure data collection unit estimates the user's emotion in real time when entering expenditure data and makes suggestions to elicit positive emotions. For example, it analyzes the user's facial expressions and voice during entry and provides positive feedback. The expenditure data collection unit also uses the emotion estimation function to analyze the user's emotion during entry of expenditure data in real time and provides an interface for eliciting positive emotions. For example, it displays encouraging messages and success stories. The expenditure data collection unit also provides feedback in real time based on the emotion estimation data when the user enters expenditure data and makes suggestions to elicit positive emotions. For example, it displays appropriate encouragement or praise according to the input content. This elicits positive emotions when the user enters expenditure data, thereby maintaining motivation for expenditure management.

[0067] The asset data collection unit can analyze the liquidity and risk profile of the user's assets in detail and propose an optimal asset allocation. The asset data collection unit, for example, collects the user's asset data and analyzes the liquidity and risk profile in detail. For example, it evaluates the liquidity of assets such as cash, stocks, and real estate and proposes an optimal asset allocation. The asset data collection unit also analyzes the user's risk tolerance in detail when collecting asset data and proposes an asset allocation based on that. For example, it proposes a method of investing in different asset classes to diversify risk. The asset data collection unit also analyzes the liquidity and risk profile based on the user's asset data and proposes an optimal asset allocation. For example, it proposes an asset allocation that ensures short-term liquidity while aiming for long-term growth. In this way, the efficiency of asset management can be improved by analyzing the liquidity and risk profile of the user's assets in detail and proposing an optimal asset allocation.

[0068] The asset data collection unit can introduce an algorithm that predicts future market trends by referring to past market data and economic indicators. For example, when analyzing asset data, the asset data collection unit introduces an algorithm that predicts future market trends by referring to past market data and economic indicators. For example, future stock price trends are predicted based on past stock price data. The asset data collection unit also analyzes economic indicators and market data to develop an algorithm that predicts future market trends. For example, market trends are predicted based on economic indicators such as GDP growth rate and unemployment rate. The asset data collection unit also introduces an algorithm that predicts future market trends based on past market data and reflects the results in the asset management plan. For example, future real estate market trends are predicted based on past real estate price data. This makes it possible to predict future market trends based on past market data and economic indicators and reflect them in the asset management plan.

[0069] The asset data collection unit can use the emotion estimation function to analyze a user's emotions when making an investment decision and provide advice to eliminate emotional bias. The asset data collection unit, for example, analyzes a user's emotions when making an investment decision and provides advice to eliminate emotional bias. For example, specific advice is provided to avoid making investment decisions due to excessive fear or excitement. The asset data collection unit also uses the emotion estimation function to analyze a user's emotions when making an investment decision in real time and provide feedback to eliminate emotional bias. For example, a reminder is displayed to encourage a calm decision. The asset data collection unit also collects emotional data when making an investment decision and provides specific advice to eliminate emotional bias. For example, advice is provided to avoid excessive risk-taking due to emotional excitement. In this way, by analyzing a user's emotions when making an investment decision and eliminating emotional bias, it is possible to support more calm and rational investment decisions.

[0070] The asset data collection unit can utilize blockchain technology to improve the transparency and reliability of asset information. The asset data collection unit, for example, utilizes blockchain technology to collect asset data to improve the transparency and reliability of asset information. For example, asset transaction history is recorded on the blockchain to prevent fraudulent transactions. The asset data collection unit also uses blockchain technology to collect and manage asset data to ensure transparency and reliability. For example, asset ownership and transaction history are recorded on the blockchain to prevent tampering. The asset data collection unit also introduces blockchain technology to collect asset data to improve the transparency and reliability of asset information. For example, asset valuation and transaction history are recorded on the blockchain to provide highly reliable data. In this way, the transparency and reliability of asset information can be improved by utilizing blockchain technology.

