system

Abstract

The system according to this embodiment aims to centrally manage the flow of money digitally. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a recording unit, and a proposal unit. The collection unit aggregates all money flows. The analysis unit analyzes user behavior based on the information collected by the collection unit. The recording unit records the costs incurred based on the information analyzed by the analysis unit. The proposal unit makes suggestions for improving revenue and expenses based on the information recorded by the recording unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the management of money is not digitally integrated, and there is room for improvement.

[0005] The system according to the embodiment aims to digitally integrate the flow of money.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a recording unit, and a proposal unit. The collection unit aggregates all the money flows. The analysis unit analyzes the user's behavior based on the information collected by the collection unit. The recording unit records the costs generated based on the information analyzed by the analysis unit. The proposal unit makes a proposal for improving the balance based on the information recorded by the recording unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage the flow of money digitally. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The electronic payment system according to an embodiment of the present invention is a system that aggregates all money flows and manages them digitally in a centralized manner. In this electronic payment system, the user installs an electronic payment app and aggregates all money flows. Next, an AI agent analyzes the user's behavior and records the incurred costs. This makes all money flows, such as salary deposits, shopping, remittances, splitting bills, and investments, automatically visible. Furthermore, using acceleration sensors and GPS, travel costs are calculated and the AI ​​agent makes suggestions for improving income and expenses. This allows the user to centralize money management in the electronic payment app and solve future financial worries. For example, the user installs an electronic payment app and aggregates all money flows. At this time, information such as salary deposits, shopping, remittances, splitting bills, and investments is automatically aggregated in the electronic payment app. For example, when the user receives their salary, that information is automatically reflected in the electronic payment app. Also, when the user makes a purchase, the expenditure information is automatically reflected in the electronic payment app. This allows the user to centrally manage all money flows. Next, an AI agent analyzes the user's behavior and records the incurred costs. For example, when a user travels, the system uses acceleration sensors and GPS to calculate travel costs and record that information. This allows users to understand costs based on their own actions. Furthermore, the AI ​​agent provides suggestions for improving finances. For instance, the AI ​​can offer investment and savings advice based on daily income and expense movements. This allows users to receive optimal advice based on their financial situation. This system allows users to consolidate their money management into an electronic payment app and solve future financial worries. For example, when a user plans their future savings, the AI ​​agent can suggest the optimal savings method, enabling them to save efficiently. Also, when a user invests, the AI ​​agent can suggest the optimal investment destination, allowing them to invest with reduced risk. In this way, by consolidating all money flows into an electronic payment app and having the AI ​​agent analyze actions and record incurred costs, the problem of not being able to manage money digitally in a unified way can be solved.This allows the electronic payment system to centrally manage all of a user's money flows digitally and provide suggestions for improving their financial performance.

[0029] The electronic payment system according to this embodiment comprises a collection unit, an analysis unit, a recording unit, and a suggestion unit. The collection unit aggregates all money flows. The collection unit collects information such as salary deposits, purchases, remittances, splitting bills, and investments. For example, the collection unit automatically collects salary deposit information and reflects it in the electronic payment app. The collection unit can also automatically collect shopping expenditure information and reflect it in the electronic payment app. Furthermore, the collection unit can also automatically collect remittance and splitting bill information and reflect it in the electronic payment app. For example, the collection unit automatically obtains salary deposit information from bank accounts and reflects it in the electronic payment app. Shopping expenditure information is automatically obtained from credit card and debit card usage history and reflected in the electronic payment app. Remittance and splitting bill information is automatically obtained from transaction history within the electronic payment app and reflected in the electronic payment app. The analysis unit analyzes user behavior based on the information collected by the collection unit. The analysis unit calculates travel costs using, for example, an acceleration sensor and GPS. The analysis unit can, for example, collect user travel data and calculate travel costs. It can also analyze user behavior patterns and identify cost incurrs. For instance, it can use an accelerometer to measure user travel distance and time and calculate travel costs. It can also use GPS to record user travel routes and calculate travel costs. By analyzing user behavior patterns, it can understand the impact of specific actions on costs. The recording unit records costs incurred based on the information analyzed by the analysis unit. For example, the recording unit records costs based on user behavior. For instance, it can record travel costs and shopping expenditure information. Furthermore, the recording unit can record costs based on user behavior in detail. For example, it can record travel costs daily and monthly, allowing users to understand cost fluctuations. It can also record shopping expenditure information by category, allowing users to understand the breakdown of their spending. The proposal unit provides suggestions for improving income and expenses based on the information recorded by the recording unit. For example, the proposal unit provides investment and savings advice based on daily income and expense movements. The proposal department can, for example, suggest the most suitable investment destinations based on the user's financial situation.Furthermore, the proposal unit can also suggest the optimal savings method based on the user's income and expenditure situation. For example, the proposal unit can analyze the user's income and expenditure data and suggest investment options that minimize risk. By suggesting the optimal savings method when creating a savings plan, it enables users to save efficiently. As a result, the electronic payment system according to this embodiment can centrally manage all of the user's money flows digitally and make suggestions for improving their income and expenditure.

[0030] The data collection unit aggregates all money flows. For example, it collects information on salary deposits, purchases, remittances, splitting bills, and investments. Specifically, it automatically retrieves salary deposit information from bank accounts and reflects it in electronic payment apps. This allows users to instantly check their salary when it is deposited. Shopping expenditure information is automatically retrieved from credit and debit card transaction history and reflected in electronic payment apps. This allows users to track their daily spending in real time. Remittance and bill-splitting information is automatically retrieved from transaction history within electronic payment apps and reflected in the apps. For example, if a user splits the bill for a meal with a friend, that information is automatically collected and reflected in the app. Furthermore, investment information is also automatically retrieved by the data collection unit. For example, if a user invests in stocks or mutual funds, the transaction information is automatically collected and reflected in the app. This allows users to track their investment status in real time. The data collection unit centrally manages all of this information, making it easy for users to check their income and expenses. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, it can be set to collect salary payment information once a month, while shopping expenditure information is collected daily. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes user behavior based on information collected by the data collection department. Specifically, it calculates travel costs using accelerometers and GPS. For example, it uses the accelerometer installed in the user's smartphone to measure the user's travel distance and time and calculate travel costs. It can also use GPS to record the user's travel route and calculate travel costs. This allows for a detailed understanding of how users travel. The analysis department can also analyze user behavior patterns and identify the factors that cause costs. For example, if a user frequently travels to a specific place at a specific time, the department can understand the impact of that behavior on costs. Furthermore, the analysis department can analyze collected shopping expenditure information to understand users' spending trends. For example, if a user frequently purchases products in a specific category, the department can understand the extent of their spending in that category. This allows for a detailed analysis of users' spending trends and enables the department to make suggestions for reducing unnecessary spending. Based on this information, the analysis department helps users manage their money efficiently by analyzing user behavior in detail and identifying the factors that cause costs.

