System

Abstract

An object of a system according to an embodiment is to enable a user to easily select an optimal investment.SOLUTION: A system includes an investment simulation part, an investment analysis part, and an option presentation part. The investment simulation unit executes a plurality of investment simulations based on information such as an investment target, a risk tolerance, and an investment period of a user. An investment analysis part performs investment analysis on the basis of the simulation result executed by the investment simulation part. The option presentation unit presents a result of the investment analysis performed by the investment analysis unit as an option.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that the wide variety of information about investments makes it difficult for users to make optimal investment choices.

[0005] The system according to the embodiment aims to enable a user to easily make optimal investment choices. [Means for solving the problem]

[0006] The system according to the embodiment includes an investment simulation unit, an investment analysis unit, and an option presentation unit. The investment simulation unit executes multiple investment simulations based on information such as the user's investment goals, risk tolerance, and investment period. The investment analysis unit performs investment analysis based on the results of the simulations executed by the investment simulation unit. The option presentation unit presents the results of the investment analysis executed by the investment analysis unit as options. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to easily make optimal investment choices. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The investment analysis system according to the embodiment of the present invention is a system that uses AI to perform investment analysis and presents the results in a selection format, which allows users to use the investment analysis system as a reference when purchasing financial products.

[0029] An investment analysis system according to an embodiment includes an investment simulation unit, an investment analysis unit, and an option presentation unit. The investment simulation unit executes multiple investment simulations based on information such as a user's investment goals, risk tolerance, and investment period. For example, if a user sets a goal of "aiming to earn 1 million yen in five years," the investment simulation unit simulates an optimal investment strategy for that goal. The investment simulation unit can also execute simulations involving financial products such as stocks, bonds, investment trusts, and real estate. Furthermore, the investment simulation unit receives prompts containing instructions on what the user wants the AI ​​to do as input to the AI, and executes simulations based on the prompts. The investment analysis unit performs investment analysis based on the results of the simulations executed by the investment simulation unit. For example, the investment analysis unit evaluates the returns and risks of each simulation and derives an optimal investment strategy for the user. The investment analysis unit can also analyze past market data and economic indicators to predict future market trends. Furthermore, the investment analysis unit proposes the most suitable investment strategy for the user based on the analysis results. The option presentation unit presents the results of the investment analysis executed by the investment analysis unit as options. For example, the option presentation unit presents options such as "stable investment with low risk," "medium-risk investment aiming for high return," and "high-risk investment aiming for high return." The option presentation unit also allows the user to select the option that best suits them from these options. This allows the investment analysis system according to the embodiment to select the optimal investment strategy according to the user's investment goals and risk tolerance. For example, the user can maximize the tax-free allowance by using the New NISA, or the elderly can earn stable income by investing their hidden savings. Furthermore, based on the options provided by AI, the user can select the optimal investment strategy according to the user's investment goals and risk tolerance.

[0030] The investment simulation unit can analyze a user's past investment history and behavioral patterns and run an individually customized simulation. For example, the investment simulation unit analyzes a user's past investment history to understand tendencies toward specific investment products and markets. For example, for a user who has been successful in stock investments in the past, a simulation centered on stocks is run. The investment simulation unit also analyzes a user's behavioral patterns and identifies the user's risk tolerance and investment style. For example, for a user who is risk-averse, a simulation centered on low-risk investment products is run. The investment simulation unit also combines the user's investment history and behavioral patterns to run an individually customized simulation. For example, for a user who has previously preferred high risk and high return, a similar investment strategy is suggested. This makes it possible to provide a more appropriate investment simulation based on the user's past investment history and behavioral patterns.

[0031] The investment simulation unit can consider different economic scenarios and present investment strategies based on multiple scenarios. For example, the investment simulation unit considers an economic recession scenario and performs a simulation centered on low-risk investment products. For example, it proposes safe assets such as bonds and gold. The investment simulation unit also considers an inflation scenario and performs a simulation centered on investment products that are resistant to inflation. For example, it proposes real estate and commodities. The investment simulation unit also considers a deflation scenario and performs a simulation centered on investment products that are resistant to deflation. For example, it proposes cash and short-term government bonds. This allows the user to respond to diverse market conditions by providing investment strategies based on different economic scenarios.

