system

Abstract

We provide the system. [Solution] Means for obtaining information on salary receipts, expenses, and savings, A means of integrating the acquired information to create a user profile, A means of analyzing spending patterns and proposing the optimal budget allocation, A means of automatically allocating the remaining amount after deducting fixed expenses and budget from the aforementioned salary to savings or investments, A means of generating and providing financial data as a report in natural language, A means of generating answers to user questions and providing educational content, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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] Young working people, especially those in their 20s who are company employees, face the complexity of financial management such as salary management, daily expenses, savings, and investment, and it is difficult to have consistent management. It is not easy to efficiently build assets with a limited starting salary level, and there is also a problem that integrated management from an overall perspective is not provided because salary transfer, expense management, investment, etc. are carried out with separate services. Furthermore, due to the lack of opportunities for financial education, the knowledge for making effective decisions is insufficient.

Means for Solving the Problems

[0005] This invention provides a system that acquires salary receipt information, expenditure information, and savings information, and creates an integrated user profile. Furthermore, by providing means to analyze spending patterns and propose optimal budget allocation, it enables users to curb wasteful spending and build assets efficiently. In addition, it realizes automated asset management through a function that automatically allocates the remaining amount after deducting fixed expenses and budget from salary to savings or investments. It supports the improvement of financial knowledge by providing financial status data as a report in natural language, generating answers to user questions, and providing customized educational content. As a result, users can receive integrated financial management and appropriate financial education.

[0006] "Salary payment information" refers to detailed data about the salary paid by the employer to the employee, and typically includes the payment date, amount, and bank account details.

[0007] "Expenditure information" refers to detailed records of payments made by an individual or organization for goods or services, including items such as the date, amount, and recipient of the payment.

[0008] "Savings information" refers to information about the amount, type, and history of funds that individuals or organizations have accumulated for the future.

[0009] A "user profile" is a collection of information that a system maintains about individual users, including data based on personal attributes and past behavioral history.

[0010] "Analyzing spending patterns" is the process of using past spending data to identify trends and characteristics using statistical or machine learning methods.

[0011] "Optimal budget allocation" is a plan for distributing available resources in the most effective way, aiming to minimize waste and maximize assets.

[0012] "Automatically allocating funds to savings or investments" is a process that moves a user's funds to savings accounts or investment destinations according to predetermined rules, without manual intervention.

[0013] "Generating reports in natural language" refers to the process of converting numerical data and analysis results into language that is easy for humans to understand and expressing them in written form.

[0014] "Customized educational content" refers to creating and providing educational content that is optimized according to the user's knowledge level and needs. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a 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.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention relates to a financial management system, the embodiment of which mainly consists of the interaction between a server, a terminal, and a user. To effectively implement this invention, the following system configuration and program flow are necessary.

[0037] This system operates based on communication between the server, terminal, and user. First, the user logs in through the terminal and provides information about their salary, expenses, and savings to the server. Based on this information, the server creates a financial profile for each user. This profile creation allows for an understanding of the user's income and expenses, enabling further analysis.

[0038] The server analyzes the collected information using IBM Watson® for Financial Services to understand the user's spending patterns. Based on the analysis results, it uses OpenAI® GPT-4® to propose the optimal budget allocation to the user. This process helps users manage their assets efficiently.

[0039] For example, the server analyzes that "User A" spends 30,000 yen on entertainment and 20,000 yen on food, and then presents an optimized budget arrangement for these expenses. It also provides a function to automatically allocate the remaining amount after deducting necessary expenses from the user's salary to savings or investments. This automatic allocation allows the user to systematically increase their assets.

[0040] Furthermore, the server converts complex numerical data into easy-to-understand reports in natural language. These reports provide users with an overview of their financial situation and aid in understanding. It also has a function to provide real-time answers to user inquiries through the terminal. In this process, educational content can be customized and delivered according to the user's knowledge level.

