system

Abstract

The system according to this embodiment aims to optimally manage the user's financial assets and provide advice on savings and investments. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and an operation unit. The collection unit collects the user's financial asset data. The analysis unit analyzes the data collected by the collection unit and classifies and analyzes the user's spending. The provision unit provides savings and investment advice based on the analysis results obtained by the analysis unit. The operation unit automatically manages assets based on the advice provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a user to optimally manage their financial assets and receive advice on savings and investments.

[0005] The system according to the embodiment aims to optimally manage a user's financial assets and provide advice on savings and investments.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and an investment unit. The data collection unit collects the user's financial asset data. The analysis unit analyzes the data collected by the data collection unit and classifies and analyzes the user's spending. The data provision unit provides savings and investment advice based on the analysis results obtained by the analysis unit. The investment unit automatically manages the assets based on the advice provided by the data provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimally manage the user's financial assets and provide advice on saving and investing. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The asset management system according to an embodiment of the present invention is a system that automatically manages a user's financial assets and provides advice on saving and investing towards their goals. The asset management system collects data on various financial assets such as payment information and bank and securities accounts when the user receives their salary. Next, a generating AI analyzes this data and classifies and analyzes the user's spending. The generating AI manages the balance between income and expenses and provides advice on saving and investing towards the user's goals. For example, if a user sets goals such as "I want to buy a specific product in one year," "I want to travel in three years," or "I want to buy a house in five years," the generating AI automatically manages daily assets in accordance with these goals. Without the user being aware of it, the system accumulates and manages financial assets and supports them towards their goals. This mechanism allows users to efficiently manage their assets and achieve their goals even if they are busy. As a result, the asset management system can efficiently manage the user's financial assets and provide advice on saving and investing towards their goals.

[0029] The asset management system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an operation unit. The collection unit collects the user's financial asset data. For example, the collection unit collects data such as salary receipt and payment information, and bank and securities data. The collection unit can collect data such as bank account information, investment portfolios, and credit card transaction history. The collection unit can also collect data using AI. The analysis unit analyzes the data collected by the collection unit and classifies and analyzes the user's spending. The analysis unit analyzes the data using generative AI. Based on the collected data, the generative AI classifies the user's spending by category and analyzes it in detail. The analysis unit performs the analysis based on the definition of the spending category and the type of analysis algorithm. The provision unit provides savings and investment advice based on the analysis results obtained by the analysis unit. The provision unit provides advice using generative AI. The generative AI manages the balance between the user's income and spending and provides savings and investment advice toward the user's goals. The provision unit provides advice based on criteria such as risk assessment, investment strategy, and savings target. The operations department automatically manages assets based on the advice provided by the service department. The operations department uses a generative AI to manage assets. The generative AI automatically manages daily assets in accordance with the user's goals. The operations department manages assets based on criteria such as the type of algorithm, the frequency of operations, and risk management. As a result, the asset management system according to this embodiment can efficiently manage the user's financial assets and provide savings and investment advice toward goals.

[0030] The data collection unit collects users' financial asset data. For example, it collects data such as salary receipts, payment information, and bank and securities data. Specifically, the data collection unit can collect data such as users' bank account information, investment portfolios, and credit card transaction history. This allows for a comprehensive understanding of the user's entire financial activity. The data collection unit can also collect data using AI. With the user's permission, the AI ​​automatically retrieves data through APIs of various financial institutions. For example, it can periodically retrieve account balances and transaction history using bank APIs, and collect the latest information on investment portfolios through securities company APIs. It can also retrieve transaction history and credit limit information using credit card company APIs. This allows the data collection unit to update users' financial data in real time and always maintain the most up-to-date information. Furthermore, the data collection unit prioritizes data security; retrieved data is encrypted and stored securely. This allows for efficient data collection while protecting user privacy. The data collection unit centrally manages users' financial data, making it accessible to the analysis and provisioning units. This improves the overall efficiency and accuracy of the system, enabling the provision of better services to users.

[0031] The analysis unit analyzes the data collected by the data collection unit to classify and analyze user spending. The analysis unit uses generative AI to analyze the data. Based on the collected data, the generative AI classifies user spending into categories and analyzes it in detail. Specifically, the generative AI uses natural language processing technology to analyze the contents of transaction details and automatically classifies them into categories such as food expenses, transportation expenses, and entertainment expenses. Furthermore, the generative AI can learn the user's spending patterns and detect abnormal spending or fraudulent transactions. For example, if there is a transaction that deviates significantly from the normal spending pattern, the generative AI marks that transaction as a flag and notifies the user. The analysis unit also performs analysis based on the definition of spending categories and the type of analysis algorithm. This allows for a detailed understanding of the user's spending trends and consumption behavior, which can be used for future spending forecasts and budget management. In addition, the analysis unit can perform trend analysis based on past data to evaluate the user's spending trends over the long term. This allows users to review their consumption behavior and gain insights for more effective asset management. The analysis unit can analyze users' financial data from multiple perspectives and provide users with useful information.

