system

Abstract

The system according to this embodiment aims to integrate asset building and health management, and to provide an optimal plan tailored to an individual's lifestyle. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a health analysis unit, and a health proposal unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an asset formation plan based on the analysis results obtained by the analysis unit. The health analysis unit analyzes the results of health checkups. The health proposal unit proposes a health management plan based on the analysis results obtained by the health analysis unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to integrally perform asset formation and health management, and it is difficult to provide an optimal plan tailored to an individual's lifestyle.

[0005] The system according to the embodiment aims to integrally perform asset formation and health management and provide an optimal plan tailored to an individual's lifestyle.

Means for Solving the Problems

[0006] [[ID=The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a health analysis unit, and a health proposal unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an asset formation plan based on the analysis results obtained by the analysis unit. The health analysis unit analyzes the results of health checkups. The health proposal unit proposes a health management plan based on the analysis results obtained by the health analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate asset building and health management, and provide an optimal plan tailored to an individual's lifestyle. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The asset formation and lifestyle management system according to an embodiment of the present invention is a system that integrates asset formation and lifestyle management for individual investors by utilizing future prediction AI. The asset formation and lifestyle management system analyzes individual asset formation plans based on the user's future lifestyle and goals, and proposes optimal investment strategies and asset allocations. Next, the asset formation and lifestyle management system also considers predicted expenses related to hobbies and personal preferences, presents recommended travel plans and goods, and guides users to relevant websites. Furthermore, the asset formation and lifestyle management system analyzes the results of health checkups and provides health management services that take into account average life expectancy, the likelihood of illness at each life stage, and treatment costs. This allows users to comprehensively manage both asset formation and health management, optimizing their future life stages. For example, the asset formation and lifestyle management system collects data such as the user's income, expenses, and investment history, and the AI ​​analyzes it. For example, if a user sets a future goal of "I want to buy a house in 10 years," the AI ​​proposes optimal investment strategies and asset allocations based on that goal. Next, the asset formation and lifestyle management system also considers predicted expenses related to hobbies and personal preferences. For example, if a user has a hobby like "I want to travel abroad every year," the AI ​​will predict the cost and suggest the optimal travel plan. The AI ​​will also guide users to relevant websites for items they are interested in. Furthermore, the asset building and lifestyle management system analyzes health checkup results and provides health management services that consider average life expectancy, the likelihood of illness at different life stages, and treatment costs. For instance, if a user is diagnosed with "high blood pressure" during a health checkup, the AI ​​will consider the treatment costs and future health risks and suggest the optimal health management plan. This allows users to manage both asset building and health comprehensively. For example, users can balance asset building and health management to optimize their future life stages. This reduces future uncertainty and allows them to plan their lives with peace of mind.The asset building and lifestyle management system aims to provide concrete planning and management support to individual investors and people focused on their future life plans who face uncertainties about future asset building and challenges in integrating lifestyle and asset building. In this way, the asset building and lifestyle management system can comprehensively support users' asset building and health management.

[0029] The asset formation and lifestyle management system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a health analysis unit, and a health proposal unit. The data collection unit collects user data. For example, the data collection unit collects data such as the user's income, expenses, investment history, and health checkup results. For example, the data collection unit collects the user's income data from pay stubs and bank account transaction history. The data collection unit can also collect the user's expense data from credit card statements and household budgeting apps. Furthermore, the data collection unit can also collect the user's investment history from securities company transaction history. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses the collected data to propose an optimal asset formation plan based on the user's future lifestyle and goals. For example, the analysis unit analyzes the balance between the user's income and expenses and sets future savings targets. The analysis unit can also analyze the user's investment history and propose an investment strategy according to their risk tolerance. Furthermore, the analysis unit can analyze the results of health checkups and assess health risks. The Proposal Department proposes an asset formation plan based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes an optimal savings plan considering the user's income and expenditure balance. The Proposal Department can also propose an investment strategy tailored to the user's risk tolerance based on their investment history. Furthermore, the Proposal Department can also propose a health management plan considering health risks. The Health Analysis Department analyzes the results of health checkups. For example, the Health Analysis Department analyzes the user's blood test results and physical measurement data to assess health risks. For example, the Health Analysis Department analyzes the user's blood pressure and cholesterol levels to assess the risk of cardiovascular disease. The Health Analysis Department can also analyze the user's weight and BMI to assess the risk of obesity. Furthermore, the Health Analysis Department can analyze the user's lifestyle data to assess the risk of lifestyle-related diseases. The Health Proposal Department proposes a health management plan based on the results obtained by the Health Analysis Department. For example, the Health Proposal Department proposes dietary guidance and exercise programs considering the user's blood pressure and cholesterol levels. The Health Proposal Department can also propose a weight loss plan considering the user's weight and BMI.Furthermore, the health proposal unit can also consider the user's lifestyle data and propose a plan for preventing lifestyle-related diseases. This allows the asset formation and lifestyle management system according to the embodiment to provide integrated support for the user's asset formation and health management.

[0030] The data collection unit collects user data. For example, it collects data such as users' income, expenses, investment history, and health checkup results. Specifically, the unit collects income data from users' pay stubs and bank account transaction history. This allows for an understanding of fluctuations and stability in users' income. The unit also collects expense data from credit card statements and budgeting apps. This enables a detailed analysis of users' consumption patterns and spending trends. Furthermore, the unit collects investment history from brokerage transaction history. This allows for an understanding of users' investment behavior and risk tolerance, providing foundational data for developing appropriate investment strategies. Regarding health checkup results, the unit collects data provided by medical institutions to gain a detailed understanding of users' health status. For example, it collects blood test results and physical measurement data to use as foundational data for evaluating users' health risks. The unit centrally manages this data, making it accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses the collected data to propose an optimal asset building plan based on the user's future lifestyle and goals. Specifically, it analyzes the balance between the user's income and expenses and sets future savings targets. For example, it compares the user's monthly income and expenses and calculates how surplus funds should be allocated to savings and investments. The analysis unit can also analyze the user's investment history and propose investment strategies according to their risk tolerance. For example, it analyzes past investment performance and market trends to construct an optimal investment portfolio for the user. Furthermore, the analysis unit can analyze the results of health checkups and assess health risks. For example, it assesses the risk of cardiovascular disease and diabetes based on blood test results and physical measurement data, and proposes an appropriate health management plan to the user. The analysis unit comprehensively analyzes this data to provide a plan that is optimal for the user's lifestyle and goals. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The proposal department proposes asset building plans based on the analysis results obtained by the analysis department. For example, the proposal department considers the balance between the user's income and expenses and proposes an optimal savings plan. Specifically, it calculates how much should be set aside for savings based on the user's monthly income and expenses and sets specific savings targets. The proposal department can also propose investment strategies tailored to the user's risk tolerance based on their investment history. For example, it evaluates the user's risk tolerance and constructs an investment portfolio to diversify risk. Furthermore, the proposal department can consider health risks and propose health management plans. For example, based on the user's health checkup results, it proposes dietary guidance and exercise programs and provides specific action plans to reduce health risks. The proposal department presents these proposals to the user in an easy-to-understand manner and provides actionable plans. In addition, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the proposal department can provide users with optimal asset building and health management plans and support their lifestyles and goals.

[0033] The Health Analysis Department analyzes the results of health checkups. For example, it analyzes users' blood test results and physical measurement data to assess health risks. Specifically, it analyzes users' blood pressure and cholesterol levels to assess the risk of cardiovascular disease. It can also analyze users' weight and BMI to assess the risk of obesity. Furthermore, the Health Analysis Department can analyze users' lifestyle data to assess the risk of lifestyle-related diseases. For example, it analyzes users' diet and exercise habits to assess the risk of diabetes and hypertension. The Health Analysis Department comprehensively analyzes this data to gain a detailed understanding of the user's health status. In addition, the Health Analysis Department can utilize historical data and statistical information to conduct long-term health risk assessments and trend analyses. As a result, the Health Analysis Department can not only grasp health status in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0034] The Health Recommendation Department proposes health management plans based on the results obtained by the Health Analysis Department. For example, the Health Recommendation Department considers the user's blood pressure and cholesterol levels and proposes dietary guidance and exercise programs. Specifically, it reviews the user's diet and provides a balanced meal plan. It also considers the user's exercise habits and proposes an appropriate exercise program. For example, it provides specific instructions on how many times a week and how much exercise should be done. Furthermore, the Health Recommendation Department can also consider the user's weight and BMI and propose a weight loss plan. For example, it provides a weight loss plan that combines calorie restriction and increased exercise. In addition, the Health Recommendation Department can consider the user's lifestyle data and propose plans for preventing lifestyle-related diseases. For example, it proposes specific lifestyle improvement measures such as quitting smoking and limiting alcohol intake. The Health Recommendation Department presents these proposals to the user in an easy-to-understand manner and provides actionable plans. Furthermore, the Health Recommendation Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the Health Recommendation Department can provide users with the optimal health management plan and support their health status.

[0035] The hobby prediction unit can predict the costs associated with hobbies. For example, it can predict future hobby costs based on the user's past hobby data. For example, it can analyze the costs of hobby activities the user has engaged in in the past and predict future costs. The hobby prediction unit can also collect market data related to the user's hobbies and predict future costs. Furthermore, it can analyze trend data related to the user's hobbies and predict future costs. This allows the hobby prediction unit to provide asset building plans tailored to the user's lifestyle. Some or all of the above processes in the hobby prediction unit may be performed using AI, for example, or without AI. For example, the hobby prediction unit can input the user's past hobby data into a generating AI and have the generating AI perform predictions of future hobby costs.