[0032] The recording unit records costs incurred based on information analyzed by the analysis unit. Specifically, it records costs based on user behavior in detail. For example, it records travel costs and shopping expenses on a daily and monthly basis, allowing users to understand cost fluctuations. Travel costs are calculated based on the user's travel distance and time and recorded on a daily and monthly basis. This allows users to understand the costs associated with their travel in detail. Shopping expenses are recorded by category, allowing users to understand the breakdown of their spending. For example, expenses are recorded in categories such as food, transportation, and entertainment. This allows users to understand their spending trends and take measures to reduce unnecessary spending. Furthermore, by recording costs based on user behavior in detail, the recording unit supports long-term financial management. For example, based on past data, it is possible to understand the trends in income and expenses over a specific period and create future financial plans. This allows users to understand their financial situation in detail and manage their money efficiently. The recording unit centrally manages this information and makes it easily accessible to users. For example, it allows users to easily check their income and expense information within an electronic payment app. This allows the recording unit to help users manage their money efficiently.

[0033] The Proposal Department makes suggestions for improving income and expenses based on information recorded by the Records Department. Specifically, it provides investment and savings advice based on daily income and expense movements. For example, it can suggest optimal investment options based on the user's income and expense situation. It analyzes the user's income and expense data and suggests investment options with reduced risk. For example, if the user has a stable income, it may suggest low-risk investment trusts or government bonds. On the other hand, if the user is seeking high returns, it may suggest stocks or real estate investments. The Proposal Department can also suggest optimal savings methods based on the user's income and expense situation. For example, it analyzes the balance between the user's income and expenses and sets monthly savings targets. Furthermore, the Proposal Department assists users in creating long-term savings plans based on their lifestyle and future goals. For example, if the user wants to buy a house in the future, it will suggest a savings plan for that purpose. This allows the user to save efficiently. The Proposal Department presents these suggestions to the user in an easy-to-understand manner and provides actionable plans. For example, it makes it possible for users to easily check the suggestions and put them into action within the electronic payment app. In this way, the Proposal Department helps users manage their money efficiently and improve their income and expenses. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to provide users with optimal revenue-improvement proposals, supporting their financial stability.

[0034] The data collection unit can collect information such as salary deposits, purchases, remittances, splitting bills, and investments. For example, the data collection unit can automatically collect salary deposit information and reflect it in an electronic payment app. The data collection unit can also automatically collect shopping expenditure information and reflect it in an electronic payment app. The data collection unit can also automatically collect remittance and split bill information and reflect it in an electronic payment app. For example, the data collection unit automatically retrieves salary deposit information from bank accounts and reflects it in an electronic payment app. Shopping expenditure information is automatically retrieved from credit and debit card usage history and reflected in an electronic payment app. Remittance and split bill information is automatically retrieved from transaction history within the electronic payment app and reflected in the electronic payment app. This allows the data collection unit to collect diverse information and manage all money flows in a centralized manner. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI automatically retrieve salary payment information from bank accounts and reflect it in electronic payment apps.

[0035] The analysis unit can calculate travel costs using acceleration sensors and GPS. For example, the analysis unit can collect user travel data and calculate travel costs. For example, the analysis unit can measure the user's travel distance and travel time using acceleration sensors and calculate travel costs. For example, the analysis unit can record the user's travel route using GPS and calculate travel costs. For example, the analysis unit can measure the user's travel distance and travel time using acceleration sensors and calculate travel costs. It can also record the user's travel route using GPS and calculate travel costs. This allows the analysis unit to calculate travel costs and understand costs based on user behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user travel data into AI and have AI calculate travel costs.

[0036] The suggestion unit can provide investment and savings advice based on daily income and expenditure movements. For example, the suggestion unit can suggest the optimal investment destination based on the user's income and expenditure situation. For example, the suggestion unit can suggest the optimal savings method based on the user's income and expenditure situation. For example, the suggestion unit can analyze the user's income and expenditure data and suggest investment destinations with reduced risk. For example, the suggestion unit can suggest the optimal savings method when creating a savings plan. For example, the suggestion unit analyzes the user's income and expenditure data and suggests investment destinations with reduced risk. By suggesting the optimal savings method when creating a savings plan, it enables users to save efficiently. As a result, by having the suggestion unit provide advice based on income and expenditure movements, users can learn the optimal investment and savings methods. 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 the user's income and expenditure data into AI and have the AI ​​suggest the optimal investment destinations and savings methods.

