system

Abstract

The system according to this embodiment aims to enable novice investors to find the optimal investment destination and investment scale, and to understand the investment results concretely. [Solution] The system according to the embodiment comprises a collection unit, an advice unit, a simulation unit, and a visualization unit. The collection unit collects investment-related information through interaction with the user. The advice unit advises on the optimal investment destination and investment scale based on the information collected by the collection unit. The simulation unit simulates investment results based on the information provided by the advice unit. The visualization unit visualizes the results generated by the simulation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for novice investors to find an optimal investment destination and investment scale, and it is difficult to specifically imagine the investment results.

[0005] The system according to the embodiment aims to enable novice investors to find an optimal investment destination and investment scale and specifically understand the investment results.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an advice unit, a simulation unit, and a visualization unit. The data collection unit collects investment-related information through interaction with the user. The advice unit advises on the optimal investment destination and investment scale based on the information collected by the data collection unit. The simulation unit simulates investment results based on the information provided by the advice unit. The visualization unit visualizes the results generated by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can help novice investors find the optimal investment destination and investment size, and to understand the investment results concretely. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 2 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An investment support system according to an embodiment of the present invention is a system that uses a generating AI to enable novice investors to invest with confidence. In this investment support system, the user interacts with the generating AI and collects information related to investment. Next, the generating AI advises the user on the optimal investment destination and investment scale based on the collected information. Furthermore, the generating AI performs a simulation of the investment results and visualizes the results. This allows the user to concretely understand the investment results and make investment decisions with greater confidence. As a result, it contributes to more people building assets with ease, alleviating anxieties about retirement life, and increasing their income. Thus, the investment support system enables users to concretely understand the investment results and make investment decisions with greater confidence. For example, by making an investment according to the advice of the generating AI and checking the results in a simulation, the user can reduce anxiety about investing. Also, by visualizing the investment results, the user can concretely grasp the risks and returns of the investment. Thus, the investment support system provides an environment in which novice investors can invest with confidence, contributing to more people building assets with ease, alleviating anxieties about retirement life, and increasing their income.

[0029] The investment support system according to this embodiment comprises a data collection unit, an advice unit, a simulation unit, and a visualization unit. The data collection unit collects investment-related information through dialogue with the user. The data collection unit can interact with the user in the form of, for example, text chat, voice dialogue, or video call. The data collection unit collects information such as the type of investment the user has, the investment amount, and the investment period. For example, the data collection unit collects investment-related information when the user asks the generating AI questions such as, "Which investment is good?" or "How much should I invest?". The advice unit advises on the optimal investment destination and investment scale based on the information collected by the data collection unit. For example, the advice unit uses the generating AI to provide specific advice such as, "It would be good to invest in this stock" or "It would be good to invest in this real estate," based on the user's risk tolerance and investment goals. The advice unit selects the optimal investment destination using the user's risk-return balance and past performance as evaluation criteria. The simulation unit simulates investment results based on the information provided by the advice unit. For example, the simulation unit performs profit forecasting using historical data analysis and statistical models. The simulation unit predicts future returns if the user invests in a specific stock and displays the results in graphs and charts. The visualization unit visualizes the results generated by the simulation unit. The visualization unit visually displays the investment results in formats such as bar graphs, line graphs, and pie charts. The visualization unit generates simple and easy-to-understand graphs and charts so that the user can concretely understand the investment results. As a result, the investment support system according to this embodiment allows the user to concretely understand the investment results and make investment decisions with greater confidence. For example, by making an investment according to the advice of the generated AI and checking the results in the simulation, the user can reduce anxiety about investing. In addition, by visualizing the investment results, the user can concretely grasp the risks and returns of the investment. As a result, the investment support system provides an environment in which novice investors can invest with peace of mind, and contributes to more people building assets without hassle, alleviating anxieties about retirement life, and increasing income.

[0030] The data collection unit gathers investment-related information through interaction with users. This can be done through various methods, such as text chat, voice chat, and video calls. Specifically, in text chat, it responds in real-time to questions and information entered by the user, collecting necessary information. In voice chat, it recognizes the user's voice and analyzes questions and answers using natural language processing technology. In video calls, it also analyzes the user's facial expressions and gestures to gain a deeper understanding. The data collection unit gathers information such as the type of investment, investment amount, and investment period. For example, the user can ask the generative AI questions like, "Which investment is best?" or "How much should I invest?" to gather investment-related information. The generative AI generates appropriate prompts in response to the user's questions, accurately understanding the user's intent. Furthermore, the data collection unit also collects detailed information such as the user's past investment history, current asset status, and risk tolerance. This allows the data collection unit to grasp the overall picture of the user's investments and provide the necessary data to the advice and simulation units. The data collection unit centrally manages the collected information and stores it in a database. This allows for continuous support based on past information when users use the system again. Furthermore, the data collection unit encrypts data and controls access to protect user privacy, ensuring the secure management of information. This allows the data collection unit to build trust with users and receive information with confidence.

[0031] The advisory department advises on optimal investment targets and investment scales based on information collected by the data collection department. For example, the advisory department uses a generating AI to provide specific advice such as "investing in this stock is a good idea" or "investing in this real estate is a good idea," based on the user's risk tolerance and investment goals. The generating AI selects the optimal investment target using criteria such as the user's risk-return balance and past performance. Specifically, the generating AI quantifies the user's risk tolerance and compares high-risk and low-risk investment targets. It also determines whether the user should aim for short-term profits or long-term wealth building, depending on their investment goals. Furthermore, the generating AI analyzes past market data and economic indicators to predict future market trends. This allows the advisory department to provide users with specific and practical investment advice. The advisory department collects user feedback and continuously improves the accuracy of its advice. For example, it collects the results of users' actual investments and evaluates the effectiveness of the advice based on those results. It also flexibly adjusts the content and format of the advice to reflect user opinions and requests. This allows the advisory department to provide users with optimal investment advice and support their investment success. Furthermore, the advisory department provides educational content and reference materials to deepen users' understanding of investing. For example, it provides articles and videos explaining basic investment knowledge, market trends, and risk management methods, supporting users in making investment decisions on their own. In this way, the advisory department can improve users' investment skills and support their long-term wealth building.

[0032] The simulation unit simulates investment results based on information provided by the advisory unit. For example, the simulation unit uses historical data analysis and statistical models to predict returns. Specifically, the simulation unit predicts the future performance of a particular investment based on historical market data. For example, it predicts future returns if an investment is made in a specific stock and displays the results in graphs and charts. The simulation unit predicts future returns if a user invests in a specific stock and displays the results in graphs and charts. The simulation unit predicts future market trends using statistical models based on historical data. For example, it predicts future price fluctuations of a specific stock based on historical stock price data and displays the results in graphs. Furthermore, the simulation unit simulates multiple scenarios to identify the most likely outcome. For example, it simulates multiple scenarios considering different economic conditions and market conditions and compares the return predictions in each scenario. This allows the simulation unit to predict investment results from multiple perspectives, supporting more reliable investment decisions. In addition, the simulation unit continuously modifies the simulation results based on real-time updated market data. For example, if stock prices or economic indicators fluctuate rapidly, the simulation unit immediately incorporates the new data and updates the simulation results. This allows the simulation unit to always provide highly accurate profit forecasts based on the latest information, supporting users' investment decisions.

[0033] The visualization unit visualizes the results generated by the simulation unit. The visualization unit visually displays investment results in formats such as bar graphs, line graphs, and pie charts. Specifically, based on the simulation results, the visualization unit generates simple and easy-to-understand graphs and charts so that users can concretely understand the investment results. For example, it displays a bar graph showing projected future returns when investing in a specific stock, visually illustrating fluctuations in returns. It also displays multiple line graphs overlaid to compare projected returns for different investments. This allows users to compare the performance of different investments at a glance. Furthermore, the visualization unit generates customized graphs and charts according to the user's investment goals and risk tolerance. For example, if a user prioritizes short-term profits, it displays a graph emphasizing short-term return projections; if aiming for long-term asset building, it displays a graph showing long-term return projections. In this way, the visualization unit provides visual information tailored to the user's needs, supporting investment decisions. The visualization unit enables users to concretely understand investment results and make investment decisions with greater confidence. For example, by following the advice of the generated AI and checking the results through simulations, users can reduce their anxiety about investing. Furthermore, by visualizing investment results, users can concretely understand the risks and returns of their investments. In this way, the visualization function provides an environment where novice investors can invest with confidence, contributing to more people building assets effortlessly, alleviating anxieties about retirement, and increasing their income.

