System

Abstract

To enable a user to easily perform a life plan simulation and receive an appropriate financial product proposal.SOLUTION: A system includes a data collection part, an additional input part, a simulation part, a proposal part, and a follow-up part. The data collector automatically collects financial data from the electronic payment app and related services with user consent. When the automatically collected data is insufficient, the additional input unit requests the user to additionally input insufficient information. The simulation part performs life plan simulation based on the collected data and the additionally inputted information. The proposal unit proposes a financial product based on the simulation result. The follow-up unit periodically collects and analyzes the user's financial data, and performs follow-up as necessary.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 requires users to actively input detailed financial data, making it difficult for general users to use.

[0005] The system according to the embodiment aims to enable users to easily perform life plan simulations and receive appropriate financial product proposals. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an additional input unit, a simulation unit, a proposal unit, and a follow-up unit. The data collection unit automatically collects financial data from electronic payment apps and related services with the user's consent. The additional input unit requests the user to input additional missing information if the automatically collected data is insufficient. The simulation unit performs a life plan simulation based on the collected data and the additionally input information. The proposal unit proposes financial products based on the simulation results. The follow-up unit periodically collects and analyzes the user's financial data and performs follow-up as necessary. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily perform a life plan simulation and receive appropriate financial product proposals. [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) A life plan simulation system according to an embodiment of the present invention automatically collects financial data from electronic payment apps and related services with the user's consent, and a generation AI proposes financial products, such as asset management, tailored to life events. This allows the user to easily perform a highly accurate life plan simulation and receive appropriate financial product proposals.

[0029] A life plan simulation system according to an embodiment includes a data collection unit, an additional input unit, a simulation unit, a proposal unit, and a follow-up unit. The data collection unit automatically collects financial data from electronic payment apps and related services with the user's consent. For example, the data collection unit acquires the user's spending history, income information, and savings status. The data collection unit also converts the data into a format that is easy for the generation AI to analyze. If the automatically collected data is insufficient, the additional input unit prompts the user to enter additional information. For example, the additional input may include information about future life events (marriage, children's education, home purchase, etc.) and specific asset management goals. The simulation unit uses the generation AI to perform a life plan simulation based on the collected data and the additional input information. For example, the simulation unit predicts the user's future asset status based on the user's current income, expenses, and savings status. The generation AI performs the simulation using a text generation AI (e.g., LLM) or a multimodal generation AI. The proposal unit proposes financial products, such as asset management tailored to the user's life events, based on the simulation results. For example, the simulation unit proposes educational loans to prepare for children's education expenses, mortgages for home purchases, and pension insurance for retirement. The follow-up unit periodically collects and analyzes the user's financial data even after the simulation, and performs follow-up as necessary. For example, it updates the simulation results and makes new proposals in response to fluctuations in income and expenses. As a result, the life plan simulation system according to the embodiment allows the user to easily perform a highly accurate life plan simulation and receive appropriate financial product proposals. For example, it allows the user to create an asset management plan in preparation for future life events, and to use necessary financial products at the appropriate time. This is expected to enable the user to manage their assets efficiently and effectively.

[0030] The data collection unit can collect financial data not only from electronic payment apps, but also from purchase histories on social media and emails. For example, with the user's consent, the data collection unit analyzes purchase histories on social media and emails to understand spending patterns and purchasing trends. For example, it analyzes social media posts and purchase notifications via email to understand the user's consumption behavior in detail. This allows for the collection of more detailed financial data and enables more accurate simulations.

[0031] The data collection unit can analyze the user's lifestyle patterns and understand spending trends during specific time periods and days of the week. For example, the data collection unit can analyze the user's lifestyle patterns and understand spending trends during specific time periods and days of the week. For example, if there is a lot of spending during the daytime on weekdays, asset management proposals tailored to that time period can be made. This makes it possible to collect highly accurate data based on the user's lifestyle patterns.

[0032] The data collection unit can link with fitness apps and health management apps in addition to electronic payment apps to make asset management suggestions based on the user's health condition and exercise habits. For example, the data collection unit can link with fitness apps and health management apps to make asset management suggestions based on the user's health condition and exercise habits. For example, a high-risk investment can be suggested to a user who is in good health. This makes it possible to make asset management suggestions based on the user's health condition and exercise habits.

