system

Abstract

The system according to this embodiment aims to efficiently manage expenditures using QR payment data and provide appropriate advice. [Solution] The system according to the embodiment comprises a collection unit, a classification unit, a report creation unit, an advice unit, and a campaign provision unit. The collection unit collects QR payment data. The classification unit classifies expenditures based on the data collected by the collection unit. The report creation unit creates an income and expenditure report based on the expenditure data classified by the classification unit. The advice unit provides advice based on the report created by the report creation unit. The campaign provision unit provides campaign information based on the advice provided by the advice unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that QR payment data was not fully utilized to efficiently manage expenditures and provide appropriate advice.

[0005] The system according to the embodiment aims to efficiently manage expenditures by utilizing QR payment data and provide appropriate advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a classification unit, a report creation unit, an advice unit, and a campaign provision unit. The data collection unit collects QR payment data. The classification unit classifies expenditures based on the data collected by the data collection unit. The report creation unit creates an income and expenditure report based on the expenditure data classified by the classification unit. The advice unit provides advice based on the report created by the report creation unit. The campaign provision unit provides campaign information based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage spending and provide appropriate advice by utilizing QR payment data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The electronic payment system according to an embodiment of the present invention is a system equipped with an AI-powered accounting ledger function. Based on QR payment data, the electronic payment system automatically classifies expenditures into categories such as food, living expenses, and entertainment using AI. This allows users to understand their spending trends. The AI ​​also creates and submits periodic income and expenditure reports to the user. This allows users to visually understand fluctuations in their spending on a weekly and monthly basis. Next, it provides an advice function. The AI ​​analyzes past spending patterns and provides specific advice on saving and spending trends. It also includes a function that allows users to set a monthly budget and notifies them of alerts when they are about to exceed that budget. Furthermore, it provides advice tailored to individual needs based on the user's spending trends and lifestyle. In addition, it includes a campaign information provision function. Based on the user's purchase and usage history, it performs analysis and notifies users of campaign and sale information tailored to frequently used stores and categories. Finally, it provides spending forecasting and goal setting functions. Based on past data, it predicts future spending patterns and provides advice to help balance income and expenses. It also allows users to set savings and budgeting goals, and the AI ​​supports them in creating plans to achieve those goals. These features allow users to efficiently manage their spending and achieve their savings and budgeting goals. Furthermore, the provision of promotional information ensures users don't miss out on special offers. This improves the user experience of the electronic payment system and increases user satisfaction. As a result, the electronic payment system allows users to efficiently manage their spending and achieve their savings and budgeting goals. Furthermore, the provision of promotional information ensures users don't miss out on special offers. This improves the user experience of the electronic payment system and increases user satisfaction.

[0029] The electronic payment system according to this embodiment comprises a collection unit, a classification unit, a report creation unit, an advice unit, and a campaign provision unit. The collection unit collects QR payment data. The QR payment data includes, but is not limited to, transaction date and time, transaction amount, and store information. The collection unit can collect QR payment data in real time, for example. The collection unit can also collect QR payment data periodically. For example, the collection unit can collect QR payment data at specific times daily, weekly, or monthly. The classification unit classifies expenditures based on the data collected by the collection unit. For example, the classification unit classifies expenditures into categories such as food expenses, living expenses, and entertainment. The classification unit can automatically classify the collected data using AI. For example, the classification unit uses an algorithm in which AI analyzes expenditure data and classifies it into each category. The report creation unit creates income and expenditure reports based on the expenditure data classified by the classification unit. The report creation unit can create monthly reports and weekly reports, for example. The report creation unit can automatically create periodic income and expenditure reports using AI. For example, the reporting unit uses an algorithm where AI analyzes spending data and generates visually easy-to-understand reports. The advice unit provides advice based on the reports created by the reporting unit. For example, the advice unit analyzes past spending patterns and provides specific advice on saving and spending trends. The advice unit can use AI to provide personalized advice to users. For example, the advice unit uses an algorithm where AI analyzes the user's spending data and makes saving suggestions and analyzes spending trends. The campaign provision unit provides campaign information based on the advice provided by the advice unit. For example, the campaign provision unit conducts analysis based on the user's purchase history and notifies users of campaign and sale information tailored to frequently used stores and categories. The campaign provision unit can use AI to provide users with the most relevant campaign information. For example, the campaign provision unit uses an algorithm where AI analyzes the user's purchase history and selects highly relevant campaign information.As a result, the electronic payment system according to this embodiment can efficiently manage users' spending and help them achieve their savings and budgeting goals. Furthermore, by providing campaign information, users can take advantage of special offers without missing out. This improves the user experience of the electronic payment system and increases user satisfaction.

[0030] The data collection unit collects QR payment data. This data includes, but is not limited to, transaction date and time, transaction amount, and store information. The data collection unit can collect QR payment data in real time, for example. Specifically, when a QR code (registered trademark) is scanned, transaction details are collected and sent to a central database. This allows for accurate tracking of which stores users paid at what times. The data collection unit can also collect QR payment data periodically. For example, it can collect QR payment data at specific times daily, weekly, or monthly. This allows for the accumulation of long-term spending data, which can then be used for analysis and reporting. Furthermore, the data collection unit can integrate data from different payment platforms and different stores. This enables centralized data management even when users utilize multiple payment methods. For example, even if a user uses credit cards or debit cards in addition to QR code payments, this data can be integrated and collected. This allows the data collection unit to understand the user's overall spending habits and build a foundation for more accurate analysis and advice.

[0031] The classification unit categorizes expenditures based on data collected by the collection unit. For example, the classification unit categorizes expenditures into categories such as food expenses, living expenses, and entertainment. Specifically, it analyzes collected QR payment data, identifies the content of each transaction, and classifies it into the appropriate category. The classification unit can use AI to automatically classify the collected data. For example, the AI ​​analyzes the store name and product name of a transaction and uses an algorithm to classify the expenditure into the appropriate category based on that. This eliminates the need for users to manually classify expenditures. Furthermore, the classification unit can learn from the user's past expenditure data to perform more accurate classifications. For example, if expenditures at a particular store are repeated, it learns which category that store belongs to and automatically classifies subsequent transactions into the correct category. The classification unit also provides a function that allows users to customize categories themselves. This allows users to add or change categories to match their lifestyle and expenditure patterns. As a result, the classification unit can efficiently and accurately classify the user's expenditures, which is useful for subsequent analysis and report generation.

[0032] The reporting unit creates income and expenditure reports based on expenditure data classified by the classification unit. For example, the reporting unit can create monthly and weekly reports. Specifically, it aggregates the classified expenditure data and generates reports in a visually easy-to-understand format. The reporting unit can use AI to automatically generate periodic income and expenditure reports. For example, the AI ​​analyzes expenditure data and uses algorithms to visualize expenditure trends and patterns using graphs and charts. This allows users to grasp their spending situation at a glance. Furthermore, the reporting unit can also incorporate the user's income data. This allows for an evaluation of the balance between income and expenditure and confirmation of financial health. For example, it can register the user's salary and other income sources in a database and create income and expenditure reports based on this information. The reporting unit can also perform trend analysis based on historical data. This allows users to understand their spending trends over the long term and use this information as a reference when planning future expenditures. In this way, the reporting unit can provide users with detailed and visually clear information on their spending situation, supporting effective expenditure management.

