System

Abstract

An object of a system according to an embodiment is to provide information on optimal investment and tax countermeasures according to the situation of each individual.SOLUTION: A system includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects information such as the annual income, occupation, residence, and family structure of the user. The analysis unit analyzes the information collected by the information collection unit. The provision unit provides information related to money according to the status and an optimal tax countermeasure based on the information analyzed by the analysis unit.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 technology has had the problem of making it difficult to efficiently provide optimal investment and tax strategy information tailored to each individual's circumstances.

[0005] The system according to the embodiment aims to provide information on optimal investments and tax strategies suited to individual circumstances. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects information such as a user's annual income, occupation, place of residence, and family structure. The analysis unit analyzes the information collected by the information collection unit. The provision unit provides financial information and optimal tax strategies tailored to the user's status based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information on optimal investments and tax strategies according to the individual circumstances. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 investment, subsidy and asset formation system according to an embodiment of the present invention is a system that collects information such as a user's annual income, occupation, place of residence, and family structure, analyzes this information using a generation AI, and provides financial information and optimal tax strategies tailored to the user's status. This allows the investment, subsidy and asset formation system to provide optimal information tailored to the user's individual circumstances.

[0029] An investment, subsidy, and asset formation system according to an embodiment includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects information such as a user's annual income, occupation, place of residence, and family structure. For example, the user enters information into an input form. The information collection unit can also collect the user's past financial transaction history. For example, it collects bank account and credit card transaction history. The analysis unit analyzes the information collected by the information collection unit. For example, a generation AI analyzes information such as annual income, occupation, place of residence, and family structure to evaluate the user's status. The analysis unit can also analyze the user's past financial transaction history and predict future income and expenses. For example, it analyzes past income and expenditure patterns to predict future income and expenses. The provision unit provides financial information and optimal tax strategies tailored to the user's status based on the information analyzed by the analysis unit. For example, it suggests high-risk, high-return investment methods to users with high annual incomes and low-risk asset formation methods to users with low annual incomes. The providing unit also provides subsidy information according to the user's place of residence and tax strategies according to the user's occupation. For example, it provides region-specific subsidy information to a user living in a specific area, and provides occupation-specific subsidy information to a user engaged in a specific occupation. This allows the investment, subsidy, and asset formation system according to the embodiment to provide optimal information according to the user's individual circumstances. For example, the user can obtain money-related information and tax strategies that are optimal for their situation.

[0030] The analysis unit can suggest high-risk, high-return investment methods to users with high annual incomes, and low-risk asset formation methods to users with low annual incomes. For example, the analysis unit suggests stock investment or real estate investment to users with high annual incomes. For example, stock investment is a high-risk, high-return investment method and is suggested according to the user's risk tolerance. The analysis unit also suggests bonds or fixed-term deposits to users with low annual incomes. For example, bonds are a low-risk asset formation method and are suggested according to the user's risk aversion. This makes it possible to provide the optimal investment method according to the user's annual income.

[0031] The analysis unit can suggest the application of dependent deductions or medical expense deductions when the family structure is complex. For example, when the family structure is complex, the analysis unit suggests the application of dependent deductions. For example, dependent deductions are applied according to the number and ages of dependents. The analysis unit also suggests the application of medical expense deductions. For example, medical expense deductions are applied according to the total amount of medical expenses and the types of medical expenses eligible for deduction. This makes it possible to provide optimal tax strategies according to family structure.

[0032] The analysis unit can suggest stock investments or real estate investments to users who can take risks, and bonds or fixed-term deposits to users who want to avoid risk. For example, the analysis unit suggests stock investments to users who can take risks. For example, stock investments are a high-risk, high-return investment method and are suggested according to the user's risk tolerance. The analysis unit also suggests real estate investments. For example, real estate investments are a long-term asset formation method and are suggested according to the user's risk tolerance. The analysis unit also suggests bonds to users who want to avoid risk. For example, bonds are a low-risk asset formation method and are suggested according to the user's risk aversion. The analysis unit also suggests fixed-term deposits. For example, fixed-term deposits are highly secure and suitable for users who want to avoid risk. This makes it possible to provide the optimal investment method according to the user's risk tolerance.