[0034] The data collection unit includes a risk collection unit that collects users' risk tolerance. The risk collection unit evaluates users' risk tolerance based, for example, on questionnaires or past investment behavior. The risk collection unit asks detailed questions to clarify how much risk users can tolerate. For example, the risk collection unit asks users questions such as, "What investments have you made in the past?" and "How much risk can you tolerate?" to evaluate the user's risk tolerance. The risk collection unit can also evaluate risk tolerance based on the user's asset situation and investment goals. For example, the risk collection unit understands the user's asset situation and evaluates their risk tolerance. As a result, the data collection unit can provide more appropriate investment advice by collecting users' risk tolerance. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input a questionnaire to evaluate the user's risk tolerance into a generation AI, and the generation AI can analyze the questionnaire results to evaluate the risk tolerance.

[0035] The advice unit includes a goal-setting unit that sets the user's investment goals. The goal-setting unit sets, for example, the user's short-term and long-term goals. The goal-setting unit asks detailed questions to clarify what kind of investment goals the user has. For example, the goal-setting unit asks the user questions such as, "What kind of return do you expect and over what period of time?" or "How do you envision your retirement plan?" and sets the user's investment goals. The goal-setting unit can also set investment goals based on the user's risk tolerance and asset situation. For example, the goal-setting unit considers the user's risk tolerance and sets realistic investment goals. This allows the advice unit to provide more specific investment advice by setting the user's investment goals. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input questions for setting the user's investment goals into a generation AI, and the generation AI can analyze the question results and set investment goals.

[0036] The simulation unit includes a forecasting unit that performs profit forecasting. The forecasting unit performs profit forecasting using, for example, historical data analysis or statistical models. The forecasting unit forecasts future profits if the user invests in a specific stock and displays the results in graphs or charts. The forecasting unit performs profit forecasting based on the type of investment and investment amount of the user. For example, the forecasting unit forecasts future profits if the user invests in a specific stock and displays the results in graphs or charts. The forecasting unit can also perform profit forecasting based on the user's investment period. For example, the forecasting unit forecasts future profits if the user invests in a specific stock and displays the results in graphs or charts. In this way, the simulation unit allows the user to concretely understand the results of their investment by performing profit forecasting. Some or all of the above processing in the simulation unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the simulation unit can input the type of investment and investment amount of the user into a generative AI, and the generative AI can perform profit forecasting.

[0037] The visualization unit includes a graph generation unit that generates graphs and charts. The graph generation unit visually displays investment results in formats such as bar graphs, line graphs, and pie charts. The graph generation unit generates simple and easy-to-understand graphs and charts so that users can concretely understand the results of their investments. The graph generation unit generates graphs and charts based on the type of investment and investment amount of the user. For example, the graph generation unit displays a graph or chart showing the projected future returns if the user invests in a particular stock. The graph generation unit can also generate graphs and charts based on the user's investment period. For example, the graph generation unit displays a graph or chart showing the projected future returns if the user invests in a particular stock. In this way, the visualization unit enables users to visually understand the results of their investments by generating graphs and charts. Some or all of the above-described processes in the visualization unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the visualization unit can input the type of investment and investment amount of the user into a generation AI, and the generation AI can generate graphs and charts.

[0038] The advisory unit advises on the optimal investment destinations based on the user's risk tolerance. For example, the advisory unit evaluates the user's risk tolerance and selects the optimal investment destinations considering the balance between risk and return. The advisory unit advises on specific investment destinations based on the user's risk tolerance. For example, the advisory unit evaluates the user's risk tolerance and provides specific advice such as "investing in this stock is a good idea" or "investing in this real estate is a good idea." The advisory unit can also advise on the optimal investment destinations based on the user's investment goals and asset situation. For example, the advisory unit considers the user's investment goals and advises on realistic investment destinations. In this way, the advisory unit enables more appropriate investment decisions by advising on the optimal investment destinations based on the user's risk tolerance. Some or all of the above processes in the advisory unit may be performed using generative AI, or they may not be performed using generative AI. For example, the advisory unit can input data to evaluate the user's risk tolerance into a generative AI, which can then analyze the risk tolerance and advise on the optimal investment destinations.

[0039] The simulation unit simulates investment results based on the user's investment scale. For example, the simulation unit makes profit forecasts based on the user's investment amount and investment period. The simulation unit makes future profit forecasts if the user invests in a specific investment target and displays the results in graphs and charts. The simulation unit makes specific profit forecasts based on the user's investment scale. For example, the simulation unit makes future profit forecasts if the user invests in a specific stock and displays the results in graphs and charts. The simulation unit can also make profit forecasts that consider the balance between risk and return based on the user's investment scale. For example, the simulation unit makes realistic profit forecasts considering the user's investment scale. This allows the simulation unit to provide a more concrete understanding of investment results by simulating investment results based on the user's investment scale. Some or all of the above processing in the simulation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the simulation unit can input the user's investment scale into a generative AI, which can then perform profit forecasts.

[0040] The data collection unit analyzes the user's past investment history and selects the optimal information collection method. For example, the data collection unit uses a generating AI to select appropriate questions based on the type and scale of investments the user has made in the past. The data collection unit prioritizes collecting information on specific investment targets from the user's past investment history. The data collection unit analyzes the user's past investment history and selects an information collection method that matches their risk tolerance. This allows the data collection unit to collect more appropriate information by analyzing the user's past investment history. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's past investment history into a generating AI, which can then analyze the investment history and select the optimal information collection method.

[0041] The data collection unit filters investment information based on the user's current financial situation and areas of interest. For example, the data collection unit uses a generating AI to filter appropriate investment information based on the user's current income and expenses. The data collection unit prioritizes collecting relevant investment information based on the user's areas of interest (e.g., technology, healthcare, etc.). The data collection unit prioritizes collecting low-risk investment information according to the user's financial situation. This allows the data collection unit to collect more appropriate information by filtering information based on the user's financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's financial situation and areas of interest into a generating AI, which can then filter appropriate investment information.

[0042] The data collection unit prioritizes collecting highly relevant information when gathering investment information, taking into account the user's geographical location. For example, the data collection unit uses a generating AI to collect relevant investment information based on the economic conditions of the area where the user lives. The data collection unit prioritizes collecting region-specific investment opportunities based on the user's geographical location. The data collection unit collects information on regional market trends, taking into account the user's geographical location. This allows the data collection unit to collect more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then collect relevant investment information.

[0043] The data collection unit analyzes the user's social media activity and collects relevant information when gathering investment information. For example, the data collection unit collects information on investors and experts that the user follows on social media. The data collection unit analyzes the content of the user's social media posts and collects investment information of interest. The data collection unit collects trending and popular investment information from the user's social media activity. This allows the data collection unit to collect more appropriate information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI, which will analyze the activity and collect relevant information.

[0044] The advisory unit adjusts the level of detail in its advice based on the importance of the investment. For example, for highly important investments, the AI ​​generates detailed advice. For less important investments, the AI ​​generates concise advice. The advisory unit adjusts the content of the advice according to the importance of the investment. This allows the advisory unit to provide more appropriate advice by adjusting the level of detail based on the importance of the investment. Some or all of the above processing in the advisory unit may be performed using the AI, or not. For example, the advisory unit can input data to evaluate the importance of an investment into the AI, which then analyzes the importance and adjusts the level of detail in the advice.

