system

Abstract

The system according to this embodiment aims to support effective budget management based on the user's income, expenses, and savings goals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a notification unit. The collection unit collects data on the user's income, expenses, and savings goals. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a budget plan based on the analysis results obtained by the analysis unit. The notification unit notifies an alert when it detects a discrepancy between the budget plan and actual results.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, effective budget management based on a user's income, expenditure, and savings goal has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to an embodiment aims to support effective budget management based on a user's income, expenditure, and savings goal.

Means for Solving the Problems

[0006] [[ID=The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a notification unit. The data collection unit collects data on the user's income, expenses, and savings goals. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a budget plan based on the analysis results obtained by the analysis unit. The notification unit issues an alert when it detects a discrepancy between the budget plan and actual results. [Effects of the Invention]

[0007] The system according to this embodiment can support effective budget management based on the user's income, expenses, and savings goals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​Assistant Personal Finance Support System according to an embodiment of the present invention is a system that analyzes a user's income, expenses, and savings goals and proposes an original budget plan. This AI Assistant Personal Finance Support System collects data on the user's income, expenses, and savings goals, and the AI ​​analyzes this data to propose a budget plan. Furthermore, it has a function to notify the user of an alert when a discrepancy is detected between the budget plan and actual results. For example, while the user manually inputs their monthly income and expenses, bank account and credit card transaction data is automatically collected. Next, the AI ​​analyzes the collected data, calculates the balance between income and expenses, and creates a budget plan to reach the savings goal. For example, if the user's monthly income is 200,000 yen and expenses are 150,000 yen, the system proposes a budget plan that allocates 50,000 yen to savings. Furthermore, it notifies the user of an alert when a discrepancy is detected between the budget plan and actual results. For example, it notifies the user of an alert when the user exceeds their budget or when a payment due date is approaching. This allows the user to manage their budget efficiently. It also provides advice on investment opportunities and interest rate optimization. The AI ​​suggests optimal investment opportunities based on the user's income and expenditure data. For example, if the user has surplus funds, it advises how to invest those funds. Furthermore, the AI ​​estimates the user's emotions and creates a budget plan based on those emotions. For example, if the user is feeling stressed, it flexibly adjusts the budget plan to reduce the user's burden. Thus, this invention is a personal finance support system AI assistant that not only analyzes the user's income, expenditure, and savings goals and proposes an original budget plan, but also provides alerts for budget overruns and payment deadlines, as well as advice on investment opportunities and interest rate optimization. As a result, the personal finance support system AI assistant can efficiently manage the user's income, expenditure, and savings goals and provide an optimal budget plan.

[0029] The AI ​​assistant personal finance support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a notification unit. The data collection unit collects data on the user's income, expenses, and savings goals. The data collection unit collects data that the user manually enters, for example. The data collection unit can also automatically acquire data from bank accounts and credit cards. For example, the data collection unit acquires transaction data from bank accounts using API integration. Furthermore, the data collection unit can also collect credit card transaction data using scraping technology. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit calculates the balance between income and expenses. The analysis unit can also grasp trends in income and expenses using statistical analysis. The analysis unit can also predict income and expenses using machine learning algorithms. The proposal unit proposes a budget plan based on the analysis results obtained by the analysis unit. For example, the proposal unit creates a budget plan to reach savings goals based on the balance between income and expenses. The proposal unit can also calculate what percentage of the user's income should be allocated to savings. Furthermore, the suggestion unit can analyze the user's spending by category and suggest points for saving. The notification unit sends an alert when it detects a discrepancy between the budget plan and actual spending. For example, the notification unit sends an alert when it detects a budget overrun. It can also send an alert when a payment due date is approaching. The notification unit can also send push notifications to the user's smartphone. It can also send alerts via email. In this way, the AI ​​assistant personal finance support system according to the embodiment efficiently supports the user's personal finance by collecting and analyzing data on the user's income, spending, and savings goals, proposing a budget plan, and sending alerts.

[0030] The data collection unit collects data on users' income, expenses, and savings goals. For example, it collects data manually entered by users. Users can enter income and expense details through a dedicated application or web interface. This includes income items such as salary, bonuses, and investment returns, and expense items such as rent, utilities, food, and entertainment. The data collection unit can also automatically retrieve bank account and credit card data. For example, it can retrieve bank account transaction data using API integration. By granting the user access to the bank's API, the data collection unit periodically retrieves transaction data and automatically updates the latest income and expense information. Furthermore, the data collection unit can collect credit card transaction data using scraping techniques. It retrieves transaction details from credit card websites, analyzes them, and stores them in a database. In this way, the data collection unit can comprehensively collect and update users' financial data in real time. Additionally, with user permission, the data collection unit can collect data from other financial services and applications. For example, it can integrate data from investment apps and insurance services to understand the user's overall financial situation. This allows the data collection department to centrally manage diverse user financial data, making it available for use by the analysis and proposal departments.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department calculates the balance between income and expenses. By calculating the balance between income and expenses, it is possible to understand how much surplus money a user has or whether they are in deficit. The analysis department can also use statistical analysis to understand trends in income and expenses. For example, based on data from the past few months, it can analyze trends in increases and decreases in income and expenses and identify seasonal fluctuations and specific spending patterns. The analysis department can also use machine learning algorithms to predict income and expenses. For example, by training with historical data, it can predict income and expenses for the following month and show the user their future financial situation. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual spending patterns and fraudulent transactions. This allows users to discover problems early and take countermeasures. The analysis department can analyze users' financial data from multiple angles and gain a detailed understanding of the user's financial situation. This provides the analysis department with a foundation to support users' financial management and make better recommendations.

