System

Abstract

An object of the system according to the embodiment is to generate digital twins based on personal information and to perform future prediction and risk assessment.SOLUTION: A system includes an information input unit, a generation unit, an option presentation unit, and a risk evaluation unit. The information input unit inputs information of a user. The generation unit generates digital twins based on the information input by the information input unit. The option presentation unit presents options based on the digital twins generated by the generation unit. The risk evaluation unit evaluates a risk level for the option presented by the option presentation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately predict the future or assess risks based on personal information, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a digital twin based on personal information and to perform future predictions and risk assessments. [Means for solving the problem]

[0006] A system according to an embodiment includes an information input unit, a generation unit, an option presentation unit, and a risk assessment unit. The information input unit inputs user information. The generation unit generates a digital twin based on the information input by the information input unit. The option presentation unit presents options based on the digital twin generated by the generation unit. The risk assessment unit evaluates the risk level of the options presented by the option presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment generates a digital twin based on personal information and can make future predictions and assess risks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A digital twin system according to an embodiment of the present invention is a system that generates a digital twin based on user information and presents options and risk levels, thereby supporting user decision-making.

[0029] A digital twin system according to an embodiment includes an information input unit, a generation unit, an option presentation unit, and a risk assessment unit. The information input unit inputs user information. For example, the user's personal information, health information, behavioral history, etc. can be input. The information input unit can also input the user's social media activities, purchase history, etc. The generation unit generates a digital twin based on the information input by the information input unit. For example, the generation AI generates a digital twin based on the user's detailed personal information. The generation AI can also generate a digital twin based on the user's health data and behavioral history. The generation AI can also generate a digital twin based on the user's emotional data. The option presentation unit presents options based on the digital twin generated by the generation unit. For example, the generation AI presents multiple investment options when a user is considering a new investment. The generation AI can also present multiple health management methods when a user makes health-related choices. The generation AI can also present multiple career plans when a user is considering a career path. The risk assessment unit evaluates the risk level of the options presented by the option presentation unit. For example, the generation AI calculates the risk level for each option and presents it to the user. The generation AI can also evaluate the return for each option and present the balance between risk and return. Furthermore, the generation AI can evaluate the risk level for each option over time and present short-, medium-, and long-term risks. This allows the digital twin system according to the embodiment to support user decision-making. For example, the user can make optimal investment choices based on the presented options and risk levels. Furthermore, when selecting a health management method, the user can select the optimal method by taking into account the risk and return. Furthermore, when considering a career plan, the user can select the optimal career path based on the risk and return.

[0030] The generation unit can collect a user's health data in real time and generate a digital twin that reflects the user's health condition. For example, the generation unit uses a wearable device to record the user's heart rate and sleep patterns to collect the user's health data in real time. For example, a smartwatch or fitness tracker is used. The generation unit also analyzes the health data and generates a digital twin that reflects the user's health condition. For example, if the user is sleep-deprived, a digital twin that reflects that condition is generated. The generation unit also predicts fluctuations in the user's health condition based on the user's health data and generates a digital twin based on that prediction. For example, if the user is likely to have a health risk in the future, a digital twin that reflects that prediction is generated. In this way, health risks can be predicted by generating a digital twin that reflects the user's health data in real time.

[0031] The generation unit can analyze a user's social media activity and generate a digital twin that takes into account their social influence and network. For example, the generation unit analyzes the user's social media activity and evaluates their social influence based on the content of their posts and reactions. For example, the generation unit analyzes the number of followers and engagement rate. The generation unit also analyzes their social media network and generates a digital twin that reflects the user's social influence. For example, if the user has many contacts with influential people, this state is reflected. The generation unit also predicts changes in social influence based on the user's social media activity and generates a digital twin based on this. For example, if the user is likely to become more influential in the future, a digital twin that reflects this prediction is generated. In this way, by generating a digital twin that takes into account the user's social media activity, it becomes possible to predict social influence.

[0032] The generation unit can also apply the digital twin to pets or at least one other family member to perform future predictions for the entire household. The generation unit, for example, collects information on all family members and generates digital twins for each member. For example, future predictions for the entire household are performed based on the family members' health data and behavioral history. The generation unit also collects information on pets and generates digital twins for the pets. For example, future predictions for the pets are performed based on the pets' health data and behavioral patterns. The generation unit also integrates the digital twins of the entire family to perform future predictions for the entire household. For example, risks and opportunities for the entire household are predicted based on the health status and behavioral patterns of all family members. In this way, future predictions for the entire household can be performed to understand risks and opportunities for all family members.

[0033] The generation unit can apply digital twins to a company's employees to help optimize work efficiency and career paths. The generation unit, for example, collects information about a company's employees and generates digital twins for each employee. For example, it supports the optimization of work efficiency based on work history and skill data. The generation unit also predicts employees' career paths and proposes optimal career plans. For example, it predicts future career paths based on employees' skills and experience. The generation unit also integrates digital twins across the entire company to help optimize work efficiency. For example, it identifies areas for improvement in work processes based on data about each employee. In this way, by applying digital twins to a company's employees, it becomes possible to optimize work efficiency and career paths.

[0034] The option presentation unit learns the user's past choices and their outcomes, allowing it to provide more accurate advice. For example, the option presentation unit stores the user's past choices and their outcomes in a database, which the generation AI then learns from. For example, future investment advice is provided based on past investment choices and their outcomes. The option presentation unit also analyzes past choices and their outcomes to find patterns. For example, it extracts characteristics of choices that the user made that were successful in the past and provides advice based on those characteristics. The option presentation unit also predicts future choices based on the user's past choices and their outcomes, providing more accurate advice. For example, it predicts the probability of success of future choices based on past data. This makes it possible to provide more accurate advice by learning the user's past choices and their outcomes.