[0036] The travel proposal department can propose travel plans. For example, the travel proposal department can propose the optimal travel plan based on the user's past travel data. For example, the travel proposal department can analyze data from the user's past trips and propose future travel plans. The travel proposal department can also collect market data related to the user's travels and propose the optimal travel plan. Furthermore, the travel proposal department can analyze trend data related to the user's travels and propose the optimal travel plan. In this way, the travel proposal department can provide asset building plans tailored to the user's lifestyle. Some or all of the above processes in the travel proposal department may be performed using AI, for example, or not using AI. For example, the travel proposal department can input the user's past travel data into a generating AI and have the generating AI propose the optimal travel plan.

[0037] The product suggestion unit can suggest products. For example, the product suggestion unit can suggest the most suitable products based on the user's past purchase data. For example, the product suggestion unit can analyze data on products the user has purchased in the past and suggest future purchase plans. The product suggestion unit can also collect market data related to the user's purchases and suggest the most suitable products. Furthermore, the product suggestion unit can analyze trend data related to the user's purchases and suggest the most suitable products. In this way, the product suggestion unit can provide asset building plans tailored to the user's lifestyle. Some or all of the above processes in the product suggestion unit may be performed using AI, for example, or without AI. For example, the product suggestion unit can input the user's past purchase data into a generating AI and have the generating AI suggest the most suitable products.

[0038] The guidance unit can guide users to relevant websites. For example, the guidance unit can guide users to websites related to goods or services that interest the user. For example, the guidance unit can guide users to shopping websites related to goods that interest the user. The guidance unit can also guide users to information websites related to services that interest the user. Furthermore, the guidance unit can guide users to reservation websites related to events that interest the user. This makes it easier for the guidance unit to access the information and services that the user needs. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data related to goods or services that interest the user into a generating AI and have the generating AI perform the guidance to relevant websites.

[0039] The data collection unit can collect data on the user's income, expenses, investment history, and health check results. For example, the data collection unit can collect the user's income data from pay stubs and bank account transaction history. For example, the data collection unit can collect the user's expense data from credit card statements and personal finance apps. Furthermore, the data collection unit can collect the user's investment history from brokerage transaction history. For example, the data collection unit can collect the user's health check results from medical institutions. By collecting diverse data on the user, the data collection unit can provide more accurate asset building plans and health management plans. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's income data into a generating AI and have the generating AI perform analysis of the collected data.

[0040] The analysis unit can analyze the collected data and propose an appropriate asset building plan based on the user's future lifestyle and goals. For example, the analysis unit can use the collected data to analyze the user's income and expenditure balance and set future savings targets. For example, the analysis unit can analyze the user's investment history and propose an investment strategy according to their risk tolerance. The analysis unit can also analyze the results of health checkups and assess health risks. This allows the analysis unit to propose an optimal asset building plan based on the user's future lifestyle and goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI generate the analysis results.

[0041] The health analysis unit can analyze the results of health checkups and assess the user's health risks. For example, the health analysis unit can analyze the user's blood test results and physical measurement data to assess health risks. For example, the health analysis unit can analyze the user's blood pressure and cholesterol levels to assess the risk of cardiovascular disease. The health analysis unit can also analyze the user's weight and BMI to assess the risk of obesity. Furthermore, the health analysis unit can analyze the user's lifestyle data to assess the risk of lifestyle-related diseases. In this way, the health analysis unit can effectively support the user's health management by assessing the user's health risks. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or without AI. For example, the health analysis unit can input the results of health checkups into a generating AI and have the generating AI perform the health risk assessment.

[0042] The Health Recommendation Department can propose an optimal health management plan to the user based on the results from the Health Analysis Department. For example, the Health Recommendation Department can propose dietary guidance and exercise programs considering the user's blood pressure and cholesterol levels. For example, the Health Recommendation Department can also propose a weight loss plan considering the user's weight and BMI. Furthermore, the Health Recommendation Department can propose a plan for preventing lifestyle-related diseases considering the user's lifestyle data. In this way, the Health Recommendation Department can effectively support the user's health management by proposing an optimal health management plan based on the results from the Health Analysis Department. Some or all of the above processing in the Health Recommendation Department may be performed using AI, for example, or without AI. For example, the Health Recommendation Department can input the results from the Health Analysis Department into a generating AI and have the generating AI execute the proposal of a health management plan.

[0043] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data that the user has frequently collected in the past. The data collection unit can also predict data to be collected at specific time periods based on the user's past data collection history and collect it efficiently. Furthermore, the data collection unit can analyze the user's past data collection history and propose the most effective collection method. In this way, the data collection unit can efficiently collect data by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0044] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also collect only the necessary data and exclude unnecessary data, depending on the user's lifestyle. Furthermore, the data collection unit can select the optimal data collection method considering the user's current lifestyle. This allows the data collection unit to efficiently collect only the necessary data by filtering it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0045] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also select the optimal data collection method based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can update the location information in real time and collect relevant data. As a result, the data collection unit can efficiently collect highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0046] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics the user has shown interest in on social media. The data collection unit can also select the optimal data collection method based on the user's social media activity. Furthermore, the data collection unit can analyze the user's social media activity in real time and collect relevant data. This allows the data collection unit to efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can also optimally allocate analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can also apply a specific health analysis algorithm to health data. Furthermore, the analysis unit can apply a specific hobby analysis algorithm to hobby-related data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0049] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. Furthermore, the analysis unit can also optimally allocate analysis resources based on the submission date. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. Furthermore, the analysis unit can also optimally allocate analysis resources based on the relevance of the data. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0051] The proposal unit can adjust the level of detail in its proposals based on the importance of the asset formation plan. For example, it can provide detailed proposals for highly important asset formation plans, and simplified proposals for less important plans. Furthermore, the proposal unit can optimally allocate resources to the proposal according to the importance of the asset formation plan. This allows the proposal unit to provide efficient proposals by adjusting the level of detail based on the importance of the asset formation plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the asset formation plan into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0052] The proposal unit can apply different proposal algorithms depending on the category of the asset formation plan when making a proposal. For example, the proposal unit can apply a specific investment proposal algorithm to an investment plan. For example, the proposal unit can also apply a specific savings proposal algorithm to a savings plan. Furthermore, the proposal unit can also apply a specific insurance proposal algorithm to an insurance plan. This allows the proposal unit to make more accurate proposals by applying different proposal algorithms depending on the category of the asset formation plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the asset formation plan into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0053] The proposal department can determine the priority of proposals based on the submission timing of the asset formation plans. For example, the proposal department may prioritize the most recent asset formation plans. The proposal department may also postpone the proposal of older asset formation plans. Furthermore, the proposal department can also optimally allocate resources to proposals based on the submission timing. This allows the proposal department to prioritize the most recent plans by determining the priority of proposals based on the submission timing of the asset formation plans. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission timing of the asset formation plans into a generating AI and have the generating AI perform the determination of proposal priorities.

[0054] The proposal unit can adjust the order of proposals based on the relevance of the asset formation plans. For example, the proposal unit can prioritize proposing highly relevant asset formation plans. For example, the proposal unit can postpone proposing less relevant asset formation plans. Furthermore, the proposal unit can also optimally allocate resources for proposals based on the relevance of the asset formation plans. This allows the proposal unit to prioritize proposing highly relevant plans by adjusting the order of proposals based on the relevance of the asset formation plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the asset formation plans into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0055] The health analysis unit can improve the accuracy of its analysis by considering the interrelationships of health data during health analysis. For example, the health analysis unit can analyze the interrelationships of health data to provide more accurate health analysis results. For example, the health analysis unit can also apply the optimal health analysis algorithm by considering the interrelationships of health data. Furthermore, the health analysis unit can predict future health risks based on the interrelationships of health data. In this way, the health analysis unit can provide more accurate health analysis results by considering the interrelationships of health data. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or without AI. For example, the health analysis unit can input the interrelationships of health data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0056] The health analysis unit can perform health analysis while considering the user's attribute information. For example, the health analysis unit can perform health analysis while considering the user's attribute information such as age, gender, and occupation. The health analysis unit can also perform health analysis while considering the user's lifestyle and eating patterns. Furthermore, the health analysis unit can assess health risks while considering the user's genetic information. As a result, the health analysis unit can provide more personalized health analysis results by considering the user's attribute information. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or without using AI. For example, the health analysis unit can input the user's attribute information into a generating AI and have the generating AI perform the analysis.

[0057] The health analysis unit can perform health analysis while considering the geographical distribution of health data. For example, the health analysis unit can analyze region-specific health risks based on the user's place of residence. The health analysis unit can also apply the optimal health analysis algorithm by considering the geographical distribution of health data. Furthermore, the health analysis unit can compare health data from different regions and evaluate the user's health risk. In this way, the health analysis unit can evaluate region-specific health risks by considering the geographical distribution of health data. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or without AI. For example, the health analysis unit can input the geographical distribution of health data into a generating AI and have the generating AI perform the analysis.

[0058] The health analysis unit can improve the accuracy of its analysis by referring to relevant literature on health data during the analysis process. For example, the health analysis unit can refer to the latest research papers related to health data to improve the accuracy of the analysis. The health analysis unit can also compare the results of the health data analysis with relevant literature to confirm reliability. Furthermore, the health analysis unit can apply the most suitable analysis algorithm based on the literature related to the analysis of health data. In this way, the health analysis unit can improve the accuracy of its analysis by referring to relevant literature on health data. Some or all of the above processes in the health analysis unit may be performed using AI, for example, or without AI. For example, the health analysis unit can input relevant literature on health data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0059] The health recommendation department can analyze the user's past health data to select the optimal recommendation method when making health recommendations. For example, the health recommendation department can make optimal health recommendations based on the user's past health data. The health recommendation department can also analyze the user's past health data to predict future health risks. Furthermore, the health recommendation department can propose an optimal health management plan based on the user's past health data. In this way, the health recommendation department can provide more personalized health recommendations by analyzing the user's past health data. Some or all of the above processes in the health recommendation department may be performed using AI, for example, or not using AI. For example, the health recommendation department can input the user's past health data into a generating AI and have the generating AI select the optimal recommendation method.

