system

Abstract

We provide the system. [Solution] A means for receiving and storing user income and expenditure information, A means for determining whether expenditures exceed a target range based on the aforementioned income and expenditure information, A means for generating a notification based on the judgment result and sending it to the user's terminal, A means of statistically analyzing spending information collected from multiple users and providing anonymized feedback, A system that includes this.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, efficient asset management is an important issue. However, for many people, manually tracking expenses and managing goals is time-consuming and often inefficient. Also, it is difficult to objectively understand one's own spending situation compared to others. This makes it difficult to appropriately maintain the balance between income and expenditure, hindering planned asset formation.

Means for Solving the Problems

[0005] To solve this problem, the present invention proposes a system comprising means for automatically receiving user income and expenditure information, means for determining whether expenditures exceed a target range based on this information, and means for generating and providing notifications to users in real time based on the determination results. Furthermore, by providing means for anonymizing expenditure data collected from multiple users, performing statistical analysis, and providing feedback to help users objectively understand their own expenditure situation, the invention realizes efficient and planned asset management.

[0006] "User" refers to an individual or legal entity that uses the system to manage their own income and expenses.

[0007] "Income information" refers to data that includes details of the salary and other income earned by the user.

[0008] "Expenditure information" refers to data that includes details about purchases and service payments made by users.

[0009] "Determination" refers to the process by which the system evaluates whether specific conditions are met based on income and expenditure information.

[0010] A "notification" is a message that a system provides to a user, including warnings, information, or suggestions.

[0011] "Statistical analysis" is a mathematical evaluation method used to identify general trends and patterns from multiple datasets.

[0012] "Anonymization" is a method of processing data in a way that prevents the identification of individual users.

[0013] "Feedback" refers to information and advice provided regarding user behavior, with the aim of improvement and optimization. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] [[ID= thirty-nine]]It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system of this invention is designed to streamline asset management, automatically managing the user's income and expenses and providing real-time feedback as needed.

[0036] The server receives initial registration information from the user's terminal, including income information, monthly spending targets, and details of multiple financial institutions that are authorized to connect. This information is securely stored in a database and forms the basis for the operation of the entire system.

[0037] When a user makes a payment, the terminal sends the spending information to the server. This information includes details such as the payment category, amount, and date. Based on the received spending information, the server immediately calculates the user's remaining budget for the month and analyzes whether it is within the target range. If the target value is exceeded, the server generates an alert and notifies the terminal. By checking the alert and reviewing spending appropriately, users can manage their assets in a planned manner.

[0038] Furthermore, the server collects anonymized data provided by multiple users and performs statistical analysis based on this data. The resulting statistical information is then fed back to the user as reference material to objectively understand their spending habits. For example, if a user's food expenses tend to be higher than those of other users under similar conditions, this information is fed back to the terminal. This gives the user an opportunity to re-evaluate their spending habits.

[0039] In this way, this system supports users in managing their assets efficiently and systematically, helping to eliminate cumbersome manual management. Furthermore, it can significantly reduce the time and effort required for asset management.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user downloads the application to their device, launches it, and enters their annual income, monthly income, and monthly spending goals. In addition, they enter and send information about bank accounts and credit cards that they wish to link to the server.

[0043] Step 2:

[0044] The server securely stores income information and spending targets submitted by users in a database. It also configures data retrieval from linked financial institutions and initializes the necessary API connections.

[0045] Step 3:

[0046] When a user makes a purchase or uses a service, such as by paying with a QR code (registered trademark), the terminal records the expenditure information. This expenditure information includes the amount, category, and date and time.

[0047] Step 4:

[0048] The terminal transmits recorded spending information to the server in real time. The server updates the received spending information as the spending information for the current month.

[0049] Step 5:

[0050] The server calculates the current total spending and compares it to a pre-set monthly spending target. If spending exceeds the target or approaches a certain threshold, the server generates an alert.

[0051] Step 6:

[0052] The server sends the generated alert to the device, and the device notifies the user. The notification is delivered via an in-app pop-up or notification bar.

[0053] Step 7:

[0054] The server periodically performs statistical analysis on anonymous spending data collected from multiple users, calculating consumption trends by category, such as food and entertainment expenses. The calculated information is provided to the user's device as feedback.

[0055] Step 8:

[0056] Based on these notifications and feedback, users set their budgets and review their spending for the following month. If they need to revise their spending targets, they set new targets on their device and send the updated information to the server.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Managing personal assets manually is cumbersome and can lead to decreased accuracy. Furthermore, many users struggle to understand and manage their spending properly, resulting in overspending. Comparing spending trends among users and centrally managing information from different financial institutions is also difficult.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] This invention includes a server that includes means for determining in real time whether spending exceeds the budget based on the user's income information, spending information, and monthly spending target; means for generating and sending a warning message to the user's terminal if spending exceeds the budget; and means for statistically analyzing spending information collected from multiple users and providing anonymized feedback. This enables efficient personal asset management and allows for appropriate spending management and review of consumption trends.

[0062] "User" refers to an individual or legal entity that uses the system and is the entity that provides information on income and expenses.

[0063] "Income information" refers to all data related to the income earned by the user, including salary, investment income, etc.

[0064] "Expenditure information" refers to data on all expenses incurred by the user, including details such as purchased items and service fees.

[0065] The "monthly spending target" refers to the maximum amount of money a user sets for spending in a month, and serves as an indicator for budget management.

[0066] A "server" refers to a central computer system that receives, processes, stores, and transmits data to user terminals.

[0067] "Terminal" refers to an electronic device used by a user for inputting information or receiving notifications, and includes smartphones, computers, and other similar devices.

[0068] "Anonymization" refers to processing data in a way that makes it impossible to identify individual users, and is intended to protect privacy.

[0069] "Statistical analysis" refers to the process of analyzing collected data using mathematical methods, and is performed to understand the trends and distribution of the data.

[0070] "Feedback" refers to the act of providing users with analysis results and warning information, which can serve as an opportunity for users to re-evaluate their own actions.

[0071] This invention is a system for efficiently managing assets, automatically managing the user's income and expenditure information and providing feedback as needed. It is implemented using the following hardware and software.

[0072] The server is equipped with a database system and securely stores user data received via encrypted communication such as SSL. This data includes user income information, monthly spending targets, and spending information. MySQL® and PostgreSQL are available as database management systems.

[0073] The terminal provides an interface for users to input information. It utilizes a dedicated application installed on a typical smartphone or tablet, which features a user interface that facilitates the input of income and expense data. It also has the functionality to display real-time feedback and warnings from the server.

[0074] When a user enters income and expenditure information into a terminal, the terminal sends this information to a server. Based on the received information, the server analyzes in real time whether the user's spending exceeds their set monthly spending target. The analysis is performed using algorithms that run using programming languages ​​such as Python or R.

[0075] The server anonymizes data collected from multiple users and performs statistical analysis to calculate the mean, median, and distribution. This statistical information is provided as feedback to help users re-evaluate their own consumption behavior.

[0076] As a concrete example, if user A sets a monthly food budget of 20,000 yen, they record their daily expenses using their device, and the server constantly updates the total expenses. If the total expenses exceed 20,000 yen midway through the month, the server issues a warning, and a notification appears on the device stating, "Please be careful not to exceed your budget."

[0077] An example of a prompt message might be: "Please recreate a scenario where you input the user's income and expenditure information, set a monthly expenditure goal, and then receive a warning."

[0078] This allows the system of the present invention to help users efficiently manage their assets and make planned expenditures.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users enter income information, monthly spending targets, and financial institution details using a dedicated application on their device. This entered information is compiled into a data structure such as JSON and transmitted from the device to the server via encrypted communication. Users input the data using a touch interface.

[0082] Step 2:

[0083] The server receives income information and monthly spending targets sent from the terminal. The received data is stored in a database management system. Here, the server verifies the data's integrity and checks for any missing information. It is then saved to the database using SQL queries.

[0084] Step 3:

[0085] When a user makes a payment, the terminal generates spending information as a data packet and sends it to the server. This spending information includes the category of purchased items, the amount spent, and the date and time of the payment. The terminal can also capture data from receipts using NFC or QR code scanning capabilities.

[0086] Step 4:

[0087] The server uses the received spending information to calculate the total monthly spending. This calculation is performed using a Python script, and the latest spending information is updated in the database in real time. The server then determines whether the spending is within the budget target.

[0088] Step 5:

[0089] If spending exceeds the budget, the server generates a warning message and sends it to the device. This message is intended to alert the user and is displayed through the device's notification function.

[0090] Step 6:

[0091] The server anonymizes spending information from multiple users and performs statistical analysis. Using statistical methods, it calculates averages, distributions, and other metrics. This allows users to compare their spending trends with those of other users and generate objective feedback.

[0092] Step 7:

[0093] The server sends the generated feedback information to the terminal and provides it to the user. The user views this information and uses it as an opportunity to review their own spending habits. The feedback helps the user manage their budget more effectively.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] Consumer activity in modern society has diversified, making it difficult for individuals to effectively manage their income and expenses. Furthermore, the lack of data aggregation among financial institutions makes it difficult for users to understand their financial situation in real time. In addition, the lack of objective guidance on improving consumption based on anonymized statistical data makes it difficult for individual users to review their consumption behavior.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] This invention includes a server that receives and stores the user's income and expenditure information, analyzes the user's individual transaction data and performs statistical comparisons with similar users, and provides the user with customized prompts using a generative AI model. This enables the user to understand their own consumption activities in detail, and at the same time, grasp their financial situation in real time through the aggregation of financial institution information, thereby enabling effective consumption improvements.

[0099] "Individual user transaction data" refers to detailed information about the income and expenses incurred by each user, including the category, amount, and date of the expenditure.

[0100] "Statistical comparison with similar users" involves comparing each user's consumption behavior with anonymized data from other users and analyzing the differences from average spending trends.

[0101] "Customized prompts using generative AI models" refer to messages that utilize artificial intelligence models to provide personalized advice and guidance based on the user's specific consumer behavior.

[0102] "Consolidation of financial institution information" refers to the process of centralizing financial information held by users across multiple financial institutions, making it possible to manage it in an integrated manner.

[0103] "Effective consumption improvement" means that users review their own consumption behavior based on statistical data and analysis by generating AI, and modify their actions to achieve optimal asset management.

[0104] This invention is a system for streamlining user asset management and utilizes multiple components, including a server, user terminals, and a generative AI model. The server receives income and expenditure information from the user's terminal and securely stores it in a database. This allows users to centrally manage their income and expenses.

[0105] Based on the received spending information, the server instantly calculates the user's budget for the month and determines whether it exceeds the target range. If the target is exceeded, the server automatically generates a notification and sends this information to the user's device in real time. This notification serves as an alert to help the user appropriately review their spending.

[0106] Furthermore, the server collects anonymized data from multiple users and performs statistical analysis. This allows the server to compare users with similar consumption patterns and provide specific suggestions for improving their spending. These suggestions include customized prompts generated using generative AI models. For example, if a user tends to spend more on food than other users, they may be offered advice to reconsider their spending.

[0107] This system integrates users' financial institution information, enabling real-time monitoring of complex financial situations. For example, it might notify users with prompts such as, "Your food expenses are higher than average. How will you adjust your budget?" to encourage improvements in specific spending habits.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user's terminal retrieves income and expense information and sends it to the server. This input data includes detailed information about income and expenses. The server receives this data and stores it in a secure database. After the data is stored, it becomes possible to centrally manage income and expenses based on this data.

[0111] Step 2:

[0112] The server analyzes the user's budget status using stored spending information. It calculates the total spending amount by adding up the spending information as input. It determines whether the budget has been exceeded and outputs the result for the next step. If the analysis reveals that the target range has been exceeded, that information is used to generate an alert.

[0113] Step 3:

[0114] The server generates an alert and sends a notification to the user's terminal if the target range is exceeded. Here, data processing is performed to generate the alert message based on the judgment result. The notification is displayed on the terminal in real time, providing information that allows the user to quickly review their spending.

