System

Abstract

An object of a system according to an embodiment is to enable a user to specifically grasp future asset formation and make an appropriate investment plan.SOLUTION: A system according to an embodiment includes a necessary information input unit, a generation unit, a visualization unit, and a proposal unit. The necessary information input unit inputs information such as the user's age, annual income, and cash loan yield. The creation unit creates an asset formation planning table based on the information input by the necessary information input unit. The visualizing unit visualizes future asset formation from the asset formation planning table created by the generating unit. A proposal part calculates an ideal asset amount on the basis of the information visualized by the visualization part, performs comparison analysis, and proposes a monthly or annual investment amount to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to specifically grasp future asset formation and create appropriate investment plans.

[0005] The system according to the embodiment aims to enable users to specifically grasp future asset formation and create appropriate investment plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a required information input unit, a generation unit, a visualization unit, and a proposal unit. The required information input unit inputs information such as the user's age, annual income, and current financial assets. The generation unit creates an asset formation planning table based on the information input by the required information input unit. The visualization unit visualizes future asset formation from the asset formation planning table created by the generation unit. The proposal unit calculates the ideal asset amount based on the information visualized by the visualization unit, performs a comparative analysis, and proposes monthly and annual investment amounts to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows users to specifically grasp future asset formation and create appropriate investment plans. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 asset formation support system according to an embodiment of the present invention is a system in which a user inputs information such as age, annual income, and current financial assets, and a generation AI creates an asset formation planning table, visualizes future asset formation, and proposes investment amounts. In this way, the asset formation support system supports the user's asset formation and, by visualizing future asset formation, can help the user recognize the need for financial investment.

[0029] An asset formation support system according to an embodiment includes a required information input unit, a generation unit, a visualization unit, and a proposal unit. The required information input unit inputs information such as a user's age, annual income, and current financial assets. For example, if a user is 30 years old, has an annual income of 5 million yen, and current financial assets of 1 million yen, the required information input unit inputs this information. The generation unit creates an asset formation planning table based on the information input by the required information input unit. For example, the generation AI predicts the user's financial assets at age 65 based on information such as the user's age, annual income, and current financial assets, and reflects this in the planning table. The visualization unit visualizes future asset formation from the asset formation planning table created by the generation unit. For example, the generation AI displays, "Your financial assets at age 65 will be 5 million yen," allowing the user to understand the reality of future asset formation. The proposal unit calculates the ideal asset amount based on the information visualized by the visualization unit, performs a comparative analysis, and proposes monthly and annual investment amounts to the user. For example, the generation AI may suggest, "The ideal asset amount is 20 million yen. The monthly investment amount is 50,000 yen," allowing the user to create a specific investment plan. In this way, the asset formation support system according to the embodiment supports the user's asset formation and helps the user recognize the need for financial investment by visualizing future asset formation.

[0030] The generation unit collects data on the user's past financial behavior and can predict a future asset formation plan more precisely based on that data. For example, the generation unit collects data on the user's past financial behavior, and the generation AI predicts a future asset formation plan based on that data. For example, it analyzes past investment history and consumption patterns to identify factors that will affect future asset formation. This makes it possible to provide a more precise asset formation plan based on past financial behavior data.

[0031] The generation unit generates a planning table that takes into account the user's life events, and can provide a more realistic asset formation plan. The generation unit, for example, generates a planning table that takes into account the user's life events. For example, it simulates the impact of events such as marriage and childbirth on asset formation and proposes an optimal asset formation plan. This makes it possible to provide a realistic asset formation plan that takes into account life events.

[0032] The required information input unit automatically acquires information to be entered by the user using voice input or image recognition technology, thereby eliminating the need for input. The required information input unit uses, for example, voice input technology to allow the user to enter information orally. For example, information such as age and annual income is entered by voice, and the generation AI automatically converts it into text data. This eliminates the need for input by the user using voice input or image recognition technology.