[0045] The advisory unit applies different advisory algorithms depending on the investment category when providing advice. For example, in the case of stock investment, the generating AI applies an advisory algorithm specialized for stocks. In the case of real estate investment, the generating AI applies an advisory algorithm specialized for real estate. In the case of cryptocurrency investment, the generating AI applies an advisory algorithm specialized for cryptocurrencies. This allows the advisory unit to provide more appropriate advice by applying different advisory algorithms depending on the investment category. Some or all of the above processing in the advisory unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the advisory unit can input data to evaluate the investment category into the generating AI, and the generating AI can analyze the category and apply a different advisory algorithm.

[0046] The advisory unit determines the priority of advice based on the submission timing of the investment targets. For example, the advisory unit's generating AI prioritizes providing advice to investment targets with upcoming submission deadlines. For investment targets with later submission deadlines, the generating AI postpones providing advice. The advisory unit adjusts the priority of advice based on the submission timing. This allows the advisory unit to provide more appropriate advice by determining the priority of advice based on the submission timing of the investment targets. Some or all of the above processing in the advisory unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the advisory unit can input data to evaluate the submission timing of investment targets into the generating AI, and the generating AI can analyze the submission timing to determine the priority of advice.

[0047] The advisory unit adjusts the order of advice based on the relevance of the investments when providing advice. For example, the advisory unit prioritizes providing advice on highly relevant investments using the generating AI. For less relevant investments, the advisory unit postpones providing advice using the generating AI. The advisory unit adjusts the order of advice based on the relevance of the investments using the generating AI. This allows the advisory unit to provide more appropriate advice by adjusting the order of advice based on the relevance of the investments. Some or all of the above processing in the advisory unit may be performed using the generating AI or not. For example, the advisory unit can input data to evaluate the relevance of investments into the generating AI, and the generating AI can analyze the relevance and adjust the order of advice.

[0048] The simulation unit improves the accuracy of the simulation by considering the interrelationships of the investment targets during the simulation. For example, the simulation unit uses a generating AI to perform the simulation while considering the correlation of the stock prices of the investment targets. The simulation unit uses a generating AI to perform the simulation while considering the interrelationships of the industries of the investment targets. The simulation unit uses a generating AI to perform the simulation while considering the economic conditions of the region of the investment target. In this way, the simulation unit improves the accuracy of the simulation by considering the interrelationships of the investment targets. Some or all of the above processing in the simulation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the simulation unit can input data for evaluating the interrelationships of investment targets into a generating AI, and the generating AI can analyze the interrelationships and perform the simulation.

[0049] The simulation unit performs simulations while considering the attribute information of the investment target. For example, the simulation unit uses a generating AI to perform simulations while considering the financial situation of the investment target company. The simulation unit uses a generating AI to perform simulations while considering the growth potential of the investment target company. The simulation unit uses a generating AI to perform simulations while considering the market share of the investment target company. As a result, the simulation unit can perform more accurate simulations by considering the attribute information of the investment target. Some or all of the above processing in the simulation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the simulation unit can input data for evaluating the attribute information of the investment target into the generating AI, and the generating AI can analyze the attribute information and perform simulations.

[0050] The simulation unit performs simulations while considering the geographical distribution of investment targets. For example, the simulation unit uses a generating AI to perform simulations while considering the economic conditions of the investment target regions. The simulation unit uses a generating AI to perform simulations while considering the political conditions of the investment target regions. The simulation unit uses a generating AI to perform simulations while considering market trends of the investment target regions. As a result, the simulation unit can perform more accurate simulations by considering the geographical distribution of investment targets. Some or all of the above processing in the simulation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the simulation unit can input data to evaluate the geographical distribution of investment targets into a generating AI, and the generating AI can analyze the geographical distribution and perform simulations.

[0051] The simulation unit improves the accuracy of the simulation by referring to relevant literature on the investment target during the simulation. For example, the simulation unit uses the latest research papers on the investment target for the generating AI to perform the simulation. The simulation unit uses the market reports on the investment target for the generating AI to perform the simulation. The simulation unit uses industry analysis reports on the investment target for the generating AI to perform the simulation. In this way, the simulation unit improves the accuracy of the simulation by referring to relevant literature on the investment target. Some or all of the above processing in the simulation unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the simulation unit can input relevant literature on the investment target into the generating AI, and the generating AI can analyze the literature and perform the simulation.

[0052] The visualization unit optimizes the current visualization by referring to past visualization data during visualization. For example, the visualization unit uses a generating AI to optimize the current visualization based on visualization formats preferred by the user in the past. The visualization unit analyzes past visualization data and provides the user with the most suitable visualization format. The visualization unit provides visualizations that aid user understanding by referring to past visualization data. In this way, the visualization unit can optimize the current visualization by referring to past visualization data. Some or all of the above processes in the visualization unit may be performed using a generating AI or not. For example, the visualization unit can input past visualization data into a generating AI, and the generating AI can analyze the data to optimize the current visualization.

[0053] The visualization unit applies different visualization methods to each investment category during visualization. For example, in the case of stock investment, the generating AI visualizes the stock price trend in a graph. In the case of real estate investment, the generating AI visualizes the property value trend in a chart. In the case of cryptocurrency investment, the generating AI visualizes the price fluctuation in a graph. This allows the visualization unit to provide more appropriate visualizations by applying different visualization methods to each investment category. Some or all of the above processing in the visualization unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the visualization unit can input data for evaluating investment categories into the generating AI, and the generating AI can analyze the categories and apply different visualization methods.

[0054] The visualization unit analyzes changes in the visualization based on the submission timing of the investment targets during the visualization process. For example, the visualization unit uses a generating AI to analyze changes in the visualization for investment targets with upcoming submission times. The visualization unit uses a generating AI to analyze changes in the visualization for investment targets with distant submission times. The visualization unit uses a generating AI to analyze changes in the visualization according to the submission timing. This allows the visualization unit to perform more appropriate visualizations by analyzing changes in the visualization based on the submission timing of the investment targets. Some or all of the above-described processes in the visualization unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the visualization unit can input data for evaluating the submission timing of investment targets into a generating AI, and the generating AI can analyze the submission timing and analyze changes in the visualization.

[0055] The visualization unit analyzes the visualization by referring to relevant market data of the investment target during the visualization process. For example, the visualization unit uses a generating AI to analyze the visualization based on the market data of the investment target. The visualization unit uses a generating AI to analyze the visualization based on the industry data of the investment target. The visualization unit uses a generating AI to analyze the visualization based on the regional data of the investment target. As a result, the visualization unit improves the accuracy of the visualization by referring to relevant market data of the investment target. Some or all of the above processing in the visualization unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the visualization unit can input relevant market data of the investment target into a generating AI, and the generating AI can analyze the data and perform visualization.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can assess the user's level of investment knowledge and adjust the method of information provision based on that assessment. For example, if the user is a novice investor, the data collection unit provides information including explanations of basic investment concepts and terminology. If the user is an intermediate investor, it provides more specific investment strategies and risk management methods. If the user is an advanced investor, it provides information on the latest market trends and advanced investment techniques. This allows the data collection unit to provide more effective investment support by providing information tailored to the user's level of knowledge. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input questions to the generative AI to assess the user's level of knowledge, and the generative AI can analyze the question results and adjust the method of information provision.

[0058] The advisory unit can analyze the user's investment style and advise on the optimal investment destinations based on that style. For example, if the user prefers short-term investments, the advisory unit will suggest investments that can aim for short-term profits. If the user prefers long-term investments, it will suggest investments that can be expected to grow steadily. If the user wants to avoid risk, it will suggest low-risk investments. In this way, the advisory unit can provide advice tailored to the user's investment style, enabling more appropriate investment decisions. Some or all of the above processing in the advisory unit may be performed using generative AI, or it may be performed without using generative AI. For example, the advisory unit can input data to evaluate the user's investment style into a generative AI, which can then analyze the style and advise on the optimal investment destinations.