[0032] The investment simulation unit can add a function to compare simulation results with other users, allowing users to refer to success stories of other users who have the same goal. The investment simulation unit, for example, compares simulation results with other users and displays success stories of users who have the same goal. For example, investment strategies of users with the same risk tolerance can be used as reference. The investment simulation unit also displays simulation results of other users in a ranking format, allowing users to refer to success stories. For example, it displays the investment strategy of the user who achieved the highest return. The investment simulation unit also adds a function to share simulation results and receive feedback from other users. For example, users with the same goal can exchange opinions with each other. This allows users to refer to success stories of other users and select a more appropriate investment strategy.

[0033] The investment simulation unit can present simulation results for different combinations of investment products, allowing the user to choose from a variety of options. For example, the investment simulation unit presents simulation results for different combinations of investment products, allowing the user to compare options. For example, it presents a combination of stocks and bonds, or a combination of stocks and real estate. The investment simulation unit also adds a function to simulate different combinations of investment products and compare returns and risks. For example, it compares a low-risk combination with a high-risk, high-return combination. The investment simulation unit also suggests an optimal combination of investment products depending on the user's investment goals. For example, it suggests highly liquid products for short-term goals and products with high growth potential for long-term goals. In this way, by presenting different combinations of investment products, the user can choose from a variety of options.

[0034] The investment analysis unit can propose a long-term investment strategy taking into account the user's life events. The investment analysis unit proposes a long-term investment strategy taking into account, for example, the user's life events (marriage, children's education, retirement, etc.). For example, it proposes an investment strategy that takes into account children's education expenses. The investment analysis unit also adjusts the investment period and risk tolerance based on the user's life events. For example, it proposes an investment strategy aimed at achieving stable income after retirement. The investment analysis unit also selects investment products according to life events and proposes an optimal investment strategy. For example, it proposes an investment strategy that takes into account purchasing a home after marriage. In this way, it is possible to provide a long-term investment strategy that takes into account the user's life events.

[0035] The investment analysis unit can analyze the investment behavior of the user's social network and propose an investment strategy that utilizes collective intelligence. For example, the investment analysis unit analyzes the investment behavior of the user's social network (friends and family) and proposes an investment strategy that utilizes collective intelligence. For example, it uses investment strategies that have been successful for friends as a reference. The investment analysis unit also analyzes the investment behavior of the social network and identifies groups with common investment goals. For example, it proposes an investment strategy for a group with the same risk tolerance. The investment analysis unit also utilizes collective intelligence to propose an optimal investment strategy for the user. For example, it proposes an investment strategy that takes into account the investment behavior of the entire family. In this way, it is possible to provide a more appropriate investment strategy by utilizing collective intelligence.

[0036] The investment analysis unit can present investment analysis results for different investment periods, allowing the user to select the period that best suits their investment goals. For example, the investment analysis unit may present investment analysis results for short-term, medium-term, and long-term investment periods, allowing the user to compare options. For example, it may present investment strategies for one year, five years, and ten years. The investment analysis unit may also add a function to compare returns and risks according to different investment periods. For example, it may compare the risks and returns of short-term investments and medium-term investments. The investment analysis unit may also suggest the optimal investment period according to the user's investment goals. For example, it may suggest highly liquid products for short-term goals and products with high growth potential for long-term goals. In this way, by presenting investment analysis results for different investment periods, the user may be able to select the optimal investment period.

[0037] The investment analysis unit can present investment analysis results with different risk profiles, allowing users to make choices according to their risk tolerance. For example, the investment analysis unit presents investment analysis results with conservative, balanced, and aggressive risk profiles, allowing users to compare options. For example, it presents low-risk, medium-risk, and high-risk investment strategies. The investment analysis unit also adds a function to compare returns and risks according to different risk profiles. For example, it compares the risk and return of a conservative investment strategy with the risk and return of an aggressive investment strategy. The investment analysis unit also suggests an optimal risk profile according to the user's risk tolerance. For example, it suggests a conservative investment strategy for risk-averse users and an aggressive investment strategy for risk-taking users. In this way, by presenting investment analysis results with different risk profiles, users can make choices according to their risk tolerance.

[0038] The option presentation unit can analyze the user's past selection history and present individually customized options. For example, the option presentation unit analyzes the user's past selection history to understand tendencies toward specific investment products and markets. For example, for a user who has previously chosen stock investment, the option presentation unit presents options centered on stocks. The option presentation unit also identifies the user's risk tolerance and investment style based on the user's selection history and presents customized options. For example, for a risk-averse user, the option presentation unit presents options centered on low-risk investment products. The option presentation unit also combines the user's selection history and behavioral patterns to present individually customized options. For example, for a user who has previously preferred high risk and high return, the option presentation unit suggests a similar investment strategy. This makes it possible to provide more appropriate options based on the user's past selection history.

