System

Abstract

To provide a system to analyze a user's payment patterns, create an optimal household account book, and perform money saving advice or spending assessment.SOLUTION: A system comprises a collection part, an analysis part, a trial calculation part, a household account book creation part, an advice part, and a target setting part. The collection part collects a user's payment patterns. The analysis part analyzes the data collected by the collection part. The trial calculation part performs a trial calculation of a future payment amount on the basis of the data analyzed by the analysis part. The household account book creation part creates an appropriate household account book on the basis of the payment amount obtained by the trail calculation performed by the trial calculation part. The advice part performs money saving advice or spending assessment on the basis of the household account book created by the household account book creation part. The target setting part automatically sets a monthly payment amount on the basis of the target set by the user.SELECTED DRAWING: Figure 1

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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately estimate future payment amounts based on a user's payment patterns and create an optimal household account book, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's payment patterns, create an optimal household account book, and provide saving advice and expenditure evaluation. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an estimate unit, a household account book creation unit, an advice unit, and a goal setting unit. The collection unit collects a user's payment pattern. The analysis unit analyzes the data collected by the collection unit. The estimate unit estimates future payment amounts based on the data analyzed by the analysis unit. The household account book creation unit creates an appropriate household account book based on the payment amount estimated by the estimate unit. The advice unit provides saving advice or expenditure evaluation based on the household account book created by the household account book creation unit. The goal setting unit automatically sets a monthly payment amount based on a goal set by the user. Effect of the Invention

[0007] The system according to the embodiment can analyze a user's payment patterns, create an optimal household account book, provide saving advice, and evaluate spending. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., a pen or a finger) to receive user input by the touch of 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The electronic payment system according to the embodiment of the present invention is a system that learns the payment patterns of a user, estimates future payment amounts, and creates an optimal household account book. This electronic payment system collects the payment patterns of the user, and the generation AI analyzes the data to estimate future payment amounts. For example, data on daily payments made by the user is collected, and the generation AI analyzes the data. Next, the generation AI estimates future payment amounts based on the data analyzed by the generation AI. For example, the generation AI predicts the next month's payment amount from past payment patterns. The generation AI creates an optimal household account book based on the estimated payment amount. For example, the system provides advice on reducing wasteful spending, taking into account the balance between income and expenditure. In addition, the system automatically sets the monthly payment amount based on the goal set by the user. For example, if a goal such as "I want to buy a car in three years" is set, the system calculates the monthly savings amount based on the goal and reviews the expenditure. In addition, the generation AI evaluates the user's expenditure and teaches efficient savings methods. For example, the system provides advice on reducing wasteful spending and saving efficiently. The system also reviews the user's income and makes suggestions for increasing income as necessary. This system allows the user to efficiently manage their household finances and make plans to achieve their goals. For example, users can plan and save money in preparation for future large expenditures. In addition, users can achieve financial stability by cutting down on wasteful spending and saving efficiently. This allows the electronic payment system to learn the user's payment patterns, estimate future payment amounts, and create an optimal household budget.

[0029] The electronic payment system according to the embodiment includes a collection unit, an analysis unit, a trial calculation unit, a household accounting book creation unit, an advice unit, and a goal setting unit. The collection unit collects the user's payment pattern. The user's payment pattern includes, for example, a credit card usage history, a record of cash expenditure, and the like, but is not limited to such examples. The collection unit, for example, collects the credit card usage history and stores it in a database. The collection unit can also manually input the record of cash expenditure. Furthermore, the collection unit can also collect the electronic money usage history. For example, the collection unit automatically collects the electronic money usage history and stores it in a database. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis is performed, for example, based on learning of payment patterns and trend analysis, but is not limited to such examples. For example, the generation AI learns the user's payment pattern based on past payment data. The analysis unit can also analyze trends in payment data using the generation AI. For example, the generation AI analyzes seasonal fluctuations and cyclical patterns of payment data. The trial calculation unit trial calculates future payment amounts based on the data analyzed by the analysis unit. The trial calculation is performed, for example, based on trend analysis of past payment data or a prediction algorithm, but is not limited to such examples. For example, the trial calculation unit predicts the payment amount for the next month based on past payment data. The trial calculation unit can also estimate the future payment amount using the generation AI. For example, the generation AI performs trial calculation using a prediction model that inputs past payment data and outputs future payment amounts. The household account book creation unit creates an optimal household account book based on the payment amount estimated by the trial calculation unit. The household account book is created, for example, based on the balance between income and expenditure and a savings goal, but is not limited to such examples. For example, the household account book creation unit takes into account the balance between income and expenditure and provides advice for reducing wasteful expenditure. The household account book creation unit can also create an optimal household account book using the generation AI. For example, the generation AI inputs income and expenditure data and creates a household account book using a model that outputs an optimal household account book. The advice unit provides saving advice and expenditure evaluation based on the household account book.The advice is provided, for example, based on the reduction of wasteful spending and an efficient saving method, but is not limited to such an example. For example, the advice unit provides advice for reducing wasteful spending based on a household account book. The advice unit can also teach an efficient saving method using a generation AI. For example, the generation AI provides advice using a model that takes data from a household account book as input and outputs an efficient saving method. The goal setting unit automatically sets a monthly payment amount based on a goal set by the user, and reviews the income. The goal setting is performed, for example, based on a savings goal or an expenditure reduction goal, but is not limited to such an example. For example, the goal setting unit calculates a monthly savings amount and reviews the expenditure based on a goal set by the user. The goal setting unit can also review the income using a generation AI. For example, the generation AI sets goals using a model that takes income data of the user as input and outputs a review of the income. As a result, the electronic payment system according to the embodiment can learn the payment pattern of the user, estimate future payment amounts, and create an optimal household account book.

[0030] The collection unit collects the user's payment pattern. The user's payment pattern includes, but is not limited to, for example, a credit card usage history, a record of cash expenditure, and the like. The collection unit, for example, collects the credit card usage history and stores it in a database. Specifically, the API of a credit card company is used to periodically obtain the user's usage history and store it in the database. The collection unit can also manually input the record of cash expenditure. The user enters details of cash expenditure through a smartphone app or a web interface and stores it in the database. Furthermore, the collection unit can collect the usage history of electronic money. For example, the API of an electronic money service is used to automatically collect the usage history of electronic money and store it in a database. As a result, every time a user uses electronic money, the usage history is automatically collected and stored in the database. The collection unit can centrally manage these data and comprehensively grasp the user's payment pattern. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if a user makes a large expenditure during a specific period, the data for that period is collected in more detail, allowing the analysis unit and the estimation unit to perform a more accurate analysis. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0031] The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed, for example, based on learning of payment patterns and trend analysis, but is not limited to such examples. Specifically, the generation AI learns the user's payment patterns based on past payment data. The generation AI uses deep learning technology to input the user's payment data into a multi-layered neural network and extract payment patterns. This makes it possible to grasp the user's payment tendency and preferences for specific spending categories. The analysis unit can also analyze trends in the payment data using the generation AI. For example, the generation AI analyzes seasonal fluctuations and cyclical patterns in the payment data. This makes it possible to grasp increases and decreases in spending at specific times and spending patterns related to specific events. Furthermore, the analysis unit can also detect unusual payment patterns using an anomaly detection algorithm. For example, if a user makes spending that deviates significantly from the normal payment pattern, the anomaly is detected and the user is notified. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the user's payment patterns and trends. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analysis. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The estimation unit estimates the future payment amount based on the data analyzed by the analysis unit. The estimation is performed, for example, based on trend analysis of past payment data or a prediction algorithm, but is not limited to such examples. Specifically, the estimation unit predicts the next month's payment amount based on the past payment data. The generation AI performs the estimation using a prediction model that takes past payment data as input and outputs future payment amounts. For example, a recurrent neural network (RNN) or a long short-term memory (LSTM) model is used to analyze payment data as time-series data and predict future payment amounts with high accuracy. The estimation unit can also predict fluctuations in the payment amount by taking into account the user's life events and seasonal factors. For example, when a user experiences a major life event such as marriage or moving, the estimation unit estimates the payment amount by taking into account the influence of the event. Furthermore, the estimation unit can also estimate the future payment amount by using the generation AI. The generation AI performs the estimation using a prediction model that takes past payment data as input and outputs future payment amounts. This allows the estimation unit to predict the future payment amount with high accuracy based on the user's payment patterns and trends. Furthermore, the calculation unit can update the prediction results in real time to respond to the latest situation. For example, if the user's payment pattern changes suddenly, the calculation unit immediately imports new data and updates the prediction results. This allows the calculation unit to always provide highly accurate trial payment amounts based on the latest information, supporting the user's financial management.

[0033] The household account book creation unit creates an optimal household account book based on the payment amount estimated by the estimation unit. The household account book is created based on, for example, the balance between income and expenditure and a saving goal, but is not limited to such examples. Specifically, the household account book creation unit provides advice for reducing wasteful expenditure, taking into account the balance between income and expenditure. For example, based on the user's income and expenditure data, the monthly expenditure is classified into categories and wasteful expenditure is identified. The household account book creation unit can also create an optimal household account book using a generation AI. The generation AI creates a household account book using a model that takes the income and expenditure data as input and outputs an optimal household account book. For example, the generation AI learns the user's spending pattern and provides specific advice for reducing wasteful expenditure. Furthermore, the household account book creation unit can customize the household account book based on the user's saving goal or savings goal. For example, if the user sets a goal to save a specific amount, the monthly expenditure is adjusted based on the goal, and a plan is made to achieve the savings goal. This allows the household account book creation unit to provide specific advice for optimizing the balance between the user's income and expenditure and reducing wasteful expenditure. Furthermore, the household account book creation unit can collect user feedback and continuously improve the accuracy and effectiveness of the household account book. This allows the household account book creation unit to support the user's financial management and realize efficient savings and optimization of expenditure.

[0034] The advice unit provides advice on saving and expenditure evaluation based on the household account book. The advice is based on, for example, the reduction of wasteful expenditure and an efficient saving method, but is not limited to such examples. Specifically, the advice unit provides advice for reducing wasteful expenditure based on the household account book. For example, the user's expenditure data is analyzed, wasteful expenditure in a specific category is identified, and a method for reducing the wasteful expenditure is proposed. The advice unit can also use the generation AI to teach an efficient saving method. The generation AI provides advice using a model that inputs data from the household account book and outputs an efficient saving method. For example, the generation AI proposes an optimal savings plan based on the user's income and expenditure data, and sets a specific savings goal. The advice unit can also provide customized advice according to the user's lifestyle and goals. For example, if the user is planning a trip or a big purchase, the advice unit can suggest a saving method and a method for adjusting expenditure based on the plan. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. As a result, the advice unit can support the user's financial management and realize efficient savings and optimization of expenditure. Furthermore, the advice unit can provide motivation and support to encourage the user to change his / her behavior. For example, the advice unit can periodically report the progress of the user in achieving the goal set by the user and provide rewards and incentives according to the degree of achievement. In this way, the advice unit can provide support for the user to save and save continuously and assist in achieving financial goals.