[0037] The recording unit can record costs based on user behavior. For example, the recording unit can record travel costs and shopping expenditure information. For example, the recording unit can record costs based on user behavior in detail. For example, the recording unit can record travel costs on a daily or monthly basis, allowing users to understand cost fluctuations. For example, the recording unit can record shopping expenditure information by category, allowing users to understand the breakdown of their spending. For example, the recording unit can record travel costs on a daily or monthly basis, allowing users to understand cost fluctuations. For example, it can record shopping expenditure information by category, allowing users to understand the breakdown of their spending. In this way, by recording costs based on user behavior, users can understand costs based on their own behavior. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input user behavior data into AI and have AI perform cost recording.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information that the user has frequently collected in the past. For example, the data collection unit can predict the information to be collected at a specific time period based on the user's past data collection history and select the optimal data collection method. For example, the data collection unit can analyze the user's past data collection history and select the most efficient data collection method. For example, the data collection unit can prioritize collecting information that the user has frequently collected in the past. For example, the data collection unit can predict the information to be collected at a specific time period based on the user's past data collection history and select the optimal data collection method. For example, the data collection unit can input the user's past data collection history into an AI and have the AI ​​select the optimal data collection method. For example, the data collection unit can input the user's past data collection history into an AI and have the AI ​​select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and areas of interest during collection. For example, the data collection unit can prioritize collecting information in areas of interest to the user. For example, the data collection unit can filter and collect necessary information based on the user's current lifestyle. For example, the data collection unit can filter and collect relevant information based on the user's areas of interest. For example, the data collection unit can prioritize collecting information in areas of interest to the user. For example, the data collection unit can filter and collect necessary information based on the user's current lifestyle. For example, the data collection unit can filter and collect relevant information based on the user's areas of interest. This allows for efficient collection of necessary information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user area of ​​interest data into AI and have AI perform information filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can filter and collect highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit can prioritize the collection of information related to their destination. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can filter and collect highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit will prioritize the collection of information related to their destination. This allows for the priority collection of highly relevant information 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 data into AI and have AI perform the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during collection. For example, the data collection unit can prioritize collecting information that the user has shown interest in on social media. For example, the data collection unit can filter and collect relevant information based on the user's social media activity. For example, the data collection unit can collect relevant information based on information that the user has shared on social media. For example, the data collection unit can prioritize collecting information that the user has shown interest in on social media. For example, the data collection unit can filter and collect relevant information based on the user's social media activity. For example, the data collection unit can input the user's social media activity data into an AI and have the AI ​​collect relevant information. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into an AI and have the AI ​​perform the collection of relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a simplified analysis on information of low importance. The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, it can perform a simplified analysis on information of low importance. The level of detail of the analysis can be adjusted based on the importance of the collected information. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the collected information into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an income analysis algorithm to income information. For example, the analysis unit can apply an expense analysis algorithm to expense information. For example, the analysis unit can apply an investment analysis algorithm to investment information. For example, the analysis unit can apply an income analysis algorithm to income information. For example, it can apply an expense analysis algorithm to expense information. For example, it can apply an investment analysis algorithm to investment information. By applying different analysis algorithms depending on the category of information, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into AI and have AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can perform a simplified analysis on older information. The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can perform a simplified analysis on older information. The analysis unit can determine the priority of analysis based on when the information was collected. This enables efficient analysis by determining the priority of analysis based on when the information was collected. 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 data on when the information was collected into the AI ​​and have the AI ​​determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can perform a simplified analysis on less relevant information. The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can perform a simplified analysis on less relevant information. The order of analysis can be adjusted based on the relevance of the information. This makes efficient analysis possible by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0046] The recording unit can analyze the user's past behavior and select the optimal recording method at the time of recording. For example, the recording unit can preferentially suggest recording methods that the user has used in the past. For example, the recording unit can select the optimal recording method from the user's past behavior history. For example, the recording unit can analyze the user's past behavior and select the most efficient recording method. For example, the recording unit can preferentially suggest recording methods that the user has used in the past. For example, the recording unit can select the optimal recording method from the user's past behavior history. For example, the recording unit can input the user's past behavior data into the AI ​​and have the AI ​​select the optimal recording method.

[0047] The recording unit can customize the recording method based on the user's current living situation at the time of recording. For example, the recording unit can provide a simplified recording method when the user is busy. For example, the recording unit can provide a detailed recording method when the user is relaxed. The recording unit can customize the optimal recording method based on the user's current living situation. For example, the recording unit can provide a simplified recording method when the user is busy. For example, it can provide a detailed recording method when the user is relaxed. The optimal recording method can be customized based on the user's current living situation. This makes it possible to record more appropriately by customizing the recording method based on the user's current living situation. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's current living situation data into the AI ​​and have the AI ​​perform the customization of the recording method.

[0048] The recording unit can select the optimal recording method by considering the user's geographical location information during recording. For example, if the user is in a specific region, the recording unit will prioritize recording information related to that region. For example, the recording unit can select the optimal recording method based on the user's geographical location information. For example, if the user is on the move, the recording unit can prioritize recording information related to the destination. For example, if the user is in a specific region, the recording unit will prioritize recording information related to that region. For example, the recording unit can select the optimal recording method based on the user's geographical location information. If the user is on the move, it can prioritize recording information related to the destination. This allows the optimal recording method to be selected by considering the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's geographical location information data into AI and have AI select the optimal recording method.

[0049] The recording unit can analyze the user's social media activity and suggest recording methods at the time of recording. For example, the recording unit can suggest the optimal recording method based on information shared by the user on social media. For example, the recording unit can suggest a method for recording relevant information based on the user's social media activity. For example, the recording unit can suggest a method for recording based on information the user has shown interest in on social media. For example, the recording unit can suggest the optimal recording method based on information shared by the user on social media. For example, it can suggest a method for recording relevant information based on the user's social media activity. For example, it can suggest a method for recording based on information the user has shown interest in on social media. In this way, by analyzing the user's social media activity, the optimal recording method can be suggested. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's social media activity data into AI and have AI perform the task of suggesting recording methods.

[0050] The proposal unit can adjust the level of detail of its proposals based on the importance of the financial items. For example, the proposal unit can provide detailed proposals for high-importance financial items. For example, it can provide simplified proposals for low-importance financial items. The proposal unit can adjust the level of detail of its proposals based on the importance of the financial items. For example, the proposal unit can provide detailed proposals for high-importance financial items. For example, it can provide simplified proposals for low-importance financial items. The level of detail of the proposals can be adjusted based on the importance of the financial items. This allows for more efficient proposals by adjusting the level of detail of the proposals based on the importance of the financial items. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input financial importance data into AI and have AI perform the adjustment of the level of detail of the proposals.

[0051] The proposal unit can apply different proposal algorithms depending on the income and expenditure categories when making a proposal. For example, the proposal unit can apply an income improvement proposal algorithm to income. For example, the proposal unit can apply an expenditure reduction proposal algorithm to expenses. For example, the proposal unit can apply an investment proposal algorithm to investments. For example, the proposal unit can apply an income improvement proposal algorithm to income. For expenses, it can apply an expenditure reduction proposal algorithm. For investments, it can apply an investment proposal algorithm. This makes it possible to make more accurate proposals by applying different proposal algorithms depending on the income and expenditure categories. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input income and expenditure category data into AI and have the AI ​​execute the application of different proposal algorithms.

[0052] The proposal department can determine the priority of proposals based on the movement of income and expenses when making a proposal. For example, the proposal department will prioritize proposals when the movement of income and expenses is large. For example, the proposal department can make simplified proposals when the movement of income and expenses is small. The proposal department can determine the priority of proposals based on the movement of income and expenses. For example, the proposal department will prioritize proposals when the movement of income and expenses is large. For example, it can make simplified proposals when the movement of income and expenses is small. The proposal department can determine the priority of proposals based on the movement of income and expenses. This makes it possible to make efficient proposals by determining the priority of proposals based on the movement of income and expenses. 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 income and expense data into AI and have AI perform the determination of proposal priority.