[0060] The health suggestion unit can customize the means of making health suggestions based on the user's current lifestyle. For example, the health suggestion unit can make optimal health suggestions by considering the user's current lifestyle. The health suggestion unit can also provide customized health suggestions based on the user's lifestyle habits and eating patterns. Furthermore, the health suggestion unit can analyze the user's current lifestyle and propose an optimal health management plan. In this way, the health suggestion unit can provide more appropriate health suggestions by customizing the means of making suggestions based on the user's current lifestyle. Some or all of the above processing in the health suggestion unit may be performed using AI, for example, or without AI. For example, the health suggestion unit can input the user's current lifestyle into a generating AI and have the generating AI perform the customization of the suggestion means.

[0061] The health suggestion department can select the optimal suggestion method when making health suggestions, taking into account the user's geographical location information. For example, the health suggestion department can make suggestions that take into account region-specific health risks based on the user's place of residence. The health suggestion department can also provide optimal health suggestions based on the user's geographical location information. Furthermore, the health suggestion department can propose an optimal health management plan, taking into account the user's geographical location information. This enables the health suggestion department to make suggestions that take into account region-specific health risks by considering the user's geographical location information. Some or all of the above processing in the health suggestion department may be performed using AI, for example, or without AI. For example, the health suggestion department can input the user's geographical location information into a generating AI and have the generating AI select the suggestion method.

[0062] The health suggestion department can analyze the user's social media activity and propose methods for making health suggestions. For example, the health suggestion department can make optimal health suggestions based on the user's social media activity. The health suggestion department can also analyze the user's social media activity and provide relevant health information. Furthermore, the health suggestion department can propose an optimal health management plan based on the user's social media activity. In this way, the health suggestion department can provide relevant health information by analyzing the user's social media activity. Some or all of the above processing in the health suggestion department may be performed using AI, for example, or without AI. For example, the health suggestion department can input the user's social media activity data into a generating AI and have the generating AI select the suggestion method.

[0063] The hobby prediction unit can analyze the user's past hobby data to select the optimal prediction method when making a hobby prediction. For example, the hobby prediction unit can make the optimal hobby prediction based on the user's past hobby data. The hobby prediction unit can also analyze the user's past hobby data to predict future hobbies. Furthermore, the hobby prediction unit can propose the optimal hobby prediction method based on the user's past hobby data. In this way, the hobby prediction unit can provide a more personalized hobby prediction by analyzing the user's past hobby data. Some or all of the above processing in the hobby prediction unit may be performed using AI, for example, or without AI. For example, the hobby prediction unit can input the user's past hobby data into a generating AI and have the generating AI select the optimal prediction method.

[0064] The hobby prediction unit can select the optimal prediction method when predicting a hobby, taking into account the user's geographical location information. For example, the hobby prediction unit predicts region-specific hobbies based on the user's place of residence. The hobby prediction unit can also provide the optimal hobby prediction based on the user's geographical location information. Furthermore, the hobby prediction unit can propose the optimal hobby prediction method, taking into account the user's geographical location information. In this way, the hobby prediction unit can predict region-specific hobbies by taking into account the user's geographical location information. Some or all of the above processing in the hobby prediction unit may be performed using AI, for example, or without using AI. For example, the hobby prediction unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal prediction method.

[0065] The travel suggestion department can analyze the user's past travel data to select the optimal suggestion method when making travel suggestions. For example, the travel suggestion department can make optimal travel suggestions based on the user's past travel data. The travel suggestion department can also analyze the user's past travel data to suggest future travel plans. Furthermore, the travel suggestion department can suggest the optimal travel suggestion method by referring to the user's past travel data. In this way, the travel suggestion department can provide more personalized travel suggestions by analyzing the user's past travel data. Some or all of the above processes in the travel suggestion department may be performed using AI, for example, or without AI. For example, the travel suggestion department can input the user's past travel data into a generating AI and have the generating AI select the optimal suggestion method.

[0066] The travel suggestion unit can select the optimal suggestion method when suggesting travel, taking into account the user's geographical location information. For example, the travel suggestion unit can suggest region-specific travel plans based on the user's place of residence. The travel suggestion unit can also provide optimal travel suggestions based on the user's geographical location information. Furthermore, the travel suggestion unit can suggest the optimal travel suggestion method, taking into account the user's geographical location information. As a result, the travel suggestion unit can suggest region-specific travel plans by taking into account the user's geographical location information. Some or all of the above processing in the travel suggestion unit may be performed using AI, for example, or without AI. For example, the travel suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal suggestion method.

[0067] The product suggestion department can analyze the user's past purchase data to select the optimal suggestion method when suggesting products. For example, the product suggestion department can make optimal product suggestions based on the user's past purchase data. The product suggestion department can also analyze the user's past purchase data to propose future purchase plans. Furthermore, the product suggestion department can propose the optimal product suggestion method by referring to the user's past purchase data. In this way, the product suggestion department can provide more personalized product suggestions by analyzing the user's past purchase data. Some or all of the above processes in the product suggestion department may be performed using AI, for example, or without AI. For example, the product suggestion department can input the user's past purchase data into a generating AI and have the generating AI select the optimal suggestion method.

[0068] The product suggestion unit can select the optimal suggestion method when suggesting products, taking into account the user's geographical location information. For example, the product suggestion unit can suggest region-specific products based on the user's place of residence. The product suggestion unit can also provide optimal product suggestions based on the user's geographical location information. Furthermore, the product suggestion unit can suggest the optimal product suggestion method, taking into account the user's geographical location information. As a result, the product suggestion unit can suggest region-specific products by taking into account the user's geographical location information. Some or all of the above processing in the product suggestion unit may be performed using AI, for example, or without AI. For example, the product suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal suggestion method.

[0069] The guidance unit can analyze the user's past guidance history and select the optimal guidance method during guidance. For example, the guidance unit can propose the optimal guidance method based on the user's past guidance history. The guidance unit can also analyze the user's past guidance history and optimize future guidance methods. Furthermore, the guidance unit can select the optimal guidance method by referring to the user's past guidance history. In this way, the guidance unit can provide more personalized guidance by analyzing the user's past guidance history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's past guidance history into a generating AI and have the generating AI select the optimal guidance method.

[0070] The guidance unit can select the optimal guidance method while considering the user's geographical location information. For example, the guidance unit can propose a region-specific guidance method based on the user's place of residence. The guidance unit can also provide the optimal guidance method based on the user's geographical location information. Furthermore, the guidance unit can propose the optimal guidance method while considering the user's geographical location information. In this way, the guidance unit can provide a region-specific guidance method by considering the user's geographical location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal guidance method.

[0071] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0072] The asset building and lifestyle management system can further collect and analyze the user's social network data. For example, the collection unit can collect data on friends and followers from the user's social media accounts. The analysis unit can analyze the collected social network data and evaluate the user's influence and social connections. Based on the analysis results, the proposal unit can provide asset building plans and lifestyle suggestions that leverage the user's social network. For example, if the user is an influential person, the system can propose investment strategies and marketing plans that utilize that influence. It can also provide travel plans and product suggestions that take into account the hobbies and interests of the user's friends and followers. In this way, the asset building and lifestyle management system can provide more personalized suggestions by leveraging the user's social network.

[0073] The asset building and lifestyle management system can further monitor the user's health data in real time and propose asset building plans tailored to their health status. For example, the data collection unit can collect heart rate, activity levels, sleep data, etc., from the user's wearable device. The health analysis unit can analyze the collected data in real time and evaluate the user's health status. Based on the results from the health analysis unit, the proposal unit can propose an asset building plan tailored to the user's health status. For example, if the user is healthy and active, it can propose a high-risk investment strategy, and if the user has health risks, it can propose a stable investment strategy. In this way, the asset building and lifestyle management system can provide flexible proposals tailored to the user's health status.

[0074] The asset building and lifestyle management system can further customize asset building plans based on the user's hobbies and interests. For example, the hobby prediction unit can analyze the user's past hobby data and predict future expenses related to those hobbies. Based on the results from the hobby prediction unit, the proposal unit can propose an asset building plan tailored to the user's hobbies. For example, if the user enjoys traveling, the system can propose a savings plan that takes travel expenses into account; if the user enjoys sports, it can propose an asset building plan that takes into account the cost of purchasing sports equipment. In this way, the asset building and lifestyle management system can provide personalized suggestions that match the user's hobbies and interests.

[0075] The asset building and lifestyle management system can further utilize the user's geographical location information to propose region-specific asset building plans. For example, the data collection unit can collect the user's place of residence and travel history. The analysis unit can analyze the collected geographical location information and evaluate region-specific economic conditions and market trends. Based on the analysis results, the proposal unit can propose an asset building plan that is optimal for the user's place of residence. For example, if the user lives in an urban area, it can propose real estate investment or urban businesses; if the user lives in a rural area, it can propose agricultural investment or businesses specific to the region. In this way, the asset building and lifestyle management system can provide personalized proposals based on the user's geographical location information.

[0076] The asset building and lifestyle management system can further analyze the user's past data collection history and select the optimal data collection method. For example, the collection unit can analyze the user's past data collection history and prioritize the collection of frequently collected data. The analysis unit can analyze the collected data and evaluate the user's data collection trends. Based on the analysis results, the proposal unit can provide asset building plans and lifestyle suggestions that are optimal for the user's data collection history. For example, it can propose specific investment strategies or health management plans based on data the user has frequently collected in the past. This allows the asset building and lifestyle management system to provide personalized suggestions based on the user's past data collection history.

[0077] The following briefly describes the processing flow for example form 1.

