system

Abstract

The system according to this embodiment aims to provide optimal asset management advice even without specialized knowledge. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives information related to asset management. The generation unit analyzes the information received by the reception unit and generates optimal asset management advice. The navigation unit optimizes the investment strategy based on the advice generated by the generation unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult for individuals lacking specialized knowledge regarding asset management to make appropriate investment decisions.

[0005] The system according to the embodiment aims to provide optimal asset management advice even without specialized knowledge.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit inputs information regarding asset management. The generation unit analyzes the information input by the reception unit and generates optimal asset management advice. The navigation unit optimizes an investment strategy based on the advice generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal asset management advice even without specialized knowledge. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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, 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 optimization system according to an embodiment of the present invention is a system that optimizes an individual's asset management and maximizes asset returns by utilizing generative AI. In this system, the user inputs information related to asset management, and the generative AI analyzes that information to generate optimal asset management advice. The generated advice is of high quality because it grasps market information in real time and optimizes risk management and investment strategies. For example, when a user inputs information such as investment amount, risk tolerance, and investment period, the generative AI analyzes this information and proposes an investment strategy that is optimal for the user's needs. The generative AI analyzes a large amount of data and proposes highly secure investment options for users with low risk tolerance and high-return investment options for users with high risk tolerance. Furthermore, the generative AI constantly collects the latest market information and optimizes the user's investment strategy. For example, it provides advice to change investment options in response to market fluctuations. Through this mechanism, an individual's asset management is optimized and asset returns are maximized. Even without specialized knowledge, users can make appropriate investment decisions by following the advice of the generative AI. In addition, the generative AI has the ability to make quick and flexible decisions and optimizes risk management and investment strategies, so users can manage their assets with peace of mind. For example, if a user inputs information such as an investment amount of 1 million yen, a moderate risk tolerance, and an investment period of 5 years, the generating AI analyzes this information and proposes an optimal investment strategy. Based on the latest market information, the generating AI optimizes risk management and investment strategies, providing users with high-quality advice. In this way, asset management services utilizing generating AI are a groundbreaking solution for optimizing individual asset management and maximizing asset returns. Even without specialized knowledge, users can make appropriate investment decisions by following the advice of the generating AI, allowing them to manage their assets with peace of mind. Thus, asset management optimization systems can optimize individual asset management and maximize asset returns.

[0029] The asset management optimization system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives information about asset management from the user. The information entered by the user includes, but is not limited to, investment amount, risk tolerance, and investment period. The reception unit stores the information entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to analyze the information entered by the reception unit and generate optimal asset management advice. The generation AI uses, for example, deep learning and reinforcement learning to analyze large amounts of data and propose an investment strategy that is best suited to the user's needs. For example, the generation unit proposes highly secure investments to users with low risk tolerance and high-return investments to users with high risk tolerance. The navigation unit optimizes the investment strategy based on the advice generated by the generation unit. The navigation unit, for example, grasps market information in real time and optimizes risk management and investment strategies. The navigation unit provides, for example, advice to change investment destinations in response to market fluctuations. As a result, the asset management optimization system according to this embodiment can optimize the user's asset management and maximize asset returns. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs user input information into the generation AI, which then analyzes it and proposes an optimal investment strategy. Some or all of the above-described processes in the navigation unit may be performed using, for example, an AI, or without a AI. For example, the navigation unit inputs real-time market information into the AI, which then performs risk management and optimizes the investment strategy.

[0030] The reception desk receives information about asset management from the user. This information may include, but is not limited to, investment amount, risk tolerance, and investment period. The reception desk, for example, stores the user-entered information in a database and provides it to the generation desk. Specifically, the reception desk provides an interface for receiving user-entered information in real time and saving it to the database. Users can easily enter information through a web browser or mobile application. The entered information is stored in a secure database, protecting user privacy. Furthermore, the reception desk checks the consistency of the user-entered information and notifies the user of any missing or inaccurate information, prompting corrections. For example, if the investment amount is unusually high or the risk tolerance is inconsistent, a warning message is displayed, and the user is asked to re-enter the information. This allows the reception desk to provide accurate and reliable data to the generation desk. The reception desk can also record and reuse information previously entered by the user. This eliminates the need for users to enter the same information repeatedly, improving convenience. For example, by referring to past investment history and changes in risk tolerance, it is possible to understand the user's investment behavior trends. This allows the reception department to respond flexibly to the user's needs, contributing to the optimization of asset management.

[0031] The generation unit uses a generation AI to analyze information entered by the reception unit and generate optimal asset management advice. The generation AI analyzes large amounts of data using technologies such as deep learning and reinforcement learning, and proposes investment strategies that are best suited to the user's needs. Specifically, the generation AI learns from historical market data, economic indicators, and corporate financial information, and selects the best investment destinations based on the user's investment goals and risk tolerance. For example, it suggests highly secure government bonds and stocks of blue-chip companies to users with low risk tolerance, and suggests emerging markets or high-risk, high-return investments to users with high risk tolerance. Based on the user's input information, the generation AI simulates multiple investment scenarios and selects the most effective strategy. Furthermore, the generation unit provides an interface to present the investment strategies proposed by the generation AI to the user in an easy-to-understand manner. For example, it uses graphs and charts to visually show the balance between risk and return of investment destinations, providing information in a way that is easy for the user to understand. In addition, the generation unit collects user feedback and uses it as training data for the generation AI to continuously improve the accuracy of its suggestions. This allows the generation unit to provide optimal asset management advice tailored to the user's needs, maximizing the results of asset management.