[0053] The proposal unit can adjust the order of proposals based on the relevance of income and expenses when making proposals. For example, the proposal unit can prioritize proposals for highly relevant income and expenses. For example, the proposal unit can make simplified proposals for less relevant income and expenses. The proposal unit can adjust the order of proposals based on the relevance of income and expenses. For example, the proposal unit can prioritize proposals for highly relevant income and expenses. For example, it can make simplified proposals for less relevant income and expenses. The order of proposals can be adjusted based on the relevance of income and expenses. This makes it possible to make efficient proposals by adjusting the order of proposals based on the relevance of 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 the relevance data of income and expenses into AI and have AI perform the adjustment of the order of proposals.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can collect users' health data and make suggestions for improving their spending based on that data. For example, the unit can collect users' step counts and exercise levels and suggest a review of their spending based on their health status. It can also collect users' dietary data and suggest spending suggestions to maintain a healthy diet. Furthermore, the unit can collect users' sleep data and suggest spending suggestions to improve sleep quality. In this way, by making suggestions for improving spending based on the user's health status, it can support a healthier lifestyle.

[0056] The analytics department can analyze users' hobbies and preferences and propose optimizations for their hobby-related spending. For example, if a user enjoys watching movies, the analytics department can propose optimizations for movie-related spending. Similarly, if a user enjoys traveling, the analytics department can propose optimizations for travel-related spending. Furthermore, if a user enjoys music, the analytics department can propose optimizations for music-related spending. By proposing spending optimizations based on users' hobbies and preferences, the department can support them in leading more fulfilling hobby lives.

[0057] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. Based on the user's geographical location, it can filter and collect highly relevant information. If the user is on the move, it can prioritize the collection of information related to their destination. In this way, by considering the user's geographical location, highly relevant information can be prioritized.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, it can perform a detailed analysis on highly important information and a simplified analysis on less important information. By adjusting the level of detail of the analysis based on the importance of the collected information, efficient analysis becomes possible.

[0059] The proposal section can apply different proposal algorithms depending on the income and expenditure category. For example, an income improvement proposal algorithm can be applied to income, an expenditure reduction proposal algorithm can be applied to expenses, and an investment proposal algorithm can be applied to investments. By applying different proposal algorithms depending on the income and expenditure category, more accurate proposals can be made.

[0060] The recording unit can analyze a user's social media activity and suggest recording methods. For example, the recording unit can suggest the optimal recording method based on information shared by the user on social media. It can suggest methods for recording relevant information based on the user's social media activity. It can suggest recording methods based on information the user has shown interest in on social media. In this way, by analyzing the user's social media activity, the optimal recording method can be suggested.

[0061] The proposal department can determine the priority of proposals based on the movement of revenue and expenses. For example, if the movement of revenue and expenses is large, proposals will be given priority. If the movement of revenue and expenses is small, simplified proposals can be made. By determining the priority of proposals based on the movement of revenue and expenses, efficient proposals can be made.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit aggregates all money flows. For example, it collects information on salary deposits, purchases, remittances, splitting bills, and investments, and reflects this information in the electronic payment app. The data collection unit automatically retrieves salary deposit information from bank accounts, purchase expenditure information from credit and debit card transaction history, and remittance and bill-splitting information from transaction history within the electronic payment app. Step 2: The analysis unit analyzes user behavior based on the information collected by the collection unit. For example, it uses acceleration sensors and GPS to calculate travel costs and analyzes user behavior patterns to identify the factors that cause costs. Step 3: The recording unit records costs incurred based on the information analyzed by the analysis unit. For example, it records travel costs and shopping expenses on a daily or monthly basis, and categorizes them so that users can understand cost fluctuations and the breakdown of their spending. Step 4: The proposal department makes suggestions for improving income and expenses based on the information recorded by the recording department. For example, it provides investment and savings advice based on daily income and expense movements and proposes the optimal investment destinations and savings methods based on the user's financial situation.

[0064] (Example of form 2) The electronic payment system according to an embodiment of the present invention is a system that aggregates all money flows and manages them digitally in a centralized manner. In this electronic payment system, the user installs an electronic payment app and aggregates all money flows. Next, an AI agent analyzes the user's behavior and records the incurred costs. This makes all money flows, such as salary deposits, shopping, remittances, splitting bills, and investments, automatically visible. Furthermore, using acceleration sensors and GPS, travel costs are calculated and the AI ​​agent makes suggestions for improving income and expenses. This allows the user to centralize money management in the electronic payment app and solve future financial worries. For example, the user installs an electronic payment app and aggregates all money flows. At this time, information such as salary deposits, shopping, remittances, splitting bills, and investments is automatically aggregated in the electronic payment app. For example, when the user receives their salary, that information is automatically reflected in the electronic payment app. Also, when the user makes a purchase, the expenditure information is automatically reflected in the electronic payment app. This allows the user to centrally manage all money flows. Next, an AI agent analyzes the user's behavior and records the incurred costs. For example, when a user travels, the system uses acceleration sensors and GPS to calculate travel costs and record that information. This allows users to understand costs based on their own actions. Furthermore, the AI ​​agent provides suggestions for improving finances. For instance, the AI ​​can offer investment and savings advice based on daily income and expense movements. This allows users to receive optimal advice based on their financial situation. This system allows users to consolidate their money management into an electronic payment app and solve future financial worries. For example, when a user plans their future savings, the AI ​​agent can suggest the optimal savings method, enabling them to save efficiently. Also, when a user invests, the AI ​​agent can suggest the optimal investment destination, allowing them to invest with reduced risk. In this way, by consolidating all money flows into an electronic payment app and having the AI ​​agent analyze actions and record incurred costs, the problem of not being able to manage money digitally in a unified way can be solved.This allows the electronic payment system to centrally manage all of a user's money flows digitally and provide suggestions for improving their financial performance.