[0059] The simulation unit can perform simulations while taking the user's investment history into consideration. For example, the simulation unit can predict future investment results based on the performance of investments the user has made in the past. It can reproduce investment strategies that the user has succeeded with in the past and predict returns under similar conditions. It can also perform simulations to help the user avoid investment strategies that have failed in the past. In this way, the simulation unit can perform more realistic simulations by taking the user's investment history into consideration. Some or all of the above-described processes in the simulation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the simulation unit can input the user's investment history into a generative AI, which can then analyze the history and perform simulations.

[0060] The visualization unit can adjust the format of the visualization based on the user's investment goals. For example, if the user is aiming for short-term profits, the visualization unit provides a graph that emphasizes short-term return forecasts. If the user is aiming for long-term wealth building, it provides a chart that shows long-term growth forecasts. If the user prioritizes risk management, it visualizes the balance between risk and return. This allows the visualization unit to provide more effective information by creating visualizations that match the user's investment goals. Some or all of the above processing in the visualization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the visualization unit can input data to evaluate the user's investment goals into a generative AI, which can then analyze the goals and adjust the format of the visualization.

[0061] The data collection unit can evaluate the user's level of interest in investment and adjust the frequency of information provision based on that evaluation. For example, if the user shows high interest, the data collection unit will provide the latest investment information frequently. If the user shows low interest, it will provide only the minimum necessary information. If the user shows moderate interest, it will provide information at a moderate frequency. In this way, the data collection unit can provide more effective investment support by providing information according to the user's level of interest. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input data to evaluate the user's level of interest into a generation AI, and the generation AI can analyze the level of interest and adjust the frequency of information provision.

[0062] The advisory unit can assess the user's investment experience and adjust the level of detail in its advice based on that assessment. For example, if the user is a novice investor, the advisory unit will provide detailed explanations of basic investment strategies and risk management methods. For intermediate investors, it will provide advice considering specific investment targets and the balance between risk and return. For advanced investors, it will provide advice on the latest market trends and advanced investment techniques. This allows the advisory unit to provide advice tailored to the user's experience, enabling more appropriate investment decisions. Some or all of the above processing in the advisory unit may be performed using or without a generative AI. For example, the advisory unit can input data to assess the user's experience into a generative AI, which can then analyze the experience and adjust the level of detail in its advice.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit gathers investment information through interaction with the user. The data collection unit interacts with the user in various forms, such as text chat, voice chat, and video calls, to collect information such as the type of investment the user is considering, the amount invested, and the investment period. For example, the user might ask the generating AI questions such as, "Which investment is good?" or "How much money should I invest?" to gather investment information. Step 2: The advisory department advises on the optimal investment destinations and investment size based on the information collected by the data collection department. Based on the user's risk tolerance and investment goals, the advisory department's generating AI provides specific advice such as "Investing in this stock would be good" or "Investing in this real estate would be good." The advisory department selects the optimal investment destinations based on evaluation criteria such as the user's risk-return balance and past performance. Step 3: The simulation unit simulates investment results based on the information provided by the advice unit. The simulation unit uses historical data analysis and statistical models to predict returns. For example, it predicts future returns if the user invests in a specific stock and displays the results in graphs and charts. Step 4: The visualization unit visualizes the results generated by the simulation unit. The visualization unit visually displays the investment results in the form of bar graphs, line graphs, pie charts, etc. This allows the user to concretely understand the results of the investment and make investment decisions with greater confidence.

[0065] (Example of form 2) An investment support system according to an embodiment of the present invention is a system that uses a generating AI to enable novice investors to invest with confidence. In this investment support system, the user interacts with the generating AI and collects information related to investment. Next, the generating AI advises the user on the optimal investment destination and investment scale based on the collected information. Furthermore, the generating AI performs a simulation of the investment results and visualizes the results. This allows the user to concretely understand the investment results and make investment decisions with greater confidence. As a result, it contributes to more people building assets with ease, alleviating anxieties about retirement life, and increasing their income. Thus, the investment support system enables users to concretely understand the investment results and make investment decisions with greater confidence. For example, by making an investment according to the advice of the generating AI and checking the results in a simulation, the user can reduce anxiety about investing. Also, by visualizing the investment results, the user can concretely grasp the risks and returns of the investment. Thus, the investment support system provides an environment in which novice investors can invest with confidence, contributing to more people building assets with ease, alleviating anxieties about retirement life, and increasing their income.

[0066] The investment support system according to this embodiment comprises a data collection unit, an advice unit, a simulation unit, and a visualization unit. The data collection unit collects investment-related information through dialogue with the user. The data collection unit can interact with the user in the form of, for example, text chat, voice dialogue, or video call. The data collection unit collects information such as the type of investment the user has, the investment amount, and the investment period. For example, the data collection unit collects investment-related information when the user asks the generating AI questions such as, "Which investment is good?" or "How much should I invest?". The advice unit advises on the optimal investment destination and investment scale based on the information collected by the data collection unit. For example, the advice unit uses the generating AI to provide specific advice such as, "It would be good to invest in this stock" or "It would be good to invest in this real estate," based on the user's risk tolerance and investment goals. The advice unit selects the optimal investment destination using the user's risk-return balance and past performance as evaluation criteria. The simulation unit simulates investment results based on the information provided by the advice unit. For example, the simulation unit performs profit forecasting using historical data analysis and statistical models. The simulation unit predicts future returns if the user invests in a specific stock and displays the results in graphs and charts. The visualization unit visualizes the results generated by the simulation unit. The visualization unit visually displays the investment results in formats such as bar graphs, line graphs, and pie charts. The visualization unit generates simple and easy-to-understand graphs and charts so that the user can concretely understand the investment results. As a result, the investment support system according to this embodiment allows the user to concretely understand the investment results and make investment decisions with greater confidence. For example, by making an investment according to the advice of the generated AI and checking the results in the simulation, the user can reduce anxiety about investing. In addition, by visualizing the investment results, the user can concretely grasp the risks and returns of the investment. As a result, the investment support system provides an environment in which novice investors can invest with peace of mind, and contributes to more people building assets without hassle, alleviating anxieties about retirement life, and increasing income.

[0067] The data collection unit gathers investment-related information through interaction with users. This can be done through various methods, such as text chat, voice chat, and video calls. Specifically, in text chat, it responds in real-time to questions and information entered by the user, collecting necessary information. In voice chat, it recognizes the user's voice and analyzes questions and answers using natural language processing technology. In video calls, it also analyzes the user's facial expressions and gestures to gain a deeper understanding. The data collection unit gathers information such as the type of investment, investment amount, and investment period. For example, the user can ask the generative AI questions like, "Which investment is best?" or "How much should I invest?" to gather investment-related information. The generative AI generates appropriate prompts in response to the user's questions, accurately understanding the user's intent. Furthermore, the data collection unit also collects detailed information such as the user's past investment history, current asset status, and risk tolerance. This allows the data collection unit to grasp the overall picture of the user's investments and provide the necessary data to the advice and simulation units. The data collection unit centrally manages the collected information and stores it in a database. This allows for continuous support based on past information when users use the system again. Furthermore, the data collection unit encrypts data and controls access to protect user privacy, ensuring the secure management of information. This allows the data collection unit to build trust with users and receive information with confidence.

[0068] The advisory department advises on optimal investment targets and investment scales based on information collected by the data collection department. For example, the advisory department uses a generating AI to provide specific advice such as "investing in this stock is a good idea" or "investing in this real estate is a good idea," based on the user's risk tolerance and investment goals. The generating AI selects the optimal investment target using criteria such as the user's risk-return balance and past performance. Specifically, the generating AI quantifies the user's risk tolerance and compares high-risk and low-risk investment targets. It also determines whether the user should aim for short-term profits or long-term wealth building, depending on their investment goals. Furthermore, the generating AI analyzes past market data and economic indicators to predict future market trends. This allows the advisory department to provide users with specific and practical investment advice. The advisory department collects user feedback and continuously improves the accuracy of its advice. For example, it collects the results of users' actual investments and evaluates the effectiveness of the advice based on those results. It also flexibly adjusts the content and format of the advice to reflect user opinions and requests. This allows the advisory department to provide users with optimal investment advice and support their investment success. Furthermore, the advisory department provides educational content and reference materials to deepen users' understanding of investing. For example, it provides articles and videos explaining basic investment knowledge, market trends, and risk management methods, supporting users in making investment decisions on their own. In this way, the advisory department can improve users' investment skills and support their long-term wealth building.

