System

Abstract

A system according to an embodiment aims to provide personalized financial advice based on a user's individual needs and to automate asset management.SOLUTION: A system includes a reception unit, a generation unit, a monitoring unit, a learning unit, and a provision unit. A reception part inputs the financial situation and target of a user. The generation unit generates advice based on the information input by the reception unit. The monitoring unit inputs the asset information of the user, and the AI automatically monitors the situation of the asset and performs rebalancing as necessary. The learning component improves financial knowledge through interactive learning tools. In a providing part, an AI analyzes a market trend and financial products, and provides a market insight to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology faced challenges in financial management and asset management, including lack of knowledge, lack of time, lack of personalization, and information overload.

[0005] The system according to the embodiment aims to provide personalized financial advice based on the individual needs of the user and to automate asset management. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a monitoring unit, a learning unit, and a provision unit. The reception unit inputs the user's financial situation and goals. The generation unit generates advice based on the information input by the reception unit. The monitoring unit inputs the user's asset information, and AI automatically monitors the asset status and rebalances as necessary. The learning unit improves financial knowledge through interactive learning tools. The provision unit uses AI to analyze market trends and financial products and provide market insights to the user. [Effects of the Invention]

[0007] Systems according to embodiments can provide personalized financial advice based on a user's individual needs and automate wealth management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A financial management system according to an embodiment of the present invention allows a user to input their financial situation and goals, and a generating AI generates optimal advice, monitors their asset status, rebalances assets as needed, improves their financial knowledge, and provides market insights through interactive learning tools. For example, a user inputs their financial situation and goals into the financial management system. For example, the user inputs information such as income, expenses, assets, liabilities, and investment goals. The financial management system then uses the generating AI to perform risk assessments and asset allocation proposals based on the input information. Examples of such proposals include risk assessments for specific investment products and asset allocation proposals. The financial management system then inputs the user's asset information, and the generating AI automatically monitors their asset status. The generating AI periodically monitors their asset status and uses a rebalancing criterion, for example, when the deviation from the asset allocation target ratio exceeds a certain level. The financial management system then improves their financial knowledge through interactive learning tools. Users can deepen their financial knowledge using learning content provided by the generating AI. For example, they can learn the basics of investment and risk management methods. The generating AI then analyzes market trends and financial products to provide users with market insights. Generative AI analyzes past market data, news articles, corporate financial information, and more to provide users with the latest market trends and investment tips. This allows the financial management system to better understand the user's financial situation and manage their assets effectively. This allows the financial management system to better understand the user's financial situation and manage their assets effectively. For example, novice users can make their first investment with confidence based on personalized advice provided by Generative AI. Experienced users can also utilize market insights provided by Generative AI to implement more advanced investment strategies.

[0029] A financial management system according to an embodiment includes a receiving unit, a generating unit, a monitoring unit, a learning unit, and a providing unit. The receiving unit inputs a user's financial situation and goals. The user's financial situation includes, but is not limited to, income, expenses, assets, liabilities, and investment goals. The receiving unit provides, for example, an interface through which the user inputs income and expense information. The receiving unit can also provide an interface through which the user inputs asset and liability information. The receiving unit can also provide an interface through which the user sets investment goals. The generating unit uses a generation AI to generate advice based on the information input by the receiving unit. The advice is provided, for example, based on risk assessment and asset allocation proposals, but is not limited to, examples. For example, the generation AI can evaluate the user's risk tolerance and propose an optimal asset allocation based on the risk tolerance. The generating unit can also use the generation AI to perform risk assessment of specific investment products. The generating unit can also use the generation AI to propose an investment strategy based on the user's financial goals. The monitoring unit uses the generation AI to monitor the status of the user's assets and rebalance as necessary. The monitoring may be performed periodically, for example, but is not limited to such an example. For example, the monitoring unit rebalances when the deviation from the asset allocation target ratio reaches a certain level. The monitoring unit may also use the generation AI to monitor the asset status in real time. Furthermore, the monitoring unit may also use the generation AI to monitor the asset status over the long term and optimize the timing of rebalancing. The learning unit improves financial knowledge through interactive learning tools. The learning may be performed in the form of a quiz, for example, but is not limited to such an example. For example, the learning unit allows the user to progress through the learning in the form of a quiz, and the generation AI provides appropriate content according to the user's learning progress. The learning unit may also use the generation AI to customize learning content based on the user's learning history. Furthermore, the learning unit may use the generation AI to generate new learning content according to the user's learning needs. The provision unit uses the generation AI to analyze market trends and financial products and provide market insights to the user.Market insights are generated based on, for example, past market data, news articles, and corporate financial information, but are not limited to these examples. For example, the providing unit uses the generation AI to analyze past market data and predict current market trends. The providing unit can also use the generation AI to analyze news articles and provide users with the latest market trends. Furthermore, the providing unit can also use the generation AI to analyze corporate financial information and provide users with investment tips. This enables the financial management system according to the embodiment to better understand the user's financial situation and effectively manage assets. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs user input information into the generation AI, which then generates advice. Some or all of the above-described processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit inputs the user's asset information into the generation AI, which then monitors the asset status. Some or all of the above-described processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's learning progress into the generation AI, which then provides appropriate learning content. Some or all of the above-described processing in the provision unit may be performed using, or without, the generation AI. For example, the provision unit inputs past market data into the generation AI, which then predicts current market trends.

[0030] The reception unit can input information on income, expenses, assets, liabilities, and investment goals. Income includes, but is not limited to, examples of salary, business income, and investment income. The reception unit, for example, provides an interface through which the user inputs salary information. The reception unit can also provide an interface through which the user inputs business income and investment income information. Expenses include, but are not limited to, examples of living expenses, education expenses, and medical expenses. The reception unit, for example, provides an interface through which the user inputs living expenses information. The reception unit can also provide an interface through which the user inputs education expenses and medical expenses information. Assets include, but are not limited to, examples of cash, stocks, and real estate. The reception unit, for example, provides an interface through which the user inputs cash information. The reception unit can also provide an interface through which the user inputs stock and real estate information. Liabilities include, but are not limited to, examples of mortgages and credit card debts. The reception unit, for example, provides an interface through which the user inputs mortgage information. The reception unit can also provide an interface through which the user inputs credit card debt information. Investment goals include, but are not limited to, retirement funds, education funds, and the like. For example, the reception unit provides an interface for the user to set retirement fund goals. The reception unit can also provide an interface for the user to set education fund goals. This allows the user's financial situation to be understood in detail. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's input information into the generation AI, and the generation AI analyzes the information.

[0031] The generation unit can perform risk assessment and asset allocation suggestions based on the input information. Risk assessment includes, but is not limited to, assessment of risk tolerance and risk profile. The generation unit can, for example, use a generation AI to assess the user's risk tolerance. The generation unit can also use the generation AI to assess the user's risk profile. Asset allocation suggestions include, but are not limited to, suggestions for the ratio of stocks to bonds and allocation by region. The generation unit can, for example, use a generation AI to suggest the user's asset allocation. The generation unit can also use the generation AI to suggest asset allocation by region. Furthermore, the generation unit can use the generation AI to suggest an investment strategy based on the user's financial goals. For example, the generation unit inputs the user's risk tolerance into the generation AI, which then performs a risk assessment. This allows optimal financial advice to be provided to the user. Some or all of the above-described processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI.

[0032] The monitoring unit periodically monitors the asset status and can rebalance when the deviation from the target asset allocation ratio exceeds a predetermined standard. Periodically includes, but is not limited to, monthly or quarterly. The monitoring unit, for example, monitors the asset status monthly using the generation AI. The monitoring unit can also monitor the asset status quarterly using the generation AI. The predetermined standard includes, but is not limited to, a deviation threshold and rebalancing timing. The monitoring unit, for example, uses the generation AI to rebalance when the deviation from the target asset allocation ratio exceeds a certain level. The monitoring unit can also optimize the timing of rebalancing using the generation AI. This allows for efficient management of the user's assets. Some or all of the above-described processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit inputs the asset status into the generation AI, and the generation AI performs rebalancing.

[0033] The learning unit allows the user to progress through learning in a quiz format, and the generation AI can provide content according to the user's learning progress. Quiz formats include, but are not limited to, multiple-choice questions and written questions. The learning unit, for example, provides an interface for the user to answer multiple-choice questions. The learning unit can also provide an interface for the user to answer written questions. Learning progress includes, but is not limited to, the correct answer rate and study time. For example, the learning unit, using the generation AI, provides learning content based on the user's correct answer rate. The learning unit can also use the generation AI to provide learning content based on the user's study time. Furthermore, the learning unit, using the generation AI, can customize learning content based on the user's study history. For example, the learning unit inputs the user's correct answer rate into the generation AI, and the generation AI provides appropriate learning content. This effectively improves the user's financial knowledge. Some or all of the above-described processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0034] The providing unit can analyze past market data, news articles, and corporate financial information to provide users with the latest market trends and investment tips. Examples of past market data include, but are not limited to, stock price data and economic indicators. For example, the providing unit can use a generation AI to analyze past stock price data. The providing unit can also use a generation AI to analyze past economic indicators. Examples of news articles include, but are not limited to, economic news and corporate news. For example, the providing unit can analyze economic news using a generation AI. The providing unit can also analyze corporate news using a generation AI. Examples of corporate financial information include, but are not limited to, financial reports and financial statements. For example, the providing unit can use a generation AI to analyze corporate financial reports. The providing unit can also use a generation AI to analyze corporate financial statements. This allows users to manage their assets more effectively. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the provider inputs past market data into the generation AI, which then predicts current market trends.

[0035] The reception unit can analyze the user's past financial information input history and select the optimal input method. The past financial information input history includes, for example, past input data and input frequency, but is not limited to these examples. The reception unit can, for example, use a generation AI to analyze the user's past input data. The reception unit can also use the generation AI to analyze the user's past input frequency. The optimal input method can, for example, include, but is not limited to, voice input, text input, image input, etc. The reception unit can, for example, use the generation AI to preferentially suggest input methods that the user has frequently used in the past. The reception unit can also use the generation AI to automatically customize the input form based on information previously input by the user. Furthermore, the reception unit can also use the generation AI to predict and suggest an input method to be used during a specific time period based on the user's past input history. This allows the optimal input method to be provided based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs the user's past input data into the generation AI, which then selects the optimal input method.