[0032] The service provider offers savings and investment advice based on the analysis results obtained by the analysis department. The service provider uses generative AI to provide advice. The generative AI manages the balance between the user's income and expenses and provides savings and investment advice towards the user's goals. Specifically, the generative AI proposes optimal savings plans and investment strategies based on information such as the user's income, expenses, savings goals, and risk tolerance. For example, if a user wants to save a certain amount within a specific period, the generative AI will create a concrete savings plan to achieve that goal and suggest monthly savings amounts and points to review regarding expenses. Regarding investments, it will propose an optimal investment portfolio according to the user's risk tolerance and investment goals. The generative AI provides advice based on criteria such as risk assessment, investment strategy, and savings goals. This allows users to efficiently manage their assets towards their financial goals. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it collects feedback on the results of actions taken by users based on the advice provided and adjusts the generative AI algorithm based on that data. This allows the service provider to provide users with more appropriate and effective advice.

[0033] The Investment Department automatically manages assets based on advice provided by the Investment Department. The Investment Department uses Generative AI to manage assets. Generative AI automatically manages daily assets in accordance with the user's goals. Specifically, Generative AI monitors the user's investment portfolio in real time and makes optimal buy and sell decisions in response to market fluctuations. For example, if the stock market fluctuates sharply, Generative AI immediately re-evaluates the portfolio and makes buy and sell decisions to minimize risk. Generative AI also periodically rebalances the portfolio based on the user's risk tolerance and investment goals. This allows users to manage their assets stably, regardless of market fluctuations. The Investment Department manages assets based on criteria such as algorithm type, trading frequency, and risk management. For example, Generative AI sets stop-loss orders for risk management and automatically sells when a certain level of loss occurs. Furthermore, the trading frequency can be adjusted according to the user's wishes. This allows the Investment Department to provide flexible asset management tailored to the user's needs. In addition, the Investment Department regularly reports the investment results to the user, making the investment status transparent. This allows users to always understand the status of their asset management and entrust their asset management to the Investment Department with peace of mind.

[0034] The data collection unit can collect data such as salary receipts, payment information, and bank and securities data. For example, the data collection unit can collect salary receipt methods including bank transfers, cash payments, and electronic money. The data collection unit collects payment information such as credit card payments, utility bill payments, and loan repayments. The data collection unit collects bank and securities data such as deposit balances, transaction history, and securities transaction information. This allows for more accurate asset management by collecting diverse financial data from users. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's bank account information into an AI, which can then automatically collect the data.

[0035] The analysis unit can analyze the collected data and classify and analyze user spending. For example, the analysis unit classifies user spending by category based on the collected data. The analysis unit includes spending categories such as food expenses, transportation expenses, and entertainment expenses. The analysis unit analyzes the data using a generative AI. The generative AI analyzes user spending in detail based on the collected data. The analysis unit performs the analysis based on the definition of the spending category and the type of analysis algorithm. This allows for the provision of appropriate advice by analyzing user spending in detail. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the collected data into the generative AI, and the generative AI can automatically analyze the data.

[0036] The service provider can manage the balance between income and expenses and provide savings and investment advice to help users achieve their goals. For example, the service provider can perform income-expense ratio and cash flow analysis based on the user's income and expense data. The service provider uses a generative AI to provide advice. The generative AI manages the balance between the user's income and expenses and provides savings and investment advice to help users achieve their goals. The service provider provides advice based on criteria such as risk assessment, investment strategy, and savings targets. This allows the service provider to provide savings and investment advice tailored to the user's goals. Some or all of the above processing in the service provider may be performed using the generative AI or not. For example, the service provider can input the user's income and expense data into the generative AI, which can then automatically provide advice.

[0037] The investment department can automatically manage assets based on the advice provided. For example, the investment department can use a generative AI to manage assets. The generative AI automatically manages daily assets in accordance with the user's goals. The investment department manages assets based on criteria such as the type of algorithm, the frequency of operations, and risk management. This allows for automatic asset management without the user's awareness. Some or all of the above processes in the investment department may be performed using the generative AI or not. For example, the investment department can input the provided advice into the generative AI, which can then automatically manage assets.

[0038] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the data collection unit may prioritize collection methods that the user has frequently used in the past. The data collection unit proposes the most efficient collection method based on the user's past collection history. The data collection unit analyzes the user's past collection history and optimizes the collection frequency. This enables efficient data collection by selecting the optimal collection method based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past collection history data into AI, which can then automatically select the optimal collection method.

[0039] The data collection unit can filter financial data based on the user's current lifestyle and areas of interest. For example, the data collection unit prioritizes collecting financial data related to areas of interest the user currently has. The data collection unit collects only the necessary data according to the user's lifestyle. The data collection unit selects the types of data to collect based on the user's areas of interest. This enables data collection tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into an AI, which can then automatically perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of financial data related to that region. The data collection unit will select the optimal data collection points based on the user's geographical location. If the user is on the move, the data collection unit will collect data in real time based on their current location. This enables more accurate data collection by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into the AI, which can then automatically prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can collect financial data related to topics the user has shown interest in on social media. The data collection unit determines the priority of data to collect based on the user's social media activity. The data collection unit analyzes the user's social media activity and selects the optimal timing for data collection. This enables more appropriate data collection by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI, which can then automatically collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the financial data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance and a simplified analysis on data with low importance. The analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the financial data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the financial data into the generative AI, and the generative AI can automatically adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of financial data during analysis. For example, the analysis unit applies a specific analysis algorithm to stock data. The analysis unit applies a different analysis algorithm to bank transaction data. The analysis unit applies yet another different analysis algorithm to insurance data. This allows for more accurate analysis by applying analysis algorithms appropriate to the category of financial data. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the category of financial data into a generating AI, and the generating AI can automatically apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of financial data collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may analyze current data while referring to past data. The analysis unit may adjust the order of analysis based on the timing of data collection. This enables efficient analysis by determining the priority of analysis based on the timing of financial data collection. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the timing of financial data collection into a generating AI, and the generating AI can automatically determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of financial data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit determines the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of financial data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of financial data into a generative AI, which can then automatically adjust the order of analysis.

