system

Abstract

The system according to this embodiment aims to provide advice optimized for the individual financial needs of the user. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a tracking unit, and an identification unit. The collection unit collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. The analysis unit analyzes the data collected by the collection unit in detail. The proposal unit proposes an optimal savings plan based on the analysis results obtained by the analysis unit. The tracking unit tracks the user's spending based on the savings plan proposed by the proposal unit. The identification unit identifies unnecessary spending based on the spending data tracked by the tracking 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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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 conventional technology, it is difficult to provide advice optimized for individual financial needs of users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide advice optimized for individual financial needs of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a tracking unit, and an identification unit. The data collection unit collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. The analysis unit analyzes the data collected by the data collection unit in detail. The proposal unit proposes an optimal savings plan based on the analysis results obtained by the analysis unit. The tracking unit tracks the user's spending based on the savings plan proposed by the proposal unit. The identification unit identifies unnecessary spending based on the spending data tracked by the tracking unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide advice optimized for the user's individual financial needs. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The financial advice and budget management support system according to an embodiment of the present invention is a system that provides personalized financial advice and budget management support to each user by utilizing the latest artificial intelligence technology. This system analyzes in detail the user's personal data, such as income, spending patterns, savings goals, and risk tolerance, and provides advice optimized for each life stage and financial needs. Through natural dialogue with the AI, users can seamlessly manage a wide range of financial tasks, from tracking daily expenses to developing long-term asset management strategies. For example, the AI ​​analyzes spending patterns to identify unnecessary expenses and proposes an optimal savings plan based on the user's income and goals. Furthermore, it monitors market trends and economic indicators in real time and issues timely alerts about investment opportunities and potential risks. This service employs advanced encryption technology and a strict data protection policy to prioritize the security of users' personal information and financial data. Through the AI ​​financial concierge, users can access expert-level financial knowledge and insights 24 / 7, enabling smarter and more effective financial management. For example, it collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. This data is obtained from information provided by the user, as well as from bank transaction history and credit card usage history. Next, the collected data is analyzed in detail by AI. The AI ​​analyzes the user's income and spending patterns and identifies unnecessary expenses. For example, it analyzes the cost of monthly subscription services and frequently used dining out, and suggests ways to save money. Furthermore, the AI ​​proposes an optimal savings plan based on the user's savings goals and risk tolerance. For example, if the user is aiming for a large future expense (e.g., buying a house or paying for children's education), the AI ​​will create a savings plan to achieve that goal. It also suggests safe and high-risk investment options depending on the risk tolerance. In addition, the AI ​​monitors market trends and economic indicators in real time and issues timely alerts about investment opportunities and potential risks. For example, it may suggest that the user reconsider their investments based on sudden fluctuations in the stock market or the release of economic indicators. This allows the user to always make investment decisions based on the latest information.This service employs advanced encryption technology and a strict data protection policy to prioritize the security of users' personal and financial data. For example, it uses the SSL / TLS protocol for data transmission and applies encryption technology to its databases. Furthermore, it will not provide data to third parties without the user's consent. Through the AI ​​Financial Concierge, users can access expert-level financial knowledge and insights 24 / 7, enabling smarter and more effective financial management. For instance, users can interact with the AI ​​anytime, anywhere, and receive financial advice, allowing for quick responses to unexpected expenses or investment reassessments. This enables the financial advice and budget management support system to analyze users' personal data, such as income, spending patterns, savings goals, and risk tolerance, in detail, providing advice optimized for each life stage and financial needs.

[0029] The financial advice and budget management support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a tracking unit, and an identification unit. The data collection unit collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. The data collection unit obtains data from, for example, information provided by the user, bank transaction history, and credit card usage history. For example, the data collection unit can collect income and spending patterns based on information provided by the user. The data collection unit can also collect the user's income and spending patterns based on bank transaction history. Furthermore, the data collection unit can also collect the user's spending patterns based on credit card usage history. The analysis unit analyzes the data collected by the data collection unit in detail. For example, the analysis unit analyzes the user's income and spending patterns and identifies unnecessary expenses. For example, the analysis unit can analyze the user's income and spending patterns using statistical analysis. Furthermore, the analysis unit can analyze the user's income and spending patterns using machine learning algorithms. Furthermore, the analysis unit can analyze the user's income and spending patterns using data mining techniques. The proposal unit proposes an optimal savings plan based on the analysis results obtained by the analysis unit. The proposal unit proposes an optimal savings plan based, for example, the user's savings goals and risk tolerance. The proposal unit can propose a savings plan that takes risk diversification into consideration. The proposal unit can also propose a savings plan that takes investment destination selection into consideration. Furthermore, the proposal unit can propose a savings plan that is tailored to the user's life stage. The tracking unit tracks the user's spending based on the savings plan proposed by the proposal unit. The tracking unit records the user's spending data and identifies unnecessary expenses. The tracking unit can record the user's spending data using, for example, an application. The tracking unit can also record the user's spending data using manual input. Furthermore, the tracking unit can also record the user's spending data using sensor data. The identification unit identifies unnecessary expenses based on the spending data tracked by the tracking unit. The identification unit identifies unnecessary expenses such as unnecessary subscriptions or excessive entertainment expenses.The system can, for example, use a notification function to point out unnecessary expenses to the user. It can also use a reporting function to point out unnecessary expenses to the user. Furthermore, it can use a dashboard function to point out unnecessary expenses to the user. As a result, the financial advice and budget management support system according to this embodiment can analyze in detail the user's personal data, such as income, spending patterns, savings goals, and risk tolerance, and provide advice optimized for each life stage and financial needs.

[0030] The data collection unit collects personal data such as users' income, spending patterns, savings goals, and risk tolerance. The unit obtains data from sources such as user-provided information, bank transaction history, and credit card usage history. Specifically, it can collect income and spending patterns based on user-provided information. Users can input information about their income and expenses through applications and web portals. This includes pay stubs, rent and utility payment information, and daily shopping and entertainment expenses. The data collection unit can also collect users' income and spending patterns based on bank transaction history. Using bank APIs, it automatically retrieves transaction data from users' accounts to understand income and spending details. Furthermore, the data collection unit can collect users' spending patterns based on credit card usage history. Through credit card company APIs, it retrieves usage details and analyzes how much is spent in each category. This allows the data collection unit to comprehensively understand users' financial behavior and collect detailed data. In addition, the data collection unit also collects information on users' savings goals and risk tolerance. Users can set their own savings goals and risk tolerance within the application, and the data collection unit collects the necessary data based on these settings. For example, it collects data to assess how achievable the user's current income and expenditure situation is against their set savings goals. Regarding risk tolerance, it collects information through questionnaires about the level of risk the user is willing to accept, and uses this to collect data for developing future investment plans. In this way, the data collection unit can collect detailed data on users' financial behavior and goals, providing foundational information for the entire system.

[0031] The analysis department analyzes the data collected by the data collection department in detail. For example, the analysis department analyzes users' income and spending patterns to identify unnecessary expenses. Specifically, it can use statistical analysis to analyze users' income and spending patterns. It analyzes fluctuations in income and spending trends as time-series data to understand seasonal fluctuations and increases or decreases in spending related to specific events. The analysis department can also use machine learning algorithms to analyze users' income and spending patterns. For example, it can use clustering methods to classify users' spending patterns into multiple categories and analyze spending trends for each category. This makes it possible to identify which categories users are overspending in. Furthermore, the analysis department can also use data mining techniques to analyze users' income and spending patterns. For example, it can use association rule mining to analyze how specific spending items relate to other spending items and identify patterns of unnecessary spending. In this way, the analysis department can analyze the collected data from multiple angles and gain deep insights into users' financial behavior. Furthermore, the analytics department can utilize historical data and external economic indicators to predict future income and expenditures. For example, it can predict future increases or decreases in income based on historical income data and assess the feasibility of achieving the user's savings goals. By incorporating external economic indicators, it can also evaluate the impact of economic fluctuations on the user's income and expenditures and provide information for risk management. This allows the analytics department to analyze the user's financial behavior in detail, identify unnecessary spending, and make future predictions, thereby providing a foundation for optimizing the user's financial situation.