[0065] The electronic payment system according to this embodiment comprises a collection unit, an analysis unit, a recording unit, and a suggestion unit. The collection unit aggregates all money flows. The collection unit collects information such as salary deposits, purchases, remittances, splitting bills, and investments. For example, the collection unit automatically collects salary deposit information and reflects it in the electronic payment app. The collection unit can also automatically collect shopping expenditure information and reflect it in the electronic payment app. Furthermore, the collection unit can also automatically collect remittance and splitting bill information and reflect it in the electronic payment app. For example, the collection unit automatically obtains salary deposit information from bank accounts and reflects it in the electronic payment app. Shopping expenditure information is automatically obtained from credit card and debit card usage history and reflected in the electronic payment app. Remittance and splitting bill information is automatically obtained from transaction history within the electronic payment app and reflected in the electronic payment app. The analysis unit analyzes user behavior based on the information collected by the collection unit. The analysis unit calculates travel costs using, for example, an acceleration sensor and GPS. The analysis unit can, for example, collect user travel data and calculate travel costs. It can also analyze user behavior patterns and identify cost incurrs. For instance, it can use an accelerometer to measure user travel distance and time and calculate travel costs. It can also use GPS to record user travel routes and calculate travel costs. By analyzing user behavior patterns, it can understand the impact of specific actions on costs. The recording unit records costs incurred based on the information analyzed by the analysis unit. For example, the recording unit records costs based on user behavior. For instance, it can record travel costs and shopping expenditure information. Furthermore, the recording unit can record costs based on user behavior in detail. For example, it can record travel costs daily and monthly, allowing users to understand cost fluctuations. It can also record shopping expenditure information by category, allowing users to understand the breakdown of their spending. The proposal unit provides suggestions for improving income and expenses based on the information recorded by the recording unit. For example, the proposal unit provides investment and savings advice based on daily income and expense movements. The proposal department can, for example, suggest the most suitable investment destinations based on the user's financial situation.Furthermore, the proposal unit can also suggest the optimal savings method based on the user's income and expenditure situation. For example, the proposal unit can analyze the user's income and expenditure data and suggest investment options that minimize risk. By suggesting the optimal savings method when creating a savings plan, it enables users to save efficiently. As a result, the electronic payment system according to this embodiment can centrally manage all of the user's money flows digitally and make suggestions for improving their income and expenditure.

[0066] The data collection unit aggregates all money flows. For example, it collects information on salary deposits, purchases, remittances, splitting bills, and investments. Specifically, it automatically retrieves salary deposit information from bank accounts and reflects it in electronic payment apps. This allows users to instantly check their salary when it is deposited. Shopping expenditure information is automatically retrieved from credit and debit card transaction history and reflected in electronic payment apps. This allows users to track their daily spending in real time. Remittance and bill-splitting information is automatically retrieved from transaction history within electronic payment apps and reflected in the apps. For example, if a user splits the bill for a meal with a friend, that information is automatically collected and reflected in the app. Furthermore, investment information is also automatically retrieved by the data collection unit. For example, if a user invests in stocks or mutual funds, the transaction information is automatically collected and reflected in the app. This allows users to track their investment status in real time. The data collection unit centrally manages all of this information, making it easy for users to check their income and expenses. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, it can be set to collect salary payment information once a month, while shopping expenditure information is collected daily. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis department analyzes user behavior based on information collected by the data collection department. Specifically, it calculates travel costs using accelerometers and GPS. For example, it uses the accelerometer installed in the user's smartphone to measure the user's travel distance and time and calculate travel costs. It can also use GPS to record the user's travel route and calculate travel costs. This allows for a detailed understanding of how users travel. The analysis department can also analyze user behavior patterns and identify the factors that cause costs. For example, if a user frequently travels to a specific place at a specific time, the department can understand the impact of that behavior on costs. Furthermore, the analysis department can analyze collected shopping expenditure information to understand users' spending trends. For example, if a user frequently purchases products in a specific category, the department can understand the extent of their spending in that category. This allows for a detailed analysis of users' spending trends and enables the department to make suggestions for reducing unnecessary spending. Based on this information, the analysis department helps users manage their money efficiently by analyzing user behavior in detail and identifying the factors that cause costs.

[0068] The recording unit records costs incurred based on information analyzed by the analysis unit. Specifically, it records costs based on user behavior in detail. For example, it records travel costs and shopping expenses on a daily and monthly basis, allowing users to understand cost fluctuations. Travel costs are calculated based on the user's travel distance and time and recorded on a daily and monthly basis. This allows users to understand the costs associated with their travel in detail. Shopping expenses are recorded by category, allowing users to understand the breakdown of their spending. For example, expenses are recorded in categories such as food, transportation, and entertainment. This allows users to understand their spending trends and take measures to reduce unnecessary spending. Furthermore, by recording costs based on user behavior in detail, the recording unit supports long-term financial management. For example, based on past data, it is possible to understand the trends in income and expenses over a specific period and create future financial plans. This allows users to understand their financial situation in detail and manage their money efficiently. The recording unit centrally manages this information and makes it easily accessible to users. For example, it allows users to easily check their income and expense information within an electronic payment app. This allows the recording unit to help users manage their money efficiently.

[0069] The Proposal Department makes suggestions for improving income and expenses based on information recorded by the Records Department. Specifically, it provides investment and savings advice based on daily income and expense movements. For example, it can suggest optimal investment options based on the user's income and expense situation. It analyzes the user's income and expense data and suggests investment options with reduced risk. For example, if the user has a stable income, it may suggest low-risk investment trusts or government bonds. On the other hand, if the user is seeking high returns, it may suggest stocks or real estate investments. The Proposal Department can also suggest optimal savings methods based on the user's income and expense situation. For example, it analyzes the balance between the user's income and expenses and sets monthly savings targets. Furthermore, the Proposal Department assists users in creating long-term savings plans based on their lifestyle and future goals. For example, if the user wants to buy a house in the future, it will suggest a savings plan for that purpose. This allows the user to save efficiently. The Proposal Department presents these suggestions to the user in an easy-to-understand manner and provides actionable plans. For example, it makes it possible for users to easily check the suggestions and put them into action within the electronic payment app. In this way, the Proposal Department helps users manage their money efficiently and improve their income and expenses. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to provide users with optimal revenue-improvement proposals, supporting their financial stability.

[0070] The data collection unit can collect information such as salary deposits, purchases, remittances, splitting bills, and investments. For example, the data collection unit can automatically collect salary deposit information and reflect it in an electronic payment app. The data collection unit can also automatically collect shopping expenditure information and reflect it in an electronic payment app. The data collection unit can also automatically collect remittance and split bill information and reflect it in an electronic payment app. For example, the data collection unit automatically retrieves salary deposit information from bank accounts and reflects it in an electronic payment app. Shopping expenditure information is automatically retrieved from credit and debit card usage history and reflected in an electronic payment app. Remittance and split bill information is automatically retrieved from transaction history within the electronic payment app and reflected in the electronic payment app. This allows the data collection unit to collect diverse information and manage all money flows in a centralized manner. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI automatically retrieve salary payment information from bank accounts and reflect it in electronic payment apps.

[0071] The analysis unit can calculate travel costs using acceleration sensors and GPS. For example, the analysis unit can collect user travel data and calculate travel costs. For example, the analysis unit can measure the user's travel distance and travel time using acceleration sensors and calculate travel costs. For example, the analysis unit can record the user's travel route using GPS and calculate travel costs. For example, the analysis unit can measure the user's travel distance and travel time using acceleration sensors and calculate travel costs. It can also record the user's travel route using GPS and calculate travel costs. This allows the analysis unit to calculate travel costs and understand costs based on user behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user travel data into AI and have AI calculate travel costs.