[0071] The asset data collection unit can add a function to anonymously share the results of asset data analysis with other investors and propose an investment strategy utilizing collective wisdom. The asset data collection unit, for example, provides a function to anonymously share the results of asset data analysis with other investors and propose an investment strategy utilizing collective wisdom. For example, an investment strategy is proposed based on data of investors with the same risk profile. The asset data collection unit also anonymously compares the asset data of other investors and proposes an investment strategy utilizing collective wisdom. For example, an optimal investment strategy is proposed based on data of investors with the same investment goals. The asset data collection unit also anonymously shares the results of asset data analysis with other investors and compares it with other investors to provide an investment strategy utilizing collective wisdom. For example, an investment strategy is proposed based on data of investors investing in the same market. In this way, by anonymously sharing data with other investors, an optimal investment strategy utilizing collective wisdom can be proposed.

[0072] The asset data collection unit can use the emotion estimation function to estimate the user's emotion in real time when entering asset data and make suggestions to elicit positive emotions. For example, the asset data collection unit estimates the user's emotion in real time when entering asset data and makes suggestions to elicit positive emotions. For example, the asset data collection unit analyzes the user's facial expression and voice during entry and provides positive feedback. The asset data collection unit also uses the emotion estimation function to analyze the user's emotion during asset data entry in real time and provide an interface for eliciting positive emotions. For example, it displays encouraging messages and success stories. The asset data collection unit also provides feedback in real time based on the emotion estimation data when the user enters asset data and makes suggestions to elicit positive emotions. For example, it displays appropriate encouragement or praise according to the input content. This elicits positive emotions when the user enters asset data, thereby maintaining motivation for asset management.

[0073] The expenditure management proposal unit can predict seasonal spending patterns based on the user's past spending history and propose an optimal budget. The expenditure management proposal unit, for example, analyzes the user's past spending history and predicts seasonal spending patterns. For example, if there is a tendency for air conditioning costs to increase in the summer, the expenditure management proposal unit proposes a budget that takes that into account. The expenditure management proposal unit also predicts seasonal spending patterns based on past spending data and proposes an optimal budget. For example, if there is a tendency for spending to increase during the year-end and New Year holidays, the expenditure management proposal unit sets a budget for that period. The expenditure management proposal unit also analyzes the user's spending history and develops an algorithm that predicts seasonal spending patterns. For example, it identifies a tendency for spending in a specific category to increase in a specific season and reflects that amount in the budget. In this way, by predicting seasonal spending patterns and proposing an optimal budget, the user's spending can be managed more effectively.

[0074] The expenditure management suggestion unit can provide expenditure advice to promote a healthy lifestyle by taking into account the user's health data. The expenditure management suggestion unit, for example, collects the user's health data and suggests an expenditure management method based on the data. For example, to promote a healthy diet, advice is provided to prioritize expenditures related to health foods and fitness. The expenditure management suggestion unit also provides expenditure advice to promote a healthy lifestyle by taking into account the health data. For example, regular exercise is recommended and gym membership fees and the purchase of exercise equipment are incorporated into the expenditure plan. The expenditure management suggestion unit also proposes an expenditure management plan to promote a healthy lifestyle based on the user's health data. For example, a budget is set for health checkups and medical expenses, and expenditures for maintaining health are planned. In this way, the expenditure advice to promote a healthy lifestyle by taking into account the user's health data can be provided, thereby supporting the user in maintaining their health.

[0075] The expenditure management suggestion unit can use the emotion estimation function to analyze the user's emotions regarding expenditure management and provide advice to maintain motivation. The expenditure management suggestion unit, for example, analyzes the user's emotions regarding expenditure management and provides advice to maintain motivation. For example, it provides positive feedback when expenditure management is going well. The expenditure management suggestion unit also uses the emotion estimation function to analyze the user's emotions regarding expenditure management in real time and provides feedback to maintain motivation. For example, it displays an encouraging message when a goal is achieved. The expenditure management suggestion unit also collects emotion data regarding expenditure management and provides specific advice to maintain motivation. For example, it suggests setting a simple goal to a user who finds expenditure management difficult. In this way, it is possible to support continued expenditure management by analyzing the user's emotions regarding expenditure management and providing advice to maintain motivation.

[0076] The expenditure management proposal unit can propose an expenditure management plan for the entire family, taking into account the user's family structure and life stage. The expenditure management proposal unit, for example, proposes an expenditure management plan for the entire family, taking into account the user's family structure and life stage. For example, it sets budgets for children's education expenses and family trips. The expenditure management proposal unit also customizes the expenditure management plan based on the family structure and life stage. For example, it proposes a budget for a newlywed family that takes into account expenses necessary for their new life. The expenditure management proposal unit also collects the user's life stage data and proposes an expenditure management plan for the entire family based on that data. For example, it sets a budget that takes into account living expenses after retirement and medical expenses. In this way, by providing an expenditure management plan that takes into account the user's family structure and life stage, it is possible to effectively manage the expenditures of the entire family.