[0033] The generation unit can integrate data from different financial institutions and obtain a unified understanding of the user's overall financial situation. The generation unit, for example, automatically collects data from different financial institutions and builds a system that provides a unified understanding of the user's overall financial situation. For example, data from bank accounts, securities accounts, and credit cards can be integrated. This allows the data from different financial institutions to be integrated and the user's overall financial situation to be obtained in a unified manner.

[0034] The visualization unit visualizes the user's future asset formation using 3D graphics, allowing for a more intuitive understanding. The visualization unit, for example, constructs a system in which the generation AI visualizes the user's future asset formation using 3D graphics. For example, it displays the increase or decrease in assets in a 3D chart, allowing the user to understand intuitively. This allows the user to visualize the user's future asset formation using 3D graphics, allowing for a more intuitive understanding.

[0035] The visualization unit periodically notifies the user of the progress of their asset formation, allowing them to maintain their motivation. For example, the visualization unit constructs a system in which the generation AI periodically notifies the user of the progress of their asset formation. For example, it reports increases or decreases in assets on a monthly or quarterly basis, maintaining the user's motivation. This allows the user to periodically notify the user of the progress of their asset formation, allowing them to maintain their motivation.

[0036] The visualization unit can visualize future asset formation using AR (augmented reality) technology, allowing the user to actually experience it. The visualization unit, for example, uses AR technology to build a system that visualizes the user's future asset formation. For example, the visualization unit displays asset growth superimposed on real space via a smartphone or tablet. This allows the future asset formation to be visualized using AR technology, allowing the user to actually experience it.

[0037] The visualization unit can provide multiple asset formation plans that take into account different scenarios (economic growth, recession, etc.) and allow the user to select the optimal plan. The visualization unit, for example, builds a system in which a generation AI provides multiple asset formation plans that take into account different scenarios. For example, it compares an economic growth scenario with a recession scenario and allows the user to select the optimal plan. This makes it possible to provide multiple asset formation plans that take into account different scenarios and allow the user to select the optimal plan.

[0038] The suggestion unit can analyze the user's risk tolerance in detail and propose an investment amount based on that. For example, the suggestion unit collects past investment history and survey results in order to analyze the user's risk tolerance in detail. For example, the suggestion unit asks questions about risk tolerance and proposes an investment amount based on the answers. This allows the user's risk tolerance to be analyzed in detail and an investment amount to be proposed based on that analysis.

[0039] The suggestion unit can suggest an investment amount taking into consideration the user's past investment performance. For example, the suggestion unit collects the user's past investment performance, and the generation AI suggests an investment amount based on that data. For example, the suggestion unit analyzes past investment success rates and failure rates and calculates the optimal investment amount. This makes it possible to suggest an investment amount taking into consideration the user's past investment performance.

[0040] The suggestion unit can provide different investment scenarios (such as high risk / high return, low risk / low return, etc.) and allow the user to select. The suggestion unit, for example, builds a system in which the generation AI provides different investment scenarios. For example, it compares high risk / high return and low risk / low return scenarios and allows the user to select the optimal scenario. This makes it possible to provide different investment scenarios and allow the user to select.

[0041] The proposal unit can propose an investment amount according to the user's life stage (youth, middle age, old age). The proposal unit, for example, builds a system that proposes an investment amount according to the user's life stage. For example, it proposes risky investments for the youth and stable investments for the middle age. This makes it possible to propose an investment amount according to the user's life stage.

[0042] The generation unit can perform a detailed analysis of the user's investment history and suggest individual stocks based on that. The generation unit, for example, builds a system in which the generation AI analyzes the user's investment history in detail and suggests individual stocks based on that. For example, it analyzes past investment success and failure rates and suggests optimal stocks. This makes it possible to perform a detailed analysis of the user's investment history and suggest individual stocks based on that.

[0043] The generation unit can suggest individual stocks taking into consideration the user's interests and concerns. The generation unit, for example, builds a system that suggests individual stocks taking into consideration the user's interests and concerns. For example, for a user who is interested in a particular industry or theme, stocks in that field are suggested. This makes it possible to suggest individual stocks taking into consideration the user's interests and concerns.