[0072] The suggestion unit can provide investment and savings advice based on daily income and expenditure movements. For example, the suggestion unit can suggest the optimal investment destination based on the user's income and expenditure situation. For example, the suggestion unit can suggest the optimal savings method based on the user's income and expenditure situation. For example, the suggestion unit can analyze the user's income and expenditure data and suggest investment destinations with reduced risk. For example, the suggestion unit can suggest the optimal savings method when creating a savings plan. For example, the suggestion unit analyzes the user's income and expenditure data and suggests investment destinations with reduced risk. By suggesting the optimal savings method when creating a savings plan, it enables users to save efficiently. As a result, by having the suggestion unit provide advice based on income and expenditure movements, users can learn the optimal investment and savings methods. 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 the user's income and expenditure data into AI and have the AI ​​suggest the optimal investment destinations and savings methods.

[0073] The recording unit can record costs based on user behavior. For example, the recording unit can record travel costs and shopping expenditure information. For example, the recording unit can record costs based on user behavior in detail. For example, the recording unit can record travel costs on a daily or monthly basis, allowing users to understand cost fluctuations. For example, the recording unit can record shopping expenditure information by category, allowing users to understand the breakdown of their spending. For example, the recording unit can record travel costs on a daily or monthly basis, allowing users to understand cost fluctuations. For example, it can record shopping expenditure information by category, allowing users to understand the breakdown of their spending. In this way, by recording costs based on user behavior, users can understand costs based on their own behavior. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input user behavior data into AI and have AI perform cost recording.

[0074] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting important spending information. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed income information. For example, if the user is in a hurry, the data collection unit may prioritize collecting information that is immediately needed. For example, if the user is stressed, the data collection unit may prioritize collecting important spending information. If the user is relaxed, the data collection unit may prioritize collecting detailed income information. If the user is in a hurry, the data collection unit may prioritize collecting information that is immediately needed. This allows for the collection of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of the information.

[0075] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information that the user has frequently collected in the past. For example, the data collection unit can predict the information to be collected at a specific time period based on the user's past data collection history and select the optimal data collection method. For example, the data collection unit can analyze the user's past data collection history and select the most efficient data collection method. For example, the data collection unit can prioritize collecting information that the user has frequently collected in the past. For example, the data collection unit can predict the information to be collected at a specific time period based on the user's past data collection history and select the optimal data collection method. For example, the data collection unit can input the user's past data collection history into an AI and have the AI ​​select the optimal data collection method. For example, the data collection unit can input the user's past data collection history into an AI and have the 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 collection. For example, the data collection unit can prioritize collecting information in areas of interest to the user. For example, the data collection unit can filter and collect necessary information based on the user's current lifestyle. For example, the data collection unit can filter and collect relevant information based on the user's areas of interest. For example, the data collection unit can prioritize collecting information in areas of interest to the user. For example, the data collection unit can filter and collect necessary information based on the user's current lifestyle. For example, the data collection unit can filter and collect relevant information based on the user's areas of interest. This allows for efficient collection of necessary information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user area of ​​interest data into AI and have AI perform information filtering.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the timing of information collection. For example, if the user is relaxed, the data collection unit can speed up the timing of information collection. For example, if the user is in a hurry, the data collection unit can collect information immediately. For example, if the user is stressed, the data collection unit can delay the timing of information collection. If the user is relaxed, the data collection unit can speed up the timing of information collection. If the user is in a hurry, the data collection unit can collect information immediately. This allows information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of information collection.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can filter and collect highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit can prioritize the collection of information related to their destination. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can filter and collect highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit will prioritize the collection of information related to their destination. This allows for the priority collection of highly relevant information 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 data into AI and have AI perform the collection of highly relevant information.

[0079] The data collection unit can analyze the user's social media activity and collect relevant information during collection. For example, the data collection unit can prioritize collecting information that the user has shown interest in on social media. For example, the data collection unit can filter and collect relevant information based on the user's social media activity. For example, the data collection unit can collect relevant information based on information that the user has shared on social media. For example, the data collection unit can prioritize collecting information that the user has shown interest in on social media. For example, the data collection unit can filter and collect relevant information based on the user's social media activity. For example, the data collection unit can input the user's social media activity data into an AI and have the AI ​​collect relevant information. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into an AI and have the AI ​​perform the collection of relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can input user emotion data into the AI ​​and have the AI ​​adjust how the analysis is presented.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a simplified analysis on information of low importance. The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, it can perform a simplified analysis on information of low importance. The level of detail of the analysis can be adjusted based on the importance of the collected information. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the collected information into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an income analysis algorithm to income information. For example, the analysis unit can apply an expense analysis algorithm to expense information. For example, the analysis unit can apply an investment analysis algorithm to investment information. For example, the analysis unit can apply an income analysis algorithm to income information. For example, it can apply an expense analysis algorithm to expense information. For example, it can apply an investment analysis algorithm to investment information. By applying different analysis algorithms depending on the category of information, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into AI and have 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, if the user is stressed, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is in a hurry, the analysis unit can provide the analysis results quickly. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, it can provide a detailed analysis. If the user is in a hurry, it can provide the analysis results quickly. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 an AI and have the AI ​​adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can perform a simplified analysis on older information. The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can perform a simplified analysis on older information. The analysis unit can determine the priority of analysis based on when the information was collected. This enables efficient analysis by determining the priority of analysis based on when the information was collected. 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 data on when the information was collected into the AI ​​and have the AI ​​determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can perform a simplified analysis on less relevant information. The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can perform a simplified analysis on less relevant information. The order of analysis can be adjusted based on the relevance of the information. This makes efficient analysis possible by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0086] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is stressed, the recording unit can provide a simple recording method. For example, if the user is relaxed, the recording unit can provide a detailed recording method. For example, if the user is in a hurry, the recording unit can provide a method that allows for quick recording. For example, if the user is stressed, the recording unit can provide a simple recording method. If the user is relaxed, the recording unit can provide a detailed recording method. If the user is in a hurry, the recording unit can provide a method that allows for quick recording. This allows for more appropriate recording by adjusting the recording method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input user emotion data into an AI and have the AI ​​adjust the recording method.

[0087] The recording unit can analyze the user's past behavior and select the optimal recording method at the time of recording. For example, the recording unit can preferentially suggest recording methods that the user has used in the past. For example, the recording unit can select the optimal recording method from the user's past behavior history. For example, the recording unit can analyze the user's past behavior and select the most efficient recording method. For example, the recording unit can preferentially suggest recording methods that the user has used in the past. For example, the recording unit can select the optimal recording method from the user's past behavior history. For example, the recording unit can input the user's past behavior data into the AI ​​and have the AI ​​select the optimal recording method.