[0032] The proposal department proposes a budget plan based on the analysis results obtained by the analysis department. For example, the proposal department creates a budget plan to reach savings goals based on the balance of income and expenses. Specifically, it calculates what percentage of the user's income should be saved and sets a monthly savings target. The proposal department can also analyze the user's expenses by category and suggest points for saving. For example, it can analyze expense categories such as food and entertainment in detail and provide specific advice on reducing unnecessary spending. Furthermore, the proposal department can propose a customized budget plan tailored to the user's lifestyle and goals. For example, for a user planning a trip or a major purchase, it will propose a savings plan for that purpose and show specific steps toward achieving the goal. In addition, the proposal department can continuously review and optimize the budget plan based on user feedback. In this way, the proposal department provides a concrete action plan to support the user in achieving their financial goals and realizing efficient fund management.

[0033] The notification unit sends alerts when it detects discrepancies between budget plans and actual results. For example, it will send an alert when it detects a budget overrun. Specifically, if the user exceeds their budget, it will send a push notification to their smartphone to immediately inform them. The notification unit can also send alerts when payment deadlines are approaching. For example, it can send reminders when credit card payment deadlines or utility bill payment deadlines are approaching to prevent users from forgetting to pay. The notification unit can also send push notifications to the user's smartphone, allowing them to receive important information in real time. The notification unit can also send alerts via email, allowing users to receive alerts through multiple means and ensuring they don't miss important information. Furthermore, the notification unit can customize notification methods according to user preferences. For example, users can set it to receive notifications only during specific time periods or only for specific alerts. This allows the notification unit to provide users with important information at the right time and support their financial management.

[0034] The notification unit can notify users of budget overruns or payment deadlines. For example, the notification unit can send an alert when it detects a budget overrun. The notification unit can also send push notifications to the user's smartphone. It can also send alerts via email. The notification unit can also send alerts when payment deadlines are approaching. For example, the notification unit can send an alert one week before the payment deadline. This allows users to take appropriate action by notifying them of budget overruns or payment deadlines. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input budget overrun and payment deadline data into a generating AI and have the generating AI generate alerts.

[0035] The data collection unit can automatically acquire data manually entered by users and data from bank accounts or credit cards. For example, the data collection unit can collect data manually entered by users. The data collection unit can collect data using text input or numerical input. The data collection unit can also automatically acquire bank account data using API integration. For example, the data collection unit can acquire transaction data using a bank's API. Furthermore, the data collection unit can automatically acquire credit card data using scraping technology. For example, the data collection unit can scrape transaction data from a credit card company's website. By combining data manually entered by users with automatically acquired data, the efficiency of data collection is improved. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input bank account or credit card data into a generating AI and have the generating AI perform the data collection.

[0036] The suggestion unit can calculate the balance between income and expenses and create a budget plan to reach savings goals. For example, the suggestion unit can calculate the balance between income and expenses. The suggestion unit can also calculate what percentage of income should be saved. Furthermore, the suggestion unit can analyze expenses by category and suggest points for saving. For example, the suggestion unit can analyze expense categories such as food and entertainment expenses and suggest points for saving. In this way, by calculating the balance between income and expenses and creating a budget plan to reach savings goals, it helps users achieve their savings goals. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input income and expense data into a generating AI and have the generating AI create a budget plan.

[0037] The advisory department can propose investment opportunities. For example, the advisory department can propose optimal investment opportunities based on the user's income and expenditure data. The advisory department can propose investment opportunities such as stock investments and real estate investments. For example, if the user has surplus funds, the advisory department can advise on how to invest those funds. In this way, it supports the user's asset management by proposing investment opportunities. Some or all of the above processes in the advisory department may be performed using AI, for example, or not using AI. For example, the advisory department can input the user's income and expenditure data into a generating AI and have the generating AI execute investment opportunity proposals.

[0038] The data collection unit can analyze the user's past income, expenditure, and savings goal data and select the optimal data collection method. For example, the data collection unit can suggest the most efficient data collection method based on data previously entered by the user. The data collection unit can also analyze the user's past income and expenditure patterns and determine the priority of the data to be collected. Furthermore, the data collection unit can select and collect the necessary data based on the user's savings goals. In this way, the optimal data collection method can be selected by analyzing the user's past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user inputs their current lifestyle, the data collection unit will prioritize collecting relevant data based on that information. The data collection unit can also customize the data it collects based on the user's areas of interest. Furthermore, the data collection unit can filter out unnecessary data according to the user's lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of data related to that region. If the user is traveling, the data collection unit can also collect data related to their current location. Furthermore, the data collection unit can select the optimal data collection method based on the user's geographical location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media posts and collect data related to income and expenses. The data collection unit can also collect relevant data based on the user's areas of interest on social media. Furthermore, the data collection unit can collect data related to savings goals from the user's social media activity. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of its analysis based on the balance between income and expenses. For example, if the balance between income and expenses is good, the analysis unit will perform a detailed analysis. If the balance between income and expenses is poor, the analysis unit can perform a simplified analysis. The analysis unit can also adjust the level of detail of its analysis according to the balance between income and expenses. This allows the analysis unit to provide appropriate analytical results by adjusting the level of detail of its analysis based on the balance between income and expenses. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input income and expense data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm specifically for income data. It can also apply an analysis algorithm specifically for expenditure data. Furthermore, it can apply an analysis algorithm specifically for savings target data. By applying different analysis algorithms depending on the data category, it is possible to provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input income, expenditure, and savings target data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis department can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis department may prioritize the analysis of the most recent data. The analysis department may also postpone the analysis of older data. Furthermore, the analysis department can determine the priority of analysis based on the submission date. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of data with a high correlation between income and expenses. It may also prioritize the analysis of data related to savings goals. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for the prioritization of analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data relevance into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The proposal unit can adjust the level of detail of its proposals based on the balance between income and expenses. For example, if the balance between income and expenses is good, the proposal unit will provide a detailed proposal. If the balance between income and expenses is poor, the proposal unit may provide a concise proposal. The proposal unit can also adjust the level of detail of its proposals according to the balance between income and expenses. This allows the proposal unit to provide appropriate proposals by adjusting the level of detail based on the balance between income and expenses. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input income and expense data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0047] The suggestion unit can apply different suggestion algorithms depending on the degree of achievement of the savings goal when making a suggestion. For example, if the savings goal has been achieved, the suggestion unit will make suggestions for the next goal. If the savings goal has not been achieved, the suggestion unit can also make specific suggestions for achieving it. Furthermore, the suggestion unit can adjust the suggestion algorithm according to the degree of achievement of the savings goal. In this way, by applying different suggestion algorithms according to the degree of achievement of the savings goal, it supports the user in achieving their goal. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the degree of achievement of the savings goal into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0048] The proposal department can prioritize proposals based on the submission timing of income and expenditure data. For example, the proposal department can make proposals based on the most recent income and expenditure data. The proposal department can also postpone older data submissions. Furthermore, the proposal department can prioritize proposals based on submission timing. This allows the proposal department to provide proposals based on the latest data by prioritizing proposals based on the submission timing of income and expenditure data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission timing of income and expenditure data into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0049] The suggestion unit can adjust the order of suggestions based on the relationship between income and expenses when making suggestions. For example, the suggestion unit can make suggestions based on data with a high correlation between income and expenses. The suggestion unit can also make suggestions based on data related to savings goals. Furthermore, the suggestion unit can adjust the order of suggestions based on the relationship between income and expenses. This allows the suggestion unit to provide suggestions based on highly relevant data by adjusting the order of suggestions based on the relationship between income and expenses. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relationship between income and expenses into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0050] The notification unit can adjust the level of detail of notifications based on the importance of budget overruns and payment deadlines. For example, if the budget overrun is large, the notification unit will provide a detailed notification. The notification unit can also provide an important notification if a payment deadline is approaching. Furthermore, the notification unit can adjust the level of detail of notifications according to the importance of budget overruns and payment deadlines. This allows important information to be appropriately notified by adjusting the level of detail of notifications based on the importance of budget overruns and payment deadlines. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input budget overrun and payment deadline data into a generating AI and have the generating AI perform the adjustment of the level of detail of notifications.

