system

Abstract

The system according to the embodiment aims to efficiently manage daily payment information, review budgets, and set goals. [Solution] The system according to the embodiment comprises a collection unit, a setting unit, an analysis unit, an advice unit, an introduction unit, and a points unit. The collection unit collects payment information. The setting unit reviews budgets and sets goals based on the information collected by the collection unit. The analysis unit analyzes expenditures based on the budget set by the setting unit. The advice unit provides advice to users based on the analysis results obtained by the analysis unit. The introduction unit recommends consulting with a financial planner based on the advice provided by the advice unit. The points unit awards points based on the advice provided by the advice unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of making it difficult to efficiently manage daily payment information, review budgets, and set goals.

[0005] The system according to the embodiment aims to efficiently manage daily payment information, review budgets, and set goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a setting unit, an analysis unit, an advice unit, an introduction unit, and a points unit. The collection unit collects payment information. The setting unit reviews budgets and sets goals based on the information collected by the collection unit. The analysis unit analyzes expenditures based on the budget set by the setting unit. The advice unit provides advice to the user based on the analysis results obtained by the analysis unit. The introduction unit recommends consulting with a financial planner based on the advice provided by the advice unit. The points unit awards points based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to the embodiment efficiently manages daily payment information, and allows for budget review and goal setting. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) An expense management system according to an embodiment of the present invention automatically manages daily payments and provides advice to users. This expense management system automatically updates payment information from electronic payment systems and coordinates with financial institutions to track spending. Next, a generation AI periodically reviews the budget and sets goals, identifying savings and reduction opportunities. Furthermore, it analyzes spending, manages budgets by category, and automatically provides advice to users. This advice may include referrals to financial planners. Users are also awarded points based on the use of specific services. For example, payment information from electronic payment systems is automatically updated. When a user makes a payment through an electronic payment system, that information is automatically imported into the system. Next, spending is tracked in coordination with financial institutions. By linking the user's bank account and credit card information to the system, all spending is centrally managed. Next, the generation AI periodically reviews the budget and sets goals. Based on monthly income and expenses, the system automatically sets the next month's budget. It also identifies savings and reduction opportunities according to the user's goals. It suggests areas where savings can be achieved by category, such as food and utility bills. In addition, the system analyzes expenses and manages budgets by category. It divides users' expenses into categories such as food, transportation, and entertainment, and sets a budget for each category. The generation AI then analyzes spending trends and automatically provides advice to the user. If food expenses are likely to exceed the budget, the system provides specific advice on how to save money. This advice may include referrals to consult with a financial planner. If the user is planning a large expense, the system suggests consulting with a financial planner. Users are also awarded points based on their use of specific services. Additional points can be earned by using specific services. This allows users to efficiently manage their daily expenses and receive specific advice on saving money and achieving their goals. This allows the expense management system to efficiently manage users' expenses and provide specific advice on saving money and achieving their goals.

[0029] An expense management system according to an embodiment includes a collection unit, a setting unit, an analysis unit, an advice unit, an introduction unit, and a points unit. The collection unit collects payment information. For example, the collection unit automatically collects payment information from an electronic payment system. The collection unit can also work with financial institutions to understand expenses. For example, the collection unit centrally manages all expenses by linking the user's bank account and credit card information to the system. The setting unit reviews the budget and sets goals based on the information collected by the collection unit. For example, the setting unit automatically sets the next month's budget based on the user's monthly income and expenses. The setting unit can also identify saving techniques and reduction points according to the user's goals. For example, the setting unit suggests areas where savings can be made by category, such as food expenses and utility bills. The analysis unit analyzes expenses based on the budget set by the setting unit. For example, the analysis unit divides the user's expenses by category and sets a budget for each category. The analysis unit can also analyze spending trends and automatically provide advice to the user. For example, the analysis unit provides specific advice on saving money if food expenses are likely to exceed budget. The advice unit provides advice to the user based on the analysis results obtained by the analysis unit. For example, the advice unit suggests consulting a financial planner if the user is planning a large expenditure. The introduction unit introduces consulting a financial planner based on the advice provided by the advice unit. For example, the introduction unit suggests consulting a financial planner if the user is planning a large expenditure. The points unit awards points based on the advice provided by the advice unit. The points unit awards additional points by using a specific service, for example. As a result, the expenditure management system according to the embodiment can consistently perform processes from collecting payment information to setting a budget, analyzing expenditures, providing advice, introducing a financial planner, and awarding points.

[0030] The collection unit can automatically collect payment information for the electronic payment system. For example, the collection unit automatically collects payment information for the electronic payment system. When a user makes a payment through the electronic payment system, the collection unit automatically imports the information into the system. This eliminates the need for manual input. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input payment information for the electronic payment system into the generation AI and have the generation AI collect the payment information.

[0031] The collection unit can grasp expenditures in cooperation with financial institutions. The collection unit grasps expenditures, for example, in cooperation with financial institutions. The collection unit centrally manages all expenditures by linking the user's bank account and credit card information to the system. In this way, by linking with financial institutions, all expenditures can be centrally managed. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the API of a financial institution into the generation AI and have the generation AI grasp expenditures.

[0032] The setting unit can automatically set the next month's budget based on monthly income and expenses. The setting unit, for example, automatically sets the next month's budget based on monthly income and expenses. The setting unit collects the user's income and expense data and sets the next month's budget based on that data. This automatically sets the next month's budget based on monthly income and expenses, thereby reducing the user's effort. Some or all of the above-mentioned processing in the setting unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input income and expense data into the generation AI and have the generation AI set the budget.

[0033] The setting unit can identify saving methods and reduction points according to the user's goals. For example, the setting unit identifies saving techniques and reduction points according to the user's goals. The setting unit collects the user's goals and identifies saving techniques and reduction points based on the collected goals. This makes it possible to provide specific saving methods by identifying saving techniques and reduction points according to the user's goals. Some or all of the above-described processing in the setting unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the setting unit can input the user's goals into the generation AI and cause the generation AI to identify saving techniques and reduction points.

[0034] The analysis unit can divide the user's expenses into categories and set a budget for each category. For example, the analysis unit divides the user's expenses into categories and sets a budget for each category. The analysis unit collects the user's expenditure data and divides it into categories such as food expenses, transportation expenses, and entertainment expenses. Then, it sets a budget for each category. This enables more detailed expenditure management by dividing expenses into categories and setting budgets. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input expenditure data into the generation AI and have the generation AI set budgets for each category.