[0033] The Advice Department provides advice based on reports created by the Reporting Department. For example, the Advice Department analyzes past spending patterns and provides specific advice on saving and spending trends. Specifically, it analyzes the user's spending data and provides suggestions for reducing unnecessary spending or advice on controlling spending in specific categories. The Advice Department can use AI to provide personalized advice to users. For example, the AI ​​uses algorithms to analyze the user's spending data and provide savings suggestions and spending trend analysis. This allows users to receive specific advice tailored to their spending situation. Furthermore, the Advice Department also provides features to support user goal setting. For example, if a user sets a specific savings goal, it provides specific advice and monitors progress towards achieving that goal. The Advice Department can also provide customized advice based on the user's lifestyle and spending patterns. This allows users to receive specific advice tailored to their life and achieve effective spending management.

[0034] The Campaign Provider Department provides campaign information based on advice provided by the Advice Department. For example, the Campaign Provider Department conducts analysis based on the user's purchase history and notifies users of campaign and sale information tailored to the stores and categories they frequently use. Specifically, it identifies the stores and categories that users frequently use and provides campaign information related to them. The Campaign Provider Department can use AI to provide users with the most relevant campaign information. For example, the AI ​​uses an algorithm that analyzes the user's purchase history and selects highly relevant campaign information. This allows users to receive campaign information that is beneficial to them. Furthermore, the Campaign Provider Department can collect user feedback and continuously improve the accuracy and relevance of the campaign information it provides. For example, it monitors users' reactions to and usage of the campaign information they receive and optimizes the information provided based on that. The Campaign Provider Department can also provide campaign information through multiple channels. For example, it uses smartphone notifications, email, SMS, etc., to ensure that information reaches users reliably. This allows the Campaign Provider Department to provide users with the most relevant campaign information and increase their purchasing intent.

[0035] The collection unit can collect QR payment data. The collection unit can collect QR payment data in real time, for example. The collection unit can also collect QR payment data periodically. For example, the collection unit can collect QR payment data at specific times daily, weekly, or monthly. This allows for spending management by collecting QR payment data. QR payment data includes, but is not limited to, transaction date and time, transaction amount, and store information. Some or all of the processing described above in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can automate data collection using an AI model for collecting QR payment data.

[0036] The classification unit can categorize expenditures into categories such as food expenses, living expenses, and entertainment based on the collected data. For example, the classification unit categorizes expenditures into categories such as food expenses, living expenses, and entertainment. The classification unit can automatically categorize the collected data using AI. For example, the classification unit uses an algorithm in which AI analyzes expenditure data and categorizes it into each category. This makes it easier for users to understand their spending trends by categorizing expenditures. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the collected data into an AI model and have the AI ​​perform the expenditure classification.

[0037] The reporting unit can create periodic income and expense reports based on classified expenditure data. For example, the reporting unit can create monthly or weekly reports. The reporting unit can also use AI to automatically create periodic income and expense reports. For example, the reporting unit uses an algorithm in which AI analyzes expenditure data and generates visually easy-to-understand reports. This allows users to visually understand fluctuations in their expenditures by creating periodic income and expense reports. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input classified expenditure data into an AI model and have the AI ​​create the income and expense reports.

[0038] The advice unit can analyze past spending patterns and provide specific advice on saving and spending trends. For example, the advice unit can analyze past spending patterns and provide specific advice on saving and spending trends. The advice unit can use AI to provide personalized advice to users. For example, the advice unit uses an algorithm where AI analyzes the user's spending data and makes saving suggestions and analyzes spending trends. This allows the advice unit to provide specific saving advice to users by analyzing past spending patterns. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input past spending data into an AI model and have the AI ​​provide saving advice.

[0039] The campaign provision unit can perform analysis based on the user's purchase history and notify them of campaign and sale information tailored to the stores and categories they frequently use. For example, the campaign provision unit can perform analysis based on the user's purchase history and notify them of campaign and sale information tailored to the stores and categories they frequently use. The campaign provision unit can use AI to provide users with the most suitable campaign information. For example, the campaign provision unit uses an algorithm in which AI analyzes the user's purchase history and selects highly relevant campaign information. This allows users to receive more advantageous information by providing campaign information based on their purchase history. Some or all of the above processing in the campaign provision unit may be performed using AI, for example, or without AI. For example, the campaign provision unit can input the user's purchase history into an AI model and have the AI ​​provide campaign information.

[0040] The data collection unit can analyze the user's past payment history and select the optimal data collection method. For example, the data collection unit can prioritize collecting payment data from stores the user frequently uses. The data collection unit can also concentrate data collection during specific time periods based on the user's past payment history. Furthermore, the data collection unit can analyze the user's past payment patterns and select the most efficient data collection method. This enables efficient data collection by selecting the optimal data collection method through analysis of past payment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past payment history into an AI model and have the AI ​​select the optimal data collection method.

[0041] The data collection unit can filter QR payment data based on the user's current purchasing behavior and areas of interest. For example, the data collection unit can filter data based on the product categories the user is currently purchasing. The data collection unit can also prioritize the collection of payment data related to the user's areas of interest. Furthermore, the data collection unit can analyze the user's purchasing behavior in real time and collect highly relevant data. This allows for the collection of highly relevant data by filtering the data based on the user's purchasing behavior and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user purchasing behavior data into an AI model and have the AI ​​perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting QR payment data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of payment data from stores in that region. The data collection unit can also prioritize the collection of payment data from stores close to the user's current location. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of payment data from their travel destination. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​determine the data prioritization.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting QR payment data. For example, the data collection unit can prioritize collecting payment data from stores mentioned by the user on social media. The data collection unit can also filter data based on the user's areas of interest on social media. Furthermore, the data collection unit can collect data according to the user's social media activity time. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into an AI model and have the AI ​​perform the data collection.

[0044] The classification unit can improve the accuracy of its classifications by considering detailed item information when classifying expenditures. For example, the classification unit can classify items by considering the place of purchase and brand information. It can also classify items by considering their price range. Furthermore, the classification unit can classify items by considering their intended use. This improves the accuracy of the classification by considering detailed item information. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input detailed item information into an AI model and have the AI ​​perform the classification accuracy improvement.

[0045] The classification unit can classify expenses by referring to the user's past spending patterns. For example, the classification unit can refer to items that the user has previously classified into the same category. The classification unit can also analyze the user's tendency to classify items into specific categories based on their past spending patterns. Furthermore, the classification unit can select the optimal classification method based on the user's past spending history. This allows for more appropriate classification by referring to past spending patterns. Some or all of the above processes in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input the user's past spending data into an AI model and have the AI ​​perform the classification.

[0046] The classification unit can classify items while considering their geographical distribution when classifying expenditures. For example, the classification unit can classify items based on where they were purchased. It can also classify items by region, taking into account their geographical distribution. Furthermore, it can classify items while considering the characteristics of where they were purchased. This allows for more appropriate classification by considering the geographical distribution of items. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input geographical distribution data of items into an AI model and have the AI ​​perform the classification.

[0047] The classification unit can improve the accuracy of its classifications by referring to relevant literature for items when classifying expenditures. For example, the classification unit can refer to relevant literature for items to classify them into the correct category. It can also classify items by referring to literature on how to use them. Furthermore, it can classify items by referring to literature on their characteristics. This improves the accuracy of the classification by referring to relevant literature. Some or all of the above processes in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input relevant literature data for items into an AI model and have the AI ​​perform the classification accuracy improvement.