[0032] The navigation unit optimizes investment strategies based on advice generated by the generation unit. For example, the navigation unit grasps market information in real time to optimize risk management and investment strategies. Specifically, it collects information such as financial market trends, economic news, and corporate earnings announcements in real time and incorporates it into the investment strategies proposed by the generation unit. For instance, in the event of a sudden market fluctuation, the navigation unit immediately performs a risk assessment and proposes changes to investment targets or portfolio restructuring as needed. The navigation unit uses AI to analyze market information and optimize risk management and investment strategies. For example, the AI ​​predicts current market conditions based on historical market data and detects increased risk early. The AI ​​also monitors the user's investment portfolio in real time and issues an alert immediately if the risk exceeds acceptable levels. This allows the navigation unit to help users respond quickly to market fluctuations. Furthermore, the navigation unit records the user's investment behavior to improve future investment strategies. For example, by analyzing past investment history and market fluctuation patterns, it can understand the user's investment behavior trends and provide more accurate advice. This allows the navigation unit to optimize the user's asset management and maximize the return on their assets.

[0033] The generation unit can analyze multiple data sets using a generation AI and propose an investment strategy that is optimal for the user's needs. For example, the generation unit uses a generation AI to analyze multiple data sets such as historical market data, economic indicators, and the user's investment history. The generation AI uses deep learning technology to learn from large amounts of data and propose an investment strategy that is optimal for the user's needs. The generation unit can also use reinforcement learning technology to learn the user's investment behavior and propose an optimal investment strategy. For example, the generation unit proposes an investment strategy that considers the balance between risk and return based on the user's risk tolerance and investment goals. In this way, the generation AI can propose an investment strategy that is optimal for the user's needs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs user input information into the generation AI, the generation AI performs the analysis, and proposes an optimal investment strategy.

[0034] The navigation unit can instantly grasp market information and optimize risk management and investment strategies. For example, the navigation unit instantly grasps market information such as real-time stock price information and economic news. For example, the navigation unit uses a risk assessment model to evaluate the user's investment risk and proposes risk hedging methods. The navigation unit can also optimize investment strategies through methods such as portfolio rebalancing and investment target review. For example, the navigation unit rebalances the user's investment portfolio based on real-time market information and optimizes the balance between risk and return. This allows for real-time market information to be grasped and risk management and investment strategy optimization to be achieved. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs real-time market information into AI, and the AI ​​performs risk management and investment strategy optimization.

[0035] The navigation unit can provide advice on changing investment destinations in response to market fluctuations. The navigation unit grasps market fluctuations, such as stock price fluctuations and changes in economic indicators. The navigation unit provides advice on changing investment destinations based on, for example, investment selection criteria and timing of changes. For example, the navigation unit provides advice on changing from high-risk investments to low-risk investments in response to market fluctuations. The navigation unit can also provide advice on changing to high-return investments in response to market fluctuations. This enables the provision of advice on changing investment destinations in response to market fluctuations. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs market fluctuation data into the AI, and the AI ​​generates advice on changing investment destinations.

[0036] The generation unit can suggest low-risk investment options to users with low risk tolerance. For example, the generation unit evaluates the user's risk tolerance based on survey results or past investment behavior. The generation unit suggests low-risk investment options such as government bonds or time deposits. For example, the generation unit suggests highly secure investment options to users with low risk tolerance and provides an investment strategy that minimizes risk. The generation unit can also suggest diversified investment to users with low risk tolerance to spread risk. This allows the generation unit to suggest highly secure investment options to users with low risk tolerance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's risk tolerance data into the generation AI, and the generation AI suggests low-risk investment options.

[0037] The generation unit can suggest high-return investment options to users with a high risk tolerance. For example, the generation unit evaluates the user's risk tolerance based on survey results or past investment behavior. The generation unit suggests high-return investment options such as stocks or venture capital. For example, the generation unit suggests high-return investment options to users with a high risk tolerance and provides investment strategies aimed at high returns. The generation unit can also suggest investment strategies that aim for high returns while diversifying risk to users with a high risk tolerance. This allows the generation unit to suggest high-return investment options to users with a high risk tolerance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's risk tolerance data into a generation AI, and the generation AI suggests high-return investment options.

[0038] The reception desk can analyze the user's past asset management history and select the optimal input method. For example, the reception desk can retrieve and analyze the user's past asset management history from a database. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest input methods to be used at specific times based on the user's past input history. For example, the reception desk can automatically display information that the user has frequently entered in the past as a candidate. This allows the optimal input method to be selected by analyzing past history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can select the optimal input method.

[0039] The reception desk can filter the input of asset management information based on the user's current financial situation and areas of interest. For example, the reception desk can suggest an appropriate investment amount based on the user's current income and expenditure information. For example, the reception desk can prioritize displaying relevant investment options based on the user's areas of interest (e.g., environmental protection, technology, etc.). The reception desk can also suggest low-risk investment options according to the user's financial situation. For example, the reception desk can suggest low-risk investment options based on the user's income and expenditure information. This allows for filtering of appropriate information based on the user's financial situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's financial data into AI, and the AI ​​can suggest appropriate investment options.

[0040] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting asset management information. For example, the reception desk can obtain the user's geographical location information from GPS data or address information. For example, the reception desk can suggest appropriate investment destinations considering the economic conditions of the area where the user lives. The reception desk can also prioritize displaying region-specific investment opportunities based on the user's geographical location. For example, the reception desk can provide investment advice that reflects regional market trends based on the user's geographical location information. This allows for the priority input of highly relevant information by considering geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's geographical location information into AI, and the AI ​​prioritizes inputting highly relevant information.