[0046] The service provider can adjust the level of detail in the advice based on the importance of the user's goals. For example, the service provider will provide detailed advice for high-importance goals and concise advice for low-importance goals. The service provider will determine the priority of the advice according to the importance of the goals. This allows for the provision of more appropriate advice by adjusting the level of detail according to the importance of the user's goals. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the importance of the user's goals into the generative AI, which can then automatically adjust the level of detail in the advice.

[0047] The service provider can apply different advice algorithms depending on the user's goal category when providing advice. For example, the service provider might apply a specific advice algorithm to savings goals, another to investment goals, and yet another to insurance goals. This allows for more appropriate advice to be provided by applying an advice algorithm tailored to the user's goal category. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's goal category into a generative AI, which can then automatically apply an appropriate advice algorithm.

[0048] The service provider can determine the priority of advice based on the user's goal achievement timeline when providing advice. For example, if the goal achievement timeline is approaching, the service provider will prioritize providing advice. If the goal achievement timeline is far off, the service provider will postpone providing advice. The service provider adjusts the order of advice based on the goal achievement timeline. This allows for the provision of more appropriate advice by determining the priority of advice based on the user's goal achievement timeline. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's goal achievement timeline into a generative AI, and the generative AI can automatically determine the priority of advice.

[0049] The advice delivery unit can adjust the order of advice based on the relevance of the user's goals when providing advice. For example, the delivery unit will prioritize advice for highly relevant goals. The delivery unit will postpone advice for less relevant goals. The delivery unit determines the order of advice based on the relevance of the goals. This allows for the provision of more appropriate advice by adjusting the order of advice based on the relevance of the user's goals. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the relevance of the user's goals into a generative AI, and the generative AI can automatically adjust the order of advice.

[0050] The investment department can analyze a user's past investment history to select the optimal investment method when managing assets. For example, the investment department may prioritize selecting investment methods that have been successful for the user in the past. The investment department may also propose methods to minimize risk based on the user's past investment history. The investment department analyzes the user's past investment history and selects the most efficient investment method. This allows for more efficient asset management by selecting the optimal investment method based on the user's past history. Some or all of the above processes in the investment department may be performed using or without a generating AI. For example, the investment department can input the user's past investment history data into a generating AI, which can then automatically select the optimal investment method.

[0051] The investment department can customize investment strategies based on the user's current living situation when managing assets. For example, the investment department can propose risk-reducing investment methods according to the user's current living situation. The investment department flexibly adjusts investment strategies based on the user's living situation. The investment department selects the optimal investment strategy according to the user's living situation. This allows for more appropriate asset management by customizing investment strategies based on the user's current living situation. Some or all of the above processes in the investment department may be performed using a generation AI, or they may be performed without a generation AI. For example, the investment department can input the user's living situation data into a generation AI, which can then automatically customize investment strategies.

[0052] The investment department can select the optimal investment method when managing assets, taking into account the user's geographical location. For example, if the user is in a specific region, the investment department will select an investment method based on the economic conditions of that region. The investment department will select the optimal investment destination based on the user's geographical location. If the user is on the move, the investment department will adjust the investment method in real time based on their current location. This allows for more appropriate asset management by selecting the optimal investment method based on the user's geographical location. Some or all of the above processes in the investment department may be performed using a generative AI, or they may be performed without a generative AI. For example, the investment department can input the user's geographical location information into a generative AI, which can then automatically select the optimal investment method.

[0053] The operations department can analyze users' social media activity and propose investment strategies when managing assets. For example, the operations department can propose investment strategies related to topics that users have shown interest in on social media. The operations department selects investment strategies from users' social media activity. The operations department analyzes users' social media activity and proposes the optimal investment strategy. This makes it possible to manage assets more appropriately by proposing investment strategies based on users' social media activity. Some or all of the above processes in the operations department may be performed using or without a generative AI. For example, the operations department can input user social media activity data into a generative AI, which can then automatically propose investment strategies.

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

[0055] Asset management systems can collect users' health data and provide investment advice based on their health status. For example, if a user provides the results of a health checkup, the system will analyze the data and suggest high-risk investments if their health is good, and low-risk investments if their health is deteriorating. It can also collect data on users' exercise habits and dietary habits and provide advice to improve their health. This allows for more appropriate asset management by providing investment advice tailored to the user's health condition.

[0056] Asset management systems can suggest investment opportunities based on users' hobbies and interests. For example, if a user is interested in sports, it can suggest investments in sports-related companies or funds. If a user is interested in environmental protection, it can suggest investments in environmentally conscious companies or green funds. Furthermore, if a user is interested in technology, it can suggest investments in companies with cutting-edge technology. By suggesting investment opportunities based on users' hobbies and interests, this system can increase the enjoyment of investing and boost motivation for asset management.