[0035] The goal setting unit automatically sets the monthly payment amount based on the goal set by the user and reviews the income. The goal setting is performed, for example, based on a savings goal or an expenditure reduction goal, but is not limited to such examples. Specifically, the goal setting unit calculates the monthly savings amount and reviews the expenditure based on the goal set by the user. For example, if the user sets a goal to save a specific amount, the monthly expenditure is adjusted based on the goal, and a plan is made to achieve the savings goal. The goal setting unit can also review the income using the generation AI. The generation AI sets the goal using a model that inputs the user's income data and outputs the review of the income. For example, the generation AI predicts the increase / decrease or fluctuation of income based on the user's income data, and proposes an expenditure plan accordingly. Furthermore, the goal setting unit can provide customized goal setting according to the user's lifestyle and goals. For example, if the user is planning a trip or a big purchase, the system suggests a method of saving and a method of adjusting expenditure based on the plan. As a result, the goal setting unit can provide specific advice to optimize the balance between the user's income and expenditure and reduce wasteful expenditure. Furthermore, the goal setting unit can collect feedback from the user and continuously improve the accuracy and effectiveness of goal setting, thereby supporting the user's financial management and enabling efficient savings and optimization of spending.

[0036] The collection unit can collect daily payment data of the user. Daily payment data includes, for example, credit card usage history, cash expenditure records, and the like, but is not limited to such examples. For example, the collection unit collects credit card usage history and stores it in a database. The collection unit can also manually input the cash expenditure records. Furthermore, the collection unit can collect electronic money usage history. For example, the collection unit automatically collects electronic money usage history and stores it in a database. In this way, by collecting daily payment data of the user, the payment pattern can be accurately grasped. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the credit card usage history into the generation AI and cause the generation AI to collect the payment data.

[0037] The analysis unit can analyze the collected data and learn the payment pattern. The analysis unit, for example, analyzes the collected data and learns the payment pattern. The payment pattern includes, for example, the monthly payment frequency and the fluctuation of the payment amount, but is not limited to such examples. The analysis unit learns the user's payment pattern based on, for example, past payment data. The analysis unit can also analyze trends in the payment data using the generation AI. For example, the generation AI analyzes seasonal fluctuations and cyclical patterns of the payment data. In this way, the collected data can be analyzed to learn the user's payment pattern. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the collected payment data to the generation AI and cause the generation AI to execute learning of the payment pattern.

[0038] The estimation unit can estimate the future payment amount based on the analyzed data. The estimation unit, for example, estimates the future payment amount based on the analyzed data. The future payment amount includes, for example, the payment amount for the next month, the payment amount for the year, etc., but is not limited to such examples. The estimation unit, for example, predicts the payment amount for the next month based on past payment data. The estimation unit can also estimate the future payment amount using the generation AI. For example, the generation AI performs estimation using a prediction model that inputs past payment data and outputs future payment amounts. This makes it possible to predict future expenditures by estimating the future payment amount based on the analyzed data. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input the analyzed payment data to the generation AI and cause the generation AI to execute an estimate of the future payment amount.

[0039] The household account book creation unit can create an appropriate household account book based on the estimated payment amount. The household account book creation unit creates an appropriate household account book based on the estimated payment amount, for example. The appropriate household account book includes, for example, the balance between income and expenditure, a method for setting a savings goal, and the like, but is not limited to such examples. The household account book creation unit provides advice for reducing wasteful expenditure, for example, by considering the balance between income and expenditure. The household account book creation unit can also create an optimal household account book using a generation AI. For example, the generation AI creates a household account book using a model that inputs data on income and expenditure and outputs an optimal household account book. This enables efficient household management by creating an optimal household account book based on the estimated payment amount. A part or all of the above-mentioned processing in the household account book creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the household account book creation unit can input the estimated payment amount to the generation AI and cause the generation AI to create a household account book.

[0040] The advice unit can provide advice on saving and an expenditure evaluation based on the household account book. The advice unit provides advice on saving and an expenditure evaluation based on the household account book, for example. The advice on saving includes, but is not limited to, for example, reducing wasteful expenditure and efficient savings methods. The advice unit provides advice for reducing wasteful expenditure, for example, based on the household account book. The advice unit can also teach an efficient savings method using a generation AI. For example, the generation AI provides advice using a model that inputs data from the household account book and outputs an efficient savings method. This makes it possible to reduce wasteful expenditure by providing advice on saving and an expenditure evaluation based on the household account book. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the advice unit can input data from the household account book to the generation AI and cause the generation AI to execute advice on saving and an expenditure evaluation.

[0041] The goal setting unit can automatically set the monthly payment amount based on the goal set by the user and review the income. The goal setting unit, for example, automatically sets the monthly payment amount based on the goal set by the user and reviews the income. The goal includes, for example, a savings goal and an expenditure reduction goal, but is not limited to such examples. The goal setting unit, for example, calculates the monthly savings amount based on the goal set by the user and reviews the expenditure. The goal setting unit can also review the income using a generation AI. For example, the generation AI sets the goal using a model that inputs the income data of the user and outputs the review of the income. As a result, the monthly payment amount is automatically set based on the goal set by the user and the income is reviewed, thereby making a plan for achieving the goal. A part or all of the above-mentioned processing in the goal setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the goal setting unit can input the goal set by the user to the generation AI and cause the generation AI to automatically set the monthly payment amount and review the income.

[0042] The collection unit can analyze the user's past payment history and select the optimal collection method. The collection unit, for example, analyzes the user's past payment history and selects the optimal collection method. The optimal collection method includes, for example, the frequency of data collection, the collection means, etc., but is not limited to such examples. The collection unit, for example, preferentially collects payment methods (credit cards, electronic money, etc.) that the user frequently uses. The collection unit can also preferentially collect payments made in a specific time period from the user's past payment history. Furthermore, the collection unit can analyze the user's payment history and focus on collecting payment data at specific stores or services. This makes it possible to select the optimal collection method by analyzing the user's past payment history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past payment history to the generation AI and cause the generation AI to select the optimal collection method.

[0043] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting payment data. The collection unit performs filtering based on the user's current living situation and areas of interest when collecting payment data, for example. Living situations include, but are not limited to, income status, family structure, and the like. Areas of interest include, but are not limited to, hobbies, products and services of interest, and the like. For example, when the user is traveling, the collection unit preferentially collects travel-related payment data. In addition, when the user is interested in health, the collection unit can also focus on collecting health-related payment data. Furthermore, when the user starts a new hobby, the collection unit can also collect payment data related to the hobby. As a result, highly relevant data can be collected by filtering based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to filter the payment data.

[0044] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting payment data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting payment data. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a specific area, the collection unit preferentially collects payment data in that area. In addition, when the user is traveling, the collection unit can also focus on collecting payment data at the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect daily payment data. In this way, by considering the geographical location information of the user, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of payment data.

[0045] The collection unit can analyze the social media activity of the user and collect related data when collecting the payment data. For example, the collection unit analyzes the social media activity of the user and collects related data when collecting the payment data. The social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. For example, the collection unit collects related payment data based on purchase information shared by the user on social media. The collection unit can also collect payment data related to products and services in which the user has shown interest on social media. Furthermore, the collection unit can collect payment data related to specific events and campaigns from the user's social media activity. In this way, the related payment data can be collected by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect the payment data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the payment data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the payment data during the analysis. The importance of the payment data includes, for example, the amount of expenditure, the frequency of expenditure, and the like, but is not limited to such examples. The analysis unit, for example, performs a detailed analysis on payment data with high importance. The analysis unit can also perform a simplified analysis on payment data with low importance. Furthermore, the analysis unit can also determine the priority of the analysis according to the importance of the payment data. As a result, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the payment data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the payment data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the payment data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of the payment data during analysis. The categories of payment data include, for example, food expenses, transportation expenses, entertainment expenses, etc., but are not limited to such examples. The analysis unit applies a specific analysis algorithm to food-related payment data, for example. The analysis unit can also apply another analysis algorithm to transportation-related payment data. Furthermore, the analysis unit can apply yet another analysis algorithm to entertainment-related payment data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the payment data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the payment data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0048] The analysis unit can determine the priority of the analysis based on the submission time of the payment data during the analysis. The analysis unit, for example, determines the priority of the analysis based on the submission time of the payment data during the analysis. The submission time of the payment data includes, for example, the end of the month, payday, etc., but is not limited to such examples. The analysis unit, for example, preferentially analyzes the most recently submitted payment data. The analysis unit can also postpone payment data submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the priority of the analysis based on the submission time of the payment data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the payment data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the payment data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the payment data during analysis. The relevance of the payment data includes, for example, expenditures in the same category, expenditures at the same store, etc., but is not limited to such examples. For example, the analysis unit preferentially analyzes payment data with high relevance. In addition, the analysis unit can postpone payment data with low relevance. Furthermore, the analysis unit can adjust the schedule of analysis based on the relevance of the payment data. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the payment data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the payment data to the generation AI and cause the generation AI to adjust the order of analysis.

[0050] The trial calculation unit can optimize the trial calculation algorithm by referring to past payment data at the time of trial calculation. The trial calculation unit, for example, optimizes the trial calculation algorithm by referring to past payment data at the time of trial calculation. The past payment data includes, for example, an expenditure history for the past year, expenditure data for a specific period, etc., but is not limited to such examples. The trial calculation unit adjusts the trial calculation algorithm, for example, based on the past payment data. The trial calculation unit can also extract parameters for improving the accuracy of the trial calculation from the past payment data. Furthermore, the trial calculation unit can also optimize the trial calculation algorithm by referring to the past payment data. As a result, the trial calculation algorithm can be optimized by referring to the past payment data, and the accuracy of the trial calculation can be improved. A part or all of the above-mentioned processing in the trial calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the trial calculation unit can input the past payment data to the generation AI and cause the generation AI to execute optimization of the trial calculation algorithm.

[0051] The estimation unit can apply different estimation methods to each category of payment data during estimation. The estimation unit, for example, applies different estimation methods to each category of payment data during estimation. The categories of payment data include, for example, food expenses, transportation expenses, and entertainment expenses, but are not limited to such examples. The estimation unit applies a specific estimation method to food-related payment data, for example. The estimation unit can also apply a different estimation method to transportation-related payment data. Furthermore, the estimation unit can apply yet another estimation method to entertainment-related payment data. This allows for more accurate estimation by applying different estimation methods to each category of payment data. Part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input the category of payment data to the generation AI and cause the generation AI to apply the estimation method.