[0051] The notification unit can prioritize highly relevant notifications by considering the user's geographical location when sending notifications. For example, if the user is in a specific region, the notification unit can prioritize notifications related to that region. If the user is traveling, the notification unit can also prioritize notifications related to the user's current location. Furthermore, the notification unit can select the most appropriate notification based on the user's geographical location. This allows the notification unit to prioritize highly relevant notifications by considering the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location into a generation AI and have the generation AI select highly relevant notifications.

[0052] The advice unit can adjust the level of detail in its advice based on the balance between income and expenses. For example, if the balance between income and expenses is good, the advice unit will provide detailed advice. If the balance between income and expenses is poor, the advice unit can also provide concise advice. The advice unit can also adjust the level of detail in its advice according to the balance between income and expenses. This allows it to provide appropriate advice by adjusting the level of detail based on the balance between income and expenses. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input income and expense data into a generating AI and have the generating AI perform the adjustment of the level of detail in the advice.

[0053] The advisory unit can apply different advisory algorithms depending on the category of investment opportunity when providing advice. For example, the advisory unit can apply a stock-specific advisory algorithm to stock investments. The advisory unit can also apply a real estate-specific advisory algorithm to real estate investments. Furthermore, the advisory unit can apply a bond-specific advisory algorithm to bond investments. This allows the advisory unit to provide appropriate investment advice by applying different advisory algorithms depending on the category of investment opportunity. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input the investment opportunity category into a generating AI and have the generating AI execute the application of the advisory algorithm.

[0054] The advice unit can prioritize advice based on the submission dates of income and expenses. For example, the advice unit provides advice based on the most recent income and expense data. The advice unit can also postpone older data submissions. The advice unit can also prioritize advice based on the submission dates. This allows the advice unit to provide advice based on the most recent data by prioritizing advice based on the submission dates of income and expenses. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the submission dates of income and expenses into a generating AI and have the generating AI determine the priority of advice.

[0055] The advice unit can adjust the order of advice based on the relationship between income and expenses. For example, the advice unit provides advice based on data with a high correlation between income and expenses. The advice unit can also provide advice based on data related to savings goals. Furthermore, the advice unit can adjust the order of advice based on the relationship between income and expenses. This allows the advice unit to provide advice based on highly relevant data by adjusting the order of advice based on the relationship between income and expenses. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the relationship between income and expenses into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] Personal finance support systems, such as AI assistants, can collect and analyze data on users' income, expenses, and savings goals, and propose budget plans that also take into account the user's life events. For example, when a user experiences life events such as marriage, childbirth, or moving, it can propose a budget plan tailored to those events. If a user is planning to get married, it can create a budget plan that takes into account the costs of the wedding and starting a new life. Similarly, if a user is planning to have a child, it can propose a budget plan that takes into account the costs of childcare. This allows for more realistic personal finance support by providing budget plans that are tailored to the user's life events.

[0058] Personal finance support systems, such as AI assistants, can collect and analyze data on a user's income, expenses, and savings goals, and propose budget plans, while also taking into account the user's hobbies and interests. For example, if a user has a particular hobby or interest, it can suggest prioritizing spending related to that hobby or interest. If a user enjoys traveling, it can suggest increasing spending on travel-related activities. Similarly, if a user is interested in sports or fitness, it can suggest prioritizing spending on sports equipment and gym memberships. This allows for more personalized personal finance support by providing budget plans that take into account the user's hobbies and interests.

[0059] Personal finance support systems with AI assistants can collect and analyze data on a user's income, expenses, and savings goals, and propose budget plans, while also taking into account the user's environmental awareness. For example, if a user wants to live an environmentally conscious life, it can suggest prioritizing spending on eco-friendly products and services. If a user wants to use renewable energy, it can suggest increasing spending on that. Furthermore, if a user is interested in reducing plastic waste and recycling, it can suggest prioritizing spending on those areas. This allows for more sustainable personal finance support by providing budget plans that take the user's environmental awareness into account.