[0036] When inputting financial information, the reception unit can perform filtering based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, family structure and income status. The reception unit can, for example, use a generation AI to analyze the user's family structure. The reception unit can also use the generation AI to analyze the user's income status. Examples of areas of interest include, but are not limited to, investment targets and hobbies. The reception unit can, for example, use a generation AI to analyze the user's investment targets. The reception unit can also use the generation AI to analyze the user's hobbies. This allows financial information to be input according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs data on the user's living situation and areas of interest into the generation AI, and the generation AI performs filtering.

[0037] When inputting financial information, the reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, and image input. For example, the reception unit can use a generation AI to provide voice input preferentially if the user prefers voice input. The reception unit can also use a generation AI to provide text input preferentially if the user prefers text input. Furthermore, the reception unit can use a generation AI to provide image input preferentially if the user prefers image input. This makes it possible to provide an input means according to the user's preferences. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs data on the user's input method into the generation AI, and the generation AI selects the optimal input means.

[0038] When inputting financial information, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, addresses and GPS data. For example, the reception unit can use a generation AI to analyze the user's address. The reception unit can also use the generation AI to analyze the user's GPS data. Examples of highly relevant information include, but are not limited to, financial information specific to a region. For example, the reception unit can use the generation AI to prioritize inputting financial information related to a region when the user is in that region. The reception unit can also use the generation AI to automatically suggest relevant financial information based on the user's geographical location information. Furthermore, the reception unit can use the generation AI to determine the priority of the financial information to be input taking into account the user's geographical location information. This allows optimal financial information to be input based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs the user's geographical location information to the generation AI, which then selects highly relevant information.

[0039] When inputting financial information, the reception unit can analyze the user's social media activity and input related information. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. The reception unit can, for example, use a generation AI to analyze the content of the user's social media posts. The reception unit can also use the generation AI to analyze the number of the user's social media followers. Related information includes, for example, but is not limited to, financial information based on social media posts. The reception unit can, for example, use a generation AI to input related financial information based on the content of the user's social media posts. The reception unit can also use a generation AI to input related financial information based on the activities of the user's friends on social media. Furthermore, the reception unit can also use a generation AI to input related financial information based on the user's social media check-in information. This allows optimal financial information to be input based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit inputs the user's social media data into the generation AI, which selects related information.

[0040] When entering financial information, the reception unit can customize the input method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings and comments. The reception unit, for example, uses a generation AI to analyze feedback previously provided by the user. The reception unit can also customize the input method based on the user's past feedback by using the generation AI. Customizing the input method includes, for example, adjustments based on the user's feedback, but is not limited to, the example. The reception unit, for example, uses the generation AI to customize the input method based on the user's past feedback. The reception unit can also use the generation AI to suggest an optimal input method based on the user's past feedback. Furthermore, the reception unit can use the generation AI to automatically customize the input form by reflecting the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed by, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's past feedback data into the generation AI, which then customizes the input method.

[0041] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the financial information. Examples of the importance of the financial information include, but are not limited to, asset size and risk level. The generation unit, for example, uses a generation AI to evaluate the importance of the financial information. The generation unit can also adjust the level of detail of the advice based on the importance of the financial information using the generation AI. For example, the generation unit provides detailed advice for important financial information. For example, the generation unit can provide concise advice for less important financial information. Furthermore, the generation unit can automatically adjust the level of detail of the advice based on the importance of the financial information using the generation AI. This allows optimal advice to be provided based on the importance of the financial information. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs financial information importance data into the generation AI, and the generation AI adjusts the level of detail of the advice.

[0042] When generating advice, the generation unit can apply different advice algorithms depending on the category of financial information. Examples of financial information categories include, but are not limited to, investment, insurance, and savings. The generation unit, for example, uses a generation AI to classify the categories of financial information. The generation unit can also use the generation AI to apply different advice algorithms depending on the category of financial information. Examples of advice algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the generation unit can use the generation AI to provide advice that emphasizes risk assessment for financial information related to investment. For financial information related to savings, the generation unit can also use the generation AI to provide advice that emphasizes savings methods. For financial information related to insurance, the generation unit can also use the generation AI to provide advice that emphasizes coverage details. This allows optimal advice to be provided depending on the category of financial information. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit inputs financial information category data into the generation AI, which then applies the advice algorithm.

[0043] When generating advice, the generation unit can improve the accuracy of the advice by referring to past advice results for the user. Past advice results include, but are not limited to, successful advice and unsuccessful advice. The generation unit, for example, uses a generation AI to analyze the past advice results for the user. The generation unit can also improve the accuracy of the advice based on the past advice results for the user using the generation AI. The accuracy of the advice can include, but is not limited to, accuracy and reliability. The generation unit, for example, uses the generation AI to analyze the results of advice received by the user in the past and reflect the results in the next advice. The generation unit can also use the generation AI to provide optimal advice based on the past advice results for the user. Furthermore, the generation unit can use the generation AI to adjust the advice algorithm based on the past advice results for the user. This allows optimal advice to be provided based on the past advice results for the user. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's past advice result data into the generation AI, which then improves the accuracy of the advice.

[0044] When generating advice, the generation unit can determine the priority of advice based on the timing of financial information submission. Examples of financial information submission times include, but are not limited to, monthly reports and quarterly reports. The generation unit, for example, uses a generation AI to evaluate the timing of financial information submission. The generation unit can also use the generation AI to determine the priority of advice based on the timing of financial information submission. Examples of advice prioritization include, but are not limited to, setting priorities based on the timing of submission. The generation unit, for example, uses the generation AI to provide advice with priority for financial information with high urgency. The generation unit can also use the generation AI to provide advice with priority for financial information whose submission date is approaching. Furthermore, the generation unit can use the generation AI to automatically determine the priority of advice based on the timing of financial information submission. This allows optimal advice to be provided depending on the timing of financial information submission. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs financial information submission time data into the generation AI, which then determines the priority of advice.

[0045] When generating advice, the generation unit can adjust the order of advice based on the relevance of the financial information. The relevance of the financial information includes, but is not limited to, highly relevant information and less relevant information. The generation unit, for example, uses a generation AI to evaluate the relevance of the financial information. The generation unit can also adjust the order of advice based on the relevance of the financial information using the generation AI. The order of advice includes, but is not limited to, setting an order based on relevance. For example, the generation unit can use the generation AI to provide advice preferentially for highly relevant financial information. The generation unit can also use the generation AI to provide advice later for less relevant financial information. Furthermore, the generation unit can use the generation AI to automatically adjust the order of advice based on the relevance of the financial information. This allows optimal advice to be provided depending on the relevance of the financial information. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs relevance data of the financial information into the generation AI, and the generation AI adjusts the order of advice.

[0046] When generating advice, the generation unit can adjust the use of technical terms in the advice depending on the user's level of expertise. Examples of technical terms include, but are not limited to, beginner, intermediate, and advanced. The generation unit, for example, uses a generation AI to evaluate the user's level of expertise. The generation unit can also adjust the use of technical terms in the advice depending on the user's level of expertise using the generation AI. Examples of the use of technical terms include, but are not limited to, definitions and frequency of use of technical terms. For example, the generation unit can use the generation AI to provide easy-to-understand advice by avoiding technical terms when the user is a beginner. For example, the generation unit can use the generation AI to provide advice by using appropriate technical terms when the user is an intermediate user. For example, the generation unit can use the generation AI to provide detailed advice by using a lot of technical terms when the user is an advanced user. This allows the provision of optimal advice depending on the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generator inputs the user's expertise level data into the generator AI, which then adjusts the use of technical terminology in the advice.

[0047] The monitoring unit can improve the accuracy of asset monitoring by taking into account the interrelationships between assets. Examples of asset interrelationships include, but are not limited to, correlation coefficients and covariances. The monitoring unit can, for example, use a generation AI to analyze the interrelationships between assets. The monitoring unit can also improve the accuracy of monitoring by taking into account the interrelationships between assets using the generation AI. Examples of monitoring accuracy include, but are not limited to, error rates and detection rates. The monitoring unit can, for example, use a generation AI to analyze the interrelationships between assets and prioritize monitoring of high-risk assets. The monitoring unit can also improve the accuracy of monitoring by taking into account the interrelationships between assets using the generation AI. Furthermore, the monitoring unit can use the generation AI to determine monitoring priorities based on the interrelationships between assets. This improves the accuracy of monitoring by taking into account the interrelationships between assets. Some or all of the above-described processing in the monitoring unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the monitoring unit inputs asset interrelationship data into the generation AI, which then improves the accuracy of monitoring.

[0048] The monitoring unit may monitor assets while taking into account the user's attribute information. Attribute information may include, but is not limited to, age, occupation, and income. For example, the monitoring unit may use a generation AI to analyze the user's age. The monitoring unit may also use a generation AI to analyze the user's occupation. The monitoring unit may also use a generation AI to analyze the user's income. Examples of monitoring include, but are not limited to, periodic checks and real-time monitoring. For example, the monitoring unit may use a generation AI to monitor assets while taking into account the user's attribute information, such as age and occupation. The monitoring unit may also use a generation AI to determine monitoring priorities based on the user's attribute information. Furthermore, the monitoring unit may use a generation AI to improve the accuracy of monitoring based on the user's attribute information. This allows optimal asset monitoring based on the user's attribute information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit inputs the user's attribute information data into the generation AI, which then performs monitoring.

[0049] During asset monitoring, the monitoring unit can weight the monitoring based on the asset fluctuation frequency. The asset fluctuation frequency includes, but is not limited to, daily fluctuations and monthly fluctuations. The monitoring unit, for example, uses a generation AI to evaluate the asset fluctuation frequency. The monitoring unit can also weight the monitoring based on the asset fluctuation frequency using the generation AI. The monitoring weighting includes, but is not limited to, weighting based on the fluctuation frequency. The monitoring unit, for example, uses the generation AI to prioritize monitoring of assets with high fluctuation frequency. The monitoring unit can also use the generation AI to reduce the monitoring frequency of assets with low fluctuation frequency. The monitoring unit can also use the generation AI to automatically adjust the monitoring weight based on the asset fluctuation frequency. This allows optimal monitoring according to the asset fluctuation frequency. Some or all of the above-described processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit inputs asset fluctuation frequency data into the generation AI, which then weights the monitoring.