[0115] Step 4:

[0116] The server performs statistical analysis based on anonymized spending data collected from multiple users. It uses anonymized data as input to derive analysis results. This includes analyzing means and distributions, and comparing data with similar users. Specific operations include data calculations using statistical models.

[0117] Step 5:

[0118] Based on the analysis results, the server uses a generative AI model to generate customized prompts for the user. In this step, the generative AI model uses the output of the statistical analysis and the user's individual data as input to create prompts. Specific prompt statements are generated and presented to the user. This includes suggestions for specific consumption improvements for the user.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] The system of this invention enables income and expenditure management, as well as feedback based on the user's emotions. By combining standard asset management functions with an emotion engine, this system achieves more personalized expenditure management.

[0121] The server stores basic information such as annual income, monthly income, and spending targets obtained from users, and operates in the same way as a typical asset management system. At the same time, it analyzes user input data and behavioral history, and uses an emotion engine to understand the user's current emotional state. This information is dynamically incorporated into the asset management information and used to customize spending alerts and feedback.

[0122] For example, when a user repeatedly makes large purchases in a short period, the server considers the possibility that it is impulsive purchasing behavior. If the emotion engine analyzes that the user is stressed or acting impulsively, the alert displayed on the device will change from the usual "overspending" notification to a message encouraging relaxation or suggesting a reassessment of the purchase.

[0123] Furthermore, the server statistically compares anonymous sentiment data collected from other users to provide general spending trends of users with similar financial situations and emotional states. This information forms the basis for strengthening spending improvement suggestions and providing users with more accurate feedback.

[0124] Thus, this system not only manages the balance between direct income and expenses, but also provides government feedback based on the user's behavior and emotions, enabling more personalized asset management. As a result, users can maintain their financial health and make daily expenses with greater peace of mind.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] Users download the application to their device and enter basic information such as their annual income, monthly income, and monthly spending goals. They also complete the initial setup required for the emotion engine and send this information to the server.

[0128] Step 2:

[0129] The server stores basic information submitted by the user in a database and builds a user profile. This profile forms the basis for monitoring financial fluctuations and emotional states.

[0130] Step 3:

[0131] When a user makes a payment for shopping or using a service, the terminal records the expenditure information. This expenditure information includes the amount, category, payment method, and date and time, and is sent to the server.

[0132] Step 4:

[0133] Based on the received spending information, the server calculates the cumulative spending for the current month and categorizes it into individual spending categories. Simultaneously, the server passes the accumulated behavioral data to the emotion engine to analyze the user's emotional state.

[0134] Step 5:

[0135] The emotion engine infers the user's current emotions from their past operation history and input data, and analyzes their stress level and purchasing motivations. Based on these results, it reports the emotional state to the server.

[0136] Step 6:

[0137] The server generates specific alerts and feedback based on cumulative spending and sentiment analysis results. For example, if an unexpected large expense occurs and the sentiment engine suggests impulsive behavior, it will generate alerts to encourage relaxation and suggestions to review spending.

[0138] Step 7:

[0139] The server sends generated alerts and feedback messages to the terminal. The terminal notifies the user of the received information and displays it clearly on the screen.

[0140] Step 8:

[0141] The server compares sentiment and spending data obtained from other anonymous users to calculate statistical trends. This information is sent to the device as feedback indicating the financial and emotional state compared to users under similar conditions.

[0142] Step 9:

[0143] Users can check alerts and feedback information through their devices, allowing them to review their spending and re-evaluate their budget settings. This enables personalized and planned asset management.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] Traditional financial management systems typically only provide simple records and warnings based on users' income and expenses, and are unable to offer personalized feedback that takes into account users' emotional states. As a result, it is difficult to prevent impulsive purchases and unplanned spending caused by stress, and there is a problem in that users cannot be given sufficient suggestions for improving their spending.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for receiving and storing information on the user's income and expenses, means for determining whether expenses exceed a target range based on the information, and emotion recognition means for analyzing the user's behavioral history and estimating their current emotional state. This makes it possible to provide feedback that reflects the emotional state based on the user's behavior, thereby enabling more appropriate spending management and improvement suggestions.

[0149] A "user" is an individual or organization that inputs information about their income and expenses through the system and receives feedback on their financial management and behavior.

[0150] "Income-related information" refers to data on a user's income, such as their annual or monthly income, and is information that forms the basis for judging their financial situation.

[0151] "Expenditure information" refers to data based on users' daily purchasing behavior and expenses, and serves as the basis for budget management and analysis of spending trends.

[0152] "Emotion recognition means" refers to a technology or process for analyzing a user's input information and behavioral history to estimate their current emotional state.

[0153] A "notification" is a message sent from a system to a user's information processing device, and includes information such as warnings, suggestions, and feedback.

[0154] "Anonymization" is the process of making it impossible to identify specific individuals or groups from data, and is used to obtain statistical information from a group while ensuring privacy.

[0155] "Statistical analysis" refers to processing collected data using mathematical and statistical methods to derive trends and patterns.

[0156] This invention is a system that enables personalized spending management by gaining a deeper understanding of the user's financial situation and providing emotion-based feedback. The system primarily operates between three parties: a server, a terminal, and the user.

[0157] The server receives information about users' income and expenses from their terminals and stores it in a database. The databases used are typically MySQL or PostgreSQL. The server also uses analytical tools such as Python or R to perform detailed analyses of users' purchase history and behavioral data.

[0158] Based on information entered by the user and their purchase history, the server uses an "emotion recognition engine" to estimate the user's current emotional state. This emotion recognition engine can utilize technologies such as "IBM Watson®". The results of the emotional analysis are integrated with asset management information and used to dynamically generate feedback.

[0159] The terminal displays feedback and warnings received from the server to the user. The server also uses a generative AI model to input prompts when generating customized messages. An example of such a prompt is: "Your recent purchasing behavior indicates you are experiencing stress. Please generate suggestions to help you relax."

[0160] For example, if a user repeatedly makes large purchases in a short period, the server analyzes this behavior and uses an emotion engine to estimate the user's emotional state. If the analysis suggests that the user is experiencing stress, a message such as "Review your spending and make time to relax" will be displayed on the device.

[0161] In this way, we can go beyond mere numerical management and achieve more precise and personalized financial assistance that is attentive to the user's emotions.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server receives income and expense information entered by the user. Specifically, the user enters their annual income, monthly income, and daily expenses through a dedicated application or web interface. The server stores this information in a database such as MySQL. The input is the user's financial information, and the output is the storage of that information in the database.

[0165] Step 2:

[0166] The server analyzes the received spending information and extracts spending patterns from the user's purchase history. Specifically, the server uses Python or R to perform data analysis and determine whether there have been high-value purchases in a short period. The input is the user's purchase history data, and the output is a report of the analyzed spending patterns.

[0167] Step 3:

[0168] The server uses an emotion recognition engine to estimate the user's emotional state based on their behavioral data. Specifically, it utilizes tools such as IBM Watson to analyze emotions like stress and impulsivity. The input consists of analysis results of purchase history and spending patterns, while the output is data on the estimated emotional state.

[0169] Step 4:

[0170] The server inputs a prompt message into a generative AI model, which then generates customized feedback based on emotion recognition. For example, the prompt message "Your recent purchasing behavior indicates you are experiencing stress. Please generate suggestions to help you relax." is used. The input consists of the emotional state and the prompt message, while the output is an emotion-based feedback message.

[0171] Step 5:

[0172] The device displays feedback received from the server to the user. Specifically, a message is displayed on the user's device, notifying the user of an alert such as, "Review your spending and make time to relax." The input is the feedback message generated by the server, and the output is the notification to the user.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] Traditional income and expense management systems simply focus on balancing income and expenses, lacking features to mitigate impulsive purchasing behavior driven by user emotions and stress. This made it difficult for users to accurately understand their own emotional state and maintain financial health while engaging in economic activities. Furthermore, systems lacked the ability to personalize improvement suggestions through anonymous data comparisons with other users. These issues need to be addressed.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes means for analyzing the user's emotional state and generating personalized advice based on the analysis results; means for providing a user interface for visually presenting the advice; and means for statistically analyzing spending information collected from multiple users and providing anonymized feedback. This enables users to achieve more appropriate spending management based on their emotional state, suppress impulsive purchasing behavior, and engage in economic activities that are best suited to them.

[0178] "Income information" refers to all financial benefits obtained by the user, including information such as salary, stocks, and investment returns.

[0179] "Expenditure information" refers to information about all activities in which a user pays money and the transactions associated with them.

[0180] "Analysis results" refer to the results of data analysis conducted to determine the emotional state of the user.

[0181] "Personalized advice" refers to specific action plans and suggestions generated based on the user's individual emotional state and financial situation.

[0182] "Visual presentation" refers to showing information to users in a visual format, and includes, but is not limited to, graphs, text, and icons.

[0183] A "user interface" is a means for a user to interact with a system, and usually refers to a device or program that includes a graphical user interface (GUI).

[0184] "Anonymized feedback" refers to ratings and information provided in a form that does not allow for personal identification in order to protect user privacy.

[0185] "Anonymous data comparison with other users" refers to the comparative analysis of spending and emotional state data from multiple users, with personal information removed.

[0186] This invention is an electronic payment system that analyzes the user's emotional state, primarily based on income and expenditure information, and provides personalized feedback. This system utilizes the user's smartphone as the primary hardware and is implemented using software such as an emotion analysis module and a database management system.

[0187] First, the server collects users' income and expenditure information and stores it in a database. This includes obtaining data from financial institutions and user input. Furthermore, sentiment analysis software such as Google® Cloud Natural Language API and Microsoft® Azure® Text Analytics are used to analyze users' emotional states. This makes it possible to extract emotions such as stress and impulsivity from text data.

[0188] The server combines analyzed emotional states with income and expenditure information to generate personalized advice. This advice is presented visually through the user interface, allowing users to easily take optimal actions based on their own emotional state.

[0189] For example, if a user is making large expenditures in a short period of time, the system will display a message on their smartphone saying, "It seems you've been under a lot of stress lately. Why not take some time to relax?" It also provides comparative information based on anonymous data from multiple users, which can be used as a reference to improve individual spending habits.

[0190] Examples of prompts in generative AI models are as follows:

[0191] "User behavior history: Recent increase in spending; Emotional state: High stress rating. Please generate an appropriate feedback message."

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server obtains user income and expenditure information through financial institution APIs and user input. This information is stored in a database for later analysis. The input to this process is the user's income and expenditure information, and the output is the financial information recorded in the database.

[0195] Step 2:

[0196] The system acquires the user's purchase history and text input and sends this to an emotion analysis module. The emotion analysis module uses the collected data to analyze the user's emotional state. The input for this process is the user's text input and purchase history, and the emotion analysis module outputs emotional states such as stress and impulsivity.

[0197] Step 3:

[0198] The server combines the analyzed emotional state with the financial information obtained in the previous step. Based on this information, it generates personalized advice when certain conditions are met. Here, it utilizes prompts generated by a generative AI model. The input to this process is the emotional state and financial information, and the output is the advice provided to the user.

[0199] Step 4:

[0200] The terminal visually displays the generated advice through a user interface. It presents the advice in a user-friendly format, for example, offering suggestions for relaxation or encouraging users to reconsider their purchases. The input for this process is the advice received from the server, and the output is the feedback message displayed on the terminal screen.

[0201] Step 5:

[0202] The server statistically analyzes anonymous data collected from other users to compare users' financial and emotional states. This analysis is then presented to users as feedback to help them improve their spending habits. The input here is statistical data, and the output is feedback based on comparative analysis.

[0203] 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.

[0204] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0210] 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.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0212] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] 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.

[0214] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The 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.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] The system of this invention is designed to streamline asset management, automatically managing the user's income and expenses and providing real-time feedback as needed.

[0220] The server receives initial registration information from the user's terminal, including income information, monthly spending targets, and details of multiple financial institutions that are authorized to connect. This information is securely stored in a database and forms the basis for the operation of the entire system.