[0060] Personal finance support systems, such as AI assistants, can not only collect and analyze data on a user's income, expenses, and savings goals to propose a budget plan, but also take into account the user's social connections. For example, if a user values ​​spending with friends and family, the system can suggest prioritizing spending on those activities. If a user is planning meals or trips with friends, the system can suggest increasing spending on those. Similarly, if a user values ​​spending time with family, the system can suggest prioritizing spending on family events and activities. This allows for more comprehensive personal finance support by providing a budget plan that considers the user's social connections.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data on the user's income, expenses, and savings goals. In addition to collecting data manually entered by the user, the data collection unit can also automatically obtain data from bank accounts and credit cards. For example, it can use API integration to obtain bank account transaction data and scraping techniques to collect credit card transaction data. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit calculates the balance between income and expenses and uses statistical analysis to understand income and expense trends. It can also use machine learning algorithms to predict income and expenses. Step 3: The proposal team proposes a budget plan based on the analysis results obtained by the analysis team. The proposal team creates a budget plan to reach the savings goal based on the balance of income and expenses, and calculates what percentage of the user's income should be allocated to savings. They also analyze expenses by category and suggest points for saving. Step 4: The notification unit will send an alert if it detects a discrepancy between the budget plan and actual results. The notification unit will also send an alert if it detects a budget overrun or if a payment deadline is approaching, sending a push notification to the user's smartphone, as well as an alert via email.

[0063] (Example of form 2) The AI ​​Assistant Personal Finance Support System according to an embodiment of the present invention is a system that analyzes a user's income, expenses, and savings goals and proposes an original budget plan. This AI Assistant Personal Finance Support System collects data on the user's income, expenses, and savings goals, and the AI ​​analyzes this data to propose a budget plan. Furthermore, it has a function to notify the user of an alert when a discrepancy is detected between the budget plan and actual results. For example, while the user manually inputs their monthly income and expenses, bank account and credit card transaction data is automatically collected. Next, the AI ​​analyzes the collected data, calculates the balance between income and expenses, and creates a budget plan to reach the savings goal. For example, if the user's monthly income is 200,000 yen and expenses are 150,000 yen, the system proposes a budget plan that allocates 50,000 yen to savings. Furthermore, it notifies the user of an alert when a discrepancy is detected between the budget plan and actual results. For example, it notifies the user of an alert when the user exceeds their budget or when a payment due date is approaching. This allows the user to manage their budget efficiently. It also provides advice on investment opportunities and interest rate optimization. The AI ​​suggests optimal investment opportunities based on the user's income and expenditure data. For example, if the user has surplus funds, it advises how to invest those funds. Furthermore, the AI ​​estimates the user's emotions and creates a budget plan based on those emotions. For example, if the user is feeling stressed, it flexibly adjusts the budget plan to reduce the user's burden. Thus, this invention is a personal finance support system AI assistant that not only analyzes the user's income, expenditure, and savings goals and proposes an original budget plan, but also provides alerts for budget overruns and payment deadlines, as well as advice on investment opportunities and interest rate optimization. As a result, the personal finance support system AI assistant can efficiently manage the user's income, expenditure, and savings goals and provide an optimal budget plan.

[0064] The AI ​​assistant personal finance support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a notification unit. The data collection unit collects data on the user's income, expenses, and savings goals. The data collection unit collects data that the user manually enters, for example. The data collection unit can also automatically acquire data from bank accounts and credit cards. For example, the data collection unit acquires transaction data from bank accounts using API integration. Furthermore, the data collection unit can also collect credit card transaction data using scraping technology. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit calculates the balance between income and expenses. The analysis unit can also grasp trends in income and expenses using statistical analysis. The analysis unit can also predict income and expenses using machine learning algorithms. The proposal unit proposes a budget plan based on the analysis results obtained by the analysis unit. For example, the proposal unit creates a budget plan to reach savings goals based on the balance between income and expenses. The proposal unit can also calculate what percentage of the user's income should be allocated to savings. Furthermore, the suggestion unit can analyze the user's spending by category and suggest points for saving. The notification unit sends an alert when it detects a discrepancy between the budget plan and actual spending. For example, the notification unit sends an alert when it detects a budget overrun. It can also send an alert when a payment due date is approaching. The notification unit can also send push notifications to the user's smartphone. It can also send alerts via email. In this way, the AI ​​assistant personal finance support system according to the embodiment efficiently supports the user's personal finance by collecting and analyzing data on the user's income, spending, and savings goals, proposing a budget plan, and sending alerts.

[0065] The data collection unit collects data on users' income, expenses, and savings goals. For example, it collects data manually entered by users. Users can enter income and expense details through a dedicated application or web interface. This includes income items such as salary, bonuses, and investment returns, and expense items such as rent, utilities, food, and entertainment. The data collection unit can also automatically retrieve bank account and credit card data. For example, it can retrieve bank account transaction data using API integration. By granting the user access to the bank's API, the data collection unit periodically retrieves transaction data and automatically updates the latest income and expense information. Furthermore, the data collection unit can collect credit card transaction data using scraping techniques. It retrieves transaction details from credit card websites, analyzes them, and stores them in a database. In this way, the data collection unit can comprehensively collect and update users' financial data in real time. Additionally, with user permission, the data collection unit can collect data from other financial services and applications. For example, it can integrate data from investment apps and insurance services to understand the user's overall financial situation. This allows the data collection department to centrally manage diverse user financial data, making it available for use by the analysis and proposal departments.

[0066] The analysis department analyzes the data collected by the data collection department. For example, the analysis department calculates the balance between income and expenses. By calculating the balance between income and expenses, it is possible to understand how much surplus money a user has or whether they are in deficit. The analysis department can also use statistical analysis to understand trends in income and expenses. For example, based on data from the past few months, it can analyze trends in increases and decreases in income and expenses and identify seasonal fluctuations and specific spending patterns. The analysis department can also use machine learning algorithms to predict income and expenses. For example, by training with historical data, it can predict income and expenses for the following month and show the user their future financial situation. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual spending patterns and fraudulent transactions. This allows users to discover problems early and take countermeasures. The analysis department can analyze users' financial data from multiple angles and gain a detailed understanding of the user's financial situation. This provides the analysis department with a foundation to support users' financial management and make better recommendations.