[0035] The option presentation unit can present options that reflect region-specific risks and opportunities by taking into account the user's geographical location information. The option presentation unit, for example, collects the user's geographical location information and evaluates region-specific risks and opportunities based on that information. For example, it presents investment risks and business opportunities in a specific region. The option presentation unit also presents region-specific options based on the geographical location information. For example, it provides options that correspond to the characteristics of the region in which the user lives. The option presentation unit also analyzes geographical data to present options that reflect region-specific risks and opportunities. For example, it presents options based on the region's economic situation and social trends. In this way, it is possible to provide options that reflect region-specific risks and opportunities by taking into account the user's geographical location information.

[0036] The option presentation unit can collect feedback from other users on the user's options and provide advice utilizing collective intelligence. For example, the option presentation unit collects feedback from other users on the user's options and provides advice based on that information. For example, it presents examples of success and failure of other users who chose the same option. The option presentation unit also utilizes collective intelligence to provide advice on the user's options. For example, it aggregates the opinions of many users and presents the most popular option. The option presentation unit also evaluates the risks and opportunities for the user's options based on the feedback from other users. For example, it analyzes past feedback data and predicts the probability of success of the options. In this way, more multifaceted advice can be provided by utilizing the feedback of other users.

[0037] The option presentation unit can visualize the simulation results for the user's options and present them in a visually easy-to-understand format. The option presentation unit, for example, visualizes the simulation results for the user's options and displays them in graphs or charts. For example, it visually shows the risk and return of an investment. The option presentation unit also provides an interactive dashboard to visualize the simulation results. For example, the simulation results are updated in real time every time the user changes their options. The option presentation unit also visualizes the simulation results to enable the user to intuitively understand them. For example, it displays the results of the options as a 3D model or animation. In this way, visualizing the simulation results makes it easier for the user to intuitively understand them.

[0038] The risk assessment unit can learn the user's past risk preferences and present an individually customized risk level. For example, the risk assessment unit stores the user's past risk preferences in a database, and the generation AI learns them. For example, it customizes future risk levels based on past investment choices and their risk levels. The risk assessment unit also analyzes past risk preferences and evaluates the user's risk tolerance. For example, it presents a risk level based on the success rate of high-risk options chosen by the user in the past. The risk assessment unit also customizes the risk level for future options based on the user's past risk preferences. For example, it presents an optimal risk level to the user based on past data. In this way, by learning the user's past risk preferences, it is possible to present an individually customized risk level.

[0039] The risk assessment unit can analyze the user's behavioral history and present a risk level based on the behavioral patterns. For example, the risk assessment unit stores the user's behavioral history in a database, and the generation AI learns from it. For example, a future risk level is presented based on past behavioral patterns and their risk levels. The risk assessment unit also analyzes the behavioral history and evaluates the risk level based on the user's behavioral patterns. For example, a risk level is presented based on the success rate of high-risk actions chosen by the user in the past. The risk assessment unit also presents a risk level for future options based on the user's behavioral history. For example, the optimal risk level is presented to the user based on past data. In this way, by analyzing the user's behavioral history, a risk level based on the behavioral patterns can be presented.

[0040] The risk assessment unit can present risk levels on different time axes and evaluate short-, medium-, and long-term risks. For example, the risk assessment unit evaluates risk levels on short-, medium-, and long-term time axes and presents the respective risk levels. For example, it presents options that have low short-term risk but high long-term risk. The risk assessment unit also analyzes past data to evaluate risk levels on different time axes. For example, it presents future risk levels based on past short-, medium-, and long-term risk levels. The risk assessment unit also performs simulations to present risk levels on different time axes. For example, it simulates short-, medium-, and long-term scenarios and presents the respective risk levels. This makes it possible to evaluate short-, medium-, and long-term risks by presenting risk levels on different time axes.

[0041] The risk assessment unit can simulate risk levels under different scenarios and present the worst and best cases. For example, the risk assessment unit simulates risk levels under different scenarios and presents the worst and best cases. For example, it simulates scenarios that take into account fluctuations in economic conditions and market changes. The risk assessment unit also analyzes past data to evaluate risk levels under different scenarios. For example, it presents future risk levels based on past worst and best cases. The risk assessment unit also performs simulations to present risk levels under different scenarios. For example, it simulates worst and best scenarios and presents the respective risk levels. In this way, by simulating risk levels under different scenarios, it is possible to present the worst and best cases.

[0042] The future prediction unit can predict changes in health status based on the user's health data and present health risks. The future prediction unit, for example, collects the user's health data and predicts changes in health status based on that data. For example, it analyzes heart rate and sleep patterns and presents future health risks. The future prediction unit also predicts changes in the user's health status based on the health data and makes a future prediction based on that. For example, if the user is likely to face a health risk in the future, it makes a future prediction that reflects that prediction. The future prediction unit also analyzes the user's health data and develops an algorithm for predicting changes in health status. For example, it predicts future health risks based on past health data. This makes it possible to present health risks by predicting changes in health status based on the user's health data.

[0043] The future prediction unit can analyze a user's social media activity and predict changes in social influence. The future prediction unit, for example, analyzes a user's social media activity and predicts changes in social influence based on the content of posts and reactions. For example, it analyzes the number of followers and engagement rate to predict future influence. The future prediction unit also analyzes the social media network and predicts changes in the user's social influence. For example, if the user has many contacts with influential people, it makes a future prediction that reflects that state. The future prediction unit also develops an algorithm for predicting changes in social influence based on the user's social media activity. For example, it predicts future influence based on past social media data. In this way, it is possible to predict changes in social influence by analyzing a user's social media activity.