[0069] The simulation unit simulates investment results based on information provided by the advisory unit. For example, the simulation unit uses historical data analysis and statistical models to predict returns. Specifically, the simulation unit predicts the future performance of a particular investment based on historical market data. For example, it predicts future returns if an investment is made in a specific stock and displays the results in graphs and charts. The simulation unit predicts future returns if a user invests in a specific stock and displays the results in graphs and charts. The simulation unit predicts future market trends using statistical models based on historical data. For example, it predicts future price fluctuations of a specific stock based on historical stock price data and displays the results in graphs. Furthermore, the simulation unit simulates multiple scenarios to identify the most likely outcome. For example, it simulates multiple scenarios considering different economic conditions and market conditions and compares the return predictions in each scenario. This allows the simulation unit to predict investment results from multiple perspectives, supporting more reliable investment decisions. In addition, the simulation unit continuously modifies the simulation results based on real-time updated market data. For example, if stock prices or economic indicators fluctuate rapidly, the simulation unit immediately incorporates the new data and updates the simulation results. This allows the simulation unit to always provide highly accurate profit forecasts based on the latest information, supporting users' investment decisions.

[0070] The visualization unit visualizes the results generated by the simulation unit. The visualization unit visually displays investment results in formats such as bar graphs, line graphs, and pie charts. Specifically, based on the simulation results, the visualization unit generates simple and easy-to-understand graphs and charts so that users can concretely understand the investment results. For example, it displays a bar graph showing projected future returns when investing in a specific stock, visually illustrating fluctuations in returns. It also displays multiple line graphs overlaid to compare projected returns for different investments. This allows users to compare the performance of different investments at a glance. Furthermore, the visualization unit generates customized graphs and charts according to the user's investment goals and risk tolerance. For example, if a user prioritizes short-term profits, it displays a graph emphasizing short-term return projections; if aiming for long-term asset building, it displays a graph showing long-term return projections. In this way, the visualization unit provides visual information tailored to the user's needs, supporting investment decisions. The visualization unit enables users to concretely understand investment results and make investment decisions with greater confidence. For example, by following the advice of the generated AI and checking the results through simulations, users can reduce their anxiety about investing. Furthermore, by visualizing investment results, users can concretely understand the risks and returns of their investments. In this way, the visualization function provides an environment where novice investors can invest with confidence, contributing to more people building assets effortlessly, alleviating anxieties about retirement, and increasing their income.

[0071] The data collection unit includes a risk collection unit that collects users' risk tolerance. The risk collection unit evaluates users' risk tolerance based, for example, on questionnaires or past investment behavior. The risk collection unit asks detailed questions to clarify how much risk users can tolerate. For example, the risk collection unit asks users questions such as, "What investments have you made in the past?" and "How much risk can you tolerate?" to evaluate the user's risk tolerance. The risk collection unit can also evaluate risk tolerance based on the user's asset situation and investment goals. For example, the risk collection unit understands the user's asset situation and evaluates their risk tolerance. As a result, the data collection unit can provide more appropriate investment advice by collecting users' risk tolerance. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input a questionnaire to evaluate the user's risk tolerance into a generation AI, and the generation AI can analyze the questionnaire results to evaluate the risk tolerance.

[0072] The advice unit includes a goal-setting unit that sets the user's investment goals. The goal-setting unit sets, for example, the user's short-term and long-term goals. The goal-setting unit asks detailed questions to clarify what kind of investment goals the user has. For example, the goal-setting unit asks the user questions such as, "What kind of return do you expect and over what period of time?" or "How do you envision your retirement plan?" and sets the user's investment goals. The goal-setting unit can also set investment goals based on the user's risk tolerance and asset situation. For example, the goal-setting unit considers the user's risk tolerance and sets realistic investment goals. This allows the advice unit to provide more specific investment advice by setting the user's investment goals. Some or all of the above processing in the advice unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the advice unit can input questions for setting the user's investment goals into a generation AI, and the generation AI can analyze the question results and set investment goals.

[0073] The simulation unit includes a forecasting unit that performs profit forecasting. The forecasting unit performs profit forecasting using, for example, historical data analysis or statistical models. The forecasting unit forecasts future profits if the user invests in a specific stock and displays the results in graphs or charts. The forecasting unit performs profit forecasting based on the type of investment and investment amount of the user. For example, the forecasting unit forecasts future profits if the user invests in a specific stock and displays the results in graphs or charts. The forecasting unit can also perform profit forecasting based on the user's investment period. For example, the forecasting unit forecasts future profits if the user invests in a specific stock and displays the results in graphs or charts. In this way, the simulation unit allows the user to concretely understand the results of their investment by performing profit forecasting. Some or all of the above processing in the simulation unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the simulation unit can input the type of investment and investment amount of the user into a generative AI, and the generative AI can perform profit forecasting.

[0074] The visualization unit includes a graph generation unit that generates graphs and charts. The graph generation unit visually displays investment results in formats such as bar graphs, line graphs, and pie charts. The graph generation unit generates simple and easy-to-understand graphs and charts so that users can concretely understand the results of their investments. The graph generation unit generates graphs and charts based on the type of investment and investment amount of the user. For example, the graph generation unit displays a graph or chart showing the projected future returns if the user invests in a particular stock. The graph generation unit can also generate graphs and charts based on the user's investment period. For example, the graph generation unit displays a graph or chart showing the projected future returns if the user invests in a particular stock. In this way, the visualization unit enables users to visually understand the results of their investments by generating graphs and charts. Some or all of the above-described processes in the visualization unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the visualization unit can input the type of investment and investment amount of the user into a generation AI, and the generation AI can generate graphs and charts.

[0075] The advisory unit advises on the optimal investment destinations based on the user's risk tolerance. For example, the advisory unit evaluates the user's risk tolerance and selects the optimal investment destinations considering the balance between risk and return. The advisory unit advises on specific investment destinations based on the user's risk tolerance. For example, the advisory unit evaluates the user's risk tolerance and provides specific advice such as "investing in this stock is a good idea" or "investing in this real estate is a good idea." The advisory unit can also advise on the optimal investment destinations based on the user's investment goals and asset situation. For example, the advisory unit considers the user's investment goals and advises on realistic investment destinations. In this way, the advisory unit enables more appropriate investment decisions by advising on the optimal investment destinations based on the user's risk tolerance. Some or all of the above processes in the advisory unit may be performed using generative AI, or they may not be performed using generative AI. For example, the advisory unit can input data to evaluate the user's risk tolerance into a generative AI, which can then analyze the risk tolerance and advise on the optimal investment destinations.

[0076] The simulation unit simulates investment results based on the user's investment scale. For example, the simulation unit makes profit forecasts based on the user's investment amount and investment period. The simulation unit makes future profit forecasts if the user invests in a specific investment target and displays the results in graphs and charts. The simulation unit makes specific profit forecasts based on the user's investment scale. For example, the simulation unit makes future profit forecasts if the user invests in a specific stock and displays the results in graphs and charts. The simulation unit can also make profit forecasts that consider the balance between risk and return based on the user's investment scale. For example, the simulation unit makes realistic profit forecasts considering the user's investment scale. This allows the simulation unit to provide a more concrete understanding of investment results by simulating investment results based on the user's investment scale. Some or all of the above processing in the simulation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the simulation unit can input the user's investment scale into a generative AI, which can then perform profit forecasts.

[0077] The data collection unit estimates the user's emotions and adjusts the method of collecting investment information based on the estimated user emotions. For example, if the user is feeling anxious, the generating AI starts with simple questions and gradually collects more detailed information. If the user is excited, the generating AI asks detailed questions all at once to quickly collect information. If the user is relaxed, the generating AI collects information naturally through dialogue. This allows the data collection unit to collect more appropriate information by adjusting the information collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generating AI. For example, the data collection unit can input data to estimate the user's emotions into a generating AI, which can then analyze the emotions and adjust the information collection method.