[0052] The trial calculation unit can weight the trial calculation based on the submission time of the payment data at the time of the trial calculation. The trial calculation unit, for example, weights the trial calculation based on the submission time of the payment data at the time of the trial calculation. The submission time of the payment data includes, for example, the end of the month, payday, etc., but is not limited to such examples. The trial calculation unit, for example, weights the payment data submitted recently. In addition, the trial calculation unit can also reduce the weight of payment data submitted earlier. Furthermore, the trial calculation unit can adjust the weight of the trial calculation based on the submission time. As a result, more accurate trial calculation is possible by weighting the trial calculation based on the submission time of the payment data. Part or all of the above-mentioned processing in the trial calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the trial calculation unit can input the submission time of the payment data to the generation AI and cause the generation AI to execute the weighting of the trial calculation.

[0053] The estimation unit can improve the accuracy of the estimation by referring to the market data related to the payment data at the time of the estimation. The estimation unit, for example, improves the accuracy of the estimation by referring to the market data related to the payment data at the time of the estimation. The related market data includes, for example, data of other companies in the same industry, industry trend data, etc., but is not limited to such examples. The estimation unit, for example, improves the accuracy of the estimation by referring to market data related to the payment data. In addition, the estimation unit can also adjust the estimation algorithm based on the market data. Furthermore, the estimation unit can also combine the payment data and the market data to improve the accuracy of the estimation. Thereby, the accuracy of the estimation can be improved by referring to the market data related to the payment data. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input the related market data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the estimation.

[0054] The household account book creation unit can adjust the level of detail of the household account book based on the importance of the payment data when creating the household account book. For example, the household account book creation unit adjusts the level of detail of the household account book based on the importance of the payment data when creating the household account book. The importance of the payment data includes, for example, the amount of expenditure, the frequency of expenditure, and the like, but is not limited to such examples. For example, the household account book creation unit writes detailed items in the household account book for payment data with high importance. In addition, the household account book creation unit can also write simplified items in the household account book for payment data with low importance. Furthermore, the household account book creation unit can also determine the priority order of items in the household account book according to the importance of the payment data. This allows efficient household management by adjusting the level of detail of the household account book based on the importance of the payment data. A part or all of the above-mentioned processing in the household account book creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the household account book creation unit can input the importance of the payment data to the generation AI and cause the generation AI to adjust the level of detail of the household account book.

[0055] The household account book creation unit can apply different household account book creation algorithms according to the category of payment data when creating the household account book. For example, the household account book creation unit applies different household account book creation algorithms according to the category of payment data when creating the household account book. The categories of payment data include, for example, food expenses, transportation expenses, and entertainment expenses, but are not limited to such examples. For example, the household account book creation unit applies a specific household account book creation algorithm to food-related payment data. In addition, the household account book creation unit can also apply another household account book creation algorithm to transportation-related payment data. Furthermore, the household account book creation unit can apply yet another household account book creation algorithm to entertainment-related payment data. In this way, by applying different household account book creation algorithms according to the category of payment data, a more accurate household account book can be created. Part or all of the above-mentioned processing in the household account book creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the household account book creation unit can input the category of payment data to the generation AI and cause the generation AI to apply the household account book creation algorithm.

[0056] The household account book creation unit can determine the priority of the household account book based on the submission time of the payment data when creating the household account book. The household account book creation unit, for example, determines the priority of the household account book based on the submission time of the payment data when creating the household account book. The submission time of the payment data includes, for example, the end of the month, payday, etc., but is not limited to such examples. The household account book creation unit, for example, preferentially writes the most recently submitted payment data in the household account book. In addition, the household account book creation unit can also postpone payment data submitted earlier. Furthermore, the household account book creation unit can adjust the priority of the items in the household account book based on the submission time. This enables efficient household management by determining the priority of the household account book based on the submission time of the payment data. Part or all of the above-mentioned processing in the household account book creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the household account book creation unit can input the submission time of the payment data to the generation AI and cause the generation AI to execute the determination of the priority of the household account book.

[0057] The household account book creation unit can adjust the order of the household account book based on the relevance of the payment data when creating the household account book. The household account book creation unit, for example, adjusts the order of the household account book based on the relevance of the payment data when creating the household account book. The relevance of the payment data includes, for example, expenditures in the same category, expenditures at the same store, and the like, but is not limited to such examples. The household account book creation unit, for example, preferentially writes highly relevant payment data in the household account book. In addition, the household account book creation unit can also postpone payment data with low relevance. Furthermore, the household account book creation unit can also adjust the order of items in the household account book based on the relevance of the payment data. As a result, efficient household management is possible by adjusting the order of the household account book based on the relevance of the payment data. A part or all of the above-mentioned processing in the household account book creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the household account book creation unit can input the relevance of the payment data to the generation AI and cause the generation AI to adjust the order of the household account book.

[0058] The advice unit can adjust the level of detail of the advice based on the importance of the payment data when giving advice. The advice unit, for example, adjusts the level of detail of the advice based on the importance of the payment data when giving advice. The importance of the payment data includes, for example, the amount of expenditure, the frequency of expenditure, and the like, but is not limited to such examples. The advice unit, for example, provides detailed advice for payment data with high importance. In addition, the advice unit can also provide simplified advice for payment data with low importance. Furthermore, the advice unit can also determine the priority of advice according to the importance of the payment data. As a result, efficient advice can be provided by adjusting the level of detail of advice based on the importance of the payment data. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the importance of the payment data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0059] The advice unit can apply different advice algorithms depending on the category of payment data when giving advice. The advice unit, for example, applies different advice algorithms depending on the category of payment data when giving advice. The categories of payment data include, for example, food expenses, transportation expenses, entertainment expenses, etc., but are not limited to such examples. The advice unit, for example, applies a specific advice algorithm to food-related payment data. The advice unit can also apply another advice algorithm to transportation-related payment data. Furthermore, the advice unit can apply yet another advice algorithm to entertainment-related payment data. This allows for more accurate advice by applying different advice algorithms depending on the category of payment data. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the category of payment data to the generation AI and cause the generation AI to apply the advice algorithm.

[0060] The advice unit can determine the priority of advice based on the submission time of payment data when giving advice. The advice unit, for example, determines the priority of advice based on the submission time of payment data when giving advice. The submission time of payment data includes, for example, the end of the month, payday, etc., but is not limited to such examples. The advice unit, for example, gives priority to advice on recently submitted payment data. The advice unit can also postpone payment data submitted earlier. Furthermore, the advice unit can adjust the schedule of advice based on the submission time. As a result, efficient advice can be provided by determining the priority of advice based on the submission time of payment data. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the submission time of payment data to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0061] The advice unit can adjust the order of advice based on the relevance of the payment data when giving advice. The advice unit, for example, adjusts the order of advice based on the relevance of the payment data when giving advice. The relevance of the payment data includes, for example, expenditures in the same category, expenditures at the same store, etc., but is not limited to such examples. For example, the advice unit gives advice preferentially to payment data with high relevance. The advice unit can also postpone payment data with low relevance. Furthermore, the advice unit can adjust the schedule of advice based on the relevance of the payment data. As a result, efficient advice can be provided by adjusting the order of advice based on the relevance of the payment data. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the relevance of the payment data to the generation AI and cause the generation AI to adjust the order of advice.

[0062] The goal setting unit can select the optimal goal setting method by referring to the user's past goal achievement history when setting a goal. For example, the goal setting unit selects the optimal goal setting method by referring to the user's past goal achievement history when setting a goal. The past goal achievement history includes, for example, past goal achievement rates, types of goals achieved, and the like, but is not limited to such examples. For example, the goal setting unit proposes the optimal goal setting method based on the user's past goal achievement history. In addition, the goal setting unit can also extract a successful goal setting method from the past goal achievement history. Furthermore, the goal setting unit can also improve the accuracy of goal setting by referring to the user's past goal achievement history. As a result, the optimal goal setting method can be selected by referring to the user's past goal achievement history. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the past goal achievement history to the generation AI and cause the generation AI to select the optimal goal setting method.

[0063] The goal setting unit can customize the goal setting means based on the user's current living situation when setting a goal. The goal setting unit customizes the goal setting means based on the user's current living situation when setting a goal, for example. Living situations include, but are not limited to, income situation, family structure, and the like. For example, when the user is busy, the goal setting unit sets an easily achievable goal. In addition, when the user has time, the goal setting unit can also set a challenging goal. Furthermore, the goal setting unit can customize the goal setting means according to the user's living situation. This enables more realistic goal setting by customizing the goal setting means based on the user's current living situation. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the user's living situation data to the generation AI and cause the generation AI to execute customization of the goal setting means.

[0064] The goal setting unit can select the optimal goal setting method in consideration of the geographical location information of the user when setting a goal. For example, the goal setting unit selects the optimal goal setting method in consideration of the geographical location information of the user when setting a goal. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a specific area, the goal setting unit sets a goal related to that area. In addition, when the user is traveling, the goal setting unit can also set a goal that is achievable at the travel destination. Furthermore, the goal setting unit can also suggest the optimal goal setting method based on the geographical location information of the user. In this way, the optimal goal setting method can be selected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the goal setting method.

[0065] The goal setting unit can analyze the user's social media activity and suggest a means for setting the goal when setting the goal. For example, the goal setting unit analyzes the user's social media activity and suggests a means for setting the goal when setting the goal. The social media activity includes, for example, the content of the post, the number of likes, and the like, but is not limited to such examples. For example, the goal setting unit suggests a related goal setting means based on the goal shared by the user on social media. The goal setting unit can also suggest a goal setting means based on the activity in which the user showed interest on social media. Furthermore, the goal setting unit can also suggest a goal setting means related to a specific event or campaign from the user's social media activity. In this way, the user's social media activity can be analyzed to suggest a related goal setting means. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the suggestion of the goal setting means.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The electronic payment system can also collect health data from the user and correlate it with spending patterns. For example, data from the user's fitness tracker or smartwatch can be collected to analyze the relationship between health status and spending. This can help determine whether spending tends to increase during periods of good health. The system can also recommend spending habits to lead a healthier life based on the health data, such as gym membership fees or healthy food purchases. Furthermore, the system can predict future medical expenses based on the health data and reflect them in the household budget. This allows the user to integrate health management with financial management.

[0068] The collection unit can analyze the user's purchase history and ascertain preferences for specific brands and products. For example, if the user frequently purchases products of a specific brand, the collection unit can prioritize collecting new product and sale information for that brand. Also, if the user likes to purchase products in a specific category, the collection unit can focus on collecting payment data related to that category. Furthermore, the collection unit can analyze seasonal purchasing patterns from the user's purchase history and make seasonal expenditure predictions. This allows for more accurate expenditure predictions based on the user's purchasing trends.