[0044] The future prediction unit can perform future predictions using different scenarios and present the worst- and best-case scenarios. For example, the future prediction unit performs future predictions using different scenarios and presents the worst- and best-case scenarios. For example, it simulates scenarios that take into account fluctuations in economic conditions and market changes. The future prediction unit also analyzes past data to perform future predictions using different scenarios. For example, it predicts the future based on the worst- and best-case scenarios of the past. The future prediction unit also performs simulations to present future predictions using different scenarios. For example, it simulates the worst- and best-case scenarios and presents the respective future predictions. In this way, by performing future predictions using different scenarios, it is possible to present the worst- and best-case scenarios.

[0045] The future prediction unit can visualize the future prediction and present it in a form that is visually easy to understand. For example, the future prediction unit visualizes the future prediction and displays it in a graph or chart. For example, future risk and return are visually shown. The future prediction unit also provides an interactive dashboard to visualize the future prediction. For example, the future prediction is updated in real time every time the user changes their choice. The future prediction unit also visualizes the future prediction to enable the user to intuitively understand it. For example, future scenarios are displayed as 3D models or animations. In this way, visualizing the future prediction makes it easier for the user to intuitively understand.

[0046] The digital twin utilization unit can use the digital twin to simulate the user's health condition and predict health risks. For example, the digital twin utilization unit uses the digital twin to analyze the user's health data and simulate the health condition. For example, future health risks are predicted based on heart rate and sleep patterns. The digital twin utilization unit also simulates fluctuations in the user's health condition based on the health data and predicts health risks based on that. For example, if the user is likely to face a health risk in the future, it performs a simulation that reflects that prediction. The digital twin utilization unit also uses the digital twin to develop an algorithm for simulating the user's health condition. For example, future health risks are predicted based on past health data. In this way, the digital twin can be used to predict the user's health risks.

[0047] The digital twin utilization unit can use the digital twin to simulate a user's social influence and predict changes in the network. For example, the digital twin utilization unit uses the digital twin to analyze a user's social media activity and simulate social influence. For example, future influence is predicted based on the number of followers and engagement rate. The digital twin utilization unit also analyzes the social media network and simulates changes in the user's social influence. For example, if a user has many contacts with influential people, a simulation is performed that reflects that state. The digital twin utilization unit also uses the digital twin to develop an algorithm for simulating a user's social influence. For example, future influence is predicted based on past social media data. In this way, changes in a user's social influence can be predicted using the digital twin.

[0048] The digital twin utilization unit can apply the digital twin to other individuals such as pets and family members to make future predictions for the entire household. The digital twin utilization unit, for example, collects information on all family members and generates digital twins for each member. For example, it makes future predictions for the entire household based on the family members' health data and behavioral history. The digital twin utilization unit also collects information on pets and generates digital twins for the pets. For example, it makes future predictions for the pets based on the pets' health data and behavioral patterns. The digital twin utilization unit also integrates the digital twins of the entire family to make future predictions for the entire household. For example, it predicts risks and opportunities for the entire household based on the health status and behavioral patterns of all family members. In this way, by making future predictions for the entire household, it is possible to understand the risks and opportunities for all family members.

[0049] The Digital Twin Utilization Department can apply digital twins to a company's employees to help optimize work efficiency and career paths. The Digital Twin Utilization Department, for example, collects information about a company's employees and generates a digital twin for each employee. For example, it supports the optimization of work efficiency based on work history and skill data. The Digital Twin Utilization Department also predicts employees' career paths and proposes optimal career plans. For example, it predicts future career paths based on employees' skills and experience. The Digital Twin Utilization Department also integrates digital twins across the entire company to help optimize work efficiency. For example, it identifies areas for improvement in work processes based on data about each employee. In this way, applying digital twins to a company's employees makes it possible to optimize work efficiency and career paths.

[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] Digital twin systems can also analyze a user's hobbies and interests and provide recommendations based on them. For example, if a user likes music, they can provide the latest music trends and concert information. If a user likes traveling, they can suggest recommended spots and travel plans. Furthermore, if a user is interested in cooking, they can provide information on new recipes and cooking classes. This makes it possible to provide information based on the user's hobbies and interests, thereby increasing user satisfaction.

[0052] The generation unit can also collect lifestyle data about the user and generate a digital twin based on that lifestyle. For example, it can analyze the user's exercise habits and eating patterns to generate a digital twin that supports a healthy lifestyle. It can also analyze the user's sleep patterns and suggest an optimal sleeping environment. It can also generate a digital twin that takes the user's work schedule into account to support efficient time management. This allows for support based on the user's lifestyle.

[0053] The generation unit can also analyze a user's purchasing history and generate a digital twin based on their purchasing patterns. For example, it can analyze the products and brands that the user frequently purchases and provide discount information for specific products or information on new products. It can also predict future purchasing trends based on the user's purchasing history and make recommendations based on those predictions. It can also analyze a user's purchasing history to suggest eco-friendly products and services. This makes it possible to provide information based on the user's purchasing patterns.

[0054] The generation unit can also analyze the user's learning history and generate a digital twin based on their learning patterns. For example, it can analyze the content the user has learned in the past and their areas of interest and suggest related learning resources and courses. It can also track the user's learning progress in real time and provide advice to improve learning efficiency. Furthermore, it can set future learning goals based on the user's learning history and suggest a learning plan based on those goals. This allows support to be provided based on the user's learning patterns.

[0055] The generator can also analyze the user's fitness data and generate a digital twin based on their fitness patterns. For example, it can analyze the user's exercise history and fitness goals and suggest an optimal training plan. It can also track the user's fitness data in real time and provide advice to maximize the benefits of exercise. It can also set future fitness goals based on the user's fitness data and suggest training plans based on those goals. This allows the device to provide support based on the user's fitness patterns.