[0048] The reporting unit can adjust the level of detail in a report based on the importance of the expenditures. For example, the reporting unit can create a detailed report on high-importance expenditures. It can also create a concise report on low-importance expenditures. Furthermore, the reporting unit can adjust the level of detail in the report according to the importance of the expenditures. This allows for a detailed understanding of important information by adjusting the level of detail in the report according to the importance of the expenditures. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input expenditure importance data into an AI model and have the AI ​​perform the adjustment of the level of detail in the report.

[0049] The report generation unit can apply different report formats depending on the expenditure category when creating reports. For example, the report generation unit can create reports on food expenses in a format that includes detailed graphs. It can also create reports on living expenses in a concise tabular format. Furthermore, it can create reports on entertainment expenses in a visually appealing format. By applying report formats according to expenditure categories, it is possible to provide visually easy-to-understand reports. Some or all of the above processing in the report generation unit may be performed using AI, for example, or not. For example, the report generation unit can input expenditure category data into an AI model and have the AI ​​perform the application of report formats.

[0050] The reporting unit can prioritize reports based on the timing of expenditure submissions when creating reports. For example, the reporting unit can prioritize reports on recent expenditures. It can also prioritize reports on recurring expenditures. Furthermore, it can prioritize reports on expenditures concentrated in a specific period. This allows for the prioritization of important reports by determining report priorities based on the timing of expenditure submissions. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input expenditure submission timing data into an AI model and have the AI ​​perform the report prioritization.

[0051] The reporting unit can adjust the order of reports based on the relevance of expenses when creating reports. For example, the reporting unit can prioritize the inclusion of highly relevant expenses in the report. It can also postpone the inclusion of less relevant expenses. Furthermore, the reporting unit can adjust the order of reports based on the relevance of expenses. This allows for the priority provision of highly relevant information by adjusting the order of reports based on the relevance of expenses. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input expense relevance data into an AI model and have the AI ​​perform the adjustment of the report order.

[0052] The advice unit can analyze past spending patterns to select the most appropriate advice when providing it. For example, the advice unit can identify areas for saving money based on the user's past spending patterns and provide advice. The advice unit can also analyze the user's past spending patterns and provide advice to reduce wasteful spending. Furthermore, the advice unit can provide advice on optimal budget setting based on the user's past spending patterns. In this way, by analyzing past spending patterns, the advice unit can provide the most appropriate advice. Some or all of the above processes in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's past spending data into an AI model and have the AI ​​select the advice.

[0053] The advice unit can customize the content of advice based on the user's current living situation when providing advice. For example, if the user starts a new job, the advice unit can provide advice based on that income. The advice unit can also provide advice based on the user's new living environment if the user moves. Furthermore, if the user changes their family structure, the advice unit can provide advice tailored to that situation. This allows for more appropriate advice to be provided by customizing it according to the user's living situation. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's living situation data into an AI model and have the AI ​​customize the advice.

[0054] The advice unit can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the advice unit can provide advice based on the cost of living in that region. The advice unit can also provide advice based on special offers at stores near the user's current location. Furthermore, if the user is traveling, the advice unit can provide advice based on the cost of living at the travel destination. In this way, optimal advice can be provided by considering the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into an AI model and have the AI ​​perform the provision of advice.

[0055] The advice unit can analyze the user's social media activity when providing advice and propose content accordingly. For example, the advice unit can provide advice based on money-saving methods mentioned by the user on social media. It can also provide advice based on the user's areas of interest on social media. Furthermore, the advice unit can provide advice tailored to the user's social media activity time. This allows for the provision of highly relevant advice by analyzing the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input the user's social media activity data into an AI model and have the AI ​​generate advice suggestions.

[0056] The campaign provision unit can analyze a user's past purchase history to select the most relevant campaign information when providing campaign information. For example, the campaign provision unit can prioritize providing campaign information from stores that the user has frequently visited in the past. It can also provide campaign information related to specific categories based on the user's past purchase history. Furthermore, the campaign provision unit can analyze the user's past purchase patterns and provide the most relevant campaign information. This allows for the provision of optimal campaign information by analyzing past purchase history. Some or all of the above processing in the campaign provision unit may be performed using AI, for example, or without AI. For example, the campaign provision unit can input the user's past purchase history data into an AI model and have the AI ​​select the campaign information.

[0057] The campaign delivery unit can customize the content of campaign information based on the user's current purchasing behavior when providing campaign information. For example, the campaign delivery unit can provide campaign information based on the product category the user is currently purchasing. The campaign delivery unit can also analyze the user's current purchasing behavior in real time and provide highly relevant campaign information. Furthermore, the campaign delivery unit can customize the optimal campaign information based on the user's purchasing behavior. This allows for the provision of highly relevant information by customizing campaign information based on current purchasing behavior. Some or all of the above processing in the campaign delivery unit may be performed using AI, for example, or without AI. For example, the campaign delivery unit can input user purchasing behavior data into an AI model and have the AI ​​perform the customization of campaign information.

[0058] The campaign delivery unit can provide optimal campaign information by considering the user's geographical location when providing campaign information. For example, if the user is in a specific region, the campaign delivery unit can prioritize providing campaign information from stores in that region. It can also prioritize providing campaign information from stores close to the user's current location. Furthermore, if the user is traveling, the campaign delivery unit can prioritize providing campaign information from their travel destination. In this way, optimal campaign information can be provided by considering the user's geographical location. Some or all of the above processing in the campaign delivery unit may be performed using AI, for example, or not using AI. For example, the campaign delivery unit can input the user's geographical location information into an AI model and have the AI ​​perform the provision of campaign information.

[0059] The campaign delivery unit can analyze users' social media activity and suggest campaign information when providing campaign information. For example, the campaign delivery unit can prioritize providing campaign information for stores mentioned by users on social media. The campaign delivery unit can also provide campaign information based on the user's areas of interest on social media. Furthermore, the campaign delivery unit can provide campaign information according to the user's social media activity time. This allows the delivery unit to provide highly relevant campaign information by analyzing users' social media activity. Some or all of the above processing in the campaign delivery unit may be performed using AI, for example, or not using AI. For example, the campaign delivery unit can input user social media activity data into an AI model and have the AI ​​suggest campaign information.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] Electronic payment systems can also collect users' health data and combine it with spending data to provide health management advice. For example, they can combine users' food spending data with health data to provide advice on nutritional balance. They can also combine users' exercise data with spending data to provide advice on maintaining a healthy lifestyle. Furthermore, they can combine users' sleep data with spending data to provide advice on stress management. This allows users to efficiently manage not only their spending but also their health.

[0062] Electronic payment systems can further analyze users' purchase history and predict spending based on their purchasing patterns. For example, if a user purchases the same item every month, the system can predict when that item will be purchased and notify the user in advance. It can also predict that spending will be higher during certain seasons based on the user's purchasing patterns and provide advice on saving money. Furthermore, by analyzing user purchasing patterns, the system can provide advice on reducing unnecessary spending. This allows users to manage their spending more efficiently.

[0063] Electronic payment systems can further consider the user's geographical location to provide the most relevant campaign information. For example, if a user is in a specific region, the system can prioritize providing campaign information from stores in that region. It can also prioritize providing campaign information from stores close to the user's current location. Furthermore, if a user is traveling, the system can prioritize providing campaign information from their travel destination. In this way, the system can provide the most relevant campaign information by considering the user's geographical location.

