system

Abstract

The system according to this embodiment aims to propose optimal asset allocation and investment strategies tailored to the individual circumstances of each user. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects the user's expenditure and income data, risk tolerance, and long-term goals. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes asset allocation and investment strategies based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to propose a specific and realistic asset allocation and investment strategy according to the individual situation of the user.

[0005] The system according to the embodiment aims to propose an optimal asset allocation and investment strategy according to the individual situation of the user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects the user's expenditure and income data, risk tolerance, and long-term goals. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an asset allocation and investment strategy based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose optimal asset allocation and investment strategies tailored to the individual circumstances of the user. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 financial advisor AI system according to an embodiment of the present invention is a system that proposes optimal asset allocation and investment strategies based on the user's spending and income data, risk tolerance, and long-term goals. This financial advisor AI system collects the user's spending and income data, risk tolerance, and long-term goals, and the AI ​​analyzes the collected data to propose specific and realistic asset allocation and investment strategies tailored to the user's situation. The proposals are not generic advice but are tailored to individual needs. For example, the financial advisor AI system understands the user's daily spending and proposes items and methods for saving money. For example, it may suggest reviewing utility bills and streamlining subscriptions based on the usage history of credit cards and electronic payment systems. Next, the financial advisor AI system analyzes market trends and economic news in real time and proposes appropriate investment timing and actions for the user. Furthermore, the financial advisor AI system reviews the asset status monthly and quarterly, and revises the plan while checking progress. It checks whether progress is on track according to the plan and updates the advice as needed. In this way, the financial advisor AI system provides specific and realistic plans tailored to the user's individual situation and supports the optimization of asset management and investment strategies. This allows the financial advisor AI system to propose specific and realistic asset allocation and investment strategies tailored to the user's situation.

[0029] The financial advisor AI system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects the user's spending and income data, risk tolerance, and long-term goals. The data collection unit can collect spending and income data such as the user's salary, rent, food expenses, and utility costs. The data collection unit can also collect survey results and past investment history to assess risk tolerance. The data collection unit can collect goals such as the user's retirement funds and children's education funds to set long-term goals. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using statistical analysis or machine learning algorithms, for example. The analysis unit can analyze the user's spending and income data and evaluate the user's spending patterns and income stability. The analysis unit can analyze risk tolerance and create a risk profile for the user. The analysis unit can analyze long-term goals and evaluate the user's degree of goal achievement. The proposal unit proposes asset allocation and investment strategies based on the analysis results obtained by the analysis unit. The proposal unit can propose asset allocations such as stocks, bonds, and real estate, for example. The proposal department can suggest investment strategies such as short-term investments, long-term investments, and risk diversification. It can understand the user's daily spending and suggest areas for saving and methods of saving. Based on credit card and electronic payment system usage history, it can suggest reviewing utility bills and streamlining subscriptions. It can analyze market trends and economic news in real time and suggest appropriate investment timings and actions for the user. The proposal department can review asset status monthly and quarterly, monitoring progress and revising plans as needed. It can check whether progress is on track according to the plan and update advice as necessary. As a result, the financial advisor AI system according to this embodiment can propose specific and realistic asset allocation and investment strategies tailored to the user's situation.

[0030] The data collection unit collects user spending and income data, risk tolerance, and long-term goals. For example, it can collect spending and income data such as the user's salary, rent, food expenses, and utility bills. Specifically, it automatically retrieves transaction history from the user's bank account and credit card to collect detailed income and spending data. This allows for an accurate understanding of the user's monthly income and expenditure balance and spending trends. Furthermore, the data collection unit can also collect survey data and past investment history to assess the user's risk tolerance. Survey data asks detailed questions about the user's attitude towards investment and risk, collecting data to create the user's risk profile. Past investment history data analyzes the types of investments the user has made in the past and their results to more accurately assess the user's risk tolerance. The data collection unit can also collect user goals, such as retirement funds or children's education funds, to set long-term goals. Based on the goals set by the user, it calculates the required amount of funds and the time required to achieve them, collecting data to create a concrete goal achievement plan. This allows the data collection unit to collect detailed data tailored to the user's economic situation and goals, providing a foundation for the analysis and proposal units to function effectively.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis and machine learning algorithms. Specifically, it analyzes user spending and income data to evaluate the user's spending patterns and income stability. For example, it classifies the user's monthly spending items to identify which items account for the largest portion of spending. Regarding income stability, it analyzes income data from the past several years to evaluate income fluctuation patterns and future income prospects. The analysis unit can also analyze risk tolerance and create a user risk profile. The risk profile quantifies the user's attitude towards investment and their tolerance for risk, serving as foundational data for proposing the optimal investment strategy. Furthermore, the analysis unit can analyze long-term goals and evaluate the user's degree of goal achievement. For example, it evaluates the likelihood of achieving user-defined goals such as retirement funds or children's education funds based on current asset status and future income prospects. The analysis unit comprehensively analyzes this data to generate foundational data for providing specific advice tailored to the user's financial situation and goals. This allows the analysis unit to gain a detailed understanding of the user's financial situation, providing a foundation for the proposal unit to suggest effective asset allocation and investment strategies.