[0032] The Proposal Department proposes the optimal savings plan based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes the optimal savings plan based on the user's savings goals and risk tolerance. Specifically, it can propose savings plans that take risk diversification into consideration. Depending on the user's risk tolerance, it proposes a balanced portfolio ranging from low-risk deposit products to high-risk investment products. The Proposal Department can also propose savings plans that take investment destination selection into consideration. For example, based on the user's interests and values, it can propose investments in environmentally conscious companies or companies that fulfill their social responsibilities. Furthermore, the Proposal Department can propose savings plans that are tailored to the user's life stage. For example, it can propose long-term growth-oriented investment plans to younger generations and bond investments with stable returns to middle-aged and older generations. In this way, the Proposal Department can provide the optimal savings plan that is tailored to the user's individual needs and circumstances. Furthermore, the Proposal Department can continuously improve its proposals based on user feedback. It collects how users reacted to the proposed plans and revises the proposals based on this. For example, if a user feels unwilling to take risks, a more conservative plan can be proposed. Furthermore, the proposal department can provide proposals that reflect the latest market trends and economic conditions. They will update their proposals as needed in response to market fluctuations and changes in economic indicators, providing users with the most suitable advice. This allows the proposal department to respond flexibly to users' needs and circumstances, always providing the optimal savings plan.

[0033] The tracking unit tracks the user's spending based on the savings plan proposed by the proposal unit. For example, the tracking unit records the user's spending data and identifies unnecessary expenses. Specifically, the user can record their spending data using an application. The user enters their daily expenses into the application, and the tracking unit manages the spending data based on this. The tracking unit can also record the user's spending data using manual input. The tracking unit collects detailed spending data by having the user manually enter receipts and invoices. Furthermore, the tracking unit can record the user's spending data using sensor data. For example, it can automatically record the user's purchasing behavior and spending patterns using wearable devices such as smartwatches and fitness trackers. This allows the tracking unit to track the user's spending in detail and identify unnecessary expenses. In addition, the tracking unit analyzes the user's spending data and evaluates the progress toward the savings plan proposed by the proposal unit. For example, it evaluates how achievable the user's current spending situation is towards their set savings goal and suggests spending adjustments as needed. Furthermore, the tracking unit can monitor changes in the user's spending patterns in real time and immediately issue a warning if abnormal spending occurs. This allows the tracking unit to continuously monitor the user's spending and provide support to reduce unnecessary expenses.

[0034] The feedback function identifies unnecessary spending based on expenditure data tracked by the tracking function. For example, it identifies unnecessary subscriptions and excessive entertainment expenses. Specifically, it can use a notification function to alert users to unnecessary spending. For each expenditure category set by the user, it sends real-time notifications when the budget is exceeded or unnecessary spending occurs. The feedback function can also use a reporting function to alert users to unnecessary spending. By generating and sending expenditure reports periodically, it clearly shows trends in unnecessary spending and areas for improvement. Furthermore, the feedback function can use a dashboard function to alert users to unnecessary spending. Through the dashboard within the application, users can grasp their spending situation at a glance and visually see which categories are concentrating unnecessary spending. This allows the feedback function to effectively alert users to unnecessary spending and encourage them to review their spending. Additionally, the feedback function can continuously improve its feedback based on user feedback. It collects how users react to the feedback and uses this to revise the feedback. For example, if a user does not perceive a particular expenditure as wasteful, the system can reduce the number of suggestions for that category, allowing for flexible responses tailored to the user's needs. Furthermore, the suggestions section updates its recommendations as the user's spending patterns change, providing advice based on the latest information. This enables the suggestions section to effectively manage the user's spending and support them in reducing unnecessary expenses.

[0035] The analytics department can analyze users' income and spending patterns and identify unnecessary expenses. For example, the analytics department can use statistical analysis to analyze users' income and spending patterns. It can also use machine learning algorithms to analyze users' income and spending patterns. Furthermore, it can use data mining techniques to analyze users' income and spending patterns. This allows for the optimization of users' spending by analyzing their income and spending patterns and identifying unnecessary expenses.

[0036] The proposal department can propose an optimal savings plan based on the user's savings goals and risk tolerance. For example, the proposal department can propose a savings plan that takes risk diversification into consideration. For example, the proposal department can propose a savings plan that takes investment destination selection into consideration. For example, the proposal department can propose a savings plan that is tailored to the user's life stage. In this way, by proposing an optimal savings plan based on the user's savings goals and risk tolerance, it can support the user in achieving their savings goals.

[0037] The tracking unit can track user spending and record spending data. The tracking unit can record user spending data, for example, using an application. The tracking unit can also record user spending data, for example, using manual input. The tracking unit can also record user spending data, for example, using sensor data. This allows the system to track user spending and record spending data, thereby supporting user spending management.

[0038] The identification unit can identify unnecessary spending based on spending data tracked by the tracking unit. For example, the identification unit can identify unnecessary spending such as unnecessary subscriptions or excessive entertainment expenses. The identification unit can, for example, notify users of unnecessary spending using a notification function. The identification unit can also notify users of unnecessary spending using a reporting function. The identification unit can also notify users of unnecessary spending using a dashboard function. This allows for the optimization of user spending by identifying unnecessary spending based on spending data tracked by the tracking unit.

[0039] The recommendation function can suggest safe and high-risk investment options based on the user's risk tolerance. For example, the recommendation function can suggest government bonds or time deposits as safe investment options. For example, the recommendation function can suggest stocks or cryptocurrencies as high-risk investment options. For example, the recommendation function can suggest a balanced investment portfolio based on the user's risk tolerance. In this way, by suggesting safe and high-risk investment options according to the user's risk tolerance, the user's investment strategy can be optimized.

[0040] The analytics department can monitor market trends and economic indicators in real time and issue alerts about investment opportunities and potential risks. For example, the analytics department can monitor stock indices and economic indicators to identify investment opportunities. For example, the analytics department can monitor economic indicators such as GDP and unemployment rates to identify potential risks. For example, the analytics department can evaluate investment opportunities and risks based on real-time data. This allows the analytics department to support users' investment decisions by monitoring market trends and economic indicators in real time and issuing alerts about investment opportunities and potential risks.

[0041] The system employs advanced encryption technology and strict data protection policies to ensure the security of users' personal and financial data. For example, the system uses the SSL / TLS protocol for data transmission and reception. The system can also apply encryption technology to its databases. Furthermore, the system can implement access control to prevent unauthorized access to data. Thus, by employing advanced encryption technology and strict data protection policies, the system can ensure the security of users' personal and financial data.

[0042] The data collection unit can analyze a user's past income and expenditure history and select the optimal data collection method. For example, based on a user's past income history, the data collection unit can focus on collecting data during periods of significant income fluctuation. For example, by analyzing a user's expenditure history, the data collection unit can collect detailed data on a particular category if spending is high in that category. For example, the data collection unit can consider the balance between a user's income and expenditure and collect data by comparing months with high and low income. By analyzing a user's past income and expenditure history, the optimal data collection method can be selected, enabling efficient data collection.

[0043] The data collection unit can filter data based on the user's current life stage and financial needs during collection. For example, if the user is newly married, the unit will prioritize collecting data related to home purchase and children's education expenses. If the user is nearing retirement, the unit can also collect data related to post-retirement living expenses and pensions. If the user is a student, the unit can also collect data related to tuition fees and scholarships. This allows for appropriate data collection by filtering data based on the user's current life stage and financial needs.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user lives in an urban area, the unit will prioritize the collection of data related to living expenses and transportation costs specific to urban areas. If the user lives in a rural area, the unit can also prioritize the collection of data related to living expenses and transportation costs specific to rural areas. If the user lives overseas, the unit can also prioritize the collection of data related to living expenses and taxes in that country. This allows for appropriate data collection by prioritizing the collection of highly relevant data while considering the user's geographical location.