[0057] Asset management systems can provide investment advice based on a user's family structure and life stage. For example, if a user is raising children, it can suggest saving for their education or investing for their children's future. If a user is nearing retirement, it can suggest securing funds for retirement and managing their pension. If a user is single, it can also suggest financial planning for future marriage or home purchase. By providing investment advice tailored to the user's family structure and life stage, more appropriate asset management becomes possible.

[0058] Asset management systems can provide investment advice based on regional economic conditions, taking into account the user's geographical location. For example, if a user lives in a specific region, the system can suggest investment opportunities based on the economic conditions and market trends of that region. If a user is traveling, it can provide short-term investment advice based on the economic conditions of their destination. Furthermore, if a user is planning to move, the system can suggest an asset management plan based on the economic conditions of their new place of residence. This allows for more appropriate asset management by providing investment advice based on the user's geographical location.

[0059] Asset management systems can analyze users' social media activity and provide investment advice based on topics they show interest in. For example, if a user shows interest in a particular company or industry on social media, the system can suggest investment opportunities related to that company or industry. If a user shows interest in environmental protection, it can suggest investments in environmentally conscious companies or green funds. Furthermore, if a user shows interest in technology, it can suggest investments in companies with cutting-edge technology. This allows for more appropriate asset management by providing investment advice based on users' social media activity.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects the user's financial asset data. Specifically, it collects data such as salary receipts, payment information, and bank and securities information. Furthermore, it can also collect data such as bank account information, investment portfolios, and credit card transaction history. The data collection unit can also use AI to collect data. Step 2: The analysis unit analyzes the data collected by the collection unit and classifies and analyzes user spending. The analysis unit uses generative AI to analyze the data, classifying user spending into categories based on the collected data and analyzing it in detail. The analysis unit performs the analysis based on the definition of spending categories and the type of analysis algorithm. Step 3: The service provider provides savings and investment advice based on the analysis results obtained by the analysis unit. The service provider uses generated AI to provide advice, manage the balance between the user's income and expenses, and provide savings and investment advice towards the user's goals. The service provider provides advice based on criteria such as risk assessment, investment strategy, and savings goals. Step 4: The investment department automatically manages assets based on the advice provided by the service provider. The investment department uses generated AI to manage assets and automatically manages daily assets in accordance with the user's goals. The investment department manages assets based on criteria such as the type of algorithm, the frequency of operations, and risk management.

[0062] (Example of form 2) The asset management system according to an embodiment of the present invention is a system that automatically manages a user's financial assets and provides advice on saving and investing towards their goals. The asset management system collects data on various financial assets such as payment information and bank and securities accounts when the user receives their salary. Next, a generating AI analyzes this data and classifies and analyzes the user's spending. The generating AI manages the balance between income and expenses and provides advice on saving and investing towards the user's goals. For example, if a user sets goals such as "I want to buy a specific product in one year," "I want to travel in three years," or "I want to buy a house in five years," the generating AI automatically manages daily assets in accordance with these goals. Without the user being aware of it, the system accumulates and manages financial assets and supports them towards their goals. This mechanism allows users to efficiently manage their assets and achieve their goals even if they are busy. As a result, the asset management system can efficiently manage the user's financial assets and provide advice on saving and investing towards their goals.

[0063] The asset management system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an operation unit. The collection unit collects the user's financial asset data. For example, the collection unit collects data such as salary receipt and payment information, and bank and securities data. The collection unit can collect data such as bank account information, investment portfolios, and credit card transaction history. The collection unit can also collect data using AI. The analysis unit analyzes the data collected by the collection unit and classifies and analyzes the user's spending. The analysis unit analyzes the data using generative AI. Based on the collected data, the generative AI classifies the user's spending by category and analyzes it in detail. The analysis unit performs the analysis based on the definition of the spending category and the type of analysis algorithm. The provision unit provides savings and investment advice based on the analysis results obtained by the analysis unit. The provision unit provides advice using generative AI. The generative AI manages the balance between the user's income and spending and provides savings and investment advice toward the user's goals. The provision unit provides advice based on criteria such as risk assessment, investment strategy, and savings target. The operations department automatically manages assets based on the advice provided by the service department. The operations department uses a generative AI to manage assets. The generative AI automatically manages daily assets in accordance with the user's goals. The operations department manages assets based on criteria such as the type of algorithm, the frequency of operations, and risk management. As a result, the asset management system according to this embodiment can efficiently manage the user's financial assets and provide savings and investment advice toward goals.

[0064] The data collection unit collects users' financial asset data. For example, it collects data such as salary receipts, payment information, and bank and securities data. Specifically, the data collection unit can collect data such as users' bank account information, investment portfolios, and credit card transaction history. This allows for a comprehensive understanding of the user's entire financial activity. The data collection unit can also collect data using AI. With the user's permission, the AI ​​automatically retrieves data through APIs of various financial institutions. For example, it can periodically retrieve account balances and transaction history using bank APIs, and collect the latest information on investment portfolios through securities company APIs. It can also retrieve transaction history and credit limit information using credit card company APIs. This allows the data collection unit to update users' financial data in real time and always maintain the most up-to-date information. Furthermore, the data collection unit prioritizes data security; retrieved data is encrypted and stored securely. This allows for efficient data collection while protecting user privacy. The data collection unit centrally manages users' financial data, making it accessible to the analysis and provisioning units. This improves the overall efficiency and accuracy of the system, enabling the provision of better services to users.