[0033] The data collection unit, with the user's consent, can collect data from smart home devices and reflect the energy consumption and home appliance usage status within the home in asset management. For example, with the user's consent, the data collection unit can collect data from smart home devices and reflect the energy consumption and home appliance usage status within the home in asset management. For example, the data collection unit can suggest energy-saving investments to households with high energy consumption. This makes it possible to suggest asset management based on the energy consumption and home appliance usage status within the home.

[0034] The additional input unit adds a function to automatically complete information entered by the user, thereby minimizing the effort required for input. The additional input unit, for example, adds a function to automatically complete information entered by the user, thereby minimizing the effort required for input. For example, it presents auto-completion candidates based on past input history. This reduces the effort required for input by the user and enables smoother data input.

[0035] The additional input unit can analyze the user's past input history and predict and suggest future input content. The additional input unit, for example, analyzes the user's past input history and predicts and suggests future input content. For example, it suggests information about future life events based on past input data. This predicts future input content based on the user's past input history, reducing the effort required for input.

[0036] The additional input unit may use voice input or image recognition technology to allow the user to provide information verbally or with a photograph. The additional input unit may use, for example, voice input technology to allow the user to provide information verbally. For example, voice recognition technology may be used to convert the user's verbal input into text data. This allows the user to provide information verbally or with a photograph, thereby reducing the effort required for input.

[0037] The additional input unit can accept information provided by the user's family and friends and collect more diversified data. The additional input unit, for example, accepts information provided by the user's family and friends and collects more diversified data. For example, a life plan simulation of the user is performed based on information entered by family and friends. This allows for accepting information provided by the user's family and friends and collecting more diversified data.

[0038] The simulation unit can analyze the user's past financial behavior and implement an algorithm that predicts future behavior. The simulation unit, for example, analyzes the user's past financial behavior and implements an algorithm that predicts future behavior. For example, the simulation unit predicts future spending patterns based on past spending history and income data. This allows future behavior to be predicted based on the user's past financial behavior, enabling more accurate simulations.

[0039] The simulation unit can collect user feedback on the simulation results and continuously improve the accuracy of the simulation. For example, the simulation unit collects user feedback on the simulation results and continuously improves the accuracy of the simulation. For example, the simulation unit improves the simulation algorithm based on the user's opinions and impressions. In this way, the accuracy of the simulation is continuously improved based on the user's feedback.

[0040] The simulation unit can perform simulations that take into account different scenarios (e.g., economic crises and natural disasters) to strengthen risk management. The simulation unit, for example, performs simulations that take into account different scenarios (e.g., economic crises and natural disasters) to strengthen risk management. For example, a decrease in income or an increase in expenditure during an economic crisis can be reflected in the simulation. In this way, risk management can be strengthened by performing simulations that take into account different scenarios.

[0041] The simulation unit can perform a simulation based on the user's lifestyle and hobbies to provide more personalized results. The simulation unit can perform a simulation based on the user's lifestyle and hobbies to provide more personalized results. For example, expenses for hobbies and lifestyle-based expenditures can be reflected in the simulation. In this way, by performing a simulation based on the user's lifestyle and hobbies, more personalized results can be provided.

[0042] The suggestion unit can introduce an algorithm that analyzes the user's past investment history and suggests optimal financial products. The suggestion unit, for example, introduces an algorithm that analyzes the user's past investment history and suggests optimal financial products. For example, the suggestion unit suggests financial products according to the user's risk tolerance based on past investment performance. This allows for more effective asset management by suggesting optimal financial products based on the user's past investment history.

[0043] The proposal unit can add a function to analyze the risks and returns of proposed financial products in detail and explain them to the user in an easy-to-understand manner. For example, the proposal unit can add a function to analyze the risks and returns of proposed financial products in detail and explain them to the user in an easy-to-understand manner. For example, the balance between risk and return can be displayed in a graph. This allows the user to easily understand the risks and returns of financial products, enabling more appropriate investment decisions.

[0044] The proposal unit can compare proposals from different financial institutions and service providers and provide the optimal option. The proposal unit, for example, builds a system that compares proposals from different financial institutions and service providers and provides the optimal option. For example, it compares the risks and returns of each financial product and proposes the optimal product. This allows the proposals from different financial institutions and service providers to be compared and the optimal option to be provided, thereby enabling the user to select the optimal financial product.