[0221] When a user makes a payment, the terminal sends the spending information to the server. This information includes details such as the payment category, amount, and date. Based on the received spending information, the server immediately calculates the user's remaining budget for the month and analyzes whether it is within the target range. If the target value is exceeded, the server generates an alert and notifies the terminal. By checking the alert and reviewing spending appropriately, users can manage their assets in a planned manner.

[0222] Furthermore, the server collects anonymized data provided by multiple users and performs statistical analysis based on this data. The resulting statistical information is then fed back to the user as reference material to objectively understand their spending habits. For example, if a user's food expenses tend to be higher than those of other users under similar conditions, this information is fed back to the terminal. This gives the user an opportunity to re-evaluate their spending habits.

[0223] In this way, this system supports users in managing their assets efficiently and systematically, helping to eliminate cumbersome manual management. Furthermore, it can significantly reduce the time and effort required for asset management.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The user downloads the application to their device, launches it, and enters their annual income, monthly income, and monthly spending goals. In addition, they enter and send information about bank accounts and credit cards that they wish to link to the server.

[0227] Step 2:

[0228] The server securely stores income information and spending targets submitted by users in a database. It also configures data retrieval from linked financial institutions and initializes the necessary API connections.

[0229] Step 3:

[0230] When a user makes a purchase or uses a service, such as by using QR code payment, the terminal records the expenditure information. This expenditure information includes the amount, category, and date and time.

[0231] Step 4:

[0232] The terminal transmits recorded spending information to the server in real time. The server updates the received spending information as the spending information for the current month.

[0233] Step 5:

[0234] The server calculates the current total spending and compares it to a pre-set monthly spending target. If spending exceeds the target or approaches a certain threshold, the server generates an alert.

[0235] Step 6:

[0236] The server sends the generated alert to the device, and the device notifies the user. The notification is delivered via an in-app pop-up or notification bar.

[0237] Step 7:

[0238] The server periodically performs statistical analysis on anonymous spending data collected from multiple users, calculating consumption trends by category, such as food and entertainment expenses. The calculated information is provided to the user's device as feedback.

[0239] Step 8:

[0240] Based on these notifications and feedback, users set their budgets and review their spending for the following month. If they need to revise their spending targets, they set new targets on their device and send the updated information to the server.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0243] Managing personal assets manually is cumbersome and can lead to decreased accuracy. Furthermore, many users struggle to understand and manage their spending properly, resulting in overspending. Comparing spending trends among users and centrally managing information from different financial institutions is also difficult.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] This invention includes a server that includes means for determining in real time whether spending exceeds the budget based on the user's income information, spending information, and monthly spending target; means for generating and sending a warning message to the user's terminal if spending exceeds the budget; and means for statistically analyzing spending information collected from multiple users and providing anonymized feedback. This enables efficient personal asset management and allows for appropriate spending management and review of consumption trends.

[0246] "User" refers to an individual or legal entity that uses the system and is the entity that provides information on income and expenses.

[0247] "Income information" refers to all data related to the income earned by the user, including salary, investment income, etc.

[0248] "Expenditure information" refers to data on all expenses incurred by the user, including details such as purchased items and service fees.

[0249] The "monthly spending target" refers to the maximum amount of money a user sets for spending in a month, and serves as an indicator for budget management.

[0250] A "server" refers to a central computer system that receives, processes, stores, and transmits data to user terminals.

[0251] "Terminal" refers to an electronic device used by a user for inputting information or receiving notifications, and includes smartphones, computers, and other similar devices.

[0252] "Anonymization" refers to processing data in a way that makes it impossible to identify individual users, and is intended to protect privacy.

[0253] "Statistical analysis" refers to the process of analyzing collected data using mathematical methods, and is performed to understand the trends and distribution of the data.

[0254] "Feedback" refers to the act of providing users with analysis results and warning information, which can serve as an opportunity for users to re-evaluate their own actions.

[0255] This invention is a system for efficiently managing assets, automatically managing the user's income and expenditure information and providing feedback as needed. It is implemented using the following hardware and software.

[0256] The server is equipped with a database system and securely stores user data received via encrypted communication such as SSL. This data includes user income information, monthly spending targets, and spending information. MySQL and PostgreSQL are available as database management systems.

[0257] The terminal provides an interface for users to input information. It utilizes a dedicated application installed on a typical smartphone or tablet, which features a user interface that facilitates the input of income and expense data. It also has the functionality to display real-time feedback and warnings from the server.

[0258] When a user enters income and expenditure information into a terminal, the terminal sends this information to a server. Based on the received information, the server analyzes in real time whether the user's spending exceeds their set monthly spending target. The analysis is performed using algorithms that run using programming languages ​​such as Python or R.

[0259] The server anonymizes data collected from multiple users and performs statistical analysis to calculate the mean, median, and distribution. This statistical information is provided as feedback to help users re-evaluate their own consumption behavior.

[0260] As a concrete example, if user A sets a monthly food budget of 20,000 yen, they record their daily expenses using their device, and the server constantly updates the total expenses. If the total expenses exceed 20,000 yen midway through the month, the server issues a warning, and a notification appears on the device stating, "Please be careful not to exceed your budget."

[0261] An example of a prompt message might be: "Please recreate a scenario where you input the user's income and expenditure information, set a monthly expenditure goal, and then receive a warning."

[0262] This allows the system of the present invention to help users efficiently manage their assets and make planned expenditures.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] Users enter income information, monthly spending targets, and financial institution details using a dedicated application on their device. This entered information is compiled into a data structure such as JSON and transmitted from the device to the server via encrypted communication. Users input the data using a touch interface.

[0266] Step 2:

[0267] The server receives income information and monthly spending targets sent from the terminal. The received data is stored in a database management system. Here, the server verifies the data's integrity and checks for any missing information. It is then saved to the database using SQL queries.

[0268] Step 3:

[0269] When a user makes a payment, the terminal generates spending information as a data packet and sends it to the server. This spending information includes the category of purchased items, the amount spent, and the date and time of the payment. The terminal can also capture data from receipts using NFC or QR code scanning capabilities.

[0270] Step 4:

[0271] The server uses the received spending information to calculate the total monthly spending. This calculation is performed using a Python script, and the latest spending information is updated in the database in real time. The server then determines whether the spending is within the budget target.

[0272] Step 5:

[0273] If spending exceeds the budget, the server generates a warning message and sends it to the device. This message is intended to alert the user and is displayed through the device's notification function.

[0274] Step 6:

[0275] The server anonymizes spending information from multiple users and performs statistical analysis. Using statistical methods, it calculates averages, distributions, and other metrics. This allows users to compare their spending trends with those of other users and generate objective feedback.

[0276] Step 7:

[0277] The server sends the generated feedback information to the terminal and provides it to the user. The user views this information and uses it as an opportunity to review their own spending habits. The feedback helps the user manage their budget more effectively.

[0278] (Application Example 1)

[0279] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0280] Consumer activity in modern society has diversified, making it difficult for individuals to effectively manage their income and expenses. Furthermore, the lack of data aggregation among financial institutions makes it difficult for users to understand their financial situation in real time. In addition, the lack of objective guidance on improving consumption based on anonymized statistical data makes it difficult for individual users to review their consumption behavior.

[0281] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0282] This invention includes a server that receives and stores the user's income and expenditure information, analyzes the user's individual transaction data and performs statistical comparisons with similar users, and provides the user with customized prompts using a generative AI model. This enables the user to understand their own consumption activities in detail, and at the same time, grasp their financial situation in real time through the aggregation of financial institution information, thereby enabling effective consumption improvements.

[0283] "Individual user transaction data" refers to detailed information about the income and expenses incurred by each user, including the category, amount, and date of the expenditure.

[0284] "Statistical comparison with similar users" means comparing the consumption behavior of each user with the anonymized data of other users and analyzing the differences from the average spending trend.

[0285] "Customized prompts using generative AI models" refers to messages that utilize artificial intelligence models to provide advice and guidance individually created based on specific consumption behaviors of users.

[0286] "Aggregation of financial institution information" means unifying the financial information held by a user at multiple financial institutions into a state where it can be managed integratively.

[0287] "Effective consumption improvement" means that based on statistical data and generative AI analysis, the user reviews their own consumption behavior and modifies their actions to achieve optimal asset management.

[0288] This invention is a system for optimizing a user's asset management, using a plurality of components including a server, a user terminal, and a generative AI model. The server receives income and expenditure information from the user's terminal and securely stores it in a database. As a result, the user can manage their income and expenditure in a unified manner.

[0289] Based on the received expenditure information, the server immediately calculates the user's budget for the month and determines whether it exceeds the target range. If it exceeds the target value, the server automatically generates a notification and transmits the information to the user terminal in real time. This notification functions as an alert for the user to appropriately review their expenditure.

[0290] Furthermore, the server collects anonymized data from multiple users and performs statistical analysis. This allows the server to compare users with similar consumption patterns and provide specific suggestions for improving their spending. These suggestions include customized prompts generated using generative AI models. For example, if a user tends to spend more on food than other users, they may be offered advice to reconsider their spending.

[0291] This system integrates users' financial institution information, enabling real-time monitoring of complex financial situations. For example, it might notify users with prompts such as, "Your food expenses are higher than average. How will you adjust your budget?" to encourage improvements in specific spending habits.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The user's terminal retrieves income and expense information and sends it to the server. This input data includes detailed information about income and expenses. The server receives this data and stores it in a secure database. After the data is stored, it becomes possible to centrally manage income and expenses based on this data.

[0295] Step 2:

[0296] The server analyzes the user's budget status using stored spending information. It calculates the total spending amount by adding up the spending information as input. It determines whether the budget has been exceeded and outputs the result for the next step. If the analysis reveals that the target range has been exceeded, that information is used to generate an alert.

[0297] Step 3:

[0298] The server generates an alert and sends a notification to the user's terminal if the target range is exceeded. Here, data processing is performed to generate the alert message based on the judgment result. The notification is displayed on the terminal in real time, providing information that allows the user to quickly review their spending.

[0299] Step 4:

[0300] The server performs statistical analysis based on anonymized spending data collected from multiple users. It uses anonymized data as input to derive analysis results. This includes analyzing means and distributions, and comparing data with similar users. Specific operations include data calculations using statistical models.

[0301] Step 5:

[0302] Based on the analysis results, the server uses a generative AI model to generate customized prompts for the user. In this step, the generative AI model uses the output of the statistical analysis and the user's individual data as input to create prompts. Specific prompt statements are generated and presented to the user. This includes suggestions for specific consumption improvements for the user.

[0303] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0304] The system of this invention enables income and expenditure management, as well as feedback based on the user's emotions. By combining standard asset management functions with an emotion engine, this system achieves more personalized expenditure management.

[0305] The server accumulates basic information such as annual income, monthly income, and expenditure targets obtained from users and operates in the same way as a normal asset management system. On the other hand, it analyzes the input data and behavior history of users and uses an emotion engine to grasp the current emotional state. This information is dynamically incorporated into the asset management information and used for customizing expenditure alerts and feedback.

[0306] For example, when a user repeatedly makes high-value expenditures in a short period, the server considers the possibility that this is an impulsive purchasing behavior. At this time, if the emotion engine analyzes that the user is feeling stressed or impulsive, the alert displayed on the terminal will be a message that promotes relaxation or a content that proposes a review of the purchase instead of the usual "expenditure exceeded" notification.

[0307] Furthermore, the server statistically compares with anonymous emotion data collected from other users and provides the general expenditure tendencies of users with similar financial situations and emotional states. This information serves as a basis for strengthening the proposal for expenditure improvement and providing more accurate feedback to users.

[0308] In this way, this system not only manages the direct balance of income and expenditure, but also realizes more personalized asset management by providing government feedback based on the behavior and emotion of users. As a result, users can spend their daily expenses more reassuringly while maintaining their financial health.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] The user downloads an application on the terminal, enters the annual income, monthly income, and monthly expenditure target as basic information. Also, complete the initial settings required for the emotion engine and send this information to the server.

[0312] Step 2:

[0313] The server stores basic information submitted by the user in a database and builds a user profile. This profile forms the basis for monitoring financial fluctuations and emotional states.