[0078] The data collection unit analyzes the user's past investment history and selects the optimal information collection method. For example, the data collection unit uses a generating AI to select appropriate questions based on the type and scale of investments the user has made in the past. The data collection unit prioritizes collecting information on specific investment targets from the user's past investment history. The data collection unit analyzes the user's past investment history and selects an information collection method that matches their risk tolerance. This allows the data collection unit to collect more appropriate information by analyzing the user's past investment history. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's past investment history into a generating AI, which can then analyze the investment history and select the optimal information collection method.

[0079] The data collection unit filters investment information based on the user's current financial situation and areas of interest. For example, the data collection unit uses a generating AI to filter appropriate investment information based on the user's current income and expenses. The data collection unit prioritizes collecting relevant investment information based on the user's areas of interest (e.g., technology, healthcare, etc.). The data collection unit prioritizes collecting low-risk investment information according to the user's financial situation. This allows the data collection unit to collect more appropriate information by filtering information based on the user's financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's financial situation and areas of interest into a generating AI, which can then filter appropriate investment information.

[0080] The data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the generating AI will prioritize collecting low-risk investment information. If the user is excited, the generating AI will prioritize collecting high-risk, high-return investment information. If the user is relaxed, the generating AI will prioritize collecting balanced investment information. This allows the data collection unit to collect more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using the generating AI or not. For example, the data collection unit can input data to estimate the user's emotions into the generating AI, which can then analyze the emotions and determine the priority of information.

[0081] The data collection unit prioritizes collecting highly relevant information when gathering investment information, taking into account the user's geographical location. For example, the data collection unit uses a generating AI to collect relevant investment information based on the economic conditions of the area where the user lives. The data collection unit prioritizes collecting region-specific investment opportunities based on the user's geographical location. The data collection unit collects information on regional market trends, taking into account the user's geographical location. This allows the data collection unit to collect more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then collect relevant investment information.

[0082] The data collection unit analyzes the user's social media activity and collects relevant information when gathering investment information. For example, the data collection unit collects information on investors and experts that the user follows on social media. The data collection unit analyzes the content of the user's social media posts and collects investment information of interest. The data collection unit collects trending and popular investment information from the user's social media activity. This allows the data collection unit to collect more appropriate information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI, which will analyze the activity and collect relevant information.

[0083] The advice unit estimates the user's emotions and adjusts the way it expresses the advice based on those emotions. For example, if the user is feeling anxious, the generative AI will provide advice in gentle language. If the user is excited, the generative AI will provide advice in an energetic tone. If the user is relaxed, the generative AI will provide advice in a calm tone. This allows the advice unit to provide more appropriate advice by adjusting the way it expresses the advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using the generative AI or not. For example, the advice unit can input data to estimate the user's emotions into the generative AI, which can then analyze the emotions and adjust the way it expresses the advice.

[0084] The advisory unit adjusts the level of detail in its advice based on the importance of the investment. For example, for highly important investments, the AI ​​generates detailed advice. For less important investments, the AI ​​generates concise advice. The advisory unit adjusts the content of the advice according to the importance of the investment. This allows the advisory unit to provide more appropriate advice by adjusting the level of detail based on the importance of the investment. Some or all of the above processing in the advisory unit may be performed using the AI, or not. For example, the advisory unit can input data to evaluate the importance of an investment into the AI, which then analyzes the importance and adjusts the level of detail in the advice.

[0085] The advisory unit applies different advisory algorithms depending on the investment category when providing advice. For example, in the case of stock investment, the generating AI applies an advisory algorithm specialized for stocks. In the case of real estate investment, the generating AI applies an advisory algorithm specialized for real estate. In the case of cryptocurrency investment, the generating AI applies an advisory algorithm specialized for cryptocurrencies. This allows the advisory unit to provide more appropriate advice by applying different advisory algorithms depending on the investment category. Some or all of the above processing in the advisory unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the advisory unit can input data to evaluate the investment category into the generating AI, and the generating AI can analyze the category and apply a different advisory algorithm.

[0086] The advice unit estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. For example, if the user is feeling anxious, the generative AI provides short, to-the-point advice. If the user is excited, the generative AI provides detailed advice. If the user is relaxed, the generative AI provides balanced advice. This allows the advice unit to provide more appropriate advice by adjusting the length of the advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using the generative AI or not. For example, the advice unit can input data to estimate the user's emotions into the generative AI, which can then analyze the emotions and adjust the length of the advice.

[0087] The advisory unit determines the priority of advice based on the submission timing of the investment targets. For example, the advisory unit's generating AI prioritizes providing advice to investment targets with upcoming submission deadlines. For investment targets with later submission deadlines, the generating AI postpones providing advice. The advisory unit adjusts the priority of advice based on the submission timing. This allows the advisory unit to provide more appropriate advice by determining the priority of advice based on the submission timing of the investment targets. Some or all of the above processing in the advisory unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the advisory unit can input data to evaluate the submission timing of investment targets into the generating AI, and the generating AI can analyze the submission timing to determine the priority of advice.

[0088] The advisory unit adjusts the order of advice based on the relevance of the investments when providing advice. For example, the advisory unit prioritizes providing advice on highly relevant investments using the generating AI. For less relevant investments, the advisory unit postpones providing advice using the generating AI. The advisory unit adjusts the order of advice based on the relevance of the investments using the generating AI. This allows the advisory unit to provide more appropriate advice by adjusting the order of advice based on the relevance of the investments. Some or all of the above processing in the advisory unit may be performed using the generating AI or not. For example, the advisory unit can input data to evaluate the relevance of investments into the generating AI, and the generating AI can analyze the relevance and adjust the order of advice.

[0089] The simulation unit estimates the user's emotions and adjusts the simulation criteria based on the estimated emotions. For example, if the user is feeling anxious, the generation AI will perform a low-risk simulation. If the user is excited, the generation AI will perform a high-risk simulation. If the user is relaxed, the generation AI will perform a balanced simulation. This allows the simulation unit to perform more appropriate simulations by adjusting the simulation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using or without the generation AI. For example, the simulation unit can input data for estimating the user's emotions into the generation AI, which can then analyze the emotions and adjust the simulation criteria.

[0090] The simulation unit improves the accuracy of the simulation by considering the interrelationships of the investment targets during the simulation. For example, the simulation unit uses a generating AI to perform the simulation while considering the correlation of the stock prices of the investment targets. The simulation unit uses a generating AI to perform the simulation while considering the interrelationships of the industries of the investment targets. The simulation unit uses a generating AI to perform the simulation while considering the economic conditions of the region of the investment target. In this way, the simulation unit improves the accuracy of the simulation by considering the interrelationships of the investment targets. Some or all of the above processing in the simulation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the simulation unit can input data for evaluating the interrelationships of investment targets into a generating AI, and the generating AI can analyze the interrelationships and perform the simulation.

[0091] The simulation unit performs simulations while considering the attribute information of the investment target. For example, the simulation unit uses a generating AI to perform simulations while considering the financial situation of the investment target company. The simulation unit uses a generating AI to perform simulations while considering the growth potential of the investment target company. The simulation unit uses a generating AI to perform simulations while considering the market share of the investment target company. As a result, the simulation unit can perform more accurate simulations by considering the attribute information of the investment target. Some or all of the above processing in the simulation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the simulation unit can input data for evaluating the attribute information of the investment target into the generating AI, and the generating AI can analyze the attribute information and perform simulations.

[0092] The simulation unit estimates the user's emotions and adjusts the order in which the simulation results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the generation AI will prioritize displaying low-risk results. If the user is excited, the generation AI will prioritize displaying high-risk results. If the user is relaxed, the generation AI will display balanced results. This allows the simulation unit to provide more appropriate information by adjusting the display order of simulation results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using or without the generation AI. For example, the simulation unit can input data for estimating the user's emotions into the generation AI, which can then analyze the emotions and adjust the display order of the simulation results.