[0078] Step 1: The data collection unit collects user data. The data collection unit collects data such as the user's income, expenses, investment history, and health check results. The data collection unit can collect the user's income data from pay stubs and bank account transaction history, and expense data from credit card statements and budgeting apps. It can also collect the user's investment history from brokerage transaction history. Step 2: The analysis unit analyzes the data collected by the data collection unit. Using the collected data, the analysis unit proposes an optimal asset building plan based on the user's future lifestyle and goals. For example, it analyzes the user's income and expenditure balance and sets future savings targets. It can also analyze the user's investment history and propose investment strategies according to their risk tolerance. Furthermore, it can analyze the results of health checkups and assess health risks. Step 3: The proposal department proposes an asset building plan based on the analysis results obtained by the analysis department. The proposal department considers the user's income and expenditure balance and proposes an optimal savings plan. It can also propose an investment strategy tailored to the user's risk tolerance based on their investment history. Furthermore, it can consider health risks and propose a health management plan. Step 4: The health analysis unit analyzes the results of the health checkup. The health analysis unit analyzes the user's blood test results and physical measurement data to assess health risks. For example, it analyzes the user's blood pressure and cholesterol levels to assess the risk of cardiovascular disease. It can also analyze the user's weight and BMI to assess the risk of obesity. Furthermore, it can analyze the user's lifestyle data to assess the risk of lifestyle-related diseases. Step 5: The Health Recommendation Department proposes a health management plan based on the results obtained by the Health Analysis Department. The Health Recommendation Department proposes dietary guidance and exercise programs, taking into account the user's blood pressure and cholesterol levels. It can also propose a weight loss plan, taking into account the user's weight and BMI. Furthermore, it can propose a plan for preventing lifestyle-related diseases, taking into account the user's lifestyle data.

[0079] (Example of form 2) The asset formation and lifestyle management system according to an embodiment of the present invention is a system that integrates asset formation and lifestyle management for individual investors by utilizing future prediction AI. The asset formation and lifestyle management system analyzes individual asset formation plans based on the user's future lifestyle and goals, and proposes optimal investment strategies and asset allocations. Next, the asset formation and lifestyle management system also considers predicted expenses related to hobbies and personal preferences, presents recommended travel plans and goods, and guides users to relevant websites. Furthermore, the asset formation and lifestyle management system analyzes the results of health checkups and provides health management services that take into account average life expectancy, the likelihood of illness at each life stage, and treatment costs. This allows users to comprehensively manage both asset formation and health management, optimizing their future life stages. For example, the asset formation and lifestyle management system collects data such as the user's income, expenses, and investment history, and the AI ​​analyzes it. For example, if a user sets a future goal of "I want to buy a house in 10 years," the AI ​​proposes optimal investment strategies and asset allocations based on that goal. Next, the asset formation and lifestyle management system also considers predicted expenses related to hobbies and personal preferences. For example, if a user has a hobby like "I want to travel abroad every year," the AI ​​will predict the cost and suggest the optimal travel plan. The AI ​​will also guide users to relevant websites for items they are interested in. Furthermore, the asset building and lifestyle management system analyzes health checkup results and provides health management services that consider average life expectancy, the likelihood of illness at different life stages, and treatment costs. For instance, if a user is diagnosed with "high blood pressure" during a health checkup, the AI ​​will consider the treatment costs and future health risks and suggest the optimal health management plan. This allows users to manage both asset building and health comprehensively. For example, users can balance asset building and health management to optimize their future life stages. This reduces future uncertainty and allows them to plan their lives with peace of mind.The asset building and lifestyle management system aims to provide concrete planning and management support to individual investors and people focused on their future life plans who face uncertainties about future asset building and challenges in integrating lifestyle and asset building. In this way, the asset building and lifestyle management system can comprehensively support users' asset building and health management.

[0080] The asset formation and lifestyle management system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a health analysis unit, and a health proposal unit. The data collection unit collects user data. For example, the data collection unit collects data such as the user's income, expenses, investment history, and health checkup results. For example, the data collection unit collects the user's income data from pay stubs and bank account transaction history. The data collection unit can also collect the user's expense data from credit card statements and household budgeting apps. Furthermore, the data collection unit can also collect the user's investment history from securities company transaction history. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses the collected data to propose an optimal asset formation plan based on the user's future lifestyle and goals. For example, the analysis unit analyzes the balance between the user's income and expenses and sets future savings targets. The analysis unit can also analyze the user's investment history and propose an investment strategy according to their risk tolerance. Furthermore, the analysis unit can analyze the results of health checkups and assess health risks. The Proposal Department proposes an asset formation plan based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes an optimal savings plan considering the user's income and expenditure balance. The Proposal Department can also propose an investment strategy tailored to the user's risk tolerance based on their investment history. Furthermore, the Proposal Department can also propose a health management plan considering health risks. The Health Analysis Department analyzes the results of health checkups. For example, the Health Analysis Department analyzes the user's blood test results and physical measurement data to assess health risks. For example, the Health Analysis Department analyzes the user's blood pressure and cholesterol levels to assess the risk of cardiovascular disease. The Health Analysis Department can also analyze the user's weight and BMI to assess the risk of obesity. Furthermore, the Health Analysis Department can analyze the user's lifestyle data to assess the risk of lifestyle-related diseases. The Health Proposal Department proposes a health management plan based on the results obtained by the Health Analysis Department. For example, the Health Proposal Department proposes dietary guidance and exercise programs considering the user's blood pressure and cholesterol levels. The Health Proposal Department can also propose a weight loss plan considering the user's weight and BMI.Furthermore, the health proposal unit can also consider the user's lifestyle data and propose a plan for preventing lifestyle-related diseases. This allows the asset formation and lifestyle management system according to the embodiment to provide integrated support for the user's asset formation and health management.

[0081] The data collection unit collects user data. For example, it collects data such as users' income, expenses, investment history, and health checkup results. Specifically, the unit collects income data from users' pay stubs and bank account transaction history. This allows for an understanding of fluctuations and stability in users' income. The unit also collects expense data from credit card statements and budgeting apps. This enables a detailed analysis of users' consumption patterns and spending trends. Furthermore, the unit collects investment history from brokerage transaction history. This allows for an understanding of users' investment behavior and risk tolerance, providing foundational data for developing appropriate investment strategies. Regarding health checkup results, the unit collects data provided by medical institutions to gain a detailed understanding of users' health status. For example, it collects blood test results and physical measurement data to use as foundational data for evaluating users' health risks. The unit centrally manages this data, making it accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0082] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses the collected data to propose an optimal asset building plan based on the user's future lifestyle and goals. Specifically, it analyzes the balance between the user's income and expenses and sets future savings targets. For example, it compares the user's monthly income and expenses and calculates how surplus funds should be allocated to savings and investments. The analysis unit can also analyze the user's investment history and propose investment strategies according to their risk tolerance. For example, it analyzes past investment performance and market trends to construct an optimal investment portfolio for the user. Furthermore, the analysis unit can analyze the results of health checkups and assess health risks. For example, it assesses the risk of cardiovascular disease and diabetes based on blood test results and physical measurement data, and proposes an appropriate health management plan to the user. The analysis unit comprehensively analyzes this data to provide a plan that is optimal for the user's lifestyle and goals. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0083] The proposal department proposes asset building plans based on the analysis results obtained by the analysis department. For example, the proposal department considers the balance between the user's income and expenses and proposes an optimal savings plan. Specifically, it calculates how much should be set aside for savings based on the user's monthly income and expenses and sets specific savings targets. The proposal department can also propose investment strategies tailored to the user's risk tolerance based on their investment history. For example, it evaluates the user's risk tolerance and constructs an investment portfolio to diversify risk. Furthermore, the proposal department can consider health risks and propose health management plans. For example, based on the user's health checkup results, it proposes dietary guidance and exercise programs and provides specific action plans to reduce health risks. The proposal department presents these proposals to the user in an easy-to-understand manner and provides actionable plans. In addition, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the proposal department can provide users with optimal asset building and health management plans and support their lifestyles and goals.

[0084] The Health Analysis Department analyzes the results of health checkups. For example, it analyzes users' blood test results and physical measurement data to assess health risks. Specifically, it analyzes users' blood pressure and cholesterol levels to assess the risk of cardiovascular disease. It can also analyze users' weight and BMI to assess the risk of obesity. Furthermore, the Health Analysis Department can analyze users' lifestyle data to assess the risk of lifestyle-related diseases. For example, it analyzes users' diet and exercise habits to assess the risk of diabetes and hypertension. The Health Analysis Department comprehensively analyzes this data to gain a detailed understanding of the user's health status. In addition, the Health Analysis Department can utilize historical data and statistical information to conduct long-term health risk assessments and trend analyses. As a result, the Health Analysis Department can not only grasp health status in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0085] The Health Recommendation Department proposes health management plans based on the results obtained by the Health Analysis Department. For example, the Health Recommendation Department considers the user's blood pressure and cholesterol levels and proposes dietary guidance and exercise programs. Specifically, it reviews the user's diet and provides a balanced meal plan. It also considers the user's exercise habits and proposes an appropriate exercise program. For example, it provides specific instructions on how many times a week and how much exercise should be done. Furthermore, the Health Recommendation Department can also consider the user's weight and BMI and propose a weight loss plan. For example, it provides a weight loss plan that combines calorie restriction and increased exercise. In addition, the Health Recommendation Department can consider the user's lifestyle data and propose plans for preventing lifestyle-related diseases. For example, it proposes specific lifestyle improvement measures such as quitting smoking and limiting alcohol intake. The Health Recommendation Department presents these proposals to the user in an easy-to-understand manner and provides actionable plans. Furthermore, the Health Recommendation Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the Health Recommendation Department can provide users with the optimal health management plan and support their health status.

[0086] The hobby prediction unit can predict the costs associated with hobbies. For example, it can predict future hobby costs based on the user's past hobby data. For example, it can analyze the costs of hobby activities the user has engaged in in the past and predict future costs. The hobby prediction unit can also collect market data related to the user's hobbies and predict future costs. Furthermore, it can analyze trend data related to the user's hobbies and predict future costs. This allows the hobby prediction unit to provide asset building plans tailored to the user's lifestyle. Some or all of the above processes in the hobby prediction unit may be performed using AI, for example, or without AI. For example, the hobby prediction unit can input the user's past hobby data into a generating AI and have the generating AI perform predictions of future hobby costs.