[0065] The analysis unit analyzes the data collected by the data collection unit to classify and analyze user spending. The analysis unit uses generative AI to analyze the data. Based on the collected data, the generative AI classifies user spending into categories and analyzes it in detail. Specifically, the generative AI uses natural language processing technology to analyze the contents of transaction details and automatically classifies them into categories such as food expenses, transportation expenses, and entertainment expenses. Furthermore, the generative AI can learn the user's spending patterns and detect abnormal spending or fraudulent transactions. For example, if there is a transaction that deviates significantly from the normal spending pattern, the generative AI marks that transaction as a flag and notifies the user. The analysis unit also performs analysis based on the definition of spending categories and the type of analysis algorithm. This allows for a detailed understanding of the user's spending trends and consumption behavior, which can be used for future spending forecasts and budget management. In addition, the analysis unit can perform trend analysis based on past data to evaluate the user's spending trends over the long term. This allows users to review their consumption behavior and gain insights for more effective asset management. The analysis unit can analyze users' financial data from multiple perspectives and provide users with useful information.

[0066] The service provider offers savings and investment advice based on the analysis results obtained by the analysis department. The service provider uses generative AI to provide advice. The generative AI manages the balance between the user's income and expenses and provides savings and investment advice towards the user's goals. Specifically, the generative AI proposes optimal savings plans and investment strategies based on information such as the user's income, expenses, savings goals, and risk tolerance. For example, if a user wants to save a certain amount within a specific period, the generative AI will create a concrete savings plan to achieve that goal and suggest monthly savings amounts and points to review regarding expenses. Regarding investments, it will propose an optimal investment portfolio according to the user's risk tolerance and investment goals. The generative AI provides advice based on criteria such as risk assessment, investment strategy, and savings goals. This allows users to efficiently manage their assets towards their financial goals. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it collects feedback on the results of actions taken by users based on the advice provided and adjusts the generative AI algorithm based on that data. This allows the service provider to provide users with more appropriate and effective advice.

[0067] The Investment Department automatically manages assets based on advice provided by the Investment Department. The Investment Department uses Generative AI to manage assets. Generative AI automatically manages daily assets in accordance with the user's goals. Specifically, Generative AI monitors the user's investment portfolio in real time and makes optimal buy and sell decisions in response to market fluctuations. For example, if the stock market fluctuates sharply, Generative AI immediately re-evaluates the portfolio and makes buy and sell decisions to minimize risk. Generative AI also periodically rebalances the portfolio based on the user's risk tolerance and investment goals. This allows users to manage their assets stably, regardless of market fluctuations. The Investment Department manages assets based on criteria such as algorithm type, trading frequency, and risk management. For example, Generative AI sets stop-loss orders for risk management and automatically sells when a certain level of loss occurs. Furthermore, the trading frequency can be adjusted according to the user's wishes. This allows the Investment Department to provide flexible asset management tailored to the user's needs. In addition, the Investment Department regularly reports the investment results to the user, making the investment status transparent. This allows users to always understand the status of their asset management and entrust their asset management to the Investment Department with peace of mind.

[0068] The data collection unit can collect data such as salary receipts, payment information, and bank and securities data. For example, the data collection unit can collect salary receipt methods including bank transfers, cash payments, and electronic money. The data collection unit collects payment information such as credit card payments, utility bill payments, and loan repayments. The data collection unit collects bank and securities data such as deposit balances, transaction history, and securities transaction information. This allows for more accurate asset management by collecting diverse financial data from users. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's bank account information into an AI, which can then automatically collect the data.

[0069] The analysis unit can analyze the collected data and classify and analyze user spending. For example, the analysis unit classifies user spending by category based on the collected data. The analysis unit includes spending categories such as food expenses, transportation expenses, and entertainment expenses. The analysis unit analyzes the data using a generative AI. The generative AI analyzes user spending in detail based on the collected data. The analysis unit performs the analysis based on the definition of the spending category and the type of analysis algorithm. This allows for the provision of appropriate advice by analyzing user spending in detail. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the collected data into the generative AI, and the generative AI can automatically analyze the data.

[0070] The service provider can manage the balance between income and expenses and provide savings and investment advice to help users achieve their goals. For example, the service provider can perform income-expense ratio and cash flow analysis based on the user's income and expense data. The service provider uses a generative AI to provide advice. The generative AI manages the balance between the user's income and expenses and provides savings and investment advice to help users achieve their goals. The service provider provides advice based on criteria such as risk assessment, investment strategy, and savings targets. This allows the service provider to provide savings and investment advice tailored to the user's goals. Some or all of the above processing in the service provider may be performed using the generative AI or not. For example, the service provider can input the user's income and expense data into the generative AI, which can then automatically provide advice.

[0071] The investment department can automatically manage assets based on the advice provided. For example, the investment department can use a generative AI to manage assets. The generative AI automatically manages daily assets in accordance with the user's goals. The investment department manages assets based on criteria such as the type of algorithm, the frequency of operations, and risk management. This allows for automatic asset management without the user's awareness. Some or all of the above processes in the investment department may be performed using the generative AI or not. For example, the investment department can input the provided advice into the generative AI, which can then automatically manage assets.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of financial data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. If the user is relaxed, the data collection unit can collect data immediately and perform real-time analysis. If the user is busy, the data collection unit can adjust the collection timing to match the user's schedule. This allows for the collection of financial data at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then automatically adjust the collection timing.