[0093] The simulation unit performs simulations while considering the geographical distribution of investment targets. For example, the simulation unit uses a generating AI to perform simulations while considering the economic conditions of the investment target regions. The simulation unit uses a generating AI to perform simulations while considering the political conditions of the investment target regions. The simulation unit uses a generating AI to perform simulations while considering market trends of the investment target regions. As a result, the simulation unit can perform more accurate simulations by considering the geographical distribution of investment targets. Some or all of the above processing in the simulation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the simulation unit can input data to evaluate the geographical distribution of investment targets into a generating AI, and the generating AI can analyze the geographical distribution and perform simulations.

[0094] The simulation unit improves the accuracy of the simulation by referring to relevant literature on the investment target during the simulation. For example, the simulation unit uses the latest research papers on the investment target for the generating AI to perform the simulation. The simulation unit uses the market reports on the investment target for the generating AI to perform the simulation. The simulation unit uses industry analysis reports on the investment target for the generating AI to perform the simulation. In this way, the simulation unit improves the accuracy of the simulation by referring to relevant literature on the investment target. Some or all of the above processing in the simulation unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the simulation unit can input relevant literature on the investment target into the generating AI, and the generating AI can analyze the literature and perform the simulation.

[0095] The visualization unit estimates the user's emotions and adjusts the visualization method based on the estimated emotions. For example, if the user is feeling anxious, the generating AI provides a simple and easy-to-understand graph. If the user is excited, the generating AI provides a visualization that includes detailed data. If the user is relaxed, the generating AI provides a balanced visualization. This allows the visualization unit to provide more appropriate visualizations by adjusting the visualization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using or without a generating AI. For example, the visualization unit can input data for estimating the user's emotions into a generating AI, which can then analyze the emotions and adjust the visualization method.

[0096] The visualization unit optimizes the current visualization by referring to past visualization data during visualization. For example, the visualization unit uses a generating AI to optimize the current visualization based on visualization formats preferred by the user in the past. The visualization unit analyzes past visualization data and provides the user with the most suitable visualization format. The visualization unit provides visualizations that aid user understanding by referring to past visualization data. In this way, the visualization unit can optimize the current visualization by referring to past visualization data. Some or all of the above processes in the visualization unit may be performed using a generating AI or not. For example, the visualization unit can input past visualization data into a generating AI, and the generating AI can analyze the data to optimize the current visualization.

[0097] The visualization unit applies different visualization methods to each investment category during visualization. For example, in the case of stock investment, the generating AI visualizes the stock price trend in a graph. In the case of real estate investment, the generating AI visualizes the property value trend in a chart. In the case of cryptocurrency investment, the generating AI visualizes the price fluctuation in a graph. This allows the visualization unit to provide more appropriate visualizations by applying different visualization methods to each investment category. Some or all of the above processing in the visualization unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the visualization unit can input data for evaluating investment categories into the generating AI, and the generating AI can analyze the categories and apply different visualization methods.

[0098] The visualization unit estimates the user's emotions and adjusts the importance of the visualizations based on the estimated emotions. For example, if the user is feeling anxious, the generation AI in the visualization unit highlights and visualizes important information. If the user is excited, the generation AI visualizes detailed information. If the user is relaxed, the generation AI visualizes balanced information. This allows the visualization unit to provide more appropriate visualizations by adjusting the importance of the visualizations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using or without the generation AI. For example, the visualization unit can input data to estimate the user's emotions into the generation AI, which can then analyze the emotions and adjust the importance of the visualizations.

[0099] The visualization unit analyzes changes in the visualization based on the submission timing of the investment targets during the visualization process. For example, the visualization unit uses a generating AI to analyze changes in the visualization for investment targets with upcoming submission times. The visualization unit uses a generating AI to analyze changes in the visualization for investment targets with distant submission times. The visualization unit uses a generating AI to analyze changes in the visualization according to the submission timing. This allows the visualization unit to perform more appropriate visualizations by analyzing changes in the visualization based on the submission timing of the investment targets. Some or all of the above-described processes in the visualization unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the visualization unit can input data for evaluating the submission timing of investment targets into a generating AI, and the generating AI can analyze the submission timing and analyze changes in the visualization.

[0100] The visualization unit analyzes the visualization by referring to relevant market data of the investment target during the visualization process. For example, the visualization unit uses a generating AI to analyze the visualization based on the market data of the investment target. The visualization unit uses a generating AI to analyze the visualization based on the industry data of the investment target. The visualization unit uses a generating AI to analyze the visualization based on the regional data of the investment target. As a result, the visualization unit improves the accuracy of the visualization by referring to relevant market data of the investment target. Some or all of the above processing in the visualization unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the visualization unit can input relevant market data of the investment target into a generating AI, and the generating AI can analyze the data and perform visualization.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The data collection unit can assess the user's level of investment knowledge and adjust the method of information provision based on that assessment. For example, if the user is a novice investor, the data collection unit provides information including explanations of basic investment concepts and terminology. If the user is an intermediate investor, it provides more specific investment strategies and risk management methods. If the user is an advanced investor, it provides information on the latest market trends and advanced investment techniques. This allows the data collection unit to provide more effective investment support by providing information tailored to the user's level of knowledge. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input questions to the generative AI to assess the user's level of knowledge, and the generative AI can analyze the question results and adjust the method of information provision.

[0103] The advisory unit can analyze the user's investment style and advise on the optimal investment destinations based on that style. For example, if the user prefers short-term investments, the advisory unit will suggest investments that can aim for short-term profits. If the user prefers long-term investments, it will suggest investments that can be expected to grow steadily. If the user wants to avoid risk, it will suggest low-risk investments. In this way, the advisory unit can provide advice tailored to the user's investment style, enabling more appropriate investment decisions. Some or all of the above processing in the advisory unit may be performed using generative AI, or it may be performed without using generative AI. For example, the advisory unit can input data to evaluate the user's investment style into a generative AI, which can then analyze the style and advise on the optimal investment destinations.

[0104] The simulation unit can perform simulations while taking the user's investment history into consideration. For example, the simulation unit can predict future investment results based on the performance of investments the user has made in the past. It can reproduce investment strategies that the user has succeeded with in the past and predict returns under similar conditions. It can also perform simulations to help the user avoid investment strategies that have failed in the past. In this way, the simulation unit can perform more realistic simulations by taking the user's investment history into consideration. Some or all of the above-described processes in the simulation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the simulation unit can input the user's investment history into a generative AI, which can then analyze the history and perform simulations.

[0105] The visualization unit can adjust the format of the visualization based on the user's investment goals. For example, if the user is aiming for short-term profits, the visualization unit provides a graph that emphasizes short-term return forecasts. If the user is aiming for long-term wealth building, it provides a chart that shows long-term growth forecasts. If the user prioritizes risk management, it visualizes the balance between risk and return. This allows the visualization unit to provide more effective information by creating visualizations that match the user's investment goals. Some or all of the above processing in the visualization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the visualization unit can input data to evaluate the user's investment goals into a generative AI, which can then analyze the goals and adjust the format of the visualization.

[0106] The data collection unit can estimate the user's emotions and adjust the tone of the conversation based on the estimated emotions. For example, if the user is feeling anxious, the generating AI will engage in conversation in a gentle tone to provide reassurance. If the user is excited, the generating AI will engage in conversation in an energetic tone to maintain the user's excitement. If the user is relaxed, the generating AI will engage in conversation in a calm tone to maintain a relaxed state. This allows the data collection unit to collect more appropriate information by adjusting the tone of the conversation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using the generating AI or not. For example, the data collection unit can input data for estimating the user's emotions into the generating AI, which can then analyze the emotions and adjust the tone of the conversation.

[0107] The advice unit can estimate the user's emotions and adjust the timing of advice based on the estimated emotions. For example, if the user is feeling anxious, the generative AI will provide advice at an appropriate time so that the user can receive it calmly. If the user is excited, the generative AI will provide advice immediately, taking advantage of the user's excitement. If the user is relaxed, the generative AI will provide advice at an appropriate time to maintain a relaxed state. In this way, the advice unit can provide more effective advice by adjusting the timing of advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using the generative AI or not. For example, the advice unit can input data for estimating the user's emotions into the generative AI, and the generative AI can analyze the emotions and adjust the timing of advice.