[0045] The data collection unit can analyze users' social media activity and collect relevant data during the collection process. For example, the data collection unit can analyze the content of social media posts that users frequently make and collect relevant spending data. For example, the data collection unit can analyze users' social media friendships and collect spending data that is easily influenced by their friends. For example, the data collection unit can analyze users' social media activity times and collect spending data related to specific time periods. This allows for appropriate data collection by analyzing users' social media activity and collecting relevant data.

[0046] The analysis department can adjust the level of detail in its analysis based on the importance of income and expenditure patterns. For example, if there is a large difference between months with high and low income, the analysis department will analyze that difference in detail to identify the cause. For example, if expenditure patterns are consistent, the analysis department can also perform a concise analysis of overall expenditure trends. For example, if expenditures are high in a particular category, the analysis department can perform a detailed analysis of that category. By adjusting the level of detail in the analysis based on the importance of income and expenditure patterns, the analysis department can provide appropriate analytical results.

[0047] The analysis unit can apply different analysis algorithms depending on the income and expenditure categories during the analysis. For example, for the income category, the analysis unit can apply an algorithm that analyzes the trend of increase or decrease in income. For example, for the expenditure category, the analysis unit can apply an algorithm that analyzes the breakdown of expenditures in detail. For example, for the savings category, the analysis unit can apply an algorithm that analyzes the trend of increase or decrease in savings. In this way, by applying different analysis algorithms depending on the income and expenditure categories, appropriate analysis results can be provided.

[0048] The analysis department can prioritize its analysis based on the timing of income and expenses. For example, if there is a large difference between months with high and low income, the analysis department will prioritize analyzing the period when that difference occurs. Similarly, if there is a large difference between months with high and low expenses, the analysis department can prioritize analyzing the period when that difference occurs. For example, if there is a large difference between periods when savings increase and decrease, the analysis department can prioritize analyzing the period when that difference occurs. By prioritizing the analysis based on the timing of income and expenses, the analysis department can provide timely analysis results.

[0049] The analysis unit can adjust the order of analysis based on the relationship between income and expenses. For example, if there is a high correlation between income and expenses, the analysis unit can analyze income first, followed by expenses. Similarly, if there is a high correlation between expenses and savings, the analysis unit can analyze expenses first, followed by savings. By adjusting the order of analysis based on the relationship between income and expenses, the analysis unit can provide analysis results in an appropriate order.

[0050] The proposal department can adjust the level of detail in its proposals based on the importance of savings goals and risk tolerance. For example, if the savings goal is high, the proposal department will provide detailed proposals aimed at that goal. If the risk tolerance is low, the proposal department can also provide detailed proposals regarding safe investment options. If the savings goal is low, the proposal department can also provide concise proposals. By adjusting the level of detail in proposals based on the importance of savings goals and risk tolerance, the proposal department can provide appropriate proposals.

[0051] The proposal function can apply different proposal algorithms depending on the savings goal and risk tolerance category when making a proposal. For example, for savings goals, the proposal function can apply a proposal algorithm aimed at achieving the goal. For risk tolerance, the proposal function can also apply a proposal algorithm related to risk management. For example, the proposal function can apply a balanced proposal algorithm to both savings goals and risk tolerance. This allows the proposal function to provide appropriate proposals by applying different proposal algorithms depending on the savings goal and risk tolerance category.

[0052] The proposal department can prioritize proposals based on the timing of savings goals and risk tolerance changes. For example, if the savings goal is nearing completion, the proposal department will prioritize proposals that address that goal. If risk tolerance fluctuates significantly, the proposal department can also prioritize proposals that address those fluctuations. If the savings goal is far off, the proposal department can also prioritize long-term proposals. By prioritizing proposals based on the timing of savings goals and risk tolerance changes, the proposal department can provide proposals at the appropriate time.

[0053] The proposal department can adjust the order of proposals based on the relationship between savings goals and risk tolerance. For example, if the relationship between savings goals and risk tolerance is high, the proposal department will propose savings goals first, followed by proposals for risk tolerance. If the relationship between savings goals and risk tolerance is low, the proposal department can also make separate proposals for each. If the relationship between savings goals and risk tolerance is moderate, the proposal department can make proposals in a balanced order. By adjusting the order of proposals based on the relationship between savings goals and risk tolerance, the proposal department can provide proposals in an appropriate order.

[0054] The tracking unit can analyze the user's past spending history to select the optimal tracking method during tracking. For example, the tracking unit can focus on tracking categories with high spending based on the user's past spending history. For example, the tracking unit can analyze the user's spending history and focus on tracking periods when spending is concentrated. For example, the tracking unit can analyze the user's spending history and focus on tracking categories with a lot of unnecessary spending. In this way, by analyzing the user's past spending history, the optimal tracking method can be selected, enabling efficient spending tracking.

[0055] The tracking unit can customize the means of tracking expenses based on the user's current living situation. For example, if the user is newly married, the tracking unit can focus on tracking wedding-related expenses. If the user is about to retire, the tracking unit can focus on tracking post-retirement living expenses. If the user is a student, the tracking unit can focus on tracking tuition and living expenses. This allows for appropriate expense tracking by customizing the means of tracking expenses based on the user's current living situation.

[0056] The tracking unit can select the optimal spending tracking method by considering the user's geographical location during tracking. For example, if the user lives in an urban area, the tracking unit will focus on tracking urban-specific spending. If the user lives in a rural area, the tracking unit can also focus on tracking rural-specific spending. If the user lives overseas, the tracking unit can also focus on tracking country-specific spending. By selecting the optimal spending tracking method by considering the user's geographical location, appropriate spending tracking can be performed.

[0057] The tracking unit can analyze the user's social media activity during tracking and suggest means of tracking spending. For example, the tracking unit can analyze the content of social media posts that the user frequently makes and track related spending. For example, the tracking unit can analyze the user's social media friendships and track spending that is easily influenced by friends. For example, the tracking unit can analyze the user's social media activity time and track spending related to specific time periods. This enables appropriate spending tracking by analyzing the user's social media activity and suggesting means of tracking spending.

[0058] The feedback system can analyze the user's past spending history to select the most appropriate feedback method when providing feedback. For example, based on the user's past spending history, the feedback system can focus on categories with high levels of unnecessary spending. For example, by analyzing the user's spending history, the feedback system can focus on periods when unnecessary spending is concentrated. For example, by analyzing the user's spending history, the feedback system can focus on months with high levels of unnecessary spending. In this way, by analyzing the user's past spending history, the system can select the most appropriate feedback method and provide efficient feedback on unnecessary expenses.

[0059] The feedback system can customize how it identifies unnecessary expenses based on the user's current living situation. For example, if the user is newly married, the feedback system will focus on identifying unnecessary expenses related to marriage. If the user is about to retire, the feedback system can focus on identifying unnecessary expenses related to post-retirement living expenses. If the user is a student, the feedback system can focus on identifying unnecessary expenses related to tuition and living expenses. By customizing the feedback system based on the user's current living situation, it can provide appropriate feedback on unnecessary expenses.

[0060] The feedback system can select the most appropriate method for identifying unnecessary expenses, taking into account the user's geographical location. For example, if the user lives in an urban area, the feedback system will focus on identifying unnecessary expenses specific to urban areas. If the user lives in a rural area, the feedback system can focus on identifying unnecessary expenses specific to rural areas. If the user lives overseas, the feedback system can focus on identifying unnecessary expenses specific to that country. By selecting the most appropriate method for identifying unnecessary expenses, taking into account the user's geographical location, the system can provide appropriate feedback on unnecessary expenses.