[0044] The generation unit can suggest individual stocks according to different investment styles (growth stocks, dividend stocks, etc.). The generation unit, for example, builds a system that suggests individual stocks according to different investment styles. For example, it suggests stocks that are expected to grow to a user who likes growth stocks, and stocks that provide stable dividends to a user who likes dividend stocks. This makes it possible to suggest individual stocks according to different investment styles.

[0045] The generation unit can suggest individual stocks taking into consideration the user's social values ​​(ESG investment, etc.). The generation unit, for example, builds a system that suggests individual stocks taking into consideration the user's social values. For example, it suggests stocks of companies that are considerate of the environment, society, and governance (ESG). This makes it possible to suggest individual stocks taking into consideration the user's social values.

[0046] The generation unit allows the generation AI to automatically improve the planning table based on user feedback. The generation unit, for example, collects user feedback and builds a system in which the generation AI automatically improves the planning table based on that data. For example, the planning table is updated to reflect user opinions and requests. This allows the generation AI to automatically improve the planning table based on user feedback.

[0047] The generation unit can automatically update the planning table in accordance with the user's life events. For example, the generation unit builds a system in which the generation AI automatically updates the planning table in accordance with the user's life events. For example, the planning table is adjusted when an event such as marriage or childbirth occurs. This makes it possible to automatically update the planning table in accordance with the user's life events.

[0048] The generation unit can provide different pricing plans and allow the user to select one. The generation unit, for example, builds a system that provides different pricing plans and allows the user to select one. For example, multiple pricing plans such as a basic plan, a premium plan, and a VIP plan are prepared. This allows the user to select one of the different pricing plans.

[0049] The generation unit can provide an individual consultation service by an expert for paid members. The generation unit, for example, builds a system that provides an individual consultation service by an expert for paid members. For example, it provides a function that allows a user to reserve an individual consultation with a financial planner or investment advisor. This makes it possible to provide an individual consultation service by an expert for paid members.

[0050] The generation unit can analyze user behavior data in detail and propose optimal marketing measures. The generation unit, for example, analyzes user behavior data in detail and builds a system in which the generation AI proposes optimal marketing measures. For example, it analyzes past purchase history and browsing history and provides targeted advertisements. This makes it possible to analyze user behavior data in detail and propose optimal marketing measures.

[0051] The generation unit can develop marketing measures according to the user's life stage. The generation unit, for example, builds a system that develops marketing measures according to the user's life stage. For example, educational loans and first investment products are proposed to young people, and home loans and asset management products are proposed to middle-aged people. This makes it possible to develop marketing measures according to the user's life stage.

[0052] The generation unit can develop marketing measures that are tailored to the user's region and culture. The generation unit, for example, builds a system that develops marketing measures that are tailored to the user's region and culture. For example, the generation unit implements a campaign that takes into account events and cultural backgrounds in each region. This allows the development of marketing measures that are tailored to the user's region and culture.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The generation unit can also collect the user's health data and predict future medical expenses based on that data. For example, it can analyze the user's past health checkup results and medical history to estimate future medical expenses. This allows the user to include medical expenses in their asset formation plan, resulting in a more realistic asset formation plan.

[0055] The generation unit can also provide asset formation plans based on the user's hobbies and lifestyle. For example, a plan that takes travel funds into consideration can be proposed to a user who likes to travel, and a plan that includes the expenses of hobbies can be proposed to a user who spends a lot of time on hobbies. This makes it possible to provide an asset formation plan that suits the user's lifestyle.

[0056] The generation unit can also provide an asset formation plan that takes into account the user's family structure. For example, it can create a plan that includes the cost of children's education and the cost of nursing care for parents, allowing the user to form assets with an eye to the future of the entire family. This makes it possible to provide a realistic asset formation plan that takes into account the family structure.

[0057] The generation unit can also provide a function for comparing the user's asset formation plan with that of other users. For example, the user can compare the asset formation plan with that of users of the same age and income and check the progress of their plan. This allows the user to understand the progress of their asset formation by comparing with other users.