[0050] The monitoring unit may monitor assets while taking into account the geographic distribution of assets. Examples of geographic distribution include, but are not limited to, asset distribution by region and investment ratio by country. The monitoring unit may, for example, use a generation AI to analyze the geographic distribution of assets. The monitoring unit may also use a generation AI to monitor while taking into account the geographic distribution of assets. Examples of monitoring include, but are not limited to, periodic checks and real-time monitoring. The monitoring unit may, for example, use a generation AI to analyze the geographic distribution of assets and prioritize monitoring of assets in high-risk areas. The monitoring unit may also use a generation AI to improve the accuracy of monitoring while taking into account the geographic distribution of assets. Furthermore, the monitoring unit may use a generation AI to determine monitoring priorities based on the geographic distribution of assets. This allows optimal monitoring based on the geographic distribution of assets. Some or all of the above-described processing in the monitoring unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit inputs asset geographic distribution data into a generation AI, which then performs monitoring.

[0051] During asset monitoring, the monitoring unit can improve the accuracy of monitoring by referring to related literature. Examples of related literature include, but are not limited to, academic papers and industry reports. The monitoring unit can, for example, use a generation AI to refer to related literature. The monitoring unit can also improve the accuracy of monitoring based on the related literature using the generation AI. Examples of monitoring accuracy include, but are not limited to, error rates and detection rates. The monitoring unit can, for example, use a generation AI to refer to related literature and incorporate the latest monitoring techniques. The monitoring unit can also improve the accuracy of monitoring based on the related literature using the generation AI. Furthermore, the monitoring unit can also use the generation AI to determine monitoring priorities by referring to related literature. Thus, referring to related literature improves the accuracy of monitoring. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit inputs related literature data into the generation AI, which then improves the accuracy of monitoring.

[0052] The monitoring unit may monitor assets while taking into account the market value of the assets. Market value includes, but is not limited to, market valuation and valuation gains and losses. The monitoring unit may, for example, use a generation AI to evaluate the market value of the assets. The monitoring unit may also, using the generation AI, monitor assets while taking into account the market value of the assets. Monitoring may include, but is not limited to, periodic checks and real-time monitoring. The monitoring unit may, for example, use a generation AI to prioritize monitoring of assets with high market value. The monitoring unit may also, using the generation AI, reduce the monitoring frequency of assets with low market value. Furthermore, the monitoring unit may, using the generation AI, automatically adjust the monitoring weighting based on the market value of the assets. This allows optimal monitoring based on the market value of the assets. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit inputs market value data of assets into the generation AI, which then performs monitoring.

[0053] When providing learning content, the learning unit can select optimal content by referring to the user's past learning history. Past learning history includes, but is not limited to, learning time, learning content, and accuracy rate. The learning unit, for example, uses a generation AI to analyze the user's past learning history. The learning unit can also use the generation AI to select optimal content based on the user's past learning history. Optimal content includes, but is not limited to, customization based on the user's learning history. The learning unit, for example, uses the generation AI to suggest optimal learning content based on the user's past learning history. The learning unit can also use the generation AI to provide content based on the user's past learning history according to the user's learning progress. Furthermore, the learning unit can also use the generation AI to determine learning priorities by referring to the user's past learning history. This allows optimal learning content to be provided based on the user's past learning history. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's past learning history data into the generation AI, which then selects optimal content.

[0054] When providing learning content, the learning unit can customize the content based on the user's current knowledge level. Knowledge levels include, but are not limited to, beginner, intermediate, and advanced. The learning unit, for example, uses a generation AI to evaluate the user's knowledge level. The learning unit can also customize the content based on the user's knowledge level using the generation AI. Content customization includes, but is not limited to, adjustments based on the user's knowledge level. For example, the learning unit can use the generation AI to provide beginner, intermediate, and advanced learning content based on the user's knowledge level. The learning unit can also use the generation AI to provide content based on the user's learning progress based on the user's knowledge level. Furthermore, the learning unit can use the generation AI to determine learning priorities based on the user's knowledge level. This allows the provision of optimal learning content based on the user's knowledge level. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's knowledge level data into the generation AI, which then customizes the content.

[0055] When providing learning content, the learning unit can improve the content by reflecting user feedback. Examples of feedback include, but are not limited to, user ratings and comments. The learning unit can, for example, use a generation AI to analyze the user feedback. The learning unit can also improve the content based on the user feedback using the generation AI. Examples of content improvement include, but are not limited to, adjustments based on the feedback. The learning unit can, for example, use a generation AI to improve the learning content based on the user feedback. The learning unit can also use a generation AI to provide optimal learning content based on the user feedback. Furthermore, the learning unit can also use a generation AI to determine learning priorities by reflecting the user feedback. This allows optimal learning content to be provided based on the user feedback. Some or all of the above-described processing in the learning unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the learning unit inputs user feedback data into the generation AI, which then improves the content.

[0056] When providing learning content, the learning unit can provide optimal content by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, addresses and GPS data. For example, the learning unit can use a generation AI to analyze the user's address. The learning unit can also use the generation AI to analyze the user's GPS data. Examples of optimal content include, but are not limited to, customization based on geographical location information. For example, the learning unit can use a generation AI to provide relevant learning content based on the user's geographical location information. The learning unit can also use a generation AI to determine learning priorities based on the user's geographical location information. Furthermore, the learning unit can use a generation AI to suggest optimal learning content based on the user's geographical location information. This allows optimal learning content to be provided based on the user's geographical location information. Some or all of the above-described processing in the learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit inputs the user's geographical location information data into the generation AI, which then provides optimal content.

[0057] When providing learning content, the learning unit can analyze the user's social media activity and provide relevant content. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. The learning unit, for example, uses a generation AI to analyze the content of the user's social media posts. The learning unit can also use the generation AI to analyze the number of the user's social media followers. Related content includes, for example, learning content based on social media posts, but is not limited to, the example. The learning unit, for example, uses a generation AI to provide relevant learning content based on the content of the user's social media posts. The learning unit can also use a generation AI to provide relevant learning content based on the activity of the user's friends on social media. Furthermore, the learning unit can use a generation AI to provide relevant learning content based on the user's social media check-in information. This allows optimal learning content to be provided based on the user's social media activity. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs the user's social media data into a generation AI, which then provides relevant content.

[0058] When providing learning content, the learning unit can customize the content by reflecting the user's past feedback. Past feedback includes, for example, user ratings and comments, but is not limited to, examples thereof. The learning unit, for example, uses a generation AI to analyze the user's past feedback. The learning unit can also customize the content based on the user's past feedback using the generation AI. Content customization includes, for example, adjustments based on feedback, but is not limited to, examples thereof. The learning unit, for example, uses a generation AI to customize the learning content based on the user's past feedback. The learning unit can also use the generation AI to provide optimal learning content based on the user's past feedback. Furthermore, the learning unit can also use the generation AI to determine learning priorities by reflecting the user's past feedback. This allows optimal learning content to be provided based on the user's past feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's past feedback data into the generation AI, which then customizes the content.

[0059] When providing market insights, the providing unit can predict current market trends by referring to past market data. Examples of past market data include, but are not limited to, stock price data and economic indicators. The providing unit can, for example, use a generation AI to analyze past market data. The providing unit can also predict current market trends by referring to past market data using the generation AI. Examples of current market trends include, but are not limited to, trend analysis and prediction models. The providing unit can, for example, use a generation AI to predict current market trends based on past market data. The providing unit can also, for example, use a generation AI to suggest optimal investment timing by referring to past market data. Furthermore, the providing unit can also provide advice to avoid high-risk investments based on past market data using the generation AI. This allows current market trends to be predicted based on past market data. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit inputs past market data into the generation AI, which then predicts current market trends.

[0060] When providing market insights, the provision unit can apply different analytical methods to each category of financial products. Examples of financial product categories include, but are not limited to, stocks, bonds, and investment trusts. The provision unit, for example, uses a generation AI to classify financial product categories. The provision unit can also use the generation AI to apply different analytical methods to each category of financial products. Examples of analytical methods include, but are not limited to, technical analysis and fundamental analysis. For example, the provision unit can apply fundamental analysis to stocks using the generation AI. The provision unit can also apply credit risk analysis to bonds using the generation AI. Furthermore, the provision unit can also apply cash flow analysis to real estate using the generation AI. This allows optimal market insights to be provided according to the category of financial products. Some or all of the above-mentioned processing in the provision unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the provision unit inputs financial product category data into the generation AI, and the generation AI applies the analytical method.

[0061] When providing market insights, the providing unit can provide the insights by taking into account the user's attribute information. Attribute information includes, for example, age, occupation, income, etc., but is not limited to these examples. The providing unit, for example, uses a generation AI to analyze the user's age. The providing unit can also use the generation AI to analyze the user's occupation. The providing unit can also use the generation AI to analyze the user's income. Insights include, for example, investment tips and market forecasts, but are not limited to these examples. The providing unit, for example, uses the generation AI to provide market insights by taking into account the user's attribute information, such as age and occupation. The providing unit can also use the generation AI to propose an optimal investment strategy based on the user's attribute information. The providing unit can also use the generation AI to provide advice to avoid high-risk investments based on the user's attribute information. This makes it possible to provide optimal market insights based on the user's attribute information. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs user attribute information data into the generating AI, and the generating AI provides insights.

[0062] When providing market insights, the providing unit can analyze changes in the insights based on the submission timing of the financial products. Examples of the submission timing of the financial products include, but are not limited to, monthly reports and quarterly reports. The providing unit, for example, uses a generation AI to evaluate the submission timing of the financial products. The providing unit can also use the generation AI to analyze changes in the insights based on the submission timing of the financial products. Examples of changes in the insights include, but are not limited to, analyzing changes based on the submission timing. The providing unit, for example, uses the generation AI to analyze changes in the market insights based on the submission timing of the financial products. The providing unit can also use the generation AI to provide market insights preferentially for financial products whose submission timing is approaching. Furthermore, the providing unit can use the generation AI to determine the priority of insights, taking into account the submission timing of the financial products. This allows optimal market insights to be provided based on the submission timing of the financial products. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs financial product submission timing data into the generation AI, which then analyzes changes in the insights.

[0063] When providing market insights, the providing unit can analyze the insights by referring to related market data. Examples of related market data include, but are not limited to, stock price data and economic indicators. The providing unit can, for example, use a generation AI to analyze the related market data. The providing unit can also, for example, use the generation AI to analyze the insights by referring to the related market data. Examples of insight analysis include, but are not limited to, data analysis and trend analysis. The providing unit can, for example, use the generation AI to provide market insights based on the related market data. The providing unit can also, for example, use the generation AI to suggest optimal investment timing by referring to the related market data. Furthermore, the providing unit can also, for example, use the generation AI to provide advice to avoid high-risk investments based on the related market data. This makes it possible to provide optimal market insights based on the related market data. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit inputs the relevant market data into the generation AI, which then analyzes the insights.