[0087] The travel proposal department can propose travel plans. For example, the travel proposal department can propose the optimal travel plan based on the user's past travel data. For example, the travel proposal department can analyze data from the user's past trips and propose future travel plans. The travel proposal department can also collect market data related to the user's travels and propose the optimal travel plan. Furthermore, the travel proposal department can analyze trend data related to the user's travels and propose the optimal travel plan. In this way, the travel proposal department can provide asset building plans tailored to the user's lifestyle. Some or all of the above processes in the travel proposal department may be performed using AI, for example, or not using AI. For example, the travel proposal department can input the user's past travel data into a generating AI and have the generating AI propose the optimal travel plan.

[0088] The product suggestion unit can suggest products. For example, the product suggestion unit can suggest the most suitable products based on the user's past purchase data. For example, the product suggestion unit can analyze data on products the user has purchased in the past and suggest future purchase plans. The product suggestion unit can also collect market data related to the user's purchases and suggest the most suitable products. Furthermore, the product suggestion unit can analyze trend data related to the user's purchases and suggest the most suitable products. In this way, the product suggestion unit can provide asset building plans tailored to the user's lifestyle. Some or all of the above processes in the product suggestion unit may be performed using AI, for example, or without AI. For example, the product suggestion unit can input the user's past purchase data into a generating AI and have the generating AI suggest the most suitable products.

[0089] The guidance unit can guide users to relevant websites. For example, the guidance unit can guide users to websites related to goods or services that interest the user. For example, the guidance unit can guide users to shopping websites related to goods that interest the user. The guidance unit can also guide users to information websites related to services that interest the user. Furthermore, the guidance unit can guide users to reservation websites related to events that interest the user. This makes it easier for the guidance unit to access the information and services that the user needs. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data related to goods or services that interest the user into a generating AI and have the generating AI perform the guidance to relevant websites.

[0090] The data collection unit can collect data on the user's income, expenses, investment history, and health check results. For example, the data collection unit can collect the user's income data from pay stubs and bank account transaction history. For example, the data collection unit can collect the user's expense data from credit card statements and personal finance apps. Furthermore, the data collection unit can collect the user's investment history from brokerage transaction history. For example, the data collection unit can collect the user's health check results from medical institutions. By collecting diverse data on the user, the data collection unit can provide more accurate asset building plans and health management plans. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's income data into a generating AI and have the generating AI perform analysis of the collected data.

[0091] The analysis unit can analyze the collected data and propose an appropriate asset building plan based on the user's future lifestyle and goals. For example, the analysis unit can use the collected data to analyze the user's income and expenditure balance and set future savings targets. For example, the analysis unit can analyze the user's investment history and propose an investment strategy according to their risk tolerance. The analysis unit can also analyze the results of health checkups and assess health risks. This allows the analysis unit to propose an optimal asset building plan based on the user's future lifestyle and goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI generate the analysis results.

[0092] The health analysis unit can analyze the results of health checkups and assess the user's health risks. For example, the health analysis unit can analyze the user's blood test results and physical measurement data to assess health risks. For example, the health analysis unit can analyze the user's blood pressure and cholesterol levels to assess the risk of cardiovascular disease. The health analysis unit can also analyze the user's weight and BMI to assess the risk of obesity. Furthermore, the health analysis unit can analyze the user's lifestyle data to assess the risk of lifestyle-related diseases. In this way, the health analysis unit can effectively support the user's health management by assessing the user's health risks. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or without AI. For example, the health analysis unit can input the results of health checkups into a generating AI and have the generating AI perform the health risk assessment.

[0093] The Health Recommendation Department can propose an optimal health management plan to the user based on the results from the Health Analysis Department. For example, the Health Recommendation Department can propose dietary guidance and exercise programs considering the user's blood pressure and cholesterol levels. For example, the Health Recommendation Department can also propose a weight loss plan considering the user's weight and BMI. Furthermore, the Health Recommendation Department can propose a plan for preventing lifestyle-related diseases considering the user's lifestyle data. In this way, the Health Recommendation Department can effectively support the user's health management by proposing an optimal health management plan based on the results from the Health Analysis Department. Some or all of the above processing in the Health Recommendation Department may be performed using AI, for example, or without AI. For example, the Health Recommendation Department can input the results from the Health Analysis Department into a generating AI and have the generating AI execute the proposal of a health management plan.

[0094] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can collect detailed data to obtain more accurate information. Furthermore, if the user is in a hurry, the data collection unit can collect only the minimum necessary data to quickly obtain information. In this way, the data collection unit can reduce the user's burden and collect more accurate data by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0095] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data that the user has frequently collected in the past. The data collection unit can also predict data to be collected at specific time periods based on the user's past data collection history and collect it efficiently. Furthermore, the data collection unit can analyze the user's past data collection history and propose the most effective collection method. In this way, the data collection unit can efficiently collect data by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0096] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also collect only the necessary data and exclude unnecessary data, depending on the user's lifestyle. Furthermore, the data collection unit can select the optimal data collection method considering the user's current lifestyle. This allows the data collection unit to efficiently collect only the necessary data by filtering it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0097] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can also collect detailed data to help predict the future. Furthermore, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This allows the data collection unit to prioritize the collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the data prioritization.

[0098] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also select the optimal data collection method based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can update the location information in real time and collect relevant data. As a result, the data collection unit can efficiently collect highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0099] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics the user has shown interest in on social media. The data collection unit can also select the optimal data collection method based on the user's social media activity. Furthermore, the data collection unit can analyze the user's social media activity in real time and collect relevant data. This allows the data collection unit to efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0100] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0101] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can also optimally allocate analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0102] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can also apply a specific health analysis algorithm to health data. Furthermore, the analysis unit can apply a specific hobby analysis algorithm to hobby-related data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0103] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. In this way, the analysis unit can provide an analysis of an appropriate length for the user by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0104] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. Furthermore, the analysis unit can also optimally allocate analysis resources based on the submission date. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0105] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. Furthermore, the analysis unit can also optimally allocate analysis resources based on the relevance of the data. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0106] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions. In this way, by adjusting the way it presents suggestions based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents suggestions.

[0107] The proposal unit can adjust the level of detail in its proposals based on the importance of the asset formation plan. For example, it can provide detailed proposals for highly important asset formation plans, and simplified proposals for less important plans. Furthermore, the proposal unit can optimally allocate resources to the proposal according to the importance of the asset formation plan. This allows the proposal unit to provide efficient proposals by adjusting the level of detail based on the importance of the asset formation plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the asset formation plan into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0108] The proposal unit can apply different proposal algorithms depending on the category of the asset formation plan when making a proposal. For example, the proposal unit can apply a specific investment proposal algorithm to an investment plan. For example, the proposal unit can also apply a specific savings proposal algorithm to a savings plan. Furthermore, the proposal unit can also apply a specific insurance proposal algorithm to an insurance plan. This allows the proposal unit to make more accurate proposals by applying different proposal algorithms depending on the category of the asset formation plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the asset formation plan into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0109] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. In this way, the suggestion unit can provide suggestions of an appropriate length for the user by adjusting the length of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0110] The proposal department can determine the priority of proposals based on the submission timing of the asset formation plans. For example, the proposal department may prioritize the most recent asset formation plans. The proposal department may also postpone the proposal of older asset formation plans. Furthermore, the proposal department can also optimally allocate resources to proposals based on the submission timing. This allows the proposal department to prioritize the most recent plans by determining the priority of proposals based on the submission timing of the asset formation plans. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission timing of the asset formation plans into a generating AI and have the generating AI perform the determination of proposal priorities.

[0111] The proposal unit can adjust the order of proposals based on the relevance of the asset formation plans. For example, the proposal unit can prioritize proposing highly relevant asset formation plans. For example, the proposal unit can postpone proposing less relevant asset formation plans. Furthermore, the proposal unit can also optimally allocate resources for proposals based on the relevance of the asset formation plans. This allows the proposal unit to prioritize proposing highly relevant plans by adjusting the order of proposals based on the relevance of the asset formation plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the asset formation plans into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0112] The health analysis unit can estimate the user's emotions and adjust the health analysis criteria based on the estimated emotions. For example, if the user is stressed, the health analysis unit can provide simple and easy-to-understand health analysis results. For example, if the user is relaxed, the health analysis unit can also provide detailed health analysis results. Furthermore, if the user is in a hurry, the health analysis unit can provide concise health analysis results. In this way, the health analysis unit can provide health analysis results that are easy for the user to understand by adjusting the health analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or not using AI. For example, the health analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the health analysis criteria.

[0113] The health analysis unit can improve the accuracy of its analysis by considering the interrelationships of health data during health analysis. For example, the health analysis unit can analyze the interrelationships of health data to provide more accurate health analysis results. For example, the health analysis unit can also apply the optimal health analysis algorithm by considering the interrelationships of health data. Furthermore, the health analysis unit can predict future health risks based on the interrelationships of health data. In this way, the health analysis unit can provide more accurate health analysis results by considering the interrelationships of health data. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or without AI. For example, the health analysis unit can input the interrelationships of health data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0114] The health analysis unit can perform health analysis while considering the user's attribute information. For example, the health analysis unit can perform health analysis while considering the user's attribute information such as age, gender, and occupation. The health analysis unit can also perform health analysis while considering the user's lifestyle and eating patterns. Furthermore, the health analysis unit can assess health risks while considering the user's genetic information. As a result, the health analysis unit can provide more personalized health analysis results by considering the user's attribute information. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or without using AI. For example, the health analysis unit can input the user's attribute information into a generating AI and have the generating AI perform the analysis.