[0033] The analysis unit can provide region-specific subsidy information to users who live in a specific region, and occupation-specific subsidy information to users who work in a specific occupation. The analysis unit, for example, provides region-specific subsidy information to users who live in a specific region. Examples include subsidies from local governments and regional grants. The analysis unit also provides occupation-specific subsidy information to users who work in a specific occupation. Examples include subsidies for specific industries and vocational training subsidies. This makes it possible to provide optimal subsidy information according to the user's place of residence and occupation.

[0034] The analysis unit can analyze the user's past financial transaction history and predict future income and expenditure. The analysis unit, for example, collects the user's bank account and credit card transaction history and analyzes past income and expenditure patterns. For example, it grasps monthly income and expenditure trends and predicts future income and expenditure. The analysis unit also predicts future income and expenditure based on past transaction records. For example, it predicts future income and expenditure trends using past income and expenditure data. This makes it possible to predict future income and expenditure based on the user's past financial transaction history.

[0035] The analysis unit can predict the user's life events and collect information based on them. The analysis unit predicts future life events based on information such as the user's age, occupation, and family structure. For example, it predicts the timing of marriage and childbirth and provides asset formation advice based on that. The analysis unit also collects information based on the user's life events. For example, it collects information corresponding to life events such as marriage, childbirth, and job change, and provides it to the user. This makes it possible to collect information based on the user's life events.

[0036] The analysis unit collects information about the user's health condition and lifestyle habits, and can make asset formation suggestions based on health risks. The analysis unit, for example, collects the user's health checkup results and medical records and analyzes health risks. For example, if the health risk is high, the analysis unit suggests asset formation methods to prepare for medical expenses. The analysis unit also collects information about the user's lifestyle habits and analyzes health risks. For example, it collects information such as dietary habits and exercise habits and evaluates health risks. This makes it possible to make optimal asset formation suggestions based on the user's health condition and lifestyle habits.

[0037] The analysis unit can collect information to suggest investments based on the user's hobbies and interests. The analysis unit, for example, collects information about the user's hobbies and interests and suggests investments based on that information. For example, a user who likes sports is suggested to invest in stocks of sports-related companies. The analysis unit also suggests investments based on the user's interests. For example, a user who is interested in technology is suggested to invest in stocks of technology-related companies. This makes it possible to suggest optimal investments based on the user's hobbies and interests.

[0038] The analysis unit can analyze the user's past investment performance and provide an optimal investment strategy. The analysis unit, for example, analyzes the user's past investment history and evaluates investment performance. For example, it proposes an optimal investment strategy based on past stock and bond trading data. The analysis unit also provides an optimal investment strategy based on the user's investment performance. For example, it evaluates past investment performance and proposes methods of risk management and diversification investment. This makes it possible to provide an optimal investment strategy based on the user's past investment performance.

[0039] The analysis unit can analyze the user's consumption patterns and provide advice for reducing wasteful spending. For example, the analysis unit can analyze the user's past consumption data and identify wasteful spending. For example, the analysis unit can suggest a periodic review of subscription services. The analysis unit can also provide advice for reducing wasteful spending based on the user's consumption patterns. For example, the analysis unit can suggest methods for reducing unnecessary purchases and excessive spending. This makes it possible to provide advice for reducing wasteful spending based on the user's consumption patterns.

[0040] The analysis unit can analyze the user's social network and provide reliable investment information. The analysis unit can, for example, analyze the user's social media and contact data to provide reliable investment information. For example, the analysis unit can provide investment advice based on information from reliable investors and experts. The analysis unit can also provide reliable investment information based on the user's social network. For example, the analysis unit can analyze the user's friendships and work network to provide reliable investment information. This makes it possible to provide reliable investment information based on the user's social network.