[0314] Step 3:

[0315] When a user makes a payment for shopping or using a service, the terminal records the expenditure information. This expenditure information includes the amount, category, payment method, and date and time, and is sent to the server.

[0316] Step 4:

[0317] Based on the received spending information, the server calculates the cumulative spending for the current month and categorizes it into individual spending categories. Simultaneously, the server passes the accumulated behavioral data to the emotion engine to analyze the user's emotional state.

[0318] Step 5:

[0319] The emotion engine infers the user's current emotions from their past operation history and input data, and analyzes their stress level and purchasing motivations. Based on these results, it reports the emotional state to the server.

[0320] Step 6:

[0321] The server generates specific alerts and feedback based on cumulative spending and sentiment analysis results. For example, if an unexpected large expense occurs and the sentiment engine suggests impulsive behavior, it will generate alerts to encourage relaxation and suggestions to review spending.

[0322] Step 7:

[0323] The server sends generated alerts and feedback messages to the terminal. The terminal notifies the user of the received information and displays it clearly on the screen.

[0324] Step 8:

[0325] The server compares sentiment and spending data obtained from other anonymous users to calculate statistical trends. This information is sent to the device as feedback indicating the financial and emotional state compared to users under similar conditions.

[0326] Step 9:

[0327] Users can check alerts and feedback information through their devices, allowing them to review their spending and re-evaluate their budget settings. This enables personalized and planned asset management.

[0328] (Example 2)

[0329] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0330] Traditional financial management systems typically only provide simple records and warnings based on users' income and expenses, and are unable to offer personalized feedback that takes into account users' emotional states. As a result, it is difficult to prevent impulsive purchases and unplanned spending caused by stress, and there is a problem in that users cannot be given sufficient suggestions for improving their spending.

[0331] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0332] In this invention, the server includes means for receiving and storing information on the user's income and expenses, means for determining whether expenses exceed a target range based on the information, and emotion recognition means for analyzing the user's behavioral history and estimating their current emotional state. This makes it possible to provide feedback that reflects the emotional state based on the user's behavior, thereby enabling more appropriate spending management and improvement suggestions.

[0333] A "user" is an individual or organization that inputs information about their income and expenses through the system and receives feedback on their financial management and behavior.

[0334] "Income-related information" refers to data on a user's income, such as their annual or monthly income, and is information that forms the basis for judging their financial situation.

[0335] "Expenditure information" refers to data based on users' daily purchasing behavior and expenses, and serves as the basis for budget management and analysis of spending trends.

[0336] "Emotion recognition means" refers to a technology or process for analyzing a user's input information and behavioral history to estimate their current emotional state.

[0337] A "notification" is a message sent from a system to a user's information processing device, and includes information such as warnings, suggestions, and feedback.

[0338] "Anonymization" is the process of making it impossible to identify specific individuals or groups from data, and is used to obtain statistical information from a group while ensuring privacy.

[0339] "Statistical analysis" refers to processing collected data using mathematical and statistical methods to derive trends and patterns.

[0340] This invention is a system that enables personalized spending management by gaining a deeper understanding of the user's financial situation and providing emotion-based feedback. The system primarily operates between three parties: a server, a terminal, and the user.

[0341] The server receives information about users' income and expenses from their terminals and stores it in a database. The databases used are typically MySQL or PostgreSQL. The server also uses analytical tools such as Python or R to perform detailed analyses of users' purchase history and behavioral data.

[0342] Based on information entered by the user and their purchase history, the server uses an "emotion recognition engine" to estimate the user's current emotional state. This emotion recognition engine can utilize technologies such as "IBM Watson." The results of the emotional analysis are integrated with asset management information and used to dynamically generate feedback.

[0343] The terminal displays feedback and warnings received from the server to the user. The server also uses a generative AI model to input prompts when generating customized messages. An example of such a prompt is: "Your recent purchasing behavior indicates you are experiencing stress. Please generate suggestions to help you relax."

[0344] For example, if a user repeatedly makes large purchases in a short period, the server analyzes this behavior and uses an emotion engine to estimate the user's emotional state. If the analysis suggests that the user is experiencing stress, a message such as "Review your spending and make time to relax" will be displayed on the device.

[0345] In this way, we can go beyond mere numerical management and achieve more precise and personalized financial assistance that is attentive to the user's emotions.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The server receives income and expense information entered by the user. Specifically, the user enters their annual income, monthly income, and daily expenses through a dedicated application or web interface. The server stores this information in a database such as MySQL. The input is the user's financial information, and the output is the storage of that information in the database.

[0349] Step 2:

[0350] The server analyzes the received spending information and extracts spending patterns from the user's purchase history. Specifically, the server uses Python or R to perform data analysis and determine whether there have been high-value purchases in a short period. The input is the user's purchase history data, and the output is a report of the analyzed spending patterns.

[0351] Step 3:

[0352] The server uses an emotion recognition engine to estimate the user's emotional state based on their behavioral data. Specifically, it utilizes tools such as IBM Watson to analyze emotions like stress and impulsivity. The input consists of analysis results of purchase history and spending patterns, while the output is data on the estimated emotional state.

[0353] Step 4:

[0354] The server inputs a prompt message into a generative AI model, which then generates customized feedback based on emotion recognition. For example, the prompt message "Your recent purchasing behavior indicates you are experiencing stress. Please generate suggestions to help you relax." is used. The input consists of the emotional state and the prompt message, while the output is an emotion-based feedback message.

[0355] Step 5:

[0356] The device displays feedback received from the server to the user. Specifically, a message is displayed on the user's device, notifying the user of an alert such as, "Review your spending and make time to relax." The input is the feedback message generated by the server, and the output is the notification to the user.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0359] Traditional income and expense management systems simply focus on balancing income and expenses, lacking features to mitigate impulsive purchasing behavior driven by user emotions and stress. This made it difficult for users to accurately understand their own emotional state and maintain financial health while engaging in economic activities. Furthermore, systems lacked the ability to personalize improvement suggestions through anonymous data comparisons with other users. These issues need to be addressed.

[0360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0361] In this invention, the server includes means for analyzing the user's emotional state and generating personalized advice based on the analysis results; means for providing a user interface for visually presenting the advice; and means for statistically analyzing spending information collected from multiple users and providing anonymized feedback. This enables users to achieve more appropriate spending management based on their emotional state, suppress impulsive purchasing behavior, and engage in economic activities that are best suited to them.

[0362] "Income information" refers to all financial benefits obtained by the user, including information such as salary, stocks, and investment returns.

[0363] "Expenditure information" refers to information about all activities in which a user pays money and the transactions associated with them.

[0364] "Analysis results" refer to the results of data analysis conducted to determine the emotional state of the user.

[0365] "Personalized advice" refers to specific action plans and suggestions generated based on the user's individual emotional state and financial situation.

[0366] "Visual presentation" refers to showing information to users in a visual format, and includes, but is not limited to, graphs, text, and icons.

[0367] A "user interface" is a means for a user to interact with a system, and usually refers to a device or program that includes a graphical user interface (GUI).

[0368] "Anonymized feedback" refers to ratings and information provided in a form that does not allow for personal identification in order to protect user privacy.

[0369] "Anonymous data comparison with other users" refers to the comparative analysis of spending and emotional state data from multiple users, with personal information removed.

[0370] This invention is an electronic payment system that analyzes the user's emotional state, primarily based on income and expenditure information, and provides personalized feedback. This system utilizes the user's smartphone as the primary hardware and is implemented using software such as an emotion analysis module and a database management system.

[0371] First, the server collects users' income and expenditure information and stores it in a database. This includes obtaining data from financial institutions and user input. Furthermore, sentiment analysis software such as Google Cloud Natural Language API and Microsoft Azure Text Analytics is used to analyze users' emotional states. This makes it possible to extract emotions such as stress and impulsivity from text data.

[0372] The server combines analyzed emotional states with income and expenditure information to generate personalized advice. This advice is presented visually through the user interface, allowing users to easily take optimal actions based on their own emotional state.

[0373] For example, if a user is making large expenditures in a short period of time, the system will display a message on their smartphone saying, "It seems you've been under a lot of stress lately. Why not take some time to relax?" It also provides comparative information based on anonymous data from multiple users, which can be used as a reference to improve individual spending habits.

[0374] Examples of prompts in generative AI models are as follows:

[0375] "User behavior history: Recent increase in spending; Emotional state: High stress rating. Please generate an appropriate feedback message."

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] The server obtains user income and expenditure information through financial institution APIs and user input. This information is stored in a database for later analysis. The input to this process is the user's income and expenditure information, and the output is the financial information recorded in the database.

[0379] Step 2:

[0380] The system acquires the user's purchase history and text input and sends this to an emotion analysis module. The emotion analysis module uses the collected data to analyze the user's emotional state. The input for this process is the user's text input and purchase history, and the emotion analysis module outputs emotional states such as stress and impulsivity.

[0381] Step 3:

[0382] The server combines the analyzed emotional state with the financial information obtained in the previous step. Based on this information, it generates personalized advice when certain conditions are met. Here, it utilizes prompts generated by a generative AI model. The input to this process is the emotional state and financial information, and the output is the advice provided to the user.

[0383] Step 4:

[0384] The terminal visually displays the generated advice through a user interface. It presents the advice in a user-friendly format, for example, offering suggestions for relaxation or encouraging users to reconsider their purchases. The input for this process is the advice received from the server, and the output is the feedback message displayed on the terminal screen.

[0385] Step 5:

[0386] The server statistically analyzes anonymous data collected from other users to compare users' financial and emotional states. This analysis is then presented to users as feedback to help them improve their spending habits. The input here is statistical data, and the output is feedback based on comparative analysis.

[0387] 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.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0394] 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.

[0395] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0396] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0397] 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.

[0398] 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.

[0399] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0400] The 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.

[0401] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0402] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0403] The system of this invention is designed to streamline asset management, automatically managing the user's income and expenses and providing real-time feedback as needed.

[0404] The server receives initial registration information from the user's terminal, including income information, monthly spending targets, and details of multiple financial institutions that are authorized to connect. This information is securely stored in a database and forms the basis for the operation of the entire system.

[0405] When a user makes a payment, the terminal sends the spending information to the server. This information includes details such as the payment category, amount, and date. Based on the received spending information, the server immediately calculates the user's remaining budget for the month and analyzes whether it is within the target range. If the target value is exceeded, the server generates an alert and notifies the terminal. By checking the alert and reviewing spending appropriately, users can manage their assets in a planned manner.

[0406] Furthermore, the server collects anonymized data provided by multiple users and performs statistical analysis based on this data. The resulting statistical information is then fed back to the user as reference material to objectively understand their spending habits. For example, if a user's food expenses tend to be higher than those of other users under similar conditions, this information is fed back to the terminal. This gives the user an opportunity to re-evaluate their spending habits.

[0407] In this way, this system supports users in managing their assets efficiently and systematically, helping to eliminate cumbersome manual management. Furthermore, it can significantly reduce the time and effort required for asset management.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The user downloads the application to their device, launches it, and enters their annual income, monthly income, and monthly spending goals. In addition, they enter and send information about bank accounts and credit cards that they wish to link to the server.

[0411] Step 2:

[0412] The server securely stores income information and spending targets submitted by users in a database. It also configures data retrieval from linked financial institutions and initializes the necessary API connections.

[0413] Step 3:

[0414] When a user makes a purchase or uses a service, such as by using QR code payment, the terminal records the expenditure information. This expenditure information includes the amount, category, and date and time.

[0415] Step 4:

[0416] The terminal transmits recorded spending information to the server in real time. The server updates the received spending information as the spending information for the current month.

[0417] Step 5:

[0418] The server calculates the current total spending and compares it to a pre-set monthly spending target. If spending exceeds the target or approaches a certain threshold, the server generates an alert.

[0419] Step 6:

[0420] The server sends the generated alert to the device, and the device notifies the user. The notification is delivered via an in-app pop-up or notification bar.

[0421] Step 7:

[0422] The server periodically performs statistical analysis on anonymous spending data collected from multiple users, calculating consumption trends by category, such as food and entertainment expenses. The calculated information is provided to the user's device as feedback.