[0064] When providing market insights, the providing unit can analyze the insights taking into account the technological maturity of the financial product. Examples of technological maturity include, but are not limited to, the stage of technological evolution and the presence or absence of patents. The providing unit, for example, uses a generation AI to evaluate the technological maturity of the financial product. The providing unit can also use the generation AI to analyze the insights taking into account the technological maturity of the financial product. Examples of insight analysis include, but are not limited to, analysis based on technological maturity. The providing unit, for example, uses the generation AI to provide market insights based on the technological maturity of the financial product. The providing unit can also use the generation AI to prioritize market insights for financial products with high technical maturity. Furthermore, the providing unit can use the generation AI to determine the priority of insights taking into account the technical maturity of the financial product. This allows optimal market insights to be provided based on the technical maturity of the financial product. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs technical maturity data of the financial product into the generation AI, which then analyzes the insights.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The reception unit can analyze the user's past financial information input history and select the optimal input method. The past financial information input history includes, for example, past input data and input frequency, but is not limited to these examples. The reception unit can, for example, use a generation AI to analyze the user's past input data. The reception unit can also use the generation AI to analyze the user's past input frequency. The optimal input method can, for example, include, but is not limited to, voice input, text input, image input, etc. The reception unit can, for example, use the generation AI to preferentially suggest input methods that the user has frequently used in the past. The reception unit can also use the generation AI to automatically customize the input form based on information previously input by the user. Furthermore, the reception unit can also use the generation AI to predict and suggest an input method to be used during a specific time period based on the user's past input history. This allows the optimal input method to be provided based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs the user's past input data into the generation AI, which then selects the optimal input method.

[0067] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the financial information. Examples of the importance of the financial information include, but are not limited to, asset size and risk level. The generation unit, for example, uses a generation AI to evaluate the importance of the financial information. The generation unit can also adjust the level of detail of the advice based on the importance of the financial information using the generation AI. For example, the generation unit provides detailed advice for important financial information. For example, the generation unit can provide concise advice for less important financial information. Furthermore, the generation unit can automatically adjust the level of detail of the advice based on the importance of the financial information using the generation AI. This allows optimal advice to be provided based on the importance of the financial information. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs financial information importance data into the generation AI, and the generation AI adjusts the level of detail of the advice.

[0068] The monitoring unit can improve the accuracy of asset monitoring by taking into account the interrelationships between assets. Examples of asset interrelationships include, but are not limited to, correlation coefficients and covariances. The monitoring unit can, for example, use a generation AI to analyze the interrelationships between assets. The monitoring unit can also improve the accuracy of monitoring by taking into account the interrelationships between assets using the generation AI. Examples of monitoring accuracy include, but are not limited to, error rates and detection rates. The monitoring unit can, for example, use a generation AI to analyze the interrelationships between assets and prioritize monitoring of high-risk assets. The monitoring unit can also improve the accuracy of monitoring by taking into account the interrelationships between assets using the generation AI. Furthermore, the monitoring unit can use the generation AI to determine monitoring priorities based on the interrelationships between assets. This improves the accuracy of monitoring by taking into account the interrelationships between assets. Some or all of the above-described processing in the monitoring unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the monitoring unit inputs asset interrelationship data into the generation AI, which then improves the accuracy of monitoring.

[0069] When providing market insights, the provision unit can apply different analytical methods to each category of financial products. Examples of financial product categories include, but are not limited to, stocks, bonds, and investment trusts. The provision unit, for example, uses a generation AI to classify financial product categories. The provision unit can also use the generation AI to apply different analytical methods to each category of financial products. Examples of analytical methods include, but are not limited to, technical analysis and fundamental analysis. For example, the provision unit can apply fundamental analysis to stocks using the generation AI. The provision unit can also apply credit risk analysis to bonds using the generation AI. Furthermore, the provision unit can also apply cash flow analysis to real estate using the generation AI. This allows optimal market insights to be provided according to the category of financial products. Some or all of the above-mentioned processing in the provision unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the provision unit inputs financial product category data into the generation AI, and the generation AI applies the analytical method.

[0070] When providing learning content, the learning unit can select optimal content by referring to the user's past learning history. Past learning history includes, but is not limited to, learning time, learning content, and accuracy rate. The learning unit, for example, uses a generation AI to analyze the user's past learning history. The learning unit can also use the generation AI to select optimal content based on the user's past learning history. Optimal content includes, but is not limited to, customization based on the user's learning history. The learning unit, for example, uses the generation AI to suggest optimal learning content based on the user's past learning history. The learning unit can also use the generation AI to provide content based on the user's past learning history according to the user's learning progress. Furthermore, the learning unit can also use the generation AI to determine learning priorities by referring to the user's past learning history. This allows optimal learning content to be provided based on the user's past learning history. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's past learning history data into the generation AI, which then selects optimal content.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit inputs the user's financial situation and goals. The user's financial situation includes income, expenses, assets, liabilities, investment goals, etc. The reception unit provides an interface for the user to input this information. Step 2: The generator generates advice based on the information entered by the receiver. Using the generator AI, it performs risk assessments, proposes asset allocation, assesses the risk of specific investment products, and proposes investment strategies. Step 3: The monitoring unit inputs the user's asset information, monitors the asset status using the generated AI, and rebalances as necessary. Monitoring is performed periodically, and rebalancing is performed when the deviation from the asset allocation target ratio exceeds a certain level. Step 4: The learning department improves financial knowledge through interactive learning tools. Learning is done in the form of quizzes, and generative AI provides appropriate content based on the user's learning progress. Step 5: The provider uses the generative AI to analyze market trends and financial products and provide market insights to users. It predicts market trends based on past market data, news articles, and corporate financial information, and provides users with the latest market trends and investment tips.

[0073] (Example 2) A financial management system according to an embodiment of the present invention allows a user to input their financial situation and goals, and a generating AI generates optimal advice, monitors their asset status, rebalances assets as needed, improves their financial knowledge, and provides market insights through interactive learning tools. For example, a user inputs their financial situation and goals into the financial management system. For example, the user inputs information such as income, expenses, assets, liabilities, and investment goals. The financial management system then uses the generating AI to perform risk assessments and asset allocation proposals based on the input information. Examples of such proposals include risk assessments for specific investment products and asset allocation proposals. The financial management system then inputs the user's asset information, and the generating AI automatically monitors their asset status. The generating AI periodically monitors their asset status and uses a rebalancing criterion, for example, when the deviation from the asset allocation target ratio exceeds a certain level. The financial management system then improves their financial knowledge through interactive learning tools. Users can deepen their financial knowledge using learning content provided by the generating AI. For example, they can learn the basics of investment and risk management methods. The generating AI then analyzes market trends and financial products to provide users with market insights. Generative AI analyzes past market data, news articles, corporate financial information, and more to provide users with the latest market trends and investment tips. This allows the financial management system to better understand the user's financial situation and manage their assets effectively. This allows the financial management system to better understand the user's financial situation and manage their assets effectively. For example, novice users can make their first investment with confidence based on personalized advice provided by Generative AI. Experienced users can also utilize market insights provided by Generative AI to implement more advanced investment strategies.

[0074] A financial management system according to an embodiment includes a receiving unit, a generating unit, a monitoring unit, a learning unit, and a providing unit. The receiving unit inputs a user's financial situation and goals. The user's financial situation includes, but is not limited to, income, expenses, assets, liabilities, and investment goals. The receiving unit provides, for example, an interface through which the user inputs income and expense information. The receiving unit can also provide an interface through which the user inputs asset and liability information. The receiving unit can also provide an interface through which the user sets investment goals. The generating unit uses a generation AI to generate advice based on the information input by the receiving unit. The advice is provided, for example, based on risk assessment and asset allocation proposals, but is not limited to, examples. For example, the generation AI can evaluate the user's risk tolerance and propose an optimal asset allocation based on the risk tolerance. The generating unit can also use the generation AI to perform risk assessment of specific investment products. The generating unit can also use the generation AI to propose an investment strategy based on the user's financial goals. The monitoring unit uses the generation AI to monitor the status of the user's assets and rebalance as necessary. The monitoring may be performed periodically, for example, but is not limited to such an example. For example, the monitoring unit rebalances when the deviation from the asset allocation target ratio reaches a certain level. The monitoring unit may also use the generation AI to monitor the asset status in real time. Furthermore, the monitoring unit may also use the generation AI to monitor the asset status over the long term and optimize the timing of rebalancing. The learning unit improves financial knowledge through interactive learning tools. The learning may be performed in the form of a quiz, for example, but is not limited to such an example. For example, the learning unit allows the user to progress through the learning in the form of a quiz, and the generation AI provides appropriate content according to the user's learning progress. The learning unit may also use the generation AI to customize learning content based on the user's learning history. Furthermore, the learning unit may use the generation AI to generate new learning content according to the user's learning needs. The provision unit uses the generation AI to analyze market trends and financial products and provide market insights to the user.Market insights are generated based on, for example, past market data, news articles, and corporate financial information, but are not limited to these examples. For example, the providing unit uses the generation AI to analyze past market data and predict current market trends. The providing unit can also use the generation AI to analyze news articles and provide users with the latest market trends. Furthermore, the providing unit can also use the generation AI to analyze corporate financial information and provide users with investment tips. This enables the financial management system according to the embodiment to better understand the user's financial situation and effectively manage assets. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs user input information into the generation AI, which then generates advice. Some or all of the above-described processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit inputs the user's asset information into the generation AI, which then monitors the asset status. Some or all of the above-described processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's learning progress into the generation AI, which then provides appropriate learning content. Some or all of the above-described processing in the provision unit may be performed using, or without, the generation AI. For example, the provision unit inputs past market data into the generation AI, which then predicts current market trends.

[0075] The reception unit can input information on income, expenses, assets, liabilities, and investment goals. Income includes, but is not limited to, examples of salary, business income, and investment income. The reception unit, for example, provides an interface through which the user inputs salary information. The reception unit can also provide an interface through which the user inputs business income and investment income information. Expenses include, but are not limited to, examples of living expenses, education expenses, and medical expenses. The reception unit, for example, provides an interface through which the user inputs living expenses information. The reception unit can also provide an interface through which the user inputs education expenses and medical expenses information. Assets include, but are not limited to, examples of cash, stocks, and real estate. The reception unit, for example, provides an interface through which the user inputs cash information. The reception unit can also provide an interface through which the user inputs stock and real estate information. Liabilities include, but are not limited to, examples of mortgages and credit card debts. The reception unit, for example, provides an interface through which the user inputs mortgage information. The reception unit can also provide an interface through which the user inputs credit card debt information. Investment goals include, but are not limited to, retirement funds, education funds, and the like. For example, the reception unit provides an interface for the user to set retirement fund goals. The reception unit can also provide an interface for the user to set education fund goals. This allows the user's financial situation to be understood in detail. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's input information into the generation AI, and the generation AI analyzes the information.