[0077] The expenditure management suggestion unit can provide a plan that reflects the user's hobbies and interests, allowing the user to manage their expenses while having fun. The expenditure management suggestion unit, for example, proposes an expenditure management plan that reflects the user's hobbies and interests. For example, for a user whose hobby is traveling, it proposes a method for systematically saving for travel expenses. The expenditure management suggestion unit also provides a plan that allows the user to manage their expenses while having fun, based on the user's hobbies and interests. For example, for a user who likes music, it sets a budget for concert tickets. The expenditure management suggestion unit also customizes the expenditure management plan taking the user's hobbies and interests into consideration. For example, for a user who likes sports, it incorporates sports event and gym membership fees into the spending plan. In this way, by providing an expenditure management plan that reflects the user's hobbies and interests, the user can manage their expenses while having fun.

[0078] The expenditure management suggestion unit can use the emotion estimation function to monitor the user's emotions in real time when the user is performing the expenditure management method and make suggestions to elicit positive emotions. The expenditure management suggestion unit, for example, monitors the user's emotions in real time when the user is performing the expenditure management method and makes suggestions to elicit positive emotions. For example, it provides positive feedback when a goal is achieved. The expenditure management suggestion unit also uses the emotion estimation function to analyze the user's emotions in real time when the user is performing the expenditure management method and provides an interface for eliciting positive emotions. For example, it displays encouraging messages and success stories. The expenditure management suggestion unit also provides feedback in real time based on the emotion estimation data when the user is performing the expenditure management method and makes suggestions to elicit positive emotions. For example, it displays appropriate encouragement or praise according to the input content. This allows the user to elicit positive emotions when performing the expenditure management method, thereby maintaining motivation for expenditure management.

[0079] The asset management proposal unit can analyze the risk tolerance in detail based on the user's past investment history and propose an optimal investment strategy. The asset management proposal unit, for example, analyzes the user's past investment history and evaluates the risk tolerance in detail. For example, it proposes an optimal investment strategy based on past investment performance and risk-taking tendencies. The asset management proposal unit also analyzes the user's risk tolerance in detail based on investment history data and proposes an investment strategy based on that. For example, it proposes a method of investing in different asset classes to diversify risk. The asset management proposal unit also analyzes the user's past investment history in detail and develops an algorithm to evaluate the risk tolerance. For example, it creates a risk profile based on past investment behavior and proposes an investment strategy based on that. In this way, the efficiency of asset management can be improved by analyzing the risk tolerance in detail based on the user's past investment history and proposing an optimal investment strategy.

[0080] The asset management proposal unit can take into account the user's long-term goals and provide a specific plan for achieving those goals. For example, the asset management proposal unit can take into account the user's long-term goals and propose an asset management plan based on those goals. For example, it can provide a specific investment strategy for securing living expenses after retirement. The asset management proposal unit can also customize the asset management plan based on the long-term goals. For example, it can propose an investment plan targeting children's education expenses and home purchase funds. The asset management proposal unit can also hear the user's long-term goals in detail and provide a specific asset management plan based on those goals. For example, it can propose a step-by-step investment strategy for achieving the goals. In this way, the efficiency of asset management can be improved by taking into account the user's long-term goals and providing a specific plan for achieving the goals.

[0081] The asset management proposal unit can use the emotion estimation function to analyze the user's emotions when making an investment decision and provide advice to eliminate emotional bias. The asset management proposal unit, for example, analyzes the user's emotions when making an investment decision and provides advice to eliminate emotional bias. For example, specific advice is provided to avoid making investment decisions due to excessive fear or excitement. The asset management proposal unit also uses the emotion estimation function to analyze the user's emotions when making an investment decision in real time and provide feedback to eliminate emotional bias. For example, a reminder is displayed to encourage a calm decision. The asset management proposal unit also collects emotional data when making an investment decision and provides specific advice to eliminate emotional bias. For example, advice is provided to avoid excessive risk-taking due to emotional excitement. In this way, the asset management proposal unit can analyze the user's emotions when making an investment decision and eliminate emotional bias, thereby supporting more calm and rational investment decisions.