[0058] The generator can also periodically review the user's wealth formation plan to reflect the latest economic conditions and market trends. For example, it can update the plan to take into account scenarios such as economic growth and recession. This allows the user to always have a wealth formation plan based on the latest information.

[0059] The generation unit may also provide a function for comparing the user's asset formation plan with other financial products. For example, the generation unit may compare the performance of different investment trusts and stocks and provide information for selecting the optimal financial product. This allows the user to compare multiple financial products and make the optimal selection.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The required information input unit inputs information such as the user's age, annual income, current financial assets, etc. For example, if the user is 30 years old, has an annual income of 5 million yen, and current financial assets of 1 million yen, the required information input unit inputs this information. Step 2: The generation unit creates an asset formation planning table based on the information entered by the necessary information input unit. For example, the generation AI predicts the user's financial assets at age 65 based on information such as their age, annual income, and current financial assets, and reflects this in the planning table. Step 3: The visualization unit visualizes future asset formation from the asset formation planning table created by the generation unit. For example, the generation AI may display, "Your financial assets at age 65 will be 5 million yen," allowing the user to understand the reality of future asset formation. Step 4: The proposal unit calculates the ideal asset amount based on the information visualized by the visualization unit, performs a comparative analysis, and proposes monthly and annual investment amounts to the user. For example, the generation AI may suggest, "The ideal asset amount is 20 million yen. The monthly investment amount is 50,000 yen," allowing the user to create a specific investment plan.

[0062] (Example 2) The asset formation support system according to an embodiment of the present invention is a system in which a user inputs information such as age, annual income, and current financial assets, and a generation AI creates an asset formation planning table, visualizes future asset formation, and proposes investment amounts. In this way, the asset formation support system supports the user's asset formation and, by visualizing future asset formation, can help the user recognize the need for financial investment.

[0063] An asset formation support system according to an embodiment includes a required information input unit, a generation unit, a visualization unit, and a proposal unit. The required information input unit inputs information such as a user's age, annual income, and current financial assets. For example, if a user is 30 years old, has an annual income of 5 million yen, and current financial assets of 1 million yen, the required information input unit inputs this information. The generation unit creates an asset formation planning table based on the information input by the required information input unit. For example, the generation AI predicts the user's financial assets at age 65 based on information such as the user's age, annual income, and current financial assets, and reflects this in the planning table. The visualization unit visualizes future asset formation from the asset formation planning table created by the generation unit. For example, the generation AI displays, "Your financial assets at age 65 will be 5 million yen," allowing the user to understand the reality of future asset formation. The proposal unit calculates the ideal asset amount based on the information visualized by the visualization unit, performs a comparative analysis, and proposes monthly and annual investment amounts to the user. For example, the generation AI may suggest, "The ideal asset amount is 20 million yen. The monthly investment amount is 50,000 yen," allowing the user to create a specific investment plan. In this way, the asset formation support system according to the embodiment supports the user's asset formation and helps the user recognize the need for financial investment by visualizing future asset formation.

[0064] The generation unit collects data on the user's past financial behavior and can predict a future asset formation plan more precisely based on that data. For example, the generation unit collects data on the user's past financial behavior, and the generation AI predicts a future asset formation plan based on that data. For example, it analyzes past investment history and consumption patterns to identify factors that will affect future asset formation. This makes it possible to provide a more precise asset formation plan based on past financial behavior data.

[0065] The generation unit generates a planning table that takes into account the user's life events, and can provide a more realistic asset formation plan. The generation unit, for example, generates a planning table that takes into account the user's life events. For example, it simulates the impact of events such as marriage and childbirth on asset formation and proposes an optimal asset formation plan. This makes it possible to provide a realistic asset formation plan that takes into account life events.

[0066] The generation unit can use the emotion estimation function to analyze the emotion of the user when inputting information and provide an interface for reducing stress. The generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when inputting information in real time. For example, the generation unit can analyze the user's facial expression and voice, and display a message to help the user relax if the user is feeling stressed. This makes it possible to analyze the user's emotion and provide an interface for reducing stress.