[0064] Electronic payment systems can further analyze users' social media activity and provide relevant campaign information. For example, they can prioritize providing campaign information from stores mentioned by users on social media. They can also provide campaign information based on users' areas of interest on social media. Furthermore, they can provide campaign information tailored to the time users spend on social media. This allows for the provision of highly relevant campaign information by analyzing users' social media activity.

[0065] Electronic payment systems can further analyze users' past spending patterns and provide advice on optimal budgeting. For example, they can offer advice on reducing wasteful spending based on users' past spending patterns. They can also analyze users' past spending patterns to identify areas for saving and provide advice. Furthermore, they can provide advice on optimal budgeting based on users' past spending patterns. In this way, by analyzing past spending patterns, they can provide optimal advice.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects QR payment data. This data includes transaction date and time, transaction amount, and store information. The collection unit can collect QR payment data in real time, as well as periodically. For example, it can collect QR payment data at specific times daily, weekly, or monthly. Step 2: The classification unit classifies expenditures based on the data collected by the collection unit. The classification unit categorizes expenditures into categories such as food expenses, living expenses, and entertainment. The classification unit can automatically classify the collected data using AI. For example, it can use an algorithm in which AI analyzes expenditure data and classifies it into each category. Step 3: The reporting unit creates an income and expense report based on the expenditure data classified by the classification unit. The reporting unit can create monthly and weekly reports. The reporting unit can also automatically create regular income and expense reports using AI. For example, it can use an algorithm in which the AI ​​analyzes expenditure data and generates a visually easy-to-understand report. Step 4: The advice department provides advice based on the report created by the report generation department. The advice department analyzes past spending patterns and provides specific advice on saving and spending trends. The advice department can use AI to provide personalized advice to users. For example, it can use an algorithm where AI analyzes the user's spending data and makes saving suggestions and analyzes spending trends. Step 5: The Campaign Provider Department provides campaign information based on the advice provided by the Advice Department. The Campaign Provider Department conducts analysis based on the user's purchase history and notifies users of campaign and sale information tailored to frequently used stores and categories. The Campaign Provider Department can use AI to provide users with the most relevant campaign information. For example, the AI ​​can analyze the user's purchase history and use an algorithm to select highly relevant campaign information.

[0068] (Example of form 2) The electronic payment system according to an embodiment of the present invention is a system equipped with an AI-powered accounting ledger function. Based on QR payment data, the electronic payment system automatically classifies expenditures into categories such as food, living expenses, and entertainment using AI. This allows users to understand their spending trends. The AI ​​also creates and submits periodic income and expenditure reports to the user. This allows users to visually understand fluctuations in their spending on a weekly and monthly basis. Next, it provides an advice function. The AI ​​analyzes past spending patterns and provides specific advice on saving and spending trends. It also includes a function that allows users to set a monthly budget and notifies them of alerts when they are about to exceed that budget. Furthermore, it provides advice tailored to individual needs based on the user's spending trends and lifestyle. In addition, it includes a campaign information provision function. Based on the user's purchase and usage history, it performs analysis and notifies users of campaign and sale information tailored to frequently used stores and categories. Finally, it provides spending forecasting and goal setting functions. Based on past data, it predicts future spending patterns and provides advice to help balance income and expenses. It also allows users to set savings and budgeting goals, and the AI ​​supports them in creating plans to achieve those goals. These features allow users to efficiently manage their spending and achieve their savings and budgeting goals. Furthermore, the provision of promotional information ensures users don't miss out on special offers. This improves the user experience of the electronic payment system and increases user satisfaction. As a result, the electronic payment system allows users to efficiently manage their spending and achieve their savings and budgeting goals. Furthermore, the provision of promotional information ensures users don't miss out on special offers. This improves the user experience of the electronic payment system and increases user satisfaction.

[0069] The electronic payment system according to this embodiment comprises a collection unit, a classification unit, a report creation unit, an advice unit, and a campaign provision unit. The collection unit collects QR payment data. The QR payment data includes, but is not limited to, transaction date and time, transaction amount, and store information. The collection unit can collect QR payment data in real time, for example. The collection unit can also collect QR payment data periodically. For example, the collection unit can collect QR payment data at specific times daily, weekly, or monthly. The classification unit classifies expenditures based on the data collected by the collection unit. For example, the classification unit classifies expenditures into categories such as food expenses, living expenses, and entertainment. The classification unit can automatically classify the collected data using AI. For example, the classification unit uses an algorithm in which AI analyzes expenditure data and classifies it into each category. The report creation unit creates income and expenditure reports based on the expenditure data classified by the classification unit. The report creation unit can create monthly reports and weekly reports, for example. The report creation unit can automatically create periodic income and expenditure reports using AI. For example, the reporting unit uses an algorithm where AI analyzes spending data and generates visually easy-to-understand reports. The advice unit provides advice based on the reports created by the reporting unit. For example, the advice unit analyzes past spending patterns and provides specific advice on saving and spending trends. The advice unit can use AI to provide personalized advice to users. For example, the advice unit uses an algorithm where AI analyzes the user's spending data and makes saving suggestions and analyzes spending trends. The campaign provision unit provides campaign information based on the advice provided by the advice unit. For example, the campaign provision unit conducts analysis based on the user's purchase history and notifies users of campaign and sale information tailored to frequently used stores and categories. The campaign provision unit can use AI to provide users with the most relevant campaign information. For example, the campaign provision unit uses an algorithm where AI analyzes the user's purchase history and selects highly relevant campaign information.As a result, the electronic payment system according to this embodiment can efficiently manage users' spending and help them achieve their savings and budgeting goals. Furthermore, by providing campaign information, users can take advantage of special offers without missing out. This improves the user experience of the electronic payment system and increases user satisfaction.

[0070] The data collection unit collects QR payment data. This data includes, but is not limited to, transaction date and time, transaction amount, and store information. The data collection unit can collect QR payment data in real time, for example. Specifically, when a QR code is scanned, transaction details are collected and sent to a central database. This allows for accurate tracking of which stores users paid at what times. The data collection unit can also collect QR payment data periodically. For example, it can collect QR payment data at specific times daily, weekly, or monthly. This allows for the accumulation of long-term spending data, which can then be used for analysis and reporting. Furthermore, the data collection unit can integrate data from different payment platforms and different stores. This enables centralized data management even when users utilize multiple payment methods. For example, even if a user uses credit cards or debit cards in addition to QR code payments, this data can be integrated and collected. This allows the data collection unit to understand the user's overall spending habits and build a foundation for more accurate analysis and advice.

[0071] The classification unit categorizes expenditures based on data collected by the collection unit. For example, the classification unit categorizes expenditures into categories such as food expenses, living expenses, and entertainment. Specifically, it analyzes collected QR payment data, identifies the content of each transaction, and classifies it into the appropriate category. The classification unit can use AI to automatically classify the collected data. For example, the AI ​​analyzes the store name and product name of a transaction and uses an algorithm to classify the expenditure into the appropriate category based on that. This eliminates the need for users to manually classify expenditures. Furthermore, the classification unit can learn from the user's past expenditure data to perform more accurate classifications. For example, if expenditures at a particular store are repeated, it learns which category that store belongs to and automatically classifies subsequent transactions into the correct category. The classification unit also provides a function that allows users to customize categories themselves. This allows users to add or change categories to match their lifestyle and expenditure patterns. As a result, the classification unit can efficiently and accurately classify the user's expenditures, which is useful for subsequent analysis and report generation.