[0039] The option presentation unit can take into account the user's current market conditions and economic indicators and update the optimal options in real time. The option presentation unit, for example, analyzes the current market conditions and economic indicators in real time and presents the optimal options. For example, if the stock market is doing well, options centered on stocks are presented. The option presentation unit also presents options that take into account the current market conditions in accordance with the user's investment goals and risk tolerance. For example, options centered on low-risk investment products are presented to a risk-averse user. The option presentation unit also updates the options in real time in accordance with fluctuations in economic indicators. For example, if interest rates rise, options centered on bonds are presented. This makes it possible to provide optimal options in real time based on market conditions and economic indicators.

[0040] The option presentation unit can propose combinations of different investment products, allowing the user to choose from a variety of options. The option presentation unit, for example, proposes combinations of different investment products, allowing the user to compare the options. For example, it presents a combination of stocks and bonds, or a combination of stocks and real estate. The option presentation unit also adds a function to simulate combinations of different investment products and compare returns and risks. For example, it compares a low-risk combination with a high-risk, high-return combination. The option presentation unit also proposes an optimal combination of investment products according to the user's investment goals. For example, it proposes highly liquid products for short-term goals and products with high growth potential for long-term goals. In this way, by presenting combinations of different investment products, the user can choose from a variety of options.

[0041] The option presentation unit can present different scenarios according to the user's investment goals. The option presentation unit, for example, presents different scenarios according to the user's investment goals, allowing the user to compare options. For example, it presents a worst-case scenario, a best-case scenario, and an intermediate scenario. The option presentation unit also adds a function for comparing returns and risks based on different scenarios. For example, it compares the risk and return of the worst-case scenario with the risk and return of the best scenario. The option presentation unit also proposes the optimal scenario according to the user's investment goals. For example, it proposes the worst-case scenario to a user who is risk-averse, and the best scenario to a user who can take risk. In this way, it is possible to provide the user with optimal options by presenting different scenarios according to the user's investment goals.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The investment analysis system may further include an education unit. The education unit provides educational content to deepen the user's knowledge about investing. For example, the education unit may provide video courses on basic investment concepts, risk management methods, and the characteristics of different investment products. The education unit may also create a customized learning plan according to the user's investment experience. For example, the education unit may provide basic content for beginners and advanced investment strategies for experienced investors. The education unit may also provide quizzes and tests to check what the user has learned and evaluate the user's learning progress. This allows the user to deepen their knowledge about investing and make investments with confidence.

[0044] The investment analysis system may further include a community section. The community section provides a platform for users to exchange information and opinions with each other. For example, an investment forum or chat room may be provided, allowing users to share their investment experiences and strategies. The community section may also provide expert advice and webinars, allowing users to obtain the latest investment information. Furthermore, the community section may allow users to create discussion groups on specific investment themes or markets, encouraging users with common interests to share information. This allows users to gain more knowledge and insights through interactions with other investors.

[0045] The investment analysis system may further include an environmental assessment unit. The environmental assessment unit assesses the environmental impact of target companies and projects and proposes environmentally friendly investment strategies. For example, it may analyze data on a company's environmental performance and sustainability and recommend investment in environmentally friendly companies. The environmental assessment unit may also provide guidelines and resources to help users make environmentally friendly investments. For example, it may provide information on green bonds and renewable energy investments. Furthermore, the environmental assessment unit may provide a function to visualize the social impact and benefits that users will gain from making environmentally friendly investments. This allows users to realize the social contribution they are making by making environmentally friendly investments.

[0046] The investment analysis system may further include a feedback unit. The feedback unit provides an interface for users to provide feedback on investment strategies and simulation results. For example, users can input their satisfaction with the simulation results and suggestions for improvement. The feedback unit may also collect data for improving the system based on user feedback. For example, new functions may be added or existing functions may be improved based on user opinions. Furthermore, the feedback unit may provide a function that allows users to view other users' feedback and share their opinions and sympathy. This allows users to contribute to improving the system and obtain a better investment experience.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The investment simulation unit runs multiple investment simulations based on information such as the user's investment goals, risk tolerance, and investment period. For example, if a user sets a goal of "aiming to have 1 million yen in five years," the unit simulates the optimal investment strategy for that goal. It can also run simulations that include financial products such as stocks, bonds, investment trusts, and real estate. Furthermore, as input to the AI, it receives prompts containing instructions on what the user wants the AI ​​to do, and runs simulations based on those prompts. Step 2: The investment analysis unit performs investment analysis based on the results of the simulations performed by the investment simulation unit. For example, it evaluates the returns and risks of each simulation and derives the optimal investment strategy for the user. It can also analyze past market data and economic indicators to predict future market trends. Based on the results of this analysis, it proposes the most suitable investment strategy for the user. Step 3: The option presentation unit presents the results of the investment analysis performed by the investment analysis unit as options. For example, it may present options such as "stable investment with low risk," "investment with medium risk aiming for high return," and "investment with high risk aiming for high return." This allows the user to select the investment strategy that best suits them.