[0069] The estimation unit can perform risk assessment for future expenditures based on the user's past payment data. For example, risk factors in a specific expenditure category are identified from the past payment data, and the risk for future expenditures is assessed. In addition, advice for reducing the risk can be provided based on the risk assessment. For example, if the risk in a specific expenditure category is high, advice for reducing expenditures in that category is provided. Furthermore, insurance and savings suggestions for future expenditures can be made based on the risk assessment. This allows the user to manage risks for future expenditures and achieve financial stability.

[0070] The advice unit can suggest environmentally friendly spending methods based on the user's spending data. For example, it can suggest environmentally friendly alternatives to products that the user frequently purchases. It can also provide advice for reducing energy consumption based on the user's spending data. Furthermore, when the user makes environmentally friendly spending, it can visualize the effect and provide feedback to the user. This allows the user to learn environmentally friendly spending methods and live a sustainable life.

[0071] When collecting the user's payment data, the collection unit can estimate the user's purchasing intent and adjust the data collection method based on the estimated purchasing intent. For example, if the user intends to purchase a specific product, data related to that product can be collected preferentially. Also, if the user intends to participate in a specific event, payment data related to that event can be collected with a focus. Furthermore, the timing of data collection can be adjusted based on the user's purchasing intent. This makes it possible to collect more appropriate data by adjusting the data collection method according to the user's purchasing intent.

[0072] When analyzing the user's payment data, the analysis unit can improve the accuracy of the analysis by taking into account the user's life events. For example, when the user faces a life event such as marriage, childbirth, or moving, the analysis focuses on the expenditures related to that event. Future expenditure predictions can also be made based on the life event. Furthermore, spending trends can be analyzed and appropriate advice can be provided to the user according to the life event. This allows for more accurate analysis by taking into account the user's life events.

[0073] The process flow of the first embodiment will be briefly described below.

[0074] Step 1: The collection unit collects the user's payment patterns. The user's payment patterns include credit card usage history, cash expenditure records, electronic money usage history, etc. The collection unit collects this data automatically or manually and stores it in a database. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is based on learning payment patterns and trend analysis, and also analyzes seasonal fluctuations and cyclical patterns. Step 3: The estimation unit estimates future payment amounts based on the data analyzed by the analysis unit. The estimation is based on trend analysis of past payment data and predictive algorithms, and can also use predictive models using generative AI. Step 4: The household accounting creation unit creates an optimal household accounting book based on the payment amount estimated by the estimation unit. The household accounting book is created based on the balance of income and expenditure and savings goals, and the optimal household accounting book can also be created using generation AI. Step 5: The advice section provides savings advice and expenditure evaluation based on the household budget. Advice is based on reducing wasteful spending and efficient savings methods, and can also teach efficient savings methods using generative AI. Step 6: The goal setting unit automatically sets the monthly payment amount based on the goal set by the user and reviews the income. Goal setting is based on savings goals and expenditure reduction goals, and income can also be reviewed using generation AI.

[0075] (Example 2) The electronic payment system according to the embodiment of the present invention is a system that learns the payment patterns of a user, estimates future payment amounts, and creates an optimal household account book. This electronic payment system collects the payment patterns of the user, and the generation AI analyzes the data to estimate future payment amounts. For example, data on daily payments made by the user is collected, and the generation AI analyzes the data. Next, the generation AI estimates future payment amounts based on the data analyzed by the generation AI. For example, the generation AI predicts the next month's payment amount from past payment patterns. The generation AI creates an optimal household account book based on the estimated payment amount. For example, the system provides advice on reducing wasteful spending, taking into account the balance between income and expenditure. In addition, the system automatically sets the monthly payment amount based on the goal set by the user. For example, if a goal such as "I want to buy a car in three years" is set, the system calculates the monthly savings amount based on the goal and reviews the expenditure. In addition, the generation AI evaluates the user's expenditure and teaches efficient savings methods. For example, the system provides advice on reducing wasteful spending and saving efficiently. The system also reviews the user's income and makes suggestions for increasing income as necessary. This system allows the user to efficiently manage their household finances and make plans to achieve their goals. For example, users can plan and save money in preparation for future large expenditures. In addition, users can achieve financial stability by cutting down on wasteful spending and saving efficiently. This allows the electronic payment system to learn the user's payment patterns, estimate future payment amounts, and create an optimal household budget.

[0076] The electronic payment system according to the embodiment includes a collection unit, an analysis unit, a trial calculation unit, a household accounting book creation unit, an advice unit, and a goal setting unit. The collection unit collects the user's payment pattern. The user's payment pattern includes, for example, a credit card usage history, a record of cash expenditure, and the like, but is not limited to such examples. The collection unit, for example, collects the credit card usage history and stores it in a database. The collection unit can also manually input the record of cash expenditure. Furthermore, the collection unit can also collect the electronic money usage history. For example, the collection unit automatically collects the electronic money usage history and stores it in a database. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis is performed, for example, based on learning of payment patterns and trend analysis, but is not limited to such examples. For example, the generation AI learns the user's payment pattern based on past payment data. The analysis unit can also analyze trends in payment data using the generation AI. For example, the generation AI analyzes seasonal fluctuations and cyclical patterns of payment data. The trial calculation unit trial calculates future payment amounts based on the data analyzed by the analysis unit. The trial calculation is performed, for example, based on trend analysis of past payment data or a prediction algorithm, but is not limited to such examples. For example, the trial calculation unit predicts the payment amount for the next month based on past payment data. The trial calculation unit can also estimate the future payment amount using the generation AI. For example, the generation AI performs trial calculation using a prediction model that inputs past payment data and outputs future payment amounts. The household account book creation unit creates an optimal household account book based on the payment amount estimated by the trial calculation unit. The household account book is created, for example, based on the balance between income and expenditure and a savings goal, but is not limited to such examples. For example, the household account book creation unit takes into account the balance between income and expenditure and provides advice for reducing wasteful expenditure. The household account book creation unit can also create an optimal household account book using the generation AI. For example, the generation AI inputs income and expenditure data and creates a household account book using a model that outputs an optimal household account book. The advice unit provides saving advice and expenditure evaluation based on the household account book.The advice is provided, for example, based on the reduction of wasteful spending and an efficient saving method, but is not limited to such an example. For example, the advice unit provides advice for reducing wasteful spending based on a household account book. The advice unit can also teach an efficient saving method using a generation AI. For example, the generation AI provides advice using a model that takes data from a household account book as input and outputs an efficient saving method. The goal setting unit automatically sets a monthly payment amount based on a goal set by the user, and reviews the income. The goal setting is performed, for example, based on a savings goal or an expenditure reduction goal, but is not limited to such an example. For example, the goal setting unit calculates a monthly savings amount and reviews the expenditure based on a goal set by the user. The goal setting unit can also review the income using a generation AI. For example, the generation AI sets goals using a model that takes income data of the user as input and outputs a review of the income. As a result, the electronic payment system according to the embodiment can learn the payment pattern of the user, estimate future payment amounts, and create an optimal household account book.

[0077] The collection unit collects the user's payment pattern. The user's payment pattern includes, but is not limited to, for example, a credit card usage history, a record of cash expenditure, and the like. The collection unit, for example, collects the credit card usage history and stores it in a database. Specifically, the API of a credit card company is used to periodically obtain the user's usage history and store it in the database. The collection unit can also manually input the record of cash expenditure. The user enters details of cash expenditure through a smartphone app or a web interface and stores it in the database. Furthermore, the collection unit can collect the usage history of electronic money. For example, the API of an electronic money service is used to automatically collect the usage history of electronic money and store it in a database. As a result, every time a user uses electronic money, the usage history is automatically collected and stored in the database. The collection unit can centrally manage these data and comprehensively grasp the user's payment pattern. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if a user makes a large expenditure during a specific period, the data for that period is collected in more detail, allowing the analysis unit and the estimation unit to perform a more accurate analysis. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0078] The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed, for example, based on learning of payment patterns and trend analysis, but is not limited to such examples. Specifically, the generation AI learns the user's payment patterns based on past payment data. The generation AI uses deep learning technology to input the user's payment data into a multi-layered neural network and extract payment patterns. This makes it possible to grasp the user's payment tendency and preferences for specific spending categories. The analysis unit can also analyze trends in the payment data using the generation AI. For example, the generation AI analyzes seasonal fluctuations and cyclical patterns in the payment data. This makes it possible to grasp increases and decreases in spending at specific times and spending patterns related to specific events. Furthermore, the analysis unit can also detect unusual payment patterns using an anomaly detection algorithm. For example, if a user makes spending that deviates significantly from the normal payment pattern, the anomaly is detected and the user is notified. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the user's payment patterns and trends. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analysis. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0079] The estimation unit estimates the future payment amount based on the data analyzed by the analysis unit. The estimation is performed, for example, based on trend analysis of past payment data or a prediction algorithm, but is not limited to such examples. Specifically, the estimation unit predicts the next month's payment amount based on the past payment data. The generation AI performs the estimation using a prediction model that takes past payment data as input and outputs future payment amounts. For example, a recurrent neural network (RNN) or a long short-term memory (LSTM) model is used to analyze payment data as time-series data and predict future payment amounts with high accuracy. The estimation unit can also predict fluctuations in the payment amount by taking into account the user's life events and seasonal factors. For example, when a user experiences a major life event such as marriage or moving, the estimation unit estimates the payment amount by taking into account the influence of the event. Furthermore, the estimation unit can also estimate the future payment amount by using the generation AI. The generation AI performs the estimation using a prediction model that takes past payment data as input and outputs future payment amounts. This allows the estimation unit to predict the future payment amount with high accuracy based on the user's payment patterns and trends. Furthermore, the calculation unit can update the prediction results in real time to respond to the latest situation. For example, if the user's payment pattern changes suddenly, the calculation unit immediately imports new data and updates the prediction results. This allows the calculation unit to always provide highly accurate trial payment amounts based on the latest information, supporting the user's financial management.

[0080] The household account book creation unit creates an optimal household account book based on the payment amount estimated by the estimation unit. The household account book is created based on, for example, the balance between income and expenditure and a saving goal, but is not limited to such examples. Specifically, the household account book creation unit provides advice for reducing wasteful expenditure, taking into account the balance between income and expenditure. For example, based on the user's income and expenditure data, the monthly expenditure is classified into categories and wasteful expenditure is identified. The household account book creation unit can also create an optimal household account book using a generation AI. The generation AI creates a household account book using a model that takes the income and expenditure data as input and outputs an optimal household account book. For example, the generation AI learns the user's spending pattern and provides specific advice for reducing wasteful expenditure. Furthermore, the household account book creation unit can customize the household account book based on the user's saving goal or savings goal. For example, if the user sets a goal to save a specific amount, the monthly expenditure is adjusted based on the goal, and a plan is made to achieve the savings goal. This allows the household account book creation unit to provide specific advice for optimizing the balance between the user's income and expenditure and reducing wasteful expenditure. Furthermore, the household account book creation unit can collect user feedback and continuously improve the accuracy and effectiveness of the household account book. This allows the household account book creation unit to support the user's financial management and realize efficient savings and optimization of expenditure.