[0072] The reporting unit creates income and expenditure reports based on expenditure data classified by the classification unit. For example, the reporting unit can create monthly and weekly reports. Specifically, it aggregates the classified expenditure data and generates reports in a visually easy-to-understand format. The reporting unit can use AI to automatically generate periodic income and expenditure reports. For example, the AI ​​analyzes expenditure data and uses algorithms to visualize expenditure trends and patterns using graphs and charts. This allows users to grasp their spending situation at a glance. Furthermore, the reporting unit can also incorporate the user's income data. This allows for an evaluation of the balance between income and expenditure and confirmation of financial health. For example, it can register the user's salary and other income sources in a database and create income and expenditure reports based on this information. The reporting unit can also perform trend analysis based on historical data. This allows users to understand their spending trends over the long term and use this information as a reference when planning future expenditures. In this way, the reporting unit can provide users with detailed and visually clear information on their spending situation, supporting effective expenditure management.

[0073] The Advice Department provides advice based on reports created by the Reporting Department. For example, the Advice Department analyzes past spending patterns and provides specific advice on saving and spending trends. Specifically, it analyzes the user's spending data and provides suggestions for reducing unnecessary spending or advice on controlling spending in specific categories. The Advice Department can use AI to provide personalized advice to users. For example, the AI ​​uses algorithms to analyze the user's spending data and provide savings suggestions and spending trend analysis. This allows users to receive specific advice tailored to their spending situation. Furthermore, the Advice Department also provides features to support user goal setting. For example, if a user sets a specific savings goal, it provides specific advice and monitors progress towards achieving that goal. The Advice Department can also provide customized advice based on the user's lifestyle and spending patterns. This allows users to receive specific advice tailored to their life and achieve effective spending management.

[0074] The Campaign Provider Department provides campaign information based on advice provided by the Advice Department. For example, the Campaign Provider Department conducts analysis based on the user's purchase history and notifies users of campaign and sale information tailored to the stores and categories they frequently use. Specifically, it identifies the stores and categories that users frequently use and provides campaign information related to them. The Campaign Provider Department can use AI to provide users with the most relevant campaign information. For example, the AI ​​uses an algorithm that analyzes the user's purchase history and selects highly relevant campaign information. This allows users to receive campaign information that is beneficial to them. Furthermore, the Campaign Provider Department can collect user feedback and continuously improve the accuracy and relevance of the campaign information it provides. For example, it monitors users' reactions to and usage of the campaign information they receive and optimizes the information provided based on that. The Campaign Provider Department can also provide campaign information through multiple channels. For example, it uses smartphone notifications, email, SMS, etc., to ensure that information reaches users reliably. This allows the Campaign Provider Department to provide users with the most relevant campaign information and increase their purchasing intent.

[0075] The collection unit can collect QR payment data. The collection unit can collect QR payment data in real time, for example. The collection unit can also collect QR payment data periodically. For example, the collection unit can collect QR payment data at specific times daily, weekly, or monthly. This allows for spending management by collecting QR payment data. QR payment data includes, but is not limited to, transaction date and time, transaction amount, and store information. Some or all of the processing described above in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can automate data collection using an AI model for collecting QR payment data.

[0076] The classification unit can categorize expenditures into categories such as food expenses, living expenses, and entertainment based on the collected data. For example, the classification unit categorizes expenditures into categories such as food expenses, living expenses, and entertainment. The classification unit can automatically categorize the collected data using AI. For example, the classification unit uses an algorithm in which AI analyzes expenditure data and categorizes it into each category. This makes it easier for users to understand their spending trends by categorizing expenditures. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the collected data into an AI model and have the AI ​​perform the expenditure classification.

[0077] The reporting unit can create periodic income and expense reports based on classified expenditure data. For example, the reporting unit can create monthly or weekly reports. The reporting unit can also use AI to automatically create periodic income and expense reports. For example, the reporting unit uses an algorithm in which AI analyzes expenditure data and generates visually easy-to-understand reports. This allows users to visually understand fluctuations in their expenditures by creating periodic income and expense reports. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input classified expenditure data into an AI model and have the AI ​​create the income and expense reports.

[0078] The advice unit can analyze past spending patterns and provide specific advice on saving and spending trends. For example, the advice unit can analyze past spending patterns and provide specific advice on saving and spending trends. The advice unit can use AI to provide personalized advice to users. For example, the advice unit uses an algorithm where AI analyzes the user's spending data and makes saving suggestions and analyzes spending trends. This allows the advice unit to provide specific saving advice to users by analyzing past spending patterns. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input past spending data into an AI model and have the AI ​​provide saving advice.

[0079] The campaign provision unit can perform analysis based on the user's purchase history and notify them of campaign and sale information tailored to the stores and categories they frequently use. For example, the campaign provision unit can perform analysis based on the user's purchase history and notify them of campaign and sale information tailored to the stores and categories they frequently use. The campaign provision unit can use AI to provide users with the most suitable campaign information. For example, the campaign provision unit uses an algorithm in which AI analyzes the user's purchase history and selects highly relevant campaign information. This allows users to receive more advantageous information by providing campaign information based on their purchase history. Some or all of the above processing in the campaign provision unit may be performed using AI, for example, or without AI. For example, the campaign provision unit can input the user's purchase history into an AI model and have the AI ​​provide campaign information.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of QR payment data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. Alternatively, if the user is relaxed, the data collection unit can immediately collect QR payment data and perform real-time analysis. Furthermore, if the user is in a hurry, the data collection unit can optimize the collection timing and collect the necessary data in a short time. By adjusting the timing of data collection according to the user's emotions, it is possible to reduce user stress and enable efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI model and have the AI ​​adjust the collection timing.

[0081] The data collection unit can analyze the user's past payment history and select the optimal data collection method. For example, the data collection unit can prioritize collecting payment data from stores the user frequently uses. The data collection unit can also concentrate data collection during specific time periods based on the user's past payment history. Furthermore, the data collection unit can analyze the user's past payment patterns and select the most efficient data collection method. This enables efficient data collection by selecting the optimal data collection method through analysis of past payment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past payment history into an AI model and have the AI ​​select the optimal data collection method.

[0082] The data collection unit can filter QR payment data based on the user's current purchasing behavior and areas of interest. For example, the data collection unit can filter data based on the product categories the user is currently purchasing. The data collection unit can also prioritize the collection of payment data related to the user's areas of interest. Furthermore, the data collection unit can analyze the user's purchasing behavior in real time and collect highly relevant data. This allows for the collection of highly relevant data by filtering the data based on the user's purchasing behavior and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user purchasing behavior data into an AI model and have the AI ​​perform the filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of QR payment data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important data. If the user is relaxed, the data collection unit can collect all data equally. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting more important data. This allows for the priority collection of important data by determining data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI model and have the AI ​​determine the data priority.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting QR payment data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of payment data from stores in that region. The data collection unit can also prioritize the collection of payment data from stores close to the user's current location. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of payment data from their travel destination. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​determine the data prioritization.

[0085] The data collection unit can analyze the user's social media activity and collect relevant data when collecting QR payment data. For example, the data collection unit can prioritize collecting payment data from stores mentioned by the user on social media. The data collection unit can also filter data based on the user's areas of interest on social media. Furthermore, the data collection unit can collect data according to the user's social media activity time. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into an AI model and have the AI ​​perform the data collection.