[0067] The proposal department proposes a budget plan based on the analysis results obtained by the analysis department. For example, the proposal department creates a budget plan to reach savings goals based on the balance of income and expenses. Specifically, it calculates what percentage of the user's income should be saved and sets a monthly savings target. The proposal department can also analyze the user's expenses by category and suggest points for saving. For example, it can analyze expense categories such as food and entertainment in detail and provide specific advice on reducing unnecessary spending. Furthermore, the proposal department can propose a customized budget plan tailored to the user's lifestyle and goals. For example, for a user planning a trip or a major purchase, it will propose a savings plan for that purpose and show specific steps toward achieving the goal. In addition, the proposal department can continuously review and optimize the budget plan based on user feedback. In this way, the proposal department provides a concrete action plan to support the user in achieving their financial goals and realizing efficient fund management.

[0068] The notification unit sends alerts when it detects discrepancies between budget plans and actual results. For example, it will send an alert when it detects a budget overrun. Specifically, if the user exceeds their budget, it will send a push notification to their smartphone to immediately inform them. The notification unit can also send alerts when payment deadlines are approaching. For example, it can send reminders when credit card payment deadlines or utility bill payment deadlines are approaching to prevent users from forgetting to pay. The notification unit can also send push notifications to the user's smartphone, allowing them to receive important information in real time. The notification unit can also send alerts via email, allowing users to receive alerts through multiple means and ensuring they don't miss important information. Furthermore, the notification unit can customize notification methods according to user preferences. For example, users can set it to receive notifications only during specific time periods or only for specific alerts. This allows the notification unit to provide users with important information at the right time and support their financial management.

[0069] The notification unit can notify users of budget overruns or payment deadlines. For example, the notification unit can send an alert when it detects a budget overrun. The notification unit can also send push notifications to the user's smartphone. It can also send alerts via email. The notification unit can also send alerts when payment deadlines are approaching. For example, the notification unit can send an alert one week before the payment deadline. This allows users to take appropriate action by notifying them of budget overruns or payment deadlines. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input budget overrun and payment deadline data into a generating AI and have the generating AI generate alerts.

[0070] The data collection unit can automatically acquire data manually entered by users and data from bank accounts or credit cards. For example, the data collection unit can collect data manually entered by users. The data collection unit can collect data using text input or numerical input. The data collection unit can also automatically acquire bank account data using API integration. For example, the data collection unit can acquire transaction data using a bank's API. Furthermore, the data collection unit can automatically acquire credit card data using scraping technology. For example, the data collection unit can scrape transaction data from a credit card company's website. By combining data manually entered by users with automatically acquired data, the efficiency of data collection is improved. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input bank account or credit card data into a generating AI and have the generating AI perform the data collection.

[0071] The suggestion unit can calculate the balance between income and expenses and create a budget plan to reach savings goals. For example, the suggestion unit can calculate the balance between income and expenses. The suggestion unit can also calculate what percentage of income should be saved. Furthermore, the suggestion unit can analyze expenses by category and suggest points for saving. For example, the suggestion unit can analyze expense categories such as food and entertainment expenses and suggest points for saving. In this way, by calculating the balance between income and expenses and creating a budget plan to reach savings goals, it helps users achieve their savings goals. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input income and expense data into a generating AI and have the generating AI create a budget plan.

[0072] The advisory department can propose investment opportunities. For example, the advisory department can propose optimal investment opportunities based on the user's income and expenditure data. The advisory department can propose investment opportunities such as stock investments and real estate investments. For example, if the user has surplus funds, the advisory department can advise on how to invest those funds. In this way, it supports the user's asset management by proposing investment opportunities. Some or all of the above processes in the advisory department may be performed using AI, for example, or not using AI. For example, the advisory department can input the user's income and expenditure data into a generating AI and have the generating AI execute investment opportunity proposals.

[0073] The proposal unit can estimate the user's emotions and create a budget plan based on those emotions. For example, the proposal unit might use an emotion analysis algorithm to estimate the user's emotions. If the user is feeling stressed, the proposal unit can flexibly adjust the budget plan. Furthermore, if the user is relaxed, the proposal unit can create a more detailed budget plan. This reduces the user's burden by creating a budget plan based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input user emotion data into a generative AI and have the generative AI create the budget plan.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using an emotion analysis algorithm. If the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. If the user is relaxed, the data collection unit can collect detailed data to create a more accurate budget plan. Furthermore, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This reduces the user's burden by adjusting the timing of data collection based on the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0075] The data collection unit can analyze the user's past income, expenditure, and savings goal data and select the optimal data collection method. For example, the data collection unit can suggest the most efficient data collection method based on data previously entered by the user. The data collection unit can also analyze the user's past income and expenditure patterns and determine the priority of the data to be collected. Furthermore, the data collection unit can select and collect the necessary data based on the user's savings goals. In this way, the optimal data collection method can be selected by analyzing the user's past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user inputs their current lifestyle, the data collection unit will prioritize collecting relevant data based on that information. The data collection unit can also customize the data it collects based on the user's areas of interest. Furthermore, the data collection unit can filter out unnecessary data according to the user's lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, the data collection unit might use an emotion analysis algorithm to estimate the user's emotions. If the user is stressed, the data collection unit can prioritize collecting only the most important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of data related to that region. If the user is traveling, the data collection unit can also collect data related to their current location. Furthermore, the data collection unit can select the optimal data collection method based on the user's geographical location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media posts and collect data related to income and expenses. The data collection unit can also collect relevant data based on the user's areas of interest on social media. Furthermore, the data collection unit can collect data related to savings goals from the user's social media activity. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion analysis algorithm. If the user is stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be made easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of its analysis based on the balance between income and expenses. For example, if the balance between income and expenses is good, the analysis unit will perform a detailed analysis. If the balance between income and expenses is poor, the analysis unit can perform a simplified analysis. The analysis unit can also adjust the level of detail of its analysis according to the balance between income and expenses. This allows the analysis unit to provide appropriate analytical results by adjusting the level of detail of its analysis based on the balance between income and expenses. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input income and expense data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm specifically for income data. It can also apply an analysis algorithm specifically for expenditure data. Furthermore, it can apply an analysis algorithm specifically for savings target data. By applying different analysis algorithms depending on the data category, it is possible to provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input income, expenditure, and savings target data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion analysis algorithm. If the user is stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can 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, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0084] The analysis department can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis department may prioritize the analysis of the most recent data. The analysis department may also postpone the analysis of older data. Furthermore, the analysis department can determine the priority of analysis based on the submission date. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of data with a high correlation between income and expenses. It may also prioritize the analysis of data related to savings goals. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for the prioritization of analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data relevance into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, the suggestion unit might use an emotion analysis algorithm to estimate the user's emotions. If the user is stressed, the suggestion unit can provide simple, visually clear suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise, to-the-point suggestions. By adjusting the presentation of suggestions based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of its suggestions.

