system

Abstract

The system according to this embodiment aims to efficiently manage user spending data and provide personalized advice. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a classification unit. The collection unit collects user spending data. The analysis unit analyzes the spending data collected by the collection unit. The provision unit provides personalized advice based on the analysis results obtained by the analysis unit. The classification unit automatically classifies the spending data based on the advice provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, it is difficult for users to accurately grasp their expenses, and there are problems such as the trouble of manual input and the lack of personalized advice.

[0005] The system according to the embodiment aims to efficiently manage the user's expense data and provide personalized advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a classification unit. The collection unit collects user spending data. The analysis unit analyzes the spending data collected by the collection unit. The provision unit provides personalized advice based on the analysis results obtained by the analysis unit. The classification unit automatically classifies the spending data based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage user spending data and provide personalized advice. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". 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 expenditure management system according to an embodiment of the present invention is a system that streamlines user expenditure management by utilizing cashless payment data. This expenditure management system is designed so that when a user makes a cashless payment, the payment data is automatically reflected in the household ledger in real time. This eliminates the need for manual input and allows users to instantly visualize their expenditures. Next, a generating AI analyzes the expenditure data and provides personalized advice for each user. For example, it provides future predictions and savings suggestions that take into account past spending trends. It also has a function to automatically classify each expenditure into the appropriate category, reducing the manual burden on the user. With this system, users can grasp their expenditures in real time, prevent exceeding their budget, and reduce unnecessary spending. Furthermore, the AI ​​enables individually optimized advice, allowing each user to achieve more effective household financial management. As a result, users can gain a firm grasp of their own financial situation and control it, gaining long-term financial security and making it easier to create future financial plans. Thus, the expenditure management system can efficiently manage users' expenditures and provide them with financial security.

[0029] The expenditure management system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a classification unit. The collection unit collects user expenditure data. The collection unit can, for example, collect payment data from a cashless payment system in real time. The collection unit automatically acquires data from the cashless payment system and stores it as user expenditure data. The collection unit can, for example, collect credit card usage history and bank account transaction history. The collection unit can set the data acquisition method and collection frequency. The analysis unit analyzes the expenditure data collected by the collection unit. The analysis unit can, for example, analyze the collected expenditure data and make future predictions that take into account the user's past expenditure trends. The analysis unit analyzes the expenditure data in detail based on the algorithm used and the purpose of the analysis. The analysis unit can, for example, use a prediction algorithm to predict the user's future expenditures. The provision unit provides personalized advice based on the analysis results obtained by the analysis unit. The provision unit can, for example, provide personalized savings suggestions to the user based on future predictions. The provision unit provides useful information to the user, such as suggestions for saving methods and investment advice. The provision unit can set the content and method of providing advice. The classification unit automatically classifies expenditure data based on the advice provided by the provision unit. The classification unit can, for example, automatically classify each expenditure into an appropriate category. The classification unit classifies expenditure data based on category definitions and classification algorithms. The classification unit can, for example, classify expenditure data into categories such as food expenses, transportation expenses, and entertainment expenses. As a result, the expenditure management system according to this embodiment can efficiently collect, analyze, provide advice on, and automatically classify the user's expenditure data.

[0030] The data collection unit collects user spending data. For example, the data collection unit can collect payment data from cashless payment systems in real time. Specifically, the data collection unit obtains data from various cashless payment methods, such as credit and debit card usage history, electronic money transaction history, and bank account transaction history. This data is automatically collected each time a user makes a purchase and stored in a central database. The data collection unit can configure the data acquisition method and frequency, allowing for flexible responses to user needs and system requirements. For example, the data collection unit can select various collection methods, such as collecting daily transaction data in a batch overnight or collecting data in real time. Furthermore, the data collection unit has functions to detect data duplication and omissions, and correct or supplement them as needed, to ensure data accuracy and consistency. This allows the data collection unit to efficiently and accurately collect user spending data, improving the overall reliability of the system. In addition, the data collection unit implements data encryption and access control as security measures to protect user privacy. This allows the data collection unit to securely collect user spending data and enhance the overall system security.

[0031] The analysis department analyzes the spending data collected by the data collection department. For example, the analysis department can analyze the collected spending data and make future predictions that take into account the user's past spending trends. Specifically, the analysis department uses machine learning algorithms and statistical models to analyze the user's spending patterns in detail. For example, based on the user's past spending data, it can analyze monthly spending trends and fluctuations in spending within specific categories. Furthermore, based on these analysis results, the analysis department predicts the user's future spending. For example, it can predict seasonal spending fluctuations and increases in spending associated with specific events, and provide appropriate advice to the user. The analysis department analyzes the spending data in detail based on the algorithms used and the purpose of the analysis. For example, it can use clustering algorithms to classify the user's spending patterns into multiple groups and provide different advice to each group. It can also use anomaly detection algorithms to detect unusual spending patterns and issue warnings to the user. In this way, the analysis department can analyze the user's spending data from multiple angles and provide useful information to the user. Furthermore, the analysis department visualizes the analysis results so that the user can understand them intuitively. For example, graphs and charts can be used to display spending trends and forecasts, making it easier for users to understand their own spending habits. This allows the analytics department to efficiently analyze users' spending data and provide them with useful information.

[0032] The service provider will provide personalized advice based on the analysis results obtained by the analysis department. Specifically, the service provider can offer personalized savings suggestions to users based on future predictions. For example, it can suggest ways to save money in specific categories, taking into account the user's spending habits. Based on the user's spending data, the service provider will provide specific advice to reduce unnecessary spending. For example, it can provide advice tailored to the user's lifestyle, such as how to save on food expenses or transportation costs. The service provider can also provide investment advice. For example, based on the user's spending data, it can suggest investment destinations to efficiently manage surplus funds. The service provider can set the content and method of providing advice, allowing it to respond flexibly to the user's needs and preferences. For example, various methods can be selected for providing advice, such as email, push notifications, and in-app messages. Furthermore, the service provider can collect user feedback and continuously improve the content and method of providing advice. This allows the service provider to provide the best possible advice for the user and support their spending management. In addition, the service provider will take appropriate security measures when providing advice to protect user privacy. This allows the service provider to offer users safe and reliable advice.