[0032] The Proposal Department proposes asset allocation and investment strategies based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can propose asset allocations such as stocks, bonds, and real estate. Specifically, it calculates and proposes the optimal asset allocation based on the user's risk profile and goal achievement level. For example, it proposes a portfolio with a higher proportion of stocks for users with high risk tolerance, and a portfolio with a higher proportion of bonds and real estate for users with low risk tolerance. The Proposal Department can propose investment strategies such as short-term investment, long-term investment, and risk diversification. For example, it proposes short-term investment strategies to respond to short-term market fluctuations and long-term investment strategies aimed at long-term wealth building. It can also propose strategies for diversifying investments across different asset classes and regions to diversify risk. The Proposal Department can understand the user's daily spending and propose areas where savings can be made and methods for doing so. For example, it can suggest reviewing utility bills and streamlining subscriptions based on credit card and electronic payment system usage history. This allows users to review and save on daily expenses, thereby allocating more funds to investment. The Proposal Department can analyze market trends and economic news in real time and propose appropriate investment timings and actions for the user. For example, it can suggest buying and selling timings based on rapid fluctuations in the stock market or the release of economic indicators. The suggestion unit can review asset status monthly or quarterly, and revise plans while checking progress. This allows users to constantly review their investment strategies based on the latest information and take optimal actions to achieve their goals. The suggestion unit can check whether progress is on track according to the plan and update advice as needed. As a result, the financial advisor AI system according to this embodiment can propose specific and realistic asset allocation and investment strategies tailored to the user's situation.

[0033] The suggestion unit can understand the user's daily spending and suggest items and methods for saving money. For example, the suggestion unit can understand the user's daily spending such as food expenses, transportation expenses, and entertainment expenses. The suggestion unit can identify items that can be saved, such as wasteful spending and unnecessary subscriptions. The suggestion unit can suggest methods for saving money, such as using coupons and saving electricity. This enables efficient asset management by understanding the user's daily spending and suggesting ways to save money. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's daily spending data into a generating AI and have the generating AI generate suggestions for items and methods for saving money.

[0034] The proposal unit can suggest a review of utility bills and streamlining of subscriptions based on credit card and electronic payment system usage history. For example, the proposal unit can analyze credit card purchase and payment history. The proposal unit can collect electronic payment system usage history and suggest a review of utility bills. The proposal unit can suggest streamlining of unnecessary subscriptions. This allows for the suggestion of specific savings methods based on credit card and electronic payment system usage history. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input credit card usage history data into a generating AI and have the generating AI execute suggestions for reviewing utility bills and streamlining subscriptions.

[0035] The proposal unit can analyze market trends and economic news in real time and propose suitable investment timings and actions to users. For example, the proposal unit analyzes stock price trends and industry trends. The proposal unit can collect economic news such as economic indicators and corporate earnings announcements and propose suitable investment timings to users. The proposal unit can propose suitable investment actions to users. In this way, by analyzing market trends and economic news in real time, it is possible to propose appropriate investment timings. 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 market trend data and economic news data into a generating AI and have the generating AI execute suggestions for investment timings and actions.

[0036] The proposal department can review the asset status monthly and quarterly, and revise the plan while checking progress. For example, the proposal department can review the user's asset types and valuations. The proposal department can propose changes to goals and investment strategies. The proposal department can periodically review the user's asset status and check the progress of the plan. This allows for checking the progress of the plan by periodically reviewing the asset status and revising the plan. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the user's asset status data into a generating AI and have the generating AI execute proposals for revisions to the plan.

[0037] The proposal department can check whether the plan is progressing smoothly and update its advice as needed. For example, the proposal department can evaluate the degree of goal achievement and the progress rate. The proposal department can propose providing new investment information or revising the strategy. The proposal department can evaluate the progress of the plan and update its advice as needed. This allows for support in achieving the plan by checking its progress and updating advice as necessary. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input plan progress data into a generating AI and have the generating AI update the advice.