[0076] The generation unit can perform risk assessment and asset allocation suggestions based on the input information. Risk assessment includes, but is not limited to, assessment of risk tolerance and risk profile. The generation unit can, for example, use a generation AI to assess the user's risk tolerance. The generation unit can also use the generation AI to assess the user's risk profile. Asset allocation suggestions include, but are not limited to, suggestions for the ratio of stocks to bonds and allocation by region. The generation unit can, for example, use a generation AI to suggest the user's asset allocation. The generation unit can also use the generation AI to suggest asset allocation by region. Furthermore, the generation unit can use the generation AI to suggest an investment strategy based on the user's financial goals. For example, the generation unit inputs the user's risk tolerance into the generation AI, which then performs a risk assessment. This allows optimal financial advice to be provided to the user. Some or all of the above-described processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI.

[0077] The monitoring unit periodically monitors the asset status and can rebalance when the deviation from the target asset allocation ratio exceeds a predetermined standard. Periodically includes, but is not limited to, monthly or quarterly. The monitoring unit, for example, monitors the asset status monthly using the generation AI. The monitoring unit can also monitor the asset status quarterly using the generation AI. The predetermined standard includes, but is not limited to, a deviation threshold and rebalancing timing. The monitoring unit, for example, uses the generation AI to rebalance when the deviation from the target asset allocation ratio exceeds a certain level. The monitoring unit can also optimize the timing of rebalancing using the generation AI. This allows for efficient management of the user's assets. Some or all of the above-described processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit inputs the asset status into the generation AI, and the generation AI performs rebalancing.

[0078] The learning unit allows the user to progress through learning in a quiz format, and the generation AI can provide content according to the user's learning progress. Quiz formats include, but are not limited to, multiple-choice questions and written questions. The learning unit, for example, provides an interface for the user to answer multiple-choice questions. The learning unit can also provide an interface for the user to answer written questions. Learning progress includes, but is not limited to, the correct answer rate and study time. For example, the learning unit, using the generation AI, provides learning content based on the user's correct answer rate. The learning unit can also use the generation AI to provide learning content based on the user's study time. Furthermore, the learning unit, using the generation AI, can customize learning content based on the user's study history. For example, the learning unit inputs the user's correct answer rate into the generation AI, and the generation AI provides appropriate learning content. This effectively improves the user's financial knowledge. Some or all of the above-described processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0079] The providing unit can analyze past market data, news articles, and corporate financial information to provide users with the latest market trends and investment tips. Examples of past market data include, but are not limited to, stock price data and economic indicators. For example, the providing unit can use a generation AI to analyze past stock price data. The providing unit can also use a generation AI to analyze past economic indicators. Examples of news articles include, but are not limited to, economic news and corporate news. For example, the providing unit can analyze economic news using a generation AI. The providing unit can also analyze corporate news using a generation AI. Examples of corporate financial information include, but are not limited to, financial reports and financial statements. For example, the providing unit can use a generation AI to analyze corporate financial reports. The providing unit can also use a generation AI to analyze corporate financial statements. This allows users to manage their assets more effectively. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the provider inputs past market data into the generation AI, which then predicts current market trends.

[0080] The reception unit can estimate the user's emotions and adjust the timing of financial information input based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and stress. The reception unit, for example, uses a generation AI to estimate the user's emotions. The reception unit can also adjust the timing of financial information input based on the user's emotions using the generation AI. For example, if the user is feeling stressed, the reception unit can prompt the user to input financial information during a time when the user can relax. If the user is relaxed, the reception unit can also prompt the user to input detailed financial information. Furthermore, if the user is in a hurry, the reception unit can provide a simplified input form. This allows the user to input financial information at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit inputs the user's emotion data into the generation AI, and the generation AI estimates the emotion.

[0081] The reception unit can analyze the user's past financial information input history and select the optimal input method. The past financial information input history includes, for example, past input data and input frequency, but is not limited to these examples. The reception unit can, for example, use a generation AI to analyze the user's past input data. The reception unit can also use the generation AI to analyze the user's past input frequency. The optimal input method can, for example, include, but is not limited to, voice input, text input, image input, etc. The reception unit can, for example, use the generation AI to preferentially suggest input methods that the user has frequently used in the past. The reception unit can also use the generation AI to automatically customize the input form based on information previously input by the user. Furthermore, the reception unit can also use the generation AI to predict and suggest an input method to be used during a specific time period based on the user's past input history. This allows the optimal input method to be provided based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs the user's past input data into the generation AI, which then selects the optimal input method.

[0082] When inputting financial information, the reception unit can perform filtering based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, family structure and income status. The reception unit can, for example, use a generation AI to analyze the user's family structure. The reception unit can also use the generation AI to analyze the user's income status. Examples of areas of interest include, but are not limited to, investment targets and hobbies. The reception unit can, for example, use a generation AI to analyze the user's investment targets. The reception unit can also use the generation AI to analyze the user's hobbies. This allows financial information to be input according to the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs data on the user's living situation and areas of interest into the generation AI, and the generation AI performs filtering.

[0083] When inputting financial information, the reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, and image input. For example, the reception unit can use a generation AI to provide voice input preferentially if the user prefers voice input. The reception unit can also use a generation AI to provide text input preferentially if the user prefers text input. Furthermore, the reception unit can use a generation AI to provide image input preferentially if the user prefers image input. This makes it possible to provide an input means according to the user's preferences. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs data on the user's input method into the generation AI, and the generation AI selects the optimal input means.

[0084] The reception unit can estimate the user's emotions and determine the priority of the financial information to be input based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. The reception unit can estimate the user's emotions using, for example, a generation AI. The reception unit can also determine the priority of the financial information to be input based on the user's emotions using the generation AI. For example, if the user is feeling stressed, the reception unit can prompt the user to input only important financial information. If the user is relaxed, the reception unit can prompt the user to input detailed financial information. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input simplified financial information. This allows the user to input optimal financial information based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the reception unit can be performed using, for example, a generation AI, or without a generation AI. For example, the reception unit inputs the user's emotion data into the generation AI, and the generation AI estimates the emotion.

[0085] When inputting financial information, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, addresses and GPS data. For example, the reception unit can use a generation AI to analyze the user's address. The reception unit can also use the generation AI to analyze the user's GPS data. Examples of highly relevant information include, but are not limited to, financial information specific to a region. For example, the reception unit can use the generation AI to prioritize inputting financial information related to a region when the user is in that region. The reception unit can also use the generation AI to automatically suggest relevant financial information based on the user's geographical location information. Furthermore, the reception unit can use the generation AI to determine the priority of the financial information to be input taking into account the user's geographical location information. This allows optimal financial information to be input based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs the user's geographical location information to the generation AI, which then selects highly relevant information.

[0086] When inputting financial information, the reception unit can analyze the user's social media activity and input related information. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. The reception unit can, for example, use a generation AI to analyze the content of the user's social media posts. The reception unit can also use the generation AI to analyze the number of the user's social media followers. Related information includes, for example, but is not limited to, financial information based on social media posts. The reception unit can, for example, use a generation AI to input related financial information based on the content of the user's social media posts. The reception unit can also use a generation AI to input related financial information based on the activities of the user's friends on social media. Furthermore, the reception unit can also use a generation AI to input related financial information based on the user's social media check-in information. This allows optimal financial information to be input based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit inputs the user's social media data into the generation AI, which selects related information.

[0087] When entering financial information, the reception unit can customize the input method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings and comments. The reception unit, for example, uses a generation AI to analyze feedback previously provided by the user. The reception unit can also customize the input method based on the user's past feedback by using the generation AI. Customizing the input method includes, for example, adjustments based on the user's feedback, but is not limited to, the example. The reception unit, for example, uses the generation AI to customize the input method based on the user's past feedback. The reception unit can also use the generation AI to suggest an optimal input method based on the user's past feedback. Furthermore, the reception unit can use the generation AI to automatically customize the input form by reflecting the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed by, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's past feedback data into the generation AI, which then customizes the input method.

[0088] The generation unit can estimate the user's emotion and adjust the way the advice is presented based on the estimated user's emotion. Emotions include, but are not limited to, joy, sadness, stress, etc. The generation unit, for example, uses a generation AI to estimate the user's emotion. The generation unit can also adjust the way the advice is presented based on the user's emotion using the generation AI. For example, if the user is stressed, the generation unit can provide concise and easy-to-understand advice. If the user is relaxed, the generation unit can provide detailed advice. If the user is in a hurry, the generation unit can provide advice that focuses on the main points. This allows optimal advice to be provided based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit inputs the user's emotional data into the generation AI, which then adjusts the way the advice is expressed.

[0089] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the financial information. Examples of the importance of the financial information include, but are not limited to, asset size and risk level. The generation unit, for example, uses a generation AI to evaluate the importance of the financial information. The generation unit can also adjust the level of detail of the advice based on the importance of the financial information using the generation AI. For example, the generation unit provides detailed advice for important financial information. For example, the generation unit can provide concise advice for less important financial information. Furthermore, the generation unit can automatically adjust the level of detail of the advice based on the importance of the financial information using the generation AI. This allows optimal advice to be provided based on the importance of the financial information. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs financial information importance data into the generation AI, and the generation AI adjusts the level of detail of the advice.

[0090] When generating advice, the generation unit can apply different advice algorithms depending on the category of financial information. Examples of financial information categories include, but are not limited to, investment, insurance, and savings. The generation unit, for example, uses a generation AI to classify the categories of financial information. The generation unit can also use the generation AI to apply different advice algorithms depending on the category of financial information. Examples of advice algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the generation unit can use the generation AI to provide advice that emphasizes risk assessment for financial information related to investment. For financial information related to savings, the generation unit can also use the generation AI to provide advice that emphasizes savings methods. For financial information related to insurance, the generation unit can also use the generation AI to provide advice that emphasizes coverage details. This allows optimal advice to be provided depending on the category of financial information. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit inputs financial information category data into the generation AI, which then applies the advice algorithm.