[0033] The classification unit automatically categorizes expenditure data based on advice provided by the service provider. Specifically, the classification unit can automatically categorize each expenditure into the appropriate category. For example, it can categorize expenditure data into categories such as food expenses, transportation expenses, and entertainment expenses. The classification unit categorizes expenditure data based on category definitions and classification algorithms. For example, it uses machine learning algorithms to analyze information such as the content, amount, and date of each expenditure and categorize it into the appropriate category. This allows the classification unit to efficiently manage the user's expenditure data and make it easier for the user to understand their spending situation. Furthermore, the classification unit can continuously improve the accuracy of its classification algorithm based on user feedback. For example, it learns from classification results manually corrected by the user and improves the accuracy of classification in subsequent uses. The classification unit can also add or change categories according to user needs. This allows the classification unit to flexibly manage the user's expenditure data and support expenditure management tailored to the user's needs. In addition, the classification unit visualizes the classification results so that users can understand them intuitively. For example, it uses graphs and charts to display the expenditure percentage for each category and the trend of expenditures, making it easier for users to understand their spending situation. This allows the classification unit to efficiently manage user spending data and support users in managing their spending.

[0034] The data collection unit can collect payment data from cashless payment systems in real time. For example, the data collection unit can automatically acquire data from cashless payment systems and store it as user spending data. By acquiring data from cashless payment systems in real time, the data collection unit can immediately reflect spending data. For example, the data collection unit can collect credit card usage history and bank account transaction history in real time. This enables the immediate reflection of spending data by collecting payment data from cashless payment systems in real time. The specific definition and criteria of real time are set based on factors such as the data update frequency and delay time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from cashless payment systems into a generating AI and have the generating AI perform real-time data collection.

[0035] The analysis unit can analyze collected spending data and make future predictions that take into account the user's past spending trends. For example, the analysis unit can analyze collected spending data and make future predictions that take into account the user's past spending trends. The analysis unit analyzes spending data in detail based on the algorithm used and the purpose of the analysis. For example, the analysis unit can predict the user's future spending using a prediction algorithm. This enables more accurate spending management by making future predictions that take into account the user's past spending trends. The specific methods and criteria for future predictions are set based on the prediction algorithm and the accuracy of the prediction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected spending data into a generating AI and have the generating AI perform future predictions.

[0036] The service provider can provide users with personalized savings suggestions based on future predictions. For example, the service provider can provide users with personalized savings suggestions based on future predictions. The service provider can provide users with useful information such as suggestions for saving methods and investment advice. The service provider can set the content and method of providing advice. This makes users' spending management more effective by providing personalized savings suggestions based on future predictions. The specific content and method of providing savings suggestions are set based on the specific means of saving and the frequency of suggestions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the results of future predictions into a generating AI and have the generating AI execute personalized savings suggestions.

[0037] The classification unit can automatically categorize each expenditure into the appropriate category. For example, the classification unit automatically categorizes each expenditure into the appropriate category. The classification unit categorizes expenditure data based on category definitions and classification algorithms. For example, the classification unit can categorize expenditure data into categories such as food expenses, transportation expenses, and entertainment expenses. This reduces the manual burden on the user and streamlines expenditure management by automatically categorizing each expenditure into the appropriate category. The specific definitions and classification criteria for appropriate categories are set based on category definitions and classification algorithms. 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 expenditure data into a generating AI and have the generating AI perform automatic categorization into the appropriate category.

[0038] The data collection unit can analyze the user's past spending history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on payment methods frequently used by the user. Based on the user's spending patterns, the data collection unit can concentrate data collection during specific time periods. The data collection unit can prioritize collecting spending from specific categories based on the user's past spending history. This allows for efficient collection of spending data by analyzing the user's past spending history and selecting the optimal data collection method. The specific criteria and selection methods for the optimal data collection method are set based on factors such as the type of data and the timing of collection. Some or all of the above-described processes 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 spending history into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter expenditure data based on the user's current lifestyle and areas of interest. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related expenditure data. If the user is interested in health, the data collection unit can prioritize collecting health-related expenditure data. If the user has started a new hobby, the data collection unit can prioritize collecting expenditure data related to that hobby. This allows for the priority collection of highly relevant expenditure data by filtering based on the user's lifestyle and areas of interest. The specific definitions of lifestyle and areas of interest, as well as the filtering methods, are set based on factors such as income, family structure, hobbies, and products of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting expenditure data. For example, if the user is in a specific region, the data collection unit can prioritize the collection of expenditure data in that region. If the user is traveling, the data collection unit can prioritize the collection of expenditure data at the travel destination. If the user is at home, the data collection unit can prioritize the collection of expenditure data around the user's home. This allows for the priority collection of highly relevant expenditure data by considering the user's geographical location. The specific methods for acquiring and using geographical location information are set based on GPS data, location information services, etc. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data when collecting spending data. For example, the data collection unit can prioritize collecting spending data at locations shared by the user on social media. The data collection unit can collect spending data related to products and services that the user has shown interest in on social media. The data collection unit can prioritize collecting spending data at brands and stores that the user follows on social media. This allows for the priority collection of relevant spending data by analyzing the user's social media activity. The specific methods for analyzing and collecting social media activity are set based on factors such as the content of posts and the number of likes. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the expenditure data. For example, the analysis unit can perform a detailed analysis on highly important expenditure data. For less important expenditure data, it can perform a simplified analysis. For moderately important expenditure data, it can perform an analysis with an appropriate level of detail. This allows for efficient analysis by adjusting the level of detail based on the importance of the expenditure data. The specific evaluation criteria and determination methods for importance are set based on factors such as the size of the amount and frequency. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the importance of the expenditure data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of expenditure data during analysis. For example, the analysis unit can apply a consumption pattern analysis algorithm to food-related expenditure data. For transportation-related expenditure data, it can apply a travel pattern analysis algorithm. For entertainment-related expenditure data, it can apply an entertainment consumption pattern analysis algorithm. By applying different analysis algorithms depending on the category of expenditure data, more accurate analysis becomes possible. The specific types and application methods of the analysis algorithms are determined based on methods such as regression analysis and clustering. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of expenditure data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis department can prioritize analysis based on the submission timing of expenditure data. For example, it can prioritize the analysis of recently submitted expenditure data. It can also prioritize the analysis of expenditure data submitted in a concentrated period. It can postpone the analysis of older expenditure data. This allows for efficient analysis by prioritizing analysis based on the submission timing of expenditure data. The specific evaluation criteria and methods for determining priority based on submission timing are set based on factors such as submission date and frequency. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the submission timing of expenditure data into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of expenditure data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant expenditure data. The analysis unit can postpone the analysis of less relevant expenditure data. The analysis unit can analyze expenditure data with moderate relevance in an appropriate order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of expenditure data. The specific criteria for evaluating relevance and the method for adjusting the order are set based on data correlation and relevance scores, etc. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of expenditure data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The service provider can adjust the level of detail of advice based on the importance of the expenditure data when providing advice. For example, the service provider can provide detailed advice for high-importance expenditure data, simplified advice for low-importance expenditure data, and advice with a moderate level of detail for medium-importance expenditure data. This allows for efficient advice by adjusting the level of detail based on the importance of the expenditure data. The specific methods and criteria for adjusting the level of detail of the advice are set based on factors such as concise advice and detailed explanations. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the importance of the expenditure data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0047] The service provider can apply different advice algorithms depending on the category of expenditure data when providing advice. For example, the service provider can apply an advice algorithm that suggests a healthy diet to food-related expenditure data. For transportation-related expenditure data, it can apply an advice algorithm that suggests efficient ways of traveling. For entertainment-related expenditure data, it can apply an advice algorithm that suggests a balance of entertainment. By applying different advice algorithms depending on the category of expenditure data, more accurate advice becomes possible. The specific types and application methods of the advice algorithms are set based on rule-based or machine learning-based methods, etc. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the categories of expenditure data into a generating AI and have the generating AI execute the application of different advice algorithms.