[0038] The data collection unit can analyze the user's past income and expense data and select the optimal data collection method. For example, the data collection unit can determine the priority of data to collect from the user's past income and expense data. The data collection unit can adjust the frequency of data collection based on the user's past income and expense data. The data collection unit can analyze the user's past income and expense data and select the types of data to collect. This allows the optimal data collection method to be selected by analyzing past income and expense data. Some or all of the above processes 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 income and expense data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and areas of interest during collection. For example, the data collection unit can filter the types of data to be collected based on the user's current lifestyle. The data collection unit can determine the priority of data to be collected based on the user's areas of interest. The data collection unit can adjust the frequency of data collection, taking into account the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering data based on the user's 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 user lifestyle and area of ​​interest data into a generating AI and have the generating AI perform data filtering.

[0040] 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, the data collection unit can select the type of data to collect based on the user's geographical location information. The data collection unit can determine the priority of the data to collect by considering the user's geographical location information. The data collection unit can adjust the frequency of data collection based on the user's geographical location information. This allows for the priority collection of 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 data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can select the types of data to collect based on the user's social media activity. The data collection unit can analyze the user's social media activity and determine the priority of the data to collect. The data collection unit can adjust the frequency of data collection based on the user's social media activity. This allows for the collection of 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, or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also adjust the frequency of analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes 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 collected data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to analyze spending patterns to spending data. For income data, it can apply an algorithm to analyze income stability. For risk tolerance data, it can apply an algorithm to analyze risk profiles. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. 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.

[0044] 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. The analysis unit may postpone the analysis of older data. The analysis unit can adjust the frequency of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes 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 determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis based on the relevance of the data. This allows for efficient analysis 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 order of analysis.

[0046] The proposal unit can adjust the level of detail in its proposals based on the importance of asset allocations and investment strategies. For example, it can provide detailed proposals for important asset allocations and investment strategies, and simplified proposals for less important ones. The proposal unit can also adjust the frequency of proposals according to the importance of asset allocations and investment strategies. This allows for efficient proposals by adjusting the level of detail based on the importance of asset allocations and investment strategies. 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 data on the importance of asset allocations and investment strategies into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the asset allocation and investment strategy categories when making a proposal. For example, the proposal unit can apply a proposal algorithm that emphasizes risk management to asset allocation. For investment strategies, the proposal unit can apply a proposal algorithm that maximizes returns. The proposal unit can apply different proposal algorithms depending on the asset allocation and investment strategy categories. This allows for more accurate proposals by applying different proposal algorithms depending on the asset allocation and investment strategy categories. 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 asset allocation and investment strategy category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0048] The proposal department can prioritize proposals based on the timing of asset allocation and investment strategy submissions. For example, the proposal department may prioritize the most recent asset allocation and investment strategies. Older asset allocation and investment strategies may be postponed. The proposal department can also adjust the frequency of proposals based on the submission timing. This enables efficient proposals by prioritizing proposals based on the submission timing of asset allocation and investment strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input asset allocation and investment strategy submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of asset allocations and investment strategies. For example, the proposal unit can prioritize proposing highly relevant asset allocations and investment strategies. The proposal unit can postpone proposing less relevant asset allocations and investment strategies. The proposal unit can adjust the order of proposals based on the relevance of asset allocations and investment strategies. This allows for efficient proposals by adjusting the order of proposals based on the relevance of asset allocations and investment strategies. 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 data on the relevance of asset allocations and investment strategies into a generating AI and have the generating AI perform the adjustment of the order of proposals.

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

[0051] The analysis unit can analyze a user's past investment history and identify their investment behavior patterns. For example, it can identify when a user tends to invest based on their past investment history and make future investment suggestions based on those patterns. It can also identify investment strategies that have been successful for the user in the past and make suggestions based on those strategies. Furthermore, it can identify investment strategies that have failed for the user in the past and provide advice on how to avoid those failures. In this way, it is possible to make more appropriate investment suggestions by utilizing the user's past investment history.

[0052] The data collection unit can gather regional economic conditions and market trends, taking into account the user's geographical location. For example, it can collect economic growth rates, unemployment rates, and real estate market trends in the user's area of ​​residence, and use this information to make investment suggestions. It can also collect economic conditions and market trends in areas the user visits for travel or business trips, and use this information to make short-term investment suggestions. Furthermore, it can collect economic conditions and market trends in areas the user is considering relocating to in the future, and use this information to make long-term investment suggestions. This allows for more appropriate investment suggestions by utilizing the user's geographical location.