[0041] For example, if a user asks about "alternative savings methods," the server will provide advice tailored to their individual circumstances based on the latest financial data. This could include information on low-risk, fee-free savings methods.

[0042] The above describes an embodiment of the present invention, which functions as a system that provides users with efficient financial management and educational support.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user operates the device and logs into the application. The device sends user authentication information to the server.

[0046] Step 2:

[0047] The server uses the transmitted authentication information to authenticate the user. If authentication is successful, the server retrieves data about the user's salary, expenses, and savings.

[0048] Step 3:

[0049] The server uses the acquired data to create a financial profile for each user. This profile includes amounts for each category of income and expenditure, as well as savings status.

[0050] Step 4:

[0051] The server uses IBM Watson for Financial Services to analyze the user's spending patterns and identify areas for improvement in their spending, as well as frequently used categories.

[0052] Step 5:

[0053] Based on the analysis results, the server uses OpenAI GPT-4 to propose the optimal budget allocation to the user. Specifically, it presents a plan outlining ways to save money on necessary expenditure items and the optimal spending amounts for each category.

[0054] Step 6:

[0055] The server calculates the remaining amount after subtracting fixed expenses and the proposed budget from the user's salary. It then automatically allocates part or all of this remaining amount to savings or investments.

[0056] Step 7:

[0057] The server converts the user's financial data into a natural language report. The report includes the user's current financial situation, future spending plans, savings, and investment status.

[0058] Step 8:

[0059] When a user enters a question through their device, the server uses OpenAI GPT-4 to generate an answer to the question. The answer is provided in language that is easy for the user to understand.

[0060] Step 9:

[0061] The server generates customized educational content based on the user's knowledge level and sends it to the device. The device displays the received content to the user, supporting continuous learning.

[0062] (Example 1)

[0063] 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."

[0064] In today's lifestyle, proper financial management is becoming increasingly important for individuals. However, many individuals do not have an accurate grasp of their own spending and savings, and are unable to manage their assets efficiently. Therefore, there is a need for a system that effectively manages income and expenses, prevents wasteful spending, and enables optimal budget allocation.

[0065] 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.

[0066] In this invention, the server includes means for acquiring reward information, consumption information, and storage information; means for integrating the acquired information to create a user profile; and means for analyzing consumption patterns and proposing an optimal resource allocation. This enables individuals to accurately understand their financial situation and achieve efficient asset management and planned savings.

[0067] "Reward information" refers to information about the monetary rewards an individual receives for their work or transactions.

[0068] "Consumer information" refers to spending information about goods and services that individuals purchase on a daily basis.

[0069] "Accumulated information" refers to information about an individual's accumulated assets and savings.

[0070] A "user profile" is data that shows an individual's financial situation, created by integrating financial information such as an individual's income, expenses, and savings.

[0071] "Consumption patterns" refer to analytical information about the tendencies and behaviors of how individuals spend their money.

[0072] "Resource allocation" refers to proposals and plans regarding the appropriate allocation of users' income and assets.

[0073] "Wasteful spending" refers to irrational spending patterns or unnecessary consumption behaviors.

[0074] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and makes predictions and suggestions for specific tasks.

[0075] "Real-time" refers to the immediate response and processing of user actions and inquiries.

[0076] The financial management system based on this invention is implemented through the interaction of three parties: the user, the terminal, and the server. The user first logs into the system using the terminal and inputs their reward information, spending information, and accumulated information. This information is securely transmitted to the server via the terminal.

[0077] The server collects the received information and generates user profiles. This profile creation involves data integration and organization to provide an accurate understanding of an individual's financial situation. A data analytics platform is used for data analysis, enabling the identification of consumption patterns and analysis of wasteful spending tendencies.

[0078] Using a generative AI model, the server performs a process of proposing customized resource allocations for each user. This proposal includes the automatic accumulation or allocation of remaining funds from earnings, taking into account fixed expenses and budgets. This allows users to manage their assets efficiently.