[0035] The analysis unit can analyze spending trends and automatically provide advice to the user. For example, the analysis unit analyzes spending trends and automatically provides advice to the user. The analysis unit collects the user's spending data, analyzes it to understand spending trends, and provides appropriate advice to the user. In this way, by analyzing spending trends, appropriate advice can be provided to the user. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input spending data into the generation AI and have the generation AI perform spending trend analysis and provide advice.

[0036] The advice unit can provide specific advice for saving money when food expenses are likely to exceed the budget. For example, the advice unit provides specific advice for saving money when food expenses are likely to exceed the budget. The advice unit collects the user's food expense data and provides specific advice for saving money when the data is likely to exceed the budget. By providing specific advice when food expenses are likely to exceed the budget, the user can take appropriate saving actions. Some or all of the above-mentioned processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input food expense data into the generation AI and have the generation AI execute advice for saving money.

[0037] The introduction unit can suggest consulting a financial planner if the user is planning a large expenditure. For example, the introduction unit suggests consulting a financial planner if the user is planning a large expenditure. The introduction unit collects the user's spending plan and suggests consulting a financial planner if the expenditure is large. By suggesting consulting a financial planner if a large expenditure is planned, the user can receive appropriate advice. Some or all of the above-mentioned processing in the introduction unit may be performed using or without the generation AI. For example, the introduction unit can input spending plan data into the generation AI and cause the generation AI to execute a suggestion to consult a financial planner.

[0038] The point unit can award additional points by using a specific service. For example, the point unit awards additional points by using a specific service. The point unit awards additional points when a user uses a specific service. This can encourage users to use the service by awarding additional points by using a specific service. Some or all of the above-mentioned processing in the point unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the point unit can input service usage data into the generation AI and have the generation AI award points.

[0039] The collection unit can analyze the user's past payment history and select an appropriate collection method. For example, the collection unit prioritizes collection of payment methods that the user has frequently used in the past. The collection unit can also perform collection during a specific time period based on the user's past payment history. The collection unit can also analyze the user's past payment history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past payment history. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input past payment history data into the generation AI and have the generation AI select the collection method.

[0040] When collecting payment information, the collection unit can perform filtering based on the user's current living situation and areas of interest. For example, the collection unit collects only necessary payment information based on the user's current living situation. The collection unit can also prioritize collecting relevant payment information based on the user's areas of interest. The collection unit can also filter unnecessary payment information taking into account the user's living situation and areas of interest. In this way, filtering based on the living situation and areas of interest can collect only necessary payment information. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input living situation and area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0041] When collecting payment information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting relevant payment information based on the user's current location. The collection unit can also collect region-specific payment information taking into account the user's geographical location information. The collection unit can also prioritize collecting the most relevant payment information based on the user's location information. In this way, by taking into account the geographical location information, highly relevant payment information can be preferentially collected. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to collect highly relevant information.

[0042] When collecting payment information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit analyzes the user's social media activity and collects related payment information. The collection unit can also collect payment information based on the user's social media interests. The collection unit can also collect the most relevant payment information based on the user's social media activity. In this way, related payment information can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input social media activity data into the generation AI and cause the generation AI to collect related information.

[0043] When setting a budget, the setting unit can set an appropriate budget by referring to the user's past income and expenditure data. The setting unit, for example, sets an optimal budget based on the user's past income and expenditure data. The setting unit can also analyze the user's past income and expenditure data to improve the accuracy of budget setting. The setting unit can also adjust the budget setting criteria by referring to the user's past income and expenditure data. In this way, an optimal budget can be set by referring to the past income and expenditure data. Some or all of the above-mentioned processing in the setting unit may be performed using or without the generation AI. For example, the setting unit can input past income and expenditure data into the generation AI and have the generation AI execute budget setting.

[0044] When setting a budget, the setting unit can apply different budget setting algorithms depending on the user's living situation and goals. For example, the setting unit applies an optimal budget setting algorithm depending on the user's living situation. The setting unit can also apply different budget setting algorithms based on the user's goals. The setting unit can also select an optimal budget setting algorithm taking into account the user's living situation and goals. This enables optimal budget setting depending on the living situation and goals. Some or all of the above-mentioned processing in the setting unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input living situation and goal data into the generation AI and cause the generation AI to apply the budget setting algorithm.

[0045] The setting unit can suggest region-specific money-saving tips based on the user's geographical location information when setting a budget. The setting unit can, for example, suggest region-specific money-saving tips based on the user's geographical location information. The setting unit can also provide optimal money-saving tips by taking the user's location information into consideration. The setting unit can also suggest region-specific money-saving points by referring to the user's geographical location information. In this way, region-specific money-saving tips can be suggested by taking the geographical location information into consideration. Some or all of the above-described processing in the setting unit may be performed using or without the generation AI. For example, the setting unit can input geographical location information data to the generation AI and cause the generation AI to suggest region-specific money-saving tips.

[0046] The setting unit can analyze the user's social media activity and set relevant goals when setting a budget. For example, the setting unit can analyze the user's social media activity and set relevant goals. The setting unit can also set goals based on the user's social media interests. The setting unit can also set the most relevant goals based on the user's social media activity. This allows relevant goals to be set by analyzing the social media activity. Some or all of the above-mentioned processing in the setting unit can be performed using or without the generation AI. For example, the setting unit can input social media activity data into the generation AI and have the generation AI perform goal setting.

[0047] When analyzing expenditures, the analysis unit can improve the accuracy of the analysis by referring to the user's past expenditure data. The analysis unit improves the accuracy of the analysis, for example, based on the user's past expenditure data. The analysis unit can also analyze the user's past expenditure data to understand spending trends. The analysis unit can also adjust the analysis criteria by referring to the user's past expenditure data. In this way, the accuracy of the analysis can be improved by referring to the past expenditure data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past expenditure data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0048] When analyzing expenses, the analysis unit can apply different analysis methods depending on the user's lifestyle situation and goals. For example, the analysis unit applies the optimal analysis method depending on the user's lifestyle situation. The analysis unit can also apply different analysis methods based on the user's goals. The analysis unit can also select the optimal analysis method taking into account the user's lifestyle situation and goals. This makes it possible to apply the optimal analysis method depending on the lifestyle situation and goals. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input lifestyle situation and goal data into the generation AI and have the generation AI apply the analysis method.