[0091] When generating advice, the generation unit can improve the accuracy of the advice by referring to past advice results for the user. Past advice results include, but are not limited to, successful advice and unsuccessful advice. The generation unit, for example, uses a generation AI to analyze the past advice results for the user. The generation unit can also improve the accuracy of the advice based on the past advice results for the user using the generation AI. The accuracy of the advice can include, but is not limited to, accuracy and reliability. The generation unit, for example, uses the generation AI to analyze the results of advice received by the user in the past and reflect the results in the next advice. The generation unit can also use the generation AI to provide optimal advice based on the past advice results for the user. Furthermore, the generation unit can use the generation AI to adjust the advice algorithm based on the past advice results for the user. This allows optimal advice to be provided based on the past advice results for the user. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's past advice result data into the generation AI, which then improves the accuracy of the advice.

[0092] The generation unit can estimate the user's emotion and adjust the length of the advice based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, stress, etc. The generation unit, for example, uses a generation AI to estimate the user's emotion. The generation unit can also adjust the length of the advice based on the user's emotion using the generation AI. For example, if the user is stressed, the generation unit can provide short, concise advice. If the user is relaxed, the generation unit can provide detailed advice. If the user is in a hurry, the generation unit can provide concise advice. This allows the optimal length of advice to be provided depending on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the generation unit may be performed using, for example, the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the length of the advice.

[0093] When generating advice, the generation unit can determine the priority of advice based on the timing of financial information submission. Examples of financial information submission times include, but are not limited to, monthly reports and quarterly reports. The generation unit, for example, uses a generation AI to evaluate the timing of financial information submission. The generation unit can also use the generation AI to determine the priority of advice based on the timing of financial information submission. Examples of advice prioritization include, but are not limited to, setting priorities based on the timing of submission. The generation unit, for example, uses the generation AI to provide advice with priority for financial information with high urgency. The generation unit can also use the generation AI to provide advice with priority for financial information whose submission date is approaching. Furthermore, the generation unit can use the generation AI to automatically determine the priority of advice based on the timing of financial information submission. This allows optimal advice to be provided depending on the timing of financial information submission. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs financial information submission time data into the generation AI, which then determines the priority of advice.

[0094] When generating advice, the generation unit can adjust the order of advice based on the relevance of the financial information. The relevance of the financial information includes, but is not limited to, highly relevant information and less relevant information. The generation unit, for example, uses a generation AI to evaluate the relevance of the financial information. The generation unit can also adjust the order of advice based on the relevance of the financial information using the generation AI. The order of advice includes, but is not limited to, setting an order based on relevance. For example, the generation unit can use the generation AI to provide advice preferentially for highly relevant financial information. The generation unit can also use the generation AI to provide advice later for less relevant financial information. Furthermore, the generation unit can use the generation AI to automatically adjust the order of advice based on the relevance of the financial information. This allows optimal advice to be provided depending on the relevance of the financial information. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs relevance data of the financial information into the generation AI, and the generation AI adjusts the order of advice.

[0095] When generating advice, the generation unit can adjust the use of technical terms in the advice depending on the user's level of expertise. Examples of technical terms include, but are not limited to, beginner, intermediate, and advanced. The generation unit, for example, uses a generation AI to evaluate the user's level of expertise. The generation unit can also adjust the use of technical terms in the advice depending on the user's level of expertise using the generation AI. Examples of the use of technical terms include, but are not limited to, definitions and frequency of use of technical terms. For example, the generation unit can use the generation AI to provide easy-to-understand advice by avoiding technical terms when the user is a beginner. For example, the generation unit can use the generation AI to provide advice by using appropriate technical terms when the user is an intermediate user. For example, the generation unit can use the generation AI to provide detailed advice by using a lot of technical terms when the user is an advanced user. This allows the provision of optimal advice depending on the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generator inputs the user's expertise level data into the generator AI, which then adjusts the use of technical terminology in the advice.

[0096] The monitoring unit can estimate the user's emotions and adjust the asset monitoring criteria based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. The monitoring unit can estimate the user's emotions using, for example, a generation AI. The monitoring unit can also adjust the asset monitoring criteria based on the user's emotions using the generation AI. Asset monitoring criteria include, but are not limited to, risk levels and asset allocation target ratios. The monitoring unit can, for example, use the generation AI to prioritize monitoring only important assets when the user is feeling stressed. The monitoring unit can also use the generation AI to perform detailed asset monitoring when the user is relaxed. The monitoring unit can also use the generation AI to perform simplified asset monitoring when the user is in a hurry. This allows optimal asset monitoring to be performed according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit inputs user emotion data into the generation AI, and the generation AI adjusts the asset monitoring criteria.

[0097] The monitoring unit can improve the accuracy of asset monitoring by taking into account the interrelationships between assets. Examples of asset interrelationships include, but are not limited to, correlation coefficients and covariances. The monitoring unit can, for example, use a generation AI to analyze the interrelationships between assets. The monitoring unit can also improve the accuracy of monitoring by taking into account the interrelationships between assets using the generation AI. Examples of monitoring accuracy include, but are not limited to, error rates and detection rates. The monitoring unit can, for example, use a generation AI to analyze the interrelationships between assets and prioritize monitoring of high-risk assets. The monitoring unit can also improve the accuracy of monitoring by taking into account the interrelationships between assets using the generation AI. Furthermore, the monitoring unit can use the generation AI to determine monitoring priorities based on the interrelationships between assets. This improves the accuracy of monitoring by taking into account the interrelationships between assets. Some or all of the above-described processing in the monitoring unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the monitoring unit inputs asset interrelationship data into the generation AI, which then improves the accuracy of monitoring.

[0098] The monitoring unit may monitor assets while taking into account the user's attribute information. Attribute information may include, but is not limited to, age, occupation, and income. For example, the monitoring unit may use a generation AI to analyze the user's age. The monitoring unit may also use a generation AI to analyze the user's occupation. The monitoring unit may also use a generation AI to analyze the user's income. Examples of monitoring include, but are not limited to, periodic checks and real-time monitoring. For example, the monitoring unit may use a generation AI to monitor assets while taking into account the user's attribute information, such as age and occupation. The monitoring unit may also use a generation AI to determine monitoring priorities based on the user's attribute information. Furthermore, the monitoring unit may use a generation AI to improve the accuracy of monitoring based on the user's attribute information. This allows optimal asset monitoring based on the user's attribute information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit inputs the user's attribute information data into the generation AI, which then performs monitoring.

[0099] During asset monitoring, the monitoring unit can weight the monitoring based on the asset fluctuation frequency. The asset fluctuation frequency includes, but is not limited to, daily fluctuations and monthly fluctuations. The monitoring unit, for example, uses a generation AI to evaluate the asset fluctuation frequency. The monitoring unit can also weight the monitoring based on the asset fluctuation frequency using the generation AI. The monitoring weighting includes, but is not limited to, weighting based on the fluctuation frequency. The monitoring unit, for example, uses the generation AI to prioritize monitoring of assets with high fluctuation frequency. The monitoring unit can also use the generation AI to reduce the monitoring frequency of assets with low fluctuation frequency. The monitoring unit can also use the generation AI to automatically adjust the monitoring weight based on the asset fluctuation frequency. This allows optimal monitoring according to the asset fluctuation frequency. Some or all of the above-described processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit inputs asset fluctuation frequency data into the generation AI, which then weights the monitoring.

[0100] The monitoring unit can estimate the user's emotions and adjust the display order of the monitoring results based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. The monitoring unit can estimate the user's emotions using, for example, a generation AI. The monitoring unit can also adjust the display order of the monitoring results based on the user's emotions using the generation AI. The display order of the monitoring results can include, but is not limited to, setting a display order based on emotions. For example, the monitoring unit can use the generation AI to prioritize displaying important monitoring results when the user is feeling stressed. The monitoring unit can also use the generation AI to display detailed monitoring results when the user is relaxed. Furthermore, the monitoring unit can use the generation AI to prioritize displaying concise monitoring results when the user is in a hurry. This allows the optimal monitoring results to be displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit inputs user emotion data into the generation AI, and the generation AI adjusts the display order of the monitoring results.

[0101] The monitoring unit may monitor assets while taking into account the geographic distribution of assets. Examples of geographic distribution include, but are not limited to, asset distribution by region and investment ratio by country. The monitoring unit may, for example, use a generation AI to analyze the geographic distribution of assets. The monitoring unit may also use a generation AI to monitor while taking into account the geographic distribution of assets. Examples of monitoring include, but are not limited to, periodic checks and real-time monitoring. The monitoring unit may, for example, use a generation AI to analyze the geographic distribution of assets and prioritize monitoring of assets in high-risk areas. The monitoring unit may also use a generation AI to improve the accuracy of monitoring while taking into account the geographic distribution of assets. Furthermore, the monitoring unit may use a generation AI to determine monitoring priorities based on the geographic distribution of assets. This allows optimal monitoring based on the geographic distribution of assets. Some or all of the above-described processing in the monitoring unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit inputs asset geographic distribution data into a generation AI, which then performs monitoring.

[0102] During asset monitoring, the monitoring unit can improve the accuracy of monitoring by referring to related literature. Examples of related literature include, but are not limited to, academic papers and industry reports. The monitoring unit can, for example, use a generation AI to refer to related literature. The monitoring unit can also improve the accuracy of monitoring based on the related literature using the generation AI. Examples of monitoring accuracy include, but are not limited to, error rates and detection rates. The monitoring unit can, for example, use a generation AI to refer to related literature and incorporate the latest monitoring techniques. The monitoring unit can also improve the accuracy of monitoring based on the related literature using the generation AI. Furthermore, the monitoring unit can also use the generation AI to determine monitoring priorities by referring to related literature. Thus, referring to related literature improves the accuracy of monitoring. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit inputs related literature data into the generation AI, which then improves the accuracy of monitoring.

[0103] The monitoring unit may monitor assets while taking into account the market value of the assets. Market value includes, but is not limited to, market valuation and valuation gains and losses. The monitoring unit may, for example, use a generation AI to evaluate the market value of the assets. The monitoring unit may also, using the generation AI, monitor assets while taking into account the market value of the assets. Monitoring may include, but is not limited to, periodic checks and real-time monitoring. The monitoring unit may, for example, use a generation AI to prioritize monitoring of assets with high market value. The monitoring unit may also, using the generation AI, reduce the monitoring frequency of assets with low market value. Furthermore, the monitoring unit may, using the generation AI, automatically adjust the monitoring weighting based on the market value of the assets. This allows optimal monitoring based on the market value of the assets. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit inputs market value data of assets into the generation AI, which then performs monitoring.