[0079] Furthermore, the server generates a report on the financial situation in natural language and provides it to the user via the terminal. This report provides a foundation for users to easily understand their financial situation and make appropriate decisions. In addition, when a user makes a query, the server can generate a response in real time and provide educational content as needed. This content is customized according to the user's skill level.

[0080] For example, if a user asks about "alternative savings methods," the server can suggest low-risk savings methods based on the latest financial information. An example of a prompt might be, "If my monthly salary is 300,000 yen, what is the optimal spending ratio?"

[0081] In this way, this system integrates a set of functions to support users' financial management, providing a very convenient platform for users.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users log in to the system using their device. The information entered is a user ID and password, and user authentication is performed based on this information. If authentication is successful, the user can access their personal dashboard.

[0085] Step 2:

[0086] Users input reward information, spending information, and accumulated information from their devices. This information is processed as data showing the user's income, expenses, and savings status and sent to the server. Data transfer is performed securely using encryption protocols.

[0087] Step 3:

[0088] The server generates a user profile based on the received information. This involves integrating and organizing each data item, ultimately outputting a profile that accurately represents the user's financial situation. This process also involves compiling information in the database.

[0089] Step 4:

[0090] The server utilizes a generative AI model to analyze profile data and identify consumption patterns and wasteful spending tendencies. The input data consists of accumulated consumption information, and the output information includes the characteristics of the analyzed user's consumption behavior and recommended areas for improvement.

[0091] Step 5:

[0092] Based on the analysis results, the server proposes an optimal resource allocation tailored to the identified consumption patterns. This proposal generation process includes setting spending limits and savings targets for each item. The output is a customized budget allocation plan for each user.

[0093] Step 6:

[0094] The server generates a report that expresses the overall financial situation in natural language and provides it to the user via a terminal. User profiles and analysis results are used as input, and the output is a report in a viewable format. This report also includes a summary of overall spending.

[0095] Step 7:

[0096] The server generates answers to user inquiries and provides educational content. In this process, a generative AI model analyzes user questions and prompts. The output includes educational content tailored to the user's knowledge level and specific answers.

[0097] Step 8:

[0098] The server provides real-time responses and delivers information to the user through the terminal. Here, immediate responses are provided to each inquiry, helping users resolve their problems. The output includes detailed advice and suggestions for the next steps, tailored to the situation.

[0099] (Application Example 1)

[0100] 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."

[0101] In modern society, many individuals face difficulties in financial management. Understanding the impact of daily expenses on their budget and systematically building wealth while minimizing unnecessary spending are crucial, but achieving these goals efficiently is not easy. This invention aims to improve the efficiency of financial management by supporting users in real-time expense management and appropriate budget allocation.

[0102] 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.

[0103] In this invention, the server includes means for acquiring benefit receipt information, expenditure information, and storage information; means for integrating the acquired information to create a user information set; means for analyzing expenditure patterns and proposing an optimal budget allocation; and means for identifying items to be purchased and evaluating the impact of those expenditures on the budget in real time. This enables users to effectively manage their spending, suppress unnecessary expenses, and manage their assets in a planned manner.

[0104] "Benefits" is a general term for the remuneration or income that an individual receives on a regular basis.

[0105] "Expenses" refer to the money paid when purchasing goods or services.

[0106] "Storage" refers to the act of saving money in preparation for future use.

[0107] "User" refers to an individual or organization that uses this system.

[0108] An "information set" is a series of profile data created by integrating acquired data.

[0109] "Spending patterns" refer to information that shows the tendencies of how users spend their money.

[0110] "Budget allocation" refers to a plan for assigning available resources to specific uses.

[0111] "Identification of goods" is the process of identifying and classifying products and services.

[0112] "Real-time evaluation" means analyzing data and providing results immediately.

[0113] The server first acquires benefit receipt information, expenditure information, and savings information through the terminal, and integrates this data to create a user information set. When the user records daily expenditures, the server updates the information each time and uses IBM Watson to analyze expenditure patterns. Based on this analysis, a generative AI model is used to propose the optimal budget allocation.