[0108] The simulation unit can estimate the user's emotions and adjust the complexity of the simulation based on the estimated emotions. For example, if the user is feeling anxious, the generative AI will perform a simple and easy-to-understand simulation. If the user is excited, the generative AI will perform a detailed and complex simulation. If the user is relaxed, the generative AI will perform a balanced simulation. In this way, the simulation unit can perform a more appropriate simulation by adjusting the complexity of the simulation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using the generative AI or not. For example, the simulation unit can input data for estimating the user's emotions into the generative AI, and the generative AI can analyze the emotions and adjust the complexity of the simulation.

[0109] The visualization unit can estimate the user's emotions and adjust the colors of the visualization based on the estimated emotions. For example, if the user is feeling anxious, the generating AI will provide a graph with calm colors. If the user is excited, the generating AI will provide a graph with vibrant colors. If the user is relaxed, the generating AI will provide a graph with balanced colors. This allows the visualization unit to provide more appropriate visualizations by adjusting the colors of the visualization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using the generating AI or not. For example, the visualization unit can input data for estimating the user's emotions into the generating AI, and the generating AI can analyze the emotions and adjust the colors of the visualization.

[0110] The data collection unit can evaluate the user's level of interest in investment and adjust the frequency of information provision based on that evaluation. For example, if the user shows high interest, the data collection unit will provide the latest investment information frequently. If the user shows low interest, it will provide only the minimum necessary information. If the user shows moderate interest, it will provide information at a moderate frequency. In this way, the data collection unit can provide more effective investment support by providing information according to the user's level of interest. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input data to evaluate the user's level of interest into a generation AI, and the generation AI can analyze the level of interest and adjust the frequency of information provision.

[0111] The advisory unit can assess the user's investment experience and adjust the level of detail in its advice based on that assessment. For example, if the user is a novice investor, the advisory unit will provide detailed explanations of basic investment strategies and risk management methods. For intermediate investors, it will provide advice considering specific investment targets and the balance between risk and return. For advanced investors, it will provide advice on the latest market trends and advanced investment techniques. This allows the advisory unit to provide advice tailored to the user's experience, enabling more appropriate investment decisions. Some or all of the above processing in the advisory unit may be performed using or without a generative AI. For example, the advisory unit can input data to assess the user's experience into a generative AI, which can then analyze the experience and adjust the level of detail in its advice.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit gathers investment information through interaction with the user. The data collection unit interacts with the user in various forms, such as text chat, voice chat, and video calls, to collect information such as the type of investment the user is considering, the amount invested, and the investment period. For example, the user might ask the generating AI questions such as, "Which investment is good?" or "How much money should I invest?" to gather investment information. Step 2: The advisory department advises on the optimal investment destinations and investment size based on the information collected by the data collection department. Based on the user's risk tolerance and investment goals, the advisory department's generating AI provides specific advice such as "Investing in this stock would be good" or "Investing in this real estate would be good." The advisory department selects the optimal investment destinations based on evaluation criteria such as the user's risk-return balance and past performance. Step 3: The simulation unit simulates investment results based on the information provided by the advice unit. The simulation unit uses historical data analysis and statistical models to predict returns. For example, it predicts future returns if the user invests in a specific stock and displays the results in graphs and charts. Step 4: The visualization unit visualizes the results generated by the simulation unit. The visualization unit visually displays the investment results in the form of bar graphs, line graphs, pie charts, etc. This allows the user to concretely understand the results of the investment and make investment decisions with greater confidence.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the data collection unit, advice unit, simulation unit, and visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit interacts with the user using the reception device 38 of the smart device 14 and collects information about investments. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and advises on the optimal investment destination and investment scale based on the collected information. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a simulation of investment results. The visualization unit visualizes the simulation results using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the data collection unit, advice unit, simulation unit, and visualization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit interacts with the user using the microphone 238 of the smart glasses 214 and collects information about investments. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and advises on the optimal investment destination and investment scale based on the collected information. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a simulation of investment results. The visualization unit visualizes the simulation results using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the data collection unit, advice unit, simulation unit, and visualization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit interacts with the user using the microphone 238 of the headset terminal 314 and collects information about investments. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and advises on the optimal investment destination and investment scale based on the collected information. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a simulation of investment results. The visualization unit visualizes the simulation results using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the data collection unit, advice unit, simulation unit, and visualization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit interacts with the user using the microphone 238 of the robot 414 and collects information about investments. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and advises on the optimal investment destination and investment scale based on the collected information. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a simulation of the investment results. The visualization unit visualizes the simulation results using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A collection unit that collects investment-related information through interaction with users, Based on the information collected by the aforementioned collection unit, an advisory unit provides advice on the optimal investment destination and investment scale. A simulation unit that simulates investment results based on the information provided by the aforementioned advisory unit, The system includes a visualization unit that visualizes the results generated by the simulation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It includes a risk collection unit that collects users' risk tolerance levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, It includes a goal-setting unit for setting user investment targets. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned simulation unit, It includes a forecasting unit that performs revenue forecasting. The system described in Appendix 1, characterized by the features described herein. (Note 5) The visualization unit is, It includes a graph generation unit that generates graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, We advise users on the best investment options based on their risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned simulation unit, Simulate investment results based on the user's investment scale. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate user sentiment and adjust how we collect investment-related information based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past investment history and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting investment information, filtering is performed based on the user's current economic situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting investment information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting investment information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When providing advice, we adjust the level of detail based on the importance of the investment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When providing advice, different advisory algorithms are applied depending on the category of the investment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, we prioritize the advice based on the timing of the investment proposals. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, we adjust the order of advice based on the relevance of the investments. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During simulations, we improve the accuracy of the simulations by considering the interrelationships between investment targets. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, During the simulation, the attribute information of the investment target will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, It estimates the user's emotions and adjusts the order in which the simulation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, During the simulation, the geographical distribution of investment targets will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, During simulations, we improve the accuracy of the simulations by referring to relevant literature on investment targets. The system described in Appendix 1, characterized by the features described herein. (Note 26) The visualization unit is, It estimates the user's emotions and adjusts the visualization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The visualization unit is, When creating a visualization, refer to past visualization data to optimize the current visualization. The system described in Appendix 1, characterized by the features described herein. (Note 28) The visualization unit is, When visualizing, different visualization methods are applied to each investment category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The visualization unit is, It estimates the user's emotions and adjusts the importance of visualizations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The visualization unit is, When creating visualizations, analyze changes in visualizations based on the submission timing of the investment targets. The system described in Appendix 1, characterized by the features described herein. (Note 31) The visualization unit is, When creating visualizations, we analyze them by referring to relevant market data for the investment targets. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects investment-related information through interaction with users, Based on the information collected by the aforementioned collection unit, an advisory unit provides advice on the optimal investment destination and investment scale. A simulation unit that simulates investment results based on the information provided by the aforementioned advisory unit, The system includes a visualization unit that visualizes the results generated by the simulation unit. A system characterized by the following features.

2. The aforementioned collection unit is It includes a risk collection unit that collects users' risk tolerance levels. The system according to feature 1.

3. The aforementioned advice section, It includes a goal-setting unit for setting user investment targets. The system according to feature 1.

4. The aforementioned simulation unit, It includes a forecasting unit that performs revenue forecasting. The system according to feature 1.

5. The visualization unit, It includes a graph generation unit that generates graphs and charts. The system according to feature 1.

6. The aforementioned advice section, We advise users on the best investment options based on their risk tolerance. The system according to feature 1.

7. The aforementioned simulation unit, Simulate investment results based on the user's investment scale. The system according to feature 1.

8. The aforementioned collection unit is We estimate user sentiment and adjust how we collect investment-related information based on that estimated sentiment. The system according to feature 1.

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