[0081] The advice unit provides advice on saving and expenditure evaluation based on the household account book. The advice is based on, for example, the reduction of wasteful expenditure and an efficient saving method, but is not limited to such examples. Specifically, the advice unit provides advice for reducing wasteful expenditure based on the household account book. For example, the user's expenditure data is analyzed, wasteful expenditure in a specific category is identified, and a method for reducing the wasteful expenditure is proposed. The advice unit can also use the generation AI to teach an efficient saving method. The generation AI provides advice using a model that inputs data from the household account book and outputs an efficient saving method. For example, the generation AI proposes an optimal savings plan based on the user's income and expenditure data, and sets a specific savings goal. The advice unit can also provide customized advice according to the user's lifestyle and goals. For example, if the user is planning a trip or a big purchase, the advice unit can suggest a saving method and a method for adjusting expenditure based on the plan. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. As a result, the advice unit can support the user's financial management and realize efficient savings and optimization of expenditure. Furthermore, the advice unit can provide motivation and support to encourage the user to change his / her behavior. For example, the advice unit can periodically report the progress of the user in achieving the goal set by the user and provide rewards and incentives according to the degree of achievement. In this way, the advice unit can provide support for the user to save and save continuously and assist in achieving financial goals.

[0082] The goal setting unit automatically sets the monthly payment amount based on the goal set by the user and reviews the income. The goal setting is performed, for example, based on a savings goal or an expenditure reduction goal, but is not limited to such examples. Specifically, the goal setting unit calculates the monthly savings amount and reviews the expenditure based on the goal set by the user. For example, if the user sets a goal to save a specific amount, the monthly expenditure is adjusted based on the goal, and a plan is made to achieve the savings goal. The goal setting unit can also review the income using the generation AI. The generation AI sets the goal using a model that inputs the user's income data and outputs the review of the income. For example, the generation AI predicts the increase / decrease or fluctuation of income based on the user's income data, and proposes an expenditure plan accordingly. Furthermore, the goal setting unit can provide customized goal setting according to the user's lifestyle and goals. For example, if the user is planning a trip or a big purchase, the system suggests a method of saving and a method of adjusting expenditure based on the plan. As a result, the goal setting unit can provide specific advice to optimize the balance between the user's income and expenditure and reduce wasteful expenditure. Furthermore, the goal setting unit can collect feedback from the user and continuously improve the accuracy and effectiveness of goal setting, thereby supporting the user's financial management and enabling efficient savings and optimization of spending.

[0083] The collection unit can collect daily payment data of the user. Daily payment data includes, for example, credit card usage history, cash expenditure records, and the like, but is not limited to such examples. For example, the collection unit collects credit card usage history and stores it in a database. The collection unit can also manually input the cash expenditure records. Furthermore, the collection unit can collect electronic money usage history. For example, the collection unit automatically collects electronic money usage history and stores it in a database. In this way, by collecting daily payment data of the user, the payment pattern can be accurately grasped. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the credit card usage history into the generation AI and cause the generation AI to collect the payment data.

[0084] The analysis unit can analyze the collected data and learn the payment pattern. The analysis unit, for example, analyzes the collected data and learns the payment pattern. The payment pattern includes, for example, the monthly payment frequency and the fluctuation of the payment amount, but is not limited to such examples. The analysis unit learns the user's payment pattern based on, for example, past payment data. The analysis unit can also analyze trends in the payment data using the generation AI. For example, the generation AI analyzes seasonal fluctuations and cyclical patterns of the payment data. In this way, the collected data can be analyzed to learn the user's payment pattern. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the collected payment data to the generation AI and cause the generation AI to execute learning of the payment pattern.

[0085] The estimation unit can estimate the future payment amount based on the analyzed data. The estimation unit, for example, estimates the future payment amount based on the analyzed data. The future payment amount includes, for example, the payment amount for the next month, the payment amount for the year, etc., but is not limited to such examples. The estimation unit, for example, predicts the payment amount for the next month based on past payment data. The estimation unit can also estimate the future payment amount using the generation AI. For example, the generation AI performs estimation using a prediction model that inputs past payment data and outputs future payment amounts. This makes it possible to predict future expenditures by estimating the future payment amount based on the analyzed data. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input the analyzed payment data to the generation AI and cause the generation AI to execute an estimate of the future payment amount.

[0086] The household account book creation unit can create an appropriate household account book based on the estimated payment amount. The household account book creation unit creates an appropriate household account book based on the estimated payment amount, for example. The appropriate household account book includes, for example, the balance between income and expenditure, a method for setting a savings goal, and the like, but is not limited to such examples. The household account book creation unit provides advice for reducing wasteful expenditure, for example, by considering the balance between income and expenditure. The household account book creation unit can also create an optimal household account book using a generation AI. For example, the generation AI creates a household account book using a model that inputs data on income and expenditure and outputs an optimal household account book. This enables efficient household management by creating an optimal household account book based on the estimated payment amount. A part or all of the above-mentioned processing in the household account book creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the household account book creation unit can input the estimated payment amount to the generation AI and cause the generation AI to create a household account book.

[0087] The advice unit can provide advice on saving and an expenditure evaluation based on the household account book. The advice unit provides advice on saving and an expenditure evaluation based on the household account book, for example. The advice on saving includes, but is not limited to, for example, reducing wasteful expenditure and efficient savings methods. The advice unit provides advice for reducing wasteful expenditure, for example, based on the household account book. The advice unit can also teach an efficient savings method using a generation AI. For example, the generation AI provides advice using a model that inputs data from the household account book and outputs an efficient savings method. This makes it possible to reduce wasteful expenditure by providing advice on saving and an expenditure evaluation based on the household account book. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the advice unit can input data from the household account book to the generation AI and cause the generation AI to execute advice on saving and an expenditure evaluation.

[0088] The goal setting unit can automatically set the monthly payment amount based on the goal set by the user and review the income. The goal setting unit, for example, automatically sets the monthly payment amount based on the goal set by the user and reviews the income. The goal includes, for example, a savings goal and an expenditure reduction goal, but is not limited to such examples. The goal setting unit, for example, calculates the monthly savings amount based on the goal set by the user and reviews the expenditure. The goal setting unit can also review the income using a generation AI. For example, the generation AI sets the goal using a model that inputs the income data of the user and outputs the review of the income. As a result, the monthly payment amount is automatically set based on the goal set by the user and the income is reviewed, thereby making a plan for achieving the goal. A part or all of the above-mentioned processing in the goal setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the goal setting unit can input the goal set by the user to the generation AI and cause the generation AI to automatically set the monthly payment amount and review the income.

[0089] The collection unit can estimate the user's emotion and adjust the collection timing of the payment data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the collection timing of the payment data based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used to estimate the emotion. For example, when the user is stressed, the collection unit delays the collection timing to collect data when the user is relaxed. In addition, when the user is relaxed, the collection unit can immediately collect the payment data and perform real-time analysis. Furthermore, when the user is in a hurry, the collection unit can shorten the collection timing to quickly collect data. This enables more appropriate data collection by adjusting the collection timing of the payment data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data to the generation AI and cause the generation AI to adjust the timing of collecting payment data.

[0090] The collection unit can analyze the user's past payment history and select the optimal collection method. The collection unit, for example, analyzes the user's past payment history and selects the optimal collection method. The optimal collection method includes, for example, the frequency of data collection, the collection means, etc., but is not limited to such examples. The collection unit, for example, preferentially collects payment methods (credit cards, electronic money, etc.) that the user frequently uses. The collection unit can also preferentially collect payments made in a specific time period from the user's past payment history. Furthermore, the collection unit can analyze the user's payment history and focus on collecting payment data at specific stores or services. This makes it possible to select the optimal collection method by analyzing the user's past payment history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past payment history to the generation AI and cause the generation AI to select the optimal collection method.

[0091] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting payment data. The collection unit performs filtering based on the user's current living situation and areas of interest when collecting payment data, for example. Living situations include, but are not limited to, income status, family structure, and the like. Areas of interest include, but are not limited to, hobbies, products and services of interest, and the like. For example, when the user is traveling, the collection unit preferentially collects travel-related payment data. In addition, when the user is interested in health, the collection unit can also focus on collecting health-related payment data. Furthermore, when the user starts a new hobby, the collection unit can also collect payment data related to the hobby. As a result, highly relevant data can be collected by filtering based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to filter the payment data.

[0092] The collection unit can estimate the user's emotion and determine the priority of the payment data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of the payment data to be collected based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used for estimating the emotion. For example, when the user is stressed, the collection unit postpones the collection of payment data with low importance. In addition, when the user is relaxed, the collection unit can also collect all payment data evenly. Furthermore, when the user is in a hurry, the collection unit can also collect payment data with high importance preferentially. In this way, important data can be preferentially collected by determining the priority of the payment data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data to the generation AI and have the generation AI determine the priority of payment data.

[0093] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting payment data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting payment data. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a specific area, the collection unit preferentially collects payment data in that area. In addition, when the user is traveling, the collection unit can also focus on collecting payment data at the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect daily payment data. In this way, by considering the geographical location information of the user, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of payment data.

[0094] The collection unit can analyze the social media activity of the user and collect related data when collecting the payment data. For example, the collection unit analyzes the social media activity of the user and collects related data when collecting the payment data. The social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. For example, the collection unit collects related payment data based on purchase information shared by the user on social media. The collection unit can also collect payment data related to products and services in which the user has shown interest on social media. Furthermore, the collection unit can collect payment data related to specific events and campaigns from the user's social media activity. In this way, the related payment data can be collected by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect the payment data.