[0114] Of particular importance is the terminal's ability to identify items intended for purchase. When a user scans an item's barcode, the server evaluates in real time how that expenditure will impact the budget and provides feedback to the terminal. This system allows users to instantly understand the impact of their spending on their budget and avoid unnecessary purchases.

[0115] A concrete example would be a user receiving a message while shopping at a supermarket stating, "Purchasing this item may cause your grocery budget to exceed your budget." This would enable more planned spending. An example of a prompt for a generative AI model would be, "Considering user A's spending patterns and this month's budget, please suggest how much they can comfortably spend on eating out this weekend."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] Users use a terminal to input information about benefit receipts, expenses, and savings. The terminal sends this data to a server. This information is collected as basic data to understand the user's current financial situation.

[0119] Step 2:

[0120] The server creates a user information set based on the data sent from the terminal. During this process, information from multiple different data streams is combined into a single, unified profile through a data integration process. Once this profile is created, subsequent analysis becomes possible.

[0121] Step 3:

[0122] The server uses IBM Watson to analyze the user's spending patterns. This analysis step examines past spending data to reveal trends in specific categories and time periods. The analysis results are useful for the following suggestions.

[0123] Step 4:

[0124] The server uses a generative AI model to suggest the optimal budget allocation to the user. In this step, prompts are generated based on the analyzed data, allowing the AI ​​to derive an appropriate budget plan. For example, a prompt such as "I want to reduce my food expenses, but how much is appropriate?" might be created.

[0125] Step 5:

[0126] The user scans the items they intend to purchase using a terminal. The terminal sends this information to a server. The server evaluates the impact of the expenditure on the budget in real time and sends the results back to the terminal. For example, barcode data is input, the impact on the budget is calculated, and a message such as "Purchasing this item may cause you to exceed your budget" is output.

[0127] Step 6:

[0128] The device provides visual feedback to the user based on information provided by the server. For example, it may alert users to reconsider their purchases, preventing unnecessary spending. Visual feedback may include specific numerical information or graphs showing budget status.

[0129] 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.

[0130] This invention provides users with more personalized feedback and suggestions by incorporating emotion recognition capabilities into a financial management system. The system operates through the interaction of a server, a terminal, and a user.

[0131] Users access the system through their terminals and provide financial data. The server retrieves information about the user's salary, expenses, and savings, integrates it, and creates a user profile. Furthermore, it analyzes the user's spending information in detail to identify waste and areas for improvement.

[0132] The emotion engine incorporates algorithms to recognize emotions from user interactions and behavior. The server uses the emotion engine to evaluate the user's emotional state and adjusts feedback and suggestions based on the results. This process enables the delivery of more effective and convincing financial plans and advice that resonate with the user's emotional state.

[0133] For example, if the server detects that a user's emotions indicate stress, it can adjust budget allocation suggestions and provide a flexible spending plan to alleviate stress. Conversely, if positive emotions are detected, it can propose a proactive investment strategy to achieve goals.

[0134] The device displays information and feedback sent from the server to the user in real time, communicating in an easy-to-understand format. Furthermore, customized educational content is adjusted based on emotions, supporting learning tailored to the user's knowledge and interests.

[0135] Thus, the system of the present invention collects and analyzes information from both the financial and emotional aspects of the user, and provides optimal support. As a result, users can improve their financial management skills and build wealth effectively while reducing stress.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user operates the device and logs into the application. The device sends the user's authentication information to the server.

[0139] Step 2:

[0140] The server authenticates the user using the transmitted authentication information. If authentication is successful, it retrieves data about the user's salary, expenses, and savings.

[0141] Step 3:

[0142] The server creates a user profile based on the acquired data. This profile integrates income, expense details, and savings status.

[0143] Step 4:

[0144] The server analyzes user spending data to identify wasteful spending patterns. This analysis includes a process of reviewing past spending patterns and identifying inefficient spending items.