[0049] During expenditure analysis, the analysis unit can analyze region-specific spending trends based on the user's geographical location information. For example, the analysis unit analyzes region-specific spending trends based on the user's geographical location information. The analysis unit can also grasp region-specific spending trends by taking the user's location information into account. The analysis unit can also analyze region-specific spending trends by referring to the user's geographical location information. In this way, region-specific spending trends can be analyzed by taking the geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input geographical location information data into the generation AI and cause the generation AI to perform an analysis of region-specific spending trends.

[0050] During expenditure analysis, the analysis unit can analyze the user's social media activity and identify relevant expenditure trends. For example, the analysis unit analyzes the user's social media activity and identifies relevant expenditure trends. The analysis unit can also analyze expenditure trends based on the user's social media interests. The analysis unit can also identify the most relevant expenditure trends based on the user's social media activity. In this way, relevant expenditure trends can be identified by analyzing the social media activity. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input social media activity data into the generation AI and have the generation AI identify expenditure trends.

[0051] When providing advice, the advice unit can provide optimal advice by referring to the user's past expenditure data. The advice unit, for example, provides optimal advice based on the user's past expenditure data. The advice unit can also analyze the user's past expenditure data and provide advice based on spending trends. The advice unit can also improve the accuracy of advice by referring to the user's past expenditure data. In this way, optimal advice can be provided by referring to the past expenditure data. Some or all of the above-mentioned processing in the advice unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input past expenditure data into the generation AI and have the generation AI provide advice.

[0052] When providing advice, the advice unit can apply different advice algorithms depending on the user's living situation and goals. The advice unit applies the optimal advice algorithm depending on, for example, the user's living situation. The advice unit can also apply different advice algorithms based on the user's goals. The advice unit can also select the optimal advice algorithm taking into account the user's living situation and goals. This makes it possible to provide optimal advice depending on the living situation and goals. Some or all of the above-mentioned processing in the advice unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input living situation and goal data into the generation AI and cause the generation AI to apply the advice algorithm.

[0053] When providing advice, the advice unit can provide region-specific advice based on the user's geographical location information. The advice unit can provide region-specific advice based on, for example, the user's geographical location information. The advice unit can also provide optimal advice by taking the user's location information into consideration. The advice unit can also provide region-specific advice by referring to the user's geographical location information. In this way, region-specific advice can be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the advice unit can be performed using or without the generation AI. For example, the advice unit can input geographical location information data to the generation AI and cause the generation AI to provide region-specific advice.

[0054] When providing advice, the advice unit can analyze the user's social media activity and provide relevant advice. For example, the advice unit can analyze the user's social media activity and provide relevant advice. The advice unit can also provide advice based on the user's social media interests. The advice unit can also provide the most relevant advice based on the user's social media activity. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input social media activity data into the generation AI and have the generation AI provide the advice.

[0055] When introducing a financial planner, the introduction unit can introduce the most suitable planner by referring to the user's past expenditure data. The introduction unit, for example, introduces the most suitable planner based on the user's past expenditure data. The introduction unit can also analyze the user's past expenditure data and introduce a planner based on spending trends. The introduction unit can also improve the accuracy of planner introductions by referring to the user's past expenditure data. In this way, the most suitable planner can be introduced by referring to the past expenditure data. Some or all of the above-mentioned processing in the introduction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the introduction unit can input past expenditure data into the generation AI and have the generation AI perform planner introductions.

[0056] When introducing a financial planner, the introduction unit can introduce a planner specific to a region based on the user's geographical location information. The introduction unit, for example, introduces a planner specific to a region based on the user's geographical location information. The introduction unit can also introduce the most suitable planner by taking the user's location information into consideration. The introduction unit can also introduce a planner specific to a region by referring to the user's geographical location information. In this way, a planner specific to a region can be introduced by taking the geographical location information into consideration. Some or all of the above-described processing in the introduction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the introduction unit can input geographical location information data into the generation AI and cause the generation AI to introduce a planner specific to a region.

[0057] When awarding points, the point unit can select the optimal point awarding method by referring to the user's past expenditure data. The point unit, for example, selects the optimal point awarding method based on the user's past expenditure data. The point unit can also analyze the user's past expenditure data to improve the accuracy of point awarding. The point unit can also adjust the point awarding criteria by referring to the user's past expenditure data. This allows the optimal point awarding method to be selected by referring to the past expenditure data. Some or all of the above-mentioned processing in the point unit may be performed using or without the generation AI. For example, the point unit can input past expenditure data into the generation AI and have the generation AI select the point awarding method.

[0058] When awarding points, the point unit can select a region-specific point awarding method based on the user's geographical location information. For example, the point unit selects a region-specific point awarding method based on the user's geographical location information. The point unit can also provide an optimal point awarding method by taking the user's location information into consideration. The point unit can also select a region-specific point awarding method by referring to the user's geographical location information. In this way, a region-specific point awarding method can be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the point unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the point unit can input geographical location information data into the generation AI and cause the generation AI to select a region-specific point awarding method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can analyze a user's purchasing history and detect specific patterns. For example, if a user tends to purchase products from a specific category on a specific day of the week or at a specific time of day, the collection unit can detect that pattern and automatically reflect that information the next time the user purchases. Also, if a user frequently purchases from a specific store, the collection unit can preferentially collect information about special offers and discounts offered by that store. Furthermore, the collection unit can predict future purchases based on the user's purchasing history and collect necessary payment information in advance. This allows payment information to be collected efficiently based on the user's purchasing patterns.

[0061] The setting unit can refer to the user's health data and set a budget according to the user's health condition. For example, the setting unit can set a medical expense budget based on the user's health checkup results. The setting unit can also set a budget for maintaining health based on the user's exercise data. Furthermore, the setting unit can refer to the user's dietary data and set a food budget for maintaining a healthy diet. This makes it possible to set a budget according to the user's health condition, allowing for both health management and expense management.

[0062] The analysis unit can evaluate the environmental impact based on the user's expenditure data. For example, the analysis unit can calculate carbon dioxide emissions from the user's expenditure data and evaluate the environmental impact. The analysis unit can also evaluate energy consumption based on the user's expenditure data and suggest improvements to energy efficiency. Furthermore, the analysis unit can evaluate the percentage of recyclable products purchased based on the user's expenditure data and provide advice to promote environmentally friendly purchasing behavior. This makes it possible to evaluate the environmental impact based on the user's expenditure data and support sustainable lifestyles.