[0041] The analysis unit can analyze the user's past tax return data and propose optimal tax saving methods. The analysis unit, for example, collects the user's past tax return data and proposes methods with high tax saving effects. For example, the analysis unit proposes optimal tax saving methods based on past data on medical expense deductions and dependent deductions. The analysis unit also proposes optimal tax saving methods based on the user's tax return data. For example, the analysis unit analyzes past tax return content and proposes tax deductions and tax preferential treatment measures. This makes it possible to propose optimal tax saving methods based on the user's past tax return data.

[0042] The analysis unit can provide long-term tax strategies based on the user's future income predictions. The analysis unit, for example, proposes long-term tax strategies based on the user's future income predictions. For example, it proposes tax-saving strategies that should be started now if future income is expected to increase. The analysis unit also provides long-term tax strategies based on the user's income predictions. For example, it proposes a long-term tax-saving plan to reduce future tax burdens. This makes it possible to provide long-term tax strategies based on the user's future income predictions.

[0043] The analysis unit can propose optimal tax strategies based on the user's asset composition. The analysis unit, for example, analyzes the user's asset composition and proposes optimal tax strategies. For example, for a user who owns a lot of real estate, it proposes tax-saving methods related to real estate. The analysis unit also proposes optimal tax strategies based on the user's asset composition. For example, it proposes tax strategies according to the type and proportion of assets. This makes it possible to propose optimal tax strategies based on the user's asset composition.

[0044] The analysis unit can provide tax strategies according to the user's life stage. For example, the analysis unit proposes tax strategies according to the user's life stage (marriage, childbirth, job change, etc.). For example, it proposes tax-saving methods related to marriage to a user who has just gotten married. The analysis unit also provides tax strategies based on the user's life stage. For example, it proposes tax strategies according to life events such as childbirth or job change. This makes it possible to provide tax strategies according to the user's life stage.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The analysis unit can analyze the user's health condition and make asset formation suggestions based on health risks. For example, it collects the user's health checkup results and medical records and evaluates the health risk. If the health risk is high, it suggests asset formation methods to prepare for medical expenses, and if the health risk is low, it suggests riskier investment methods. The analysis unit can also collect information about the user's lifestyle habits and analyze health risks. For example, it collects information on eating habits and exercise habits and evaluates health risks. This makes it possible to make optimal asset formation suggestions based on the user's health condition and lifestyle habits.

[0047] The analysis unit can suggest investment options based on the user's hobbies and interests. For example, it can collect information about the user's hobbies and interests and suggest investment options based on that information. For a user who likes sports, it can suggest investments in stocks of sports-related companies, and for a user who is interested in technology, it can suggest investments in stocks of technology-related companies. The analysis unit can also evaluate the user's investment risk tolerance based on their hobbies and interests. For example, it can suggest high-risk, high-return investment options to an adventurous user, and low-risk investment options to a cautious user. This makes it possible to suggest optimal investment options based on the user's hobbies and interests.

[0048] The analysis unit can predict the user's life events and collect information based on them. For example, future life events can be predicted based on information such as the user's age, occupation, and family structure. The analysis unit can predict the timing of marriage and childbirth and provide asset formation advice based on that. The analysis unit can also collect information based on the user's life events. Information corresponding to life events such as marriage, childbirth, and job changes is collected and provided to the user. This makes it possible to collect information based on the user's life events.

[0049] The analysis unit can analyze the user's consumption patterns and provide advice for reducing wasteful spending. For example, the analysis unit can analyze the user's past consumption data and identify wasteful spending. The analysis unit can suggest a periodic review of subscription services and suggest methods for reducing unnecessary purchases and excessive spending. The analysis unit can also provide specific advice for saving money based on the user's consumption patterns. For example, the analysis unit can suggest methods for reducing food and utility expenses and provide advice for reducing wasteful spending based on the user's consumption patterns. In this way, advice for reducing wasteful spending can be provided based on the user's consumption patterns.