[0145] Step 5:

[0146] A server equipped with an emotion engine analyzes the user's emotional state based on their conversation history and input. This analysis helps determine whether the user is feeling stressed or in a positive mood.

[0147] Step 6:

[0148] The server adjusts budget allocation and advice based on the evaluation results of the emotion engine. For example, if a user is experiencing stress, it will suggest increasing flexibility in their spending plan.

[0149] Step 7:

[0150] The server generates tailored advice and budget allocations as natural language reports. These reports are easy for users to understand and provide guidance for deciding on their next course of action.

[0151] Step 8:

[0152] The device displays generated reports and sentiment-based feedback to the user, which then uses this information to make financial decisions.

[0153] Step 9:

[0154] The server creates educational content optimized for the user's knowledge level and emotional state, and delivers it to the user through their device. This content helps to facilitate user learning and deepen their understanding.

[0155] (Example 2)

[0156] 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".

[0157] Traditional financial management systems provide general advice and suggestions based on users' financial data, but they do not take into account feedback optimized for individual psychological states and emotions. As a result, users may find it difficult to accept suggestions readily or may experience stress. Therefore, there is a need to provide personalized advice and learning support that takes into account not only the user's financial situation but also their emotional state.

[0158] 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.

[0159] In this invention, the server includes means for creating a user profile, means for recognizing the user's emotional state, and means for adjusting feedback based on the emotional state. This enables the provision of effective financial plans and educational suggestions that are in tune with the user's emotions.

[0160] "Salary receipt information" refers to detailed data about the salary a user receives from their employer, including the amount, payment date, and payment frequency.

[0161] "Spending information" refers to data about the amount of money a user has spent on purchasing goods and services, and includes purchase history and spending categories.

[0162] "Savings information" refers to data about funds that users have saved for the future, including savings account balances and savings plans.

[0163] A "user profile" refers to an individual profile generated by integrating financial and emotional data about a user, reflecting the user's overall financial and emotional state.

[0164] "Optimal budget allocation" refers to the most effective and efficient way to allocate funds based on the user's income and expenses, supporting the achievement of financial goals.

[0165] "Emotional state" refers to the user's psychological or emotional condition, and is evaluated based on past actions and current input.

[0166] "Feedback" refers to the advice and suggestions that a system provides to the user, which are adjusted based on financial management and sentiment recognition.

[0167] "Educational content" refers to information and learning materials aimed at improving the user's knowledge and skills, and is customized according to the user's level and interests.

[0168] This invention incorporates emotion recognition capabilities into a financial management system, providing users with personalized feedback and suggestions. The system is realized through the interaction of a server, a terminal, and a user.

[0169] Users access the system using a terminal and enter financial information such as salary, expenses, and savings. The terminal sends this data to the server. The server registers the received information in a database and generates a unified user profile for each user. This profile reflects the user's overall financial situation.

[0170] The server uses an emotion engine to recognize the user's emotional state from past user interaction data and real-time input data. For example, if the user's input suddenly decreases or positive content declines, the server can determine that the user is experiencing stress. Based on this, the server can generate feedback tailored to the emotional state and adjust individual financial advice for the user.

[0171] For example, if the user's emotions indicate stress, the server will offer a plan to flexibly adjust spending items. Conversely, if the user's emotions are positive, it will suggest a proactive investment strategy. This feedback is presented to the user in real time via the terminal and displayed in a visually easy-to-understand format.

[0172] An example of a prompt might be, "Analyze the biggest wasteful spending category in your spending this month, and then suggest ways to improve it."

[0173] In this way, this system comprehensively analyzes users' financial and emotional data, and by providing more appropriate financial management and emotionally responsive support, it can improve users' financial management capabilities.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The user uses a terminal to input financial data regarding salary, expenses, and savings. The terminal receives this data and prepares to send it to the server. The input here is raw data about the user's financial situation, and the output is a digital file containing that data. The terminal sends this file to the server.