[0088] The recording unit can customize the recording method based on the user's current living situation at the time of recording. For example, the recording unit can provide a simplified recording method when the user is busy. For example, the recording unit can provide a detailed recording method when the user is relaxed. The recording unit can customize the optimal recording method based on the user's current living situation. For example, the recording unit can provide a simplified recording method when the user is busy. For example, it can provide a detailed recording method when the user is relaxed. The optimal recording method can be customized based on the user's current living situation. This makes it possible to record more appropriately by customizing the recording method based on the user's current living situation. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's current living situation data into the AI ​​and have the AI ​​perform the customization of the recording method.

[0089] The recording unit can estimate the user's emotions and determine the priority of recordings based on the estimated user emotions. For example, if the user is stressed, the recording unit will prioritize recording important information. For example, if the user is relaxed, the recording unit can prioritize recording detailed information. For example, if the user is in a hurry, the recording unit can prioritize recording information that is immediately needed. For example, if the user is stressed, the recording unit will prioritize recording important information. If the user is relaxed, it can prioritize recording detailed information. If the user is in a hurry, it can prioritize recording information that is immediately needed. This allows for prioritizing the recording of more important information by determining the priority of recordings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of the recordings.

[0090] The recording unit can select the optimal recording method by considering the user's geographical location information during recording. For example, if the user is in a specific region, the recording unit will prioritize recording information related to that region. For example, the recording unit can select the optimal recording method based on the user's geographical location information. For example, if the user is on the move, the recording unit can prioritize recording information related to the destination. For example, if the user is in a specific region, the recording unit will prioritize recording information related to that region. For example, the recording unit can select the optimal recording method based on the user's geographical location information. If the user is on the move, it can prioritize recording information related to the destination. This allows the optimal recording method to be selected by considering the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's geographical location information data into AI and have AI select the optimal recording method.

[0091] The recording unit can analyze the user's social media activity and suggest recording methods at the time of recording. For example, the recording unit can suggest the optimal recording method based on information shared by the user on social media. For example, the recording unit can suggest a method for recording relevant information based on the user's social media activity. For example, the recording unit can suggest a method for recording based on information the user has shown interest in on social media. For example, the recording unit can suggest the optimal recording method based on information shared by the user on social media. For example, it can suggest a method for recording relevant information based on the user's social media activity. For example, it can suggest a method for recording based on information the user has shown interest in on social media. In this way, by analyzing the user's social media activity, the optimal recording method can be suggested. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's social media activity data into AI and have AI perform the task of suggesting recording methods.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. This allows for more appropriate suggestions by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 an AI and have the AI ​​adjust the way suggestions are presented.

[0093] The proposal unit can adjust the level of detail of its proposals based on the importance of the financial items. For example, the proposal unit can provide detailed proposals for high-importance financial items. For example, it can provide simplified proposals for low-importance financial items. The proposal unit can adjust the level of detail of its proposals based on the importance of the financial items. For example, the proposal unit can provide detailed proposals for high-importance financial items. For example, it can provide simplified proposals for low-importance financial items. The level of detail of the proposals can be adjusted based on the importance of the financial items. This allows for more efficient proposals by adjusting the level of detail of the proposals based on the importance of the financial items. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input financial importance data into AI and have AI perform the adjustment of the level of detail of the proposals.

[0094] The proposal unit can apply different proposal algorithms depending on the income and expenditure categories when making a proposal. For example, the proposal unit can apply an income improvement proposal algorithm to income. For example, the proposal unit can apply an expenditure reduction proposal algorithm to expenses. For example, the proposal unit can apply an investment proposal algorithm to investments. For example, the proposal unit can apply an income improvement proposal algorithm to income. For expenses, it can apply an expenditure reduction proposal algorithm. For investments, it can apply an investment proposal algorithm. This makes it possible to make more accurate proposals by applying different proposal algorithms depending on the income and expenditure categories. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input income and expenditure category data into AI and have the AI ​​execute the application of different proposal algorithms.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can make short, to-the-point suggestions. For example, if the user is relaxed, the suggestion unit can make detailed suggestions. For example, if the user is in a hurry, the suggestion unit can make quick suggestions. For example, if the user is stressed, the suggestion unit can make short, to-the-point suggestions. If the user is relaxed, it can make detailed suggestions. If the user is in a hurry, it can make quick suggestions. This allows for more appropriate suggestions by adjusting the length of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the length of the suggestions.

[0096] The proposal department can determine the priority of proposals based on the movement of income and expenses when making a proposal. For example, the proposal department will prioritize proposals when the movement of income and expenses is large. For example, the proposal department can make simplified proposals when the movement of income and expenses is small. The proposal department can determine the priority of proposals based on the movement of income and expenses. For example, the proposal department will prioritize proposals when the movement of income and expenses is large. For example, it can make simplified proposals when the movement of income and expenses is small. The proposal department can determine the priority of proposals based on the movement of income and expenses. This makes it possible to make efficient proposals by determining the priority of proposals based on the movement of income and expenses. 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 income and expense data into AI and have AI perform the determination of proposal priority.

[0097] The proposal unit can adjust the order of proposals based on the relevance of income and expenses when making proposals. For example, the proposal unit can prioritize proposals for highly relevant income and expenses. For example, the proposal unit can make simplified proposals for less relevant income and expenses. The proposal unit can adjust the order of proposals based on the relevance of income and expenses. For example, the proposal unit can prioritize proposals for highly relevant income and expenses. For example, it can make simplified proposals for less relevant income and expenses. The order of proposals can be adjusted based on the relevance of income and expenses. This makes it possible to make efficient proposals by adjusting the order of proposals based on the relevance of 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 the relevance data of income and expenses into AI and have AI perform the adjustment of the order of proposals.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can collect users' health data and make suggestions for improving their spending based on that data. For example, the unit can collect users' step counts and exercise levels and suggest a review of their spending based on their health status. It can also collect users' dietary data and suggest spending suggestions to maintain a healthy diet. Furthermore, the unit can collect users' sleep data and suggest spending suggestions to improve sleep quality. In this way, by making suggestions for improving spending based on the user's health status, it can support a healthier lifestyle.