[0423] Step 8:

[0424] Based on these notifications and feedback, users set their budgets and review their spending for the following month. If they need to revise their spending targets, they set new targets on their device and send the updated information to the server.

[0425] (Example 1)

[0426] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0427] Managing personal assets manually is cumbersome and can lead to decreased accuracy. Furthermore, many users struggle to understand and manage their spending properly, resulting in overspending. Comparing spending trends among users and centrally managing information from different financial institutions is also difficult.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] This invention includes a server that includes means for determining in real time whether spending exceeds the budget based on the user's income information, spending information, and monthly spending target; means for generating and sending a warning message to the user's terminal if spending exceeds the budget; and means for statistically analyzing spending information collected from multiple users and providing anonymized feedback. This enables efficient personal asset management and allows for appropriate spending management and review of consumption trends.

[0430] "User" refers to an individual or legal entity that uses the system and is the entity that provides information on income and expenses.

[0431] "Income information" refers to all data related to the income earned by the user, including salary, investment income, etc.

[0432] "Expenditure information" refers to data on all expenses incurred by the user, including details such as purchased items and service fees.

[0433] The "monthly spending target" refers to the maximum amount of money a user sets for spending in a month, and serves as an indicator for budget management.

[0434] A "server" refers to a central computer system that receives, processes, stores, and transmits data to user terminals.

[0435] "Terminal" refers to an electronic device used by a user for inputting information or receiving notifications, and includes smartphones, computers, and other similar devices.

[0436] "Anonymization" refers to processing data in a way that makes it impossible to identify individual users, and is intended to protect privacy.

[0437] "Statistical analysis" refers to the process of analyzing collected data using mathematical methods, and is performed to understand the trends and distribution of the data.

[0438] "Feedback" refers to the act of providing users with analysis results and warning information, which can serve as an opportunity for users to re-evaluate their own actions.

[0439] This invention is a system for efficiently managing assets, automatically managing the user's income and expenditure information and providing feedback as needed. It is implemented using the following hardware and software.

[0440] The server is equipped with a database system and securely stores user data received via encrypted communication such as SSL. This data includes user income information, monthly spending targets, and spending information. MySQL and PostgreSQL are available as database management systems.

[0441] The terminal provides an interface for users to input information. It utilizes a dedicated application installed on a typical smartphone or tablet, which features a user interface that facilitates the input of income and expense data. It also has the functionality to display real-time feedback and warnings from the server.

[0442] When a user enters income and expenditure information into a terminal, the terminal sends this information to a server. Based on the received information, the server analyzes in real time whether the user's spending exceeds their set monthly spending target. The analysis is performed using algorithms that run using programming languages ​​such as Python or R.

[0443] The server anonymizes data collected from multiple users and performs statistical analysis to calculate the mean, median, and distribution. This statistical information is provided as feedback to help users re-evaluate their own consumption behavior.

[0444] As a concrete example, if user A sets a monthly food budget of 20,000 yen, they record their daily expenses using their device, and the server constantly updates the total expenses. If the total expenses exceed 20,000 yen midway through the month, the server issues a warning, and a notification appears on the device stating, "Please be careful not to exceed your budget."

[0445] An example of a prompt message might be: "Please recreate a scenario where you input the user's income and expenditure information, set a monthly expenditure goal, and then receive a warning."

[0446] This allows the system of the present invention to help users efficiently manage their assets and make planned expenditures.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] Users enter income information, monthly spending targets, and financial institution details using a dedicated application on their device. This entered information is compiled into a data structure such as JSON and transmitted from the device to the server via encrypted communication. Users input the data using a touch interface.

[0450] Step 2:

[0451] The server receives income information and monthly spending targets sent from the terminal. The received data is stored in a database management system. Here, the server verifies the data's integrity and checks for any missing information. It is then saved to the database using SQL queries.

[0452] Step 3:

[0453] When a user makes a payment, the terminal generates spending information as a data packet and sends it to the server. This spending information includes the category of purchased items, the amount spent, and the date and time of the payment. The terminal can also capture data from receipts using NFC or QR code scanning capabilities.

[0454] Step 4:

[0455] The server uses the received spending information to calculate the total monthly spending. This calculation is performed using a Python script, and the latest spending information is updated in the database in real time. The server then determines whether the spending is within the budget target.

[0456] Step 5:

[0457] If spending exceeds the budget, the server generates a warning message and sends it to the device. This message is intended to alert the user and is displayed through the device's notification function.

[0458] Step 6:

[0459] The server anonymizes spending information from multiple users and performs statistical analysis. Using statistical methods, it calculates averages, distributions, and other metrics. This allows users to compare their spending trends with those of other users and generate objective feedback.

[0460] Step 7:

[0461] The server sends the generated feedback information to the terminal and provides it to the user. The user views this information and uses it as an opportunity to review their own spending habits. The feedback helps the user manage their budget more effectively.

[0462] (Application Example 1)

[0463] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0464] Consumer activity in modern society has diversified, making it difficult for individuals to effectively manage their income and expenses. Furthermore, the lack of data aggregation among financial institutions makes it difficult for users to understand their financial situation in real time. In addition, the lack of objective guidance on improving consumption based on anonymized statistical data makes it difficult for individual users to review their consumption behavior.

[0465] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0466] This invention includes a server that receives and stores the user's income and expenditure information, analyzes the user's individual transaction data and performs statistical comparisons with similar users, and provides the user with customized prompts using a generative AI model. This enables the user to understand their own consumption activities in detail, and at the same time, grasp their financial situation in real time through the aggregation of financial institution information, thereby enabling effective consumption improvements.

[0467] "Individual user transaction data" refers to detailed information about the income and expenses incurred by each user, including the category, amount, and date of the expenditure.

[0468] "Statistical comparison with similar users" involves comparing each user's consumption behavior with anonymized data from other users and analyzing the differences from average spending trends.

[0469] "Customized prompts using generative AI models" refer to messages that utilize artificial intelligence models to provide personalized advice and guidance based on the user's specific consumer behavior.

[0470] "Consolidation of financial institution information" refers to the process of centralizing financial information held by users across multiple financial institutions, making it possible to manage it in an integrated manner.

[0471] "Effective consumption improvement" means that users review their own consumption behavior based on statistical data and analysis by generating AI, and modify their actions to achieve optimal asset management.

[0472] This invention is a system for streamlining user asset management and utilizes multiple components, including a server, user terminals, and a generative AI model. The server receives income and expenditure information from the user's terminal and securely stores it in a database. This allows users to centrally manage their income and expenses.

[0473] Based on the received spending information, the server instantly calculates the user's budget for the month and determines whether it exceeds the target range. If the target is exceeded, the server automatically generates a notification and sends this information to the user's device in real time. This notification serves as an alert to help the user appropriately review their spending.

[0474] Furthermore, the server collects anonymized data from multiple users and performs statistical analysis. This allows the server to compare users with similar consumption patterns and provide specific suggestions for improving their spending. These suggestions include customized prompts generated using generative AI models. For example, if a user tends to spend more on food than other users, they may be offered advice to reconsider their spending.

[0475] This system integrates users' financial institution information, enabling real-time monitoring of complex financial situations. For example, it might notify users with prompts such as, "Your food expenses are higher than average. How will you adjust your budget?" to encourage improvements in specific spending habits.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The user's terminal retrieves income and expense information and sends it to the server. This input data includes detailed information about income and expenses. The server receives this data and stores it in a secure database. After the data is stored, it becomes possible to centrally manage income and expenses based on this data.

[0479] Step 2:

[0480] The server analyzes the user's budget status using stored spending information. It calculates the total spending amount by adding up the spending information as input. It determines whether the budget has been exceeded and outputs the result for the next step. If the analysis reveals that the target range has been exceeded, that information is used to generate an alert.

[0481] Step 3:

[0482] The server generates an alert and sends a notification to the user's terminal if the target range is exceeded. Here, data processing is performed to generate the alert message based on the judgment result. The notification is displayed on the terminal in real time, providing information that allows the user to quickly review their spending.

[0483] Step 4:

[0484] The server performs statistical analysis based on anonymized spending data collected from multiple users. It uses anonymized data as input to derive analysis results. This includes analyzing means and distributions, and comparing data with similar users. Specific operations include data calculations using statistical models.

[0485] Step 5:

[0486] Based on the analysis results, the server uses a generative AI model to generate customized prompts for the user. In this step, the generative AI model uses the output of the statistical analysis and the user's individual data as input to create prompts. Specific prompt statements are generated and presented to the user. This includes suggestions for specific consumption improvements for the user.

[0487] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0488] The system of this invention enables income and expenditure management, as well as feedback based on the user's emotions. By combining standard asset management functions with an emotion engine, this system achieves more personalized expenditure management.

[0489] The server stores basic information such as annual income, monthly income, and spending targets obtained from users, and operates in the same way as a typical asset management system. At the same time, it analyzes user input data and behavioral history, and uses an emotion engine to understand the user's current emotional state. This information is dynamically incorporated into the asset management information and used to customize spending alerts and feedback.

[0490] For example, when a user repeatedly makes large purchases in a short period, the server considers the possibility that it is impulsive purchasing behavior. If the emotion engine analyzes that the user is stressed or acting impulsively, the alert displayed on the device will change from the usual "overspending" notification to a message encouraging relaxation or suggesting a reassessment of the purchase.

[0491] Furthermore, the server statistically compares anonymous sentiment data collected from other users to provide general spending trends of users with similar financial situations and emotional states. This information forms the basis for strengthening spending improvement suggestions and providing users with more accurate feedback.

[0492] Thus, this system not only manages the balance between direct income and expenses, but also provides government feedback based on the user's behavior and emotions, enabling more personalized asset management. As a result, users can maintain their financial health and make daily expenses with greater peace of mind.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] Users download the application to their device and enter basic information such as their annual income, monthly income, and monthly spending goals. They also complete the initial setup required for the emotion engine and send this information to the server.

[0496] Step 2:

[0497] The server stores basic information submitted by the user in a database and builds a user profile. This profile forms the basis for monitoring financial fluctuations and emotional states.

[0498] Step 3:

[0499] When a user makes a payment for shopping or using a service, the terminal records the expenditure information. This expenditure information includes the amount, category, payment method, and date and time, and is sent to the server.

[0500] Step 4:

[0501] Based on the received spending information, the server calculates the cumulative spending for the current month and categorizes it into individual spending categories. Simultaneously, the server passes the accumulated behavioral data to the emotion engine to analyze the user's emotional state.

[0502] Step 5:

[0503] The emotion engine infers the user's current emotions from their past operation history and input data, and analyzes their stress level and purchasing motivations. Based on these results, it reports the emotional state to the server.

[0504] Step 6:

[0505] The server generates specific alerts and feedback based on cumulative spending and sentiment analysis results. For example, if an unexpected large expense occurs and the sentiment engine suggests impulsive behavior, it will generate alerts to encourage relaxation and suggestions to review spending.

[0506] Step 7:

[0507] The server sends generated alerts and feedback messages to the terminal. The terminal notifies the user of the received information and displays it clearly on the screen.

[0508] Step 8:

[0509] The server compares sentiment and spending data obtained from other anonymous users to calculate statistical trends. This information is sent to the device as feedback indicating the financial and emotional state compared to users under similar conditions.

[0510] Step 9:

[0511] Users can check alerts and feedback information through their devices, allowing them to review their spending and re-evaluate their budget settings. This enables personalized and planned asset management.

[0512] (Example 2)

[0513] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0514] Traditional financial management systems typically only provide simple records and warnings based on users' income and expenses, and are unable to offer personalized feedback that takes into account users' emotional states. As a result, it is difficult to prevent impulsive purchases and unplanned spending caused by stress, and there is a problem in that users cannot be given sufficient suggestions for improving their spending.

[0515] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0516] In this invention, the server includes means for receiving and storing information on the user's income and expenses, means for determining whether expenses exceed a target range based on the information, and emotion recognition means for analyzing the user's behavioral history and estimating their current emotional state. This makes it possible to provide feedback that reflects the emotional state based on the user's behavior, thereby enabling more appropriate spending management and improvement suggestions.