[0045] The proposal unit can propose a long-term asset management plan based on the user's life events. The proposal unit, for example, builds a system that proposes a long-term asset management plan based on the user's life events. For example, the proposal unit proposes an asset management plan based on life events such as marriage, childbirth, and home purchase. This allows for more effective asset management by proposing a long-term asset management plan based on the user's life events.

[0046] The follow-up unit can add a function to monitor the user's financial data in real time and issue an alert if an abnormality is detected. The follow-up unit, for example, adds a function to monitor the user's financial data in real time and issue an alert if an abnormality is detected. For example, an alert is issued if a sudden increase in expenses or decrease in income is detected. This allows the user's financial data to be monitored in real time and an alert is issued if an abnormality is detected, enabling a quick response.

[0047] The follow-up unit can introduce an algorithm that takes into account changes in the user's living environment and health condition when performing a follow-up. For example, the follow-up unit introduces an algorithm that takes into account changes in the user's living environment and health condition when performing a follow-up. For example, the follow-up unit makes asset management proposals that take into account deterioration of health condition and changes in living environment. This makes it possible to perform follow-up that takes into account changes in the user's living environment and health condition.

[0048] The follow-up unit can provide a function for sharing information with the user's family and friends during follow-up and for joint asset management. The follow-up unit, for example, can provide a function for sharing information with the user's family and friends during follow-up and for joint asset management. For example, an information sharing function for sharing with family and friends is added. This allows the user to share information with the user's family and friends and jointly manage assets, thereby enabling more effective asset management.

[0049] The follow-up unit can share the results of the follow-up in communities and forums in which the user participates and obtain feedback from other users. For example, the follow-up unit can share the results of the follow-up in communities and forums in which the user participates and obtain feedback from other users. For example, a function for posting to a forum can be added. This allows the results of the follow-up to be shared in communities and forums and obtain feedback from other users, enabling more effective asset management.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The life plan simulation system can also make asset management suggestions based on the user's hobbies and interests. For example, if the user likes traveling, travel savings plans and travel insurance can be suggested. If the user likes sports, sports-related investment products and insurance can be suggested. Furthermore, if the user is interested in art, art investments and art insurance can be suggested. This makes it possible to offer asset management suggestions based on the user's hobbies and interests, allowing for more personalized services.

[0052] The life plan simulation system can also make asset management proposals based on the user's health condition. For example, if the user provides the results of a health check, health insurance and medical insurance can be proposed based on those results. Also, if the user uses a fitness app, investment products for maintaining health can be proposed based on that data. Furthermore, if the user has a specific illness, insurance products corresponding to that illness can be proposed. This makes it possible to propose asset management based on the user's health condition, allowing for more appropriate services to be provided.

[0053] The life plan simulation system can also make asset management proposals based on the user's family structure. For example, if the user has children, it will propose educational savings plans and education insurance for educational expenses. If the user has elderly parents, it will propose nursing care insurance and savings plans for nursing care expenses. Furthermore, if the user is single, it will propose investment products and insurance for singles. This makes it possible to propose asset management based on the user's family structure, allowing for more personalized services.

[0054] The life plan simulation system can also make asset management proposals based on the user's occupation. For example, if the user is a freelancer, the system will propose investment products and insurance that address the user's income instability. If the user is a civil servant, the system will propose long-term investment products based on a stable income. Furthermore, if the user is a business manager, the system will propose insurance and investment products that address business risks. This makes it possible to propose asset management proposals based on the user's occupation, allowing for the provision of more appropriate services.