[0049] (Example 2) The investment analysis system according to the embodiment of the present invention is a system that uses AI to perform investment analysis and presents the results in a selection format, which allows users to use the investment analysis system as a reference when purchasing financial products.

[0050] An investment analysis system according to an embodiment includes an investment simulation unit, an investment analysis unit, and an option presentation unit. The investment simulation unit executes multiple investment simulations based on information such as a user's investment goals, risk tolerance, and investment period. For example, if a user sets a goal of "aiming to earn 1 million yen in five years," the investment simulation unit simulates an optimal investment strategy for that goal. The investment simulation unit can also execute simulations involving financial products such as stocks, bonds, investment trusts, and real estate. Furthermore, the investment simulation unit receives prompts containing instructions on what the user wants the AI ​​to do as input to the AI, and executes simulations based on the prompts. The investment analysis unit performs investment analysis based on the results of the simulations executed by the investment simulation unit. For example, the investment analysis unit evaluates the returns and risks of each simulation and derives an optimal investment strategy for the user. The investment analysis unit can also analyze past market data and economic indicators to predict future market trends. Furthermore, the investment analysis unit proposes the most suitable investment strategy for the user based on the analysis results. The option presentation unit presents the results of the investment analysis executed by the investment analysis unit as options. For example, the option presentation unit presents options such as "stable investment with low risk," "medium-risk investment aiming for high return," and "high-risk investment aiming for high return." The option presentation unit also allows the user to select the option that best suits them from these options. This allows the investment analysis system according to the embodiment to select the optimal investment strategy according to the user's investment goals and risk tolerance. For example, the user can maximize the tax-free allowance by using the New NISA, or the elderly can earn stable income by investing their hidden savings. Furthermore, based on the options provided by AI, the user can select the optimal investment strategy according to the user's investment goals and risk tolerance.

[0051] The investment simulation unit can analyze a user's past investment history and behavioral patterns and run an individually customized simulation. For example, the investment simulation unit analyzes a user's past investment history to understand tendencies toward specific investment products and markets. For example, for a user who has been successful in stock investments in the past, a simulation centered on stocks is run. The investment simulation unit also analyzes a user's behavioral patterns and identifies the user's risk tolerance and investment style. For example, for a user who is risk-averse, a simulation centered on low-risk investment products is run. The investment simulation unit also combines the user's investment history and behavioral patterns to run an individually customized simulation. For example, for a user who has previously preferred high risk and high return, a similar investment strategy is suggested. This makes it possible to provide a more appropriate investment simulation based on the user's past investment history and behavioral patterns.

[0052] The investment simulation unit can consider different economic scenarios and present investment strategies based on multiple scenarios. For example, the investment simulation unit considers an economic recession scenario and performs a simulation centered on low-risk investment products. For example, it proposes safe assets such as bonds and gold. The investment simulation unit also considers an inflation scenario and performs a simulation centered on investment products that are resistant to inflation. For example, it proposes real estate and commodities. The investment simulation unit also considers a deflation scenario and performs a simulation centered on investment products that are resistant to deflation. For example, it proposes cash and short-term government bonds. This allows the user to respond to diverse market conditions by providing investment strategies based on different economic scenarios.

[0053] The investment simulation unit uses the emotion estimation function to perform a simulation that takes into account the user's current emotional state and proposes an investment strategy that provides emotional peace of mind. The investment simulation unit, for example, analyzes the user's emotional state in real time and proposes a low-risk investment strategy when stress is high. For example, it runs a simulation centered on bonds and fixed-term deposits. The investment simulation unit also analyzes the user's emotional state and proposes a high-risk, high-return investment strategy when positive emotions are strong. For example, it runs a simulation centered on investments in stocks and emerging markets. The investment simulation unit also takes into account the user's emotional state and proposes an investment strategy that provides emotional peace of mind. For example, it proposes a balanced investment strategy when emotions are stable. In this way, by providing an investment strategy that takes into account the user's emotional state, emotional peace of mind is realized in investments.