[0067] The required information input unit automatically acquires information to be entered by the user using voice input or image recognition technology, thereby eliminating the need for input. The required information input unit uses, for example, voice input technology to allow the user to enter information orally. For example, information such as age and annual income is entered by voice, and the generation AI automatically converts it into text data. This eliminates the need for input by the user using voice input or image recognition technology.

[0068] The generation unit can integrate data from different financial institutions and obtain a unified understanding of the user's overall financial situation. The generation unit, for example, automatically collects data from different financial institutions and builds a system that provides a unified understanding of the user's overall financial situation. For example, data from bank accounts, securities accounts, and credit cards can be integrated. This allows the data from different financial institutions to be integrated and the user's overall financial situation to be obtained in a unified manner.

[0069] The generation unit uses the emotion estimation function to provide real-time feedback on the emotions a user feels when entering text, and can make suggestions to elicit positive emotions. The generation unit, for example, uses the emotion estimation function to build a system that provides real-time feedback on the emotions a user feels when entering text. For example, if the user is feeling stressed, a message to help the user relax is displayed. This allows the generation unit to provide real-time feedback on the user's emotions and make suggestions to elicit positive emotions.

[0070] The visualization unit visualizes the user's future asset formation using 3D graphics, allowing for a more intuitive understanding. The visualization unit, for example, constructs a system in which the generation AI visualizes the user's future asset formation using 3D graphics. For example, it displays the increase or decrease in assets in a 3D chart, allowing the user to understand intuitively. This allows the user to visualize the user's future asset formation using 3D graphics, allowing for a more intuitive understanding.

[0071] The visualization unit periodically notifies the user of the progress of their asset formation, allowing them to maintain their motivation. For example, the visualization unit constructs a system in which the generation AI periodically notifies the user of the progress of their asset formation. For example, it reports increases or decreases in assets on a monthly or quarterly basis, maintaining the user's motivation. This allows the user to periodically notify the user of the progress of their asset formation, allowing them to maintain their motivation.

[0072] The visualization unit can use the emotion estimation function to analyze the emotions the user feels when viewing the future asset formation and provide feedback to elicit positive emotions. The visualization unit, for example, uses the emotion estimation function to analyze the emotions the user feels when viewing the future asset formation in real time. For example, it analyzes the user's facial expressions and voice and displays a message to elicit positive emotions. This makes it possible to analyze the emotions the user feels when viewing the future asset formation and provide feedback to elicit positive emotions.

[0073] The visualization unit can visualize future asset formation using AR (augmented reality) technology, allowing the user to actually experience it. The visualization unit, for example, uses AR technology to build a system that visualizes the user's future asset formation. For example, the visualization unit displays asset growth superimposed on real space via a smartphone or tablet. This allows the future asset formation to be visualized using AR technology, allowing the user to actually experience it.

[0074] The visualization unit can provide multiple asset formation plans that take into account different scenarios (economic growth, recession, etc.) and allow the user to select the optimal plan. The visualization unit, for example, builds a system in which a generation AI provides multiple asset formation plans that take into account different scenarios. For example, it compares an economic growth scenario with a recession scenario and allows the user to select the optimal plan. This makes it possible to provide multiple asset formation plans that take into account different scenarios and allow the user to select the optimal plan.

[0075] The visualization unit uses the emotion estimation function to monitor in real time the emotions of the user when viewing future asset formation, and can provide optimal feedback. The visualization unit, for example, uses the emotion estimation function to build a system that monitors in real time the emotions of the user when viewing future asset formation. For example, the visualization unit analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to monitor in real time the emotions of the user when viewing future asset formation, and provide optimal feedback.

[0076] The suggestion unit can analyze the user's risk tolerance in detail and propose an investment amount based on that. For example, the suggestion unit collects past investment history and survey results in order to analyze the user's risk tolerance in detail. For example, the suggestion unit asks questions about risk tolerance and proposes an investment amount based on the answers. This allows the user's risk tolerance to be analyzed in detail and an investment amount to be proposed based on that analysis.