[0061] The feedback function can analyze a user's social media activity and suggest ways to identify unnecessary spending. For example, it can analyze the content a user frequently posts on social media and identify related unnecessary spending. It can also analyze a user's social media friendships and identify unnecessary spending that is easily influenced by friends. It can also analyze a user's social media activity times and identify unnecessary spending related to specific time periods. By analyzing a user's social media activity and suggesting ways to identify unnecessary spending, the system can provide appropriate feedback on unnecessary expenses.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The proposal department can adjust the level of detail in its proposals based on the importance of savings goals and risk tolerance. For example, if the savings goal is high, the proposal department will provide detailed proposals aimed at that goal. If the risk tolerance is low, the proposal department can also provide detailed proposals regarding safe investment options. If the savings goal is low, the proposal department can also provide concise proposals. By adjusting the level of detail in proposals based on the importance of savings goals and risk tolerance, the proposal department can provide appropriate proposals.

[0064] The analysis department can adjust the level of detail in its analysis based on the importance of income and expenditure patterns. For example, if there is a large difference between months with high and low income, the analysis department will analyze that difference in detail to identify the cause. For example, if expenditure patterns are consistent, the analysis department can also perform a concise analysis of overall expenditure trends. For example, if expenditures are high in a particular category, the analysis department can perform a detailed analysis of that category. By adjusting the level of detail in the analysis based on the importance of income and expenditure patterns, the analysis department can provide appropriate analytical results.

[0065] The tracking unit can analyze the user's past spending history to select the optimal tracking method during tracking. For example, the tracking unit can focus on tracking categories with high spending based on the user's past spending history. For example, the tracking unit can analyze the user's spending history and focus on tracking periods when spending is concentrated. For example, the tracking unit can analyze the user's spending history and focus on tracking categories with a lot of unnecessary spending. In this way, by analyzing the user's past spending history, the optimal tracking method can be selected, enabling efficient spending tracking.

[0066] The feedback system can analyze the user's past spending history to select the most appropriate feedback method when providing feedback. For example, based on the user's past spending history, the feedback system can focus on categories with high levels of unnecessary spending. For example, by analyzing the user's spending history, the feedback system can focus on periods when unnecessary spending is concentrated. For example, by analyzing the user's spending history, the feedback system can focus on months with high levels of unnecessary spending. In this way, by analyzing the user's past spending history, the system can select the most appropriate feedback method and provide efficient feedback on unnecessary expenses.

[0067] The proposal department can prioritize proposals based on the timing of savings goals and risk tolerance changes. For example, if the savings goal is nearing completion, the proposal department will prioritize proposals that address that goal. If risk tolerance fluctuates significantly, the proposal department can also prioritize proposals that address those fluctuations. If the savings goal is far off, the proposal department can also prioritize long-term proposals. By prioritizing proposals based on the timing of savings goals and risk tolerance changes, the proposal department can provide proposals at the appropriate time.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. The data collection unit obtains data from sources such as information provided by the user, bank transaction history, and credit card usage history. Step 2: The analysis department analyzes the data collected by the data collection department in detail. The analysis department uses, for example, statistical analysis, machine learning algorithms, and data mining techniques to analyze users' income and spending patterns and identify unnecessary expenses. Step 3: The proposal department proposes the optimal savings plan based on the analysis results obtained by the analysis department. For example, the proposal department proposes a savings plan that takes into account risk diversification and investment selection based on the user's savings goals and risk tolerance. Step 4: The tracking unit tracks the user's spending based on the savings plan proposed by the suggestion unit. The tracking unit records the user's spending data using, for example, applications, manual input, or sensor data, and identifies unnecessary spending. Step 5: The identification unit identifies unnecessary spending based on the spending data tracked by the tracking unit. The identification unit uses notification, reporting, and dashboard functions to inform users of unnecessary spending, such as unnecessary subscriptions or excessive entertainment expenses.

[0070] (Example of form 2) The financial advice and budget management support system according to an embodiment of the present invention is a system that provides personalized financial advice and budget management support to each user by utilizing the latest artificial intelligence technology. This system analyzes in detail the user's personal data, such as income, spending patterns, savings goals, and risk tolerance, and provides advice optimized for each life stage and financial needs. Through natural dialogue with the AI, users can seamlessly manage a wide range of financial tasks, from tracking daily expenses to developing long-term asset management strategies. For example, the AI ​​analyzes spending patterns to identify unnecessary expenses and proposes an optimal savings plan based on the user's income and goals. Furthermore, it monitors market trends and economic indicators in real time and issues timely alerts about investment opportunities and potential risks. This service employs advanced encryption technology and a strict data protection policy to prioritize the security of users' personal information and financial data. Through the AI ​​financial concierge, users can access expert-level financial knowledge and insights 24 / 7, enabling smarter and more effective financial management. For example, it collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. This data is obtained from information provided by the user, as well as from bank transaction history and credit card usage history. Next, the collected data is analyzed in detail by AI. The AI ​​analyzes the user's income and spending patterns and identifies unnecessary expenses. For example, it analyzes the cost of monthly subscription services and frequently used dining out, and suggests ways to save money. Furthermore, the AI ​​proposes an optimal savings plan based on the user's savings goals and risk tolerance. For example, if the user is aiming for a large future expense (e.g., buying a house or paying for children's education), the AI ​​will create a savings plan to achieve that goal. It also suggests safe and high-risk investment options depending on the risk tolerance. In addition, the AI ​​monitors market trends and economic indicators in real time and issues timely alerts about investment opportunities and potential risks. For example, it may suggest that the user reconsider their investments based on sudden fluctuations in the stock market or the release of economic indicators. This allows the user to always make investment decisions based on the latest information.This service employs advanced encryption technology and a strict data protection policy to prioritize the security of users' personal and financial data. For example, it uses the SSL / TLS protocol for data transmission and applies encryption technology to its databases. Furthermore, it will not provide data to third parties without the user's consent. Through the AI ​​Financial Concierge, users can access expert-level financial knowledge and insights 24 / 7, enabling smarter and more effective financial management. For instance, users can interact with the AI ​​anytime, anywhere, and receive financial advice, allowing for quick responses to unexpected expenses or investment reassessments. This enables the financial advice and budget management support system to analyze users' personal data, such as income, spending patterns, savings goals, and risk tolerance, in detail, providing advice optimized for each life stage and financial needs.

[0071] The financial advice and budget management support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a tracking unit, and an identification unit. The data collection unit collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. The data collection unit obtains data from, for example, information provided by the user, bank transaction history, and credit card usage history. For example, the data collection unit can collect income and spending patterns based on information provided by the user. The data collection unit can also collect the user's income and spending patterns based on bank transaction history. Furthermore, the data collection unit can also collect the user's spending patterns based on credit card usage history. The analysis unit analyzes the data collected by the data collection unit in detail. For example, the analysis unit analyzes the user's income and spending patterns and identifies unnecessary expenses. For example, the analysis unit can analyze the user's income and spending patterns using statistical analysis. Furthermore, the analysis unit can analyze the user's income and spending patterns using machine learning algorithms. Furthermore, the analysis unit can analyze the user's income and spending patterns using data mining techniques. The proposal unit proposes an optimal savings plan based on the analysis results obtained by the analysis unit. The proposal unit proposes an optimal savings plan based, for example, the user's savings goals and risk tolerance. The proposal unit can propose a savings plan that takes risk diversification into consideration. The proposal unit can also propose a savings plan that takes investment destination selection into consideration. Furthermore, the proposal unit can propose a savings plan that is tailored to the user's life stage. The tracking unit tracks the user's spending based on the savings plan proposed by the proposal unit. The tracking unit records the user's spending data and identifies unnecessary expenses. The tracking unit can record the user's spending data using, for example, an application. The tracking unit can also record the user's spending data using manual input. Furthermore, the tracking unit can also record the user's spending data using sensor data. The identification unit identifies unnecessary expenses based on the spending data tracked by the tracking unit. The identification unit identifies unnecessary expenses such as unnecessary subscriptions or excessive entertainment expenses.The system can, for example, use a notification function to point out unnecessary expenses to the user. It can also use a reporting function to point out unnecessary expenses to the user. Furthermore, it can use a dashboard function to point out unnecessary expenses to the user. As a result, the financial advice and budget management support system according to this embodiment can analyze in detail the user's personal data, such as income, spending patterns, savings goals, and risk tolerance, and provide advice optimized for each life stage and financial needs.