[0048] The service provider can prioritize advice based on the timing of expenditure data submission when providing advice. For example, the service provider may prioritize advice on recently submitted expenditure data. The service provider may also prioritize advice on expenditure data submitted in a concentrated period. The service provider may postpone advice on older expenditure data submissions. This allows for efficient advice by prioritizing advice based on the timing of expenditure data submission. The specific methods and criteria for determining advice priority are set based on factors such as importance and urgency. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the timing of expenditure data submission into a generating AI and have the generating AI determine the priority of advice.

[0049] The service provider can adjust the order of advice based on the relevance of expenditure data when providing advice. For example, the service provider can prioritize advice on highly relevant expenditure data. For less relevant expenditure data, the service provider can postpone providing advice. For expenditure data of moderate relevance, the service provider can provide advice in an appropriate order. This allows for efficient advice by adjusting the order of advice based on the relevance of expenditure data. The specific methods and criteria for adjusting the order of advice are set based on relevance, importance, etc. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of expenditure data into a generating AI and have the generating AI adjust the order of advice.

[0050] The classification unit can improve the accuracy of classification by considering the interrelationships of expenditure data during classification. For example, the classification unit can group and classify expenditure data belonging to the same category. The classification unit can link and classify expenditure data that belongs to different categories but is related. The classification unit can analyze the interrelationships of expenditure data and apply the optimal classification method. This improves the accuracy of classification by considering the interrelationships of expenditure data. The specific evaluation criteria for interrelationships and methods for improving classification accuracy are set based on data correlation and relevance scores, etc. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input the interrelationships of expenditure data into a generating AI and have the generating AI perform the classification accuracy improvement.

[0051] The classification unit can classify expenditure data while considering the attribute information of the submitter. For example, the classification unit can classify expenditure data based on the submitter's age and gender. The classification unit can classify expenditure data based on the submitter's occupation and income. The classification unit can classify expenditure data based on the submitter's lifestyle and hobbies. This allows for more appropriate classification by considering the attribute information of the expenditure data submitter. The specific types of attribute information and classification methods are set based on age, occupation, income, etc. 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 submitter's attribute information into a generating AI and have the generating AI perform the classification.

[0052] The classification unit can perform classification while considering the geographical distribution of expenditure data. For example, the classification unit can classify based on the location where the expenditure data originates. The classification unit can group and classify geographically close expenditure data. The classification unit can analyze geographical trends and apply the optimal classification method. This allows for more appropriate classification by considering the geographical distribution of expenditure data. Specific evaluation criteria and classification methods for geographical distribution are set based on regional expenditure trends and geographical relationships. 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 geographical distribution of expenditure data into a generating AI and have the generating AI perform the classification.

[0053] The classification unit can improve the accuracy of its classification by referring to relevant literature on expenditure data during the classification process. For example, the classification unit can refer to relevant literature on expenditure data to strengthen the basis for its classification. Based on the information in the relevant literature, the classification unit can re-evaluate the categories of expenditure data. The classification unit can incorporate the latest information on relevant literature and update its classification method. This improves the accuracy of classification by referring to relevant literature on expenditure data. The specific methods for referring to relevant literature and improving classification accuracy are set based on criteria for selecting literature and the methods of reference. 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 on expenditure data into a generating AI and have the generating AI perform the classification accuracy improvement.

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

[0055] The data collection unit can adjust its collection method based on the user's health status when collecting user spending data. For example, if a user enters the results of a health checkup, it can prioritize the collection of health-related spending data based on those results. Furthermore, if a user sets a specific health goal, it can collect spending data related to that goal. Additionally, if a user uses a health-related application, data from that application can be collected and integrated with the health-related spending data. This allows for more personalized spending management by collecting spending data based on the user's health status.