[0104] The learning unit can estimate the user's emotions and adjust the method of providing learning content based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. The learning unit can estimate the user's emotions using, for example, a generation AI. The learning unit can also adjust the method of providing learning content based on the user's emotions using the generation AI. Methods of providing learning content include, but are not limited to, videos, text, interactive tools, etc. The learning unit can, for example, use the generation AI to provide relaxing learning content when the user is stressed. The learning unit can also use the generation AI to provide detailed learning content when the user is relaxed. Furthermore, the learning unit can also use the generation AI to provide concise learning content when the user is in a hurry. This allows optimal learning content to be provided according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit inputs user emotion data into the generation AI, which then adjusts how the learning content is provided.

[0105] When providing learning content, the learning unit can select optimal content by referring to the user's past learning history. Past learning history includes, but is not limited to, learning time, learning content, and accuracy rate. The learning unit, for example, uses a generation AI to analyze the user's past learning history. The learning unit can also use the generation AI to select optimal content based on the user's past learning history. Optimal content includes, but is not limited to, customization based on the user's learning history. The learning unit, for example, uses the generation AI to suggest optimal learning content based on the user's past learning history. The learning unit can also use the generation AI to provide content based on the user's past learning history according to the user's learning progress. Furthermore, the learning unit can also use the generation AI to determine learning priorities by referring to the user's past learning history. This allows optimal learning content to be provided based on the user's past learning history. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's past learning history data into the generation AI, which then selects optimal content.

[0106] When providing learning content, the learning unit can customize the content based on the user's current knowledge level. Knowledge levels include, but are not limited to, beginner, intermediate, and advanced. The learning unit, for example, uses a generation AI to evaluate the user's knowledge level. The learning unit can also customize the content based on the user's knowledge level using the generation AI. Content customization includes, but is not limited to, adjustments based on the user's knowledge level. For example, the learning unit can use the generation AI to provide beginner, intermediate, and advanced learning content based on the user's knowledge level. The learning unit can also use the generation AI to provide content based on the user's learning progress based on the user's knowledge level. Furthermore, the learning unit can use the generation AI to determine learning priorities based on the user's knowledge level. This allows the provision of optimal learning content based on the user's knowledge level. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's knowledge level data into the generation AI, which then customizes the content.

[0107] When providing learning content, the learning unit can improve the content by reflecting user feedback. Examples of feedback include, but are not limited to, user ratings and comments. The learning unit can, for example, use a generation AI to analyze the user feedback. The learning unit can also improve the content based on the user feedback using the generation AI. Examples of content improvement include, but are not limited to, adjustments based on the feedback. The learning unit can, for example, use a generation AI to improve the learning content based on the user feedback. The learning unit can also use a generation AI to provide optimal learning content based on the user feedback. Furthermore, the learning unit can also use a generation AI to determine learning priorities by reflecting the user feedback. This allows optimal learning content to be provided based on the user feedback. Some or all of the above-described processing in the learning unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the learning unit inputs user feedback data into the generation AI, which then improves the content.

[0108] The learning unit can estimate the user's emotions and prioritize learning content based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and stress. The learning unit can estimate the user's emotions using, for example, a generation AI. The learning unit can also prioritize learning content based on the user's emotions using the generation AI. Prioritizing learning content can include, but is not limited to, emotion-based prioritization. For example, the learning unit can use the generation AI to prioritize relaxing learning content when the user is stressed. For example, the learning unit can use the generation AI to prioritize detailed learning content when the user is relaxed. Furthermore, the learning unit can use the generation AI to prioritize concise learning content when the user is in a hurry. This allows optimal learning content to be prioritized based on the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI. For example, the learning unit inputs user emotion data into the generation AI, which then determines the priority of the learning content.

[0109] When providing learning content, the learning unit can provide optimal content by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, addresses and GPS data. For example, the learning unit can use a generation AI to analyze the user's address. The learning unit can also use the generation AI to analyze the user's GPS data. Examples of optimal content include, but are not limited to, customization based on geographical location information. For example, the learning unit can use a generation AI to provide relevant learning content based on the user's geographical location information. The learning unit can also use a generation AI to determine learning priorities based on the user's geographical location information. Furthermore, the learning unit can use a generation AI to suggest optimal learning content based on the user's geographical location information. This allows optimal learning content to be provided based on the user's geographical location information. Some or all of the above-described processing in the learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit inputs the user's geographical location information data into the generation AI, which then provides optimal content.

[0110] When providing learning content, the learning unit can analyze the user's social media activity and provide relevant content. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. The learning unit, for example, uses a generation AI to analyze the content of the user's social media posts. The learning unit can also use the generation AI to analyze the number of the user's social media followers. Related content includes, for example, learning content based on social media posts, but is not limited to, the example. The learning unit, for example, uses a generation AI to provide relevant learning content based on the content of the user's social media posts. The learning unit can also use a generation AI to provide relevant learning content based on the activity of the user's friends on social media. Furthermore, the learning unit can use a generation AI to provide relevant learning content based on the user's social media check-in information. This allows optimal learning content to be provided based on the user's social media activity. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs the user's social media data into a generation AI, which then provides relevant content.

[0111] When providing learning content, the learning unit can customize the content by reflecting the user's past feedback. Past feedback includes, for example, user ratings and comments, but is not limited to, examples thereof. The learning unit, for example, uses a generation AI to analyze the user's past feedback. The learning unit can also customize the content based on the user's past feedback using the generation AI. Content customization includes, for example, adjustments based on feedback, but is not limited to, examples thereof. The learning unit, for example, uses a generation AI to customize the learning content based on the user's past feedback. The learning unit can also use the generation AI to provide optimal learning content based on the user's past feedback. Furthermore, the learning unit can also use the generation AI to determine learning priorities by reflecting the user's past feedback. This allows optimal learning content to be provided based on the user's past feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's past feedback data into the generation AI, which then customizes the content.

[0112] The providing unit can estimate the user's emotions and adjust the method of providing market insights based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. The providing unit can estimate the user's emotions, for example, using a generation AI. The providing unit can also adjust the method of providing market insights based on the user's emotions, using the generation AI. Methods of providing market insights include, but are not limited to, report formats and dashboard formats. For example, the providing unit can use the generation AI to provide concise and easy-to-understand market insights when the user is stressed. The providing unit can also use the generation AI to provide detailed market insights when the user is relaxed. Furthermore, the providing unit can use the generation AI to provide market insights that focus on the main points when the user is in a hurry. This allows optimal market insights to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs user emotion data into the generation AI, and the generation AI adjusts the method of providing market insights.

[0113] When providing market insights, the providing unit can predict current market trends by referring to past market data. Examples of past market data include, but are not limited to, stock price data and economic indicators. The providing unit can, for example, use a generation AI to analyze past market data. The providing unit can also predict current market trends by referring to past market data using the generation AI. Examples of current market trends include, but are not limited to, trend analysis and prediction models. The providing unit can, for example, use a generation AI to predict current market trends based on past market data. The providing unit can also, for example, use a generation AI to suggest optimal investment timing by referring to past market data. Furthermore, the providing unit can also provide advice to avoid high-risk investments based on past market data using the generation AI. This allows current market trends to be predicted based on past market data. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit inputs past market data into the generation AI, which then predicts current market trends.

[0114] When providing market insights, the provision unit can apply different analytical methods to each category of financial products. Examples of financial product categories include, but are not limited to, stocks, bonds, and investment trusts. The provision unit, for example, uses a generation AI to classify financial product categories. The provision unit can also use the generation AI to apply different analytical methods to each category of financial products. Examples of analytical methods include, but are not limited to, technical analysis and fundamental analysis. For example, the provision unit can apply fundamental analysis to stocks using the generation AI. The provision unit can also apply credit risk analysis to bonds using the generation AI. Furthermore, the provision unit can also apply cash flow analysis to real estate using the generation AI. This allows optimal market insights to be provided according to the category of financial products. Some or all of the above-mentioned processing in the provision unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the provision unit inputs financial product category data into the generation AI, and the generation AI applies the analytical method.

[0115] When providing market insights, the providing unit can provide the insights by taking into account the user's attribute information. Attribute information includes, for example, age, occupation, income, etc., but is not limited to these examples. The providing unit, for example, uses a generation AI to analyze the user's age. The providing unit can also use the generation AI to analyze the user's occupation. The providing unit can also use the generation AI to analyze the user's income. Insights include, for example, investment tips and market forecasts, but are not limited to these examples. The providing unit, for example, uses the generation AI to provide market insights by taking into account the user's attribute information, such as age and occupation. The providing unit can also use the generation AI to propose an optimal investment strategy based on the user's attribute information. The providing unit can also use the generation AI to provide advice to avoid high-risk investments based on the user's attribute information. This makes it possible to provide optimal market insights based on the user's attribute information. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs user attribute information data into the generating AI, and the generating AI provides insights.

[0116] The providing unit can estimate the user's emotions and adjust the importance of market insights based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. The providing unit can estimate the user's emotions using, for example, a generation AI. The providing unit can also adjust the importance of market insights based on the user's emotions using the generation AI. The importance of market insights can include, but is not limited to, emotion-based importance settings. For example, the providing unit can use the generation AI to provide only important market insights preferentially when the user is stressed. The providing unit can also use the generation AI to provide detailed market insights when the user is relaxed. Furthermore, the providing unit can use the generation AI to provide concise market insights preferentially when the user is in a hurry. This allows the provision of optimal market insights according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs user emotion data to the generation AI, and the generation AI adjusts the importance of market insights.

[0117] When providing market insights, the providing unit can analyze changes in the insights based on the submission timing of the financial products. Examples of the submission timing of the financial products include, but are not limited to, monthly reports and quarterly reports. The providing unit, for example, uses a generation AI to evaluate the submission timing of the financial products. The providing unit can also use the generation AI to analyze changes in the insights based on the submission timing of the financial products. Examples of changes in the insights include, but are not limited to, analyzing changes based on the submission timing. The providing unit, for example, uses the generation AI to analyze changes in the market insights based on the submission timing of the financial products. The providing unit can also use the generation AI to provide market insights preferentially for financial products whose submission timing is approaching. Furthermore, the providing unit can use the generation AI to determine the priority of insights, taking into account the submission timing of the financial products. This allows optimal market insights to be provided based on the submission timing of the financial products. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs financial product submission timing data into the generation AI, which then analyzes changes in the insights.

[0118] When providing market insights, the providing unit can analyze the insights by referring to related market data. Examples of related market data include, but are not limited to, stock price data and economic indicators. The providing unit can, for example, use a generation AI to analyze the related market data. The providing unit can also, for example, use the generation AI to analyze the insights by referring to the related market data. Examples of insight analysis include, but are not limited to, data analysis and trend analysis. The providing unit can, for example, use the generation AI to provide market insights based on the related market data. The providing unit can also, for example, use the generation AI to suggest optimal investment timing by referring to the related market data. Furthermore, the providing unit can also, for example, use the generation AI to provide advice to avoid high-risk investments based on the related market data. This makes it possible to provide optimal market insights based on the related market data. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit inputs the relevant market data into the generation AI, which then analyzes the insights.