[0517] A "user" is an individual or organization that inputs information about their income and expenses through the system and receives feedback on their financial management and behavior.

[0518] "Income-related information" refers to data on a user's income, such as their annual or monthly income, and is information that forms the basis for judging their financial situation.

[0519] "Expenditure information" refers to data based on users' daily purchasing behavior and expenses, and serves as the basis for budget management and analysis of spending trends.

[0520] "Emotion recognition means" refers to a technology or process for analyzing a user's input information and behavioral history to estimate their current emotional state.

[0521] A "notification" is a message sent from a system to a user's information processing device, and includes information such as warnings, suggestions, and feedback.

[0522] "Anonymization" is the process of making it impossible to identify specific individuals or groups from data, and is used to obtain statistical information from a group while ensuring privacy.

[0523] "Statistical analysis" refers to processing collected data using mathematical and statistical methods to derive trends and patterns.

[0524] This invention is a system that enables personalized spending management by gaining a deeper understanding of the user's financial situation and providing emotion-based feedback. The system primarily operates between three parties: a server, a terminal, and the user.

[0525] The server receives information about users' income and expenses from their terminals and stores it in a database. The databases used are typically MySQL or PostgreSQL. The server also uses analytical tools such as Python or R to perform detailed analyses of users' purchase history and behavioral data.

[0526] Based on information entered by the user and their purchase history, the server uses an "emotion recognition engine" to estimate the user's current emotional state. This emotion recognition engine can utilize technologies such as "IBM Watson." The results of the emotional analysis are integrated with asset management information and used to dynamically generate feedback.

[0527] The terminal displays feedback and warnings received from the server to the user. The server also uses a generative AI model to input prompts when generating customized messages. An example of such a prompt is: "Your recent purchasing behavior indicates you are experiencing stress. Please generate suggestions to help you relax."

[0528] For example, if a user repeatedly makes large purchases in a short period, the server analyzes this behavior and uses an emotion engine to estimate the user's emotional state. If the analysis suggests that the user is experiencing stress, a message such as "Review your spending and make time to relax" will be displayed on the device.

[0529] In this way, we can go beyond mere numerical management and achieve more precise and personalized financial assistance that is attentive to the user's emotions.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The server receives income and expense information entered by the user. Specifically, the user enters their annual income, monthly income, and daily expenses through a dedicated application or web interface. The server stores this information in a database such as MySQL. The input is the user's financial information, and the output is the storage of that information in the database.

[0533] Step 2:

[0534] The server analyzes the received spending information and extracts spending patterns from the user's purchase history. Specifically, the server uses Python or R to perform data analysis and determine whether there have been high-value purchases in a short period. The input is the user's purchase history data, and the output is a report of the analyzed spending patterns.

[0535] Step 3:

[0536] The server uses an emotion recognition engine to estimate the user's emotional state based on their behavioral data. Specifically, it utilizes tools such as IBM Watson to analyze emotions like stress and impulsivity. The input consists of analysis results of purchase history and spending patterns, while the output is data on the estimated emotional state.

[0537] Step 4:

[0538] The server inputs a prompt message into a generative AI model, which then generates customized feedback based on emotion recognition. For example, the prompt message "Your recent purchasing behavior indicates you are experiencing stress. Please generate suggestions to help you relax." is used. The input consists of the emotional state and the prompt message, while the output is an emotion-based feedback message.

[0539] Step 5:

[0540] The device displays feedback received from the server to the user. Specifically, a message is displayed on the user's device, notifying the user of an alert such as, "Review your spending and make time to relax." The input is the feedback message generated by the server, and the output is the notification to the user.

[0541] (Application Example 2)

[0542] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] Traditional income and expense management systems simply focus on balancing income and expenses, lacking features to mitigate impulsive purchasing behavior driven by user emotions and stress. This made it difficult for users to accurately understand their own emotional state and maintain financial health while engaging in economic activities. Furthermore, systems lacked the ability to personalize improvement suggestions through anonymous data comparisons with other users. These issues need to be addressed.

[0544] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0545] In this invention, the server includes means for analyzing the user's emotional state and generating personalized advice based on the analysis results; means for providing a user interface for visually presenting the advice; and means for statistically analyzing spending information collected from multiple users and providing anonymized feedback. This enables users to achieve more appropriate spending management based on their emotional state, suppress impulsive purchasing behavior, and engage in economic activities that are best suited to them.

[0546] "Income information" refers to all financial benefits obtained by the user, including information such as salary, stocks, and investment returns.

[0547] "Expenditure information" refers to information about all activities in which a user pays money and the transactions associated with them.

[0548] "Analysis results" refer to the results of data analysis conducted to determine the emotional state of the user.

[0549] "Personalized advice" refers to specific action plans and suggestions generated based on the user's individual emotional state and financial situation.

[0550] "Visual presentation" refers to showing information to users in a visual format, and includes, but is not limited to, graphs, text, and icons.

[0551] A "user interface" is a means for a user to interact with a system, and usually refers to a device or program that includes a graphical user interface (GUI).

[0552] "Anonymized feedback" refers to ratings and information provided in a form that does not allow for personal identification in order to protect user privacy.

[0553] "Anonymous data comparison with other users" refers to the comparative analysis of spending and emotional state data from multiple users, with personal information removed.

[0554] This invention is an electronic payment system that analyzes the user's emotional state, primarily based on income and expenditure information, and provides personalized feedback. This system utilizes the user's smartphone as the primary hardware and is implemented using software such as an emotion analysis module and a database management system.

[0555] First, the server collects users' income and expenditure information and stores it in a database. This includes obtaining data from financial institutions and user input. Furthermore, sentiment analysis software such as Google Cloud Natural Language API and Microsoft Azure Text Analytics is used to analyze users' emotional states. This makes it possible to extract emotions such as stress and impulsivity from text data.

[0556] The server combines analyzed emotional states with income and expenditure information to generate personalized advice. This advice is presented visually through the user interface, allowing users to easily take optimal actions based on their own emotional state.

[0557] For example, if a user is making large expenditures in a short period of time, the system will display a message on their smartphone saying, "It seems you've been under a lot of stress lately. Why not take some time to relax?" It also provides comparative information based on anonymous data from multiple users, which can be used as a reference to improve individual spending habits.

[0558] Examples of prompts in generative AI models are as follows:

[0559] "User behavior history: Recent increase in spending; Emotional state: High stress rating. Please generate an appropriate feedback message."

[0560] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0561] Step 1:

[0562] The server obtains user income and expenditure information through financial institution APIs and user input. This information is stored in a database for later analysis. The input to this process is the user's income and expenditure information, and the output is the financial information recorded in the database.

[0563] Step 2:

[0564] The system acquires the user's purchase history and text input and sends this to an emotion analysis module. The emotion analysis module uses the collected data to analyze the user's emotional state. The input for this process is the user's text input and purchase history, and the emotion analysis module outputs emotional states such as stress and impulsivity.

[0565] Step 3:

[0566] The server combines the analyzed emotional state with the financial information obtained in the previous step. Based on this information, it generates personalized advice when certain conditions are met. Here, it utilizes prompts generated by a generative AI model. The input to this process is the emotional state and financial information, and the output is the advice provided to the user.

[0567] Step 4:

[0568] The terminal visually displays the generated advice through a user interface. It presents the advice in a user-friendly format, for example, offering suggestions for relaxation or encouraging users to reconsider their purchases. The input for this process is the advice received from the server, and the output is the feedback message displayed on the terminal screen.

[0569] Step 5:

[0570] The server statistically analyzes anonymous data collected from other users to compare users' financial and emotional states. This analysis is then presented to users as feedback to help them improve their spending habits. The input here is statistical data, and the output is feedback based on comparative analysis.

[0571] 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.

[0572] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0573] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0578] 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.

[0579] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0580] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0581] 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.

[0582] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0583] 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.

[0584] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0585] The 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.

[0586] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0587] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0588] The system of this invention is designed to streamline asset management, automatically managing the user's income and expenses and providing real-time feedback as needed.

[0589] The server receives initial registration information from the user's terminal, including income information, monthly spending targets, and details of multiple financial institutions that are authorized to connect. This information is securely stored in a database and forms the basis for the operation of the entire system.

[0590] When a user makes a payment, the terminal sends the spending information to the server. This information includes details such as the payment category, amount, and date. Based on the received spending information, the server immediately calculates the user's remaining budget for the month and analyzes whether it is within the target range. If the target value is exceeded, the server generates an alert and notifies the terminal. By checking the alert and reviewing spending appropriately, users can manage their assets in a planned manner.

[0591] Furthermore, the server collects anonymized data provided by multiple users and performs statistical analysis based on this data. The resulting statistical information is then fed back to the user as reference material to objectively understand their spending habits. For example, if a user's food expenses tend to be higher than those of other users under similar conditions, this information is fed back to the terminal. This gives the user an opportunity to re-evaluate their spending habits.

[0592] In this way, this system supports users in managing their assets efficiently and systematically, helping to eliminate cumbersome manual management. Furthermore, it can significantly reduce the time and effort required for asset management.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] The user downloads the application to their device, launches it, and enters their annual income, monthly income, and monthly spending goals. In addition, they enter and send information about bank accounts and credit cards that they wish to link to the server.

[0596] Step 2:

[0597] The server securely stores income information and spending targets submitted by users in a database. It also configures data retrieval from linked financial institutions and initializes the necessary API connections.

[0598] Step 3:

[0599] When a user makes a purchase or uses a service, such as by using QR code payment, the terminal records the expenditure information. This expenditure information includes the amount, category, and date and time.

[0600] Step 4:

[0601] The terminal transmits recorded spending information to the server in real time. The server updates the received spending information as the spending information for the current month.

[0602] Step 5:

[0603] The server calculates the current total spending and compares it to a pre-set monthly spending target. If spending exceeds the target or approaches a certain threshold, the server generates an alert.

[0604] Step 6:

[0605] The server sends the generated alert to the device, and the device notifies the user. The notification is delivered via an in-app pop-up or notification bar.

[0606] Step 7:

[0607] The server periodically performs statistical analysis on anonymous spending data collected from multiple users, calculating consumption trends by category, such as food and entertainment expenses. The calculated information is provided to the user's device as feedback.

[0608] Step 8:

[0609] Based on these notifications and feedback, users set their budgets and review their spending for the following month. If they need to revise their spending targets, they set new targets on their device and send the updated information to the server.

[0610] (Example 1)

[0611] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] Managing personal assets manually is cumbersome and can lead to decreased accuracy. Furthermore, many users struggle to understand and manage their spending properly, resulting in overspending. Comparing spending trends among users and centrally managing information from different financial institutions is also difficult.

[0613] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0614] This invention includes a server that includes means for determining in real time whether spending exceeds the budget based on the user's income information, spending information, and monthly spending target; means for generating and sending a warning message to the user's terminal if spending exceeds the budget; and means for statistically analyzing spending information collected from multiple users and providing anonymized feedback. This enables efficient personal asset management and allows for appropriate spending management and review of consumption trends.

[0615] "User" refers to an individual or legal entity that uses the system and is the entity that provides information on income and expenses.

[0616] "Income information" refers to all data related to the income earned by the user, including salary, investment income, etc.

[0617] "Expenditure information" refers to data on all expenses incurred by the user, including details such as purchased items and service fees.

[0618] The "monthly spending target" refers to the maximum amount of money a user sets for spending in a month, and serves as an indicator for budget management.

[0619] A "server" refers to a central computer system that receives, processes, stores, and transmits data to user terminals.

[0620] "Terminal" refers to an electronic device used by a user for inputting information or receiving notifications, and includes smartphones, computers, and other similar devices.

[0621] "Anonymization" refers to processing data in a way that makes it impossible to identify individual users, and is intended to protect privacy.

[0622] "Statistical analysis" refers to the process of analyzing collected data using mathematical methods, and is performed to understand the trends and distribution of the data.