[0073] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the data collection unit may prioritize collection methods that the user has frequently used in the past. The data collection unit proposes the most efficient collection method based on the user's past collection history. The data collection unit analyzes the user's past collection history and optimizes the collection frequency. This enables efficient data collection by selecting the optimal collection method based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past collection history data into AI, which can then automatically select the optimal collection method.

[0074] The data collection unit can filter financial data based on the user's current lifestyle and areas of interest. For example, the data collection unit prioritizes collecting financial data related to areas of interest the user currently has. The data collection unit collects only the necessary data according to the user's lifestyle. The data collection unit selects the types of data to collect based on the user's areas of interest. This enables data collection tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into an AI, which can then automatically perform the filtering.

[0075] The data collection unit can estimate the user's emotions and prioritize the financial data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important data. If the user is relaxed, the data collection unit will collect all data equally. If the user is in a hurry, the data collection unit will prioritize collecting the most important data. This enables efficient data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then automatically determine the priority of the data to collect.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of financial data related to that region. The data collection unit will select the optimal data collection points based on the user's geographical location. If the user is on the move, the data collection unit will collect data in real time based on their current location. This enables more accurate data collection by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into the AI, which can then automatically prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can collect financial data related to topics the user has shown interest in on social media. The data collection unit determines the priority of data to collect based on the user's social media activity. The data collection unit analyzes the user's social media activity and selects the optimal timing for data collection. This enables more appropriate data collection by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI, which can then automatically collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is stressed, the analysis unit provides concise and to-the-point analysis results. If the user is in a hurry, the analysis unit provides analysis results in a format that can be quickly understood. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can automatically adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the financial data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance and a simplified analysis on data with low importance. The analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the financial data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the financial data into the generative AI, and the generative AI can automatically adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of financial data during analysis. For example, the analysis unit applies a specific analysis algorithm to stock data. The analysis unit applies a different analysis algorithm to bank transaction data. The analysis unit applies yet another different analysis algorithm to insurance data. This allows for more accurate analysis by applying analysis algorithms appropriate to the category of financial data. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the category of financial data into a generating AI, and the generating AI can automatically apply an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is stressed, the analysis unit provides concise and to-the-point analysis results. If the user is in a hurry, the analysis unit provides analysis results in a format that can be quickly understood. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can automatically adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the timing of financial data collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may analyze current data while referring to past data. The analysis unit may adjust the order of analysis based on the timing of data collection. This enables efficient analysis by determining the priority of analysis based on the timing of financial data collection. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the timing of financial data collection into a generating AI, and the generating AI can automatically determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of financial data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit determines the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of financial data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of financial data into a generative AI, which can then automatically adjust the order of analysis.

[0084] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is relaxed, the service provider will provide detailed advice. If the user is stressed, the service provider will provide concise and to-the-point advice. If the user is in a hurry, the service provider will provide advice in a format that can be quickly understood. In this way, by adjusting the way advice is expressed according to the user's emotions, more easily understandable advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can automatically adjust the way advice is expressed.

[0085] The service provider can adjust the level of detail in the advice based on the importance of the user's goals. For example, the service provider will provide detailed advice for high-importance goals and concise advice for low-importance goals. The service provider will determine the priority of the advice according to the importance of the goals. This allows for the provision of more appropriate advice by adjusting the level of detail according to the importance of the user's goals. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the importance of the user's goals into the generative AI, which can then automatically adjust the level of detail in the advice.

[0086] The service provider can apply different advice algorithms depending on the user's goal category when providing advice. For example, the service provider might apply a specific advice algorithm to savings goals, another to investment goals, and yet another to insurance goals. This allows for more appropriate advice to be provided by applying an advice algorithm tailored to the user's goal category. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's goal category into a generative AI, which can then automatically apply an appropriate advice algorithm.

[0087] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is relaxed, the service provider will provide detailed advice. If the user is stressed, the service provider will provide concise and to-the-point advice. If the user is in a hurry, the service provider will provide advice in a format that can be quickly understood. By adjusting the length of the advice according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then automatically adjust the length of the advice.

[0088] The service provider can determine the priority of advice based on the user's goal achievement timeline when providing advice. For example, if the goal achievement timeline is approaching, the service provider will prioritize providing advice. If the goal achievement timeline is far off, the service provider will postpone providing advice. The service provider adjusts the order of advice based on the goal achievement timeline. This allows for the provision of more appropriate advice by determining the priority of advice based on the user's goal achievement timeline. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's goal achievement timeline into a generative AI, and the generative AI can automatically determine the priority of advice.

[0089] The advice delivery unit can adjust the order of advice based on the relevance of the user's goals when providing advice. For example, the delivery unit will prioritize advice for highly relevant goals. The delivery unit will postpone advice for less relevant goals. The delivery unit determines the order of advice based on the relevance of the goals. This allows for the provision of more appropriate advice by adjusting the order of advice based on the relevance of the user's goals. Some or all of the above processing in the delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the delivery unit can input the relevance of the user's goals into a generative AI, and the generative AI can automatically adjust the order of advice.