[0056] The analytics department can adjust its analysis methods when analyzing user spending data, taking into account the user's life events. For example, if a user experiences a life event such as marriage or childbirth, the analytics department can focus its analysis on spending data related to that event. Similarly, if a user changes jobs or moves, the analytics department can analyze spending data related to those changes in detail. Furthermore, if a user sets a specific goal, the analytics department can analyze spending data toward achieving that goal and evaluate progress. This allows for more appropriate advice to be provided by analyzing spending data based on the user's life events.

[0057] The service provider can tailor advice based on user spending data, taking into account the user's hobbies and interests. For example, if a user spends a lot on a particular hobby, the service can provide money-saving tips and deals related to that hobby. If a user starts a new hobby, the service can analyze spending data related to that hobby and suggest efficient spending methods. Furthermore, if a user prefers certain brands or stores, the service can provide advice related to those brands or stores. This allows for more personalized spending management by providing advice based on the user's hobbies and interests.

[0058] The classification unit can adjust the classification method when classifying user spending data, taking into account the user's family structure. For example, if a user has spending data shared with their family, that data can be classified by family member. Furthermore, if a user has specific family-related spending, such as children's education expenses or family medical expenses, that spending data can be classified into the appropriate category. Additionally, if a user has spending data related to family life events, that data can be prioritized for classification. This allows for more appropriate spending management by classifying spending data based on the user's family structure.

[0059] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting user spending data. For example, if a user is in a specific region, it can prioritize the collection of spending data in that region. Similarly, if a user is traveling, it can prioritize the collection of spending data at their travel destination. Furthermore, if a user is at home, it can prioritize the collection of spending data around their home. This allows for the priority collection of highly relevant spending data by considering the user's geographical location.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects user spending data. The data collection unit can, for example, collect payment data from cashless payment systems in real time. The data collection unit automatically acquires data from cashless payment systems and stores it as user spending data. The data collection unit can also collect, for example, credit card usage history and bank account transaction history. The data collection unit can configure the data acquisition method and collection frequency. Step 2: The analysis unit analyzes the spending data collected by the collection unit. For example, the analysis unit can analyze the collected spending data and make future predictions that take into account the user's past spending trends. The analysis unit analyzes the spending data in detail based on the algorithm used and the purpose of the analysis. For example, the analysis unit can use a prediction algorithm to predict the user's future spending. Step 3: The service provider provides personalized advice based on the analysis results obtained by the analysis provider. For example, the service provider can provide users with personalized savings suggestions based on future predictions. The service provider provides useful information to users, such as suggestions for saving methods and investment advice. The service provider can set the content and method of providing the advice. Step 4: The classification unit automatically classifies the expenditure data based on the advice provided by the provision unit. The classification unit can, for example, automatically classify each expenditure into the appropriate category. The classification unit classifies the expenditure data based on the category definition and classification algorithm. The classification unit can, for example, classify the expenditure data into categories such as food expenses, transportation expenses, and entertainment expenses.

[0062] (Example of form 2) The expenditure management system according to an embodiment of the present invention is a system that streamlines user expenditure management by utilizing cashless payment data. This expenditure management system is designed so that when a user makes a cashless payment, the payment data is automatically reflected in the household ledger in real time. This eliminates the need for manual input and allows users to instantly visualize their expenditures. Next, a generating AI analyzes the expenditure data and provides personalized advice for each user. For example, it provides future predictions and savings suggestions that take into account past spending trends. It also has a function to automatically classify each expenditure into the appropriate category, reducing the manual burden on the user. With this system, users can grasp their expenditures in real time, prevent exceeding their budget, and reduce unnecessary spending. Furthermore, the AI ​​enables individually optimized advice, allowing each user to achieve more effective household financial management. As a result, users can gain a firm grasp of their own financial situation and control it, gaining long-term financial security and making it easier to create future financial plans. Thus, the expenditure management system can efficiently manage users' expenditures and provide them with financial security.

[0063] The expenditure management system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a classification unit. The collection unit collects user expenditure data. The collection unit can, for example, collect payment data from a cashless payment system in real time. The collection unit automatically acquires data from the cashless payment system and stores it as user expenditure data. The collection unit can, for example, collect credit card usage history and bank account transaction history. The collection unit can set the data acquisition method and collection frequency. The analysis unit analyzes the expenditure data collected by the collection unit. The analysis unit can, for example, analyze the collected expenditure data and make future predictions that take into account the user's past expenditure trends. The analysis unit analyzes the expenditure data in detail based on the algorithm used and the purpose of the analysis. The analysis unit can, for example, use a prediction algorithm to predict the user's future expenditures. The provision unit provides personalized advice based on the analysis results obtained by the analysis unit. The provision unit can, for example, provide personalized savings suggestions to the user based on future predictions. The provision unit provides useful information to the user, such as suggestions for saving methods and investment advice. The provision unit can set the content and method of providing advice. The classification unit automatically classifies expenditure data based on the advice provided by the provision unit. The classification unit can, for example, automatically classify each expenditure into an appropriate category. The classification unit classifies expenditure data based on category definitions and classification algorithms. The classification unit can, for example, classify expenditure data into categories such as food expenses, transportation expenses, and entertainment expenses. As a result, the expenditure management system according to this embodiment can efficiently collect, analyze, provide advice on, and automatically classify the user's expenditure data.

[0064] The data collection unit collects user spending data. For example, the data collection unit can collect payment data from cashless payment systems in real time. Specifically, the data collection unit obtains data from various cashless payment methods, such as credit and debit card usage history, electronic money transaction history, and bank account transaction history. This data is automatically collected each time a user makes a purchase and stored in a central database. The data collection unit can configure the data acquisition method and frequency, allowing for flexible responses to user needs and system requirements. For example, the data collection unit can select various collection methods, such as collecting daily transaction data in a batch overnight or collecting data in real time. Furthermore, the data collection unit has functions to detect data duplication and omissions, and correct or supplement them as needed, to ensure data accuracy and consistency. This allows the data collection unit to efficiently and accurately collect user spending data, improving the overall reliability of the system. In addition, the data collection unit implements data encryption and access control as security measures to protect user privacy. This allows the data collection unit to securely collect user spending data and enhance the overall system security.