[0177] Step 2:

[0178] The server receives financial data sent from the terminal and stores it in a database. The server uses this data to generate individual user profiles. The input is raw financial data, stored in the database. The output is a user-specific profile, used for subsequent analysis. The server then proceeds to the next step based on this profile.

[0179] Step 3:

[0180] The server uses an emotion engine to analyze the user's emotional state from user interactions and input data. Specifically, it analyzes patterns and rate changes in the input data to infer emotions. Inputs include stored profile data and real-time user data. The output is the estimated user emotional state, which is used for feedback adjustments.

[0181] Step 4:

[0182] The server uses a generative AI model to generate feedback tailored to the user profile and emotional state. The inputs are profile data and emotional state. The output is personalized feedback and advice, including appropriate financial management and educational content. The generated feedback is then sent to the terminal.

[0183] Step 5:

[0184] The terminal presents the user with feedback received from the server. The feedback is displayed in a visually easy-to-understand format, such as text or graphs. The input is feedback data from the server, and the output is the information displayed on the user's screen. The user reviews and understands this information.

[0185] Through this series of steps, the system becomes capable of providing personalized support based on the user's financial situation and emotions.

[0186] (Application Example 2)

[0187] 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".

[0188] Existing financial management systems have been unable to provide personalized feedback and suggestions that take into account the user's emotional state, making it difficult to offer flexible advice that addresses diverse emotional needs. Therefore, there is a need for support that comprehensively considers both the user's emotions and their financial situation.

[0189] 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.

[0190] In this invention, the server includes means for acquiring salary receipt information, expenditure information, and savings information; means for integrating the acquired information to create a user profile; means for analyzing expenditure patterns and proposing an optimal budget allocation; means for automatically allocating the remaining amount after deducting fixed expenses and budget from the salary to savings or investments; means for generating and providing financial status data as a report in natural language; means for generating answers to user questions and providing educational content; and means for recognizing the user's emotional state and adjusting financial advice based on that state. This enables optimal support that simultaneously considers the user's emotions and financial situation.

[0191] "Salary receipt information" refers to detailed information about a user's income, including data on salary, bonuses, and other sources of income.

[0192] "Expense information" refers to data on all expenses incurred by the user in their daily life, including details such as food expenses, transportation expenses, and entertainment expenses.

[0193] "Savings information" refers to data related to a user's asset building, including information such as deposit balances and investment amounts in investment products.

[0194] A "user profile" is an integrated dataset representing a user's financial situation and spending patterns, used to gain a comprehensive understanding of an individual's economic status.

[0195] "Spending patterns" refer to a set of data that represents the user's consumption behavior trends, and include the results of analyzing past purchase history and daily spending trends.

[0196] "Budget allocation" refers to the method of efficiently distributing available funds across various expenditure items, and serves as a guideline for managing consumption in a planned manner.

[0197] A "report in natural language" is a report that provides specialized financial data in a format that is easy for the general public to understand, using conversational expressions to convey information in a way that is close to human language.

[0198] "Educational content" refers to a collection of learning resources provided to users to improve their financial knowledge, including textbooks, tutorials, and advice.

[0199] "Emotional state" refers to the user's psychological state at a given moment, and is evaluated based on psychological indicators such as joy, sadness, and stress.

[0200] "Financial advice" refers to a set of recommendations provided to support users in improving their financial situation and achieving their goals, including spending control, increasing savings, and investment strategies.

[0201] The system for realizing this invention is provided as an application accessible from the user's smartphone or personal computer. The user inputs information about income, expenses, and savings through the device. This information is transmitted to a cloud server using a secure transmission protocol.

[0202] The server first retrieves the user's income, expenditure, and savings information, and integrates this data to create a user profile. This profile includes the user's past spending history, savings status, and financial goals. The server analyzes this information through data processing using the Pandas library to identify the user's spending patterns. Based on the analysis of these spending patterns, it provides a means to suggest an optimal budget allocation.