[0086] The classification unit can estimate the user's emotions and adjust the spending classification criteria based on the estimated emotions. For example, if the user is stressed, the classification unit can apply simple classification criteria. If the user is relaxed, it can also apply detailed classification criteria. Furthermore, if the user is in a hurry, it can apply criteria that allow for quick classification. This allows for more appropriate classification by adjusting the classification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, or not using AI. For example, the classification unit can input user emotion data into an AI model and have the AI ​​perform the adjustment of the classification criteria.

[0087] The classification unit can improve the accuracy of its classifications by considering detailed item information when classifying expenditures. For example, the classification unit can classify items by considering the place of purchase and brand information. It can also classify items by considering their price range. Furthermore, the classification unit can classify items by considering their intended use. This improves the accuracy of the classification by considering detailed item information. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input detailed item information into an AI model and have the AI ​​perform the classification accuracy improvement.

[0088] The classification unit can classify expenses by referring to the user's past spending patterns. For example, the classification unit can refer to items that the user has previously classified into the same category. The classification unit can also analyze the user's tendency to classify items into specific categories based on their past spending patterns. Furthermore, the classification unit can select the optimal classification method based on the user's past spending history. This allows for more appropriate classification by referring to past spending patterns. Some or all of the above processes in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input the user's past spending data into an AI model and have the AI ​​perform the classification.

[0089] The classification unit can estimate the user's emotions and adjust the order in which it displays the classification results of spending based on the estimated emotions. For example, if the user is stressed, the classification unit may postpone displaying less important categories. If the user is relaxed, the classification unit may display all categories equally. Furthermore, if the user is in a hurry, the classification unit may prioritize displaying more important categories. This allows the user to prioritize viewing important information by adjusting the display order of classification results according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into an AI model and have the AI ​​adjust the display order of the classification results.

[0090] The classification unit can classify items while considering their geographical distribution when classifying expenditures. For example, the classification unit can classify items based on where they were purchased. It can also classify items by region, taking into account their geographical distribution. Furthermore, it can classify items while considering the characteristics of where they were purchased. This allows for more appropriate classification by considering the geographical distribution of items. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input geographical distribution data of items into an AI model and have the AI ​​perform the classification.

[0091] The classification unit can improve the accuracy of its classifications by referring to relevant literature for items when classifying expenditures. For example, the classification unit can refer to relevant literature for items to classify them into the correct category. It can also classify items by referring to literature on how to use them. Furthermore, it can classify items by referring to literature on their characteristics. This improves the accuracy of the classification by referring to relevant literature. Some or all of the above processes in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input relevant literature data for items into an AI model and have the AI ​​perform the classification accuracy improvement.

[0092] The report generation unit can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is stressed, the report generation unit can create a simple and easy-to-read report. If the user is relaxed, the report generation unit can create a report with more detailed information. Furthermore, if the user is in a hurry, the report generation unit can create a concise report. By adjusting the report's presentation according to the user's emotions, the report can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the report generation unit may be performed using AI or not. For example, the report generation unit can input user emotion data into an AI model and have the AI ​​adjust the report's presentation.

[0093] The reporting unit can adjust the level of detail in a report based on the importance of the expenditures. For example, the reporting unit can create a detailed report on high-importance expenditures. It can also create a concise report on low-importance expenditures. Furthermore, the reporting unit can adjust the level of detail in the report according to the importance of the expenditures. This allows for a detailed understanding of important information by adjusting the level of detail in the report according to the importance of the expenditures. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input expenditure importance data into an AI model and have the AI ​​perform the adjustment of the level of detail in the report.

[0094] The report generation unit can apply different report formats depending on the expenditure category when creating reports. For example, the report generation unit can create reports on food expenses in a format that includes detailed graphs. It can also create reports on living expenses in a concise tabular format. Furthermore, it can create reports on entertainment expenses in a visually appealing format. By applying report formats according to expenditure categories, it is possible to provide visually easy-to-understand reports. Some or all of the above processing in the report generation unit may be performed using AI, for example, or not. For example, the report generation unit can input expenditure category data into an AI model and have the AI ​​perform the application of report formats.

[0095] The report generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is stressed, the report generation unit can create a short, concise report. If the user is relaxed, the report generation unit can create a longer report with detailed explanations. Furthermore, if the user is in a hurry, the report generation unit can create a short report that can be read quickly. By adjusting the length of the report according to the user's emotions, the optimal amount of information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the report generation unit may be performed using AI or not. For example, the report generation unit can input user emotion data into an AI model and have the AI ​​adjust the length of the report.

[0096] The reporting unit can prioritize reports based on the timing of expenditure submissions when creating reports. For example, the reporting unit can prioritize reports on recent expenditures. It can also prioritize reports on recurring expenditures. Furthermore, it can prioritize reports on expenditures concentrated in a specific period. This allows for the prioritization of important reports by determining report priorities based on the timing of expenditure submissions. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input expenditure submission timing data into an AI model and have the AI ​​perform the report prioritization.

[0097] The reporting unit can adjust the order of reports based on the relevance of expenses when creating reports. For example, the reporting unit can prioritize the inclusion of highly relevant expenses in the report. It can also postpone the inclusion of less relevant expenses. Furthermore, the reporting unit can adjust the order of reports based on the relevance of expenses. This allows for the priority provision of highly relevant information by adjusting the order of reports based on the relevance of expenses. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input expense relevance data into an AI model and have the AI ​​perform the adjustment of the report order.

[0098] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit can provide simple and easy-to-understand advice. If the user is relaxed, the advice unit can also provide detailed advice. Furthermore, if the user is in a hurry, the advice unit can provide advice that can be acted upon quickly. In this way, by adjusting the way advice is expressed according to the user's emotions, it is possible to provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into an AI model and have the AI ​​adjust the way advice is expressed.

[0099] The advice unit can analyze past spending patterns to select the most appropriate advice when providing it. For example, the advice unit can identify areas for saving money based on the user's past spending patterns and provide advice. The advice unit can also analyze the user's past spending patterns and provide advice to reduce wasteful spending. Furthermore, the advice unit can provide advice on optimal budget setting based on the user's past spending patterns. In this way, by analyzing past spending patterns, the advice unit can provide the most appropriate advice. Some or all of the above processes in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's past spending data into an AI model and have the AI ​​select the advice.

[0100] The advice unit can customize the content of advice based on the user's current living situation when providing advice. For example, if the user starts a new job, the advice unit can provide advice based on that income. The advice unit can also provide advice based on the user's new living environment if the user moves. Furthermore, if the user changes their family structure, the advice unit can provide advice tailored to that situation. This allows for more appropriate advice to be provided by customizing it according to the user's living situation. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's living situation data into an AI model and have the AI ​​customize the advice.

[0101] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the advice unit may postpone less important advice. If the user is relaxed, the advice unit may provide all advice equally. Furthermore, if the user is in a hurry, the advice unit may prioritize providing more important advice. This ensures that important advice is prioritized by determining the priority of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into an AI model and have the AI ​​determine the priority of advice.

[0102] The advice unit can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the advice unit can provide advice based on the cost of living in that region. The advice unit can also provide advice based on special offers at stores near the user's current location. Furthermore, if the user is traveling, the advice unit can provide advice based on the cost of living at the travel destination. In this way, optimal advice can be provided by considering the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into an AI model and have the AI ​​perform the provision of advice.