[0053] The analytics unit can analyze users' past spending data and identify spending patterns. For example, it can identify which categories users spend the most money on and provide saving suggestions for those categories. It can also provide saving suggestions for periods when users tend to spend more. Furthermore, if users spend a lot on specific stores or services, it can provide discount information and coupons for those stores or services. This allows for more appropriate saving suggestions by leveraging users' past spending data.

[0054] The data collection unit can analyze users' social media activity and collect data based on their interests and preferences. For example, it can analyze topics users frequently mention and accounts they follow on social media, and collect relevant data based on that information. It can also analyze content and comments users share on social media and identify their interests and preferences based on that information. Furthermore, it can analyze groups and events users participate in on social media and collect relevant data based on that information. This makes it possible to collect more appropriate data by utilizing users' social media activity.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit collects user spending and income data, risk tolerance, and long-term goals. For example, it can collect spending and income data such as the user's salary, rent, food expenses, and utility costs. It can also collect survey data and past investment history to assess risk tolerance, and collect user goals such as retirement funds and children's education funds to set long-term objectives. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze the data using statistical analysis and machine learning algorithms to evaluate the user's spending patterns and income stability. It can also analyze risk tolerance to create a user risk profile and analyze long-term goals to evaluate the user's goal achievement. Step 3: The proposal department proposes asset allocation and investment strategies based on the analysis results obtained by the analysis department. For example, it can propose asset allocations such as stocks, bonds, and real estate, and investment strategies such as short-term investment, long-term investment, and risk diversification. It can also understand the user's daily spending and propose areas where savings can be made and methods for saving, and propose a review of utility bills and streamlining subscriptions based on credit card and electronic payment system usage history. Furthermore, it can analyze market trends and economic news in real time to propose investment timing and actions suitable for the user, review asset status monthly or quarterly, and revise the plan while checking progress. It can check whether progress is on track according to the plan and update advice as needed.

[0057] (Example of form 2) The financial advisor AI system according to an embodiment of the present invention is a system that proposes optimal asset allocation and investment strategies based on the user's spending and income data, risk tolerance, and long-term goals. This financial advisor AI system collects the user's spending and income data, risk tolerance, and long-term goals, and the AI ​​analyzes the collected data to propose specific and realistic asset allocation and investment strategies tailored to the user's situation. The proposals are not generic advice but are tailored to individual needs. For example, the financial advisor AI system understands the user's daily spending and proposes items and methods for saving money. For example, it may suggest reviewing utility bills and streamlining subscriptions based on the usage history of credit cards and electronic payment systems. Next, the financial advisor AI system analyzes market trends and economic news in real time and proposes appropriate investment timing and actions for the user. Furthermore, the financial advisor AI system reviews the asset status monthly and quarterly, and revises the plan while checking progress. It checks whether progress is on track according to the plan and updates the advice as needed. In this way, the financial advisor AI system provides specific and realistic plans tailored to the user's individual situation and supports the optimization of asset management and investment strategies. This allows the financial advisor AI system to propose specific and realistic asset allocation and investment strategies tailored to the user's situation.

[0058] The financial advisor AI system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects the user's spending and income data, risk tolerance, and long-term goals. The data collection unit can collect spending and income data such as the user's salary, rent, food expenses, and utility costs. The data collection unit can also collect survey results and past investment history to assess risk tolerance. The data collection unit can collect goals such as the user's retirement funds and children's education funds to set long-term goals. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using statistical analysis or machine learning algorithms, for example. The analysis unit can analyze the user's spending and income data and evaluate the user's spending patterns and income stability. The analysis unit can analyze risk tolerance and create a risk profile for the user. The analysis unit can analyze long-term goals and evaluate the user's degree of goal achievement. The proposal unit proposes asset allocation and investment strategies based on the analysis results obtained by the analysis unit. The proposal unit can propose asset allocations such as stocks, bonds, and real estate, for example. The proposal department can suggest investment strategies such as short-term investments, long-term investments, and risk diversification. It can understand the user's daily spending and suggest areas for saving and methods of saving. Based on credit card and electronic payment system usage history, it can suggest reviewing utility bills and streamlining subscriptions. It can analyze market trends and economic news in real time and suggest appropriate investment timings and actions for the user. The proposal department can review asset status monthly and quarterly, monitoring progress and revising plans as needed. It can check whether progress is on track according to the plan and update advice as necessary. As a result, the financial advisor AI system according to this embodiment can propose specific and realistic asset allocation and investment strategies tailored to the user's situation.