[0082] The asset management proposal department can reflect the user's preferences for socially responsible investment (SRI) and environmental, social, and governance (ESG) investment. For example, the asset management proposal department proposes an asset management plan that reflects the user's preferences for socially responsible investment (SRI) and environmental, social, and governance (ESG) investment. For example, it recommends investments in environmentally conscious companies. The asset management proposal department also considers the user's preferences for SRI and ESG investment and proposes optimal investment destinations for the user. For example, it recommends investments in socially responsible companies and projects. The asset management proposal department also listens to the user's investment preferences in detail and provides an asset management plan that reflects SRI and ESG investment based on the interview. For example, it proposes an investment strategy that emphasizes environmental protection and social contribution. In this way, by providing an asset management plan that reflects the user's preferences for socially responsible investment (SRI) and environmental, social, and governance (ESG) investment, it is possible to support asset management that matches the user's investment preferences.

[0083] The asset management proposal unit can provide a locally tailored investment strategy taking into account the user's local economy and industrial trends. The asset management proposal unit, for example, proposes a locally tailored investment strategy taking into account the user's local economy and industrial trends. For example, it recommends investments in local growth industries and local companies. The asset management proposal unit also analyzes local economic data and provides the user with an optimal locally tailored investment strategy based on that data. For example, it selects investment destinations based on the local economic growth rate and industrial trends. The asset management proposal unit also analyzes local economic and industrial trends in detail to propose an investment strategy tailored to the user's region. For example, it recommends investments in local infrastructure projects and emerging companies. In this way, by providing a locally tailored investment strategy taking into account the user's local economy and industrial trends, it is possible to support the user's asset management while contributing to the development of the local economy.

[0084] The asset management proposal unit can use the emotion estimation function to monitor the user's emotions in real time when the user is implementing an asset management plan and make suggestions that will elicit positive emotions. For example, the asset management proposal unit monitors the user's emotions in real time when the user is implementing an asset management plan and makes suggestions that will elicit positive emotions. For example, it provides positive feedback when investments are progressing smoothly. The asset management proposal unit also uses the emotion estimation function to analyze the user's emotions in real time when the user is implementing an asset management plan and provides an interface for eliciting positive emotions. For example, it displays encouraging messages and success stories. The asset management proposal unit also provides feedback in real time based on the emotion estimation data when the user is implementing an asset management plan and makes suggestions that will elicit positive emotions. For example, it displays appropriate encouragement and praise according to the progress of the investment. This allows the user to elicit positive emotions when implementing the asset management plan, thereby maintaining motivation for asset management.

[0085] The expenditure data collection unit and the asset data collection unit can monitor a user's overall financial situation in real time and immediately detect abnormal expenditure or investment patterns. The expenditure data collection unit and the asset data collection unit, for example, build a system that monitors a user's overall financial situation in real time and immediately detects abnormal expenditure or investment patterns. For example, they detect high-value transactions that deviate from normal expenditure patterns and issue alerts. The expenditure data collection unit and the asset data collection unit also integrate expenditure data and asset data to monitor the financial situation in real time. For example, they detect abnormal investment patterns or sudden asset fluctuations and notify the user. The expenditure data collection unit and the asset data collection unit also develop algorithms that analyze a user's financial situation in real time and immediately detect abnormal expenditure or investment patterns. For example, they detect transactions that deviate from normal expenditure or investment patterns and issue alerts. This makes it possible to monitor a user's overall financial situation in real time and immediately detect abnormal expenditure or investment patterns, enabling rapid response.

[0086] The expenditure management proposal unit and the asset management proposal unit can provide a financial plan appropriate to a user's life stage by taking into account the user's life events. For example, the expenditure management proposal unit and the asset management proposal unit can provide a comprehensive expenditure management and asset management plan based on the user's life events. For example, they can propose plans for expenses and asset management related to marriage and childbirth. The expenditure management proposal unit and the asset management proposal unit can also collect data on the user's life events and customize the expenditure management and asset management plan based on the data to provide a financial plan appropriate to the user's life stage. For example, they can provide a plan that takes into account living expenses and medical expenses after retirement. The expenditure management proposal unit and the asset management proposal unit can also hear the user's life events in detail and provide a financial plan appropriate to the user's life stage based on the data. For example, they can propose an expenditure management and asset management plan targeting children's education expenses and home purchase funds. This can support the user's life planning by providing a financial plan appropriate to the user's life stage by taking into account the user's life events.