[0063] The advice unit can suggest optimization of spending based on the user's hobbies and interests. For example, if the user is interested in music, the advice unit can provide advice for optimizing music-related spending. Also, if the user's hobby is traveling, the advice unit can provide advice for optimizing travel-related spending. Furthermore, if the user is interested in sports, the advice unit can provide advice for optimizing sports-related spending. In this way, it is possible to suggest optimization of spending based on the user's hobbies and interests and support a more fulfilling life.

[0064] The introduction unit can introduce an appropriate financial planner based on the user's occupation or career. For example, if the user is a freelancer, the introduction unit can introduce a financial planner for freelancers. Also, if the user is a corporate manager, the introduction unit can introduce a financial planner for managers. Furthermore, if the user is a new graduate, the introduction unit can introduce a financial planner for new graduates. This allows the user to be introduced to an appropriate financial planner based on the user's occupation or career, and supports financial management tailored to their career.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects payment information. For example, the collection unit can automatically collect payment information from electronic payment systems and keep track of expenditures in conjunction with financial institutions. Specifically, by linking the user's bank account and credit card information to the system, all expenditures can be managed centrally. Step 2: The setting unit reviews the budget and sets goals based on the information collected by the collection unit. For example, the setting unit automatically sets the next month's budget based on monthly income and expenses, and identifies saving techniques and reduction points according to the user's goals. Specifically, it suggests areas where savings can be made, categorized by food expenses, utility bills, etc. Step 3: The analysis unit analyzes expenses based on the budget set by the setting unit. For example, the analysis unit divides the user's expenses into categories and sets a budget for each category. Furthermore, the analysis unit can analyze spending trends and automatically provide advice to the user. Specifically, if food expenses are likely to exceed the budget, the analysis unit provides specific advice on how to save money. Step 4: The advice unit provides advice to the user based on the analysis results obtained by the analysis unit. For example, if the user is planning a large expenditure, the advice unit suggests consulting a financial planner. Step 5: The introduction unit introduces the user to a financial planner based on the advice provided by the advice unit. For example, the introduction unit suggests consulting a financial planner if the user is planning a large expenditure. Step 6: The point unit awards points based on the advice provided by the advice unit. The point unit awards additional points, for example, by using a specific service.

[0067] (Example 2) An expense management system according to an embodiment of the present invention automatically manages daily payments and provides advice to users. This expense management system automatically updates payment information from electronic payment systems and coordinates with financial institutions to track spending. Next, a generation AI periodically reviews the budget and sets goals, identifying savings and reduction opportunities. Furthermore, it analyzes spending, manages budgets by category, and automatically provides advice to users. This advice may include referrals to financial planners. Users are also awarded points based on the use of specific services. For example, payment information from electronic payment systems is automatically updated. When a user makes a payment through an electronic payment system, that information is automatically imported into the system. Next, spending is tracked in coordination with financial institutions. By linking the user's bank account and credit card information to the system, all spending is centrally managed. Next, the generation AI periodically reviews the budget and sets goals. Based on monthly income and expenses, the system automatically sets the next month's budget. It also identifies savings and reduction opportunities according to the user's goals. It suggests areas where savings can be achieved by category, such as food and utility bills. In addition, the system analyzes expenses and manages budgets by category. It divides users' expenses into categories such as food, transportation, and entertainment, and sets a budget for each category. The generation AI then analyzes spending trends and automatically provides advice to the user. If food expenses are likely to exceed the budget, the system provides specific advice on how to save money. This advice may include referrals to consult with a financial planner. If the user is planning a large expense, the system suggests consulting with a financial planner. Users are also awarded points based on their use of specific services. Additional points can be earned by using specific services. This allows users to efficiently manage their daily expenses and receive specific advice on saving money and achieving their goals. This allows the expense management system to efficiently manage users' expenses and provide specific advice on saving money and achieving their goals.

[0068] An expense management system according to an embodiment includes a collection unit, a setting unit, an analysis unit, an advice unit, an introduction unit, and a points unit. The collection unit collects payment information. For example, the collection unit automatically collects payment information from an electronic payment system. The collection unit can also work with financial institutions to understand expenses. For example, the collection unit centrally manages all expenses by linking the user's bank account and credit card information to the system. The setting unit reviews the budget and sets goals based on the information collected by the collection unit. For example, the setting unit automatically sets the next month's budget based on the user's monthly income and expenses. The setting unit can also identify saving techniques and reduction points according to the user's goals. For example, the setting unit suggests areas where savings can be made by category, such as food expenses and utility bills. The analysis unit analyzes expenses based on the budget set by the setting unit. For example, the analysis unit divides the user's expenses by category and sets a budget for each category. The analysis unit can also analyze spending trends and automatically provide advice to the user. For example, the analysis unit provides specific advice on saving money if food expenses are likely to exceed budget. The advice unit provides advice to the user based on the analysis results obtained by the analysis unit. For example, the advice unit suggests consulting a financial planner if the user is planning a large expenditure. The introduction unit introduces consulting a financial planner based on the advice provided by the advice unit. For example, the introduction unit suggests consulting a financial planner if the user is planning a large expenditure. The points unit awards points based on the advice provided by the advice unit. The points unit awards additional points by using a specific service, for example. As a result, the expenditure management system according to the embodiment can consistently perform processes from collecting payment information to setting a budget, analyzing expenditures, providing advice, introducing a financial planner, and awarding points.

[0069] The collection unit can automatically collect payment information for the electronic payment system. For example, the collection unit automatically collects payment information for the electronic payment system. When a user makes a payment through the electronic payment system, the collection unit automatically imports the information into the system. This eliminates the need for manual input. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input payment information for the electronic payment system into the generation AI and have the generation AI collect the payment information.

[0070] The collection unit can grasp expenditures in cooperation with financial institutions. The collection unit grasps expenditures, for example, in cooperation with financial institutions. The collection unit centrally manages all expenditures by linking the user's bank account and credit card information to the system. In this way, by linking with financial institutions, all expenditures can be centrally managed. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the API of a financial institution into the generation AI and have the generation AI grasp expenditures.

[0071] The setting unit can automatically set the next month's budget based on monthly income and expenses. The setting unit, for example, automatically sets the next month's budget based on monthly income and expenses. The setting unit collects the user's income and expense data and sets the next month's budget based on that data. This automatically sets the next month's budget based on monthly income and expenses, thereby reducing the user's effort. Some or all of the above-mentioned processing in the setting unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input income and expense data into the generation AI and have the generation AI set the budget.