[0072] The data collection unit collects personal data such as users' income, spending patterns, savings goals, and risk tolerance. The unit obtains data from sources such as user-provided information, bank transaction history, and credit card usage history. Specifically, it can collect income and spending patterns based on user-provided information. Users can input information about their income and expenses through applications and web portals. This includes pay stubs, rent and utility payment information, and daily shopping and entertainment expenses. The data collection unit can also collect users' income and spending patterns based on bank transaction history. Using bank APIs, it automatically retrieves transaction data from users' accounts to understand income and spending details. Furthermore, the data collection unit can collect users' spending patterns based on credit card usage history. Through credit card company APIs, it retrieves usage details and analyzes how much is spent in each category. This allows the data collection unit to comprehensively understand users' financial behavior and collect detailed data. In addition, the data collection unit also collects information on users' savings goals and risk tolerance. Users can set their own savings goals and risk tolerance within the application, and the data collection unit collects the necessary data based on these settings. For example, it collects data to assess how achievable the user's current income and expenditure situation is against their set savings goals. Regarding risk tolerance, it collects information through questionnaires about the level of risk the user is willing to accept, and uses this to collect data for developing future investment plans. In this way, the data collection unit can collect detailed data on users' financial behavior and goals, providing foundational information for the entire system.

[0073] The analysis department analyzes the data collected by the data collection department in detail. For example, the analysis department analyzes users' income and spending patterns to identify unnecessary expenses. Specifically, it can use statistical analysis to analyze users' income and spending patterns. It analyzes fluctuations in income and spending trends as time-series data to understand seasonal fluctuations and increases or decreases in spending related to specific events. The analysis department can also use machine learning algorithms to analyze users' income and spending patterns. For example, it can use clustering methods to classify users' spending patterns into multiple categories and analyze spending trends for each category. This makes it possible to identify which categories users are overspending in. Furthermore, the analysis department can also use data mining techniques to analyze users' income and spending patterns. For example, it can use association rule mining to analyze how specific spending items relate to other spending items and identify patterns of unnecessary spending. In this way, the analysis department can analyze the collected data from multiple angles and gain deep insights into users' financial behavior. Furthermore, the analytics department can utilize historical data and external economic indicators to predict future income and expenditures. For example, it can predict future increases or decreases in income based on historical income data and assess the feasibility of achieving the user's savings goals. By incorporating external economic indicators, it can also evaluate the impact of economic fluctuations on the user's income and expenditures and provide information for risk management. This allows the analytics department to analyze the user's financial behavior in detail, identify unnecessary spending, and make future predictions, thereby providing a foundation for optimizing the user's financial situation.

[0074] The Proposal Department proposes the optimal savings plan based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes the optimal savings plan based on the user's savings goals and risk tolerance. Specifically, it can propose savings plans that take risk diversification into consideration. Depending on the user's risk tolerance, it proposes a balanced portfolio ranging from low-risk deposit products to high-risk investment products. The Proposal Department can also propose savings plans that take investment destination selection into consideration. For example, based on the user's interests and values, it can propose investments in environmentally conscious companies or companies that fulfill their social responsibilities. Furthermore, the Proposal Department can propose savings plans that are tailored to the user's life stage. For example, it can propose long-term growth-oriented investment plans to younger generations and bond investments with stable returns to middle-aged and older generations. In this way, the Proposal Department can provide the optimal savings plan that is tailored to the user's individual needs and circumstances. Furthermore, the Proposal Department can continuously improve its proposals based on user feedback. It collects how users reacted to the proposed plans and revises the proposals based on this. For example, if a user feels unwilling to take risks, a more conservative plan can be proposed. Furthermore, the proposal department can provide proposals that reflect the latest market trends and economic conditions. They will update their proposals as needed in response to market fluctuations and changes in economic indicators, providing users with the most suitable advice. This allows the proposal department to respond flexibly to users' needs and circumstances, always providing the optimal savings plan.

[0075] The tracking unit tracks the user's spending based on the savings plan proposed by the proposal unit. For example, the tracking unit records the user's spending data and identifies unnecessary expenses. Specifically, the user can record their spending data using an application. The user enters their daily expenses into the application, and the tracking unit manages the spending data based on this. The tracking unit can also record the user's spending data using manual input. The tracking unit collects detailed spending data by having the user manually enter receipts and invoices. Furthermore, the tracking unit can record the user's spending data using sensor data. For example, it can automatically record the user's purchasing behavior and spending patterns using wearable devices such as smartwatches and fitness trackers. This allows the tracking unit to track the user's spending in detail and identify unnecessary expenses. In addition, the tracking unit analyzes the user's spending data and evaluates the progress toward the savings plan proposed by the proposal unit. For example, it evaluates how achievable the user's current spending situation is towards their set savings goal and suggests spending adjustments as needed. Furthermore, the tracking unit can monitor changes in the user's spending patterns in real time and immediately issue a warning if abnormal spending occurs. This allows the tracking unit to continuously monitor the user's spending and provide support to reduce unnecessary expenses.

[0076] The feedback function identifies unnecessary spending based on expenditure data tracked by the tracking function. For example, it identifies unnecessary subscriptions and excessive entertainment expenses. Specifically, it can use a notification function to alert users to unnecessary spending. For each expenditure category set by the user, it sends real-time notifications when the budget is exceeded or unnecessary spending occurs. The feedback function can also use a reporting function to alert users to unnecessary spending. By generating and sending expenditure reports periodically, it clearly shows trends in unnecessary spending and areas for improvement. Furthermore, the feedback function can use a dashboard function to alert users to unnecessary spending. Through the dashboard within the application, users can grasp their spending situation at a glance and visually see which categories are concentrating unnecessary spending. This allows the feedback function to effectively alert users to unnecessary spending and encourage them to review their spending. Additionally, the feedback function can continuously improve its feedback based on user feedback. It collects how users react to the feedback and uses this to revise the feedback. For example, if a user does not perceive a particular expenditure as wasteful, the system can reduce the number of suggestions for that category, allowing for flexible responses tailored to the user's needs. Furthermore, the suggestions section updates its recommendations as the user's spending patterns change, providing advice based on the latest information. This enables the suggestions section to effectively manage the user's spending and support them in reducing unnecessary expenses.

[0077] The analytics department can analyze users' income and spending patterns and identify unnecessary expenses. For example, the analytics department can use statistical analysis to analyze users' income and spending patterns. It can also use machine learning algorithms to analyze users' income and spending patterns. Furthermore, it can use data mining techniques to analyze users' income and spending patterns. This allows for the optimization of users' spending by analyzing their income and spending patterns and identifying unnecessary expenses.

[0078] The proposal department can propose an optimal savings plan based on the user's savings goals and risk tolerance. For example, the proposal department can propose a savings plan that takes risk diversification into consideration. For example, the proposal department can propose a savings plan that takes investment destination selection into consideration. For example, the proposal department can propose a savings plan that is tailored to the user's life stage. In this way, by proposing an optimal savings plan based on the user's savings goals and risk tolerance, it can support the user in achieving their savings goals.

[0079] The tracking unit can track user spending and record spending data. The tracking unit can record user spending data, for example, using an application. The tracking unit can also record user spending data, for example, using manual input. The tracking unit can also record user spending data, for example, using sensor data. This allows the system to track user spending and record spending data, thereby supporting user spending management.

[0080] The identification unit can identify unnecessary spending based on spending data tracked by the tracking unit. For example, the identification unit can identify unnecessary spending such as unnecessary subscriptions or excessive entertainment expenses. The identification unit can, for example, notify users of unnecessary spending using a notification function. The identification unit can also notify users of unnecessary spending using a reporting function. The identification unit can also notify users of unnecessary spending using a dashboard function. This allows for the optimization of user spending by identifying unnecessary spending based on spending data tracked by the tracking unit.