[0095] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used for estimating the emotion. For example, the analysis unit can provide a simple and highly visible analysis result when the user is stressed. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a concise analysis result with the main points when the user is in a hurry. This allows the analysis to be adjusted according to the user's emotion, thereby providing a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0096] The analysis unit can adjust the level of detail of the analysis based on the importance of the payment data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the payment data during the analysis. The importance of the payment data includes, for example, the amount of expenditure, the frequency of expenditure, and the like, but is not limited to such examples. The analysis unit, for example, performs a detailed analysis on payment data with high importance. The analysis unit can also perform a simplified analysis on payment data with low importance. Furthermore, the analysis unit can also determine the priority of the analysis according to the importance of the payment data. As a result, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the payment data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the payment data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0097] The analysis unit can apply different analysis algorithms depending on the category of the payment data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of the payment data during analysis. The categories of payment data include, for example, food expenses, transportation expenses, entertainment expenses, etc., but are not limited to such examples. The analysis unit applies a specific analysis algorithm to food-related payment data, for example. The analysis unit can also apply another analysis algorithm to transportation-related payment data. Furthermore, the analysis unit can apply yet another analysis algorithm to entertainment-related payment data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the payment data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category of the payment data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0098] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used to estimate the emotion. For example, the analysis unit provides a short and to the point analysis result when the user is stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a concise analysis result when the user is in a hurry. This allows the length of the analysis to be adjusted according to the user's emotion, thereby providing a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0099] The analysis unit can determine the priority of the analysis based on the submission time of the payment data during the analysis. The analysis unit, for example, determines the priority of the analysis based on the submission time of the payment data during the analysis. The submission time of the payment data includes, for example, the end of the month, payday, etc., but is not limited to such examples. The analysis unit, for example, preferentially analyzes the most recently submitted payment data. The analysis unit can also postpone payment data submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the priority of the analysis based on the submission time of the payment data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the payment data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0100] The analysis unit can adjust the order of analysis based on the relevance of the payment data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the payment data during analysis. The relevance of the payment data includes, for example, expenditures in the same category, expenditures at the same store, etc., but is not limited to such examples. For example, the analysis unit preferentially analyzes payment data with high relevance. In addition, the analysis unit can postpone payment data with low relevance. Furthermore, the analysis unit can adjust the schedule of analysis based on the relevance of the payment data. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the payment data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the payment data to the generation AI and cause the generation AI to adjust the order of analysis.

[0101] The estimation unit can estimate the user's emotion and adjust the estimation method based on the estimated user's emotion. The estimation unit can estimate the user's emotion and adjust the estimation method based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used to estimate the emotion. For example, the estimation unit can provide a simple estimation method when the user is stressed. Furthermore, the estimation unit can also provide a detailed estimation method when the user is relaxed. Furthermore, the estimation unit can also provide a method for performing a quick estimation when the user is in a hurry. This allows the estimation unit to adjust the estimation method according to the user's emotion and provide a more appropriate estimation result. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input a user's emotional data into the generation AI and cause the generation AI to adjust the estimation method.

[0102] The trial calculation unit can optimize the trial calculation algorithm by referring to past payment data at the time of trial calculation. The trial calculation unit, for example, optimizes the trial calculation algorithm by referring to past payment data at the time of trial calculation. The past payment data includes, for example, an expenditure history for the past year, expenditure data for a specific period, etc., but is not limited to such examples. The trial calculation unit adjusts the trial calculation algorithm, for example, based on the past payment data. The trial calculation unit can also extract parameters for improving the accuracy of the trial calculation from the past payment data. Furthermore, the trial calculation unit can also optimize the trial calculation algorithm by referring to the past payment data. As a result, the trial calculation algorithm can be optimized by referring to the past payment data, and the accuracy of the trial calculation can be improved. A part or all of the above-mentioned processing in the trial calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the trial calculation unit can input the past payment data to the generation AI and cause the generation AI to execute optimization of the trial calculation algorithm.

[0103] The estimation unit can apply different estimation methods to each category of payment data during estimation. The estimation unit, for example, applies different estimation methods to each category of payment data during estimation. The categories of payment data include, for example, food expenses, transportation expenses, and entertainment expenses, but are not limited to such examples. The estimation unit applies a specific estimation method to food-related payment data, for example. The estimation unit can also apply a different estimation method to transportation-related payment data. Furthermore, the estimation unit can apply yet another estimation method to entertainment-related payment data. This allows for more accurate estimation by applying different estimation methods to each category of payment data. Part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input the category of payment data to the generation AI and cause the generation AI to apply the estimation method.

[0104] The trial calculation unit can estimate the user's emotion and determine the priority of the trial calculation based on the estimated user's emotion. The trial calculation unit can estimate the user's emotion and determine the priority of the trial calculation based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used for estimating the emotion. For example, when the user is stressed, the trial calculation unit postpones the trial calculation of low importance. In addition, when the user is relaxed, the trial calculation unit can perform all trial calculations equally. Furthermore, when the user is in a hurry, the trial calculation unit can perform the trial calculation of high importance with priority. In this way, the priority of the trial calculation is determined according to the user's emotion, so that important trial calculations can be performed with priority. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the trial calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input a user's emotional data into the generation AI and have the generation AI determine the priorities of the estimation.

[0105] The trial calculation unit can weight the trial calculation based on the submission time of the payment data at the time of the trial calculation. The trial calculation unit, for example, weights the trial calculation based on the submission time of the payment data at the time of the trial calculation. The submission time of the payment data includes, for example, the end of the month, payday, etc., but is not limited to such examples. The trial calculation unit, for example, weights the payment data submitted recently. In addition, the trial calculation unit can also reduce the weight of payment data submitted earlier. Furthermore, the trial calculation unit can adjust the weight of the trial calculation based on the submission time. As a result, more accurate trial calculation is possible by weighting the trial calculation based on the submission time of the payment data. Part or all of the above-mentioned processing in the trial calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the trial calculation unit can input the submission time of the payment data to the generation AI and cause the generation AI to execute the weighting of the trial calculation.

[0106] The estimation unit can improve the accuracy of the estimation by referring to the market data related to the payment data at the time of the estimation. The estimation unit, for example, improves the accuracy of the estimation by referring to the market data related to the payment data at the time of the estimation. The related market data includes, for example, data of other companies in the same industry, industry trend data, etc., but is not limited to such examples. The estimation unit, for example, improves the accuracy of the estimation by referring to market data related to the payment data. In addition, the estimation unit can also adjust the estimation algorithm based on the market data. Furthermore, the estimation unit can also combine the payment data and the market data to improve the accuracy of the estimation. Thereby, the accuracy of the estimation can be improved by referring to the market data related to the payment data. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input the related market data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the estimation.

[0107] The household accounting book creation unit can estimate the user's emotion and adjust the creation method of the household accounting book based on the estimated user's emotion. The household accounting book creation unit, for example, estimates the user's emotion and adjusts the creation method of the household accounting book based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used for estimating the emotion. For example, the household accounting book creation unit creates a simple household accounting book when the user is stressed. Also, the household accounting book creation unit can create a detailed household accounting book when the user is relaxed. Furthermore, the household accounting book creation unit can provide a method for quickly creating a household accounting book when the user is in a hurry. This allows a more appropriate household accounting book to be created by adjusting the creation method of the household accounting book according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the household accounting book creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the household accounting creation unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the household accounting is created.

[0108] The household account book creation unit can adjust the level of detail of the household account book based on the importance of the payment data when creating the household account book. For example, the household account book creation unit adjusts the level of detail of the household account book based on the importance of the payment data when creating the household account book. The importance of the payment data includes, for example, the amount of expenditure, the frequency of expenditure, and the like, but is not limited to such examples. For example, the household account book creation unit writes detailed items in the household account book for payment data with high importance. In addition, the household account book creation unit can also write simplified items in the household account book for payment data with low importance. Furthermore, the household account book creation unit can also determine the priority order of items in the household account book according to the importance of the payment data. This allows efficient household management by adjusting the level of detail of the household account book based on the importance of the payment data. A part or all of the above-mentioned processing in the household account book creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the household account book creation unit can input the importance of the payment data to the generation AI and cause the generation AI to adjust the level of detail of the household account book.

[0109] The household account book creation unit can apply different household account book creation algorithms according to the category of payment data when creating the household account book. For example, the household account book creation unit applies different household account book creation algorithms according to the category of payment data when creating the household account book. The categories of payment data include, for example, food expenses, transportation expenses, and entertainment expenses, but are not limited to such examples. For example, the household account book creation unit applies a specific household account book creation algorithm to food-related payment data. In addition, the household account book creation unit can also apply another household account book creation algorithm to transportation-related payment data. Furthermore, the household account book creation unit can apply yet another household account book creation algorithm to entertainment-related payment data. In this way, by applying different household account book creation algorithms according to the category of payment data, a more accurate household account book can be created. Part or all of the above-mentioned processing in the household account book creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the household account book creation unit can input the category of payment data to the generation AI and cause the generation AI to apply the household account book creation algorithm.

[0110] The household account book creation unit can estimate the user's emotion and adjust the display method of the household account book based on the estimated user's emotion. The household account book creation unit, for example, estimates the user's emotion and adjusts the display method of the household account book based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used for estimating the emotion. For example, the household account book creation unit can provide a display method that is simple and highly visible when the user is stressed. In addition, the household account book creation unit can also provide a display method that includes detailed information when the user is relaxed. Furthermore, the household account book creation unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to display the household account book more appropriately by adjusting the display method of the household account book according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the household account book creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the household account book creation unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the household account book is displayed.

[0111] The household account book creation unit can determine the priority of the household account book based on the submission time of the payment data when creating the household account book. The household account book creation unit, for example, determines the priority of the household account book based on the submission time of the payment data when creating the household account book. The submission time of the payment data includes, for example, the end of the month, payday, etc., but is not limited to such examples. The household account book creation unit, for example, preferentially writes the most recently submitted payment data in the household account book. In addition, the household account book creation unit can also postpone payment data submitted earlier. Furthermore, the household account book creation unit can adjust the priority of the items in the household account book based on the submission time. This enables efficient household management by determining the priority of the household account book based on the submission time of the payment data. Part or all of the above-mentioned processing in the household account book creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the household account book creation unit can input the submission time of the payment data to the generation AI and cause the generation AI to execute the determination of the priority of the household account book.

[0112] The household account book creation unit can adjust the order of the household account book based on the relevance of the payment data when creating the household account book. The household account book creation unit, for example, adjusts the order of the household account book based on the relevance of the payment data when creating the household account book. The relevance of the payment data includes, for example, expenditures in the same category, expenditures at the same store, and the like, but is not limited to such examples. The household account book creation unit, for example, preferentially writes highly relevant payment data in the household account book. In addition, the household account book creation unit can also postpone payment data with low relevance. Furthermore, the household account book creation unit can also adjust the order of items in the household account book based on the relevance of the payment data. As a result, efficient household management is possible by adjusting the order of the household account book based on the relevance of the payment data. A part or all of the above-mentioned processing in the household account book creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the household account book creation unit can input the relevance of the payment data to the generation AI and cause the generation AI to adjust the order of the household account book.