[0087] The expenditure management suggestion unit and the asset management suggestion unit can use the emotion estimation function to analyze a user's emotions regarding financial management and provide advice to maintain motivation. The expenditure management suggestion unit and the asset management suggestion unit, for example, analyze a user's emotions regarding financial management and provide advice to maintain motivation. For example, they provide positive feedback when financial management is going well. The expenditure management suggestion unit and the asset management suggestion unit also use the emotion estimation function to analyze a user's emotions regarding financial management in real time and provide feedback to maintain motivation. For example, they display an encouraging message when a goal is achieved. The expenditure management suggestion unit and the asset management suggestion unit also collect emotion data regarding financial management and provide specific advice to maintain motivation. For example, they suggest setting simple goals to a user who finds financial management difficult. In this way, the user's emotions regarding financial management can be analyzed and advice to maintain motivation can be provided to support continued financial management.

[0088] The expenditure management proposal unit and the asset management proposal unit can integrate the user's health data and provide a financial plan to promote a healthy lifestyle. For example, the expenditure management proposal unit and the asset management proposal unit integrate the user's health data and provide a comprehensive expenditure management and asset management plan based on the health data. For example, they provide advice that prioritizes healthy eating habits and fitness-related expenses. The expenditure management proposal unit and the asset management proposal unit also take the health data into consideration and provide a financial plan to promote a healthy lifestyle. For example, they recommend regular exercise and incorporate gym membership fees and the purchase of exercise equipment into the spending plan. The expenditure management proposal unit and the asset management proposal unit also propose an expenditure management and asset management plan to promote a healthy lifestyle based on the user's health data. For example, they set budgets for health checkups and medical expenses and plan expenditures for maintaining health. In this way, the system can support the user in maintaining their health by integrating the user's health data and providing a financial plan to promote a healthy lifestyle.

[0089] The expenditure management proposal unit and the asset management proposal unit can provide a plan that reflects the user's hobbies and interests, allowing the user to manage their finances while having fun. The expenditure management proposal unit and the asset management proposal unit, for example, propose a financial management plan that reflects the user's hobbies and interests. For example, for a user whose hobby is traveling, they propose a method for systematically saving money for travel expenses. The expenditure management proposal unit and the asset management proposal unit also provide a plan that allows the user to manage their finances while having fun, based on the user's hobbies and interests. For example, for a user who likes music, they set a budget for concert tickets. The expenditure management proposal unit and the asset management proposal unit also customize the financial management plan taking into account the user's hobbies and interests. For example, for a user who likes sports, they incorporate sports event and gym membership fees into the spending plan. In this way, by providing a financial management plan that reflects the user's hobbies and interests, the user can manage their finances while having fun.

[0090] The expenditure management suggestion unit and the asset management suggestion unit can use the emotion estimation function to monitor the user's emotions in real time when managing their finances and make suggestions that elicit positive emotions. The expenditure management suggestion unit and the asset management suggestion unit, for example, monitor the user's emotions in real time when managing their finances and make suggestions that elicit positive emotions. For example, they provide positive feedback when a goal is achieved. The expenditure management suggestion unit and the asset management suggestion unit also use the emotion estimation function to analyze the user's emotions in real time when managing their finances and provide an interface for eliciting positive emotions. For example, they display encouraging messages and success stories. The expenditure management suggestion unit and the asset management suggestion unit also provide feedback in real time based on the emotion estimation data when the user manages their finances and make suggestions that elicit positive emotions. For example, they display appropriate encouragement or praise based on the input content. This allows the user to elicit positive emotions when managing their finances, thereby maintaining motivation for financial management.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The expenditure management suggestion unit can predict seasonal expenditure patterns based on the user's past expenditure history and propose an optimal budget. For example, if air conditioning costs tend to increase in the summer, the unit can propose a budget that takes that into account. Also, if expenditures tend to increase around the year-end and New Year holidays, the unit can set a budget for that time of year. Furthermore, it is possible to understand the tendency for expenditures in specific categories to increase during certain seasons and reflect that in the budget. In this way, by predicting seasonal expenditure patterns and proposing an optimal budget, the user can manage their expenditures more effectively.