[0055] The life plan simulation system can also make asset management proposals based on the user's place of residence. For example, if the user lives in an urban area, insurance and investment products that address risks specific to urban areas are proposed. If the user lives in a rural area, insurance and investment products that address risks specific to that area are proposed. Furthermore, if the user lives overseas, insurance and investment products that address risks specific to overseas areas are proposed. This makes it possible to propose asset management based on the user's place of residence, allowing for the provision of more appropriate services.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: With the user's consent, the data collection unit automatically collects financial data from electronic payment apps and related services. For example, the data collection unit obtains the user's spending history, income information, savings status, etc. The data collection unit also converts the data into a format that is easy for the generation AI to analyze. Step 2: The additional input section prompts the user to enter additional information if the automatically collected data is insufficient, such as information about future life events (marriage, children's education, home purchase, etc.) or specific financial management goals. Step 3: In the simulation section, the generation AI performs a life plan simulation based on the collected data and additional input information. For example, it predicts the user's future asset status based on their current income, expenses, and savings. The generation AI performs the simulation using text generation AI (e.g., LLM) or multimodal generation AI. Step 4: Based on the simulation results, the proposal unit proposes financial products such as asset management tailored to the user's life events, such as educational loans to prepare for children's education expenses, mortgages for home purchases, and pension insurance for retirement. Step 5: The follow-up department periodically collects and analyzes the user's financial data after the simulation and performs follow-up as necessary. For example, it updates the simulation results in response to fluctuations in income and expenses and makes new proposals.

[0058] (Example 2) A life plan simulation system according to an embodiment of the present invention automatically collects financial data from electronic payment apps and related services with the user's consent, and a generation AI proposes financial products, such as asset management, tailored to life events. This allows the user to easily perform a highly accurate life plan simulation and receive appropriate financial product proposals.

[0059] A life plan simulation system according to an embodiment includes a data collection unit, an additional input unit, a simulation unit, a proposal unit, and a follow-up unit. The data collection unit automatically collects financial data from electronic payment apps and related services with the user's consent. For example, the data collection unit acquires the user's spending history, income information, and savings status. The data collection unit also converts the data into a format that is easy for the generation AI to analyze. If the automatically collected data is insufficient, the additional input unit prompts the user to enter additional information. For example, the additional input may include information about future life events (marriage, children's education, home purchase, etc.) and specific asset management goals. The simulation unit uses the generation AI to perform a life plan simulation based on the collected data and the additional input information. For example, the simulation unit predicts the user's future asset status based on the user's current income, expenses, and savings status. The generation AI performs the simulation using a text generation AI (e.g., LLM) or a multimodal generation AI. The proposal unit proposes financial products, such as asset management tailored to the user's life events, based on the simulation results. For example, the simulation unit proposes educational loans to prepare for children's education expenses, mortgages for home purchases, and pension insurance for retirement. The follow-up unit periodically collects and analyzes the user's financial data even after the simulation, and performs follow-up as necessary. For example, it updates the simulation results and makes new proposals in response to fluctuations in income and expenses. As a result, the life plan simulation system according to the embodiment allows the user to easily perform a highly accurate life plan simulation and receive appropriate financial product proposals. For example, it allows the user to create an asset management plan in preparation for future life events, and to use necessary financial products at the appropriate time. This is expected to enable the user to manage their assets efficiently and effectively.

[0060] The data collection unit can collect financial data not only from electronic payment apps, but also from purchase histories on social media and emails. For example, with the user's consent, the data collection unit analyzes purchase histories on social media and emails to understand spending patterns and purchasing trends. For example, it analyzes social media posts and purchase notifications via email to understand the user's consumption behavior in detail. This allows for the collection of more detailed financial data and enables more accurate simulations.

[0061] The data collection unit can analyze the user's lifestyle patterns and understand spending trends during specific time periods and days of the week. For example, the data collection unit can analyze the user's lifestyle patterns and understand spending trends during specific time periods and days of the week. For example, if there is a lot of spending during the daytime on weekdays, asset management proposals tailored to that time period can be made. This makes it possible to collect highly accurate data based on the user's lifestyle patterns.

[0062] The data collection unit can use the emotion estimation function to analyze the user's emotions at the time of purchase and reflect the spending tendency based on the emotions in the data. The data collection unit, for example, uses the emotion estimation function to analyze the user's emotions at the time of purchase and reflect the spending tendency based on the emotions in the data. For example, the data collection unit analyzes the user's joy and satisfaction at the time of purchase and makes asset management suggestions based on the emotions. In this way, by reflecting the spending tendency based on the user's emotions, more accurate simulations are possible.

[0063] The data collection unit can link with fitness apps and health management apps in addition to electronic payment apps to make asset management suggestions based on the user's health condition and exercise habits. For example, the data collection unit can link with fitness apps and health management apps to make asset management suggestions based on the user's health condition and exercise habits. For example, a high-risk investment can be suggested to a user who is in good health. This makes it possible to make asset management suggestions based on the user's health condition and exercise habits.