[0077] The suggestion unit can suggest an investment amount taking into consideration the user's past investment performance. For example, the suggestion unit collects the user's past investment performance, and the generation AI suggests an investment amount based on that data. For example, the suggestion unit analyzes past investment success rates and failure rates and calculates the optimal investment amount. This makes it possible to suggest an investment amount taking into consideration the user's past investment performance.

[0078] The suggestion unit can provide different investment scenarios (such as high risk / high return, low risk / low return, etc.) and allow the user to select. The suggestion unit, for example, builds a system in which the generation AI provides different investment scenarios. For example, it compares high risk / high return and low risk / low return scenarios and allows the user to select the optimal scenario. This makes it possible to provide different investment scenarios and allow the user to select.

[0079] The proposal unit can propose an investment amount according to the user's life stage (youth, middle age, old age). The proposal unit, for example, builds a system that proposes an investment amount according to the user's life stage. For example, it proposes risky investments for the youth and stable investments for the middle age. This makes it possible to propose an investment amount according to the user's life stage.

[0080] The suggestion unit can use the emotion estimation function to monitor the user's emotions regarding the proposed investment amount in real time and continuously suggest the optimal investment amount. The suggestion unit, for example, uses the emotion estimation function to build a system that monitors the user's emotions regarding the proposed investment amount in real time. For example, the suggestion unit analyzes the user's facial expressions and voice and calculates an emotion score. This allows the user's emotions regarding the proposed investment amount to be monitored in real time and continuously suggest the optimal investment amount.

[0081] The generation unit can perform a detailed analysis of the user's investment history and suggest individual stocks based on that. The generation unit, for example, builds a system in which the generation AI analyzes the user's investment history in detail and suggests individual stocks based on that. For example, it analyzes past investment success and failure rates and suggests optimal stocks. This makes it possible to perform a detailed analysis of the user's investment history and suggest individual stocks based on that.

[0082] The generation unit can suggest individual stocks taking into consideration the user's interests and concerns. The generation unit, for example, builds a system that suggests individual stocks taking into consideration the user's interests and concerns. For example, for a user who is interested in a particular industry or theme, stocks in that field are suggested. This makes it possible to suggest individual stocks taking into consideration the user's interests and concerns.

[0083] The generation unit can use the emotion estimation function to analyze the user's emotions regarding the suggested stocks and re-suggest the most suitable stocks. The generation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions regarding the suggested stocks in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. This allows the generation unit to analyze the user's emotions regarding the suggested stocks and re-suggest the most suitable stocks.

[0084] The generation unit can suggest individual stocks according to different investment styles (growth stocks, dividend stocks, etc.). The generation unit, for example, builds a system that suggests individual stocks according to different investment styles. For example, it suggests stocks that are expected to grow to a user who likes growth stocks, and stocks that provide stable dividends to a user who likes dividend stocks. This makes it possible to suggest individual stocks according to different investment styles.

[0085] The generation unit can suggest individual stocks taking into consideration the user's social values ​​(ESG investment, etc.). The generation unit, for example, builds a system that suggests individual stocks taking into consideration the user's social values. For example, it suggests stocks of companies that are considerate of the environment, society, and governance (ESG). This makes it possible to suggest individual stocks taking into consideration the user's social values.

[0086] The generation unit uses the emotion estimation function to monitor the user's emotions regarding the suggested stocks in real time and continuously suggest optimal stocks. The generation unit, for example, uses the emotion estimation function to build a system that monitors the user's emotions regarding the suggested stocks in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. This allows the generation unit to monitor the user's emotions regarding the suggested stocks in real time and continuously suggest optimal stocks.

[0087] The generation unit allows the generation AI to automatically improve the planning table based on user feedback. The generation unit, for example, collects user feedback and builds a system in which the generation AI automatically improves the planning table based on that data. For example, the planning table is updated to reflect user opinions and requests. This allows the generation AI to automatically improve the planning table based on user feedback.