[0119] When providing market insights, the providing unit can analyze the insights taking into account the technological maturity of the financial product. Examples of technological maturity include, but are not limited to, the stage of technological evolution and the presence or absence of patents. The providing unit, for example, uses a generation AI to evaluate the technological maturity of the financial product. The providing unit can also use the generation AI to analyze the insights taking into account the technological maturity of the financial product. Examples of insight analysis include, but are not limited to, analysis based on technological maturity. The providing unit, for example, uses the generation AI to provide market insights based on the technological maturity of the financial product. The providing unit can also use the generation AI to prioritize market insights for financial products with high technical maturity. Furthermore, the providing unit can use the generation AI to determine the priority of insights taking into account the technical maturity of the financial product. This allows optimal market insights to be provided based on the technical maturity of the financial product. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs technical maturity data of the financial product into the generation AI, which then analyzes the insights. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, learning unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, learning unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, learning unit, and providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, learning unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The reception unit can estimate the user's emotions and adjust the timing of financial information input based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and stress. The reception unit, for example, uses a generation AI to estimate the user's emotions. The reception unit can also adjust the timing of financial information input based on the user's emotions using the generation AI. For example, if the user is feeling stressed, the reception unit can prompt the user to input financial information during a time when the user can relax. If the user is relaxed, the reception unit can also prompt the user to input detailed financial information. Furthermore, if the user is in a hurry, the reception unit can provide a simplified input form. This allows the user to input financial information at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit inputs the user's emotion data into the generation AI, and the generation AI estimates the emotion.

[0122] The generation unit can estimate the user's emotion and adjust the way the advice is presented based on the estimated user's emotion. Emotions include, but are not limited to, joy, sadness, stress, etc. The generation unit, for example, uses a generation AI to estimate the user's emotion. The generation unit can also adjust the way the advice is presented based on the user's emotion using the generation AI. For example, if the user is stressed, the generation unit can provide concise and easy-to-understand advice. If the user is relaxed, the generation unit can provide detailed advice. If the user is in a hurry, the generation unit can provide advice that focuses on the main points. This allows optimal advice to be provided based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit inputs the user's emotional data into the generation AI, which then adjusts the way the advice is expressed.

[0123] The monitoring unit can estimate the user's emotions and adjust the asset monitoring criteria based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. The monitoring unit can estimate the user's emotions using, for example, a generation AI. The monitoring unit can also adjust the asset monitoring criteria based on the user's emotions using the generation AI. Asset monitoring criteria include, but are not limited to, risk levels and asset allocation target ratios. The monitoring unit can, for example, use the generation AI to prioritize monitoring only important assets when the user is feeling stressed. The monitoring unit can also use the generation AI to perform detailed asset monitoring when the user is relaxed. The monitoring unit can also use the generation AI to perform simplified asset monitoring when the user is in a hurry. This allows optimal asset monitoring to be performed according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit inputs user emotion data into the generation AI, and the generation AI adjusts the asset monitoring criteria.

[0124] The learning unit can estimate the user's emotions and adjust the method of providing learning content based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. The learning unit can estimate the user's emotions using, for example, a generation AI. The learning unit can also adjust the method of providing learning content based on the user's emotions using the generation AI. Methods of providing learning content include, but are not limited to, videos, text, interactive tools, etc. The learning unit can, for example, use the generation AI to provide relaxing learning content when the user is stressed. The learning unit can also use the generation AI to provide detailed learning content when the user is relaxed. Furthermore, the learning unit can also use the generation AI to provide concise learning content when the user is in a hurry. This allows optimal learning content to be provided according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit inputs user emotion data into the generation AI, which then adjusts how the learning content is provided.

[0125] The providing unit can estimate the user's emotions and adjust the method of providing market insights based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. The providing unit can estimate the user's emotions, for example, using a generation AI. The providing unit can also adjust the method of providing market insights based on the user's emotions, using the generation AI. Methods of providing market insights include, but are not limited to, report formats and dashboard formats. For example, the providing unit can use the generation AI to provide concise and easy-to-understand market insights when the user is stressed. The providing unit can also use the generation AI to provide detailed market insights when the user is relaxed. Furthermore, the providing unit can use the generation AI to provide market insights that focus on the main points when the user is in a hurry. This allows optimal market insights to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs user emotion data into the generation AI, and the generation AI adjusts the method of providing market insights.

[0126] The reception unit can analyze the user's past financial information input history and select the optimal input method. The past financial information input history includes, for example, past input data and input frequency, but is not limited to these examples. The reception unit can, for example, use a generation AI to analyze the user's past input data. The reception unit can also use the generation AI to analyze the user's past input frequency. The optimal input method can, for example, include, but is not limited to, voice input, text input, image input, etc. The reception unit can, for example, use the generation AI to preferentially suggest input methods that the user has frequently used in the past. The reception unit can also use the generation AI to automatically customize the input form based on information previously input by the user. Furthermore, the reception unit can also use the generation AI to predict and suggest an input method to be used during a specific time period based on the user's past input history. This allows the optimal input method to be provided based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs the user's past input data into the generation AI, which then selects the optimal input method.

[0127] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the financial information. Examples of the importance of the financial information include, but are not limited to, asset size and risk level. The generation unit, for example, uses a generation AI to evaluate the importance of the financial information. The generation unit can also adjust the level of detail of the advice based on the importance of the financial information using the generation AI. For example, the generation unit provides detailed advice for important financial information. For example, the generation unit can provide concise advice for less important financial information. Furthermore, the generation unit can automatically adjust the level of detail of the advice based on the importance of the financial information using the generation AI. This allows optimal advice to be provided based on the importance of the financial information. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs financial information importance data into the generation AI, and the generation AI adjusts the level of detail of the advice.

[0128] The monitoring unit can improve the accuracy of asset monitoring by taking into account the interrelationships between assets. Examples of asset interrelationships include, but are not limited to, correlation coefficients and covariances. The monitoring unit can, for example, use a generation AI to analyze the interrelationships between assets. The monitoring unit can also improve the accuracy of monitoring by taking into account the interrelationships between assets using the generation AI. Examples of monitoring accuracy include, but are not limited to, error rates and detection rates. The monitoring unit can, for example, use a generation AI to analyze the interrelationships between assets and prioritize monitoring of high-risk assets. The monitoring unit can also improve the accuracy of monitoring by taking into account the interrelationships between assets using the generation AI. Furthermore, the monitoring unit can use the generation AI to determine monitoring priorities based on the interrelationships between assets. This improves the accuracy of monitoring by taking into account the interrelationships between assets. Some or all of the above-described processing in the monitoring unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the monitoring unit inputs asset interrelationship data into the generation AI, which then improves the accuracy of monitoring.

[0129] When providing market insights, the provision unit can apply different analytical methods to each category of financial products. Examples of financial product categories include, but are not limited to, stocks, bonds, and investment trusts. The provision unit, for example, uses a generation AI to classify financial product categories. The provision unit can also use the generation AI to apply different analytical methods to each category of financial products. Examples of analytical methods include, but are not limited to, technical analysis and fundamental analysis. For example, the provision unit can apply fundamental analysis to stocks using the generation AI. The provision unit can also apply credit risk analysis to bonds using the generation AI. Furthermore, the provision unit can also apply cash flow analysis to real estate using the generation AI. This allows optimal market insights to be provided according to the category of financial products. Some or all of the above-mentioned processing in the provision unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the provision unit inputs financial product category data into the generation AI, and the generation AI applies the analytical method.

[0130] When providing learning content, the learning unit can select optimal content by referring to the user's past learning history. Past learning history includes, but is not limited to, learning time, learning content, and accuracy rate. The learning unit, for example, uses a generation AI to analyze the user's past learning history. The learning unit can also use the generation AI to select optimal content based on the user's past learning history. Optimal content includes, but is not limited to, customization based on the user's learning history. The learning unit, for example, uses the generation AI to suggest optimal learning content based on the user's past learning history. The learning unit can also use the generation AI to provide content based on the user's past learning history according to the user's learning progress. Furthermore, the learning unit can also use the generation AI to determine learning priorities by referring to the user's past learning history. This allows optimal learning content to be provided based on the user's past learning history. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit inputs the user's past learning history data into the generation AI, which then selects optimal content.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit inputs the user's financial situation and goals. The user's financial situation includes income, expenses, assets, liabilities, investment goals, etc. The reception unit provides an interface for the user to input this information. Step 2: The generator generates advice based on the information entered by the receiver. Using the generator AI, it performs risk assessments, proposes asset allocation, assesses the risk of specific investment products, and proposes investment strategies. Step 3: The monitoring unit inputs the user's asset information, monitors the asset status using the generated AI, and rebalances as necessary. Monitoring is performed periodically, and rebalancing is performed when the deviation from the asset allocation target ratio exceeds a certain level. Step 4: The learning department improves financial knowledge through interactive learning tools. Learning is done in the form of quizzes, and generative AI provides appropriate content based on the user's learning progress. Step 5: The provider uses the generative AI to analyze market trends and financial products and provide market insights to users. It predicts market trends based on past market data, news articles, and corporate financial information, and provides users with the latest market trends and investment tips.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a 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.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the user's financial situation and goals; a generation unit that generates advice based on the information input by the reception unit; A monitoring unit that inputs the user's asset information and automatically monitors the asset status using AI, rebalancing as necessary; A learning department that improves financial knowledge through interactive learning tools; and a providing unit that uses AI to analyze market trends and financial products and provide market insights to users. A system characterized by:

2. The reception unit Enter your income, expenses, assets, liabilities, and investment goals 2. The system of claim 1.

3. The generation unit Based on the information you provide, we provide risk assessment and asset allocation recommendations.

2. The system of claim 1.

4. The monitoring unit Regularly monitor asset allocation and rebalance when deviation from target asset allocation ratio exceeds a predetermined threshold.

2. The system of claim 1.

5. The learning unit Users can study in a quiz format, and AI provides content according to the user's learning progress.

2. The system of claim 1.

6. The providing unit Analyzes historical market data, news articles, and corporate financial information to provide users with the latest market trends and investment tips 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the timing of financial information input based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past financial information input history and select the optimal input method 2. The system of claim 1.

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