[0041] The reception desk can analyze a user's social media activity and input relevant information when they input asset management information. For example, the reception desk can analyze a user's social media activity to understand investment trends. For example, the reception desk can prioritize suggesting investments that the user has shown interest in on social media. The reception desk can also analyze investment trends from the user's social media activity and provide appropriate advice. For example, the reception desk can estimate the user's risk tolerance based on their social media posts and suggest appropriate investments. This allows for the input of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's social media data into the AI, and the AI ​​inputs relevant information.

[0042] The generation unit can adjust the level of detail in the advice based on the importance of the asset management when generating advice. For example, the generation unit evaluates the importance of asset management based on the investment amount and risk level. For example, the generation unit generates detailed advice for asset management with high importance. The generation unit can also generate concise advice for asset management with low importance. For example, the generation unit adjusts the content of the advice in stages according to the importance of the asset management. This allows for the provision of appropriate advice by adjusting the level of detail based on the importance of the asset management. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs asset management importance data into the generation AI, and the generation AI adjusts the level of detail in the advice.

[0043] The generation unit can apply different advice algorithms depending on the asset management category when generating advice. For example, the generation unit classifies asset management categories into stock investment, real estate investment, bond investment, etc. For example, the generation unit applies an advice algorithm that emphasizes risk management to stock investment. The generation unit can also apply an advice algorithm that emphasizes a long-term perspective to real estate investment. For example, the generation unit applies an advice algorithm that emphasizes safety to bond investment. In this way, appropriate advice can be provided by applying different advice algorithms depending on the asset management category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs asset management category data into the generation AI, and the generation AI applies an appropriate advice algorithm.

[0044] The generation unit can determine the priority of advice based on the timing of asset management submissions when generating advice. For example, the generation unit evaluates the timing of asset management submissions based on submission deadlines and submission frequency. For example, the generation unit prioritizes generating advice for asset management with approaching submission deadlines. Conversely, the generation unit can also postpone generating advice for asset management with distant submission deadlines. For example, the generation unit adjusts the priority of advice in stages according to the submission timing. This allows advice to be provided at the appropriate time by determining the priority of advice based on the submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs asset management submission timing data into the generation AI, and the generation AI determines the priority of advice.

[0045] The generation unit can adjust the order of advice based on the relevance of asset management when generating advice. For example, the generation unit evaluates the relevance of asset management based on the relevance of investment targets and the relevance of risks. For example, the generation unit prioritizes generating advice for highly relevant asset management. The generation unit can also postpone generating advice for less relevant asset management. For example, the generation unit adjusts the order of advice in stages according to the relevance of asset management. This allows the advice to be provided in an appropriate order by adjusting the order of advice based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs asset management relevance data into the generation AI, and the generation AI adjusts the order of advice.

[0046] The navigation unit can improve the accuracy of navigation by considering the interrelationships of asset management during navigation. For example, the navigation unit evaluates the interrelationships of asset management based on the correlations between investment targets and the mutual impact of risks. For example, the navigation unit analyzes the interrelationships of asset management and provides optimal navigation. The navigation unit can also provide navigation with enhanced risk management based on the interrelationships of asset management. For example, the navigation unit provides navigation that optimizes investment strategies by considering the interrelationships of asset management. This improves the accuracy of navigation by considering the interrelationships of asset management. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs data on the interrelationships of asset management into the AI, and the AI ​​improves the accuracy of navigation.

[0047] The navigation unit can perform navigation while considering the attribute information of the asset management submitter. For example, the navigation unit performs navigation based on attribute information such as the submitter's age, occupation, and investment experience. For example, the navigation unit provides optimal navigation based on the submitter's age and occupation. The navigation unit can also provide navigation with enhanced risk management based on the submitter's risk tolerance. For example, the navigation unit provides appropriate navigation based on the submitter's investment experience. In this way, more appropriate navigation can be provided by considering the submitter's attribute information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs the submitter's attribute information data into the AI, and the AI ​​performs the navigation.

[0048] The navigation unit can perform navigation while considering the geographical distribution of asset management. For example, the navigation unit evaluates the geographical distribution of asset management based on investment destinations and region-specific risks for each region. For example, the navigation unit analyzes the geographical distribution of asset management and provides optimal navigation. The navigation unit can also provide navigation that takes region-specific risks into account based on the geographical distribution. For example, the navigation unit provides navigation that optimizes investment strategies by considering the geographical distribution. This makes it possible to perform navigation that reflects region-specific risks by considering the geographical distribution. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without using AI. For example, the navigation unit inputs geographical distribution data of asset management into AI, and the AI ​​performs navigation.

[0049] The navigation unit can improve the accuracy of its navigation by referring to relevant literature on asset management during navigation. For example, the navigation unit can obtain and refer to relevant literature on asset management from academic papers and industry reports. For example, the navigation unit can refer to relevant literature on asset management and provide optimal navigation. The navigation unit can also provide navigation with enhanced risk management based on relevant literature. For example, the navigation unit can provide navigation that optimizes investment strategies by considering relevant literature. In this way, the accuracy of navigation can be improved by referring to relevant literature. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs data on relevant literature on asset management into AI, and the AI ​​performs the navigation.

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

[0051] The reception desk can analyze user input information and learn the user's investment behavior patterns. For example, it can analyze the success and failure rates of the user's past investments to identify investment behavior patterns. Furthermore, the reception desk can improve the accuracy of investment advice based on the user's investment behavior patterns. For instance, it can prioritize suggesting investment strategies that the user has previously succeeded with. In addition, the reception desk can compare the user's investment behavior patterns with those of other users and set benchmarks. This allows it to learn the user's investment behavior patterns and provide more appropriate investment advice.