[0065] The analysis department analyzes the spending data collected by the data collection department. For example, the analysis department can analyze the collected spending data and make future predictions that take into account the user's past spending trends. Specifically, the analysis department uses machine learning algorithms and statistical models to analyze the user's spending patterns in detail. For example, based on the user's past spending data, it can analyze monthly spending trends and fluctuations in spending within specific categories. Furthermore, based on these analysis results, the analysis department predicts the user's future spending. For example, it can predict seasonal spending fluctuations and increases in spending associated with specific events, and provide appropriate advice to the user. The analysis department analyzes the spending data in detail based on the algorithms used and the purpose of the analysis. For example, it can use clustering algorithms to classify the user's spending patterns into multiple groups and provide different advice to each group. It can also use anomaly detection algorithms to detect unusual spending patterns and issue warnings to the user. In this way, the analysis department can analyze the user's spending data from multiple angles and provide useful information to the user. Furthermore, the analysis department visualizes the analysis results so that the user can understand them intuitively. For example, graphs and charts can be used to display spending trends and forecasts, making it easier for users to understand their own spending habits. This allows the analytics department to efficiently analyze users' spending data and provide them with useful information.

[0066] The service provider will provide personalized advice based on the analysis results obtained by the analysis department. Specifically, the service provider can offer personalized savings suggestions to users based on future predictions. For example, it can suggest ways to save money in specific categories, taking into account the user's spending habits. Based on the user's spending data, the service provider will provide specific advice to reduce unnecessary spending. For example, it can provide advice tailored to the user's lifestyle, such as how to save on food expenses or transportation costs. The service provider can also provide investment advice. For example, based on the user's spending data, it can suggest investment destinations to efficiently manage surplus funds. The service provider can set the content and method of providing advice, allowing it to respond flexibly to the user's needs and preferences. For example, various methods can be selected for providing advice, such as email, push notifications, and in-app messages. Furthermore, the service provider can collect user feedback and continuously improve the content and method of providing advice. This allows the service provider to provide the best possible advice for the user and support their spending management. In addition, the service provider will take appropriate security measures when providing advice to protect user privacy. This allows the service provider to offer users safe and reliable advice.

[0067] The classification unit automatically categorizes expenditure data based on advice provided by the service provider. Specifically, the classification unit can automatically categorize each expenditure into the appropriate category. For example, it can categorize expenditure data into categories such as food expenses, transportation expenses, and entertainment expenses. The classification unit categorizes expenditure data based on category definitions and classification algorithms. For example, it uses machine learning algorithms to analyze information such as the content, amount, and date of each expenditure and categorize it into the appropriate category. This allows the classification unit to efficiently manage the user's expenditure data and make it easier for the user to understand their spending situation. Furthermore, the classification unit can continuously improve the accuracy of its classification algorithm based on user feedback. For example, it learns from classification results manually corrected by the user and improves the accuracy of classification in subsequent uses. The classification unit can also add or change categories according to user needs. This allows the classification unit to flexibly manage the user's expenditure data and support expenditure management tailored to the user's needs. In addition, the classification unit visualizes the classification results so that users can understand them intuitively. For example, it uses graphs and charts to display the expenditure percentage for each category and the trend of expenditures, making it easier for users to understand their spending situation. This allows the classification unit to efficiently manage user spending data and support users in managing their spending.

[0068] The data collection unit can collect payment data from cashless payment systems in real time. For example, the data collection unit can automatically acquire data from cashless payment systems and store it as user spending data. By acquiring data from cashless payment systems in real time, the data collection unit can immediately reflect spending data. For example, the data collection unit can collect credit card usage history and bank account transaction history in real time. This enables the immediate reflection of spending data by collecting payment data from cashless payment systems in real time. The specific definition and criteria of real time are set based on factors such as the data update frequency and delay time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from cashless payment systems into a generating AI and have the generating AI perform real-time data collection.

[0069] The analysis unit can analyze collected spending data and make future predictions that take into account the user's past spending trends. For example, the analysis unit can analyze collected spending data and make future predictions that take into account the user's past spending trends. The analysis unit analyzes spending data in detail based on the algorithm used and the purpose of the analysis. For example, the analysis unit can predict the user's future spending using a prediction algorithm. This enables more accurate spending management by making future predictions that take into account the user's past spending trends. The specific methods and criteria for future predictions are set based on the prediction algorithm and the accuracy of the prediction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected spending data into a generating AI and have the generating AI perform future predictions.

[0070] The service provider can provide users with personalized savings suggestions based on future predictions. For example, the service provider can provide users with personalized savings suggestions based on future predictions. The service provider can provide users with useful information such as suggestions for saving methods and investment advice. The service provider can set the content and method of providing advice. This makes users' spending management more effective by providing personalized savings suggestions based on future predictions. The specific content and method of providing savings suggestions are set based on the specific means of saving and the frequency of suggestions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the results of future predictions into a generating AI and have the generating AI execute personalized savings suggestions.

[0071] The classification unit can automatically categorize each expenditure into the appropriate category. For example, the classification unit automatically categorizes each expenditure into the appropriate category. The classification unit categorizes expenditure data based on category definitions and classification algorithms. For example, the classification unit can categorize expenditure data into categories such as food expenses, transportation expenses, and entertainment expenses. This reduces the manual burden on the user and streamlines expenditure management by automatically categorizing each expenditure into the appropriate category. The specific definitions and classification criteria for appropriate categories are set based on category definitions and classification algorithms. 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 expenditure data into a generating AI and have the generating AI perform automatic categorization into the appropriate category.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of 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 the data when the user is relaxed. If the user is relaxed, the data collection unit can immediately collect the data and reflect it in real time. If the user is in a hurry, the data collection unit can shorten the collection timing and collect the data quickly. This allows for the collection of data at a more appropriate time by adjusting the collection timing based on the user's emotions. User emotions are estimated using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0073] The data collection unit can analyze the user's past spending history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on payment methods frequently used by the user. Based on the user's spending patterns, the data collection unit can concentrate data collection during specific time periods. The data collection unit can prioritize collecting spending from specific categories based on the user's past spending history. This allows for efficient collection of spending data by analyzing the user's past spending history and selecting the optimal data collection method. The specific criteria and selection methods for the optimal data collection method are set based on factors such as the type of data and the timing of collection. Some or all of the above-described processes 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 spending history into a generating AI and have the generating AI select the optimal data collection method.