[0623] "Feedback" refers to the act of providing users with analysis results and warning information, which can serve as an opportunity for users to re-evaluate their own actions.

[0624] This invention is a system for efficiently managing assets, automatically managing the user's income and expenditure information and providing feedback as needed. It is implemented using the following hardware and software.

[0625] The server is equipped with a database system and securely stores user data received via encrypted communication such as SSL. This data includes user income information, monthly spending targets, and spending information. MySQL and PostgreSQL are available as database management systems.

[0626] The terminal provides an interface for users to input information. It utilizes a dedicated application installed on a typical smartphone or tablet, which features a user interface that facilitates the input of income and expense data. It also has the functionality to display real-time feedback and warnings from the server.

[0627] When a user enters income and expenditure information into a terminal, the terminal sends this information to a server. Based on the received information, the server analyzes in real time whether the user's spending exceeds their set monthly spending target. The analysis is performed using algorithms that run using programming languages ​​such as Python or R.

[0628] The server anonymizes data collected from multiple users and performs statistical analysis to calculate the mean, median, and distribution. This statistical information is provided as feedback to help users re-evaluate their own consumption behavior.

[0629] As a concrete example, if user A sets a monthly food budget of 20,000 yen, they record their daily expenses using their device, and the server constantly updates the total expenses. If the total expenses exceed 20,000 yen midway through the month, the server issues a warning, and a notification appears on the device stating, "Please be careful not to exceed your budget."

[0630] An example of a prompt message might be: "Please recreate a scenario where you input the user's income and expenditure information, set a monthly expenditure goal, and then receive a warning."

[0631] This allows the system of the present invention to help users efficiently manage their assets and make planned expenditures.

[0632] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0633] Step 1:

[0634] Users enter income information, monthly spending targets, and financial institution details using a dedicated application on their device. This entered information is compiled into a data structure such as JSON and transmitted from the device to the server via encrypted communication. Users input the data using a touch interface.

[0635] Step 2:

[0636] The server receives income information and monthly spending targets sent from the terminal. The received data is stored in a database management system. Here, the server verifies the data's integrity and checks for any missing information. It is then saved to the database using SQL queries.

[0637] Step 3:

[0638] When a user makes a payment, the terminal generates spending information as a data packet and sends it to the server. This spending information includes the category of purchased items, the amount spent, and the date and time of the payment. The terminal can also capture data from receipts using NFC or QR code scanning capabilities.

[0639] Step 4:

[0640] The server uses the received spending information to calculate the total monthly spending. This calculation is performed using a Python script, and the latest spending information is updated in the database in real time. The server then determines whether the spending is within the budget target.

[0641] Step 5:

[0642] If spending exceeds the budget, the server generates a warning message and sends it to the device. This message is intended to alert the user and is displayed through the device's notification function.

[0643] Step 6:

[0644] The server anonymizes spending information from multiple users and performs statistical analysis. Using statistical methods, it calculates averages, distributions, and other metrics. This allows users to compare their spending trends with those of other users and generate objective feedback.

[0645] Step 7:

[0646] The server sends the generated feedback information to the terminal and provides it to the user. The user views this information and uses it as an opportunity to review their own spending habits. The feedback helps the user manage their budget more effectively.

[0647] (Application Example 1)

[0648] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0649] Consumer activity in modern society has diversified, making it difficult for individuals to effectively manage their income and expenses. Furthermore, the lack of data aggregation among financial institutions makes it difficult for users to understand their financial situation in real time. In addition, the lack of objective guidance on improving consumption based on anonymized statistical data makes it difficult for individual users to review their consumption behavior.

[0650] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0651] This invention includes a server that receives and stores the user's income and expenditure information, analyzes the user's individual transaction data and performs statistical comparisons with similar users, and provides the user with customized prompts using a generative AI model. This enables the user to understand their own consumption activities in detail, and at the same time, grasp their financial situation in real time through the aggregation of financial institution information, thereby enabling effective consumption improvements.

[0652] "Individual user transaction data" refers to detailed information about the income and expenses incurred by each user, including the category, amount, and date of the expenditure.

[0653] "Statistical comparison with similar users" involves comparing each user's consumption behavior with anonymized data from other users and analyzing the differences from average spending trends.

[0654] "Customized prompts using generative AI models" refer to messages that utilize artificial intelligence models to provide personalized advice and guidance based on the user's specific consumer behavior.

[0655] "Consolidation of financial institution information" refers to the process of centralizing financial information held by users across multiple financial institutions, making it possible to manage it in an integrated manner.

[0656] "Effective consumption improvement" means that users review their own consumption behavior based on statistical data and analysis by generating AI, and modify their actions to achieve optimal asset management.

[0657] This invention is a system for streamlining user asset management and utilizes multiple components, including a server, user terminals, and a generative AI model. The server receives income and expenditure information from the user's terminal and securely stores it in a database. This allows users to centrally manage their income and expenses.

[0658] Based on the received spending information, the server instantly calculates the user's budget for the month and determines whether it exceeds the target range. If the target is exceeded, the server automatically generates a notification and sends this information to the user's device in real time. This notification serves as an alert to help the user appropriately review their spending.

[0659] Furthermore, the server collects anonymized data from multiple users and performs statistical analysis. This allows the server to compare users with similar consumption patterns and provide specific suggestions for improving their spending. These suggestions include customized prompts generated using generative AI models. For example, if a user tends to spend more on food than other users, they may be offered advice to reconsider their spending.

[0660] This system integrates users' financial institution information, enabling real-time monitoring of complex financial situations. For example, it might notify users with prompts such as, "Your food expenses are higher than average. How will you adjust your budget?" to encourage improvements in specific spending habits.

[0661] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0662] Step 1:

[0663] The user's terminal retrieves income and expense information and sends it to the server. This input data includes detailed information about income and expenses. The server receives this data and stores it in a secure database. After the data is stored, it becomes possible to centrally manage income and expenses based on this data.

[0664] Step 2:

[0665] The server analyzes the user's budget status using stored spending information. It calculates the total spending amount by adding up the spending information as input. It determines whether the budget has been exceeded and outputs the result for the next step. If the analysis reveals that the target range has been exceeded, that information is used to generate an alert.

[0666] Step 3:

[0667] The server generates an alert and sends a notification to the user's terminal if the target range is exceeded. Here, data processing is performed to generate the alert message based on the judgment result. The notification is displayed on the terminal in real time, providing information that allows the user to quickly review their spending.

[0668] Step 4:

[0669] The server performs statistical analysis based on anonymized spending data collected from multiple users. It uses anonymized data as input to derive analysis results. This includes analyzing means and distributions, and comparing data with similar users. Specific operations include data calculations using statistical models.

[0670] Step 5:

[0671] Based on the analysis results, the server uses a generative AI model to generate customized prompts for the user. In this step, the generative AI model uses the output of the statistical analysis and the user's individual data as input to create prompts. Specific prompt statements are generated and presented to the user. This includes suggestions for specific consumption improvements for the user.

[0672] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0673] The system of this invention enables income and expenditure management, as well as feedback based on the user's emotions. By combining standard asset management functions with an emotion engine, this system achieves more personalized expenditure management.

[0674] The server stores basic information such as annual income, monthly income, and spending targets obtained from users, and operates in the same way as a typical asset management system. At the same time, it analyzes user input data and behavioral history, and uses an emotion engine to understand the user's current emotional state. This information is dynamically incorporated into the asset management information and used to customize spending alerts and feedback.

[0675] For example, when a user repeatedly makes large purchases in a short period, the server considers the possibility that it is impulsive purchasing behavior. If the emotion engine analyzes that the user is stressed or acting impulsively, the alert displayed on the device will change from the usual "overspending" notification to a message encouraging relaxation or suggesting a reassessment of the purchase.

[0676] Furthermore, the server statistically compares anonymous sentiment data collected from other users to provide general spending trends of users with similar financial situations and emotional states. This information forms the basis for strengthening spending improvement suggestions and providing users with more accurate feedback.

[0677] Thus, this system not only manages the balance between direct income and expenses, but also provides government feedback based on the user's behavior and emotions, enabling more personalized asset management. As a result, users can maintain their financial health and make daily expenses with greater peace of mind.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] Users download the application to their device and enter basic information such as their annual income, monthly income, and monthly spending goals. They also complete the initial setup required for the emotion engine and send this information to the server.

[0681] Step 2:

[0682] The server stores basic information submitted by the user in a database and builds a user profile. This profile forms the basis for monitoring financial fluctuations and emotional states.

[0683] Step 3:

[0684] When a user makes a payment for shopping or using a service, the terminal records the expenditure information. This expenditure information includes the amount, category, payment method, and date and time, and is sent to the server.

[0685] Step 4:

[0686] Based on the received spending information, the server calculates the cumulative spending for the current month and categorizes it into individual spending categories. Simultaneously, the server passes the accumulated behavioral data to the emotion engine to analyze the user's emotional state.

[0687] Step 5:

[0688] The emotion engine infers the user's current emotions from their past operation history and input data, and analyzes their stress level and purchasing motivations. Based on these results, it reports the emotional state to the server.

[0689] Step 6:

[0690] The server generates specific alerts and feedback based on cumulative spending and sentiment analysis results. For example, if an unexpected large expense occurs and the sentiment engine suggests impulsive behavior, it will generate alerts to encourage relaxation and suggestions to review spending.

[0691] Step 7:

[0692] The server sends generated alerts and feedback messages to the terminal. The terminal notifies the user of the received information and displays it clearly on the screen.

[0693] Step 8:

[0694] The server compares sentiment and spending data obtained from other anonymous users to calculate statistical trends. This information is sent to the device as feedback indicating the financial and emotional state compared to users under similar conditions.

[0695] Step 9:

[0696] Users can check alerts and feedback information through their devices, allowing them to review their spending and re-evaluate their budget settings. This enables personalized and planned asset management.

[0697] (Example 2)

[0698] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] Traditional financial management systems typically only provide simple records and warnings based on users' income and expenses, and are unable to offer personalized feedback that takes into account users' emotional states. As a result, it is difficult to prevent impulsive purchases and unplanned spending caused by stress, and there is a problem in that users cannot be given sufficient suggestions for improving their spending.

[0700] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0701] In this invention, the server includes means for receiving and storing information on the user's income and expenses, means for determining whether expenses exceed a target range based on the information, and emotion recognition means for analyzing the user's behavioral history and estimating their current emotional state. This makes it possible to provide feedback that reflects the emotional state based on the user's behavior, thereby enabling more appropriate spending management and improvement suggestions.

[0702] A "user" is an individual or organization that inputs information about their income and expenses through the system and receives feedback on their financial management and behavior.

[0703] "Income-related information" refers to data on a user's income, such as their annual or monthly income, and is information that forms the basis for judging their financial situation.

[0704] "Expenditure information" refers to data based on users' daily purchasing behavior and expenses, and serves as the basis for budget management and analysis of spending trends.

[0705] "Emotion recognition means" refers to a technology or process for analyzing a user's input information and behavioral history to estimate their current emotional state.

[0706] A "notification" is a message sent from a system to a user's information processing device, and includes information such as warnings, suggestions, and feedback.

[0707] "Anonymization" is the process of making it impossible to identify specific individuals or groups from data, and is used to obtain statistical information from a group while ensuring privacy.

[0708] "Statistical analysis" refers to processing collected data using mathematical and statistical methods to derive trends and patterns.

[0709] This invention is a system that enables personalized spending management by gaining a deeper understanding of the user's financial situation and providing emotion-based feedback. The system primarily operates between three parties: a server, a terminal, and the user.

[0710] The server receives information about users' income and expenses from their terminals and stores it in a database. The databases used are typically MySQL or PostgreSQL. The server also uses analytical tools such as Python or R to perform detailed analyses of users' purchase history and behavioral data.

[0711] Based on information entered by the user and their purchase history, the server uses an "emotion recognition engine" to estimate the user's current emotional state. This emotion recognition engine can utilize technologies such as "IBM Watson." The results of the emotional analysis are integrated with asset management information and used to dynamically generate feedback.