[0059] The data collection unit collects user spending and income data, risk tolerance, and long-term goals. For example, it can collect spending and income data such as the user's salary, rent, food expenses, and utility bills. Specifically, it automatically retrieves transaction history from the user's bank account and credit card to collect detailed income and spending data. This allows for an accurate understanding of the user's monthly income and expenditure balance and spending trends. Furthermore, the data collection unit can also collect survey data and past investment history to assess the user's risk tolerance. Survey data asks detailed questions about the user's attitude towards investment and risk, collecting data to create the user's risk profile. Past investment history data analyzes the types of investments the user has made in the past and their results to more accurately assess the user's risk tolerance. The data collection unit can also collect user goals, such as retirement funds or children's education funds, to set long-term goals. Based on the goals set by the user, it calculates the required amount of funds and the time required to achieve them, collecting data to create a concrete goal achievement plan. This allows the data collection unit to collect detailed data tailored to the user's economic situation and goals, providing a foundation for the analysis and proposal units to function effectively.

[0060] The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis and machine learning algorithms. Specifically, it analyzes user spending and income data to evaluate the user's spending patterns and income stability. For example, it classifies the user's monthly spending items to identify which items account for the largest portion of spending. Regarding income stability, it analyzes income data from the past several years to evaluate income fluctuation patterns and future income prospects. The analysis unit can also analyze risk tolerance and create a user risk profile. The risk profile quantifies the user's attitude towards investment and their tolerance for risk, serving as foundational data for proposing the optimal investment strategy. Furthermore, the analysis unit can analyze long-term goals and evaluate the user's degree of goal achievement. For example, it evaluates the likelihood of achieving user-defined goals such as retirement funds or children's education funds based on current asset status and future income prospects. The analysis unit comprehensively analyzes this data to generate foundational data for providing specific advice tailored to the user's financial situation and goals. This allows the analysis unit to gain a detailed understanding of the user's financial situation, providing a foundation for the proposal unit to suggest effective asset allocation and investment strategies.

[0061] The Proposal Department proposes asset allocation and investment strategies based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can propose asset allocations such as stocks, bonds, and real estate. Specifically, it calculates and proposes the optimal asset allocation based on the user's risk profile and goal achievement level. For example, it proposes a portfolio with a higher proportion of stocks for users with high risk tolerance, and a portfolio with a higher proportion of bonds and real estate for users with low risk tolerance. The Proposal Department can propose investment strategies such as short-term investment, long-term investment, and risk diversification. For example, it proposes short-term investment strategies to respond to short-term market fluctuations and long-term investment strategies aimed at long-term wealth building. It can also propose strategies for diversifying investments across different asset classes and regions to diversify risk. The Proposal Department can understand the user's daily spending and propose areas where savings can be made and methods for doing so. For example, it can suggest reviewing utility bills and streamlining subscriptions based on credit card and electronic payment system usage history. This allows users to review and save on daily expenses, thereby allocating more funds to investment. The Proposal Department can analyze market trends and economic news in real time and propose appropriate investment timings and actions for the user. For example, it can suggest buying and selling timings based on rapid fluctuations in the stock market or the release of economic indicators. The suggestion unit can review asset status monthly or quarterly, and revise plans while checking progress. This allows users to constantly review their investment strategies based on the latest information and take optimal actions to achieve their goals. The suggestion unit can check whether progress is on track according to the plan and update advice as needed. As a result, the financial advisor AI system according to this embodiment can propose specific and realistic asset allocation and investment strategies tailored to the user's situation.

[0062] The suggestion unit can understand the user's daily spending and suggest items and methods for saving money. For example, the suggestion unit can understand the user's daily spending such as food expenses, transportation expenses, and entertainment expenses. The suggestion unit can identify items that can be saved, such as wasteful spending and unnecessary subscriptions. The suggestion unit can suggest methods for saving money, such as using coupons and saving electricity. This enables efficient asset management by understanding the user's daily spending and suggesting ways to save money. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's daily spending data into a generating AI and have the generating AI generate suggestions for items and methods for saving money.