[0074] The data collection unit can filter expenditure data based on the user's current lifestyle and areas of interest. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related expenditure data. If the user is interested in health, the data collection unit can prioritize collecting health-related expenditure data. If the user has started a new hobby, the data collection unit can prioritize collecting expenditure data related to that hobby. This allows for the priority collection of highly relevant expenditure data by filtering based on the user's lifestyle and areas of interest. The specific definitions of lifestyle and areas of interest, as well as the filtering methods, are set based on factors such as income, family structure, hobbies, and products of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of spending data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important spending data. If the user is relaxed, the data collection unit can collect all spending data evenly. If the user is in a hurry, the data collection unit can prioritize collecting more important spending data. This ensures that important spending data is collected preferentially by prioritizing spending data based on the user's emotions. User emotions are estimated using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of spending data.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting expenditure data. For example, if the user is in a specific region, the data collection unit can prioritize the collection of expenditure data in that region. If the user is traveling, the data collection unit can prioritize the collection of expenditure data at the travel destination. If the user is at home, the data collection unit can prioritize the collection of expenditure data around the user's home. This allows for the priority collection of highly relevant expenditure data by considering the user's geographical location. The specific methods for acquiring and using geographical location information are set based on GPS data, location information services, etc. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data when collecting spending data. For example, the data collection unit can prioritize collecting spending data at locations shared by the user on social media. The data collection unit can collect spending data related to products and services that the user has shown interest in on social media. The data collection unit can prioritize collecting spending data at brands and stores that the user follows on social media. This allows for the priority collection of relevant spending data by analyzing the user's social media activity. The specific methods for analyzing and collecting social media activity are set based on factors such as the content of posts and the number of likes. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple, visually easy-to-understand graph. If the user is relaxed, the analysis unit can provide a report with detailed data. If the user is in a hurry, the analysis unit can provide a concise report that gets straight to the point. This allows for more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the expenditure data. For example, the analysis unit can perform a detailed analysis on highly important expenditure data. For less important expenditure data, it can perform a simplified analysis. For moderately important expenditure data, it can perform an analysis with an appropriate level of detail. This allows for efficient analysis by adjusting the level of detail based on the importance of the expenditure data. The specific evaluation criteria and determination methods for importance are set based on factors such as the size of the amount and frequency. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the importance of the expenditure data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of expenditure data during analysis. For example, the analysis unit can apply a consumption pattern analysis algorithm to food-related expenditure data. For transportation-related expenditure data, it can apply a travel pattern analysis algorithm. For entertainment-related expenditure data, it can apply an entertainment consumption pattern analysis algorithm. By applying different analysis algorithms depending on the category of expenditure data, more accurate analysis becomes possible. The specific types and application methods of the analysis algorithms are determined based on methods such as regression analysis and clustering. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of expenditure data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis department can prioritize analysis based on the submission timing of expenditure data. For example, it can prioritize the analysis of recently submitted expenditure data. It can also prioritize the analysis of expenditure data submitted in a concentrated period. It can postpone the analysis of older expenditure data. This allows for efficient analysis by prioritizing analysis based on the submission timing of expenditure data. The specific evaluation criteria and methods for determining priority based on submission timing are set based on factors such as submission date and frequency. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the submission timing of expenditure data into a generating AI and have the generating AI determine the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of expenditure data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant expenditure data. The analysis unit can postpone the analysis of less relevant expenditure data. The analysis unit can analyze expenditure data with moderate relevance in an appropriate order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of expenditure data. The specific criteria for evaluating relevance and the method for adjusting the order are set based on data correlation and relevance scores, etc. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of expenditure data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and visually easy-to-understand advice. If the user is relaxed, the service provider can provide detailed advice. If the user is in a hurry, the service provider can provide concise and to-the-point advice. This allows for the provision of more appropriate advice by adjusting the way advice is expressed based on the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust the way advice is expressed.

[0085] The service provider can adjust the level of detail of advice based on the importance of the expenditure data when providing advice. For example, the service provider can provide detailed advice for high-importance expenditure data, simplified advice for low-importance expenditure data, and advice with a moderate level of detail for medium-importance expenditure data. This allows for efficient advice by adjusting the level of detail based on the importance of the expenditure data. The specific methods and criteria for adjusting the level of detail of the advice are set based on factors such as concise advice and detailed explanations. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the importance of the expenditure data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0086] The service provider can apply different advice algorithms depending on the category of expenditure data when providing advice. For example, the service provider can apply an advice algorithm that suggests a healthy diet to food-related expenditure data. For transportation-related expenditure data, it can apply an advice algorithm that suggests efficient ways of traveling. For entertainment-related expenditure data, it can apply an advice algorithm that suggests a balance of entertainment. By applying different advice algorithms depending on the category of expenditure data, more accurate advice becomes possible. The specific types and application methods of the advice algorithms are set based on rule-based or machine learning-based methods, etc. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the categories of expenditure data into a generating AI and have the generating AI execute the application of different advice algorithms.

[0087] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise advice. If the user is relaxed, the service provider can provide detailed advice. If the user is excited, the service provider can provide advice with visually stimulating effects. By adjusting the length of the advice based on the user's emotions, more appropriate advice can be provided. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust the length of the advice.

[0088] The service provider can prioritize advice based on the timing of expenditure data submission when providing advice. For example, the service provider may prioritize advice on recently submitted expenditure data. The service provider may also prioritize advice on expenditure data submitted in a concentrated period. The service provider may postpone advice on older expenditure data submissions. This allows for efficient advice by prioritizing advice based on the timing of expenditure data submission. The specific methods and criteria for determining advice priority are set based on factors such as importance and urgency. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the timing of expenditure data submission into a generating AI and have the generating AI determine the priority of advice.