[0072] The setting unit can identify saving methods and reduction points according to the user's goals. For example, the setting unit identifies saving techniques and reduction points according to the user's goals. The setting unit collects the user's goals and identifies saving techniques and reduction points based on the collected goals. This makes it possible to provide specific saving methods by identifying saving techniques and reduction points according to the user's goals. Some or all of the above-described processing in the setting unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the setting unit can input the user's goals into the generation AI and cause the generation AI to identify saving techniques and reduction points.

[0073] The analysis unit can divide the user's expenses into categories and set a budget for each category. For example, the analysis unit divides the user's expenses into categories and sets a budget for each category. The analysis unit collects the user's expenditure data and divides it into categories such as food expenses, transportation expenses, and entertainment expenses. Then, it sets a budget for each category. This enables more detailed expenditure management by dividing expenses into categories and setting budgets. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input expenditure data into the generation AI and have the generation AI set budgets for each category.

[0074] The analysis unit can analyze spending trends and automatically provide advice to the user. For example, the analysis unit analyzes spending trends and automatically provides advice to the user. The analysis unit collects the user's spending data, analyzes it to understand spending trends, and provides appropriate advice to the user. In this way, by analyzing spending trends, appropriate advice can be provided to the user. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input spending data into the generation AI and have the generation AI perform spending trend analysis and provide advice.

[0075] The advice unit can provide specific advice for saving money when food expenses are likely to exceed the budget. For example, the advice unit provides specific advice for saving money when food expenses are likely to exceed the budget. The advice unit collects the user's food expense data and provides specific advice for saving money when the data is likely to exceed the budget. By providing specific advice when food expenses are likely to exceed the budget, the user can take appropriate saving actions. Some or all of the above-mentioned processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input food expense data into the generation AI and have the generation AI execute advice for saving money.

[0076] The introduction unit can suggest consulting a financial planner if the user is planning a large expenditure. For example, the introduction unit suggests consulting a financial planner if the user is planning a large expenditure. The introduction unit collects the user's spending plan and suggests consulting a financial planner if the expenditure is large. By suggesting consulting a financial planner if a large expenditure is planned, the user can receive appropriate advice. Some or all of the above-mentioned processing in the introduction unit may be performed using or without the generation AI. For example, the introduction unit can input spending plan data into the generation AI and cause the generation AI to execute a suggestion to consult a financial planner.

[0077] The point unit can award additional points by using a specific service. For example, the point unit awards additional points by using a specific service. The point unit awards additional points when a user uses a specific service. This can encourage users to use the service by awarding additional points by using a specific service. Some or all of the above-mentioned processing in the point unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the point unit can input service usage data into the generation AI and have the generation AI award points.

[0078] The collection unit can estimate the user's emotions and adjust the timing of collecting payment information based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect payment information in a relaxed state. If the user is relaxed, the collection unit can also collect payment information immediately and process it quickly. If the user is in a hurry, the collection unit can also advance the collection timing and collect payment information quickly. This allows payment information to be collected at a more appropriate time by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the collection unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0079] The collection unit can analyze the user's past payment history and select an appropriate collection method. For example, the collection unit prioritizes collection of payment methods that the user has frequently used in the past. The collection unit can also perform collection during a specific time period based on the user's past payment history. The collection unit can also analyze the user's past payment history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past payment history. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input past payment history data into the generation AI and have the generation AI select the collection method.

[0080] When collecting payment information, the collection unit can perform filtering based on the user's current living situation and areas of interest. For example, the collection unit collects only necessary payment information based on the user's current living situation. The collection unit can also prioritize collecting relevant payment information based on the user's areas of interest. The collection unit can also filter unnecessary payment information taking into account the user's living situation and areas of interest. In this way, filtering based on the living situation and areas of interest can collect only necessary payment information. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input living situation and area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0081] The collection unit can estimate the user's emotions and determine the priority of the payment information to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting important payment information. When the user is relaxed, the collection unit can also collect all payment information equally. When the user is in a hurry, the collection unit can also prioritize collecting payment information with high urgency. This allows important information to be collected preferentially by prioritizing payment information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the collection unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the collected information.

[0082] When collecting payment information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting relevant payment information based on the user's current location. The collection unit can also collect region-specific payment information taking into account the user's geographical location information. The collection unit can also prioritize collecting the most relevant payment information based on the user's location information. In this way, by taking into account the geographical location information, highly relevant payment information can be preferentially collected. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to collect highly relevant information.

[0083] When collecting payment information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit analyzes the user's social media activity and collects related payment information. The collection unit can also collect payment information based on the user's social media interests. The collection unit can also collect the most relevant payment information based on the user's social media activity. In this way, related payment information can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input social media activity data into the generation AI and cause the generation AI to collect related information.

[0084] The setting unit can estimate the user's emotions and adjust the budget review and goal setting methods based on the estimated user emotions. For example, if the user is feeling stressed, the setting unit can provide a simple budget review and goal setting method. If the user is relaxed, the setting unit can also provide a detailed budget review and goal setting method. If the user is in a hurry, the setting unit can also provide a quick budget review and goal setting method. This enables more appropriate budget management by adjusting the budget review and goal setting methods based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the setting unit can be performed using or without the generation AI. For example, the setting unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the budget review and goal setting methods.

[0085] When setting a budget, the setting unit can set an appropriate budget by referring to the user's past income and expenditure data. The setting unit, for example, sets an optimal budget based on the user's past income and expenditure data. The setting unit can also analyze the user's past income and expenditure data to improve the accuracy of budget setting. The setting unit can also adjust the budget setting criteria by referring to the user's past income and expenditure data. In this way, an optimal budget can be set by referring to the past income and expenditure data. Some or all of the above-mentioned processing in the setting unit may be performed using or without the generation AI. For example, the setting unit can input past income and expenditure data into the generation AI and have the generation AI execute budget setting.

[0086] When setting a budget, the setting unit can apply different budget setting algorithms depending on the user's living situation and goals. For example, the setting unit applies an optimal budget setting algorithm depending on the user's living situation. The setting unit can also apply different budget setting algorithms based on the user's goals. The setting unit can also select an optimal budget setting algorithm taking into account the user's living situation and goals. This enables optimal budget setting depending on the living situation and goals. Some or all of the above-mentioned processing in the setting unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input living situation and goal data into the generation AI and cause the generation AI to apply the budget setting algorithm.