[0081] The recommendation function can suggest safe and high-risk investment options based on the user's risk tolerance. For example, the recommendation function can suggest government bonds or time deposits as safe investment options. For example, the recommendation function can suggest stocks or cryptocurrencies as high-risk investment options. For example, the recommendation function can suggest a balanced investment portfolio based on the user's risk tolerance. In this way, by suggesting safe and high-risk investment options according to the user's risk tolerance, the user's investment strategy can be optimized.

[0082] The analytics department can monitor market trends and economic indicators in real time and issue alerts about investment opportunities and potential risks. For example, the analytics department can monitor stock indices and economic indicators to identify investment opportunities. For example, the analytics department can monitor economic indicators such as GDP and unemployment rates to identify potential risks. For example, the analytics department can evaluate investment opportunities and risks based on real-time data. This allows the analytics department to support users' investment decisions by monitoring market trends and economic indicators in real time and issuing alerts about investment opportunities and potential risks.

[0083] The system employs advanced encryption technology and strict data protection policies to ensure the security of users' personal and financial data. For example, the system uses the SSL / TLS protocol for data transmission and reception. The system can also apply encryption technology to its databases. Furthermore, the system can implement access control to prevent unauthorized access to data. Thus, by employing advanced encryption technology and strict data protection policies, the system can ensure the security of users' personal and financial data.

[0084] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can minimize the amount of data collected and obtain information in the form of simple questions. For example, if the user is relaxed, the data collection unit can collect more detailed data and obtain more information. For example, if the user is in a hurry, the data collection unit can prioritize the collection of only important data and obtain information quickly. In this way, by adjusting the type of data collected based on the user's emotions, the burden on the user can be reduced and appropriate data can be collected. 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.

[0085] The data collection unit can analyze a user's past income and expenditure history and select the optimal data collection method. For example, based on a user's past income history, the data collection unit can focus on collecting data during periods of significant income fluctuation. For example, by analyzing a user's expenditure history, the data collection unit can collect detailed data on a particular category if spending is high in that category. For example, the data collection unit can consider the balance between a user's income and expenditure and collect data by comparing months with high and low income. By analyzing a user's past income and expenditure history, the optimal data collection method can be selected, enabling efficient data collection.

[0086] The data collection unit can filter data based on the user's current life stage and financial needs during collection. For example, if the user is newly married, the unit will prioritize collecting data related to home purchase and children's education expenses. If the user is nearing retirement, the unit can also collect data related to post-retirement living expenses and pensions. If the user is a student, the unit can also collect data related to tuition fees and scholarships. This allows for appropriate data collection by filtering data based on the user's current life stage and financial needs.

[0087] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit can prioritize collecting only essential data to reduce the user's burden. If the user is relaxed, the unit can prioritize collecting detailed data to obtain more information. If the user is in a hurry, the unit can prioritize collecting data that can be collected quickly. This reduces the user's burden and ensures appropriate data collection by prioritizing the data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user lives in an urban area, the unit will prioritize the collection of data related to living expenses and transportation costs specific to urban areas. If the user lives in a rural area, the unit can also prioritize the collection of data related to living expenses and transportation costs specific to rural areas. If the user lives overseas, the unit can also prioritize the collection of data related to living expenses and taxes in that country. This allows for appropriate data collection by prioritizing the collection of highly relevant data while considering the user's geographical location.

[0089] The data collection unit can analyze users' social media activity and collect relevant data during the collection process. For example, the data collection unit can analyze the content of social media posts that users frequently make and collect relevant spending data. For example, the data collection unit can analyze users' social media friendships and collect spending data that is easily influenced by their friends. For example, the data collection unit can analyze users' social media activity times and collect spending data related to specific time periods. This allows for appropriate data collection by analyzing users' social media activity and collecting relevant data.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can display the results using simple, visually easy-to-understand graphs and charts. If the user is relaxed, the analysis unit can also display the results including detailed text and numerical data. If the user is in a hurry, the analysis unit can also display the results in a concise, to-the-point report format. This allows for the provision of easy-to-understand analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The analysis department can adjust the level of detail in its analysis based on the importance of income and expenditure patterns. For example, if there is a large difference between months with high and low income, the analysis department will analyze that difference in detail to identify the cause. For example, if expenditure patterns are consistent, the analysis department can also perform a concise analysis of overall expenditure trends. For example, if expenditures are high in a particular category, the analysis department can perform a detailed analysis of that category. By adjusting the level of detail in the analysis based on the importance of income and expenditure patterns, the analysis department can provide appropriate analytical results.

[0092] The analysis unit can apply different analysis algorithms depending on the income and expenditure categories during the analysis. For example, for the income category, the analysis unit can apply an algorithm that analyzes the trend of increase or decrease in income. For example, for the expenditure category, the analysis unit can apply an algorithm that analyzes the breakdown of expenditures in detail. For example, for the savings category, the analysis unit can apply an algorithm that analyzes the trend of increase or decrease in savings. In this way, by applying different analysis algorithms depending on the income and expenditure categories, appropriate analysis results can be provided.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is in a hurry, the analysis unit can also provide a brief analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The analysis department can prioritize its analysis based on the timing of income and expenses. For example, if there is a large difference between months with high and low income, the analysis department will prioritize analyzing the period when that difference occurs. Similarly, if there is a large difference between months with high and low expenses, the analysis department can prioritize analyzing the period when that difference occurs. For example, if there is a large difference between periods when savings increase and decrease, the analysis department can prioritize analyzing the period when that difference occurs. By prioritizing the analysis based on the timing of income and expenses, the analysis department can provide timely analysis results.

[0095] The analysis unit can adjust the order of analysis based on the relationship between income and expenses. For example, if there is a high correlation between income and expenses, the analysis unit can analyze income first, followed by expenses. Similarly, if there is a high correlation between expenses and savings, the analysis unit can analyze expenses first, followed by savings. By adjusting the order of analysis based on the relationship between income and expenses, the analysis unit can provide analysis results in an appropriate order.

[0096] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and visually easy-to-understand suggestions. If the user is relaxed, the suggestion function may also present suggestions that include detailed text and numerical data. If the user is in a hurry, the suggestion function may also present concise suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, the system can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The proposal department can adjust the level of detail in its proposals based on the importance of savings goals and risk tolerance. For example, if the savings goal is high, the proposal department will provide detailed proposals aimed at that goal. If the risk tolerance is low, the proposal department can also provide detailed proposals regarding safe investment options. If the savings goal is low, the proposal department can also provide concise proposals. By adjusting the level of detail in proposals based on the importance of savings goals and risk tolerance, the proposal department can provide appropriate proposals.

[0098] The proposal function can apply different proposal algorithms depending on the savings goal and risk tolerance category when making a proposal. For example, for savings goals, the proposal function can apply a proposal algorithm aimed at achieving the goal. For risk tolerance, the proposal function can also apply a proposal algorithm related to risk management. For example, the proposal function can apply a balanced proposal algorithm to both savings goals and risk tolerance. This allows the proposal function to provide appropriate proposals by applying different proposal algorithms depending on the savings goal and risk tolerance category.

[0099] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is stressed, the suggestion function will provide short, concise suggestions. If the user is relaxed, for example, the suggestion function can provide detailed suggestions. If the user is in a hurry, for example, the suggestion function can provide concise suggestions. By adjusting the length of suggestions based on the user's emotions, the system can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The proposal department can prioritize proposals based on the timing of savings goals and risk tolerance changes. For example, if the savings goal is nearing completion, the proposal department will prioritize proposals that address that goal. If risk tolerance fluctuates significantly, the proposal department can also prioritize proposals that address those fluctuations. If the savings goal is far off, the proposal department can also prioritize long-term proposals. By prioritizing proposals based on the timing of savings goals and risk tolerance changes, the proposal department can provide proposals at the appropriate time.