[0054] The investment simulation unit can add a function to compare simulation results with other users, allowing users to refer to success stories of other users who have the same goal. The investment simulation unit, for example, compares simulation results with other users and displays success stories of users who have the same goal. For example, investment strategies of users with the same risk tolerance can be used as reference. The investment simulation unit also displays simulation results of other users in a ranking format, allowing users to refer to success stories. For example, it displays the investment strategy of the user who achieved the highest return. The investment simulation unit also adds a function to share simulation results and receive feedback from other users. For example, users with the same goal can exchange opinions with each other. This allows users to refer to success stories of other users and select a more appropriate investment strategy.

[0055] The investment simulation unit can present simulation results for different combinations of investment products, allowing the user to choose from a variety of options. For example, the investment simulation unit presents simulation results for different combinations of investment products, allowing the user to compare options. For example, it presents a combination of stocks and bonds, or a combination of stocks and real estate. The investment simulation unit also adds a function to simulate different combinations of investment products and compare returns and risks. For example, it compares a low-risk combination with a high-risk, high-return combination. The investment simulation unit also suggests an optimal combination of investment products depending on the user's investment goals. For example, it suggests highly liquid products for short-term goals and products with high growth potential for long-term goals. In this way, by presenting different combinations of investment products, the user can choose from a variety of options.

[0056] The investment simulation unit can use the emotion estimation function to provide real-time feedback on the user's emotional reactions to the simulation results and re-present optimal simulation results. For example, the investment simulation unit analyzes the user's emotional reactions to the simulation results in real time, and if there are many negative reactions, re-runs the simulation. For example, it re-presents an investment strategy with reduced risk. The investment simulation unit also adjusts the simulation results based on the user's emotional reactions to obtain a positive reaction. For example, it adjusts a strategy with high returns but high risk. The investment simulation unit also presents simulation results that the user feels most comfortable with based on the emotion estimation data. For example, if emotions are stable, it proposes a balanced investment strategy. In this way, optimal simulation results can be provided by providing real-time feedback on the user's emotional reactions.

[0057] The investment analysis unit can propose a long-term investment strategy taking into account the user's life events. The investment analysis unit proposes a long-term investment strategy taking into account, for example, the user's life events (marriage, children's education, retirement, etc.). For example, it proposes an investment strategy that takes into account children's education expenses. The investment analysis unit also adjusts the investment period and risk tolerance based on the user's life events. For example, it proposes an investment strategy aimed at achieving stable income after retirement. The investment analysis unit also selects investment products according to life events and proposes an optimal investment strategy. For example, it proposes an investment strategy that takes into account purchasing a home after marriage. In this way, it is possible to provide a long-term investment strategy that takes into account the user's life events.

[0058] The investment analysis unit can analyze the investment behavior of the user's social network and propose an investment strategy that utilizes collective intelligence. For example, the investment analysis unit analyzes the investment behavior of the user's social network (friends and family) and proposes an investment strategy that utilizes collective intelligence. For example, it uses investment strategies that have been successful for friends as a reference. The investment analysis unit also analyzes the investment behavior of the social network and identifies groups with common investment goals. For example, it proposes an investment strategy for a group with the same risk tolerance. The investment analysis unit also utilizes collective intelligence to propose an optimal investment strategy for the user. For example, it proposes an investment strategy that takes into account the investment behavior of the entire family. In this way, it is possible to provide a more appropriate investment strategy by utilizing collective intelligence.

[0059] The investment analysis unit uses the emotion estimation function to perform investment analysis that takes into account the user's emotional state and can propose an investment strategy that provides emotional peace of mind. The investment analysis unit, for example, analyzes the user's emotional state in real time and proposes a low-risk investment strategy when stress levels are high. For example, it proposes an investment strategy centered on bonds and time deposits. The investment analysis unit also analyzes the user's emotional state and proposes a high-risk, high-return investment strategy when positive emotions are strong. For example, it proposes an investment strategy centered on investments in stocks and emerging markets. The investment analysis unit also considers the user's emotional state and proposes an investment strategy that provides emotional peace of mind. For example, it proposes a balanced investment strategy when emotions are stable. In this way, by providing an investment strategy that takes into account the user's emotional state, emotional peace of mind is realized.