[0052] The generation unit can predict investment risk based on the user's investment history. For example, it can analyze the risk level of past investments and predict future investment risk. It can also provide risk management advice based on the user's investment history. For instance, if the user has made high-risk investments in the past, it can suggest investment strategies to diversify risk. Furthermore, the generation unit can compare the user's investment history with that of other users and provide best practices for risk management. This allows for the provision of more appropriate risk management advice based on the user's investment history.

[0053] The navigation system can prioritize investment strategies based on the user's investment goals. For example, if the user aims for short-term profits, the navigation system will prioritize suggesting high-risk investments. Conversely, if the user aims for long-term stability, the navigation system can prioritize suggesting high-safety investments. Furthermore, the navigation system can adjust the balance of investment strategies based on the user's investment goals. For example, it can suggest investment strategies that consider the balance between short-term profits and long-term stability. This allows the system to provide more appropriate investment strategies based on the user's investment goals.

[0054] The navigation system can adjust the difficulty level of investment advice based on the user's investment experience. For example, it can suggest basic investment strategies to novice investors, and more advanced strategies to experienced users. Furthermore, the navigation system can adjust the level of detail in investment advice based on the user's investment experience. For example, it can provide detailed explanations to novice investors and concise advice to experienced users. This allows for the provision of more appropriate investment advice based on the user's investment experience.

[0055] The generation unit can monitor the user's investment portfolio in real time and suggest rebalancing as needed. For example, if the user's portfolio composition deviates from the target, the generation unit will suggest rebalancing. The generation unit can also suggest portfolio rebalancing in response to market fluctuations. For instance, to cope with rapid market fluctuations, the generation unit may suggest reducing high-risk assets and increasing high-safety assets. Furthermore, the generation unit can suggest portfolio rebalancing based on the user's investment goals. This allows for real-time monitoring of the user's investment portfolio and the provision of appropriate rebalancing suggestions.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception unit receives information about the user's asset management. This information includes, for example, the investment amount, risk tolerance, and investment period. The reception unit saves the information entered by the user to a database and provides it to the generation unit. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and generate optimal asset management advice. The generation AI uses technologies such as deep learning and reinforcement learning to analyze large amounts of data and propose investment strategies that are best suited to the user's needs. For example, it proposes highly secure investments to users with low risk tolerance and high-return investments to users with high risk tolerance. Step 3: The navigation unit optimizes the investment strategy based on the advice generated by the generation unit. The navigation unit grasps market information in real time and optimizes risk management and investment strategies. For example, it provides advice on changing investment targets in response to market fluctuations.

[0058] (Example of form 2) The asset management optimization system according to an embodiment of the present invention is a system that optimizes an individual's asset management and maximizes asset returns by utilizing generative AI. In this system, the user inputs information related to asset management, and the generative AI analyzes that information to generate optimal asset management advice. The generated advice is of high quality because it grasps market information in real time and optimizes risk management and investment strategies. For example, when a user inputs information such as investment amount, risk tolerance, and investment period, the generative AI analyzes this information and proposes an investment strategy that is optimal for the user's needs. The generative AI analyzes a large amount of data and proposes highly secure investment options for users with low risk tolerance and high-return investment options for users with high risk tolerance. Furthermore, the generative AI constantly collects the latest market information and optimizes the user's investment strategy. For example, it provides advice to change investment options in response to market fluctuations. Through this mechanism, an individual's asset management is optimized and asset returns are maximized. Even without specialized knowledge, users can make appropriate investment decisions by following the advice of the generative AI. In addition, the generative AI has the ability to make quick and flexible decisions and optimizes risk management and investment strategies, so users can manage their assets with peace of mind. For example, if a user inputs information such as an investment amount of 1 million yen, a moderate risk tolerance, and an investment period of 5 years, the generating AI analyzes this information and proposes an optimal investment strategy. Based on the latest market information, the generating AI optimizes risk management and investment strategies, providing users with high-quality advice. In this way, asset management services utilizing generating AI are a groundbreaking solution for optimizing individual asset management and maximizing asset returns. Even without specialized knowledge, users can make appropriate investment decisions by following the advice of the generating AI, allowing them to manage their assets with peace of mind. Thus, asset management optimization systems can optimize individual asset management and maximize asset returns.

[0059] The asset management optimization system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives information about asset management from the user. The information entered by the user includes, but is not limited to, investment amount, risk tolerance, and investment period. The reception unit stores the information entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to analyze the information entered by the reception unit and generate optimal asset management advice. The generation AI uses, for example, deep learning and reinforcement learning to analyze large amounts of data and propose an investment strategy that is best suited to the user's needs. For example, the generation unit proposes highly secure investments to users with low risk tolerance and high-return investments to users with high risk tolerance. The navigation unit optimizes the investment strategy based on the advice generated by the generation unit. The navigation unit, for example, grasps market information in real time and optimizes risk management and investment strategies. The navigation unit provides, for example, advice to change investment destinations in response to market fluctuations. As a result, the asset management optimization system according to this embodiment can optimize the user's asset management and maximize asset returns. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs user input information into the generation AI, which then analyzes it and proposes an optimal investment strategy. Some or all of the above-described processes in the navigation unit may be performed using, for example, an AI, or without a AI. For example, the navigation unit inputs real-time market information into the AI, which then performs risk management and optimizes the investment strategy.

[0060] The reception desk receives information about asset management from the user. This information may include, but is not limited to, investment amount, risk tolerance, and investment period. The reception desk, for example, stores the user-entered information in a database and provides it to the generation desk. Specifically, the reception desk provides an interface for receiving user-entered information in real time and saving it to the database. Users can easily enter information through a web browser or mobile application. The entered information is stored in a secure database, protecting user privacy. Furthermore, the reception desk checks the consistency of the user-entered information and notifies the user of any missing or inaccurate information, prompting corrections. For example, if the investment amount is unusually high or the risk tolerance is inconsistent, a warning message is displayed, and the user is asked to re-enter the information. This allows the reception desk to provide accurate and reliable data to the generation desk. The reception desk can also record and reuse information previously entered by the user. This eliminates the need for users to enter the same information repeatedly, improving convenience. For example, by referring to past investment history and changes in risk tolerance, it is possible to understand the user's investment behavior trends. This allows the reception department to respond flexibly to the user's needs, contributing to the optimization of asset management.