[0087] The proposal unit can adjust the level of detail of its proposals based on the balance between income and expenses. For example, if the balance between income and expenses is good, the proposal unit will provide a detailed proposal. If the balance between income and expenses is poor, the proposal unit may provide a concise proposal. The proposal unit can also adjust the level of detail of its proposals according to the balance between income and expenses. This allows the proposal unit to provide appropriate proposals by adjusting the level of detail based on the balance between income and expenses. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input income and expense data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0088] The suggestion unit can apply different suggestion algorithms depending on the degree of achievement of the savings goal when making a suggestion. For example, if the savings goal has been achieved, the suggestion unit will make suggestions for the next goal. If the savings goal has not been achieved, the suggestion unit can also make specific suggestions for achieving it. Furthermore, the suggestion unit can adjust the suggestion algorithm according to the degree of achievement of the savings goal. In this way, by applying different suggestion algorithms according to the degree of achievement of the savings goal, it supports the user in achieving their goal. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the degree of achievement of the savings goal into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit might use an emotion analysis algorithm to estimate the user's emotions. If the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide brief suggestions. By adjusting the length of the suggestion based on the user's emotions, the suggestion unit can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0090] The proposal department can prioritize proposals based on the submission timing of income and expenditure data. For example, the proposal department can make proposals based on the most recent income and expenditure data. The proposal department can also postpone older data submissions. Furthermore, the proposal department can prioritize proposals based on submission timing. This allows the proposal department to provide proposals based on the latest data by prioritizing proposals based on the submission timing of income and expenditure data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission timing of income and expenditure data into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0091] The suggestion unit can adjust the order of suggestions based on the relationship between income and expenses when making suggestions. For example, the suggestion unit can make suggestions based on data with a high correlation between income and expenses. The suggestion unit can also make suggestions based on data related to savings goals. Furthermore, the suggestion unit can adjust the order of suggestions based on the relationship between income and expenses. This allows the suggestion unit to provide suggestions based on highly relevant data by adjusting the order of suggestions based on the relationship between income and expenses. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relationship between income and expenses into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0092] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, the notification unit might use an emotion analysis algorithm to estimate the user's emotions. If the user is stressed, the notification unit can reduce the frequency of notifications. Conversely, if the user is relaxed, the notification unit can provide more detailed notifications. Furthermore, if the user is in a hurry, the notification unit can quickly deliver only important notifications. This allows for timely notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the timing of notifications.

[0093] The notification unit can adjust the level of detail of notifications based on the importance of budget overruns and payment deadlines. For example, if the budget overrun is large, the notification unit will provide a detailed notification. The notification unit can also provide an important notification if a payment deadline is approaching. Furthermore, the notification unit can adjust the level of detail of notifications according to the importance of budget overruns and payment deadlines. This allows important information to be appropriately notified by adjusting the level of detail of notifications based on the importance of budget overruns and payment deadlines. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input budget overrun and payment deadline data into a generating AI and have the generating AI perform the adjustment of the level of detail of notifications.

[0094] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, the notification unit might use an emotion analysis algorithm to estimate the user's emotions. If the user is stressed, the notification unit can prioritize only important notifications. If the user is relaxed, the notification unit can also prioritize detailed notifications. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications requiring immediate attention. This allows for prioritizing important notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0095] The notification unit can prioritize highly relevant notifications by considering the user's geographical location when sending notifications. For example, if the user is in a specific region, the notification unit can prioritize notifications related to that region. If the user is traveling, the notification unit can also prioritize notifications related to the user's current location. Furthermore, the notification unit can select the most appropriate notification based on the user's geographical location. This allows the notification unit to prioritize highly relevant notifications by considering the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location into a generation AI and have the generation AI select highly relevant notifications.

[0096] The advice unit can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, the advice unit might use an emotion analysis algorithm to estimate the user's emotions. If the user is stressed, the advice unit can provide simple, visually clear advice. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is in a hurry, it can provide concise, to-the-point advice. By adjusting the presentation of advice based on the user's emotions, the advice unit can provide advice 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, or not. For example, the advice unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the advice.

[0097] The advice unit can adjust the level of detail in its advice based on the balance between income and expenses. For example, if the balance between income and expenses is good, the advice unit will provide detailed advice. If the balance between income and expenses is poor, the advice unit can also provide concise advice. The advice unit can also adjust the level of detail in its advice according to the balance between income and expenses. This allows it to provide appropriate advice by adjusting the level of detail based on the balance between income and expenses. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input income and expense data into a generating AI and have the generating AI perform the adjustment of the level of detail in the advice.

[0098] The advisory unit can apply different advisory algorithms depending on the category of investment opportunity when providing advice. For example, the advisory unit can apply a stock-specific advisory algorithm to stock investments. The advisory unit can also apply a real estate-specific advisory algorithm to real estate investments. Furthermore, the advisory unit can apply a bond-specific advisory algorithm to bond investments. This allows the advisory unit to provide appropriate investment advice by applying different advisory algorithms depending on the category of investment opportunity. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input the investment opportunity category into a generating AI and have the generating AI execute the application of the advisory algorithm.