[0089] The service provider can adjust the order of advice based on the relevance of expenditure data when providing advice. For example, the service provider can prioritize advice on highly relevant expenditure data. For less relevant expenditure data, the service provider can postpone providing advice. For expenditure data of moderate relevance, the service provider can provide advice in an appropriate order. This allows for efficient advice by adjusting the order of advice based on the relevance of expenditure data. The specific methods and criteria for adjusting the order of advice are set based on relevance, importance, etc. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of expenditure data into a generating AI and have the generating AI adjust the order of advice.

[0090] The classification unit can estimate the user's emotions and adjust the classification method based on the estimated emotions. For example, if the user is stressed, the classification unit can use a simple and easy-to-understand classification method. If the user is relaxed, the classification unit can use a detailed classification method. If the user is in a hurry, the classification unit can use a concise and simplified classification method. This allows for more appropriate classification by adjusting the classification method based on the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, or not using AI. For example, the classification unit can input user emotion data into the generative AI and have the generative AI adjust the classification method.

[0091] The classification unit can improve the accuracy of classification by considering the interrelationships of expenditure data during classification. For example, the classification unit can group and classify expenditure data belonging to the same category. The classification unit can link and classify expenditure data that belongs to different categories but is related. The classification unit can analyze the interrelationships of expenditure data and apply the optimal classification method. This improves the accuracy of classification by considering the interrelationships of expenditure data. The specific evaluation criteria for interrelationships and methods for improving classification accuracy are set based on data correlation and relevance scores, etc. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input the interrelationships of expenditure data into a generating AI and have the generating AI perform the classification accuracy improvement.

[0092] The classification unit can classify expenditure data while considering the attribute information of the submitter. For example, the classification unit can classify expenditure data based on the submitter's age and gender. The classification unit can classify expenditure data based on the submitter's occupation and income. The classification unit can classify expenditure data based on the submitter's lifestyle and hobbies. This allows for more appropriate classification by considering the attribute information of the expenditure data submitter. The specific types of attribute information and classification methods are set based on age, occupation, income, etc. 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 submitter's attribute information into a generating AI and have the generating AI perform the classification.

[0093] The classification unit can estimate the user's emotions and adjust the order in which the classification results are displayed based on the estimated emotions. For example, if the user is stressed, the classification unit can display high-importance classification results first. If the user is relaxed, the classification unit can display all classification results equally. If the user is in a hurry, the classification unit can display concise classification results first. This allows for more appropriate classification results to be provided by adjusting the order in which the classification results are displayed based on the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, or not using AI. For example, the classification unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the classification results.

[0094] The classification unit can perform classification while considering the geographical distribution of expenditure data. For example, the classification unit can classify based on the location where the expenditure data originates. The classification unit can group and classify geographically close expenditure data. The classification unit can analyze geographical trends and apply the optimal classification method. This allows for more appropriate classification by considering the geographical distribution of expenditure data. Specific evaluation criteria and classification methods for geographical distribution are set based on regional expenditure trends and geographical relationships. 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 geographical distribution of expenditure data into a generating AI and have the generating AI perform the classification.

[0095] The classification unit can improve the accuracy of its classification by referring to relevant literature on expenditure data during the classification process. For example, the classification unit can refer to relevant literature on expenditure data to strengthen the basis for its classification. Based on the information in the relevant literature, the classification unit can re-evaluate the categories of expenditure data. The classification unit can incorporate the latest information on relevant literature and update its classification method. This improves the accuracy of classification by referring to relevant literature on expenditure data. The specific methods for referring to relevant literature and improving classification accuracy are set based on criteria for selecting literature and the methods of reference. 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 on expenditure data into a generating AI and have the generating AI perform the classification accuracy improvement.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can adjust its collection method based on the user's health status when collecting user spending data. For example, if a user enters the results of a health checkup, it can prioritize the collection of health-related spending data based on those results. Furthermore, if a user sets a specific health goal, it can collect spending data related to that goal. Additionally, if a user uses a health-related application, data from that application can be collected and integrated with the health-related spending data. This allows for more personalized spending management by collecting spending data based on the user's health status.

[0098] The analytics department can adjust its analysis methods when analyzing user spending data, taking into account the user's life events. For example, if a user experiences a life event such as marriage or childbirth, the analytics department can focus its analysis on spending data related to that event. Similarly, if a user changes jobs or moves, the analytics department can analyze spending data related to those changes in detail. Furthermore, if a user sets a specific goal, the analytics department can analyze spending data toward achieving that goal and evaluate progress. This allows for more appropriate advice to be provided by analyzing spending data based on the user's life events.

[0099] The service provider can tailor advice based on user spending data, taking into account the user's hobbies and interests. For example, if a user spends a lot on a particular hobby, the service can provide money-saving tips and deals related to that hobby. If a user starts a new hobby, the service can analyze spending data related to that hobby and suggest efficient spending methods. Furthermore, if a user prefers certain brands or stores, the service can provide advice related to those brands or stores. This allows for more personalized spending management by providing advice based on the user's hobbies and interests.

[0100] The classification unit can adjust the classification method when classifying user spending data, taking into account the user's family structure. For example, if a user has spending data shared with their family, that data can be classified by family member. Furthermore, if a user has specific family-related spending, such as children's education expenses or family medical expenses, that spending data can be classified into the appropriate category. Additionally, if a user has spending data related to family life events, that data can be prioritized for classification. This allows for more appropriate spending management by classifying spending data based on the user's family structure.

[0101] The data collection unit can estimate the user's emotions and adjust how spending data is collected based on those emotions. For example, if the user is stressed, the unit can reduce the collection frequency and collect data when the user is relaxed. If the user is excited, the unit can increase the collection frequency and collect spending data in real time. Furthermore, if the user is depressed, the unit can adjust the collection timing and collect data when the user has recovered. By adjusting how spending data is collected based on the user's emotions, spending data can be collected at a more appropriate time.