[0063] The proposal unit can suggest a review of utility bills and streamlining of subscriptions based on credit card and electronic payment system usage history. For example, the proposal unit can analyze credit card purchase and payment history. The proposal unit can collect electronic payment system usage history and suggest a review of utility bills. The proposal unit can suggest streamlining of unnecessary subscriptions. This allows for the suggestion of specific savings methods based on credit card and electronic payment system usage history. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input credit card usage history data into a generating AI and have the generating AI execute suggestions for reviewing utility bills and streamlining subscriptions.

[0064] The proposal unit can analyze market trends and economic news in real time and propose suitable investment timings and actions to users. For example, the proposal unit analyzes stock price trends and industry trends. The proposal unit can collect economic news such as economic indicators and corporate earnings announcements and propose suitable investment timings to users. The proposal unit can propose suitable investment actions to users. In this way, by analyzing market trends and economic news in real time, it is possible to propose appropriate investment timings. 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 market trend data and economic news data into a generating AI and have the generating AI execute suggestions for investment timings and actions.

[0065] The proposal department can review the asset status monthly and quarterly, and revise the plan while checking progress. For example, the proposal department can review the user's asset types and valuations. The proposal department can propose changes to goals and investment strategies. The proposal department can periodically review the user's asset status and check the progress of the plan. This allows for checking the progress of the plan by periodically reviewing the asset status and revising the plan. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the user's asset status data into a generating AI and have the generating AI execute proposals for revisions to the plan.

[0066] The proposal department can check whether the plan is progressing smoothly and update its advice as needed. For example, the proposal department can evaluate the degree of goal achievement and the progress rate. The proposal department can propose providing new investment information or revising the strategy. The proposal department can evaluate the progress of the plan and update its advice as needed. This allows for support in achieving the plan by checking its progress and updating advice as necessary. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input plan progress data into a generating AI and have the generating AI update the advice.

[0067] The data collection unit can estimate the user's emotions and adjust the type and timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can limit the types of data collected and reduce the collection frequency. If the user is relaxed, the data collection unit can collect more detailed data and increase the collection frequency. If the user is in a hurry, the data collection unit can prioritize collecting only important data. This allows for more appropriate data collection by adjusting the data collection method according to 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 adjust the type and timing of data collection.

[0068] The data collection unit can analyze the user's past income and expense data and select the optimal data collection method. For example, the data collection unit can determine the priority of data to collect from the user's past income and expense data. The data collection unit can adjust the frequency of data collection based on the user's past income and expense data. The data collection unit can analyze the user's past income and expense data and select the types of data to collect. This allows the optimal data collection method to be selected by analyzing past income and expense data. Some or all of the above processes 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 income and expense data into a generating AI and have the generating AI select the optimal data collection method.

[0069] The data collection unit can filter data based on the user's current lifestyle and areas of interest during collection. For example, the data collection unit can filter the types of data to be collected based on the user's current lifestyle. The data collection unit can determine the priority of data to be collected based on the user's areas of interest. The data collection unit can adjust the frequency of data collection, taking into account the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering data based on the user's 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 user lifestyle and area of ​​interest data into a generating AI and have the generating AI perform data filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. If the user is in a hurry, the data collection unit can limit the types of data to collect. This allows for the priority collection of important data by prioritizing data according to 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 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 determine the priority of data to collect.

[0071] 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, the data collection unit can select the type of data to collect based on the user's geographical location information. The data collection unit can determine the priority of the data to collect by considering the user's geographical location information. The data collection unit can adjust the frequency of data collection based on the user's geographical location information. This allows for the priority collection of 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 data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0072] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can select the types of data to collect based on the user's social media activity. The data collection unit can analyze the user's social media activity and determine the priority of the data to collect. The data collection unit can adjust the frequency of data collection based on the user's social media activity. This allows for the collection of 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, or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis method and algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple analysis method. If the user is relaxed, the analysis unit can use a detailed analysis method. If the user is in a hurry, the analysis unit can use an algorithm to quickly obtain analysis results. By adjusting the analysis method according to the user's emotions, a more appropriate analysis becomes possible. 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 analysis method and algorithm.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also adjust the frequency of analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes 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 collected data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to analyze spending patterns to spending data. For income data, it can apply an algorithm to analyze income stability. For risk tolerance data, it can apply an algorithm to analyze risk profiles. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. 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.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more easily understandable display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0077] 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. The analysis unit may postpone the analysis of older data. The analysis unit can adjust the frequency of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes 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 determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis based on the relevance of the data. This allows for efficient analysis 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 order of analysis.