[0050] The analysis unit can analyze a user's social network and provide reliable investment information. For example, it can analyze the user's social media and contact data to provide reliable investment information. It can provide investment advice based on information from reliable investors and experts, and analyze the user's friendship and work networks to provide reliable investment information. The analysis unit can also evaluate the user's investment risk tolerance based on the user's social network. For example, it can evaluate the risk tolerance based on successful investment cases within the user's network and suggest optimal investment methods. This makes it possible to provide reliable investment information based on the user's social network.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The information collection unit collects information such as the user's annual income, occupation, place of residence, and family composition. For example, the user enters the information into an input form. The information collection unit can also collect the user's past financial transaction history. For example, it can collect bank account and credit card transaction history. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes information such as annual income, occupation, place of residence, and family composition to evaluate the user's status. The analysis unit can also analyze the user's past financial transaction history and predict future income and expenditure. For example, it analyzes past income and expenditure patterns to predict future income and expenditure. Step 3: Based on the information analyzed by the analysis unit, the provision unit provides financial information and optimal tax strategies tailored to each user's status. For example, it suggests high-risk, high-return investment methods to users with high annual incomes, and low-risk asset formation methods to users with low annual incomes. The provision unit also provides information on subsidies according to the user's place of residence and tax strategies according to the user's occupation. For example, it provides region-specific subsidy information to users living in a specific area, and occupation-specific subsidy information to users engaged in a specific occupation.

[0053] (Example 2) The investment, subsidy and asset formation system according to an embodiment of the present invention is a system that collects information such as a user's annual income, occupation, place of residence, and family structure, analyzes this information using a generation AI, and provides financial information and optimal tax strategies tailored to the user's status. This allows the investment, subsidy and asset formation system to provide optimal information tailored to the user's individual circumstances.

[0054] An investment, subsidy, and asset formation system according to an embodiment includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects information such as a user's annual income, occupation, place of residence, and family structure. For example, the user enters information into an input form. The information collection unit can also collect the user's past financial transaction history. For example, it collects bank account and credit card transaction history. The analysis unit analyzes the information collected by the information collection unit. For example, a generation AI analyzes information such as annual income, occupation, place of residence, and family structure to evaluate the user's status. The analysis unit can also analyze the user's past financial transaction history and predict future income and expenses. For example, it analyzes past income and expenditure patterns to predict future income and expenses. The provision unit provides financial information and optimal tax strategies tailored to the user's status based on the information analyzed by the analysis unit. For example, it suggests high-risk, high-return investment methods to users with high annual incomes and low-risk asset formation methods to users with low annual incomes. The providing unit also provides subsidy information according to the user's place of residence and tax strategies according to the user's occupation. For example, it provides region-specific subsidy information to a user living in a specific area, and provides occupation-specific subsidy information to a user engaged in a specific occupation. This allows the investment, subsidy, and asset formation system according to the embodiment to provide optimal information according to the user's individual circumstances. For example, the user can obtain money-related information and tax strategies that are optimal for their situation.

[0055] The analysis unit can suggest high-risk, high-return investment methods to users with high annual incomes, and low-risk asset formation methods to users with low annual incomes. For example, the analysis unit suggests stock investment or real estate investment to users with high annual incomes. For example, stock investment is a high-risk, high-return investment method and is suggested according to the user's risk tolerance. The analysis unit also suggests bonds or fixed-term deposits to users with low annual incomes. For example, bonds are a low-risk asset formation method and are suggested according to the user's risk aversion. This makes it possible to provide the optimal investment method according to the user's annual income.