[0064] The data collection unit, with the user's consent, can collect data from smart home devices and reflect the energy consumption and home appliance usage status within the home in asset management. For example, with the user's consent, the data collection unit can collect data from smart home devices and reflect the energy consumption and home appliance usage status within the home in asset management. For example, the data collection unit can suggest energy-saving investments to households with high energy consumption. This makes it possible to suggest asset management based on the energy consumption and home appliance usage status within the home.

[0065] The data collection unit can use the emotion estimation function to analyze the emotions a user has regarding a specific life event and collect data based on those emotions. For example, the data collection unit can use the emotion estimation function to analyze the emotions a user has regarding a specific life event and collect data based on those emotions. For example, the data collection unit can analyze emotions regarding marriage and childbirth and make asset management suggestions based on those emotions. This makes it possible to collect data based on the user's emotions, enabling more accurate simulations.

[0066] The additional input unit adds a function to automatically complete information entered by the user, thereby minimizing the effort required for input. The additional input unit, for example, adds a function to automatically complete information entered by the user, thereby minimizing the effort required for input. For example, it presents auto-completion candidates based on past input history. This reduces the effort required for input by the user and enables smoother data input.

[0067] The additional input unit can analyze the user's past input history and predict and suggest future input content. The additional input unit, for example, analyzes the user's past input history and predicts and suggests future input content. For example, it suggests information about future life events based on past input data. This predicts future input content based on the user's past input history, reducing the effort required for input.

[0068] The additional input unit can use the emotion estimation function to provide an interface for reducing stress felt by the user when inputting data. The additional input unit, for example, uses the emotion estimation function to provide an interface for reducing stress felt by the user when inputting data. For example, the additional input unit analyzes the emotion felt when inputting data and displays advice for reducing stress. This reduces stress felt by the user when inputting data and provides a more comfortable input experience.

[0069] The additional input unit may use voice input or image recognition technology to allow the user to provide information verbally or with a photograph. The additional input unit may use, for example, voice input technology to allow the user to provide information verbally. For example, voice recognition technology may be used to convert the user's verbal input into text data. This allows the user to provide information verbally or with a photograph, thereby reducing the effort required for input.

[0070] The additional input unit can accept information provided by the user's family and friends and collect more diversified data. The additional input unit, for example, accepts information provided by the user's family and friends and collects more diversified data. For example, a life plan simulation of the user is performed based on information entered by family and friends. This allows for accepting information provided by the user's family and friends and collecting more diversified data.

[0071] The additional input unit can use the emotion estimation function to analyze the emotional response to information input by the user and suggest an input method that elicits positive emotions. The additional input unit, for example, uses the emotion estimation function to analyze the emotional response to information input by the user and suggest an input method that elicits positive emotions. For example, the additional input unit analyzes emotions at the time of input and displays advice for eliciting positive emotions. In this way, the emotional response of the user at the time of input is analyzed and an input method that elicits positive emotions is suggested.

[0072] The simulation unit can analyze the user's past financial behavior and implement an algorithm that predicts future behavior. The simulation unit, for example, analyzes the user's past financial behavior and implements an algorithm that predicts future behavior. For example, the simulation unit predicts future spending patterns based on past spending history and income data. This allows future behavior to be predicted based on the user's past financial behavior, enabling more accurate simulations.

[0073] The simulation unit can collect user feedback on the simulation results and continuously improve the accuracy of the simulation. For example, the simulation unit collects user feedback on the simulation results and continuously improves the accuracy of the simulation. For example, the simulation unit improves the simulation algorithm based on the user's opinions and impressions. In this way, the accuracy of the simulation is continuously improved based on the user's feedback.

[0074] The simulation unit can use the emotion estimation function to analyze the user's emotional response to the simulation results and provide simulation results based on the emotions. The simulation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the simulation results and provide simulation results based on the emotions. For example, the simulation unit analyzes the user's joy or anxiety regarding the simulation results and makes suggestions based on the emotions. This allows for more personalized suggestions by providing simulation results based on the user's emotional response.