[0100] The analytics department can analyze users' hobbies and preferences and propose optimizations for their hobby-related spending. For example, if a user enjoys watching movies, the analytics department can propose optimizations for movie-related spending. Similarly, if a user enjoys traveling, the analytics department can propose optimizations for travel-related spending. Furthermore, if a user enjoys music, the analytics department can propose optimizations for music-related spending. By proposing spending optimizations based on users' hobbies and preferences, the department can support them in leading more fulfilling hobby lives.

[0101] The suggestion function can estimate the user's emotions and, based on those emotions, suggest spending recommendations for stress reduction. For example, if the user is feeling stressed, the suggestion function can suggest spending related to relaxation or hobbies. If the user is relaxed, the suggestion function can also suggest spending related to saving or investing for the future. Furthermore, if the user is in a hurry, the suggestion function can suggest spending that will have an immediate effect. In this way, by providing spending recommendations based on the user's emotions, it can support more appropriate spending management.

[0102] The recording unit can estimate the user's emotions and adjust the recording method based on those emotions. For example, if the user is stressed, the recording unit can provide a simple recording method. If the user is relaxed, it can provide a detailed recording method. If the user is in a hurry, it can provide a method that allows for quick recording. By adjusting the recording method based on the user's emotions, more appropriate recording becomes possible.

[0103] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and highly visible suggestions. If the user is relaxed, it can present detailed suggestions. If the user is in a hurry, it can present suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made.

[0104] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. Based on the user's geographical location, it can filter and collect highly relevant information. If the user is on the move, it can prioritize the collection of information related to their destination. In this way, by considering the user's geographical location, highly relevant information can be prioritized.

[0105] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, it can perform a detailed analysis on highly important information and a simplified analysis on less important information. By adjusting the level of detail of the analysis based on the importance of the collected information, efficient analysis becomes possible.

[0106] The proposal section can apply different proposal algorithms depending on the income and expenditure category. For example, an income improvement proposal algorithm can be applied to income, an expenditure reduction proposal algorithm can be applied to expenses, and an investment proposal algorithm can be applied to investments. By applying different proposal algorithms depending on the income and expenditure category, more accurate proposals can be made.

[0107] The recording unit can analyze a user's social media activity and suggest recording methods. For example, the recording unit can suggest the optimal recording method based on information shared by the user on social media. It can suggest methods for recording relevant information based on the user's social media activity. It can suggest recording methods based on information the user has shown interest in on social media. In this way, by analyzing the user's social media activity, the optimal recording method can be suggested.

[0108] The proposal department can determine the priority of proposals based on the movement of revenue and expenses. For example, if the movement of revenue and expenses is large, proposals will be given priority. If the movement of revenue and expenses is small, simplified proposals can be made. By determining the priority of proposals based on the movement of revenue and expenses, efficient proposals can be made.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit aggregates all money flows. For example, it collects information on salary deposits, purchases, remittances, splitting bills, and investments, and reflects this information in the electronic payment app. The data collection unit automatically retrieves salary deposit information from bank accounts, purchase expenditure information from credit and debit card transaction history, and remittance and bill-splitting information from transaction history within the electronic payment app. Step 2: The analysis unit analyzes user behavior based on the information collected by the collection unit. For example, it uses acceleration sensors and GPS to calculate travel costs and analyzes user behavior patterns to identify the factors that cause costs. Step 3: The recording unit records costs incurred based on the information analyzed by the analysis unit. For example, it records travel costs and shopping expenses on a daily or monthly basis, and categorizes them so that users can understand cost fluctuations and the breakdown of their spending. Step 4: The proposal department makes suggestions for improving income and expenses based on the information recorded by the recording department. For example, it provides investment and savings advice based on daily income and expense movements and proposes the optimal investment destinations and savings methods based on the user's financial situation.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, recording unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14, which automatically collects salary deposit and shopping expenditure information and reflects it in the electronic payment application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which calculates travel costs using an acceleration sensor and GPS. The recording unit is implemented by the specific processing unit 290 of the data processing unit 12, which records costs based on user behavior. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes suggestions for improving income and expenses. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, recording unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214, which automatically collects salary deposit and shopping expenditure information and reflects it in the electronic payment application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which calculates travel costs using an acceleration sensor and GPS. The recording unit is implemented by the specific processing unit 290 of the data processing unit 12, which records costs based on the user's behavior. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes suggestions for improving income and expenses. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The 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.

[0135] 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.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 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.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the 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.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, recording unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314, which automatically collects salary deposit and shopping expenditure information and reflects it in the electronic payment application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which calculates travel costs using an acceleration sensor and GPS. The recording unit is implemented by the specific processing unit 290 of the data processing unit 12, which records costs based on user behavior. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes suggestions for improving income and expenses. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The 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.

[0151] 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.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, recording unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414, which automatically collects information on salary deposits and shopping expenses and reflects it in an electronic payment application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which calculates travel costs using an acceleration sensor and GPS. The recording unit is implemented by the specific processing unit 290 of the data processing unit 12, which records costs based on user behavior. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes suggestions for improving income and expenses. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The collection department, which aggregates all money flows, An analysis unit analyzes user behavior based on the information collected by the aforementioned collection unit, A recording unit records the costs incurred based on the information analyzed by the aforementioned analysis unit, The system includes a proposal unit that makes suggestions for improving revenue and expenditure based on the information recorded by the recording unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information on salary deposits, shopping, money transfers, splitting bills, investments, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Calculate travel costs using acceleration sensors and GPS. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We provide investment and savings advice based on your daily income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recording unit is Record costs based on user behavior. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes the user's past data collection history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recording unit is The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recording unit is During recording, the system analyzes the user's past behavior to select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recording unit is During recording, the recording method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recording unit is The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recording unit is During recording, the optimal recording method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recording unit is During recording, we analyze the user's social media activity and suggest recording methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the revenue and expenditure. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the revenue and expenditure category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making proposals, prioritize them based on the flow of income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to income and expenses. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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. The collection department, which aggregates all money flows, An analysis unit analyzes user behavior based on the information collected by the aforementioned collection unit, A recording unit records the costs incurred based on the information analyzed by the aforementioned analysis unit, The system includes a proposal unit that makes suggestions for improving revenue and expenditure based on the information recorded by the recording unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information on salary deposits, shopping, money transfers, splitting bills, investments, etc. The system according to feature 1.

3. The aforementioned analysis unit is Calculate travel costs using acceleration sensors and GPS. The system according to feature 1.

4. The aforementioned proposal section is, We provide investment and savings advice based on your daily income and expenses. The system according to feature 1.

5. The recording unit is, Record costs based on user behavior. The system according to feature 1.

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

7. The aforementioned collection unit is During data collection, the system analyzes the user's past data collection history to select the most suitable collection method. The system according to feature 1.

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

9. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

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