[0056] The analysis unit can suggest the application of dependent deductions or medical expense deductions when the family structure is complex. For example, when the family structure is complex, the analysis unit suggests the application of dependent deductions. For example, dependent deductions are applied according to the number and ages of dependents. The analysis unit also suggests the application of medical expense deductions. For example, medical expense deductions are applied according to the total amount of medical expenses and the types of medical expenses eligible for deduction. This makes it possible to provide optimal tax strategies according to family structure.

[0057] The analysis unit can suggest stock investments or real estate investments to users who can take risks, and bonds or fixed-term deposits to users who want to avoid risk. For example, the analysis unit suggests stock investments to users who can take risks. For example, stock investments are a high-risk, high-return investment method and are suggested according to the user's risk tolerance. The analysis unit also suggests real estate investments. For example, real estate investments are a long-term asset formation method and are suggested according to the user's risk tolerance. The analysis unit also suggests bonds to users who want to avoid risk. For example, bonds are a low-risk asset formation method and are suggested according to the user's risk aversion. The analysis unit also suggests fixed-term deposits. For example, fixed-term deposits are highly secure and suitable for users who want to avoid risk. This makes it possible to provide the optimal investment method according to the user's risk tolerance.

[0058] The analysis unit can provide region-specific subsidy information to users who live in a specific region, and occupation-specific subsidy information to users who work in a specific occupation. The analysis unit, for example, provides region-specific subsidy information to users who live in a specific region. Examples include subsidies from local governments and regional grants. The analysis unit also provides occupation-specific subsidy information to users who work in a specific occupation. Examples include subsidies for specific industries and vocational training subsidies. This makes it possible to provide optimal subsidy information according to the user's place of residence and occupation.

[0059] The analysis unit can analyze the user's past financial transaction history and predict future income and expenditure. The analysis unit, for example, collects the user's bank account and credit card transaction history and analyzes past income and expenditure patterns. For example, it grasps monthly income and expenditure trends and predicts future income and expenditure. The analysis unit also predicts future income and expenditure based on past transaction records. For example, it predicts future income and expenditure trends using past income and expenditure data. This makes it possible to predict future income and expenditure based on the user's past financial transaction history.

[0060] The analysis unit can predict the user's life events and collect information based on them. The analysis unit predicts future life events based on information such as the user's age, occupation, and family structure. For example, it predicts the timing of marriage and childbirth and provides asset formation advice based on that. The analysis unit also collects information based on the user's life events. For example, it collects information corresponding to life events such as marriage, childbirth, and job change, and provides it to the user. This makes it possible to collect information based on the user's life events.

[0061] The analysis unit collects information about the user's health condition and lifestyle habits, and can make asset formation suggestions based on health risks. The analysis unit, for example, collects the user's health checkup results and medical records and analyzes health risks. For example, if the health risk is high, the analysis unit suggests asset formation methods to prepare for medical expenses. The analysis unit also collects information about the user's lifestyle habits and analyzes health risks. For example, it collects information such as dietary habits and exercise habits and evaluates health risks. This makes it possible to make optimal asset formation suggestions based on the user's health condition and lifestyle habits.

[0062] The analysis unit can collect information to suggest investments based on the user's hobbies and interests. The analysis unit, for example, collects information about the user's hobbies and interests and suggests investments based on that information. For example, a user who likes sports is suggested to invest in stocks of sports-related companies. The analysis unit also suggests investments based on the user's interests. For example, a user who is interested in technology is suggested to invest in stocks of technology-related companies. This makes it possible to suggest optimal investments based on the user's hobbies and interests.

[0063] The analysis unit can analyze the user's emotions when entering information in real time and collect information to elicit positive emotions. For example, the analysis unit analyzes facial expressions and voice when the user enters information and estimates the user's emotional state in real time. For example, if a negative emotion is detected, an encouraging message to elicit positive emotions is displayed. The analysis unit also collects information to elicit positive emotions based on the user's emotional state. For example, it provides information that the user is interested in and elicits positive emotions. This makes it possible to collect information to elicit positive emotions based on the user's emotions when entering information.