[0075] The simulation unit can perform simulations that take into account different scenarios (e.g., economic crises and natural disasters) to strengthen risk management. The simulation unit, for example, performs simulations that take into account different scenarios (e.g., economic crises and natural disasters) to strengthen risk management. For example, a decrease in income or an increase in expenditure during an economic crisis can be reflected in the simulation. In this way, risk management can be strengthened by performing simulations that take into account different scenarios.

[0076] The simulation unit can perform a simulation based on the user's lifestyle and hobbies to provide more personalized results. The simulation unit can perform a simulation based on the user's lifestyle and hobbies to provide more personalized results. For example, expenses for hobbies and lifestyle-based expenditures can be reflected in the simulation. In this way, by performing a simulation based on the user's lifestyle and hobbies, more personalized results can be provided.

[0077] The simulation unit can use the emotion estimation function to identify a simulation scenario in which the user is most interested and provide results based on that scenario. For example, the simulation unit can use the emotion estimation function to identify a simulation scenario in which the user is most interested and provide results based on that scenario. For example, the simulation unit can analyze the emotions associated with a scenario in which the user is interested and make suggestions based on that scenario. This allows for more personalized suggestions by providing results based on the simulation scenario in which the user is most interested.

[0078] The suggestion unit can introduce an algorithm that analyzes the user's past investment history and suggests optimal financial products. The suggestion unit, for example, introduces an algorithm that analyzes the user's past investment history and suggests optimal financial products. For example, the suggestion unit suggests financial products according to the user's risk tolerance based on past investment performance. This allows for more effective asset management by suggesting optimal financial products based on the user's past investment history.

[0079] The proposal unit can add a function to analyze the risks and returns of proposed financial products in detail and explain them to the user in an easy-to-understand manner. For example, the proposal unit can add a function to analyze the risks and returns of proposed financial products in detail and explain them to the user in an easy-to-understand manner. For example, the balance between risk and return can be displayed in a graph. This allows the user to easily understand the risks and returns of financial products, enabling more appropriate investment decisions.

[0080] The suggestion unit can use the emotion estimation function to analyze the emotion the user has toward the proposed financial product and make a suggestion based on the emotion. For example, the suggestion unit uses the emotion estimation function to analyze the emotion the user has toward the proposed financial product and make a suggestion based on the emotion. For example, the suggestion unit analyzes the user's joy or anxiety about the proposed financial product and makes a suggestion based on the emotion. This makes it possible to make more personalized suggestions by suggesting financial products based on the user's emotions.

[0081] The proposal unit can compare proposals from different financial institutions and service providers and provide the optimal option. The proposal unit, for example, builds a system that compares proposals from different financial institutions and service providers and provides the optimal option. For example, it compares the risks and returns of each financial product and proposes the optimal product. This allows the proposals from different financial institutions and service providers to be compared and the optimal option to be provided, thereby enabling the user to select the optimal financial product.

[0082] The proposal unit can propose a long-term asset management plan based on the user's life events. The proposal unit, for example, builds a system that proposes a long-term asset management plan based on the user's life events. For example, the proposal unit proposes an asset management plan based on life events such as marriage, childbirth, and home purchase. This allows for more effective asset management by proposing a long-term asset management plan based on the user's life events.

[0083] The suggestion unit can use the emotion estimation function to identify the financial product in which the user is most interested and make a suggestion based on that product. The suggestion unit, for example, uses the emotion estimation function to identify the financial product in which the user is most interested and make a suggestion based on that product. For example, the suggestion unit analyzes the user's emotional response to identify the financial product in which the user is interested. This allows the suggestion unit to identify the financial product in which the user is most interested and make a suggestion based on that product, thereby enabling more personalized suggestions.

[0084] The follow-up unit can add a function to monitor the user's financial data in real time and issue an alert if an abnormality is detected. The follow-up unit, for example, adds a function to monitor the user's financial data in real time and issue an alert if an abnormality is detected. For example, an alert is issued if a sudden increase in expenses or decrease in income is detected. This allows the user's financial data to be monitored in real time and an alert is issued if an abnormality is detected, enabling a quick response.