[0103] The advice unit can analyze the user's social media activity when providing advice and propose content accordingly. For example, the advice unit can provide advice based on money-saving methods mentioned by the user on social media. It can also provide advice based on the user's areas of interest on social media. Furthermore, the advice unit can provide advice tailored to the user's social media activity time. This allows for the provision of highly relevant advice by analyzing the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input the user's social media activity data into an AI model and have the AI ​​generate advice suggestions.

[0104] The campaign delivery unit can estimate the user's emotions and adjust how campaign information is delivered based on those emotions. For example, if the user is stressed, the campaign delivery unit can provide simple and highly visible campaign information. If the user is relaxed, the campaign delivery unit can also provide detailed campaign information. Furthermore, if the user is in a hurry, the campaign delivery unit can provide concise campaign information. By adjusting how campaign information is delivered according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the campaign delivery unit may be performed using AI or not using AI. For example, the campaign delivery unit can input user emotion data into an AI model and have the AI ​​adjust how campaign information is delivered.

[0105] The campaign provision unit can analyze a user's past purchase history to select the most relevant campaign information when providing campaign information. For example, the campaign provision unit can prioritize providing campaign information from stores that the user has frequently visited in the past. It can also provide campaign information related to specific categories based on the user's past purchase history. Furthermore, the campaign provision unit can analyze the user's past purchase patterns and provide the most relevant campaign information. This allows for the provision of optimal campaign information by analyzing past purchase history. Some or all of the above processing in the campaign provision unit may be performed using AI, for example, or without AI. For example, the campaign provision unit can input the user's past purchase history data into an AI model and have the AI ​​select the campaign information.

[0106] The campaign delivery unit can customize the content of campaign information based on the user's current purchasing behavior when providing campaign information. For example, the campaign delivery unit can provide campaign information based on the product category the user is currently purchasing. The campaign delivery unit can also analyze the user's current purchasing behavior in real time and provide highly relevant campaign information. Furthermore, the campaign delivery unit can customize the optimal campaign information based on the user's purchasing behavior. This allows for the provision of highly relevant information by customizing campaign information based on current purchasing behavior. Some or all of the above processing in the campaign delivery unit may be performed using AI, for example, or without AI. For example, the campaign delivery unit can input user purchasing behavior data into an AI model and have the AI ​​perform the customization of campaign information.

[0107] The campaign delivery unit can estimate the user's emotions and prioritize campaign information based on those emotions. For example, if a user is stressed, the campaign delivery unit may postpone less important campaign information. Conversely, if a user is relaxed, the campaign delivery unit may provide all campaign information equally. Furthermore, if a user is in a hurry, the campaign delivery unit may prioritize providing more important campaign information. This ensures that important information is delivered preferentially by prioritizing campaign information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the campaign delivery unit may be performed using AI or not. For example, the campaign delivery unit can input user emotion data into an AI model and have the AI ​​determine the priority of campaign information.

[0108] The campaign delivery unit can provide optimal campaign information by considering the user's geographical location when providing campaign information. For example, if the user is in a specific region, the campaign delivery unit can prioritize providing campaign information from stores in that region. It can also prioritize providing campaign information from stores close to the user's current location. Furthermore, if the user is traveling, the campaign delivery unit can prioritize providing campaign information from their travel destination. In this way, optimal campaign information can be provided by considering the user's geographical location. Some or all of the above processing in the campaign delivery unit may be performed using AI, for example, or not using AI. For example, the campaign delivery unit can input the user's geographical location information into an AI model and have the AI ​​perform the provision of campaign information.

[0109] The campaign delivery unit can analyze users' social media activity and suggest campaign information when providing campaign information. For example, the campaign delivery unit can prioritize providing campaign information for stores mentioned by users on social media. The campaign delivery unit can also provide campaign information based on the user's areas of interest on social media. Furthermore, the campaign delivery unit can provide campaign information according to the user's social media activity time. This allows the delivery unit to provide highly relevant campaign information by analyzing users' social media activity. Some or all of the above processing in the campaign delivery unit may be performed using AI, for example, or not using AI. For example, the campaign delivery unit can input user social media activity data into an AI model and have the AI ​​suggest campaign information.

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

[0111] Electronic payment systems can also collect users' health data and combine it with spending data to provide health management advice. For example, they can combine users' food spending data with health data to provide advice on nutritional balance. They can also combine users' exercise data with spending data to provide advice on maintaining a healthy lifestyle. Furthermore, they can combine users' sleep data with spending data to provide advice on stress management. This allows users to efficiently manage not only their spending but also their health.

[0112] Electronic payment systems can further estimate a user's emotions and provide spending advice based on those emotions. For example, if a user is stressed, it can provide spending advice to help them relax. If a user is relaxed, it can provide spending advice to help them enjoy themselves. Furthermore, if a user is in a hurry, it can provide spending advice to help them be efficient. By providing spending advice tailored to the user's emotions, this system can increase user satisfaction.

[0113] Electronic payment systems can further analyze users' purchase history and predict spending based on their purchasing patterns. For example, if a user purchases the same item every month, the system can predict when that item will be purchased and notify the user in advance. It can also predict that spending will be higher during certain seasons based on the user's purchasing patterns and provide advice on saving money. Furthermore, by analyzing user purchasing patterns, the system can provide advice on reducing unnecessary spending. This allows users to manage their spending more efficiently.

[0114] Electronic payment systems can further estimate user emotions and adjust how campaign information is delivered based on those estimates. For example, if a user is stressed, simple and highly visible campaign information can be provided. If the user is relaxed, detailed campaign information can be provided. Furthermore, if the user is in a hurry, concise campaign information can be provided. By adjusting how campaign information is delivered according to the user's emotions, it becomes possible to provide information that is easy for the user to understand.

[0115] Electronic payment systems can further consider the user's geographical location to provide the most relevant campaign information. For example, if a user is in a specific region, the system can prioritize providing campaign information from stores in that region. It can also prioritize providing campaign information from stores close to the user's current location. Furthermore, if a user is traveling, the system can prioritize providing campaign information from their travel destination. In this way, the system can provide the most relevant campaign information by considering the user's geographical location.

[0116] Electronic payment systems can further estimate the user's emotions and adjust spending classification criteria based on those emotions. For example, if the user is stressed, a simple classification criterion can be applied. Conversely, if the user is relaxed, a more detailed classification criterion can be applied. Furthermore, if the user is in a hurry, a criterion that allows for quick classification can be applied. This allows for more appropriate classification by adjusting the classification criteria according to the user's emotions.

[0117] Electronic payment systems can further analyze users' social media activity and provide relevant campaign information. For example, they can prioritize providing campaign information from stores mentioned by users on social media. They can also provide campaign information based on users' areas of interest on social media. Furthermore, they can provide campaign information tailored to the time users spend on social media. This allows for the provision of highly relevant campaign information by analyzing users' social media activity.

[0118] Electronic payment systems can further estimate user emotions and adjust the presentation of reports based on those estimates. For example, if a user is stressed, a simple and highly visual report can be created. If the user is relaxed, a report with more detailed information can be created. Furthermore, if the user is in a hurry, a concise report can be created. By adjusting the presentation of reports according to the user's emotions, it becomes possible to provide reports that are easy for users to understand.