[0115] The health analysis unit can estimate the user's emotions and adjust the order in which the health analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the health analysis unit can display important health information first. If the user is relaxed, for example, the health analysis unit can also display detailed health information sequentially. Furthermore, if the user is in a hurry, the health analysis unit can also display concise health information first. In this way, the health analysis unit can prioritize providing information that is important to the user by adjusting the order in which the health analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or without AI. For example, the health analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the order in which the results are displayed.

[0116] The health analysis unit can perform health analysis while considering the geographical distribution of health data. For example, the health analysis unit can analyze region-specific health risks based on the user's place of residence. The health analysis unit can also apply the optimal health analysis algorithm by considering the geographical distribution of health data. Furthermore, the health analysis unit can compare health data from different regions and evaluate the user's health risk. In this way, the health analysis unit can evaluate region-specific health risks by considering the geographical distribution of health data. Some or all of the above processing in the health analysis unit may be performed using AI, for example, or without AI. For example, the health analysis unit can input the geographical distribution of health data into a generating AI and have the generating AI perform the analysis.

[0117] The health analysis unit can improve the accuracy of its analysis by referring to relevant literature on health data during the analysis process. For example, the health analysis unit can refer to the latest research papers related to health data to improve the accuracy of the analysis. The health analysis unit can also compare the results of the health data analysis with relevant literature to confirm reliability. Furthermore, the health analysis unit can apply the most suitable analysis algorithm based on the literature related to the analysis of health data. In this way, the health analysis unit can improve the accuracy of its analysis by referring to relevant literature on health data. Some or all of the above processes in the health analysis unit may be performed using AI, for example, or without AI. For example, the health analysis unit can input relevant literature on health data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0118] The health suggestion unit can estimate the user's emotions and adjust the method of health suggestions based on the estimated emotions. For example, if the user is stressed, the health suggestion unit can provide simple and easy-to-understand health suggestions. For example, if the user is relaxed, the health suggestion unit can also provide detailed health suggestions. Furthermore, if the user is in a hurry, the health suggestion unit can provide concise health suggestions. In this way, the health suggestion unit can provide health suggestions that are easy for the user to understand by adjusting the method of health suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health suggestion unit may be performed using AI or not using AI. For example, the health suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the method of health suggestions.

[0119] The health recommendation department can analyze the user's past health data to select the optimal recommendation method when making health recommendations. For example, the health recommendation department can make optimal health recommendations based on the user's past health data. The health recommendation department can also analyze the user's past health data to predict future health risks. Furthermore, the health recommendation department can propose an optimal health management plan based on the user's past health data. In this way, the health recommendation department can provide more personalized health recommendations by analyzing the user's past health data. Some or all of the above processes in the health recommendation department may be performed using AI, for example, or not using AI. For example, the health recommendation department can input the user's past health data into a generating AI and have the generating AI select the optimal recommendation method.

[0120] The health suggestion unit can customize the means of making health suggestions based on the user's current lifestyle. For example, the health suggestion unit can make optimal health suggestions by considering the user's current lifestyle. The health suggestion unit can also provide customized health suggestions based on the user's lifestyle habits and eating patterns. Furthermore, the health suggestion unit can analyze the user's current lifestyle and propose an optimal health management plan. In this way, the health suggestion unit can provide more appropriate health suggestions by customizing the means of making suggestions based on the user's current lifestyle. Some or all of the above processing in the health suggestion unit may be performed using AI, for example, or without AI. For example, the health suggestion unit can input the user's current lifestyle into a generating AI and have the generating AI perform the customization of the suggestion means.

[0121] The health suggestion unit can estimate the user's emotions and prioritize health suggestions based on those emotions. For example, if the user is stressed, the health suggestion unit will prioritize important health suggestions. If the user is relaxed, the health suggestion unit can also sequentially provide detailed health suggestions. Furthermore, if the user is in a hurry, the health suggestion unit can prioritize concise health suggestions. In this way, the health suggestion unit can prioritize important health suggestions by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health suggestion unit may be performed using AI or not. For example, the health suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0122] The health suggestion department can select the optimal suggestion method when making health suggestions, taking into account the user's geographical location information. For example, the health suggestion department can make suggestions that take into account region-specific health risks based on the user's place of residence. The health suggestion department can also provide optimal health suggestions based on the user's geographical location information. Furthermore, the health suggestion department can propose an optimal health management plan, taking into account the user's geographical location information. This enables the health suggestion department to make suggestions that take into account region-specific health risks by considering the user's geographical location information. Some or all of the above processing in the health suggestion department may be performed using AI, for example, or without AI. For example, the health suggestion department can input the user's geographical location information into a generating AI and have the generating AI select the suggestion method.

[0123] The health suggestion department can analyze the user's social media activity and propose methods for making health suggestions. For example, the health suggestion department can make optimal health suggestions based on the user's social media activity. The health suggestion department can also analyze the user's social media activity and provide relevant health information. Furthermore, the health suggestion department can propose an optimal health management plan based on the user's social media activity. In this way, the health suggestion department can provide relevant health information by analyzing the user's social media activity. Some or all of the above processing in the health suggestion department may be performed using AI, for example, or without AI. For example, the health suggestion department can input the user's social media activity data into a generating AI and have the generating AI select the suggestion method.

[0124] The hobby prediction unit can estimate the user's emotions and adjust its hobby prediction method based on the estimated emotions. For example, if the user is relaxed, the hobby prediction unit can provide a detailed hobby prediction. For example, if the user is in a hurry, the hobby prediction unit can provide a concise hobby prediction. Furthermore, if the user is excited, the hobby prediction unit can provide a visually stimulating hobby prediction. In this way, the hobby prediction unit can provide hobby predictions that are easy for the user to understand by adjusting its hobby prediction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the hobby prediction unit may be performed using AI or not using AI. For example, the hobby prediction unit can input user emotion data into the generative AI and have the generative AI adjust the hobby prediction method.

[0125] The hobby prediction unit can analyze the user's past hobby data to select the optimal prediction method when making a hobby prediction. For example, the hobby prediction unit can make the optimal hobby prediction based on the user's past hobby data. The hobby prediction unit can also analyze the user's past hobby data to predict future hobbies. Furthermore, the hobby prediction unit can propose the optimal hobby prediction method based on the user's past hobby data. In this way, the hobby prediction unit can provide a more personalized hobby prediction by analyzing the user's past hobby data. Some or all of the above processing in the hobby prediction unit may be performed using AI, for example, or without AI. For example, the hobby prediction unit can input the user's past hobby data into a generating AI and have the generating AI select the optimal prediction method.

[0126] The hobby prediction unit can estimate the user's emotions and determine the priority of hobby predictions based on the estimated emotions. For example, if the user is relaxed, the hobby prediction unit may prioritize detailed hobby predictions. If the user is in a hurry, for example, the hobby prediction unit may prioritize concise hobby predictions. Furthermore, if the user is excited, the hobby prediction unit may prioritize visually stimulating hobby predictions. In this way, the hobby prediction unit can prioritize important hobby predictions by determining the priority of hobby predictions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the hobby prediction unit may be performed using AI or not using AI. For example, the hobby prediction unit can input user emotion data into a generative AI and have the generative AI determine the priority of hobby predictions.

[0127] The hobby prediction unit can select the optimal prediction method when predicting a hobby, taking into account the user's geographical location information. For example, the hobby prediction unit predicts region-specific hobbies based on the user's place of residence. The hobby prediction unit can also provide the optimal hobby prediction based on the user's geographical location information. Furthermore, the hobby prediction unit can propose the optimal hobby prediction method, taking into account the user's geographical location information. In this way, the hobby prediction unit can predict region-specific hobbies by taking into account the user's geographical location information. Some or all of the above processing in the hobby prediction unit may be performed using AI, for example, or without using AI. For example, the hobby prediction unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal prediction method.

[0128] The travel suggestion unit can estimate the user's emotions and adjust its travel suggestion methods based on those emotions. For example, if the user is relaxed, the travel suggestion unit can provide detailed travel suggestions. If the user is in a hurry, for example, the travel suggestion unit can provide concise travel suggestions. Furthermore, if the user is excited, the travel suggestion unit can provide visually stimulating travel suggestions. In this way, by adjusting its travel suggestion methods based on the user's emotions, the travel suggestion unit can provide travel suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the travel suggestion unit may be performed using AI or not using AI. For example, the travel suggestion unit can input user emotion data into a generative AI and have the generative AI adjust its travel suggestion methods.

[0129] The travel suggestion department can analyze the user's past travel data to select the optimal suggestion method when making travel suggestions. For example, the travel suggestion department can make optimal travel suggestions based on the user's past travel data. The travel suggestion department can also analyze the user's past travel data to suggest future travel plans. Furthermore, the travel suggestion department can suggest the optimal travel suggestion method by referring to the user's past travel data. In this way, the travel suggestion department can provide more personalized travel suggestions by analyzing the user's past travel data. Some or all of the above processes in the travel suggestion department may be performed using AI, for example, or without AI. For example, the travel suggestion department can input the user's past travel data into a generating AI and have the generating AI select the optimal suggestion method.

[0130] The travel suggestion unit can estimate the user's emotions and prioritize travel suggestions based on those emotions. For example, if the user is relaxed, the travel suggestion unit may prioritize detailed travel suggestions. If the user is in a hurry, for example, the travel suggestion unit may prioritize concise travel suggestions. Furthermore, if the user is excited, the travel suggestion unit may prioritize visually stimulating travel suggestions. In this way, the travel suggestion unit can prioritize important travel suggestions by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the travel suggestion unit may be performed using AI or not using AI. For example, the travel suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of travel suggestions.