[0099] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, the advice unit might use an emotion analysis algorithm to estimate the user's emotions. If the user is stressed, the advice unit can provide short, concise advice. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is in a hurry, it can provide brief advice. By adjusting the length of the advice based on the user's emotions, the advice unit can provide advice of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0100] The advice unit can prioritize advice based on the submission dates of income and expenses. For example, the advice unit provides advice based on the most recent income and expense data. The advice unit can also postpone older data submissions. The advice unit can also prioritize advice based on the submission dates. This allows the advice unit to provide advice based on the most recent data by prioritizing advice based on the submission dates of income and expenses. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the submission dates of income and expenses into a generating AI and have the generating AI determine the priority of advice.

[0101] The advice unit can adjust the order of advice based on the relationship between income and expenses. For example, the advice unit provides advice based on data with a high correlation between income and expenses. The advice unit can also provide advice based on data related to savings goals. Furthermore, the advice unit can adjust the order of advice based on the relationship between income and expenses. This allows the advice unit to provide advice based on highly relevant data by adjusting the order of advice based on the relationship between income and expenses. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the relationship between income and expenses into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] Personal finance support systems, such as AI assistants, can not only collect and analyze data on a user's income, expenses, and savings goals to propose a budget plan, but also take into account the user's health data. For example, they can collect data from a user's fitness tracker or smartwatch and adjust the budget plan based on their health status. They can also suggest prioritizing spending on things that promote a healthy lifestyle. Furthermore, if a user is experiencing stress, they can suggest increasing spending on relaxation and health management. This enables more comprehensive personal finance support by providing a budget plan that takes the user's health into account.

[0104] Personal finance support systems, such as AI assistants, can collect and analyze data on users' income, expenses, and savings goals, and propose budget plans that also take into account the user's life events. For example, when a user experiences life events such as marriage, childbirth, or moving, it can propose a budget plan tailored to those events. If a user is planning to get married, it can create a budget plan that takes into account the costs of the wedding and starting a new life. Similarly, if a user is planning to have a child, it can propose a budget plan that takes into account the costs of childcare. This allows for more realistic personal finance support by providing budget plans that are tailored to the user's life events.

[0105] Personal finance support systems, such as AI assistants, can collect and analyze data on a user's income, expenses, and savings goals, and propose budget plans, while also taking into account the user's hobbies and interests. For example, if a user has a particular hobby or interest, it can suggest prioritizing spending related to that hobby or interest. If a user enjoys traveling, it can suggest increasing spending on travel-related activities. Similarly, if a user is interested in sports or fitness, it can suggest prioritizing spending on sports equipment and gym memberships. This allows for more personalized personal finance support by providing budget plans that take into account the user's hobbies and interests.

[0106] Personal finance support systems with AI assistants can collect and analyze data on a user's income, expenses, and savings goals, and propose budget plans, while also taking into account the user's environmental awareness. For example, if a user wants to live an environmentally conscious life, it can suggest prioritizing spending on eco-friendly products and services. If a user wants to use renewable energy, it can suggest increasing spending on that. Furthermore, if a user is interested in reducing plastic waste and recycling, it can suggest prioritizing spending on those areas. This allows for more sustainable personal finance support by providing budget plans that take the user's environmental awareness into account.

[0107] Personal finance support systems, such as AI assistants, can not only collect and analyze data on a user's income, expenses, and savings goals to propose a budget plan, but also take into account the user's social connections. For example, if a user values ​​spending with friends and family, the system can suggest prioritizing spending on those activities. If a user is planning meals or trips with friends, the system can suggest increasing spending on those. Similarly, if a user values ​​spending time with family, the system can suggest prioritizing spending on family events and activities. This allows for more comprehensive personal finance support by providing a budget plan that considers the user's social connections.

[0108] The AI ​​assistant personal finance support system can estimate the user's emotions and adjust their budget plan based on those emotions. For example, if the user is stressed, it can suggest increasing spending on relaxation and stress relief. If the user is relaxed, it can suggest increasing spending on savings and investments. Furthermore, if the user is in a hurry, it can provide a concise and quick budget plan. This allows for optimal personal finance support by adjusting the budget plan based on the user's emotions.

[0109] The AI ​​assistant personal finance support system can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, detailed data collection can be performed to create a more accurate budget plan. If the user is in a hurry, only the minimum necessary data can be quickly collected. In this way, the system reduces the user's burden by adjusting the timing of data collection based on their emotions.

[0110] The AI ​​assistant in the personal finance support system can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the frequency of notifications can be reduced. If the user is relaxed, detailed notifications can be provided. Also, if the user is in a hurry, only important notifications can be delivered quickly. In this way, by adjusting the timing of notifications based on the user's emotions, notifications can be delivered at the appropriate time for the user.

[0111] The AI ​​assistant personal finance support system can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is stressed, it can provide simple, visually easy-to-understand advice. If the user is relaxed, it can provide more detailed advice. If the user is in a hurry, it can provide concise, to-the-point advice. By adjusting the way advice is presented based on the user's emotions, it can provide advice that is easy for the user to understand.