[0113] The advice unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. The advice unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used for estimating the emotion. For example, the advice unit can provide simple and highly visible advice when the user is stressed. In addition, the advice unit can provide detailed advice when the user is relaxed. Furthermore, the advice unit can provide concise advice with the main points when the user is in a hurry. This allows more appropriate advice to be provided by adjusting the way of expressing advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotional data to the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0114] The advice unit can adjust the level of detail of the advice based on the importance of the payment data when giving advice. The advice unit, for example, adjusts the level of detail of the advice based on the importance of the payment data when giving advice. The importance of the payment data includes, for example, the amount of expenditure, the frequency of expenditure, and the like, but is not limited to such examples. The advice unit, for example, provides detailed advice for payment data with high importance. In addition, the advice unit can also provide simplified advice for payment data with low importance. Furthermore, the advice unit can also determine the priority of advice according to the importance of the payment data. As a result, efficient advice can be provided by adjusting the level of detail of advice based on the importance of the payment data. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the importance of the payment data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0115] The advice unit can apply different advice algorithms depending on the category of payment data when giving advice. The advice unit, for example, applies different advice algorithms depending on the category of payment data when giving advice. The categories of payment data include, for example, food expenses, transportation expenses, entertainment expenses, etc., but are not limited to such examples. The advice unit applies a specific advice algorithm to food-related payment data, for example. The advice unit can also apply another advice algorithm to transportation-related payment data. Furthermore, the advice unit can apply yet another advice algorithm to entertainment-related payment data. This allows for more accurate advice by applying different advice algorithms depending on the category of payment data. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the category of payment data to the generation AI and cause the generation AI to apply the advice algorithm.

[0116] The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used to estimate the emotion. For example, the advice unit can provide short and to the point advice when the user is stressed. The advice unit can also provide detailed advice when the user is relaxed. Furthermore, the advice unit can also provide concise advice when the user is in a hurry. This allows more appropriate advice to be provided by adjusting the length of the advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the advice.

[0117] The advice unit can determine the priority of advice based on the submission time of payment data when giving advice. The advice unit, for example, determines the priority of advice based on the submission time of payment data when giving advice. The submission time of payment data includes, for example, the end of the month, payday, etc., but is not limited to such examples. The advice unit, for example, gives priority to advice on recently submitted payment data. The advice unit can also postpone payment data submitted earlier. Furthermore, the advice unit can adjust the schedule of advice based on the submission time. As a result, efficient advice can be provided by determining the priority of advice based on the submission time of payment data. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the submission time of payment data to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0118] The advice unit can adjust the order of advice based on the relevance of the payment data when giving advice. The advice unit, for example, adjusts the order of advice based on the relevance of the payment data when giving advice. The relevance of the payment data includes, for example, expenditures in the same category, expenditures at the same store, etc., but is not limited to such examples. For example, the advice unit gives advice preferentially to payment data with high relevance. The advice unit can also postpone payment data with low relevance. Furthermore, the advice unit can adjust the schedule of advice based on the relevance of the payment data. As a result, efficient advice can be provided by adjusting the order of advice based on the relevance of the payment data. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the relevance of the payment data to the generation AI and cause the generation AI to adjust the order of advice.

[0119] The goal setting unit can estimate the user's emotion and adjust the goal setting method based on the estimated user's emotion. The goal setting unit, for example, estimates the user's emotion and adjusts the goal setting method based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used for estimating the emotion. For example, the goal setting unit provides a simple goal setting method when the user is stressed. In addition, the goal setting unit can also provide a detailed goal setting method when the user is relaxed. Furthermore, the goal setting unit can also provide a method for quickly setting goals when the user is in a hurry. This allows for more appropriate goal setting by adjusting the goal setting method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the goal setting unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the goal setting method.

[0120] The goal setting unit can select the optimal goal setting method by referring to the user's past goal achievement history when setting a goal. For example, the goal setting unit selects the optimal goal setting method by referring to the user's past goal achievement history when setting a goal. The past goal achievement history includes, for example, past goal achievement rates, types of goals achieved, and the like, but is not limited to such examples. For example, the goal setting unit proposes the optimal goal setting method based on the user's past goal achievement history. In addition, the goal setting unit can also extract a successful goal setting method from the past goal achievement history. Furthermore, the goal setting unit can also improve the accuracy of goal setting by referring to the user's past goal achievement history. As a result, the optimal goal setting method can be selected by referring to the user's past goal achievement history. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the past goal achievement history to the generation AI and cause the generation AI to select the optimal goal setting method.

[0121] The goal setting unit can customize the goal setting means based on the user's current living situation when setting a goal. The goal setting unit customizes the goal setting means based on the user's current living situation when setting a goal, for example. Living situations include, but are not limited to, income situation, family structure, and the like. For example, when the user is busy, the goal setting unit sets an easily achievable goal. In addition, when the user has time, the goal setting unit can also set a challenging goal. Furthermore, the goal setting unit can customize the goal setting means according to the user's living situation. This enables more realistic goal setting by customizing the goal setting means based on the user's current living situation. Part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the user's living situation data to the generation AI and cause the generation AI to execute customization of the goal setting means.

[0122] The goal setting unit can estimate the user's emotion and determine the priority of goal setting based on the estimated user's emotion. The goal setting unit can estimate the user's emotion and determine the priority of goal setting based on the estimated user's emotion. For example, an emotion recognition algorithm or an emotion engine can be used for estimating the emotion. For example, when the user is feeling stressed, the goal setting unit postpones goal setting of low importance. In addition, when the user is relaxed, the goal setting unit can also set all goals equally. Furthermore, when the user is in a hurry, the goal setting unit can also prioritize goal setting of high importance. In this way, by determining the priority of goal setting according to the user's emotion, important goals can be set preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the goal setting unit can input the user's emotional data to the generation AI and have the generation AI determine the priority of goal setting.

[0123] The goal setting unit can select the optimal goal setting method in consideration of the geographical location information of the user when setting a goal. For example, the goal setting unit selects the optimal goal setting method in consideration of the geographical location information of the user when setting a goal. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a specific area, the goal setting unit sets a goal related to that area. In addition, when the user is traveling, the goal setting unit can also set a goal that is achievable at the travel destination. Furthermore, the goal setting unit can also suggest the optimal goal setting method based on the geographical location information of the user. In this way, the optimal goal setting method can be selected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the goal setting method.

[0124] The goal setting unit can analyze the user's social media activity and suggest a means for setting the goal when setting the goal. For example, the goal setting unit analyzes the user's social media activity and suggests a means for setting the goal when setting the goal. The social media activity includes, for example, the content of the post, the number of likes, and the like, but is not limited to such examples. For example, the goal setting unit suggests a related goal setting means based on the goal shared by the user on social media. The goal setting unit can also suggest a goal setting means based on the activity in which the user showed interest on social media. Furthermore, the goal setting unit can also suggest a goal setting means related to a specific event or campaign from the user's social media activity. In this way, the user's social media activity can be analyzed to suggest a related goal setting means. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal setting unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the suggestion of the goal setting means.

[0125] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0126] The electronic payment system can also collect health data from the user and correlate it with spending patterns. For example, data from the user's fitness tracker or smartwatch can be collected to analyze the relationship between health status and spending. This can help determine whether spending tends to increase during periods of good health. The system can also recommend spending habits to lead a healthier life based on the health data, such as gym membership fees or healthy food purchases. Furthermore, the system can predict future medical expenses based on the health data and reflect them in the household budget. This allows the user to integrate health management with financial management.

[0127] The collection unit can analyze the user's purchase history and ascertain preferences for specific brands and products. For example, if the user frequently purchases products of a specific brand, the collection unit can prioritize collecting new product and sale information for that brand. Also, if the user likes to purchase products in a specific category, the collection unit can focus on collecting payment data related to that category. Furthermore, the collection unit can analyze seasonal purchasing patterns from the user's purchase history and make seasonal expenditure predictions. This allows for more accurate expenditure predictions based on the user's purchasing trends.

[0128] The analysis unit can estimate the user's emotions and adjust the timing of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis can be temporarily stopped and resumed when the user is relaxed. Also, if the user is relaxed, the analysis can be performed immediately to provide real-time feedback. Furthermore, if the user is in a hurry, the analysis priority can be adjusted to quickly analyze important data. In this way, more appropriate analysis results can be provided by adjusting the timing of analysis according to the user's emotions.

[0129] The estimation unit can perform risk assessment for future expenditures based on the user's past payment data. For example, risk factors in a specific expenditure category are identified from the past payment data, and the risk for future expenditures is assessed. In addition, advice for reducing the risk can be provided based on the risk assessment. For example, if the risk in a specific expenditure category is high, advice for reducing expenditures in that category is provided. Furthermore, insurance and savings suggestions for future expenditures can be made based on the risk assessment. This allows the user to manage risks for future expenditures and achieve financial stability.

[0130] The household account book creation unit can estimate the user's emotions and adjust the layout of the household account book based on the estimated user's emotions. For example, if the user is feeling stressed, a simple layout with high visibility can be provided. If the user is relaxed, a layout including detailed information can be provided. Furthermore, if the user is in a hurry, a simple layout focusing on the main points can be provided. In this way, a more appropriate household account book can be provided by adjusting the layout of the household account book according to the user's emotions.

[0131] The advice unit can suggest environmentally friendly spending methods based on the user's spending data. For example, it can suggest environmentally friendly alternatives to products that the user frequently purchases. It can also provide advice for reducing energy consumption based on the user's spending data. Furthermore, when the user makes environmentally friendly spending, it can visualize the effect and provide feedback to the user. This allows the user to learn environmentally friendly spending methods and live a sustainable life.

[0132] The goal setting unit can estimate the user's emotions and adjust the method of providing feedback on the progress toward goal achievement based on the estimated user's emotions. For example, if the user is stressed, simple, positive feedback can be provided. If the user is relaxed, a detailed progress report can be provided. Furthermore, if the user is in a hurry, concise feedback that focuses on the main points can be provided. In this way, more appropriate support can be provided by adjusting the method of providing feedback on the progress toward goal achievement based on the user's emotions.

[0133] When collecting the user's payment data, the collection unit can estimate the user's purchasing intent and adjust the data collection method based on the estimated purchasing intent. For example, if the user intends to purchase a specific product, data related to that product can be collected preferentially. Also, if the user intends to participate in a specific event, payment data related to that event can be collected with a focus. Furthermore, the timing of data collection can be adjusted based on the user's purchasing intent. This makes it possible to collect more appropriate data by adjusting the data collection method according to the user's purchasing intent.

[0134] When analyzing the user's payment data, the analysis unit can improve the accuracy of the analysis by taking into account the user's life events. For example, when the user faces a life event such as marriage, childbirth, or moving, the analysis focuses on the expenditures related to that event. Future expenditure predictions can also be made based on the life event. Furthermore, spending trends can be analyzed and appropriate advice can be provided to the user according to the life event. This allows for more accurate analysis by taking into account the user's life events.