[0087] The setting unit can estimate the user's emotions and adjust the frequency of budget review based on the estimated user emotions. For example, the setting unit can reduce the frequency of budget review when the user is stressed. The setting unit can also increase the frequency of budget review when the user is relaxed. The setting unit can also adjust the frequency of budget review when the user is in a hurry to respond quickly. This enables more appropriate budget management by adjusting the frequency of budget review based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the setting unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the setting unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the frequency of budget review.

[0088] The setting unit can suggest region-specific money-saving tips based on the user's geographical location information when setting a budget. The setting unit can, for example, suggest region-specific money-saving tips based on the user's geographical location information. The setting unit can also provide optimal money-saving tips by taking the user's location information into consideration. The setting unit can also suggest region-specific money-saving points by referring to the user's geographical location information. In this way, region-specific money-saving tips can be suggested by taking the geographical location information into consideration. Some or all of the above-described processing in the setting unit may be performed using or without the generation AI. For example, the setting unit can input geographical location information data to the generation AI and cause the generation AI to suggest region-specific money-saving tips.

[0089] The setting unit can analyze the user's social media activity and set relevant goals when setting a budget. For example, the setting unit can analyze the user's social media activity and set relevant goals. The setting unit can also set goals based on the user's social media interests. The setting unit can also set the most relevant goals based on the user's social media activity. This allows relevant goals to be set by analyzing the social media activity. Some or all of the above-mentioned processing in the setting unit can be performed using or without the generation AI. For example, the setting unit can input social media activity data into the generation AI and have the generation AI perform goal setting.

[0090] The analysis unit can estimate the user's emotions and adjust the expenditure analysis criteria based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide simple expenditure analysis criteria. If the user is relaxed, the analysis unit can also provide detailed expenditure analysis criteria. If the user is in a hurry, the analysis unit can also provide criteria for a quick expenditure analysis. This enables more appropriate expenditure analysis by adjusting the expenditure analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the expenditure analysis criteria.

[0091] When analyzing expenditures, the analysis unit can improve the accuracy of the analysis by referring to the user's past expenditure data. The analysis unit improves the accuracy of the analysis, for example, based on the user's past expenditure data. The analysis unit can also analyze the user's past expenditure data to understand spending trends. The analysis unit can also adjust the analysis criteria by referring to the user's past expenditure data. In this way, the accuracy of the analysis can be improved by referring to the past expenditure data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past expenditure data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0092] When analyzing expenses, the analysis unit can apply different analysis methods depending on the user's lifestyle situation and goals. For example, the analysis unit applies the optimal analysis method depending on the user's lifestyle situation. The analysis unit can also apply different analysis methods based on the user's goals. The analysis unit can also select the optimal analysis method taking into account the user's lifestyle situation and goals. This makes it possible to apply the optimal analysis method depending on the lifestyle situation and goals. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input lifestyle situation and goal data into the generation AI and have the generation AI apply the analysis method.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the expenditure analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible result display method. If the user is relaxed, the analysis unit can also provide a detailed result display method. If the user is in a hurry, the analysis unit can also provide a result display method that focuses on the main points. This allows for more appropriate result display by adjusting the result display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the result display method.

[0094] During expenditure analysis, the analysis unit can analyze region-specific spending trends based on the user's geographical location information. For example, the analysis unit analyzes region-specific spending trends based on the user's geographical location information. The analysis unit can also grasp region-specific spending trends by taking the user's location information into account. The analysis unit can also analyze region-specific spending trends by referring to the user's geographical location information. In this way, region-specific spending trends can be analyzed by taking the geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input geographical location information data into the generation AI and cause the generation AI to perform an analysis of region-specific spending trends.

[0095] During expenditure analysis, the analysis unit can analyze the user's social media activity and identify relevant expenditure trends. For example, the analysis unit analyzes the user's social media activity and identifies relevant expenditure trends. The analysis unit can also analyze expenditure trends based on the user's social media interests. The analysis unit can also identify the most relevant expenditure trends based on the user's social media activity. In this way, relevant expenditure trends can be identified by analyzing the social media activity. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input social media activity data into the generation AI and have the generation AI identify expenditure trends.

[0096] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit can provide simple and easy-to-understand advice. If the user is relaxed, the advice unit can also provide detailed and specific advice. If the user is in a hurry, the advice unit can also provide advice that can be implemented quickly. This allows for more appropriate advice to be provided by adjusting the way the advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the advice unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0097] When providing advice, the advice unit can provide optimal advice by referring to the user's past expenditure data. The advice unit, for example, provides optimal advice based on the user's past expenditure data. The advice unit can also analyze the user's past expenditure data and provide advice based on spending trends. The advice unit can also improve the accuracy of advice by referring to the user's past expenditure data. In this way, optimal advice can be provided by referring to the past expenditure data. Some or all of the above-mentioned processing in the advice unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input past expenditure data into the generation AI and have the generation AI provide advice.

[0098] When providing advice, the advice unit can apply different advice algorithms depending on the user's living situation and goals. The advice unit applies the optimal advice algorithm depending on, for example, the user's living situation. The advice unit can also apply different advice algorithms based on the user's goals. The advice unit can also select the optimal advice algorithm taking into account the user's living situation and goals. This makes it possible to provide optimal advice depending on the living situation and goals. Some or all of the above-mentioned processing in the advice unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input living situation and goal data into the generation AI and cause the generation AI to apply the advice algorithm.

[0099] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, if the user is feeling stressed, the advice unit can prioritize providing important advice. If the user is relaxed, the advice unit can also provide all advice equally. If the user is in a hurry, the advice unit can also prioritize providing advice with a high degree of urgency. This allows important advice to be provided preferentially by determining the priority of advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the advice unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of advice.

[0100] When providing advice, the advice unit can provide region-specific advice based on the user's geographical location information. The advice unit can provide region-specific advice based on, for example, the user's geographical location information. The advice unit can also provide optimal advice by taking the user's location information into consideration. The advice unit can also provide region-specific advice by referring to the user's geographical location information. In this way, region-specific advice can be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the advice unit can be performed using or without the generation AI. For example, the advice unit can input geographical location information data to the generation AI and cause the generation AI to provide region-specific advice.

[0101] When providing advice, the advice unit can analyze the user's social media activity and provide relevant advice. For example, the advice unit can analyze the user's social media activity and provide relevant advice. The advice unit can also provide advice based on the user's social media interests. The advice unit can also provide the most relevant advice based on the user's social media activity. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the advice unit may be performed using or without the generation AI. For example, the advice unit can input social media activity data into the generation AI and have the generation AI provide the advice.