[0090] The operations department can estimate the user's emotions and adjust the investment strategy based on those emotions. For example, if the user is relaxed, the operations department might suggest a high-risk investment strategy. If the user is stressed, the operations department might suggest a low-risk investment strategy. If the user is in a hurry, the operations department might suggest a strategy that yields quick results. By adjusting the investment strategy according to the user's emotions, more appropriate investment strategies can be implemented. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the operations department may be performed using generative AI or not. For example, the operations department can input user emotion data into a generative AI, which can then automatically adjust the investment strategy.

[0091] The investment department can analyze a user's past investment history to select the optimal investment method when managing assets. For example, the investment department may prioritize selecting investment methods that have been successful for the user in the past. The investment department may also propose methods to minimize risk based on the user's past investment history. The investment department analyzes the user's past investment history and selects the most efficient investment method. This allows for more efficient asset management by selecting the optimal investment method based on the user's past history. Some or all of the above processes in the investment department may be performed using or without a generating AI. For example, the investment department can input the user's past investment history data into a generating AI, which can then automatically select the optimal investment method.

[0092] The investment department can customize investment strategies based on the user's current living situation when managing assets. For example, the investment department can propose risk-reducing investment methods according to the user's current living situation. The investment department flexibly adjusts investment strategies based on the user's living situation. The investment department selects the optimal investment strategy according to the user's living situation. This allows for more appropriate asset management by customizing investment strategies based on the user's current living situation. Some or all of the above processes in the investment department may be performed using a generation AI, or they may be performed without a generation AI. For example, the investment department can input the user's living situation data into a generation AI, which can then automatically customize investment strategies.

[0093] The operations department can estimate the user's emotions and determine investment priorities based on those emotions. For example, if the user is relaxed, the operations department will prioritize high-risk investments. If the user is stressed, the operations department will prioritize low-risk investments. If the user is in a hurry, the operations department will prioritize investments that yield quick results. This allows for more appropriate investment management by determining investment priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the operations department may be performed using generative AI or not. For example, the operations department can input user emotion data into a generative AI, which can then automatically determine investment priorities.

[0094] The investment department can select the optimal investment method when managing assets, taking into account the user's geographical location. For example, if the user is in a specific region, the investment department will select an investment method based on the economic conditions of that region. The investment department will select the optimal investment destination based on the user's geographical location. If the user is on the move, the investment department will adjust the investment method in real time based on their current location. This allows for more appropriate asset management by selecting the optimal investment method based on the user's geographical location. Some or all of the above processes in the investment department may be performed using a generative AI, or they may be performed without a generative AI. For example, the investment department can input the user's geographical location information into a generative AI, which can then automatically select the optimal investment method.

[0095] The operations department can analyze users' social media activity and propose investment strategies when managing assets. For example, the operations department can propose investment strategies related to topics that users have shown interest in on social media. The operations department selects investment strategies from users' social media activity. The operations department analyzes users' social media activity and proposes the optimal investment strategy. This makes it possible to manage assets more appropriately by proposing investment strategies based on users' social media activity. Some or all of the above processes in the operations department may be performed using or without a generative AI. For example, the operations department can input user social media activity data into a generative AI, which can then automatically propose investment strategies.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] Asset management systems can collect users' health data and provide investment advice based on their health status. For example, if a user provides the results of a health checkup, the system will analyze the data and suggest high-risk investments if their health is good, and low-risk investments if their health is deteriorating. It can also collect data on users' exercise habits and dietary habits and provide advice to improve their health. This allows for more appropriate asset management by providing investment advice tailored to the user's health condition.

[0098] Asset management systems can suggest investment opportunities based on users' hobbies and interests. For example, if a user is interested in sports, it can suggest investments in sports-related companies or funds. If a user is interested in environmental protection, it can suggest investments in environmentally conscious companies or green funds. Furthermore, if a user is interested in technology, it can suggest investments in companies with cutting-edge technology. By suggesting investment opportunities based on users' hobbies and interests, this system can increase the enjoyment of investing and boost motivation for asset management.

[0099] Asset management systems can provide investment advice based on a user's family structure and life stage. For example, if a user is raising children, it can suggest saving for their education or investing for their children's future. If a user is nearing retirement, it can suggest securing funds for retirement and managing their pension. If a user is single, it can also suggest financial planning for future marriage or home purchase. By providing investment advice tailored to the user's family structure and life stage, more appropriate asset management becomes possible.

[0100] An asset management system can estimate a user's emotions and adjust their tolerance for investment risk based on those emotions. For example, if a user is relaxed, it might suggest high-risk investments. If a user is stressed, it might suggest low-risk investments. If a user is in a hurry, it might suggest investments that yield quick results. This allows for more appropriate asset management by adjusting investment risk tolerance according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] An asset management system can estimate a user's emotions and adjust the frequency of asset management based on those emotions. For example, if the user is relaxed, it can manage assets frequently. If the user is stressed, it can reduce the frequency of management. If the user is in a hurry, it can manage assets to achieve results in a short period. By adjusting the frequency of asset management according to the user's emotions, more appropriate asset management becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] An asset management system can estimate a user's emotions and adjust investment goals based on those emotions. For example, if the user is relaxed, long-term goals are set. If the user is stressed, short-term goals are set. If the user is in a hurry, goals that can be achieved quickly are set. This allows for more appropriate asset management by adjusting investment goals according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] An asset management system can estimate a user's emotions and adjust investment strategies based on those emotions. For example, if a user is relaxed, it might suggest high-risk investment strategies. If a user is stressed, it might suggest low-risk strategies. If a user is in a hurry, it might suggest strategies that yield quick results. By adjusting investment strategies according to the user's emotions, more appropriate asset management becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The asset management system can estimate the user's emotions and adjust the reporting method of asset management based on those emotions. For example, if the user is relaxed, it can provide a detailed report. If the user is stressed, it can provide a concise report. If the user is in a hurry, it can provide a report in a format that can be quickly understood. This allows for more appropriate information to be provided by adjusting the reporting method of asset management according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] Asset management systems can provide investment advice based on regional economic conditions, taking into account the user's geographical location. For example, if a user lives in a specific region, the system can suggest investment opportunities based on the economic conditions and market trends of that region. If a user is traveling, it can provide short-term investment advice based on the economic conditions of their destination. Furthermore, if a user is planning to move, the system can suggest an asset management plan based on the economic conditions of their new place of residence. This allows for more appropriate asset management by providing investment advice based on the user's geographical location.