[0061] The generation unit uses a generation AI to analyze information entered by the reception unit and generate optimal asset management advice. The generation AI analyzes large amounts of data using technologies such as deep learning and reinforcement learning, and proposes investment strategies that are best suited to the user's needs. Specifically, the generation AI learns from historical market data, economic indicators, and corporate financial information, and selects the best investment destinations based on the user's investment goals and risk tolerance. For example, it suggests highly secure government bonds and stocks of blue-chip companies to users with low risk tolerance, and suggests emerging markets or high-risk, high-return investments to users with high risk tolerance. Based on the user's input information, the generation AI simulates multiple investment scenarios and selects the most effective strategy. Furthermore, the generation unit provides an interface to present the investment strategies proposed by the generation AI to the user in an easy-to-understand manner. For example, it uses graphs and charts to visually show the balance between risk and return of investment destinations, providing information in a way that is easy for the user to understand. In addition, the generation unit collects user feedback and uses it as training data for the generation AI to continuously improve the accuracy of its suggestions. This allows the generation unit to provide optimal asset management advice tailored to the user's needs, maximizing the results of asset management.

[0062] The navigation unit optimizes investment strategies based on advice generated by the generation unit. For example, the navigation unit grasps market information in real time to optimize risk management and investment strategies. Specifically, it collects information such as financial market trends, economic news, and corporate earnings announcements in real time and incorporates it into the investment strategies proposed by the generation unit. For instance, in the event of a sudden market fluctuation, the navigation unit immediately performs a risk assessment and proposes changes to investment targets or portfolio restructuring as needed. The navigation unit uses AI to analyze market information and optimize risk management and investment strategies. For example, the AI ​​predicts current market conditions based on historical market data and detects increased risk early. The AI ​​also monitors the user's investment portfolio in real time and issues an alert immediately if the risk exceeds acceptable levels. This allows the navigation unit to help users respond quickly to market fluctuations. Furthermore, the navigation unit records the user's investment behavior to improve future investment strategies. For example, by analyzing past investment history and market fluctuation patterns, it can understand the user's investment behavior trends and provide more accurate advice. This allows the navigation unit to optimize the user's asset management and maximize the return on their assets.

[0063] The generation unit can analyze multiple data sets using a generation AI and propose an investment strategy that is optimal for the user's needs. For example, the generation unit uses a generation AI to analyze multiple data sets such as historical market data, economic indicators, and the user's investment history. The generation AI uses deep learning technology to learn from large amounts of data and propose an investment strategy that is optimal for the user's needs. The generation unit can also use reinforcement learning technology to learn the user's investment behavior and propose an optimal investment strategy. For example, the generation unit proposes an investment strategy that considers the balance between risk and return based on the user's risk tolerance and investment goals. In this way, the generation AI can propose an investment strategy that is optimal for the user's needs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs user input information into the generation AI, the generation AI performs the analysis, and proposes an optimal investment strategy.

[0064] The navigation unit can instantly grasp market information and optimize risk management and investment strategies. For example, the navigation unit instantly grasps market information such as real-time stock price information and economic news. For example, the navigation unit uses a risk assessment model to evaluate the user's investment risk and proposes risk hedging methods. The navigation unit can also optimize investment strategies through methods such as portfolio rebalancing and investment target review. For example, the navigation unit rebalances the user's investment portfolio based on real-time market information and optimizes the balance between risk and return. This allows for real-time market information to be grasped and risk management and investment strategy optimization to be achieved. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs real-time market information into AI, and the AI ​​performs risk management and investment strategy optimization.

[0065] The navigation unit can provide advice on changing investment destinations in response to market fluctuations. The navigation unit grasps market fluctuations, such as stock price fluctuations and changes in economic indicators. The navigation unit provides advice on changing investment destinations based on, for example, investment selection criteria and timing of changes. For example, the navigation unit provides advice on changing from high-risk investments to low-risk investments in response to market fluctuations. The navigation unit can also provide advice on changing to high-return investments in response to market fluctuations. This enables the provision of advice on changing investment destinations in response to market fluctuations. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs market fluctuation data into the AI, and the AI ​​generates advice on changing investment destinations.

[0066] The generation unit can suggest low-risk investment options to users with low risk tolerance. For example, the generation unit evaluates the user's risk tolerance based on survey results or past investment behavior. The generation unit suggests low-risk investment options such as government bonds or time deposits. For example, the generation unit suggests highly secure investment options to users with low risk tolerance and provides an investment strategy that minimizes risk. The generation unit can also suggest diversified investment to users with low risk tolerance to spread risk. This allows the generation unit to suggest highly secure investment options to users with low risk tolerance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's risk tolerance data into the generation AI, and the generation AI suggests low-risk investment options.

[0067] The generation unit can suggest high-return investment options to users with a high risk tolerance. For example, the generation unit evaluates the user's risk tolerance based on survey results or past investment behavior. The generation unit suggests high-return investment options such as stocks or venture capital. For example, the generation unit suggests high-return investment options to users with a high risk tolerance and provides investment strategies aimed at high returns. The generation unit can also suggest investment strategies that aim for high returns while diversifying risk to users with a high risk tolerance. This allows the generation unit to suggest high-return investment options to users with a high risk tolerance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's risk tolerance data into a generation AI, and the generation AI suggests high-return investment options.