[0203] Furthermore, the server analyzes the user's emotions using natural language processing libraries such as NLTK and Google® Cloud Natural Language API. It analyzes the voice and text data entered by the user and evaluates their current emotional state. Based on this emotional state, it utilizes a generative AI model (e.g., GPT-3®) to generate personalized financial advice in real time. If the emotion is positive, it can suggest proactive savings and investments; if it is negative, it can suggest a flexible spending plan.

[0204] One particularly interesting example is a feature that advises users to reconsider their spending when they are considering an expensive purchase right after payday, linking it to their emotional state that day. For example, it might send a message like, "You're happy your income increased today! However, we recommend you reconsider your big purchase and think about whether or not to buy it again in a few days."

[0205] An example of a prompt would be, "Based on the user's income and spending data, what savings strategy should be suggested if their current emotional state is positive?" This prompt is then used by the generative AI model to generate optimal advice.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The user enters their financial information using a terminal. This information includes salary, expense information, and savings information. This data is sent to the server using end-to-end encrypted communication. Based on the user's financial information as input, the server obtains basic data to form a user profile.

[0209] Step 2:

[0210] The server integrates the received financial information and creates a user profile. This involves processing the data using the Python Pandas library to integrate information about the user's income, expenses, and savings. The output is a user profile that provides an overall picture.

[0211] Step 3:

[0212] The server analyzes spending patterns based on this user profile data. This data analysis includes exploring average spending amounts and seasonal fluctuations for each spending category. This identifies wasteful spending and spending trends that need improvement, providing the basis for subsequent budget allocation suggestions.

[0213] Step 4:

[0214] The server proposes an optimal budget allocation based on the analyzed spending patterns. This involves financial recommendations that consider the balance between fixed costs, variable costs, and savings targets. The output of this process is the budget allocation proposal presented to the user.

[0215] Step 5:

[0216] The server analyzes the user's emotional state using text or voice data. It uses Google Cloud Natural Language API and NLTK to perform data calculations for sentiment analysis. The resulting sentiment analysis is output as data representing the user's current psychological state.

[0217] Step 6:

[0218] Based on the user's emotional state, a generative AI model is used to generate customized financial advice. The server receives the emotional analysis results and financial data from the user profile as prompts, and obtains specific advice as output from the model.

[0219] Step 7:

[0220] The server sends the generated financial advice to the terminal and presents it to the user. The terminal provides real-time feedback to the user and offers further specific guidelines for the next steps. As output, the user is provided with emotionally sensitive financial advice.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention relates to a financial management system, the embodiment of which mainly consists of the interaction between a server, a terminal, and a user. To effectively implement this invention, the following system configuration and program flow are necessary.

[0238] This system operates based on communication between the server, terminal, and user. First, the user logs in through the terminal and provides information about their salary, expenses, and savings to the server. Based on this information, the server creates a financial profile for each user. This profile creation allows for an understanding of the user's income and expenses, enabling further analysis.

[0239] The server analyzes the collected information using IBM Watson for Financial Services to understand the user's spending patterns. Based on the analysis results, it uses OpenAI GPT-4 to propose the optimal budget allocation for the user. This process helps users manage their assets efficiently.

[0240] For example, the server analyzes that "User A" spends 30,000 yen on entertainment and 20,000 yen on food, and then presents an optimized budget arrangement for these expenses. It also provides a function to automatically allocate the remaining amount after deducting necessary expenses from the user's salary to savings or investments. This automatic allocation allows the user to systematically increase their assets.

[0241] Furthermore, the server converts complex numerical data into easy-to-understand reports in natural language. These reports provide users with an overview of their financial situation and aid in understanding. It also has a function to provide real-time answers to user inquiries through the terminal. In this process, educational content can be customized and delivered according to the user's knowledge level.

[0242] For example, if a user asks about "alternative savings methods," the server will provide advice tailored to their individual circumstances based on the latest financial data. This could include information on low-risk, fee-free savings methods.