[0131] The travel suggestion unit can select the optimal suggestion method when suggesting travel, taking into account the user's geographical location information. For example, the travel suggestion unit can suggest region-specific travel plans based on the user's place of residence. The travel suggestion unit can also provide optimal travel suggestions based on the user's geographical location information. Furthermore, the travel suggestion unit can suggest the optimal travel suggestion method, taking into account the user's geographical location information. As a result, the travel suggestion unit can suggest region-specific travel plans by taking into account the user's geographical location information. Some or all of the above processing in the travel suggestion unit may be performed using AI, for example, or without AI. For example, the travel suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal suggestion method.

[0132] The product suggestion unit can estimate the user's emotions and adjust its product suggestion method based on the estimated emotions. For example, if the user is relaxed, the product suggestion unit can provide detailed product suggestions. If the user is in a hurry, for example, the product suggestion unit can provide concise product suggestions. Furthermore, if the user is excited, the product suggestion unit can provide visually stimulating product suggestions. In this way, the product suggestion unit can provide product suggestions that are easy for the user to understand by adjusting its product suggestion method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the product suggestion unit may be performed using AI or not using AI. For example, the product suggestion unit can input user emotion data into a generative AI and have the generative AI adjust its product suggestion method.

[0133] The product suggestion department can analyze the user's past purchase data to select the optimal suggestion method when suggesting products. For example, the product suggestion department can make optimal product suggestions based on the user's past purchase data. The product suggestion department can also analyze the user's past purchase data to propose future purchase plans. Furthermore, the product suggestion department can propose the optimal product suggestion method by referring to the user's past purchase data. In this way, the product suggestion department can provide more personalized product suggestions by analyzing the user's past purchase data. Some or all of the above processes in the product suggestion department may be performed using AI, for example, or without AI. For example, the product suggestion department can input the user's past purchase data into a generating AI and have the generating AI select the optimal suggestion method.

[0134] The product suggestion unit can estimate the user's emotions and prioritize product suggestions based on those emotions. For example, if the user is relaxed, the product suggestion unit may prioritize detailed product suggestions. If the user is in a hurry, the product suggestion unit may also prioritize concise product suggestions. Furthermore, if the user is excited, the product suggestion unit may prioritize visually stimulating product suggestions. This allows the product suggestion unit to prioritize important product suggestions by determining their priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the product suggestion unit may be performed using AI or not. For example, the product suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of product suggestions.

[0135] The product suggestion unit can select the optimal suggestion method when suggesting products, taking into account the user's geographical location information. For example, the product suggestion unit can suggest region-specific products based on the user's place of residence. The product suggestion unit can also provide optimal product suggestions based on the user's geographical location information. Furthermore, the product suggestion unit can suggest the optimal product suggestion method, taking into account the user's geographical location information. As a result, the product suggestion unit can suggest region-specific products by taking into account the user's geographical location information. Some or all of the above processing in the product suggestion unit may be performed using AI, for example, or without AI. For example, the product suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal suggestion method.

[0136] The guidance unit can estimate the user's emotions and adjust the guidance method based on the estimated emotions. For example, if the user is nervous, the guidance unit can provide a simple and visually clear guidance method. For example, if the user is relaxed, the guidance unit can also provide a detailed guidance method. Furthermore, if the user is in a hurry, the guidance unit can provide a concise guidance method. In this way, the guidance unit can provide guidance that is easy for the user to understand by adjusting the guidance method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the guidance method.

[0137] The guidance unit can analyze the user's past guidance history and select the optimal guidance method during guidance. For example, the guidance unit can propose the optimal guidance method based on the user's past guidance history. The guidance unit can also analyze the user's past guidance history and optimize future guidance methods. Furthermore, the guidance unit can select the optimal guidance method by referring to the user's past guidance history. In this way, the guidance unit can provide more personalized guidance by analyzing the user's past guidance history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's past guidance history into a generating AI and have the generating AI select the optimal guidance method.

[0138] The guidance unit can estimate the user's emotions and determine guidance priorities based on the estimated emotions. For example, if the user is nervous, the guidance unit can prioritize providing important guidance information. If the user is relaxed, the guidance unit can also sequentially provide detailed guidance information. Furthermore, if the user is in a hurry, the guidance unit can prioritize providing concise guidance information. In this way, the guidance unit can prioritize providing important guidance information by determining guidance priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input user emotion data into the generative AI and have the generative AI determine the guidance priorities.

[0139] The guidance unit can select the optimal guidance method while considering the user's geographical location information. For example, the guidance unit can propose a region-specific guidance method based on the user's place of residence. The guidance unit can also provide the optimal guidance method based on the user's geographical location information. Furthermore, the guidance unit can propose the optimal guidance method while considering the user's geographical location information. In this way, the guidance unit can provide a region-specific guidance method by considering the user's geographical location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal guidance method.

[0140] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0141] The asset building and lifestyle management system can further collect and analyze the user's social network data. For example, the collection unit can collect data on friends and followers from the user's social media accounts. The analysis unit can analyze the collected social network data and evaluate the user's influence and social connections. Based on the analysis results, the proposal unit can provide asset building plans and lifestyle suggestions that leverage the user's social network. For example, if the user is an influential person, the system can propose investment strategies and marketing plans that utilize that influence. It can also provide travel plans and product suggestions that take into account the hobbies and interests of the user's friends and followers. In this way, the asset building and lifestyle management system can provide more personalized suggestions by leveraging the user's social network.

[0142] The asset building and lifestyle management system can further estimate the user's emotions and adjust the asset building plan based on those emotions. For example, the analysis unit can analyze the user's emotional data and suggest a low-risk investment strategy if the user is feeling stressed. Conversely, if the user is relaxed, it can suggest a high-risk but high-return investment strategy. The suggestion unit can adjust the content and timing of the asset building plan based on the user's emotions. For example, if the user is excited, it can suggest aggressive investments, and if the user is feeling anxious, it can suggest stable investments. In this way, the asset building and lifestyle management system can provide flexible suggestions that respond to the user's emotions.

[0143] The asset building and lifestyle management system can further monitor the user's health data in real time and propose asset building plans tailored to their health status. For example, the data collection unit can collect heart rate, activity levels, sleep data, etc., from the user's wearable device. The health analysis unit can analyze the collected data in real time and evaluate the user's health status. Based on the results from the health analysis unit, the proposal unit can propose an asset building plan tailored to the user's health status. For example, if the user is healthy and active, it can propose a high-risk investment strategy, and if the user has health risks, it can propose a stable investment strategy. In this way, the asset building and lifestyle management system can provide flexible proposals tailored to the user's health status.

[0144] The asset building and lifestyle management system can further estimate the user's emotions and adjust the health management plan based on those emotions. For example, the health analysis unit can analyze the user's emotional data and suggest a relaxing health management plan if the user is feeling stressed. Conversely, if the user is relaxed, it can suggest an active exercise plan. The health suggestion unit can adjust the content and timing of the health management plan based on the user's emotions. For example, if the user is excited, it can suggest an energetic exercise plan, and if the user is feeling anxious, it can suggest a relaxing yoga or meditation plan. In this way, the asset building and lifestyle management system can provide a flexible health management plan that responds to the user's emotions.

[0145] The asset building and lifestyle management system can further customize asset building plans based on the user's hobbies and interests. For example, the hobby prediction unit can analyze the user's past hobby data and predict future expenses related to those hobbies. Based on the results from the hobby prediction unit, the proposal unit can propose an asset building plan tailored to the user's hobbies. For example, if the user enjoys traveling, the system can propose a savings plan that takes travel expenses into account; if the user enjoys sports, it can propose an asset building plan that takes into account the cost of purchasing sports equipment. In this way, the asset building and lifestyle management system can provide personalized suggestions that match the user's hobbies and interests.

[0146] The asset building and lifestyle management system can further estimate the user's emotions and propose travel plans based on those emotions. For example, the travel suggestion unit can analyze the user's emotional data and suggest a resort trip if the user is relaxed. Conversely, if the user is excited, it can suggest an active adventure tour. The travel suggestion unit can adjust the content and timing of the travel plan based on the user's emotions. For example, if the user is stressed, it can suggest a relaxing hot spring trip, and if the user is feeling energetic, it can suggest a trip to a sporting event. In this way, the asset building and lifestyle management system can provide flexible travel plans that are tailored to the user's emotions.

[0147] The asset building and lifestyle management system can further utilize the user's geographical location information to propose region-specific asset building plans. For example, the data collection unit can collect the user's place of residence and travel history. The analysis unit can analyze the collected geographical location information and evaluate region-specific economic conditions and market trends. Based on the analysis results, the proposal unit can propose an asset building plan that is optimal for the user's place of residence. For example, if the user lives in an urban area, it can propose real estate investment or urban businesses; if the user lives in a rural area, it can propose agricultural investment or businesses specific to the region. In this way, the asset building and lifestyle management system can provide personalized proposals based on the user's geographical location information.

[0148] The asset building and lifestyle management system can further estimate the user's emotions and make product recommendations based on those emotions. For example, the product recommendation unit can analyze the user's emotional data and suggest relaxation products if the user is relaxed. Conversely, if the user is excited, it can suggest products related to active hobbies. The product recommendation unit can adjust the content and timing of product recommendations based on the user's emotions. For example, if the user is stressed, it can suggest relaxing aromatherapy products, and if the user is feeling energetic, it can suggest sports equipment. In this way, the asset building and lifestyle management system can provide flexible product recommendations that are tailored to the user's emotions.

[0149] The asset building and lifestyle management system can further analyze the user's past data collection history and select the optimal data collection method. For example, the collection unit can analyze the user's past data collection history and prioritize the collection of frequently collected data. The analysis unit can analyze the collected data and evaluate the user's data collection trends. Based on the analysis results, the proposal unit can provide asset building plans and lifestyle suggestions that are optimal for the user's data collection history. For example, it can propose specific investment strategies or health management plans based on data the user has frequently collected in the past. This allows the asset building and lifestyle management system to provide personalized suggestions based on the user's past data collection history.