[0085] The follow-up unit can introduce an algorithm that takes into account changes in the user's living environment and health condition when performing a follow-up. For example, the follow-up unit introduces an algorithm that takes into account changes in the user's living environment and health condition when performing a follow-up. For example, the follow-up unit makes asset management proposals that take into account deterioration of health condition and changes in living environment. This makes it possible to perform follow-up that takes into account changes in the user's living environment and health condition.

[0086] The follow-up unit can use the emotion estimation function to analyze the anxieties and questions the user feels during follow-up and provide support accordingly. The follow-up unit, for example, uses the emotion estimation function to analyze the anxieties and questions the user feels during follow-up and provide support accordingly. For example, it analyzes the anxieties and questions during follow-up and provides appropriate advice. In this way, by providing support according to the user's anxieties and questions, asset management can be carried out with greater peace of mind.

[0087] The follow-up unit can provide a function for sharing information with the user's family and friends during follow-up and for joint asset management. The follow-up unit, for example, can provide a function for sharing information with the user's family and friends during follow-up and for joint asset management. For example, an information sharing function for sharing with family and friends is added. This allows the user to share information with the user's family and friends and jointly manage assets, thereby enabling more effective asset management.

[0088] The follow-up unit can share the results of the follow-up in communities and forums in which the user participates and obtain feedback from other users. For example, the follow-up unit can share the results of the follow-up in communities and forums in which the user participates and obtain feedback from other users. For example, a function for posting to a forum can be added. This allows the results of the follow-up to be shared in communities and forums and obtain feedback from other users, enabling more effective asset management.

[0089] The follow-up unit can use the emotion estimation function to identify information in which the user is most interested during follow-up and provide support based on that information. The follow-up unit, for example, uses the emotion estimation function to identify information in which the user is most interested during follow-up and provide support based on that information. For example, the follow-up unit analyzes emotions during follow-up and identifies information in which the user is interested. This enables more effective follow-up by providing support based on the information in which the user is most interested.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The life plan simulation system can also make asset management suggestions based on the user's hobbies and interests. For example, if the user likes traveling, travel savings plans and travel insurance can be suggested. If the user likes sports, sports-related investment products and insurance can be suggested. Furthermore, if the user is interested in art, art investments and art insurance can be suggested. This makes it possible to offer asset management suggestions based on the user's hobbies and interests, allowing for more personalized services.

[0092] The life plan simulation system can also make asset management proposals based on the user's health condition. For example, if the user provides the results of a health check, health insurance and medical insurance can be proposed based on those results. Also, if the user uses a fitness app, investment products for maintaining health can be proposed based on that data. Furthermore, if the user has a specific illness, insurance products corresponding to that illness can be proposed. This makes it possible to propose asset management based on the user's health condition, allowing for more appropriate services to be provided.

[0093] The life plan simulation system can also make asset management proposals based on the user's family structure. For example, if the user has children, it will propose educational savings plans and education insurance for educational expenses. If the user has elderly parents, it will propose nursing care insurance and savings plans for nursing care expenses. Furthermore, if the user is single, it will propose investment products and insurance for singles. This makes it possible to propose asset management based on the user's family structure, allowing for more personalized services.

[0094] The life plan simulation system can also make asset management proposals based on the user's occupation. For example, if the user is a freelancer, the system will propose investment products and insurance that address the user's income instability. If the user is a civil servant, the system will propose long-term investment products based on a stable income. Furthermore, if the user is a business manager, the system will propose insurance and investment products that address business risks. This makes it possible to propose asset management proposals based on the user's occupation, allowing for the provision of more appropriate services.

[0095] The life plan simulation system can also make asset management proposals based on the user's place of residence. For example, if the user lives in an urban area, insurance and investment products that address risks specific to urban areas are proposed. If the user lives in a rural area, insurance and investment products that address risks specific to that area are proposed. Furthermore, if the user lives overseas, insurance and investment products that address risks specific to overseas areas are proposed. This makes it possible to propose asset management based on the user's place of residence, allowing for the provision of more appropriate services.

[0096] The life plan simulation system can estimate a user's emotions and make asset management suggestions based on the estimated emotions. For example, if a user is feeling anxious about the future, stable investment products to alleviate that anxiety are suggested. If a user is feeling optimistic, high-risk investment products are suggested. Furthermore, if a user is feeling stressed, relaxation-related investment products to alleviate that stress are suggested. This makes it possible to provide asset management suggestions based on the user's emotions and more personalized services.