[0106] Asset management systems can analyze users' social media activity and provide investment advice based on topics they show interest in. For example, if a user shows interest in a particular company or industry on social media, the system can suggest investment opportunities related to that company or industry. If a user shows interest in environmental protection, it can suggest investments in environmentally conscious companies or green funds. Furthermore, if a user shows interest in technology, it can suggest investments in companies with cutting-edge technology. This allows for more appropriate asset management by providing investment advice based on users' social media activity.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects the user's financial asset data. Specifically, it collects data such as salary receipts, payment information, and bank and securities information. Furthermore, it can also collect data such as bank account information, investment portfolios, and credit card transaction history. The data collection unit can also use AI to collect data. Step 2: The analysis unit analyzes the data collected by the collection unit and classifies and analyzes user spending. The analysis unit uses generative AI to analyze the data, classifying user spending into categories based on the collected data and analyzing it in detail. The analysis unit performs the analysis based on the definition of spending categories and the type of analysis algorithm. Step 3: The service provider provides savings and investment advice based on the analysis results obtained by the analysis unit. The service provider uses generated AI to provide advice, manage the balance between the user's income and expenses, and provide savings and investment advice towards the user's goals. The service provider provides advice based on criteria such as risk assessment, investment strategy, and savings goals. Step 4: The investment department automatically manages assets based on the advice provided by the service provider. The investment department uses generated AI to manage assets and automatically manages daily assets in accordance with the user's goals. The investment department manages assets based on criteria such as the type of algorithm, the frequency of operations, and risk management.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and operation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's financial asset data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to classify and analyze the user's spending. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides savings and investment advice based on the analysis results. The operation unit is implemented by the control unit 46A of the smart device 14 and automatically manages assets based on the provided advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and operation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's financial asset data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to classify and analyze the user's spending. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides savings and investment advice based on the analysis results. The operation unit is implemented by the control unit 46A of the smart glasses 214 and automatically manages assets based on the provided advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and operation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's financial asset data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to classify and analyze the user's spending. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides savings and investment advice based on the analysis results. The operation unit is implemented by the control unit 46A of the headset terminal 314 and automatically manages assets based on the provided advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and operation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the user's financial asset data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to classify and analyze the user's spending. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides savings and investment advice based on the analysis results. The operation unit is implemented by, for example, the control unit 46A of the robot 414 and automatically manages assets based on the provided advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A data collection unit that collects users' financial asset data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that classifies and analyzes user spending, A provision unit that provides savings and investment advice based on the analysis results obtained by the aforementioned analysis unit, The system includes an investment unit that automatically manages assets based on the advice provided by the aforementioned investment unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects data on salary receipts, payment information, and bank and securities information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to classify and analyze user spending. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It manages the balance between income and expenses and provides advice on saving and investing to help users achieve their goals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned operations unit, The system automatically manages assets based on the advice provided. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past financial data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting financial data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates user sentiment and prioritizes the financial data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting financial data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting financial data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the financial data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analytical algorithms are applied depending on the category of financial data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the financial data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the financial data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the user's goal category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, we prioritize the advice based on the timeframe for achieving the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, the order of advice is adjusted based on the relevance of the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned operations unit, It estimates the user's emotions and adjusts the asset management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned operations unit, When managing assets, the system analyzes the user's past investment history to select the optimal investment method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned operations unit, When managing assets, the investment methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned operations unit, It estimates user sentiment and determines asset management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned operations unit, When managing assets, the optimal investment method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned operations unit, When managing assets, we analyze users' social media activity and propose investment strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects users' financial asset data, The analysis unit analyzes the data collected by the aforementioned collection unit and classifies and analyzes the user's spending. A provision unit that provides savings and investment advice based on the analysis results obtained by the aforementioned analysis unit, The system includes an investment unit that automatically manages assets based on the advice provided by the aforementioned investment unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects data on salary receipts, payment information, and bank and securities information. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to classify and analyze user spending. The system according to feature 1.

4. The aforementioned supply unit is, It manages the balance between income and expenses and provides advice on saving and investing to help users achieve their goals. The system according to feature 1.

5. The aforementioned operations unit, The system automatically manages assets based on the advice provided. The system according to feature 1.

6. The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial data collection based on the estimated user sentiment. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting financial data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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