[0064] The analysis unit can analyze the user's past investment performance and provide an optimal investment strategy. The analysis unit, for example, analyzes the user's past investment history and evaluates investment performance. For example, it proposes an optimal investment strategy based on past stock and bond trading data. The analysis unit also provides an optimal investment strategy based on the user's investment performance. For example, it evaluates past investment performance and proposes methods of risk management and diversification investment. This makes it possible to provide an optimal investment strategy based on the user's past investment performance.

[0065] The analysis unit can analyze the user's consumption patterns and provide advice for reducing wasteful spending. For example, the analysis unit can analyze the user's past consumption data and identify wasteful spending. For example, the analysis unit can suggest a periodic review of subscription services. The analysis unit can also provide advice for reducing wasteful spending based on the user's consumption patterns. For example, the analysis unit can suggest methods for reducing unnecessary purchases and excessive spending. This makes it possible to provide advice for reducing wasteful spending based on the user's consumption patterns.

[0066] The analysis unit can analyze the user's social network and provide reliable investment information. The analysis unit can, for example, analyze the user's social media and contact data to provide reliable investment information. For example, the analysis unit can provide investment advice based on information from reliable investors and experts. The analysis unit can also provide reliable investment information based on the user's social network. For example, the analysis unit can analyze the user's friendships and work network to provide reliable investment information. This makes it possible to provide reliable investment information based on the user's social network.

[0067] The analysis unit can provide the user with investment information that interests them most, thereby increasing their willingness to invest. For example, the analysis unit can analyze the user's emotional state in real time and provide the user with investment information that interests them most. For example, the analysis unit can provide investment information related to themes that evoke strong positive emotions. The analysis unit can also provide information to increase the user's willingness to invest based on the user's interests. For example, the analysis unit can provide investment information in areas that interest the user, thereby increasing the user's willingness to invest. This can provide the user with investment information that interests them most, thereby increasing the user's willingness to invest.

[0068] The analysis unit can analyze the user's past tax return data and propose optimal tax saving methods. The analysis unit, for example, collects the user's past tax return data and proposes methods with high tax saving effects. For example, the analysis unit proposes optimal tax saving methods based on past data on medical expense deductions and dependent deductions. The analysis unit also proposes optimal tax saving methods based on the user's tax return data. For example, the analysis unit analyzes past tax return content and proposes tax deductions and tax preferential treatment measures. This makes it possible to propose optimal tax saving methods based on the user's past tax return data.

[0069] The analysis unit can provide long-term tax strategies based on the user's future income predictions. The analysis unit, for example, proposes long-term tax strategies based on the user's future income predictions. For example, it proposes tax-saving strategies that should be started now if future income is expected to increase. The analysis unit also provides long-term tax strategies based on the user's income predictions. For example, it proposes a long-term tax-saving plan to reduce future tax burdens. This makes it possible to provide long-term tax strategies based on the user's future income predictions.

[0070] The analysis unit can provide advice to reduce the user's anxiety about taxes. For example, the analysis unit analyzes the user's emotional state in real time and provides advice to reduce the user's anxiety about taxes. For example, if the user has high anxiety about taxes, the analysis unit can suggest specific tax-saving methods. The analysis unit also provides advice to reduce the user's anxiety about taxes. For example, the analysis unit can provide tax consultations and information about taxes. This makes it possible to provide advice to reduce the user's anxiety about taxes.

[0071] The analysis unit can propose optimal tax strategies based on the user's asset composition. The analysis unit, for example, analyzes the user's asset composition and proposes optimal tax strategies. For example, for a user who owns a lot of real estate, it proposes tax-saving methods related to real estate. The analysis unit also proposes optimal tax strategies based on the user's asset composition. For example, it proposes tax strategies according to the type and proportion of assets. This makes it possible to propose optimal tax strategies based on the user's asset composition.