[0060] The investment analysis unit can present investment analysis results for different investment periods, allowing the user to select the period that best suits their investment goals. For example, the investment analysis unit may present investment analysis results for short-term, medium-term, and long-term investment periods, allowing the user to compare options. For example, it may present investment strategies for one year, five years, and ten years. The investment analysis unit may also add a function to compare returns and risks according to different investment periods. For example, it may compare the risks and returns of short-term investments and medium-term investments. The investment analysis unit may also suggest the optimal investment period according to the user's investment goals. For example, it may suggest highly liquid products for short-term goals and products with high growth potential for long-term goals. In this way, by presenting investment analysis results for different investment periods, the user may be able to select the optimal investment period.

[0061] The investment analysis unit can present investment analysis results with different risk profiles, allowing users to make choices according to their risk tolerance. For example, the investment analysis unit presents investment analysis results with conservative, balanced, and aggressive risk profiles, allowing users to compare options. For example, it presents low-risk, medium-risk, and high-risk investment strategies. The investment analysis unit also adds a function to compare returns and risks according to different risk profiles. For example, it compares the risk and return of a conservative investment strategy with the risk and return of an aggressive investment strategy. The investment analysis unit also suggests an optimal risk profile according to the user's risk tolerance. For example, it suggests a conservative investment strategy for risk-averse users and an aggressive investment strategy for risk-taking users. In this way, by presenting investment analysis results with different risk profiles, users can make choices according to their risk tolerance.

[0062] The investment analysis unit can use the emotion estimation function to provide real-time feedback on the user's emotional reactions to the investment analysis results and re-present an optimal investment strategy. For example, the investment analysis unit analyzes the user's emotional reactions to the investment analysis results in real time, and re-proposes an investment strategy if there are many negative reactions. For example, it re-presents an investment strategy with reduced risk. The investment analysis unit also adjusts the investment strategy based on the user's emotional reactions to obtain a positive reaction. For example, it adjusts a strategy with high returns but high risk. The investment analysis unit also presents an investment strategy that the user feels most comfortable with based on the emotion estimation data. For example, it proposes a balanced investment strategy if emotions are stable. In this way, the optimal investment strategy can be provided by providing real-time feedback on the user's emotional reactions.

[0063] The option presentation unit can analyze the user's past selection history and present individually customized options. For example, the option presentation unit analyzes the user's past selection history to understand tendencies toward specific investment products and markets. For example, for a user who has previously chosen stock investment, the option presentation unit presents options centered on stocks. The option presentation unit also identifies the user's risk tolerance and investment style based on the user's selection history and presents customized options. For example, for a risk-averse user, the option presentation unit presents options centered on low-risk investment products. The option presentation unit also combines the user's selection history and behavioral patterns to present individually customized options. For example, for a user who has previously preferred high risk and high return, the option presentation unit suggests a similar investment strategy. This makes it possible to provide more appropriate options based on the user's past selection history.

[0064] The option presentation unit can take into account the user's current market conditions and economic indicators and update the optimal options in real time. The option presentation unit, for example, analyzes the current market conditions and economic indicators in real time and presents the optimal options. For example, if the stock market is doing well, options centered on stocks are presented. The option presentation unit also presents options that take into account the current market conditions in accordance with the user's investment goals and risk tolerance. For example, options centered on low-risk investment products are presented to a risk-averse user. The option presentation unit also updates the options in real time in accordance with fluctuations in economic indicators. For example, if interest rates rise, options centered on bonds are presented. This makes it possible to provide optimal options in real time based on market conditions and economic indicators.

[0065] The option presentation unit uses the emotion estimation function to present options that take the user's emotional state into consideration, thereby encouraging the user to make an emotionally reassuring choice. The option presentation unit, for example, analyzes the user's emotional state in real time, and presents low-risk options when stress levels are high. For example, options centered on bonds and fixed-term deposits are presented. The option presentation unit also analyzes the user's emotional state, and presents high-risk, high-return options when positive emotions are strong. For example, options centered on investments in stocks and emerging markets are presented. The option presentation unit also considers the user's emotional state and presents emotionally reassuring options. For example, balanced options are presented when emotions are stable. In this way, by providing options that take the user's emotional state into consideration, the user is encouraged to make an emotionally reassuring choice.