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

[0057] Step 1: The information input unit inputs user information, such as the user's personal information, health information, behavioral history, social media activity, and purchase history. Step 2: The generation unit generates a digital twin based on the information input by the information input unit. For example, the generation AI generates a digital twin based on the user's detailed personal information, health data, behavioral history, and emotional data. Step 3: The option presentation unit presents options based on the digital twin generated by the generation unit. For example, the generation AI may present multiple investment options when the user is considering a new investment, multiple health management methods when making health-related choices, or multiple career plans when considering a career path. Step 4: The risk assessment unit evaluates the risk level for the options presented by the option presentation unit. For example, the generation AI calculates the risk level for each option, presents the balance between risk and return, and evaluates short-term, medium-term, and long-term risks.

[0058] (Example 2) A digital twin system according to an embodiment of the present invention is a system that generates a digital twin based on user information and presents options and risk levels, thereby supporting user decision-making.

[0059] A digital twin system according to an embodiment includes an information input unit, a generation unit, an option presentation unit, and a risk assessment unit. The information input unit inputs user information. For example, the user's personal information, health information, behavioral history, etc. can be input. The information input unit can also input the user's social media activities, purchase history, etc. The generation unit generates a digital twin based on the information input by the information input unit. For example, the generation AI generates a digital twin based on the user's detailed personal information. The generation AI can also generate a digital twin based on the user's health data and behavioral history. The generation AI can also generate a digital twin based on the user's emotional data. The option presentation unit presents options based on the digital twin generated by the generation unit. For example, the generation AI presents multiple investment options when a user is considering a new investment. The generation AI can also present multiple health management methods when a user makes health-related choices. The generation AI can also present multiple career plans when a user is considering a career path. The risk assessment unit evaluates the risk level of the options presented by the option presentation unit. For example, the generation AI calculates the risk level for each option and presents it to the user. The generation AI can also evaluate the return for each option and present the balance between risk and return. Furthermore, the generation AI can evaluate the risk level for each option over time and present short-, medium-, and long-term risks. This allows the digital twin system according to the embodiment to support user decision-making. For example, the user can make optimal investment choices based on the presented options and risk levels. Furthermore, when selecting a health management method, the user can select the optimal method by taking into account the risk and return. Furthermore, when considering a career plan, the user can select the optimal career path based on the risk and return.

[0060] The generation unit can collect the user's emotional data and generate a digital twin that takes into account emotional fluctuations using an emotion estimation function. To collect the user's emotional data, the generation unit, for example, analyzes the user's daily actions and statements and records emotional fluctuations in real time. For example, the generation unit quantifies the user's emotional state using a smartphone sensor or a wearable device. The generation unit also analyzes the user's emotional data using the emotion estimation function to generate a digital twin that takes into account emotional fluctuations. For example, if the user is feeling stressed, the generation unit generates a digital twin that reflects that state. The generation unit also predicts emotional fluctuations based on the user's emotional data and generates a digital twin based on that prediction. For example, if the user is likely to experience emotional fluctuations in the future, the generation unit generates a digital twin that reflects that prediction. This allows for more accurate future predictions by generating a digital twin that takes into account the user's emotional data.

[0061] The generation unit can collect a user's health data in real time and generate a digital twin that reflects the user's health condition. For example, the generation unit uses a wearable device to record the user's heart rate and sleep patterns to collect the user's health data in real time. For example, a smartwatch or fitness tracker is used. The generation unit also analyzes the health data and generates a digital twin that reflects the user's health condition. For example, if the user is sleep-deprived, a digital twin that reflects that condition is generated. The generation unit also predicts fluctuations in the user's health condition based on the user's health data and generates a digital twin based on that prediction. For example, if the user is likely to have a health risk in the future, a digital twin that reflects that prediction is generated. In this way, health risks can be predicted by generating a digital twin that reflects the user's health data in real time.

[0062] The generation unit can analyze a user's social media activity and generate a digital twin that takes into account their social influence and network. For example, the generation unit analyzes the user's social media activity and evaluates their social influence based on the content of their posts and reactions. For example, the generation unit analyzes the number of followers and engagement rate. The generation unit also analyzes their social media network and generates a digital twin that reflects the user's social influence. For example, if the user has many contacts with influential people, this state is reflected. The generation unit also predicts changes in social influence based on the user's social media activity and generates a digital twin based on this. For example, if the user is likely to become more influential in the future, a digital twin that reflects this prediction is generated. In this way, by generating a digital twin that takes into account the user's social media activity, it becomes possible to predict social influence.

[0063] The generation unit can also apply the digital twin to pets or at least one other family member to perform future predictions for the entire household. The generation unit, for example, collects information on all family members and generates digital twins for each member. For example, future predictions for the entire household are performed based on the family members' health data and behavioral history. The generation unit also collects information on pets and generates digital twins for the pets. For example, future predictions for the pets are performed based on the pets' health data and behavioral patterns. The generation unit also integrates the digital twins of the entire family to perform future predictions for the entire household. For example, risks and opportunities for the entire household are predicted based on the health status and behavioral patterns of all family members. In this way, future predictions for the entire household can be performed to understand risks and opportunities for all family members.

[0064] The generation unit can apply digital twins to a company's employees to help optimize work efficiency and career paths. The generation unit, for example, collects information about a company's employees and generates digital twins for each employee. For example, it supports the optimization of work efficiency based on work history and skill data. The generation unit also predicts employees' career paths and proposes optimal career plans. For example, it predicts future career paths based on employees' skills and experience. The generation unit also integrates digital twins across the entire company to help optimize work efficiency. For example, it identifies areas for improvement in work processes based on data about each employee. In this way, by applying digital twins to a company's employees, it becomes possible to optimize work efficiency and career paths.