[0079] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0080] The proposal unit can adjust the level of detail in its proposals based on the importance of asset allocations and investment strategies. For example, it can provide detailed proposals for important asset allocations and investment strategies, and simplified proposals for less important ones. The proposal unit can also adjust the frequency of proposals according to the importance of asset allocations and investment strategies. This allows for efficient proposals by adjusting the level of detail based on the importance of asset allocations and investment strategies. 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 data on the importance of asset allocations and investment strategies into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0081] The proposal unit can apply different proposal algorithms depending on the asset allocation and investment strategy categories when making a proposal. For example, the proposal unit can apply a proposal algorithm that emphasizes risk management to asset allocation. For investment strategies, the proposal unit can apply a proposal algorithm that maximizes returns. The proposal unit can apply different proposal algorithms depending on the asset allocation and investment strategy categories. This allows for more accurate proposals by applying different proposal algorithms depending on the asset allocation and investment strategy categories. 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 asset allocation and investment strategy category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0082] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with more detailed explanations. If the user is in a hurry, the suggestion unit can provide short suggestions that can be quickly understood. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. 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 the suggestions.

[0083] The proposal department can prioritize proposals based on the timing of asset allocation and investment strategy submissions. For example, the proposal department may prioritize the most recent asset allocation and investment strategies. Older asset allocation and investment strategies may be postponed. The proposal department can also adjust the frequency of proposals based on the submission timing. This enables efficient proposals by prioritizing proposals based on the submission timing of asset allocation and investment strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input asset allocation and investment strategy submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0084] The proposal unit can adjust the order of proposals based on the relevance of asset allocations and investment strategies. For example, the proposal unit can prioritize proposing highly relevant asset allocations and investment strategies. The proposal unit can postpone proposing less relevant asset allocations and investment strategies. The proposal unit can adjust the order of proposals based on the relevance of asset allocations and investment strategies. This allows for efficient proposals by adjusting the order of proposals based on the relevance of asset allocations and investment strategies. 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 data on the relevance of asset allocations and investment strategies into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The proposed solution can estimate the user's emotions and adjust the frequency of feedback based on those emotions. For example, if the user is stressed, the frequency of feedback can be reduced, and if the user is relaxed, the frequency can be increased. Also, if the user is in a hurry, only important feedback can be prioritized. By adjusting the frequency of feedback according to the user's emotions, the burden on the user can be reduced, and more effective support can be provided.

[0087] The analysis unit can analyze a user's past investment history and identify their investment behavior patterns. For example, it can identify when a user tends to invest based on their past investment history and make future investment suggestions based on those patterns. It can also identify investment strategies that have been successful for the user in the past and make suggestions based on those strategies. Furthermore, it can identify investment strategies that have failed for the user in the past and provide advice on how to avoid those failures. In this way, it is possible to make more appropriate investment suggestions by utilizing the user's past investment history.

[0088] The proposal function can estimate the user's emotions and adjust the content of the proposal based on those emotions. For example, if the user is stressed, it can propose low-risk investments, and if the user is relaxed, it can propose high-risk investments. Also, if the user is in a hurry, it can propose investments that are expected to yield short-term profits, and if the user has time and resources, it can propose investments that are expected to yield long-term profits. In this way, by adjusting the content of the proposal according to the user's emotions, it becomes possible to make more appropriate investment proposals.

[0089] The data collection unit can gather regional economic conditions and market trends, taking into account the user's geographical location. For example, it can collect economic growth rates, unemployment rates, and real estate market trends in the user's area of ​​residence, and use this information to make investment suggestions. It can also collect economic conditions and market trends in areas the user visits for travel or business trips, and use this information to make short-term investment suggestions. Furthermore, it can collect economic conditions and market trends in areas the user is considering relocating to in the future, and use this information to make long-term investment suggestions. This allows for more appropriate investment suggestions by utilizing the user's geographical location.

[0090] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those estimates. For example, if the user is stressed, the suggestion timing can be delayed; if the user is relaxed, the suggestion timing can be accelerated. Furthermore, if the user is in a hurry, only important suggestions can be prioritized. By adjusting the timing of suggestions according to the user's emotions, this reduces the user's burden and enables more effective support.

[0091] The analytics unit can analyze users' past spending data and identify spending patterns. For example, it can identify which categories users spend the most money on and provide saving suggestions for those categories. It can also provide saving suggestions for periods when users tend to spend more. Furthermore, if users spend a lot on specific stores or services, it can provide discount information and coupons for those stores or services. This allows for more appropriate saving suggestions by leveraging users' past spending data.