[0712] The terminal displays feedback and warnings received from the server to the user. The server also uses a generative AI model to input prompts when generating customized messages. An example of such a prompt is: "Your recent purchasing behavior indicates you are experiencing stress. Please generate suggestions to help you relax."

[0713] For example, if a user repeatedly makes large purchases in a short period, the server analyzes this behavior and uses an emotion engine to estimate the user's emotional state. If the analysis suggests that the user is experiencing stress, a message such as "Review your spending and make time to relax" will be displayed on the device.

[0714] In this way, we can go beyond mere numerical management and achieve more precise and personalized financial assistance that is attentive to the user's emotions.

[0715] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0716] Step 1:

[0717] The server receives income and expense information entered by the user. Specifically, the user enters their annual income, monthly income, and daily expenses through a dedicated application or web interface. The server stores this information in a database such as MySQL. The input is the user's financial information, and the output is the storage of that information in the database.

[0718] Step 2:

[0719] The server analyzes the received spending information and extracts spending patterns from the user's purchase history. Specifically, the server uses Python or R to perform data analysis and determine whether there have been high-value purchases in a short period. The input is the user's purchase history data, and the output is a report of the analyzed spending patterns.

[0720] Step 3:

[0721] The server uses an emotion recognition engine to estimate the user's emotional state based on their behavioral data. Specifically, it utilizes tools such as IBM Watson to analyze emotions like stress and impulsivity. The input consists of analysis results of purchase history and spending patterns, while the output is data on the estimated emotional state.

[0722] Step 4:

[0723] The server inputs a prompt message into a generative AI model, which then generates customized feedback based on emotion recognition. For example, the prompt message "Your recent purchasing behavior indicates you are experiencing stress. Please generate suggestions to help you relax." is used. The input consists of the emotional state and the prompt message, while the output is an emotion-based feedback message.

[0724] Step 5:

[0725] The device displays feedback received from the server to the user. Specifically, a message is displayed on the user's device, notifying the user of an alert such as, "Review your spending and make time to relax." The input is the feedback message generated by the server, and the output is the notification to the user.

[0726] (Application Example 2)

[0727] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] Traditional income and expense management systems simply focus on balancing income and expenses, lacking features to mitigate impulsive purchasing behavior driven by user emotions and stress. This made it difficult for users to accurately understand their own emotional state and maintain financial health while engaging in economic activities. Furthermore, systems lacked the ability to personalize improvement suggestions through anonymous data comparisons with other users. These issues need to be addressed.

[0729] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0730] In this invention, the server includes means for analyzing the user's emotional state and generating personalized advice based on the analysis results; means for providing a user interface for visually presenting the advice; and means for statistically analyzing spending information collected from multiple users and providing anonymized feedback. This enables users to achieve more appropriate spending management based on their emotional state, suppress impulsive purchasing behavior, and engage in economic activities that are best suited to them.

[0731] "Income information" refers to all financial benefits obtained by the user, including information such as salary, stocks, and investment returns.

[0732] "Expenditure information" refers to information about all activities in which a user pays money and the transactions associated with them.

[0733] "Analysis results" refer to the results of data analysis conducted to determine the emotional state of the user.

[0734] "Personalized advice" refers to specific action plans and suggestions generated based on the user's individual emotional state and financial situation.

[0735] "Visual presentation" refers to showing information to users in a visual format, and includes, but is not limited to, graphs, text, and icons.

[0736] A "user interface" is a means for a user to interact with a system, and usually refers to a device or program that includes a graphical user interface (GUI).

[0737] "Anonymized feedback" refers to ratings and information provided in a form that does not allow for personal identification in order to protect user privacy.

[0738] "Anonymous data comparison with other users" refers to the comparative analysis of spending and emotional state data from multiple users, with personal information removed.

[0739] This invention is an electronic payment system that analyzes the user's emotional state, primarily based on income and expenditure information, and provides personalized feedback. This system utilizes the user's smartphone as the primary hardware and is implemented using software such as an emotion analysis module and a database management system.

[0740] First, the server collects users' income and expenditure information and stores it in a database. This includes obtaining data from financial institutions and user input. Furthermore, sentiment analysis software such as Google Cloud Natural Language API and Microsoft Azure Text Analytics is used to analyze users' emotional states. This makes it possible to extract emotions such as stress and impulsivity from text data.

[0741] The server combines analyzed emotional states with income and expenditure information to generate personalized advice. This advice is presented visually through the user interface, allowing users to easily take optimal actions based on their own emotional state.

[0742] For example, if a user is making large expenditures in a short period of time, the system will display a message on their smartphone saying, "It seems you've been under a lot of stress lately. Why not take some time to relax?" It also provides comparative information based on anonymous data from multiple users, which can be used as a reference to improve individual spending habits.

[0743] Examples of prompts in generative AI models are as follows:

[0744] "User behavior history: Recent increase in spending; Emotional state: High stress rating. Please generate an appropriate feedback message."

[0745] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0746] Step 1:

[0747] The server obtains user income and expenditure information through financial institution APIs and user input. This information is stored in a database for later analysis. The input to this process is the user's income and expenditure information, and the output is the financial information recorded in the database.

[0748] Step 2:

[0749] The system acquires the user's purchase history and text input and sends this to an emotion analysis module. The emotion analysis module uses the collected data to analyze the user's emotional state. The input for this process is the user's text input and purchase history, and the emotion analysis module outputs emotional states such as stress and impulsivity.

[0750] Step 3:

[0751] The server combines the analyzed emotional state with the financial information obtained in the previous step. Based on this information, it generates personalized advice when certain conditions are met. Here, it utilizes prompts generated by a generative AI model. The input to this process is the emotional state and financial information, and the output is the advice provided to the user.

[0752] Step 4:

[0753] The terminal visually displays the generated advice through a user interface. It presents the advice in a user-friendly format, for example, offering suggestions for relaxation or encouraging users to reconsider their purchases. The input for this process is the advice received from the server, and the output is the feedback message displayed on the terminal screen.

[0754] Step 5:

[0755] The server statistically analyzes anonymous data collected from other users to compare users' financial and emotional states. This analysis is then presented to users as feedback to help them improve their spending habits. The input here is statistical data, and the output is feedback based on comparative analysis.

[0756] 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.

[0757] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0758] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0759] 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.

[0760] Figure 9 shows an 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.

[0761] 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.

[0762] 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.

[0763] 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, motorcycles, etc., 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, for example, based 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.

[0764] 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."

[0765] 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.

[0766] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0767] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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 the like 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.

[0776] 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.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] A means for receiving and storing user income and expenditure information,

[0780] A means for determining whether expenditures exceed a target range based on the aforementioned income and expenditure information,

[0781] A means for generating a notification based on the judgment result and sending it to the user's terminal,

[0782] A means of statistically analyzing spending information collected from multiple users and providing anonymized feedback,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] A means for anonymizing user spending information and calculating the mean, median, and distribution,

[0786] A means of presenting the calculation results to the user,

[0787] The system according to claim 1, characterized by comprising:

[0788] (Claim 3)

[0789] A means of consolidating a user's information from multiple financial institutions into a single profile,

[0790] A means of monitoring the financial situation in real time based on the aggregated information,

[0791] The system according to claim 1, characterized by comprising:

[0792] "Example 1"

[0793] (Claim 1)

[0794] A means for receiving and storing user income and expenditure information,

[0795] A means for determining in real time whether expenditures exceed the budget based on the aforementioned income information, expenditure information, and monthly expenditure targets,

[0796] A means for generating and sending a warning message to the user's terminal when spending exceeds the budget limit,

[0797] A means of statistically analyzing spending information collected from multiple users and providing anonymized feedback,

[0798] A means of providing information on a user's consumption trends in comparison to other users,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] A means for anonymizing user spending information and calculating the mean, median, and distribution through statistical analysis,

[0802] A means of visually presenting users' spending trends based on calculated statistical data,

[0803] The system according to claim 1, characterized by comprising:

[0804] (Claim 3)

[0805] By consolidating the user's data from multiple financial institutions into a single profile,

[0806] A means of monitoring the financial situation in real time based on the aggregated information and presenting it to the user,

[0807] The system according to claim 1, characterized by comprising:

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means for receiving and storing user income and expenditure information,

[0811] A means for determining whether expenditures exceed a target range based on the aforementioned income and expenditure information,

[0812] A means for generating a notification based on the judgment result and sending it to the user's terminal,

[0813] A means of statistically analyzing spending information collected from multiple users and providing anonymized feedback,

[0814] A means of analyzing individual user transaction data and performing statistical comparisons with similar users,

[0815] A means of providing users with suggestions for improving their consumption based on statistical comparison results,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] A means for anonymizing user spending information and calculating the mean, median, and distribution,

[0819] A means of presenting the calculation results to the user,

[0820] A means of providing spending suggestions to promote smart asset management by analyzing the spending trends of similar users,

[0821] The system according to claim 1, characterized by comprising:

[0822] (Claim 3)

[0823] A means of consolidating a user's information from multiple financial institutions into a single profile,

[0824] A means of monitoring the financial situation in real time based on the aggregated information,

[0825] A means of providing users with customized prompts using a generative AI model,

[0826] The system according to claim 1, characterized by comprising:

[0827] "Example 2 of combining an emotion engine"

[0828] (Claim 1)

[0829] Means for receiving and storing information regarding users' income and expenses,

[0830] A means for determining whether expenditures exceed a target range based on the aforementioned information on income and expenditures,

[0831] A means for generating a notification based on the judgment result and sending it to the user's information processing device,

[0832] An emotion recognition method that analyzes the user's behavioral history and estimates their current emotional state,

[0833] A means for customizing feedback, including estimated emotional states, and sending notifications to the user's information processing device,

[0834] A means of statistically analyzing spending information and emotional state-based information collected from multiple users, and providing anonymized feedback,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] A means for anonymizing information about users' spending and emotional states, and for calculating the mean, median, and distribution,

[0838] A means of presenting the calculation results to the user,

[0839] The system according to claim 1, characterized by comprising:

[0840] (Claim 3)

[0841] A means of aggregating multiple financial information of a user under the same identification information,

[0842] A means of continuously monitoring the financial condition based on the aggregated information,

[0843] The system according to claim 1, characterized by comprising:

[0844] "Application example 2 when combining with an emotional engine"

[0845] (Claim 1)

[0846] A means for receiving and storing user income and expenditure information,

[0847] A means for determining whether expenditures exceed a target range based on the aforementioned income and expenditure information,

[0848] A means for generating a notification based on the judgment result and sending it to the user's terminal,

[0849] A means of statistically analyzing spending information collected from multiple users and providing anonymized feedback,

[0850] A means for analyzing the emotional state of a user and generating personalized advice based on the analysis results,

[0851] Means for providing a user interface for visually presenting the aforementioned advice,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] A means for anonymizing user spending information and calculating the mean, median, and distribution,

[0855] Based on the calculated statistical information, a means of comparing spending trends with those of users with similar financial and emotional states,

[0856] A means of presenting the results to the user,

[0857] The system according to claim 1, characterized by comprising:

[0858] (Claim 3)

[0859] A means of consolidating a user's information from multiple financial institutions into a single profile,

[0860] A means of monitoring financial status and emotional state in real time based on the aggregated information and emotional data,

[0861] A means for customizing emotional feedback using prompt messages generated based on the aforementioned monitoring results,

[0862] The system according to claim 1, characterized by comprising: [Explanation of symbols]

[0863] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving and storing user income and expenditure information, A means for determining whether expenditures exceed a target range based on the aforementioned income and expenditure information, A means for generating a notification based on the judgment result and sending it to the user's terminal, A means of statistically analyzing spending information collected from multiple users and providing anonymized feedback, A system that includes this.

2. A means for anonymizing user spending information and calculating the mean, median, and distribution, A means of presenting the calculation results to the user, The system according to claim 1, characterized by comprising:

3. A means of consolidating a user's information from multiple financial institutions into a single profile, A means of monitoring the financial situation in real time based on the aggregated information, The system according to claim 1, characterized by comprising:

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