[0072] The analysis unit can provide tax strategies according to the user's life stage. For example, the analysis unit proposes tax strategies according to the user's life stage (marriage, childbirth, job change, etc.). For example, it proposes tax-saving methods related to marriage to a user who has just gotten married. The analysis unit also provides tax strategies based on the user's life stage. For example, it proposes tax strategies according to life events such as childbirth or job change. This makes it possible to provide tax strategies according to the user's life stage.

[0073] The analysis unit can suggest tax strategies that give the user the most peace of mind, thereby reducing tax stress. The analysis unit, for example, analyzes the user's emotional state in real time and suggests tax strategies that give the user the most peace of mind. For example, if there is a high level of anxiety about taxes, it can suggest specific tax-saving methods. The analysis unit also provides advice to reduce the user's tax stress. For example, it provides tax consultations and information about taxes. This allows the analysis unit to suggest tax strategies that give the user the most peace of mind, thereby reducing tax stress.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The analysis unit can estimate the user's emotions and determine the investment risk tolerance level based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest a low-risk investment method, and if the user is relaxed, it can suggest a high-risk, high-return investment method. The analysis unit can also suggest the timing of investments based on the user's emotions. For example, if the user is feeling positive, it can suggest an aggressive investment, and if the user is feeling negative, it can suggest a cautious investment. This makes it possible to provide the optimal investment method based on the user's emotions.

[0076] The analysis unit can analyze the user's health condition and make asset formation suggestions based on health risks. For example, it collects the user's health checkup results and medical records and evaluates the health risk. If the health risk is high, it suggests asset formation methods to prepare for medical expenses, and if the health risk is low, it suggests riskier investment methods. The analysis unit can also collect information about the user's lifestyle habits and analyze health risks. For example, it collects information on eating habits and exercise habits and evaluates health risks. This makes it possible to make optimal asset formation suggestions based on the user's health condition and lifestyle habits.

[0077] The analysis unit can estimate the user's emotions and suggest optimal tax strategies based on the estimated user emotions. For example, if the user feels anxious about taxes, the analysis unit can suggest specific tax-saving methods to provide a sense of security. The analysis unit can also adjust the timing of providing tax-related information based on the user's emotions. For example, important tax-related information can be provided when the user is relaxed. This makes it possible to provide optimal tax strategies based on the user's emotions.

[0078] The analysis unit can suggest investment options based on the user's hobbies and interests. For example, it can collect information about the user's hobbies and interests and suggest investment options based on that information. For a user who likes sports, it can suggest investments in stocks of sports-related companies, and for a user who is interested in technology, it can suggest investments in stocks of technology-related companies. The analysis unit can also evaluate the user's investment risk tolerance based on their hobbies and interests. For example, it can suggest high-risk, high-return investment options to an adventurous user, and low-risk investment options to a cautious user. This makes it possible to suggest optimal investment options based on the user's hobbies and interests.

[0079] The analysis unit can estimate the user's emotions and provide optimal subsidy information based on the estimated user's emotions. For example, if the user is feeling stressed, subsidy information with simple procedures is provided, and if the user is relaxed, detailed subsidy information is provided. The analysis unit can also adjust the method of providing subsidy information based on the user's emotions. For example, if the user is feeling positive, subsidy information is provided proactively, and if the user is feeling negative, subsidy information is provided cautiously. This makes it possible to provide optimal subsidy information based on the user's emotions.

[0080] The analysis unit can predict the user's life events and collect information based on them. For example, future life events can be predicted based on information such as the user's age, occupation, and family structure. The analysis unit can predict the timing of marriage and childbirth and provide asset formation advice based on that. The analysis unit can also collect information based on the user's life events. Information corresponding to life events such as marriage, childbirth, and job changes is collected and provided to the user. This makes it possible to collect information based on the user's life events.