[0065] The generation unit uses the emotion estimation function to generate a digital twin based on the user's emotions and provide emotional support. The generation unit, for example, uses the emotion estimation function to analyze the user's emotion data and generate a digital twin based on the emotions. For example, if the user is feeling stressed, the generation unit generates a digital twin that reflects that state. The generation unit also generates a digital twin that provides emotional support based on the user's emotion data. For example, if the user is feeling anxious, the generation unit generates a digital twin that provides a sense of security. The generation unit also uses the emotion estimation function to predict the user's emotional fluctuations and generate a digital twin based on those predictions. For example, if the user is likely to experience emotional fluctuations in the future, the generation unit generates a digital twin that reflects that prediction. In this way, emotional support can be provided by generating a digital twin based on the user's emotions.

[0066] The option presentation unit learns the user's past choices and their outcomes, allowing it to provide more accurate advice. For example, the option presentation unit stores the user's past choices and their outcomes in a database, which the generation AI then learns from. For example, future investment advice is provided based on past investment choices and their outcomes. The option presentation unit also analyzes past choices and their outcomes to find patterns. For example, it extracts characteristics of choices that the user made that were successful in the past and provides advice based on those characteristics. The option presentation unit also predicts future choices based on the user's past choices and their outcomes, providing more accurate advice. For example, it predicts the probability of success of future choices based on past data. This makes it possible to provide more accurate advice by learning the user's past choices and their outcomes.

[0067] The option presentation unit can evaluate the user's current psychological state using the emotion estimation function and provide options and advice based on that. The option presentation unit, for example, uses the emotion estimation function to evaluate the user's current psychological state in real time. For example, if the user is feeling stressed, the option presentation unit presents options that reflect that state. The option presentation unit also provides optimal options and advice based on the user's psychological state. For example, if the user is relaxed, the option presentation unit presents options that are appropriate for that state. The option presentation unit also predicts the user's psychological state based on the emotion estimation data and provides options and advice based on that prediction. For example, if the user is likely to feel stressed in the future, the option presentation unit presents options that reflect that prediction. In this way, by evaluating the user's current psychological state, more appropriate options and advice can be provided.

[0068] The option presentation unit can present options that reflect region-specific risks and opportunities by taking into account the user's geographical location information. The option presentation unit, for example, collects the user's geographical location information and evaluates region-specific risks and opportunities based on that information. For example, it presents investment risks and business opportunities in a specific region. The option presentation unit also presents region-specific options based on the geographical location information. For example, it provides options that correspond to the characteristics of the region in which the user lives. The option presentation unit also analyzes geographical data to present options that reflect region-specific risks and opportunities. For example, it presents options based on the region's economic situation and social trends. In this way, it is possible to provide options that reflect region-specific risks and opportunities by taking into account the user's geographical location information.

[0069] The option presentation unit can collect feedback from other users on the user's options and provide advice utilizing collective intelligence. For example, the option presentation unit collects feedback from other users on the user's options and provides advice based on that information. For example, it presents examples of success and failure of other users who chose the same option. The option presentation unit also utilizes collective intelligence to provide advice on the user's options. For example, it aggregates the opinions of many users and presents the most popular option. The option presentation unit also evaluates the risks and opportunities for the user's options based on the feedback from other users. For example, it analyzes past feedback data and predicts the probability of success of the options. In this way, more multifaceted advice can be provided by utilizing the feedback of other users.

[0070] The option presentation unit can visualize the simulation results for the user's options and present them in a visually easy-to-understand format. The option presentation unit, for example, visualizes the simulation results for the user's options and displays them in graphs or charts. For example, it visually shows the risk and return of an investment. The option presentation unit also provides an interactive dashboard to visualize the simulation results. For example, the simulation results are updated in real time every time the user changes their options. The option presentation unit also visualizes the simulation results to enable the user to intuitively understand them. For example, it displays the results of the options as a 3D model or animation. In this way, visualizing the simulation results makes it easier for the user to intuitively understand them.

[0071] The option presentation unit uses the emotion estimation function to present options based on the user's emotions, thereby increasing emotional satisfaction. The option presentation unit, for example, uses the emotion estimation function to analyze the user's emotion data and present options based on the emotions. For example, if the user is relaxed, options appropriate for that state are provided. The option presentation unit also presents options that increase emotional satisfaction based on the user's emotion data. For example, options that the user has positive emotions for are presented preferentially. The option presentation unit also predicts the user's emotional fluctuations based on the emotion estimation data and presents options based on the prediction. For example, options that the user is likely to have positive emotions for in the future are provided. In this way, emotional satisfaction is increased by presenting options based on the user's emotions.

[0072] The risk assessment unit can learn the user's past risk preferences and present an individually customized risk level. For example, the risk assessment unit stores the user's past risk preferences in a database, and the generation AI learns them. For example, it customizes future risk levels based on past investment choices and their risk levels. The risk assessment unit also analyzes past risk preferences and evaluates the user's risk tolerance. For example, it presents a risk level based on the success rate of high-risk options chosen by the user in the past. The risk assessment unit also customizes the risk level for future options based on the user's past risk preferences. For example, it presents an optimal risk level to the user based on past data. In this way, by learning the user's past risk preferences, it is possible to present an individually customized risk level.

[0073] The risk assessment unit can use the emotion estimation function to evaluate the emotional risk tolerance and present a risk level based on the evaluation. The risk assessment unit, for example, uses the emotion estimation function to analyze the user's emotion data and evaluate the emotional risk tolerance. For example, if the user is feeling stressed, a risk level reflecting that state is presented. The risk assessment unit also evaluates the user's emotional risk tolerance based on the user's emotion data and presents a risk level based on the evaluation. For example, if the user is relaxed, a risk level appropriate for that state is presented. The risk assessment unit also predicts the user's emotional fluctuations based on the emotion estimation data and presents a risk level based on the prediction. For example, if the user is likely to experience emotional fluctuations in the future, a risk level reflecting the prediction is presented. In this way, a more appropriate risk level can be presented by evaluating the user's emotional risk tolerance.