[0102] The analytics department can estimate the user's emotions and adjust the presentation of the analysis results based on those estimated emotions. For example, if the user is stressed, the analytics department can provide the results using simple, visually easy-to-understand graphs. If the user is relaxed, it can provide a report with detailed data. Furthermore, if the user is in a hurry, it can provide a concise report that gets straight to the point. By adjusting the presentation of the analysis results based on the user's emotions, it is possible to provide more appropriate analysis results.

[0103] The service provider can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is stressed, the service provider can suggest simple and easy-to-follow money-saving methods. If the user is relaxed, it can provide more detailed money-saving methods or investment advice. Furthermore, if the user is in a hurry, it can provide concise and to-the-point advice. In this way, by adjusting the content of the advice based on the user's emotions, more appropriate advice can be provided.

[0104] The classification unit can estimate the user's emotions and adjust how the classification results are displayed based on those emotions. For example, if the user is stressed, the classification unit can display the most important classification results first. If the user is relaxed, it can display all classification results equally. Furthermore, if the user is in a hurry, it can display the most important classification results first. By adjusting how the classification results are displayed based on the user's emotions, the system can provide more appropriate classification results.

[0105] The data collection unit can estimate the user's emotions and prioritize the spending data to collect based on those emotions. For example, if the user is stressed, less important spending data can be prioritized. If the user is relaxed, all spending data can be collected evenly. Furthermore, if the user is in a hurry, highly important spending data can be prioritized. In this way, by prioritizing spending data based on the user's emotions, important spending data can be collected preferentially.

[0106] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting user spending data. For example, if a user is in a specific region, it can prioritize the collection of spending data in that region. Similarly, if a user is traveling, it can prioritize the collection of spending data at their travel destination. Furthermore, if a user is at home, it can prioritize the collection of spending data around their home. This allows for the priority collection of highly relevant spending data by considering the user's geographical location.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects user spending data. The data collection unit can, for example, collect payment data from cashless payment systems in real time. The data collection unit automatically acquires data from cashless payment systems and stores it as user spending data. The data collection unit can also collect, for example, credit card usage history and bank account transaction history. The data collection unit can configure the data acquisition method and collection frequency. Step 2: The analysis unit analyzes the spending data collected by the collection unit. For example, the analysis unit can analyze the collected spending data and make future predictions that take into account the user's past spending trends. The analysis unit analyzes the spending data in detail based on the algorithm used and the purpose of the analysis. For example, the analysis unit can use a prediction algorithm to predict the user's future spending. Step 3: The service provider provides personalized advice based on the analysis results obtained by the analysis provider. For example, the service provider can provide users with personalized savings suggestions based on future predictions. The service provider provides useful information to users, such as suggestions for saving methods and investment advice. The service provider can set the content and method of providing the advice. Step 4: The classification unit automatically classifies the expenditure data based on the advice provided by the provision unit. The classification unit can, for example, automatically classify each expenditure into the appropriate category. The classification unit classifies the expenditure data based on the category definition and classification algorithm. The classification unit can, for example, classify the expenditure data into categories such as food expenses, transportation expenses, and entertainment expenses.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and classification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires data from the cashless payment system via the communication I / F 44 of the smart device 14 and processes it with the processor 46. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected expenditure data. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates personalized advice based on the analysis results. The classification unit is implemented in the control unit 46A of the smart device 14 and automatically classifies each expenditure into the appropriate category. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and classification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires data from a cashless payment system via the communication I / F 44 of the smart glasses 214 and processes it with the processor 46. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected expenditure data. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates personalized advice based on the analysis results. The classification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and automatically classifies each expenditure into the appropriate category. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and classification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit acquires data from the cashless payment system via the communication I / F 44 of the headset terminal 314 and processes it with the processor 46. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected expenditure data. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates personalized advice based on the analysis results. The classification unit is implemented in the control unit 46A of the headset terminal 314 and automatically classifies each expenditure into the appropriate category. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and classification unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires data from the cashless payment system via the communication I / F 44 of the robot 414 and processes it with the processor 46. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected expenditure data. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates personalized advice based on the analysis results. The classification unit is implemented, for example, by the control unit 46A of the robot 414 and automatically classifies each expenditure into the appropriate category. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects user spending data, An analysis unit analyzes the expenditure data collected by the aforementioned collection unit, A provisioning unit that provides personalized advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a classification unit that automatically classifies expenditure data based on advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect payment data from cashless payment systems in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected spending data is analyzed to make future predictions that take into account the user's past spending trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on future predictions, we provide users with personalized savings suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned classification unit is Automatically categorize each expense into the appropriate category. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of spending data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past spending history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting spending data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates user sentiment and prioritizes the spending data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting spending 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 11) The aforementioned collection unit is When collecting spending data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the spending data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of spending data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the spending data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the spending data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, 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 19) The aforementioned supply unit is, When providing advice, we adjust the level of detail based on the importance of the spending data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of the spending data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the spending data is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of spending data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned classification unit is It estimates the user's emotions and adjusts the classification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned classification unit is When classifying data, consider the interrelationships between expenditure data to improve classification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned classification unit is When classifying expenditure data, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned classification unit is It estimates the user's emotions and adjusts the order in which the classification results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned classification unit is When classifying expenditure data, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned classification unit is When classifying data, we refer to relevant literature on expenditure data to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user spending data, An analysis unit analyzes the expenditure data collected by the aforementioned collection unit, A provisioning unit that provides personalized advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a classification unit that automatically classifies expenditure data based on advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect payment data from cashless payment systems in real time. The system according to feature 1.

3. The aforementioned analysis unit is The collected spending data is analyzed to make future predictions that take into account the user's past spending trends. The system according to feature 1.

4. The aforementioned supply unit is, Based on future predictions, we provide users with personalized savings suggestions. The system according to feature 1.

5. The aforementioned classification unit is Automatically categorize each expense into the appropriate category. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of spending data collection based on those estimated emotions. The system according to feature 1.

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

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

9. The aforementioned collection unit is It estimates user sentiment and prioritizes the spending data to collect based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned collection unit is When collecting spending data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

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