[0101] The proposal department can adjust the order of proposals based on the relationship between savings goals and risk tolerance. For example, if the relationship between savings goals and risk tolerance is high, the proposal department will propose savings goals first, followed by proposals for risk tolerance. If the relationship between savings goals and risk tolerance is low, the proposal department can also make separate proposals for each. If the relationship between savings goals and risk tolerance is moderate, the proposal department can make proposals in a balanced order. By adjusting the order of proposals based on the relationship between savings goals and risk tolerance, the proposal department can provide proposals in an appropriate order.

[0102] The tracking unit can estimate the user's emotions and adjust the spending tracking method based on the estimated emotions. For example, if the user is stressed, the tracking unit can provide a simple and visually easy-to-understand spending tracking method. For example, if the user is relaxed, the tracking unit can also provide a detailed spending tracking method. For example, if the user is in a hurry, the tracking unit can also provide a concise spending tracking method. In this way, by adjusting the spending tracking method based on the user's emotions, it is possible to provide a spending tracking method that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The tracking unit can analyze the user's past spending history to select the optimal tracking method during tracking. For example, the tracking unit can focus on tracking categories with high spending based on the user's past spending history. For example, the tracking unit can analyze the user's spending history and focus on tracking periods when spending is concentrated. For example, the tracking unit can analyze the user's spending history and focus on tracking categories with a lot of unnecessary spending. In this way, by analyzing the user's past spending history, the optimal tracking method can be selected, enabling efficient spending tracking.

[0104] The tracking unit can customize the means of tracking expenses based on the user's current living situation. For example, if the user is newly married, the tracking unit can focus on tracking wedding-related expenses. If the user is about to retire, the tracking unit can focus on tracking post-retirement living expenses. If the user is a student, the tracking unit can focus on tracking tuition and living expenses. This allows for appropriate expense tracking by customizing the means of tracking expenses based on the user's current living situation.

[0105] The tracking unit can estimate the user's emotions and determine the priority of spending tracking based on the estimated emotions. For example, if the user is stressed, the tracking unit will prioritize tracking only important spending. For example, if the user is relaxed, the tracking unit may prioritize tracking detailed spending. For example, if the user is in a hurry, the tracking unit may prioritize tracking spending that can be tracked quickly. This allows for spending tracking with appropriate priorities for the user by determining the priority of spending tracking based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The tracking unit can select the optimal spending tracking method by considering the user's geographical location during tracking. For example, if the user lives in an urban area, the tracking unit will focus on tracking urban-specific spending. If the user lives in a rural area, the tracking unit can also focus on tracking rural-specific spending. If the user lives overseas, the tracking unit can also focus on tracking country-specific spending. By selecting the optimal spending tracking method by considering the user's geographical location, appropriate spending tracking can be performed.

[0107] The tracking unit can analyze the user's social media activity during tracking and suggest means of tracking spending. For example, the tracking unit can analyze the content of social media posts that the user frequently makes and track related spending. For example, the tracking unit can analyze the user's social media friendships and track spending that is easily influenced by friends. For example, the tracking unit can analyze the user's social media activity time and track spending related to specific time periods. This enables appropriate spending tracking by analyzing the user's social media activity and suggesting means of tracking spending.

[0108] The feedback function can estimate the user's emotions and adjust how it points out unnecessary spending based on those emotions. For example, if the user is stressed, it can provide a simple and visually clear feedback method. If the user is relaxed, it can provide a more detailed feedback method. If the user is in a hurry, it can provide a more concise feedback method. By adjusting how it points out unnecessary spending based on the user's emotions, it can provide feedback that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The feedback system can analyze the user's past spending history to select the most appropriate feedback method when providing feedback. For example, based on the user's past spending history, the feedback system can focus on categories with high levels of unnecessary spending. For example, by analyzing the user's spending history, the feedback system can focus on periods when unnecessary spending is concentrated. For example, by analyzing the user's spending history, the feedback system can focus on months with high levels of unnecessary spending. In this way, by analyzing the user's past spending history, the system can select the most appropriate feedback method and provide efficient feedback on unnecessary expenses.

[0110] The feedback system can customize how it identifies unnecessary expenses based on the user's current living situation. For example, if the user is newly married, the feedback system will focus on identifying unnecessary expenses related to marriage. If the user is about to retire, the feedback system can focus on identifying unnecessary expenses related to post-retirement living expenses. If the user is a student, the feedback system can focus on identifying unnecessary expenses related to tuition and living expenses. By customizing the feedback system based on the user's current living situation, it can provide appropriate feedback on unnecessary expenses.

[0111] The feedback system can estimate the user's emotions and prioritize identifying unnecessary expenses based on those emotions. For example, if the user is stressed, the feedback system will prioritize identifying only significant unnecessary expenses. If the user is relaxed, the feedback system may prioritize identifying detailed unnecessary expenses. If the user is in a hurry, the feedback system may prioritize identifying unnecessary expenses that can be identified quickly. This allows the system to prioritize unnecessary expenses based on the user's emotions, providing the user with an appropriate level of priority for identifying unnecessary expenses. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The feedback system can select the most appropriate method for identifying unnecessary expenses, taking into account the user's geographical location. For example, if the user lives in an urban area, the feedback system will focus on identifying unnecessary expenses specific to urban areas. If the user lives in a rural area, the feedback system can focus on identifying unnecessary expenses specific to rural areas. If the user lives overseas, the feedback system can focus on identifying unnecessary expenses specific to that country. By selecting the most appropriate method for identifying unnecessary expenses, taking into account the user's geographical location, the system can provide appropriate feedback on unnecessary expenses.

[0113] The feedback function can analyze a user's social media activity and suggest ways to identify unnecessary spending. For example, it can analyze the content a user frequently posts on social media and identify related unnecessary spending. It can also analyze a user's social media friendships and identify unnecessary spending that is easily influenced by friends. It can also analyze a user's social media activity times and identify unnecessary spending related to specific time periods. By analyzing a user's social media activity and suggesting ways to identify unnecessary spending, the system can provide appropriate feedback on unnecessary expenses.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and visually easy-to-understand suggestions. If the user is relaxed, the suggestion function may also present suggestions that include detailed text and numerical data. If the user is in a hurry, the suggestion function may also present concise suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, the system can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can display the results using simple, visually easy-to-understand graphs and charts. If the user is relaxed, the analysis unit can also display the results including detailed text and numerical data. If the user is in a hurry, the analysis unit can also display the results in a concise, to-the-point report format. This allows for the provision of easy-to-understand analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can minimize the amount of data collected and obtain information in the form of simple questions. For example, if the user is relaxed, the data collection unit can collect more detailed data and obtain more information. For example, if the user is in a hurry, the data collection unit can prioritize the collection of only important data and obtain information quickly. In this way, by adjusting the type of data collected based on the user's emotions, the burden on the user can be reduced and appropriate data can be collected. 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.

[0118] The tracking unit can estimate the user's emotions and adjust the spending tracking method based on the estimated emotions. For example, if the user is stressed, the tracking unit can provide a simple and visually easy-to-understand spending tracking method. For example, if the user is relaxed, the tracking unit can also provide a detailed spending tracking method. For example, if the user is in a hurry, the tracking unit can also provide a concise spending tracking method. In this way, by adjusting the spending tracking method based on the user's emotions, it is possible to provide a spending tracking method that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The feedback function can estimate the user's emotions and adjust how it points out unnecessary spending based on those emotions. For example, if the user is stressed, it can provide a simple and visually clear feedback method. If the user is relaxed, it can provide a more detailed feedback method. If the user is in a hurry, it can provide a more concise feedback method. By adjusting how it points out unnecessary spending based on the user's emotions, it can provide feedback that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The proposal department can adjust the level of detail in its proposals based on the importance of savings goals and risk tolerance. For example, if the savings goal is high, the proposal department will provide detailed proposals aimed at that goal. If the risk tolerance is low, the proposal department can also provide detailed proposals regarding safe investment options. If the savings goal is low, the proposal department can also provide concise proposals. By adjusting the level of detail in proposals based on the importance of savings goals and risk tolerance, the proposal department can provide appropriate proposals.