[0088] The generation unit can automatically update the planning table in accordance with the user's life events. For example, the generation unit builds a system in which the generation AI automatically updates the planning table in accordance with the user's life events. For example, the planning table is adjusted when an event such as marriage or childbirth occurs. This makes it possible to automatically update the planning table in accordance with the user's life events.

[0089] The generation unit uses the emotion estimation function to analyze the user's emotions regarding updating the planning table and can propose the optimal timing for updating. The generation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions regarding updating the planning table in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to analyze the user's emotions regarding updating the planning table and propose the optimal timing for updating.

[0090] The generation unit can provide different pricing plans and allow the user to select one. The generation unit, for example, builds a system that provides different pricing plans and allows the user to select one. For example, multiple pricing plans such as a basic plan, a premium plan, and a VIP plan are prepared. This allows the user to select one of the different pricing plans.

[0091] The generation unit can provide an individual consultation service by an expert for paid members. The generation unit, for example, builds a system that provides an individual consultation service by an expert for paid members. For example, it provides a function that allows a user to reserve an individual consultation with a financial planner or investment advisor. This makes it possible to provide an individual consultation service by an expert for paid members.

[0092] The generation unit uses the emotion estimation function to monitor users' emotions toward the paid membership system in real time, and can provide optimal services. The generation unit, for example, uses the emotion estimation function to build a system that monitors users' emotions toward the paid membership system in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. This allows users' emotions toward the paid membership system to be monitored in real time, and optimal services to be provided.

[0093] The generation unit can analyze user behavior data in detail and propose optimal marketing measures. The generation unit, for example, analyzes user behavior data in detail and builds a system in which the generation AI proposes optimal marketing measures. For example, it analyzes past purchase history and browsing history and provides targeted advertisements. This makes it possible to analyze user behavior data in detail and propose optimal marketing measures.

[0094] The generation unit can develop marketing measures according to the user's life stage. The generation unit, for example, builds a system that develops marketing measures according to the user's life stage. For example, educational loans and first investment products are proposed to young people, and home loans and asset management products are proposed to middle-aged people. This makes it possible to develop marketing measures according to the user's life stage.

[0095] The generation unit uses the emotion estimation function to analyze the user's emotions regarding marketing measures and re-propose optimal measures. The generation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions regarding marketing measures in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. This allows the user's emotions regarding marketing measures to be analyzed and optimal measures to be re-proposed.

[0096] The generation unit can develop marketing measures that are tailored to the user's region and culture. The generation unit, for example, builds a system that develops marketing measures that are tailored to the user's region and culture. For example, the generation unit implements a campaign that takes into account events and cultural backgrounds in each region. This allows the development of marketing measures that are tailored to the user's region and culture.

[0097] The generation unit uses the emotion estimation function to monitor users' emotions regarding marketing measures in real time and continuously propose optimal measures. The generation unit, for example, uses the emotion estimation function to build a system that monitors users' emotions regarding marketing measures in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. This allows users' emotions regarding marketing measures to be monitored in real time and continuously propose optimal measures.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The generation unit can also collect the user's health data and predict future medical expenses based on that data. For example, it can analyze the user's past health checkup results and medical history to estimate future medical expenses. This allows the user to include medical expenses in their asset formation plan, resulting in a more realistic asset formation plan.

[0100] The generation unit can also provide asset formation plans based on the user's hobbies and lifestyle. For example, a plan that takes travel funds into consideration can be proposed to a user who likes to travel, and a plan that includes the expenses of hobbies can be proposed to a user who spends a lot of time on hobbies. This makes it possible to provide an asset formation plan that suits the user's lifestyle.

[0101] The generator can also estimate the user's emotions and provide relaxation music and meditation guides to reduce stress. For example, if the user is feeling stressed, it can play relaxing music and display meditation guides. This allows the user's emotions to be analyzed and support provided to reduce stress.