[0074] The risk assessment unit can analyze the user's behavioral history and present a risk level based on the behavioral patterns. For example, the risk assessment unit stores the user's behavioral history in a database, and the generation AI learns from it. For example, a future risk level is presented based on past behavioral patterns and their risk levels. The risk assessment unit also analyzes the behavioral history and evaluates the risk level based on the user's behavioral patterns. For example, a risk level is presented based on the success rate of high-risk actions chosen by the user in the past. The risk assessment unit also presents a risk level for future options based on the user's behavioral history. For example, the optimal risk level is presented to the user based on past data. In this way, by analyzing the user's behavioral history, a risk level based on the behavioral patterns can be presented.

[0075] The risk assessment unit can present risk levels on different time axes and evaluate short-, medium-, and long-term risks. For example, the risk assessment unit evaluates risk levels on short-, medium-, and long-term time axes and presents the respective risk levels. For example, it presents options that have low short-term risk but high long-term risk. The risk assessment unit also analyzes past data to evaluate risk levels on different time axes. For example, it presents future risk levels based on past short-, medium-, and long-term risk levels. The risk assessment unit also performs simulations to present risk levels on different time axes. For example, it simulates short-, medium-, and long-term scenarios and presents the respective risk levels. This makes it possible to evaluate short-, medium-, and long-term risks by presenting risk levels on different time axes.

[0076] The risk assessment unit can simulate risk levels under different scenarios and present the worst and best cases. For example, the risk assessment unit simulates risk levels under different scenarios and presents the worst and best cases. For example, it simulates scenarios that take into account fluctuations in economic conditions and market changes. The risk assessment unit also analyzes past data to evaluate risk levels under different scenarios. For example, it presents future risk levels based on past worst and best cases. The risk assessment unit also performs simulations to present risk levels under different scenarios. For example, it simulates worst and best scenarios and presents the respective risk levels. In this way, by simulating risk levels under different scenarios, it is possible to present the worst and best cases.

[0077] The risk assessment unit can use the emotion estimation function to present a risk level based on the user's emotions, thereby providing a sense of emotional security. The risk assessment unit, for example, uses the emotion estimation function to analyze the user's emotion data and present a risk level based on the emotions. For example, if the user is relaxed, the risk assessment unit presents a risk level appropriate for that state. The risk assessment unit also presents a risk level that provides a sense of emotional security based on the user's emotion data. For example, if the user is feeling anxious, the risk assessment unit presents a risk level that reflects that state. The risk assessment unit also predicts the user's emotional fluctuations based on the emotion estimation data and presents a risk level based on that. For example, if the user is likely to experience emotional fluctuations in the future, the risk assessment unit presents a risk level that reflects that prediction. In this way, by presenting a risk level based on the user's emotions, a sense of emotional security can be provided.

[0078] The future prediction unit can use the user's emotion estimation function to make a future prediction that takes into account emotional fluctuations. The future prediction unit, for example, uses the emotion estimation function to analyze the user's emotion data and make a future prediction that takes into account emotional fluctuations. For example, if the user is feeling stressed, a future prediction that reflects that state is made. The future prediction unit also predicts emotional fluctuations based on the user's emotion data and makes a future prediction based on that. For example, if the user is likely to experience emotional fluctuations in the future, a future prediction that reflects that prediction is made. The future prediction unit also makes a future prediction that takes into account the user's emotional fluctuations based on the emotion estimation data. For example, if the user is likely to have positive emotions in the future, a future prediction that reflects that prediction is made. In this way, by making a future prediction that takes into account the user's emotional fluctuations, more accurate future predictions are possible.

[0079] The future prediction unit can predict changes in health status based on the user's health data and present health risks. The future prediction unit, for example, collects the user's health data and predicts changes in health status based on that data. For example, it analyzes heart rate and sleep patterns and presents future health risks. The future prediction unit also predicts changes in the user's health status based on the health data and makes a future prediction based on that. For example, if the user is likely to face a health risk in the future, it makes a future prediction that reflects that prediction. The future prediction unit also analyzes the user's health data and develops an algorithm for predicting changes in health status. For example, it predicts future health risks based on past health data. This makes it possible to present health risks by predicting changes in health status based on the user's health data.

[0080] The future prediction unit can analyze a user's social media activity and predict changes in social influence. The future prediction unit, for example, analyzes a user's social media activity and predicts changes in social influence based on the content of posts and reactions. For example, it analyzes the number of followers and engagement rate to predict future influence. The future prediction unit also analyzes the social media network and predicts changes in the user's social influence. For example, if the user has many contacts with influential people, it makes a future prediction that reflects that state. The future prediction unit also develops an algorithm for predicting changes in social influence based on the user's social media activity. For example, it predicts future influence based on past social media data. In this way, it is possible to predict changes in social influence by analyzing a user's social media activity.

[0081] The future prediction unit can perform future predictions using different scenarios and present the worst- and best-case scenarios. For example, the future prediction unit performs future predictions using different scenarios and presents the worst- and best-case scenarios. For example, it simulates scenarios that take into account fluctuations in economic conditions and market changes. The future prediction unit also analyzes past data to perform future predictions using different scenarios. For example, it predicts the future based on the worst- and best-case scenarios of the past. The future prediction unit also performs simulations to present future predictions using different scenarios. For example, it simulates the worst- and best-case scenarios and presents the respective future predictions. In this way, by performing future predictions using different scenarios, it is possible to present the worst- and best-case scenarios.