[0066] The option presentation unit can propose combinations of different investment products, allowing the user to choose from a variety of options. The option presentation unit, for example, proposes combinations of different investment products, allowing the user to compare the options. For example, it presents a combination of stocks and bonds, or a combination of stocks and real estate. The option presentation unit also adds a function to simulate combinations of different investment products and compare returns and risks. For example, it compares a low-risk combination with a high-risk, high-return combination. The option presentation unit also proposes an optimal combination of investment products according to the user's investment goals. For example, it proposes highly liquid products for short-term goals and products with high growth potential for long-term goals. In this way, by presenting combinations of different investment products, the user can choose from a variety of options.

[0067] The option presentation unit can present different scenarios according to the user's investment goals. The option presentation unit, for example, presents different scenarios according to the user's investment goals, allowing the user to compare options. For example, it presents a worst-case scenario, a best-case scenario, and an intermediate scenario. The option presentation unit also adds a function for comparing returns and risks based on different scenarios. For example, it compares the risk and return of the worst-case scenario with the risk and return of the best scenario. The option presentation unit also proposes the optimal scenario according to the user's investment goals. For example, it proposes the worst-case scenario to a user who is risk-averse, and the best scenario to a user who can take risk. In this way, it is possible to provide the user with optimal options by presenting different scenarios according to the user's investment goals.

[0068] The option presentation unit can use the emotion estimation function to provide feedback on the user's emotional reactions to the options in real time and re-present the optimal option. For example, the option presentation unit analyzes the user's emotional reactions to the options in real time, and re-presents the option if there are many negative reactions. For example, it re-presents an option with reduced risk. The option presentation unit also adjusts the option based on the user's emotional reaction so that a positive reaction can be obtained. For example, it adjusts an option with high return but high risk. The option presentation unit also presents the option that makes the user feel most comfortable based on the emotion estimation data. For example, it presents a balanced option if the user's emotions are stable. In this way, the optimal option can be provided by providing feedback on the user's emotional reaction in real time.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The investment analysis system may further include an education unit. The education unit provides educational content to deepen the user's knowledge about investing. For example, the education unit may provide video courses on basic investment concepts, risk management methods, and the characteristics of different investment products. The education unit may also create a customized learning plan according to the user's investment experience. For example, the education unit may provide basic content for beginners and advanced investment strategies for experienced investors. The education unit may also provide quizzes and tests to check what the user has learned and evaluate the user's learning progress. This allows the user to deepen their knowledge about investing and make investments with confidence.

[0071] The investment analysis system may further include a community section. The community section provides a platform for users to exchange information and opinions with each other. For example, an investment forum or chat room may be provided, allowing users to share their investment experiences and strategies. The community section may also provide expert advice and webinars, allowing users to obtain the latest investment information. Furthermore, the community section may allow users to create discussion groups on specific investment themes or markets, encouraging users with common interests to share information. This allows users to gain more knowledge and insights through interactions with other investors.

[0072] The investment analysis system may further include a health management unit. The health management unit monitors the user's health status and proposes investment strategies that take health into consideration. For example, it may analyze the user's stress level and sleep patterns and propose a low-risk investment strategy if the user is highly stressed. The health management unit may also provide advice to support a healthy lifestyle based on the user's health data. For example, it may recommend regular exercise and a balanced diet. Furthermore, the health management unit may provide reminders and notification functions to help the user maintain their health while engaging in investment activities. This allows the user to invest with peace of mind while maintaining their health.

[0073] The investment analysis system may further include an environmental assessment unit. The environmental assessment unit assesses the environmental impact of target companies and projects and proposes environmentally friendly investment strategies. For example, it may analyze data on a company's environmental performance and sustainability and recommend investment in environmentally friendly companies. The environmental assessment unit may also provide guidelines and resources to help users make environmentally friendly investments. For example, it may provide information on green bonds and renewable energy investments. Furthermore, the environmental assessment unit may provide a function to visualize the social impact and benefits that users will gain from making environmentally friendly investments. This allows users to realize the social contribution they are making by making environmentally friendly investments.

[0074] The investment analysis system may further include a feedback unit. The feedback unit provides an interface for users to provide feedback on investment strategies and simulation results. For example, users can input their satisfaction with the simulation results and suggestions for improvement. The feedback unit may also collect data for improving the system based on user feedback. For example, new functions may be added or existing functions may be improved based on user opinions. Furthermore, the feedback unit may provide a function that allows users to view other users' feedback and share their opinions and sympathy. This allows users to contribute to improving the system and obtain a better investment experience.