[0093] The expenditure data collection unit can use IoT devices to collect comprehensive expenditure data, including household power consumption and water usage. For example, household power consumption data can be collected through IoT devices and analyzed as comprehensive expenditure data. It can identify time periods with high power usage and provide specific advice for saving energy. It can also collect water usage data using IoT devices and integrate it as expenditure data. It can analyze time periods with high water usage and usage patterns and suggest specific methods for saving water. This allows for the collection of comprehensive expenditure data, including household power consumption and water usage, enabling more detailed expenditure management.

[0094] The expenditure data collection unit can use the emotion estimation function to analyze a user's emotions when making a purchase and provide advice to curb emotional spending. For example, for a user who tends to make expensive purchases during times of high stress, the expenditure data collection unit can suggest alternative ways to relax. It is also possible to collect emotional data when making a purchase and provide specific advice to curb emotional spending. To prevent impulsive emotional buying, the expenditure data collection unit can suggest taking some time to calm down before making a purchase. Furthermore, the emotion estimation function can be used to analyze a user's emotions when making a purchase in real time and issue an alert to curb emotional spending. This can curb a user's emotional spending and achieve more planned spending management.

[0095] The expenditure data collection unit can add a function that allows the results of expenditure data analysis to be anonymously compared with other users and used as a benchmark. For example, it is possible to compare spending patterns with users in the same income bracket and identify areas for improvement. It is also possible to compare expenditure data with other users anonymously and use it as a benchmark for expenditure management. It is possible to compare expenditure data with users living in the same area and understand spending trends specific to that area. Furthermore, it is possible to anonymously share the results of expenditure data analysis and compare it with other users to provide a benchmark for expenditure management. It is possible to compare expenditure data with users in the same age group and suggest expenditure management methods according to life stages. In this way, anonymous comparison with other users can be used as a benchmark for expenditure management and identify areas for improvement.

[0096] The expenditure data collection unit can use the emotion estimation function to estimate the user's emotions in real time when entering expenditure data and make suggestions to elicit positive emotions. For example, the emotion estimation function can analyze the user's facial expressions and voice during entry and provide positive feedback. The emotion estimation function can also be used to analyze the user's emotions in real time when entering expenditure data and provide an interface for eliciting positive emotions. Encouraging messages and success stories can be displayed. Furthermore, when the user enters expenditure data, feedback can be provided in real time based on the emotion estimation data, and suggestions to elicit positive emotions can be made. Appropriate encouragement and praise can be displayed according to the input content. This can elicit positive emotions when the user enters expenditure data, thereby maintaining motivation for expenditure management.

[0097] The asset data collection unit can perform a detailed analysis of the liquidity and risk profile of a user's assets and propose an optimal asset allocation. For example, it can collect a user's asset data and perform a detailed analysis of the liquidity and risk profile. It can evaluate the liquidity of assets such as cash, stocks, and real estate and propose an optimal asset allocation. It can also perform a detailed analysis of the user's risk tolerance when collecting asset data and propose an asset allocation based on that. It can propose a method of investing in different asset classes to diversify risk. Furthermore, it can analyze the liquidity and risk profile based on the user's asset data and propose an optimal asset allocation. It can propose an asset allocation that aims for long-term growth while ensuring short-term liquidity. This allows for a detailed analysis of the user's asset liquidity and risk profile and proposes an optimal asset allocation, thereby improving the efficiency of asset management.

[0098] The asset data collection unit can introduce an algorithm that refers to past market data and economic indicators and predicts future market trends. For example, in analyzing asset data, an algorithm can be introduced that refers to past market data and economic indicators and predicts future market trends. Future stock price trends can be predicted based on past stock price data. It is also possible to analyze economic indicators and market data and develop an algorithm that predicts future market trends. Market trends can be predicted based on economic indicators such as GDP growth rate and unemployment rate. Furthermore, an algorithm that predicts future market trends based on past market data can be introduced and reflected in asset management plans. Future real estate market trends can be predicted based on past real estate price data. This makes it possible to predict future market trends based on past market data and economic indicators and reflect them in asset management plans.