[0092] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is stressed, it can present a simple and highly visible proposal; if the user is relaxed, it can present a proposal that includes detailed information. Furthermore, if the user is in a hurry, it can present a concise proposal. By adjusting the presentation of proposals according to the user's emotions, more appropriate suggestions can be made.

[0093] The data collection unit can analyze users' social media activity and collect data based on their interests and preferences. For example, it can analyze topics users frequently mention and accounts they follow on social media, and collect relevant data based on that information. It can also analyze content and comments users share on social media and identify their interests and preferences based on that information. Furthermore, it can analyze groups and events users participate in on social media and collect relevant data based on that information. This makes it possible to collect more appropriate data by utilizing users' social media activity.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method; if the user is relaxed, it can provide a display method that includes detailed information; and if the user is in a hurry, it can provide a display method that focuses on the essentials. By adjusting the display method of the analysis results according to the user's emotions, a more easily understandable display becomes possible.

[0095] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on that estimation. For example, if the user is stressed, it can provide a short, concise suggestion; if the user is relaxed, it can provide a longer suggestion with more detailed explanations. Furthermore, if the user is in a hurry, it can provide a short, easily understandable suggestion. By adjusting the suggestion length according to the user's emotions, more appropriate suggestions can be provided.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects user spending and income data, risk tolerance, and long-term goals. For example, it can collect spending and income data such as the user's salary, rent, food expenses, and utility costs. It can also collect survey data and past investment history to assess risk tolerance, and collect user goals such as retirement funds and children's education funds to set long-term objectives. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze the data using statistical analysis and machine learning algorithms to evaluate the user's spending patterns and income stability. It can also analyze risk tolerance to create a user risk profile and analyze long-term goals to evaluate the user's goal achievement. Step 3: The proposal department proposes asset allocation and investment strategies based on the analysis results obtained by the analysis department. For example, it can propose asset allocations such as stocks, bonds, and real estate, and investment strategies such as short-term investment, long-term investment, and risk diversification. It can also understand the user's daily spending and propose areas where savings can be made and methods for saving, and propose a review of utility bills and streamlining subscriptions based on credit card and electronic payment system usage history. Furthermore, it can analyze market trends and economic news in real time to propose investment timing and actions suitable for the user, review asset status monthly or quarterly, and revise the plan while checking progress. It can check whether progress is on track according to the plan and update advice as needed.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's spending and income data, risk tolerance, and long-term goals. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes asset allocation and investment strategies based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's spending and income data, risk tolerance, and long-term goals. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes asset allocation and investment strategies based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's spending and income data, risk tolerance, and long-term goals. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes asset allocation and investment strategies based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects the user's expenditure and income data, risk tolerance, and long-term goals. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes asset allocation and investment strategies based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A data collection unit that collects user spending and income data, risk tolerance, and long-term goals, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes asset allocation and investment strategies based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It helps users understand their daily spending and suggests areas where they can save money and how to do so. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on your credit card and electronic payment system usage history, we will suggest a review of your utility bills and streamline your subscriptions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We analyze market trends and economic news in real time and propose investment timings and actions that are suitable for the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We review our asset status monthly and quarterly, and revise our plans while checking our progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Check whether the plan is progressing smoothly and update advice as needed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the type and timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past financial data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the data is filtered based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis methods and algorithms based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed 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, 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 18) 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 19) 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 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of asset allocation and investment strategy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the asset allocation and investment strategy category. 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 length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, we prioritize them based on the timing of their asset allocation and investment strategy submissions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of asset allocation and investment strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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 spending and income data, risk tolerance, and long-term goals, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes asset allocation and investment strategies based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, It helps users understand their daily spending and suggests areas where they can save money and how to do so. The system according to feature 1.

3. The aforementioned proposal section is, Based on your credit card and electronic payment system usage history, we will suggest a review of your utility bills and streamline your subscriptions. The system according to feature 1.

4. The aforementioned proposal section is, We analyze market trends and economic news in real time and propose investment timings and actions that are suitable for the user. The system according to feature 1.

5. The aforementioned proposal section is, We review our asset status monthly and quarterly, and revise our plans while checking our progress. The system according to feature 1.

6. The aforementioned proposal section is, Check whether the plan is progressing smoothly and update advice as needed. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past financial data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the data is filtered based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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