[0121] The analysis department can adjust the level of detail in its analysis based on the importance of income and expenditure patterns. For example, if there is a large difference between months with high and low income, the analysis department will analyze that difference in detail to identify the cause. For example, if expenditure patterns are consistent, the analysis department can also perform a concise analysis of overall expenditure trends. For example, if expenditures are high in a particular category, the analysis department can perform a detailed analysis of that category. By adjusting the level of detail in the analysis based on the importance of income and expenditure patterns, the analysis department can provide appropriate analytical results.

[0122] The tracking unit can analyze the user's past spending history to select the optimal tracking method during tracking. For example, the tracking unit can focus on tracking categories with high spending based on the user's past spending history. For example, the tracking unit can analyze the user's spending history and focus on tracking periods when spending is concentrated. For example, the tracking unit can analyze the user's spending history and focus on tracking categories with a lot of unnecessary spending. In this way, by analyzing the user's past spending history, the optimal tracking method can be selected, enabling efficient spending tracking.

[0123] The feedback system can analyze the user's past spending history to select the most appropriate feedback method when providing feedback. For example, based on the user's past spending history, the feedback system can focus on categories with high levels of unnecessary spending. For example, by analyzing the user's spending history, the feedback system can focus on periods when unnecessary spending is concentrated. For example, by analyzing the user's spending history, the feedback system can focus on months with high levels of unnecessary spending. In this way, by analyzing the user's past spending history, the system can select the most appropriate feedback method and provide efficient feedback on unnecessary expenses.

[0124] The proposal department can prioritize proposals based on the timing of savings goals and risk tolerance changes. For example, if the savings goal is nearing completion, the proposal department will prioritize proposals that address that goal. If risk tolerance fluctuates significantly, the proposal department can also prioritize proposals that address those fluctuations. If the savings goal is far off, the proposal department can also prioritize long-term proposals. By prioritizing proposals based on the timing of savings goals and risk tolerance changes, the proposal department can provide proposals at the appropriate time.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The data collection unit collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. The data collection unit obtains data from sources such as information provided by the user, bank transaction history, and credit card usage history. Step 2: The analysis department analyzes the data collected by the data collection department in detail. The analysis department uses, for example, statistical analysis, machine learning algorithms, and data mining techniques to analyze users' income and spending patterns and identify unnecessary expenses. Step 3: The proposal department proposes the optimal savings plan based on the analysis results obtained by the analysis department. For example, the proposal department proposes a savings plan that takes into account risk diversification and investment selection based on the user's savings goals and risk tolerance. Step 4: The tracking unit tracks the user's spending based on the savings plan proposed by the suggestion unit. The tracking unit records the user's spending data using, for example, applications, manual input, or sensor data, and identifies unnecessary spending. Step 5: The identification unit identifies unnecessary spending based on the spending data tracked by the tracking unit. The identification unit uses notification, reporting, and dashboard functions to inform users of unnecessary spending, such as unnecessary subscriptions or excessive entertainment expenses.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, tracking unit, and identification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's income and expenditure patterns. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data in detail. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes an optimal savings plan. The tracking unit is implemented by the control unit 46A of the smart device 14 and tracks the user's expenditures. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies unnecessary expenses. 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.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, tracking unit, and identification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's income and expenditure patterns. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the collected data in detail. The proposal unit is implemented by the identification unit 290 of the data processing unit 12 and proposes an optimal savings plan. The tracking unit is implemented by the control unit 46A of the smart glasses 214 and tracks the user's expenditures. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies unnecessary expenses. 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.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, tracking unit, and identification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's income and expenditure patterns. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data in detail. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes an optimal savings plan. The tracking unit is implemented by the control unit 46A of the headset terminal 314 and tracks the user's expenditures. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies unnecessary expenses. 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.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, tracking unit, and identification unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the user's income and expenditure patterns. The analysis unit is implemented, for example, by the identification unit 290 of the data processing unit 12 and analyzes the collected data in detail. The proposal unit is implemented, for example, by the identification unit 290 of the data processing unit 12 and proposes an optimal savings plan. The tracking unit is implemented, for example, by the control unit 46A of the robot 414 and tracks the user's expenditures. The identification unit is implemented, for example, by the identification unit 290 of the data processing unit 12 and identifies unnecessary expenses. 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) A data collection unit collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. An analysis unit that analyzes in detail the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal savings plan. A tracking unit tracks the user's spending based on the savings plan proposed by the aforementioned proposal unit, The system includes a detection unit that identifies unnecessary expenses based on expenditure data tracked by the aforementioned tracking unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze users' income and spending patterns to identify unnecessary expenses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose the optimal savings plan based on the user's savings goals and risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned tracking unit is Track user spending and record spending data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned point is, Based on the expenditure data tracked by the aforementioned tracking unit, unnecessary expenses are identified. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest safe and high-risk investment options based on the user's risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is It monitors market trends and economic indicators in real time and issues alerts about investment opportunities and potential risks. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned system, We employ advanced encryption technology and strict data protection policies to ensure the security of users' personal and financial data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past income and spending history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the data is filtered based on the user's current life stage and financial needs. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) 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 14) 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 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of income and expenditure patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the income and expenditure categories. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is When conducting an analysis, prioritize the analysis based on when income and expenses occur. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relationship between income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of savings goals and risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the savings goal and risk tolerance category. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) The aforementioned proposal section is, When making proposals, prioritize them based on savings goals and the timing of when risk tolerance will be reached. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on their relevance to savings goals and risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned tracking unit is We estimate user sentiment and adjust spending tracking methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned tracking unit is During tracking, the system analyzes the user's past spending history to select the most suitable tracking method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned tracking unit is During tracking, the means of tracking spending are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned tracking unit is It estimates user sentiment and prioritizes spending tracking based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned tracking unit is During tracking, the system selects the optimal spending tracking method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned tracking unit is During tracking, we analyze the user's social media activity and suggest ways to track spending. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned point is, It estimates the user's emotions and adjusts how it identifies unnecessary spending based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned point is, When issuing a complaint, the system analyzes the user's past spending history to select the most appropriate method of complaint. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned point is, When pointing out unnecessary expenses, the method of identifying them is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned point is, It estimates the user's emotions and prioritizes identifying unnecessary spending based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned point is, When pointing out unnecessary expenses, the system selects the most appropriate method for identifying them, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned point is, When pointing out issues, we propose methods for identifying unnecessary spending by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0199] 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 collects personal data such as the user's income, spending patterns, savings goals, and risk tolerance. An analysis unit that analyzes in detail the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal savings plan. A tracking unit tracks the user's spending based on the savings plan proposed by the aforementioned proposal unit, The system includes a detection unit that identifies unnecessary expenses based on expenditure data tracked by the aforementioned tracking unit. A system characterized by the following features.

2. The aforementioned analysis unit is Analyze users' income and spending patterns to identify unnecessary expenses. The system according to feature 1.

3. The aforementioned proposal section is, We propose the optimal savings plan based on the user's savings goals and risk tolerance. The system according to feature 1.

4. The aforementioned tracking unit is Track user spending and record spending data. The system according to feature 1.

5. The aforementioned point is, Based on the expenditure data tracked by the aforementioned tracking unit, unnecessary expenses are identified. The system according to feature 1.

6. The aforementioned proposal section is, We suggest safe and high-risk investment options based on the user's risk tolerance. The system according to feature 1.

7. The aforementioned analysis unit is It monitors market trends and economic indicators in real time and issues alerts about investment opportunities and potential risks. The system according to feature 1.

8. The aforementioned system, We employ advanced encryption technology and strict data protection policies to ensure the security of users' personal and financial data. The system according to feature 1.

Images