[0112] The AI ​​assistant personal finance support system can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide a simple and visually easy-to-understand analysis. If the user is relaxed, it can provide a detailed analysis. If the user is in a hurry, it can provide a concise analysis that gets straight to the point. In this way, by adjusting the presentation of the analysis based on the user's emotions, it can provide analysis results that are easy for the user to understand.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects data on the user's income, expenses, and savings goals. In addition to collecting data manually entered by the user, the data collection unit can also automatically obtain data from bank accounts and credit cards. For example, it can use API integration to obtain bank account transaction data and scraping techniques to collect credit card transaction data. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit calculates the balance between income and expenses and uses statistical analysis to understand income and expense trends. It can also use machine learning algorithms to predict income and expenses. Step 3: The proposal team proposes a budget plan based on the analysis results obtained by the analysis team. The proposal team creates a budget plan to reach the savings goal based on the balance of income and expenses, and calculates what percentage of the user's income should be allocated to savings. They also analyze expenses by category and suggest points for saving. Step 4: The notification unit will send an alert if it detects a discrepancy between the budget plan and actual results. The notification unit will also send an alert if it detects a budget overrun or if a payment deadline is approaching, sending a push notification to the user's smartphone, as well as an alert via email.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] For example, the data collection unit is implemented by the computer 36 of the smart device 14 or the processor 28 of the data processing unit 12. For example, the data collection unit can accept manual input from the user using the receiving device 38 of the smart device 14. The data collection unit can also automatically acquire bank account and credit card transaction data via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a budget plan based on the analysis results. The notification unit can notify alerts via, for example, the output device 40 of the smart device 14 or the communication I / F 26 of the data processing unit 12. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes optimal investment opportunities based on the user's income and expenditure data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 or the processor 28 of the data processing unit 12. For example, the data collection unit can accept manual input from the user using the microphone 238 of the smart glasses 214. The data collection unit can also automatically acquire bank account and credit card transaction data via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a budget plan based on the analysis results. The notification unit can notify alerts via, for example, the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing unit 12. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes optimal investment opportunities based on the user's income and expenditure data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In 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.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 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.

[0150] For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 or the processor 28 of the data processing unit 12. For example, the data collection unit can accept manual input from the user using the microphone 238 of the headset terminal 314. The data collection unit can also automatically acquire bank account and credit card transaction data via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a budget plan based on the analysis results. The notification unit can notify alerts via, for example, the speaker 240 of the headset terminal 314 or the communication I / F 26 of the data processing unit 12. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes optimal investment opportunities based on the user's income and expenditure data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] For example, the data collection unit is implemented by the computer 36 of the robot 414 or the processor 28 of the data processing unit 12. For example, the data collection unit can accept manual input from the user using the microphone 238 of the robot 414. The data collection unit can also automatically acquire bank account and credit card transaction data via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a budget plan based on the analysis results. The notification unit can notify alerts via the speaker 240 of the robot 414 or the communication I / F 26 of the data processing unit 12. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal investment opportunities based on the user's income and expenditure data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A data collection unit that collects data on users' income, expenses, and savings goals, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes a budget plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a notification unit that issues an alert when it detects a discrepancy between the budget plan and actual results. A system characterized by the following features. (Note 2) The aforementioned notification unit, Clearly specify the method for notifying of budget overruns or payment deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Clearly specify the methods for automatically retrieving data that users manually enter, as well as bank account or credit card data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Calculate the balance between income and expenses and create a budget plan to reach your savings goal. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes an advisory section that clearly outlines specific methods for proposing investment opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, This document outlines specific methods for estimating user sentiment and creating budget plans based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Clearly define the specific methods for estimating user sentiment and adjusting the timing of data collection based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is This document outlines specific methods for analyzing users' past income, spending, and savings goal data and selecting the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, clearly specify the concrete methods used to filter data based on the user's current lifestyle or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is This document outlines specific methods for estimating user sentiment and prioritizing data collection based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system will clearly specify a method for prioritizing the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, clearly specify the concrete methods for analyzing users' social media activity and collecting relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is This document will specify concrete methods for estimating user sentiment and adjusting the presentation of the analysis based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, specify how to adjust the level of detail based on the balance between income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, clearly specify how to apply different analytical algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Clearly specify how to estimate user sentiment and adjust the length of the analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, clearly specify the method for determining the priority of analyses based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, the order of analysis is adjusted based on the relevance of the data. (Note 19) The aforementioned proposal section is, Clearly specify the concrete methods for estimating user emotions and adjusting the presentation of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, clearly state how you will adjust the level of detail in the proposal based on the balance of income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, clearly specify how different proposal algorithms will be applied depending on the degree to which savings goals are achieved. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Clearly specify how to estimate the user's emotions and adjust the length of the suggestion based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, clearly specify the method for determining the priority of proposals based on the timing of income and expenditure submissions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, clearly state the specific method for adjusting the order of proposals based on the relationship between income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, Clearly specify the method for estimating user sentiment and adjusting notification timing based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending notifications, clearly state how the level of detail in the notification will be adjusted based on the importance of budget overruns or payment deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, Clearly specify the method for estimating user sentiment and determining notification priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, clearly specify the concrete methods for prioritizing highly relevant notifications by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, Clearly specify the concrete methods for estimating user emotions and adjusting the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, clearly state how to adjust the level of detail based on the balance of income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, When providing advice, clearly specify how different advisory algorithms are applied depending on the category of investment opportunity. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned advice section, Clearly specify how to estimate the user's emotions and adjust the length of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned advice section, When providing advice, clearly state the specific method for determining the priority of advice based on the timing of income and expense submissions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advice section, When providing advice, clearly state specific methods for adjusting the order of advice based on the relationship between income and expenses. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on users' income, expenses, and savings goals, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes a budget plan, The system includes a notification unit that issues an alert when it detects a discrepancy between the budget plan and actual results. A system characterized by the following features.

2. The aforementioned notification unit, Clearly specify the method for notifying of budget overruns or payment deadlines. The system according to feature 1.

3. The aforementioned collection unit is Clearly specify the methods for automatically retrieving data that users manually enter, as well as bank account or credit card data. The system according to feature 1.

4. The aforementioned proposal section is, Calculate the balance between income and expenses and create a budget plan to reach your savings goal. The system according to feature 1.

5. It includes an advisory section that clearly outlines specific methods for proposing investment opportunities. The system according to feature 1.

6. The aforementioned proposal section is, This document outlines specific methods for estimating user sentiment and creating budget plans based on that estimated sentiment. The system according to feature 1.

7. The aforementioned collection unit is This document outlines specific methods for estimating user sentiment and adjusting the timing of data collection based on that estimated sentiment. The system according to feature 1.

8. The aforementioned collection unit is This document outlines specific methods for analyzing users' past income, spending, and savings goal data and selecting the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, clearly specify the concrete methods used to filter data based on the user's current lifestyle or areas of interest. The system according to feature 1.

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