[0119] Electronic payment systems can further analyze users' past spending patterns and provide advice on optimal budgeting. For example, they can offer advice on reducing wasteful spending based on users' past spending patterns. They can also analyze users' past spending patterns to identify areas for saving and provide advice. Furthermore, they can provide advice on optimal budgeting based on users' past spending patterns. In this way, by analyzing past spending patterns, they can provide optimal advice.

[0120] Electronic payment systems can further estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if a user is stressed, they can provide simple and easy-to-understand advice. If the user is relaxed, they can provide more detailed advice. Furthermore, if the user is in a hurry, they can provide advice that can be acted upon quickly. In this way, by adjusting the way advice is presented according to the user's emotions, it is possible to provide advice that is easy for the user to understand.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects QR payment data. This data includes transaction date and time, transaction amount, and store information. The collection unit can collect QR payment data in real time, as well as periodically. For example, it can collect QR payment data at specific times daily, weekly, or monthly. Step 2: The classification unit classifies expenditures based on the data collected by the collection unit. The classification unit categorizes expenditures into categories such as food expenses, living expenses, and entertainment. The classification unit can automatically classify the collected data using AI. For example, it can use an algorithm in which AI analyzes expenditure data and classifies it into each category. Step 3: The reporting unit creates an income and expense report based on the expenditure data classified by the classification unit. The reporting unit can create monthly and weekly reports. The reporting unit can also automatically create regular income and expense reports using AI. For example, it can use an algorithm in which the AI ​​analyzes expenditure data and generates a visually easy-to-understand report. Step 4: The advice department provides advice based on the report created by the report generation department. The advice department analyzes past spending patterns and provides specific advice on saving and spending trends. The advice department can use AI to provide personalized advice to users. For example, it can use an algorithm where AI analyzes the user's spending data and makes saving suggestions and analyzes spending trends. Step 5: The Campaign Provider Department provides campaign information based on the advice provided by the Advice Department. The Campaign Provider Department conducts analysis based on the user's purchase history and notifies users of campaign and sale information tailored to frequently used stores and categories. The Campaign Provider Department can use AI to provide users with the most relevant campaign information. For example, the AI ​​can analyze the user's purchase history and use an algorithm to select highly relevant campaign information.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the data collection unit, classification unit, report creation unit, advice unit, and campaign provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects QR payment data using the control unit 46A of the smart device 14. The classification unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and classifies the expenditures into categories. The report creation unit creates an income and expenditure report using the identification processing unit 290 of the data processing unit 12. The advice unit analyzes past expenditure patterns using the identification processing unit 290 of the data processing unit 12 and provides specific advice. The campaign provision unit notifies the user of campaign information using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, classification unit, report creation unit, advice unit, and campaign provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects QR payment data using the control unit 46A of the smart glasses 214. The classification unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and classifies the expenditures by category. The report creation unit creates an income and expenditure report using the identification processing unit 290 of the data processing unit 12. The advice unit analyzes past expenditure patterns using the identification processing unit 290 of the data processing unit 12 and provides specific advice. The campaign provision unit notifies the user of campaign information using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the data collection unit, classification unit, report creation unit, advice unit, and campaign provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects QR payment data using the control unit 46A of the headset terminal 314. The classification unit analyzes the collected data using, for example, the identification processing unit 290 of the data processing unit 12 and classifies expenditures by category. The report creation unit creates an income and expenditure report using, for example, the identification processing unit 290 of the data processing unit 12. The advice unit analyzes past expenditure patterns using, for example, the identification processing unit 290 of the data processing unit 12 and provides specific advice. The campaign provision unit notifies the user of campaign information using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the data collection unit, classification unit, report creation unit, advice unit, and campaign provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects QR payment data using the control unit 46A of the robot 414. The classification unit analyzes the collected data using, for example, the identification processing unit 290 of the data processing unit 12 and classifies the expenditures by category. The report creation unit creates an income and expenditure report using, for example, the identification processing unit 290 of the data processing unit 12. The advice unit analyzes past expenditure patterns using, for example, the identification processing unit 290 of the data processing unit 12 and provides specific advice. The campaign provision unit notifies the user of campaign information using, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A collection unit that collects QR payment data, A classification unit that classifies expenditures based on the data collected by the aforementioned collection unit, A report generation unit that generates an income and expenditure report based on the expenditure data classified by the classification unit, An advice unit provides advice based on the report created by the aforementioned report creation unit, The system includes a campaign provision unit that provides campaign information based on the advice provided by the aforementioned advice unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect QR payment data The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned classification unit is Based on the collected data, expenses are categorized into categories such as food, living expenses, and entertainment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned report creation unit, Create regular income and expenditure reports based on categorized spending data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, We analyze past spending patterns and provide specific advice on saving and spending trends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned campaign provision department, Based on user purchase history, the system analyzes data and notifies users of campaign and sale information tailored to their frequently used stores and categories. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of QR payment data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past payment history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting QR payment data, filtering is performed based on the user's current purchasing behavior and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates user sentiment and prioritizes the QR payment data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting QR payment data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting QR payment data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned classification unit is It estimates user sentiment and adjusts spending classification criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned classification unit is When classifying expenses, consider detailed item information to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned classification unit is When classifying expenses, the system uses the user's past spending patterns for classification. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned classification unit is It estimates the user's sentiment and adjusts the order in which spending classifications are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned classification unit is When classifying expenditures, consider the geographical distribution of items. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned classification unit is When classifying expenses, refer to relevant literature for each item to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned report creation unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned report creation unit, When creating a report, adjust the level of detail based on the importance of the expenditure. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned report creation unit, When creating reports, apply different report formats depending on the expenditure category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned report creation unit, It estimates the user's sentiment and adjusts the length of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned report creation unit, When preparing reports, prioritize them based on when the expenses were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned report creation unit, When creating reports, adjust the order of reports based on the relevance of expenses. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When providing advice, we analyze past spending patterns to select the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, the content of the advice is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, we analyze the user's social media activity to propose appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned campaign provision department, We estimate user sentiment and adjust how campaign information is delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned campaign provision department, When providing campaign information, the system analyzes the user's past purchase history to select the most suitable campaign information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned campaign provision department, When providing campaign information, customize the content of the campaign information based on the user's current purchasing behavior. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned campaign provision department, It estimates user sentiment and prioritizes campaign information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned campaign provision department, When providing campaign information, we will provide the most relevant campaign information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned campaign provision department, When providing campaign information, we analyze users' social media activity to suggest campaign content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A collection unit that collects QR payment data, A classification unit that classifies expenditures based on the data collected by the aforementioned collection unit, A report generation unit that generates an income and expenditure report based on expenditure data classified by the classification unit, An advice unit that provides advice based on the report created by the aforementioned report creation unit, The system includes a campaign provision unit that provides campaign information based on the advice provided by the aforementioned advice unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect QR payment data The system according to feature 1.

3. The aforementioned classification unit is Based on the collected data, expenses are categorized into categories such as food, living expenses, and entertainment. The system according to feature 1.

4. The aforementioned report creation unit, Create regular income and expenditure reports based on categorized spending data. The system according to feature 1.

5. The aforementioned advice section, We analyze past spending patterns and provide specific advice on saving and spending trends. The system according to feature 1.

6. The aforementioned campaign provision department, Based on user purchase history, the system analyzes data and notifies users of campaign and sale information tailored to their frequently used stores and categories. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of QR payment data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past payment history and select the optimal data collection method. The system according to feature 1.

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