[0099] The asset data collection unit can use the emotion estimation function to analyze a user's emotions when making an investment decision and provide advice to eliminate emotional bias. For example, it can analyze a user's emotions when making an investment decision and provide advice to eliminate emotional bias. Specific advice can be provided to avoid making investment decisions based on excessive fear or excitement. It is also possible to use the emotion estimation function to analyze a user's emotions when making an investment decision in real time and provide feedback to eliminate emotional bias. It can display reminders to encourage a calm decision. Furthermore, it can collect emotional data when making an investment decision and provide specific advice to eliminate emotional bias. Advice can be provided to avoid excessive risk-taking due to emotional excitement. This allows the user's emotions when making an investment decision to be analyzed and emotional bias eliminated, thereby supporting more calm and rational investment decisions.

[0100] The asset data collection unit can utilize blockchain technology to improve the transparency and reliability of asset information. For example, blockchain technology can be utilized to collect asset data to improve the transparency and reliability of asset information. Asset transaction history can be recorded on the blockchain to prevent fraudulent transactions. Blockchain technology can also be used to collect and manage asset data to ensure transparency and reliability. Asset ownership and transaction history can be recorded on the blockchain to prevent tampering. Furthermore, blockchain technology can be introduced to collect asset data to improve the transparency and reliability of asset information. Asset valuation and transaction history can be recorded on the blockchain to provide highly reliable data. As a result, the transparency and reliability of asset information can be improved by utilizing blockchain technology.

[0101] The asset data collection unit can use the emotion estimation function to estimate the user's emotions in real time when entering asset data and make suggestions that will elicit positive emotions. For example, when entering asset data, the user's emotions can be estimated in real time and suggestions that will elicit positive emotions can be made. The user's facial expressions and voice can be analyzed during entry and positive feedback can be provided. The emotion estimation function can also be used to analyze the user's emotions in real time when entering asset data and provide an interface that elicits positive emotions. Encouraging messages and success stories can be displayed. Furthermore, when the user enters asset data, feedback can be provided in real time based on the emotion estimation data and suggestions that will elicit positive emotions can be made. Appropriate encouragement and praise can be displayed according to the input content. This can elicit positive emotions when the user enters asset data, thereby maintaining motivation for asset management.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The expenditure data collection unit collects expenditure data of the user, such as credit card usage history, bank account transaction history, and electronic money usage history. Step 2: The asset data collection unit collects asset data of the user, such as stock holdings, real estate ownership, and deposit balances. Step 3: The expenditure management proposal unit analyzes the expenditure data collected by the expenditure data collection unit and proposes optimal expenditure management methods, such as advice on reducing wasteful expenditures, how to set a budget, and analyzing expenditure trends to propose areas for improvement. Step 4: The Asset Management Proposal Department analyzes the asset data collected by the Asset Data Collection Department and proposes an optimal asset management plan. For example, it analyzes investment destinations based on risk tolerance, asset diversification methods, and asset management performance to propose areas for improvement.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

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

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

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

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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. an expenditure data collection unit that collects expenditure data of users; an asset data collection unit that collects asset data of users; an expenditure management suggestion unit that analyzes the expenditure data collected by the expenditure data collection unit and suggests an optimal expenditure management method; an asset management proposal unit that analyzes the asset data collected by the asset data collection unit and proposes an optimal asset management plan. A system characterized by:

2. The expenditure data collection unit Monitor the purchasing behavior of said users in real time and instantly detect abnormal spending patterns.

2. The system of claim 1.

3. The expenditure data collection unit Generate a personalized spending management plan that takes into account the user's lifestyle and preferences 2. The system of claim 1.

4. The expenditure data collection unit Analyzing the user's emotions at the time of purchase and providing advice to curb emotional spending 2. The system of claim 1.

5. The expenditure data collection unit The IoT devices will be used to collect comprehensive expenditure data, including household electricity consumption and water usage.

2. The system of claim 1.

6. The expenditure data collection unit Add a function to anonymously compare the analysis results of the expenditure data with those of other users and use them as a benchmark.

2. The system of claim 1.

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