[0081] The analysis unit can estimate the user's emotions and provide an optimal investment strategy based on the estimated user emotions. For example, if the user has positive emotions, it can suggest an aggressive investment strategy, and if the user has negative emotions, it can suggest a conservative investment strategy. The analysis unit can also adjust the timing of investments based on the user's emotions. For example, it can provide important investment information when the user is relaxed. This makes it possible to provide an optimal investment strategy based on the user's emotions.

[0082] The analysis unit can analyze the user's consumption patterns and provide advice for reducing wasteful spending. For example, the analysis unit can analyze the user's past consumption data and identify wasteful spending. The analysis unit can suggest a periodic review of subscription services and suggest methods for reducing unnecessary purchases and excessive spending. The analysis unit can also provide specific advice for saving money based on the user's consumption patterns. For example, the analysis unit can suggest methods for reducing food and utility expenses and provide advice for reducing wasteful spending based on the user's consumption patterns. In this way, advice for reducing wasteful spending can be provided based on the user's consumption patterns.

[0083] The analysis unit can estimate the user's emotions and suggest optimal asset formation methods based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest low-risk asset formation methods, and if the user is relaxed, it can suggest high-risk asset formation methods. The analysis unit can also adjust the timing of asset formation based on the user's emotions. For example, if the user is feeling positive, it can suggest aggressive asset formation methods, and if the user is feeling negative, it can suggest cautious asset formation methods. This makes it possible to suggest optimal asset formation methods based on the user's emotions.

[0084] The analysis unit can analyze a user's social network and provide reliable investment information. For example, it can analyze the user's social media and contact data to provide reliable investment information. It can provide investment advice based on information from reliable investors and experts, and analyze the user's friendship and work networks to provide reliable investment information. The analysis unit can also evaluate the user's investment risk tolerance based on the user's social network. For example, it can evaluate the risk tolerance based on successful investment cases within the user's network and suggest optimal investment methods. This makes it possible to provide reliable investment information based on the user's social network.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The information collection unit collects information such as the user's annual income, occupation, place of residence, and family composition. For example, the user enters the information into an input form. The information collection unit can also collect the user's past financial transaction history. For example, it can collect bank account and credit card transaction history. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes information such as annual income, occupation, place of residence, and family composition to evaluate the user's status. The analysis unit can also analyze the user's past financial transaction history and predict future income and expenditure. For example, it analyzes past income and expenditure patterns to predict future income and expenditure. Step 3: Based on the information analyzed by the analysis unit, the provision unit provides financial information and optimal tax strategies tailored to each user's status. For example, it suggests high-risk, high-return investment methods to users with high annual incomes, and low-risk asset formation methods to users with low annual incomes. The provision unit also provides information on subsidies according to the user's place of residence and tax strategies according to the user's occupation. For example, it provides region-specific subsidy information to users living in a specific area, and occupation-specific subsidy information to users engaged in a specific occupation.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects information such as the user's annual income, occupation, place of residence, and family structure; an analysis unit that analyzes the information collected by the information collection unit; and a providing unit that provides information related to money and optimal tax measures according to the status based on the information analyzed by the analyzing unit. A system characterized by:

2. The analysis unit To the user with a high annual income, a high-risk, high-return investment method is proposed, and to the user with a low annual income, a low-risk asset formation method is proposed.

2. The system of claim 1.

3. The analysis unit Provide region-specific subsidy information to the user who lives in a specific region, and provide occupation-specific subsidy information to the user who is engaged in a specific occupation.

2. The system of claim 1.

4. The analysis unit Analyzing the user's past investment performance and providing the optimal investment strategy 2. The system of claim 1.

5. The analysis unit Analyzing the user's past tax return data and proposing the optimal tax saving method 2. The system of claim 1.

6. The analysis unit Analyze the emotions of the user in real time as they input, and collect information to elicit positive emotions.

2. The system of claim 1.

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