[0082] The future prediction unit can visualize the future prediction and present it in a form that is visually easy to understand. For example, the future prediction unit visualizes the future prediction and displays it in a graph or chart. For example, future risk and return are visually shown. The future prediction unit also provides an interactive dashboard to visualize the future prediction. For example, the future prediction is updated in real time every time the user changes their choice. The future prediction unit also visualizes the future prediction to enable the user to intuitively understand it. For example, future scenarios are displayed as 3D models or animations. In this way, visualizing the future prediction makes it easier for the user to intuitively understand.

[0083] The future prediction unit uses the emotion estimation function to make future predictions based on the user's emotions, thereby providing a sense of emotional security. The future prediction unit, for example, uses the emotion estimation function to analyze the user's emotion data and make future predictions based on the emotions. For example, if the user is relaxed, a future prediction appropriate for that state is made. The future prediction unit also makes future predictions that provide a sense of emotional security based on the user's emotion data. For example, if the user is feeling anxious, a future prediction that reflects that state is made. The future prediction unit also predicts the user's emotional fluctuations based on the emotion estimation data and makes a future prediction based on that. For example, if there is a high possibility that the user will experience emotional fluctuations in the future, a future prediction that reflects that prediction is made. In this way, by making future predictions based on the user's emotions, a sense of emotional security can be provided.

[0084] The digital twin utilization unit can use the digital twin to provide emotional support by utilizing the user's emotion estimation function. The digital twin utilization unit, for example, uses the digital twin to analyze the user's emotion data and provide emotional support. For example, if the user is feeling stressed, support that reflects that state is provided. The digital twin utilization unit also generates a digital twin that provides emotional support based on the user's emotion data. For example, if the user is feeling anxious, a digital twin that provides a sense of security is generated. The digital twin utilization unit also uses the emotion estimation function to predict the user's emotional fluctuations and generate a digital twin that provides emotional support based on those predictions. For example, if the user is likely to experience emotional fluctuations in the future, support that reflects that prediction is provided. In this way, support based on the user's emotions can be provided by using the digital twin.

[0085] The digital twin utilization unit can use the digital twin to simulate the user's health condition and predict health risks. For example, the digital twin utilization unit uses the digital twin to analyze the user's health data and simulate the health condition. For example, future health risks are predicted based on heart rate and sleep patterns. The digital twin utilization unit also simulates fluctuations in the user's health condition based on the health data and predicts health risks based on that. For example, if the user is likely to face a health risk in the future, it performs a simulation that reflects that prediction. The digital twin utilization unit also uses the digital twin to develop an algorithm for simulating the user's health condition. For example, future health risks are predicted based on past health data. In this way, the digital twin can be used to predict the user's health risks.

[0086] The digital twin utilization unit can use the digital twin to simulate a user's social influence and predict changes in the network. For example, the digital twin utilization unit uses the digital twin to analyze a user's social media activity and simulate social influence. For example, future influence is predicted based on the number of followers and engagement rate. The digital twin utilization unit also analyzes the social media network and simulates changes in the user's social influence. For example, if a user has many contacts with influential people, a simulation is performed that reflects that state. The digital twin utilization unit also uses the digital twin to develop an algorithm for simulating a user's social influence. For example, future influence is predicted based on past social media data. In this way, changes in a user's social influence can be predicted using the digital twin.

[0087] The digital twin utilization unit can apply the digital twin to other individuals such as pets and family members to make future predictions for the entire household. The digital twin utilization unit, for example, collects information on all family members and generates digital twins for each member. For example, it makes future predictions for the entire household based on the family members' health data and behavioral history. The digital twin utilization unit also collects information on pets and generates digital twins for the pets. For example, it makes future predictions for the pets based on the pets' health data and behavioral patterns. The digital twin utilization unit also integrates the digital twins of the entire family to make future predictions for the entire household. For example, it predicts risks and opportunities for the entire household based on the health status and behavioral patterns of all family members. In this way, by making future predictions for the entire household, it is possible to understand the risks and opportunities for all family members.

[0088] The Digital Twin Utilization Department can apply digital twins to a company's employees to help optimize work efficiency and career paths. The Digital Twin Utilization Department, for example, collects information about a company's employees and generates a digital twin for each employee. For example, it supports the optimization of work efficiency based on work history and skill data. The Digital Twin Utilization Department also predicts employees' career paths and proposes optimal career plans. For example, it predicts future career paths based on employees' skills and experience. The Digital Twin Utilization Department also integrates digital twins across the entire company to help optimize work efficiency. For example, it identifies areas for improvement in work processes based on data about each employee. In this way, applying digital twins to a company's employees makes it possible to optimize work efficiency and career paths.

[0089] The digital twin utilization unit uses the emotion estimation function to utilize a digital twin based on the user's emotions and provide emotional support. The digital twin utilization unit, for example, uses the emotion estimation function to analyze the user's emotion data and utilize the digital twin based on the emotions. For example, if the user is feeling stressed, support that reflects that state is provided. The digital twin utilization unit also generates a digital twin that provides emotional support based on the user's emotion data. For example, if the user is feeling anxious, a digital twin that provides a sense of security is generated. The digital twin utilization unit also uses the emotion estimation function to predict the user's emotional fluctuations and generate a digital twin that provides emotional support based on those predictions. For example, if the user is likely to experience emotional fluctuations in the future, support that reflects that prediction is provided. In this way, emotional support can be provided by utilizing a digital twin based on the user's emotions.