[0102] The generation unit can also provide an asset formation plan that takes into account the user's family structure. For example, it can create a plan that includes the cost of children's education and the cost of nursing care for parents, allowing the user to form assets with an eye to the future of the entire family. This makes it possible to provide a realistic asset formation plan that takes into account the family structure.

[0103] The generation unit can also estimate the user's emotions and provide feedback to elicit positive emotions. For example, if the user feels anxious after looking at the asset formation plan, the generation unit can display an encouraging message and provide advice to elicit positive emotions. This makes it possible to analyze the user's emotions and provide support to elicit positive emotions.

[0104] The generation unit can also provide a function for comparing the user's asset formation plan with that of other users. For example, the user can compare the asset formation plan with that of users of the same age and income and check the progress of their plan. This allows the user to understand the progress of their asset formation by comparing with other users.

[0105] The generation unit can also estimate the user's emotions and provide feedback to maintain motivation according to the progress of the asset formation plan. For example, if the user is approaching a goal, a message that gives a sense of accomplishment and advice on how to maintain motivation can be displayed. This makes it possible to analyze the user's emotions and provide support to maintain motivation.

[0106] The generator can also periodically review the user's wealth formation plan to reflect the latest economic conditions and market trends. For example, it can update the plan to take into account scenarios such as economic growth and recession. This allows the user to always have a wealth formation plan based on the latest information.

[0107] The generation unit can also estimate the user's emotions and provide feedback to reduce anxiety about changing the asset formation plan. For example, if the user feels anxious about changing the plan, the generation unit explains the benefits of the change and provides advice to reduce anxiety. This makes it possible to analyze the user's emotions and provide support to reduce anxiety about changing the plan.

[0108] The generation unit may also provide a function for comparing the user's asset formation plan with other financial products. For example, the generation unit may compare the performance of different investment trusts and stocks and provide information for selecting the optimal financial product. This allows the user to compare multiple financial products and make the optimal selection.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The required information input unit inputs information such as the user's age, annual income, current financial assets, etc. For example, if the user is 30 years old, has an annual income of 5 million yen, and current financial assets of 1 million yen, the required information input unit inputs this information. Step 2: The generation unit creates an asset formation planning table based on the information entered by the necessary information input unit. For example, the generation AI predicts the user's financial assets at age 65 based on information such as their age, annual income, and current financial assets, and reflects this in the planning table. Step 3: The visualization unit visualizes future asset formation from the asset formation planning table created by the generation unit. For example, the generation AI may display, "Your financial assets at age 65 will be 5 million yen," allowing the user to understand the reality of future asset formation. Step 4: The proposal unit calculates the ideal asset amount based on the information visualized by the visualization unit, performs a comparative analysis, and proposes monthly and annual investment amounts to the user. For example, the generation AI may suggest, "The ideal asset amount is 20 million yen. The monthly investment amount is 50,000 yen," allowing the user to create a specific investment plan.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

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

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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.

[0138] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a necessary information input section for inputting information such as the user's age, annual income, and current financial assets; a generation unit that generates an asset formation planning table based on the information input by the necessary information input unit; a visualization unit that visualizes future asset formation from the asset formation planning table created by the generation unit; a proposal unit that calculates an ideal asset amount based on the information visualized by the visualization unit, performs comparative analysis, and proposes monthly and annual investment amounts to the user. A system characterized by:

2. The necessary information input unit The information entered by the user is automatically acquired using voice input and image recognition technology, eliminating the need for input.

2. The system of claim 1.

3. The visualization unit Visualize the user's future asset formation in 3D graphics to help them understand it more intuitively.

2. The system of claim 1.

4. The proposal unit Analyze the user's risk tolerance in detail and propose an investment amount based on that.

2. The system of claim 1.

5. The generation unit Analyze the user's investment history in detail and suggest individual stocks based on that.

2. The system of claim 1.

6. The generation unit Using an emotion estimation function, the emotion expressed by the user when inputting is analyzed, and an interface for reducing stress is provided.

2. The system of claim 1.

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