[0102] The introduction unit can estimate the user's emotions and adjust the introduction method for financial planners based on the estimated user emotions. For example, if the user is feeling stressed, the introduction unit can provide a simple and easy-to-understand introduction method. If the user is relaxed, the introduction unit can also provide a detailed and specific introduction method. If the user is in a hurry, the introduction unit can also provide a method for quickly making an introduction. This enables more appropriate introductions by adjusting the introduction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the introduction unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the introduction unit can input the user's emotion data into the generation AI and have the generation AI adjust the introduction method.

[0103] When introducing a financial planner, the introduction unit can introduce the most suitable planner by referring to the user's past expenditure data. The introduction unit, for example, introduces the most suitable planner based on the user's past expenditure data. The introduction unit can also analyze the user's past expenditure data and introduce a planner based on spending trends. The introduction unit can also improve the accuracy of planner introductions by referring to the user's past expenditure data. In this way, the most suitable planner can be introduced by referring to the past expenditure data. Some or all of the above-mentioned processing in the introduction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the introduction unit can input past expenditure data into the generation AI and have the generation AI perform planner introductions.

[0104] The introduction unit can estimate the user's emotions and determine the priority of introductions based on the estimated user emotions. For example, if the user is feeling stressed, the introduction unit can prioritize important introductions. If the user is relaxed, the introduction unit can also provide all introductions equally. If the user is in a hurry, the introduction unit can also prioritize urgent introductions. This allows important introductions to be provided preferentially by determining the priority of introductions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the introduction unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the introduction unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of introductions.

[0105] When introducing a financial planner, the introduction unit can introduce a planner specific to a region based on the user's geographical location information. The introduction unit, for example, introduces a planner specific to a region based on the user's geographical location information. The introduction unit can also introduce the most suitable planner by taking the user's location information into consideration. The introduction unit can also introduce a planner specific to a region by referring to the user's geographical location information. In this way, a planner specific to a region can be introduced by taking the geographical location information into consideration. Some or all of the above-described processing in the introduction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the introduction unit can input geographical location information data into the generation AI and cause the generation AI to introduce a planner specific to a region.

[0106] The point unit can estimate the user's emotions and adjust the point awarding method based on the estimated user's emotions. For example, if the user is feeling stressed, the point unit can delay the timing of point awarding and award points in a relaxed state. If the user is relaxed, the point unit can award points immediately and perform processing quickly. If the user is in a hurry, the point unit can also advance the timing of point awarding and award points quickly. This allows points to be awarded at a more appropriate time by adjusting the point awarding method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the point unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the point unit can input the user's emotion data into the generation AI and have the generation AI adjust the point awarding method.

[0107] When awarding points, the point unit can select the optimal point awarding method by referring to the user's past expenditure data. The point unit, for example, selects the optimal point awarding method based on the user's past expenditure data. The point unit can also analyze the user's past expenditure data to improve the accuracy of point awarding. The point unit can also adjust the point awarding criteria by referring to the user's past expenditure data. This allows the optimal point awarding method to be selected by referring to the past expenditure data. Some or all of the above-mentioned processing in the point unit may be performed using or without the generation AI. For example, the point unit can input past expenditure data into the generation AI and have the generation AI select the point awarding method.

[0108] The point unit can estimate the user's emotions and determine the priority of point awarding based on the estimated user's emotions. For example, if the user is stressed, the point unit can prioritize important points. If the user is relaxed, the point unit can also award all points equally. If the user is in a hurry, the point unit can also prioritize points with high urgency. This allows important points to be prioritized by determining the priority of point awarding based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the point unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the point unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of point awarding.

[0109] When awarding points, the point unit can select a region-specific point awarding method based on the user's geographical location information. For example, the point unit selects a region-specific point awarding method based on the user's geographical location information. The point unit can also provide an optimal point awarding method by taking the user's location information into consideration. The point unit can also select a region-specific point awarding method by referring to the user's geographical location information. In this way, a region-specific point awarding method can be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the point unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the point unit can input geographical location information data into the generation AI and cause the generation AI to select a region-specific point awarding method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, setting unit, analysis unit, advice unit, introduction unit, and point unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects payment information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing device 12 via the control unit 46A. The setting unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, reviews the budget and sets goals based on the collected information. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes spending trends. The advice unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides advice to the user based on the analysis results. The introduction unit, implemented, for example, by the control unit 46A of the smart device 14, suggests consulting a financial planner. The point unit, implemented, for example, by the control unit 46A of the smart device 14, awards points based on the use of specific services. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, setting unit, analysis unit, advice unit, introduction unit, and point unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects payment information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing device 12 by the control unit 46A. The setting unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, reviews the budget and sets goals based on the collected information. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes spending trends. The advice unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides advice to the user based on the analysis results. The introduction unit, implemented, for example, by the control unit 46A of the smart glasses 214, suggests consulting a financial planner. The point unit, implemented, for example, by the control unit 46A of the smart glasses 214, awards points based on the use of specific services. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, setting unit, analysis unit, advice unit, introduction unit, and point unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects payment information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing device 12 by the control unit 46A. The setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and reviews the budget and sets goals based on the collected information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes spending trends. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the analysis results. The introduction unit is implemented, for example, by the control unit 46A of the headset terminal 314 and suggests consulting with a financial planner. The point unit is implemented, for example, by the control unit 46A of the headset terminal 314 and awards points based on the use of specific services. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, setting unit, analysis unit, advice unit, introduction unit, and point unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects payment information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing device 12 by the control unit 46A. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reviews the budget and sets goals based on the collected information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes spending trends. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the analysis results. The introduction unit is realized, for example, by the control unit 46A of the robot 414 and suggests consulting a financial planner. The point unit is realized, for example, by the control unit 46A of the robot 414 and awards points based on the use of specific services.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The collection unit can analyze a user's purchasing history and detect specific patterns. For example, if a user tends to purchase products from a specific category on a specific day of the week or at a specific time of day, the collection unit can detect that pattern and automatically reflect that information the next time the user purchases. Also, if a user frequently purchases from a specific store, the collection unit can preferentially collect information about special offers and discounts offered by that store. Furthermore, the collection unit can predict future purchases based on the user's purchasing history and collect necessary payment information in advance. This allows payment information to be collected efficiently based on the user's purchasing patterns.

[0112] The setting unit can refer to the user's health data and set a budget according to the user's health condition. For example, the setting unit can set a medical expense budget based on the user's health checkup results. The setting unit can also set a budget for maintaining health based on the user's exercise data. Furthermore, the setting unit can refer to the user's dietary data and set a food budget for maintaining a healthy diet. This makes it possible to set a budget according to the user's health condition, allowing for both health management and expense management.