[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] Digital twin systems can also analyze a user's hobbies and interests and provide recommendations based on them. For example, if a user likes music, they can provide the latest music trends and concert information. If a user likes traveling, they can suggest recommended spots and travel plans. Furthermore, if a user is interested in cooking, they can provide information on new recipes and cooking classes. This makes it possible to provide information based on the user's hobbies and interests, thereby increasing user satisfaction.

[0092] The generation unit can also collect lifestyle data about the user and generate a digital twin based on that lifestyle. For example, it can analyze the user's exercise habits and eating patterns to generate a digital twin that supports a healthy lifestyle. It can also analyze the user's sleep patterns and suggest an optimal sleeping environment. It can also generate a digital twin that takes the user's work schedule into account to support efficient time management. This allows for support based on the user's lifestyle.

[0093] The generation unit can also analyze a user's purchasing history and generate a digital twin based on their purchasing patterns. For example, it can analyze the products and brands that the user frequently purchases and provide discount information for specific products or information on new products. It can also predict future purchasing trends based on the user's purchasing history and make recommendations based on those predictions. It can also analyze a user's purchasing history to suggest eco-friendly products and services. This makes it possible to provide information based on the user's purchasing patterns.

[0094] The generation unit can also analyze the user's learning history and generate a digital twin based on their learning patterns. For example, it can analyze the content the user has learned in the past and their areas of interest and suggest related learning resources and courses. It can also track the user's learning progress in real time and provide advice to improve learning efficiency. Furthermore, it can set future learning goals based on the user's learning history and suggest a learning plan based on those goals. This allows support to be provided based on the user's learning patterns.

[0095] The generator can also analyze the user's fitness data and generate a digital twin based on their fitness patterns. For example, it can analyze the user's exercise history and fitness goals and suggest an optimal training plan. It can also track the user's fitness data in real time and provide advice to maximize the benefits of exercise. It can also set future fitness goals based on the user's fitness data and suggest training plans based on those goals. This allows the device to provide support based on the user's fitness patterns.

[0096] The option presentation unit can also estimate the user's emotions and present options based on those emotions. For example, if the user is feeling stressed, options that will help them relax can be presented. Also, if the user is excited, it can suggest activities that are appropriate for that state. Furthermore, it is possible to predict emotional fluctuations based on the user's emotional data and present options based on those fluctuations. In this way, by providing options based on the user's emotions, it is possible to increase emotional satisfaction.

[0097] The risk assessment unit can also estimate the user's emotions and perform risk assessment based on the emotions. For example, if the user is feeling anxious, the risk assessment unit can perform a risk assessment that reflects that state. Also, if the user is feeling confident, the risk assessment unit can perform a risk assessment appropriate to that state. Furthermore, it is possible to predict emotional fluctuations based on the user's emotional data and perform risk assessment based on that. This allows for more appropriate risk management by performing risk assessment based on the user's emotions.

[0098] The risk assessment unit can also estimate the user's emotions and propose risk avoidance measures based on the emotions. For example, if the user is feeling stressed, it can propose risk avoidance measures that reflect that state. Also, if the user is relaxed, it can propose risk avoidance measures that are appropriate for that state. Furthermore, it is possible to predict emotional fluctuations based on the user's emotional data and propose risk avoidance measures based on those fluctuations. In this way, by providing risk avoidance measures based on the user's emotions, it is possible to increase the user's emotional security.

[0099] The future prediction unit can also estimate the user's emotions and make future predictions based on those emotions. For example, if the user has positive emotions, a future prediction that reflects that state can be made. Also, if the user has negative emotions, a future prediction appropriate to that state can be made. Furthermore, it is possible to predict emotional fluctuations based on the user's emotional data and make future predictions based on that. This makes it possible to make more accurate future predictions by making future predictions based on the user's emotions.

[0100] The future prediction unit can also estimate the user's emotions and present future risks and opportunities based on those emotions. For example, if the user is feeling stressed, future risks that reflect that state can be presented. Also, if the user is relaxed, future opportunities appropriate to that state can be presented. Furthermore, it is possible to predict emotional fluctuations based on the user's emotional data and present future risks and opportunities based on those fluctuations. In this way, by providing future risks and opportunities based on the user's emotions, it is possible to increase the user's emotional security.

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

[0102] Step 1: The information input unit inputs user information, such as the user's personal information, health information, behavioral history, social media activity, and purchase history. Step 2: The generation unit generates a digital twin based on the information input by the information input unit. For example, the generation AI generates a digital twin based on the user's detailed personal information, health data, behavioral history, and emotional data. Step 3: The option presentation unit presents options based on the digital twin generated by the generation unit. For example, the generation AI may present multiple investment options when the user is considering a new investment, multiple health management methods when making health-related choices, or multiple career plans when considering a career path. Step 4: The risk assessment unit evaluates the risk level for the options presented by the option presentation unit. For example, the generation AI calculates the risk level for each option, presents the balance between risk and return, and evaluates short-term, medium-term, and long-term risks.

[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 a 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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. an information input unit for inputting user information; a generation unit that generates a digital twin based on the information input by the information input unit; an option presentation unit that presents options based on the digital twin generated by the generation unit; a risk assessment unit that assesses the risk level of the options presented by the option presentation unit. A system characterized by:

2. The generation unit Collecting user emotional data and generating a digital twin that takes into account emotional fluctuations 2. The system of claim 1.

3. The generation unit Collecting users' health data in real time and creating a digital twin that reflects their health status 2. The system of claim 1.

4. The generation unit Analyzing users' social media activity and generating digital twins that take into account their social influence and networks 2. The system of claim 1.

5. The generation unit Applying the digital twin to pets or at least one other individual in the family to predict the future for the entire household 2. The system of claim 1.

6. The generation unit Applying digital twins to corporate employees to help optimize work efficiency and career paths 2. The system of claim 1.

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