[0075] The investment analysis system can further use an emotion estimation function to provide investment education content that takes into account the user's emotional state. For example, if the user is feeling stressed, it can provide a relaxing investment education video. The emotion estimation function can also be used to provide educational content on challenging investment strategies if the user is feeling positive. Furthermore, the emotion estimation function can also be used to adjust the learning pace according to the user's emotional state. For example, if the user's emotions are stable, more information can be provided, and if the user's emotions are unstable, the amount of information can be reduced. This allows the user to deepen their investment knowledge in an optimal learning environment that suits their emotional state.

[0076] The investment analysis system can further use an emotion estimation function to provide investment simulation results that take the user's emotional state into consideration. For example, if the user is feeling anxious, low-risk investment simulation results can be presented. Also, the emotion estimation function can be used to present high-risk investment simulation results if the user is excited. Furthermore, the emotion estimation function can be used to adjust the frequency of investment simulations according to the user's emotional state. For example, if the user's emotions are stable, the simulations can be run more frequently, and if the user's emotions are unstable, the simulations can be run less frequently. This allows the user to obtain optimal investment simulation results that suit their emotional state.

[0077] The investment analysis system can further use an emotion estimation function to provide investment advice that takes into account the user's emotional state. For example, if the user is feeling stressed, low-risk investment advice can be provided. Also, using the emotion estimation function, high-risk investment advice can be provided if the user is feeling positive. Furthermore, using the emotion estimation function, the frequency of investment advice can be adjusted according to the user's emotional state. For example, advice can be provided more frequently if the user's emotions are stable, and less frequently if the user's emotions are unstable. This allows the user to obtain optimal investment advice according to their emotional state.

[0078] The investment analysis system can further use an emotion estimation function to perform investment risk management that takes into account the user's emotional state. For example, if the user is feeling anxious, low-risk investment products can be recommended. The emotion estimation function can also be used to recommend high-risk investment products if the user is feeling positive. The emotion estimation function can also be used to adjust risk management methods according to the user's emotional state. For example, if the user's emotions are stable, a risk-taking strategy can be recommended, and if the user's emotions are unstable, a risk-avoiding strategy can be recommended. This allows the user to perform optimal risk management according to their emotional state.

[0079] The investment analysis system can further use an emotion estimation function to evaluate investment performance taking into account the user's emotional state. For example, if the user is feeling stressed, low-risk investment performance can be evaluated. Also, using the emotion estimation function, if the user is feeling positive, high-risk investment performance can be evaluated. Furthermore, using the emotion estimation function, the investment performance evaluation criteria can be adjusted according to the user's emotional state. For example, if the user's emotions are stable, the performance of a risk-taking strategy can be evaluated, and if the user's emotions are unstable, the performance of a risk-averse strategy can be evaluated. This allows the user to optimally evaluate investment performance according to their emotional state.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The investment simulation unit runs multiple investment simulations based on information such as the user's investment goals, risk tolerance, and investment period. For example, if a user sets a goal of "aiming to have 1 million yen in five years," the unit simulates the optimal investment strategy for that goal. It can also run simulations that include financial products such as stocks, bonds, investment trusts, and real estate. Furthermore, as input to the AI, it receives prompts containing instructions on what the user wants the AI ​​to do, and runs simulations based on those prompts. Step 2: The investment analysis unit performs investment analysis based on the results of the simulations performed by the investment simulation unit. For example, it evaluates the returns and risks of each simulation and derives the optimal investment strategy for the user. It can also analyze past market data and economic indicators to predict future market trends. Based on the results of this analysis, it proposes the most suitable investment strategy for the user. Step 3: The option presentation unit presents the results of the investment analysis performed by the investment analysis unit as options. For example, it may present options such as "stable investment with low risk," "investment with medium risk aiming for high return," and "investment with high risk aiming for high return." This allows the user to select the investment strategy that best suits them.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an investment simulation unit that executes multiple investment simulations based on information such as the user's investment goals, risk tolerance, and investment period; an investment analysis unit that performs investment analysis based on the results of the simulation executed by the investment simulation unit; and an option presentation unit that presents the results of the investment analysis performed by the investment analysis unit as options. A system characterized by:

2. The investment simulation unit Analyze the user's past investment history and behavioral patterns and run individually customized simulations 2. The system of claim 1.

3. The investment simulation unit Consider different economic scenarios and present investment strategies based on multiple scenarios 2. The system of claim 1.

4. The investment simulation unit Conduct a simulation that takes into account the user's current emotional state and propose an investment strategy that provides emotional comfort.

2. The system of claim 1.

5. The investment simulation unit Add a function to compare the simulation results with other users, so that users can refer to the success stories of other users with the same goals.

2. The system of claim 1.

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