[0068] The reception desk can estimate the user's emotions and adjust the timing of asset management information input based on the estimated emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on changes in facial expressions and adjust the input timing. The reception desk can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the voice, calculate an emotion score, and adjust the input timing. The reception desk can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on fluctuations in heart rate and adjust the input timing. This allows for more appropriate information input by adjusting the input timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0069] The reception desk can analyze the user's past asset management history and select the optimal input method. For example, the reception desk can retrieve and analyze the user's past asset management history from a database. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest input methods to be used at specific times based on the user's past input history. For example, the reception desk can automatically display information that the user has frequently entered in the past as a candidate. This allows the optimal input method to be selected by analyzing past history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can select the optimal input method.

[0070] The reception desk can filter the input of asset management information based on the user's current financial situation and areas of interest. For example, the reception desk can suggest an appropriate investment amount based on the user's current income and expenditure information. For example, the reception desk can prioritize displaying relevant investment options based on the user's areas of interest (e.g., environmental protection, technology, etc.). The reception desk can also suggest low-risk investment options according to the user's financial situation. For example, the reception desk can suggest low-risk investment options based on the user's income and expenditure information. This allows for filtering of appropriate information based on the user's financial situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's financial data into AI, and the AI ​​can suggest appropriate investment options.

[0071] The reception unit can estimate the user's emotions and determine the priority of the information to be input based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and determine the priority of the information to be input. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of the information to be input. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of the information to be input. This enables efficient information input by determining the priority of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0072] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting asset management information. For example, the reception desk can obtain the user's geographical location information from GPS data or address information. For example, the reception desk can suggest appropriate investment destinations considering the economic conditions of the area where the user lives. The reception desk can also prioritize displaying region-specific investment opportunities based on the user's geographical location. For example, the reception desk can provide investment advice that reflects regional market trends based on the user's geographical location information. This allows for the priority input of highly relevant information by considering geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's geographical location information into AI, and the AI ​​prioritizes inputting highly relevant information.

[0073] The reception desk can analyze a user's social media activity and input relevant information when they input asset management information. For example, the reception desk can analyze a user's social media activity to understand investment trends. For example, the reception desk can prioritize suggesting investments that the user has shown interest in on social media. The reception desk can also analyze investment trends from the user's social media activity and provide appropriate advice. For example, the reception desk can estimate the user's risk tolerance based on their social media posts and suggest appropriate investments. This allows for the input of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's social media data into the AI, and the AI ​​inputs relevant information.

[0074] The generation unit can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions and adjust the way advice is expressed. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the way advice is expressed. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate and adjust the way advice is expressed. This allows for the provision of more appropriate advice by adjusting the way advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is, 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0075] The generation unit can adjust the level of detail in the advice based on the importance of the asset management when generating advice. For example, the generation unit evaluates the importance of asset management based on the investment amount and risk level. For example, the generation unit generates detailed advice for asset management with high importance. The generation unit can also generate concise advice for asset management with low importance. For example, the generation unit adjusts the content of the advice in stages according to the importance of the asset management. This allows for the provision of appropriate advice by adjusting the level of detail based on the importance of the asset management. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs asset management importance data into the generation AI, and the generation AI adjusts the level of detail in the advice.

[0076] The generation unit can apply different advice algorithms depending on the asset management category when generating advice. For example, the generation unit classifies asset management categories into stock investment, real estate investment, bond investment, etc. For example, the generation unit applies an advice algorithm that emphasizes risk management to stock investment. The generation unit can also apply an advice algorithm that emphasizes a long-term perspective to real estate investment. For example, the generation unit applies an advice algorithm that emphasizes safety to bond investment. In this way, appropriate advice can be provided by applying different advice algorithms depending on the asset management category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs asset management category data into the generation AI, and the generation AI applies an appropriate advice algorithm.

[0077] The generation unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions and adjust the length of the advice. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the advice. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate and adjust the length of the advice. By adjusting the length of the advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0078] The generation unit can determine the priority of advice based on the timing of asset management submissions when generating advice. For example, the generation unit evaluates the timing of asset management submissions based on submission deadlines and submission frequency. For example, the generation unit prioritizes generating advice for asset management with approaching submission deadlines. Conversely, the generation unit can also postpone generating advice for asset management with distant submission deadlines. For example, the generation unit adjusts the priority of advice in stages according to the submission timing. This allows advice to be provided at the appropriate time by determining the priority of advice based on the submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs asset management submission timing data into the generation AI, and the generation AI determines the priority of advice.

[0079] The generation unit can adjust the order of advice based on the relevance of asset management when generating advice. For example, the generation unit evaluates the relevance of asset management based on the relevance of investment targets and the relevance of risks. For example, the generation unit prioritizes generating advice for highly relevant asset management. The generation unit can also postpone generating advice for less relevant asset management. For example, the generation unit adjusts the order of advice in stages according to the relevance of asset management. This allows the advice to be provided in an appropriate order by adjusting the order of advice based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs asset management relevance data into the generation AI, and the generation AI adjusts the order of advice.