[0150] The asset building and lifestyle management system can further estimate the user's emotions and adjust the guidance method based on those emotions. For example, the guidance unit can analyze the user's emotional data and provide a simple, easy-to-understand guidance method if the user is stressed. Conversely, if the user is relaxed, it can provide a more detailed guidance method. The guidance unit can adjust the content and timing of the guidance based on the user's emotions. For example, if the user is in a hurry, it can provide a concise guidance method, while if the user is relaxed, it can provide detailed information. This allows the asset building and lifestyle management system to provide flexible guidance that responds to the user's emotions.

[0151] The following briefly describes the processing flow for example form 2.

[0152] Step 1: The data collection unit collects user data. The data collection unit collects data such as the user's income, expenses, investment history, and health check results. The data collection unit can collect the user's income data from pay stubs and bank account transaction history, and expense data from credit card statements and budgeting apps. It can also collect the user's investment history from brokerage transaction history. Step 2: The analysis unit analyzes the data collected by the data collection unit. Using the collected data, the analysis unit proposes an optimal asset building plan based on the user's future lifestyle and goals. For example, it analyzes the user's income and expenditure balance and sets future savings targets. It can also analyze the user's investment history and propose investment strategies according to their risk tolerance. Furthermore, it can analyze the results of health checkups and assess health risks. Step 3: The proposal department proposes an asset building plan based on the analysis results obtained by the analysis department. The proposal department considers the user's income and expenditure balance and proposes an optimal savings plan. It can also propose an investment strategy tailored to the user's risk tolerance based on their investment history. Furthermore, it can consider health risks and propose a health management plan. Step 4: The health analysis unit analyzes the results of the health checkup. The health analysis unit analyzes the user's blood test results and physical measurement data to assess health risks. For example, it analyzes the user's blood pressure and cholesterol levels to assess the risk of cardiovascular disease. It can also analyze the user's weight and BMI to assess the risk of obesity. Furthermore, it can analyze the user's lifestyle data to assess the risk of lifestyle-related diseases. Step 5: The Health Recommendation Department proposes a health management plan based on the results obtained by the Health Analysis Department. The Health Recommendation Department proposes dietary guidance and exercise programs, taking into account the user's blood pressure and cholesterol levels. It can also propose a weight loss plan, taking into account the user's weight and BMI. Furthermore, it can propose a plan for preventing lifestyle-related diseases, taking into account the user's lifestyle data.

[0153] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0154] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0155] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, health analysis unit, health proposal unit, hobby prediction unit, travel proposal unit, item proposal unit, and guidance unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user data by the control unit 46A of the smart device 14 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes an asset formation plan based on the analysis results. The health analysis unit and health proposal unit are implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyze the results of a health checkup and propose a health management plan. The hobby prediction unit, travel proposal unit, item proposal unit, and guidance unit are implemented, for example, by the control unit 46A of the smart device 14 and make optimal plans and proposals based on data about the user's hobbies, travel, and items. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0158] As shown in Figure 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, health analysis unit, health proposal unit, hobby prediction unit, travel proposal unit, item proposal unit, and guidance unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user data by the control unit 46A of the smart glasses 214 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes an asset formation plan based on the analysis results. The health analysis unit and health proposal unit are implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyze the results of a health checkup and propose a health management plan. The hobby prediction unit, travel proposal unit, item proposal unit, and guidance unit are implemented, for example, by the control unit 46A of the smart glasses 214 and make optimal plans and proposals based on data about the user's hobbies, travel, and items. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0174] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0181] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0182] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0183] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0184] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0185] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0186] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0187] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0188] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, health analysis unit, health proposal unit, hobby prediction unit, travel proposal unit, item proposal unit, and guidance unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user data by the control unit 46A of the headset terminal 314 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes an asset formation plan based on the analysis results. The health analysis unit and health proposal unit are implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyze the results of a health checkup and propose a health management plan. The hobby prediction unit, travel proposal unit, item proposal unit, and guidance unit are implemented, for example, by the control unit 46A of the headset terminal 314 and make optimal plans and proposals based on the user's hobbies, travel, and item data. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0189] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0190] As shown in Figure 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.

[0191] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0192] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0193] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0194] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0195] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0196] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0197] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0198] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0199] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0200] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0201] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0202] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0203] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0204] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0205] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, health analysis unit, health proposal unit, hobby prediction unit, travel proposal unit, item proposal unit, and guidance unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user data by the control unit 46A of the robot 414 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes an asset formation plan based on the analysis results. The health analysis unit and health proposal unit are implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyze the results of a health checkup and propose a health management plan. The hobby prediction unit, travel proposal unit, item proposal unit, and guidance unit are implemented, for example, by the control unit 46A of the robot 414 and make optimal plans and proposals based on data about the user's hobbies, travel, and items. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0206] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0207] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0208] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0209] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0210] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0211] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0212] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0213] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0214] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0215] 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.

[0216] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0217] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0218] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0219] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0220] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0221] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0222] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0223] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0224] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an asset formation plan. The Health Analysis Department analyzes the results of health checkups, The system comprises a health proposal unit that proposes a health management plan based on the analysis results obtained by the health analysis unit. A system characterized by the following features. (Note 2) It includes a hobby prediction section that predicts the costs associated with hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The department has a travel planning section that proposes travel plans. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a product proposal department that proposes products. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a navigation section that directs users to related websites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We collect data on users' income, expenses, investment history, and health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We analyze the collected data and propose an appropriate asset building plan based on the user's future lifestyle and goals. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned health analysis unit, Analyze the results of health checkups and assess the user's health risks. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned health proposal department, Based on the results from the health analysis department, we propose the optimal health management plan for each user. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the asset building plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the asset building plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on when the asset formation plan was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance within the asset building plan. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned health analysis unit, The system estimates the user's emotions and adjusts the health analysis criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned health analysis unit, When performing health analysis, consider the interrelationships between health data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned health analysis unit, During health analysis, the analysis takes into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned health analysis unit, It estimates the user's emotions and adjusts the order in which health analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned health analysis unit, When performing health analysis, the geographical distribution of health data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned health analysis unit, When analyzing health data, we improve the accuracy of the analysis by referring to relevant literature on health data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned health proposal department, It estimates the user's emotions and adjusts the health recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned health proposal department, When providing health recommendations, the system analyzes the user's past health data to select the most suitable recommendation method. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned health proposal department, When providing health recommendations, the recommendation methods are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned health proposal department, It estimates the user's emotions and prioritizes health recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned health proposal department, When providing health recommendations, the optimal recommendation method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned health proposal department, When providing health recommendations, we analyze the user's social media activity to suggest appropriate methods for making recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 40) The hobby prediction unit described above is We estimate the user's emotions and adjust the hobby prediction method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 41) The hobby prediction unit described above is When predicting hobbies, the system analyzes the user's past hobby data to select the optimal prediction method. The system described in Appendix 2, characterized by the features described herein. (Note 42) The hobby prediction unit described above is Estimate the user's emotion and determine the priority order of hobby prediction based on the estimated user emotion The system according to appended note 2, characterized by the above. (Appended note 43) The hobby prediction unit When predicting hobbies, select the optimal prediction method considering the user's geographical location information The system according to appended note 2, characterized by the above. (Appended note 44) The travel proposal unit Estimate the user's emotion and adjust the travel proposal method based on the estimated user emotion The system according to appended note 3, characterized by the above. (Appended note 45) The travel proposal unit When making a travel proposal, analyze the user's past travel data and select the optimal proposal method The system according to appended note 3, characterized by the above. (Appended note 46) The travel proposal unit Estimate the user's emotion and determine the priority order of travel proposal based on the estimated user emotion The system according to appended note 3, characterized by the above. (Appended note 47) The travel proposal unit When making a travel proposal, consider the user's geographical location information and select the optimal proposal method The system according to appended note 3, characterized by the above. (Appended note 48) The item proposal unit Estimate the user's emotion and adjust the item proposal method based on the estimated user emotion The system according to appended note 4, characterized by the above. (Appended note 49) The item proposal unit When making an item proposal, analyze the user's past purchase data and select the optimal proposal method The system according to appended note 4, characterized by the above. (Appended note 50) The item proposal unit The system estimates the user's emotions and prioritizes product suggestions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 51) The aforementioned article proposal unit, When proposing products, the optimal proposal method is selected by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 52) The aforementioned induction unit is It estimates the user's emotions and adjusts the guidance method based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 53) The aforementioned induction unit is During the guidance process, the system analyzes the user's past guidance history to select the optimal guidance method. The system described in Appendix 5, characterized by the features described herein. (Note 54) The aforementioned induction unit is It estimates the user's emotions and determines the priority of guidance based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 55) The aforementioned induction unit is During guidance, the optimal guidance method is selected considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0225] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an asset formation plan. The Health Analysis Department analyzes the results of health checkups, The system comprises a health proposal unit that proposes a health management plan based on the analysis results obtained by the health analysis unit. A system characterized by the following features.

2. It includes a hobby prediction section that predicts the costs associated with hobbies. The system according to feature 1.

3. The department has a travel planning section that proposes travel plans. The system according to feature 1.

4. It has a product proposal department that proposes products. The system according to feature 1.

5. It features a navigation section that directs users to related websites. The system according to feature 1.

6. The aforementioned collection unit is We collect data on users' income, expenses, investment history, and health checkup results. The system according to feature 1.

7. The aforementioned analysis unit, We analyze the collected data and propose an appropriate asset building plan based on the user's future lifestyle and goals. The system according to feature 1.

8. The aforementioned health analysis unit, Analyze the results of health checkups and assess the user's health risks. The system according to feature 1.

9. The aforementioned health proposal department, Based on the results from the aforementioned health analysis unit, we propose an optimal health management plan to the user. The system according to feature 1.

10. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

Images