[0097] The life plan simulation system can estimate the user's emotions and provide follow-up based on the estimated emotions. For example, if the user feels anxious, advice to alleviate the anxiety is provided. If the user feels satisfied, follow-up is provided to maintain the satisfaction. Furthermore, if the user has questions, answers to those questions are provided. This makes it possible to provide follow-up based on the user's emotions and provide more effective support.

[0098] The life plan simulation system can estimate the user's emotions and provide simulation results based on the estimated emotions. For example, if the user is feeling anxious about the future, simulation results to alleviate that anxiety are provided. Also, if the user is feeling optimistic, simulation results based on those emotions are provided. Furthermore, if the user is feeling stressed, simulation results to alleviate that stress are provided. This makes it possible to provide simulation results based on the user's emotions, allowing for more personalized services.

[0099] The life plan simulation system can estimate the user's emotions and provide an input interface based on the estimated emotions. For example, if the user is feeling stressed, an input interface for reducing the stress is provided. Also, if the user is feeling optimistic, an input interface for maintaining the optimistic emotions is provided. Furthermore, if the user is feeling anxious, an input interface for reducing the anxiety is provided. This makes it possible to provide an input interface based on the user's emotions, thereby providing a more comfortable input experience.

[0100] The life plan simulation system can estimate the user's emotions and provide suggestions based on the estimated emotions. For example, if the user is feeling anxious about the future, the system can provide suggestions to alleviate that anxiety. If the user is feeling optimistic, the system can provide suggestions based on those emotions. Furthermore, if the user is feeling stressed, the system can provide suggestions to alleviate that stress. This makes it possible to provide suggestions based on the user's emotions, allowing for more personalized services.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: With the user's consent, the data collection unit automatically collects financial data from electronic payment apps and related services. For example, the data collection unit obtains the user's spending history, income information, savings status, etc. The data collection unit also converts the data into a format that is easy for the generation AI to analyze. Step 2: The additional input section prompts the user to enter additional information if the automatically collected data is insufficient, such as information about future life events (marriage, children's education, home purchase, etc.) or specific financial management goals. Step 3: In the simulation section, the generation AI performs a life plan simulation based on the collected data and additional input information. For example, it predicts the user's future asset status based on their current income, expenses, and savings. The generation AI performs the simulation using text generation AI (e.g., LLM) or multimodal generation AI. Step 4: Based on the simulation results, the proposal unit proposes financial products such as asset management tailored to the user's life events, such as educational loans to prepare for children's education expenses, mortgages for home purchases, and pension insurance for retirement. Step 5: The follow-up department periodically collects and analyzes the user's financial data after the simulation and performs follow-up as necessary. For example, it updates the simulation results in response to fluctuations in income and expenses and makes new proposals.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[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 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.

[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 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.

[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 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

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

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] 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.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that automatically collects financial data from electronic payment applications and related services with the user's consent; an additional input unit that, if there is any deficiency in the automatically collected data, prompts the user to input additional information that is lacking; a simulation unit that performs a life plan simulation based on the collected data and additionally entered information; a proposal unit that proposes financial products based on the simulation results; A follow-up unit that periodically collects and analyzes the user's financial data and follows up as necessary. A system characterized by:

2. The data collection unit The financial data is collected not only from electronic payment apps but also from purchase history on social media and email.

2. The system of claim 1.

3. The data collection unit In addition to electronic payment apps, the app also works with fitness and health management apps to offer asset management suggestions based on the user's health status and exercise habits.

2. The system of claim 1.

4. The additional input unit Add a function to automatically complete the information entered by the user, minimizing the effort required for input.

2. The system of claim 1.

5. The simulation unit Introducing an algorithm that analyzes the user's past financial behavior and predicts future behavior.

2. The system of claim 1.

6. The proposal unit Analyzing the user's feelings toward the proposed financial product and making the proposal based on the feelings.

2. The system of claim 1.

7. The follow-up unit Add a function to monitor the user's financial data in real time and issue an alert if an anomaly is detected.

2. The system of claim 1.

8. The data collection unit Analyze the user's emotions at the time of purchase and reflect spending trends based on emotions in the data 2. The system of claim 1.

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