[0080] The navigation unit can estimate the user's emotions and adjust the navigation criteria based on the estimated emotions. For example, the navigation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the navigation unit can calculate an emotion score based on changes in facial expressions and adjust the navigation criteria. The navigation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the navigation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the navigation criteria. The navigation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the navigation unit can calculate an emotion score based on fluctuations in heart rate and adjust the navigation criteria. By adjusting the navigation criteria based on the user's emotions, more appropriate navigation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0081] The navigation unit can improve the accuracy of navigation by considering the interrelationships of asset management during navigation. For example, the navigation unit evaluates the interrelationships of asset management based on the correlations between investment targets and the mutual impact of risks. For example, the navigation unit analyzes the interrelationships of asset management and provides optimal navigation. The navigation unit can also provide navigation with enhanced risk management based on the interrelationships of asset management. For example, the navigation unit provides navigation that optimizes investment strategies by considering the interrelationships of asset management. This improves the accuracy of navigation by considering the interrelationships of asset management. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs data on the interrelationships of asset management into the AI, and the AI ​​improves the accuracy of navigation.

[0082] The navigation unit can perform navigation while considering the attribute information of the asset management submitter. For example, the navigation unit performs navigation based on attribute information such as the submitter's age, occupation, and investment experience. For example, the navigation unit provides optimal navigation based on the submitter's age and occupation. The navigation unit can also provide navigation with enhanced risk management based on the submitter's risk tolerance. For example, the navigation unit provides appropriate navigation based on the submitter's investment experience. In this way, more appropriate navigation can be provided by considering the submitter's attribute information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs the submitter's attribute information data into the AI, and the AI ​​performs the navigation.

[0083] The navigation unit can estimate the user's emotions and adjust the order in which navigation results are displayed based on the estimated emotions. For example, the navigation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the navigation unit can calculate an emotion score based on changes in facial expressions and adjust the order in which navigation results are displayed. The navigation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the navigation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the order in which navigation results are displayed. The navigation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the navigation unit can calculate an emotion score based on fluctuations in heart rate and adjust the order in which navigation results are displayed. This allows for the provision of more appropriate information by adjusting the display order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, 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 navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0084] The navigation unit can perform navigation while considering the geographical distribution of asset management. For example, the navigation unit evaluates the geographical distribution of asset management based on investment destinations and region-specific risks for each region. For example, the navigation unit analyzes the geographical distribution of asset management and provides optimal navigation. The navigation unit can also provide navigation that takes region-specific risks into account based on the geographical distribution. For example, the navigation unit provides navigation that optimizes investment strategies by considering the geographical distribution. This makes it possible to perform navigation that reflects region-specific risks by considering the geographical distribution. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without using AI. For example, the navigation unit inputs geographical distribution data of asset management into AI, and the AI ​​performs navigation.

[0085] The navigation unit can improve the accuracy of its navigation by referring to relevant literature on asset management during navigation. For example, the navigation unit can obtain and refer to relevant literature on asset management from academic papers and industry reports. For example, the navigation unit can refer to relevant literature on asset management and provide optimal navigation. The navigation unit can also provide navigation with enhanced risk management based on relevant literature. For example, the navigation unit can provide navigation that optimizes investment strategies by considering relevant literature. In this way, the accuracy of navigation can be improved by referring to relevant literature. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit inputs data on relevant literature on asset management into AI, and the AI ​​performs the navigation.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can analyze user input information and learn the user's investment behavior patterns. For example, it can analyze the success and failure rates of the user's past investments to identify investment behavior patterns. Furthermore, the reception desk can improve the accuracy of investment advice based on the user's investment behavior patterns. For instance, it can prioritize suggesting investment strategies that the user has previously succeeded with. In addition, the reception desk can compare the user's investment behavior patterns with those of other users and set benchmarks. This allows it to learn the user's investment behavior patterns and provide more appropriate investment advice.

[0088] The generation unit can predict investment risk based on the user's investment history. For example, it can analyze the risk level of past investments and predict future investment risk. It can also provide risk management advice based on the user's investment history. For instance, if the user has made high-risk investments in the past, it can suggest investment strategies to diversify risk. Furthermore, the generation unit can compare the user's investment history with that of other users and provide best practices for risk management. This allows for the provision of more appropriate risk management advice based on the user's investment history.

[0089] The navigation system can prioritize investment strategies based on the user's investment goals. For example, if the user aims for short-term profits, the navigation system will prioritize suggesting high-risk investments. Conversely, if the user aims for long-term stability, the navigation system can prioritize suggesting high-safety investments. Furthermore, the navigation system can adjust the balance of investment strategies based on the user's investment goals. For example, it can suggest investment strategies that consider the balance between short-term profits and long-term stability. This allows the system to provide more appropriate investment strategies based on the user's investment goals.

[0090] The navigation system can adjust the difficulty level of investment advice based on the user's investment experience. For example, it can suggest basic investment strategies to novice investors, and more advanced strategies to experienced users. Furthermore, the navigation system can adjust the level of detail in investment advice based on the user's investment experience. For example, it can provide detailed explanations to novice investors and concise advice to experienced users. This allows for the provision of more appropriate investment advice based on the user's investment experience.

[0091] The generation unit can monitor the user's investment portfolio in real time and suggest rebalancing as needed. For example, if the user's portfolio composition deviates from the target, the generation unit will suggest rebalancing. The generation unit can also suggest portfolio rebalancing in response to market fluctuations. For instance, to cope with rapid market fluctuations, the generation unit may suggest reducing high-risk assets and increasing high-safety assets. Furthermore, the generation unit can suggest portfolio rebalancing based on the user's investment goals. This allows for real-time monitoring of the user's investment portfolio and the provision of appropriate rebalancing suggestions.

[0092] The reception desk can estimate the user's emotions and customize the input interface based on those emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface. Conversely, if the user is relaxed, it can provide an interface that allows for the input of more detailed information. Furthermore, the reception desk can adjust the color and design of the input interface based on the user's emotions. For example, if the user is tense, it can provide an interface with calming colors. This allows for the provision of a more appropriate input interface based on the user's emotions.