[0135] The calculation unit can estimate the user's emotions and adjust the way in which the calculation results are presented based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible calculation result can be provided. If the user is relaxed, detailed calculation results can be provided. Furthermore, if the user is in a hurry, concise calculation results that focus on the main points can be provided. In this way, by adjusting the way in which the calculation results are presented according to the user's emotions, more appropriate calculation results can be provided.

[0136] The process flow of the second embodiment will be briefly described below.

[0137] Step 1: The collection unit collects the user's payment patterns. The user's payment patterns include credit card usage history, cash expenditure records, electronic money usage history, etc. The collection unit collects this data automatically or manually and stores it in a database. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is based on learning payment patterns and trend analysis, and also analyzes seasonal fluctuations and cyclical patterns. Step 3: The estimation unit estimates future payment amounts based on the data analyzed by the analysis unit. The estimation is based on trend analysis of past payment data and predictive algorithms, and can also use predictive models using generative AI. Step 4: The household accounting creation unit creates an optimal household accounting book based on the payment amount estimated by the estimation unit. The household accounting book is created based on the balance of income and expenditure and savings goals, and the optimal household accounting book can also be created using generation AI. Step 5: The advice section provides savings advice and expenditure evaluation based on the household budget. Advice is based on reducing wasteful spending and efficient savings methods, and can also teach efficient savings methods using generative AI. Step 6: The goal setting unit automatically sets the monthly payment amount based on the goal set by the user and reviews the income. Goal setting is based on savings goals and expenditure reduction goals, and income can also be reviewed using generation AI.

[0138] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0139] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0140] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0141] For example, the collection unit can estimate the user's emotions using the camera 42 and microphone 38B of the smart device 14, and adjust the timing of collecting payment data based on the estimated emotions. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the future payment amount based on the analyzed data. The household account book creation unit is realized by the specific processing unit 290 of the data processing device 12, and creates an optimal household account book based on the estimated payment amount. The advice unit is realized by the control unit 46A of the smart device 14, and provides advice on saving and evaluation of expenditure based on the household account book. The goal setting unit is realized by the specific processing unit 290 of the data processing device 12, and automatically sets the monthly payment amount based on the goal set by the user, and reviews the income. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0142] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0145] 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.

[0146] 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 the voice according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0148] 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 performed in a secure state.

[0149] Fig. 4 shows an example of 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.

[0150] 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.

[0151] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0152] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0153] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0154] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0155] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0156] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0157] For example, the collection unit can estimate the user's emotions using the camera 42 and microphone 238 of the smart glasses 214, and adjust the timing of collecting payment data based on the estimated emotions. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the future payment amount based on the analyzed data. The household account book creation unit is realized by the specific processing unit 290 of the data processing device 12, and creates an optimal household account book based on the estimated payment amount. The advice unit is realized by the control unit 46A of the smart glasses 214, and provides advice on saving and evaluation of expenditure based on the household account book. The goal setting unit is realized by the specific processing unit 290 of the data processing device 12, and automatically sets the monthly payment amount based on the goal set by the user, and reviews the income. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0158] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0159] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0161] 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.

[0162] 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 the voice according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0164] 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 performed in a secure state.

[0165] 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.

[0166] 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.

[0167] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0169] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 voice indicating a user input for 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.

[0171] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0173] For example, the collection unit can estimate the user's emotions using the camera 42 and microphone 238 of the headset terminal 314, and adjust the timing of collecting payment data based on the estimated emotions. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the future payment amount based on the analyzed data. The household account book creation unit is realized by the specific processing unit 290 of the data processing device 12, and creates an optimal household account book based on the estimated payment amount. The advice unit is realized by the control unit 46A of the headset terminal 314, and provides advice on saving and evaluation of expenditure based on the household account book. The goal setting unit is realized by the specific processing unit 290 of the data processing device 12, and automatically sets the monthly payment amount based on the goal set by the user, and reviews the income. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0174] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0177] 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.

[0178] 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 the voice according to instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0180] 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 performed in a secure state.

[0181] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0182] 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.

[0183] 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.

[0184] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0185] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0186] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0187] 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 a voice indicating a user input for 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.

[0188] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0189] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0190] For example, the collection unit can estimate the user's emotions using the camera 42 and microphone 238 of the robot 414, and adjust the timing of collecting payment data based on the estimated emotions. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the future payment amount based on the analyzed data. The household account book creation unit is realized by the specific processing unit 290 of the data processing device 12, and creates an optimal household account book based on the estimated payment amount. The advice unit is realized by the control unit 46A of the robot 414, and provides advice on saving and evaluation of expenditure based on the household account book. The goal setting unit is realized by the specific processing unit 290 of the data processing device 12, and automatically sets the monthly payment amount based on the goal set by the user, and reviews the income. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0191] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0192] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0193] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0194] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0195] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0196] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0198] 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.

[0199] 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 Universal Serial Bus (USB) 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.

[0200] In addition, 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 upon request from the data processing device 12.

[0201] 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.

[0202] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0203] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0204] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0205] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0206] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0207] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0208] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0209] (Appendix 1) a collection unit for collecting payment patterns of users; an analysis unit that analyzes the data collected by the collection unit; an estimation unit that estimates a future payment amount based on the data analyzed by the analysis unit; a household accounting book creation unit that creates an appropriate household accounting book based on the payment amount estimated by the estimation unit; an advice unit that gives saving advice or evaluates expenditures based on the household account book created by the household account book creation unit; A goal setting unit that automatically sets a monthly payment amount based on a goal set by a user. A system characterized by: (Appendix 2) The collecting unit includes: Collecting users' daily payment data 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data and learn payment patterns 2. The system of claim 1, (Appendix 4) The estimation unit is Calculate future payments based on the analyzed data 2. The system of claim 1, (Appendix 5) The household accounting book creation unit includes: Create an appropriate household budget based on the estimated payment amount 2. The system of claim 1, (Appendix 6) The advice unit is Providing savings advice and expenditure evaluation based on household budget 2. The system of claim 1, (Appendix 7) The goal setting unit, Automatically set monthly payments based on user-defined goals and review your income 2. The system of claim 1, (Appendix 8) The collecting unit includes: Inferring user sentiment and adjusting the timing of payment data collection based on the inferred user sentiment 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze users' past payment history and choose the best collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: Filter payment data as it is collected based on the user's current life situation and interests 2. The system of claim 1, (Appendix 11) The collecting unit includes: Infer user sentiment and prioritize payment data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting payment data, take into account the user’s geographic location to prioritize collecting the most relevant data. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting payment data, analyze your social media activity and collect related data 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail based on the importance of the payment data 2. The system of claim 1, (Appendix 16) The analysis unit is During parsing, different parsing algorithms are applied depending on the category of payment data. 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when payment data was submitted 2. The system of claim 1, (Appendix 19) The analysis unit is During parsing, adjust the order of parsing based on the relevance of payment data 2. The system of claim 1, (Appendix 20) The estimation unit is Infer user emotions and adjust the estimation method based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The estimation unit is When making estimates, the calculation algorithm is optimized by referencing past payment data. 2. The system of claim 1, (Appendix 22) The estimation unit is When making estimates, different estimation methodologies are applied to different categories of payment data. 2. The system of claim 1, (Appendix 23) The estimation unit is Estimate user emotions and prioritize calculations based on the estimated user emotions 2. The system of claim 1, (Appendix 24) The estimation unit is The calculations are weighted based on when payment data was submitted. 2. The system of claim 1, (Appendix 25) The estimation unit is When making estimates, we will refer to relevant market data on payment data to improve the accuracy of the estimates. 2. The system of claim 1, (Appendix 26) The household accounting book creation unit includes: Inferring a user's emotions and adjusting the way the household budget is created based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The household accounting book creation unit includes: When creating a household budget, adjust the level of detail based on the importance of the payment data 2. The system of claim 1, (Appendix 28) The household accounting book creation unit includes: When creating a household budget, apply different budget creation algorithms depending on the category of payment data. 2. The system of claim 1, (Appendix 29) The household accounting book creation unit includes: Inferring user emotions and adjusting the display method of the household budget based on the estimated user emotions 2. The system of claim 1, (Appendix 30) The household accounting book creation unit includes: When creating a household budget, prioritize household budgets based on when payment data is submitted 2. The system of claim 1, (Appendix 31) The household accounting book creation unit includes: When creating budgets, adjust the order of budgets based on the relevance of payment data 2. The system of claim 1, (Appendix 32) The advice unit is Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 33) The advice unit is When making recommendations, adjust the level of detail based on the importance of the payment data 2. The system of claim 1, (Appendix 34) The advice unit is At the time of advising, apply different advising algorithms depending on the category of payment data 2. The system of claim 1, (Appendix 35) The advice unit is Estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 36) The advice unit is At the time of advice, prioritize advice based on when payment data was submitted 2. The system of claim 1, (Appendix 37) The advice unit is At advice time, adjust advice order based on relevance of payment data 2. The system of claim 1, (Appendix 38) The goal setting unit, Inferring a user's emotions and adjusting a goal setting method based on the estimated user's emotions 2. The system of claim 1, (Appendix 39) The goal setting unit, When setting goals, select the optimal goal setting method by referring to the user's past goal achievement history. 2. The system of claim 1, (Appendix 40) The goal setting unit, When setting goals, customize the goal-setting experience based on the user's current life situation 2. The system of claim 1, (Appendix 41) The goal setting unit, Estimate user emotions and prioritize goal setting based on the estimated user emotions 2. The system of claim 1, (Appendix 42) The goal setting unit, When setting goals, choose the optimal goal setting method by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 43) The goal setting unit, When setting goals, analyze users' social media activity to suggest ways to set goals. 2. The system of claim 1, [Explanation of symbols]

[0210] 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 for collecting payment patterns of users; an analysis unit that analyzes the data collected by the collection unit; an estimation unit that estimates a future payment amount based on the data analyzed by the analysis unit; a household accounting book creation unit that creates an appropriate household accounting book based on the payment amount estimated by the estimation unit; an advice unit that gives saving advice or evaluates expenditures based on the household account book created by the household account book creation unit; A goal setting unit that automatically sets a monthly payment amount based on a goal set by a user. A system characterized by:

2. The collecting unit includes: Collecting users' daily payment data 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data and learn payment patterns 2. The system of claim 1 .

4. The advice unit, Providing savings advice and expenditure evaluation based on household budget 2. The system of claim 1 .

5. The goal setting unit, Automatically set monthly payments based on user-defined goals and review your income 2. The system of claim 1 .

6. The collecting unit includes: Inferring user sentiment and adjusting the timing of payment data collection based on the inferred user sentiment 2. The system of claim 1 .

7. The collecting unit includes: Analyze users' past payment history and choose the best collection method 2. The system of claim 1 .

8. The collecting unit includes: Filter payment data as it is collected based on the user's current life situation and interests 2. The system of claim 1 .

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