[0113] The analysis unit can evaluate the environmental impact based on the user's expenditure data. For example, the analysis unit can calculate carbon dioxide emissions from the user's expenditure data and evaluate the environmental impact. The analysis unit can also evaluate energy consumption based on the user's expenditure data and suggest improvements to energy efficiency. Furthermore, the analysis unit can evaluate the percentage of recyclable products purchased based on the user's expenditure data and provide advice to promote environmentally friendly purchasing behavior. This makes it possible to evaluate the environmental impact based on the user's expenditure data and support sustainable lifestyles.

[0114] The advice unit can suggest optimization of spending based on the user's hobbies and interests. For example, if the user is interested in music, the advice unit can provide advice for optimizing music-related spending. Also, if the user's hobby is traveling, the advice unit can provide advice for optimizing travel-related spending. Furthermore, if the user is interested in sports, the advice unit can provide advice for optimizing sports-related spending. In this way, it is possible to suggest optimization of spending based on the user's hobbies and interests and support a more fulfilling life.

[0115] The introduction unit can introduce an appropriate financial planner based on the user's occupation or career. For example, if the user is a freelancer, the introduction unit can introduce a financial planner for freelancers. Also, if the user is a corporate manager, the introduction unit can introduce a financial planner for managers. Furthermore, if the user is a new graduate, the introduction unit can introduce a financial planner for new graduates. This allows the user to be introduced to an appropriate financial planner based on the user's occupation or career, and supports financial management tailored to their career.

[0116] The collection unit can estimate the user's emotions and adjust the method of collecting payment information based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can simplify the collection of payment information to reduce the user's burden. Also, if the user is relaxed, the collection unit can collect detailed payment information and provide more accurate data. Furthermore, if the user is in a hurry, the collection unit can quickly collect payment information and process it immediately. This allows the method of collecting payment information to be adjusted based on the user's emotions and collect information at a more appropriate time.

[0117] The setting unit can estimate the user's emotions and adjust the frequency of budget review and goal setting based on the estimated user's emotions. For example, if the user is feeling stressed, the setting unit can reduce the frequency of budget review and goal setting to reduce the burden on the user. Also, if the user is relaxed, the setting unit can increase the frequency of budget review and goal setting to perform more detailed management. Furthermore, if the user is in a hurry, the setting unit can quickly review the budget and set goals. In this way, the frequency of budget review and goal setting can be adjusted based on the user's emotions, allowing for more appropriate management.

[0118] The analysis unit can estimate the user's emotions and adjust the level of detail of the expenditure analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple and easy-to-understand expenditure analysis to reduce the user's burden. Alternatively, if the user is relaxed, the analysis unit can provide a detailed expenditure analysis to provide deeper insights. Furthermore, if the user is in a hurry, the analysis unit can quickly perform an expenditure analysis and provide results immediately. This allows the level of detail of the expenditure analysis to be adjusted based on the user's emotions, and more appropriate information to be provided.

[0119] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit can provide simple and easy-to-follow advice to reduce the user's burden. In addition, if the user is relaxed, the advice unit can provide detailed and specific advice to provide deeper insight. Furthermore, if the user is in a hurry, the advice unit can provide advice that can be quickly implemented. In this way, the content of the advice can be adjusted based on the user's emotions, and more appropriate support can be provided.

[0120] The introduction unit can estimate the user's emotions and adjust the timing of introducing a financial planner based on the estimated user's emotions. For example, if the introduction unit is feeling stressed, it can delay the timing of the introduction and make the introduction when the user is relaxed. Also, if the user is relaxed, the introduction unit can immediately introduce a financial planner and respond quickly. Furthermore, if the user is in a hurry, the introduction unit can advance the timing of the introduction and quickly introduce a financial planner. This allows the introduction timing to be adjusted based on the user's emotions, making it possible to introduce a financial planner at a more appropriate time.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects payment information. For example, the collection unit can automatically collect payment information from electronic payment systems and keep track of expenditures in conjunction with financial institutions. Specifically, by linking the user's bank account and credit card information to the system, all expenditures can be managed centrally. Step 2: The setting unit reviews the budget and sets goals based on the information collected by the collection unit. For example, the setting unit automatically sets the next month's budget based on monthly income and expenses, and identifies saving techniques and reduction points according to the user's goals. Specifically, it suggests areas where savings can be made, categorized by food expenses, utility bills, etc. Step 3: The analysis unit analyzes expenses based on the budget set by the setting unit. For example, the analysis unit divides the user's expenses into categories and sets a budget for each category. Furthermore, the analysis unit can analyze spending trends and automatically provide advice to the user. Specifically, if food expenses are likely to exceed the budget, the analysis unit provides specific advice on how to save money. Step 4: The advice unit provides advice to the user based on the analysis results obtained by the analysis unit. For example, if the user is planning a large expenditure, the advice unit suggests consulting a financial planner. Step 5: The introduction unit introduces the user to a financial planner based on the advice provided by the advice unit. For example, the introduction unit suggests consulting a financial planner if the user is planning a large expenditure. Step 6: The point unit awards points based on the advice provided by the advice unit. The point unit awards additional points, for example, by using a specific service.

[0123] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a 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.

[0161] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0174] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects payment information; a setting unit that reviews the budget and sets targets based on the information collected by the collecting unit; an analysis unit that analyzes expenditures based on the budget set by the setting unit; an advice unit that provides advice to a user based on the analysis result obtained by the analysis unit; an introduction unit that recommends consulting with a financial planner based on the advice provided by the advice unit; a point unit that awards points based on the advice provided by the advice unit. A system characterized by:

2. The collecting unit Automatically collect payment information for electronic payment systems 2. The system of claim 1.

3. The collecting unit Link with financial institutions to understand expenses 2. The system of claim 1.

4. The setting unit Automatically set your next month's budget based on your monthly income and expenses 2. The system of claim 1.

5. The setting unit Identify ways to save and cut depending on your goals 2. The system of claim 1.

6. The analysis unit Break down your spending into categories and set a budget for each.

2. The system of claim 1.

7. The analysis unit Analyze spending trends and provide automated advice to users 2. The system of claim 1.

8. The advice unit If food costs are likely to exceed your budget, we'll offer specific advice on how to save money.

2. The system of claim 1.

Images