[0093] The generation unit can estimate the user's emotions and adjust the tone of the advice based on those emotions. For example, if the user is feeling anxious, the generation unit will provide advice in a reassuring tone. Conversely, if the user is confident, the generation unit can provide advice in a positive tone. Furthermore, the generation unit can adjust the content of the advice based on the user's emotions. For example, if the user is afraid of risk, the generation unit will provide advice to mitigate that risk. This allows for the provision of more appropriate advice based on the user's emotions.

[0094] The navigation unit can estimate the user's emotions and adjust the pace of navigation based on those emotions. For example, if the user is anxious, the navigation unit will provide navigation at a slow pace. Conversely, if the user is relaxed, the navigation unit can provide navigation at a fast pace. Furthermore, the navigation unit can also adjust the content of the navigation based on the user's emotions. For example, if the user is confused, the navigation unit will provide concise and clear navigation. This allows for more appropriate navigation based on the user's emotions.

[0095] The generation unit can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is feeling stressed, the generation unit may temporarily delay providing advice. Conversely, if the user is relaxed, the generation unit may provide advice quickly. Furthermore, the generation unit can also adjust the frequency of advice based on the user's emotions. For example, if the user is feeling anxious, the generation unit may provide advice more frequently to provide reassurance. This allows for more appropriate timing of advice based on the user's emotions.

[0096] The navigation unit can estimate the user's emotions and adjust the navigation feedback based on those emotions. For example, if the user is feeling anxious, the navigation unit will provide positive feedback. If the user is confident, the navigation unit can also provide feedback that includes specific areas for improvement. Furthermore, the navigation unit can adjust the frequency of feedback based on the user's emotions. For example, if the user is feeling stressed, the navigation unit will provide frequent feedback to reassure them. This allows for more appropriate navigation feedback based on the user's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception unit receives information about the user's asset management. This information includes, for example, the investment amount, risk tolerance, and investment period. The reception unit saves the information entered by the user to a database and provides it to the generation unit. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and generate optimal asset management advice. The generation AI uses technologies such as deep learning and reinforcement learning to analyze large amounts of data and propose investment strategies that are best suited to the user's needs. For example, it proposes highly secure investments to users with low risk tolerance and high-return investments to users with high risk tolerance. Step 3: The navigation unit optimizes the investment strategy based on the advice generated by the generation unit. The navigation unit grasps market information in real time and optimizes risk management and investment strategies. For example, it provides advice on changing investment targets in response to market fluctuations.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] For example, the reception unit is implemented by the reception device 38 of the smart device 14, which transmits the information entered by the user to the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's input information using generation AI and generates optimal asset management advice. The navigation unit is implemented by the control unit 46A of the smart device 14, which optimizes the investment strategy based on the generated advice. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which transmits the information entered by the user to the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's input information using generation AI and generates optimal asset management advice. The navigation unit is implemented by the control unit 46A of the smart glasses 214, which optimizes the investment strategy based on the generated advice. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which transmits the information entered by the user to the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's input information using a generation AI and generates optimal asset management advice. The navigation unit is implemented by the control unit 46A of the headset terminal 314, which optimizes the investment strategy based on the generated advice. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] For example, the reception unit is implemented by the microphone 238 of the robot 414, which transmits the information entered by the user to the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's input information using a generation AI and generates optimal asset management advice. The navigation unit is implemented by the control unit 46A of the robot 414, which optimizes the investment strategy based on the generated advice. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A reception area for entering information about asset management, A generation unit analyzes the information entered by the reception unit and generates optimal asset management advice, The system includes a navigation unit that optimizes the investment strategy based on the advice generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Using generative AI, we analyze multiple data sets and propose investment strategies best suited to the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned navigation unit is To instantly grasp market information and optimize risk management and investment strategies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned navigation unit is We provide advice on changing investment destinations in response to market fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is For users with a low risk tolerance, we suggest low-risk investment options. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is For users with a high risk tolerance, we propose investment options with high returns. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of asset management information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past asset management history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering asset management information, filtering is performed based on the user's current financial situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering asset management information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering asset management information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating 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 14) The generating unit is When generating advice, adjust the level of detail based on the importance of the asset management. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating advice, different advice algorithms are applied depending on the asset management category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating 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 17) The generating unit is When generating advice, we prioritize the advice based on when the asset management report was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating advice, the order of advice is adjusted based on the relevance of asset management. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned navigation unit is It estimates the user's emotions and adjusts navigation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned navigation unit is During navigation, we improve the accuracy of the navigation by considering the interrelationships of asset management. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned navigation unit is During navigation, the system takes into account the attribute information of the asset management submitter. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned navigation unit is It estimates the user's sentiment and adjusts the order in which navigation results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned navigation unit is During navigation, the geographical distribution of asset management is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned navigation unit is During navigation, we refer to relevant literature on asset management to improve the accuracy of the navigation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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 reception area for entering information about asset management, A generation unit analyzes the information entered by the reception unit and generates optimal asset management advice, The system includes a navigation unit that optimizes the investment strategy based on the advice generated by the generation unit. A system characterized by the following features.

2. The generating unit is Using generative AI, we analyze multiple data sets and propose the optimal investment strategy for the user's needs. The system according to feature 1.

3. The aforementioned navigation unit is To instantly grasp market information and optimize risk management and investment strategies. The system according to feature 1.

4. The aforementioned navigation unit is We provide advice on changing investment destinations in response to market fluctuations. The system according to feature 1.

5. The generating unit is For users with a low risk tolerance, we suggest low-risk investment options. The system according to feature 1.

6. The generating unit is For users with a high risk tolerance, we propose investment options with high returns. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of asset management information input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past asset management history and select the optimal input method. The system according to feature 1.

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