[0243] The above describes an embodiment of the present invention, which functions as a system that provides users with efficient financial management and educational support.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user operates the device and logs into the application. The device sends user authentication information to the server.

[0247] Step 2:

[0248] The server uses the transmitted authentication information to authenticate the user. If authentication is successful, the server retrieves data about the user's salary, expenses, and savings.

[0249] Step 3:

[0250] The server uses the acquired data to create a financial profile for each user. This profile includes amounts for each category of income and expenditure, as well as savings status.

[0251] Step 4:

[0252] The server uses IBM Watson for Financial Services to analyze the user's spending patterns and identify areas for improvement in their spending, as well as frequently used categories.

[0253] Step 5:

[0254] Based on the analysis results, the server uses OpenAI GPT-4 to propose the optimal budget allocation to the user. Specifically, it presents a plan outlining ways to save money on necessary expenditure items and the optimal spending amounts for each category.

[0255] Step 6:

[0256] The server calculates the remaining amount after subtracting fixed expenses and the proposed budget from the user's salary. It then automatically allocates part or all of this remaining amount to savings or investments.

[0257] Step 7:

[0258] The server converts the user's financial data into a natural language report. The report includes the user's current financial situation, future spending plans, savings, and investment status.

[0259] Step 8:

[0260] When a user enters a question through their device, the server uses OpenAI GPT-4 to generate an answer to the question. The answer is provided in language that is easy for the user to understand.

[0261] Step 9:

[0262] The server generates customized educational content based on the user's knowledge level and sends it to the device. The device displays the received content to the user, supporting continuous learning.

[0263] (Example 1)

[0264] 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."

[0265] In today's lifestyle, proper financial management is becoming increasingly important for individuals. However, many individuals do not have an accurate grasp of their own spending and savings, and are unable to manage their assets efficiently. Therefore, there is a need for a system that effectively manages income and expenses, prevents wasteful spending, and enables optimal budget allocation.

[0266] 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.

[0267] In this invention, the server includes means for acquiring reward information, consumption information, and storage information; means for integrating the acquired information to create a user profile; and means for analyzing consumption patterns and proposing an optimal resource allocation. This enables individuals to accurately understand their financial situation and achieve efficient asset management and planned savings.

[0268] "Reward information" refers to information about the monetary rewards an individual receives for their work or transactions.

[0269] "Consumer information" refers to spending information about goods and services that individuals purchase on a daily basis.

[0270] "Accumulated information" refers to information about an individual's accumulated assets and savings.

[0271] A "user profile" is data that shows an individual's financial situation, created by integrating financial information such as an individual's income, expenses, and savings.

[0272] "Consumption patterns" refer to analytical information about the tendencies and behaviors of how individuals spend their money.

[0273] "Resource allocation" refers to proposals and plans regarding the appropriate allocation of users' income and assets.

[0274] "Wasteful spending" refers to irrational spending patterns or unnecessary consumption behaviors.

[0275] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and makes predictions and suggestions for specific tasks.

[0276] "Real-time" refers to the immediate response and processing of user actions and inquiries.

[0277] The financial management system based on this invention is implemented through the interaction of three parties: the user, the terminal, and the server. The user first logs into the system using the terminal and inputs their reward information, spending information, and accumulated information. This information is securely transmitted to the server via the terminal.

[0278] The server collects the received information and generates a user profile. Creating this profile involves integrating and organizing data to accurately understand an individual's financial situation. At this time, a data analysis platform is used for data analysis to enable the identification of consumption patterns and the analysis of wasteful spending tendencies.

[0279] Using a generated AI model, the server implements a process to propose customized resource allocation for each user. This proposal includes the automatic accumulation of the balance or the allocation of capital investment after considering fixed expenses and budgets from rewards. As a result, users can efficiently manage their assets.

[0280] Furthermore, the server generates a report on the financial situation in natural language and provides it to the user through the terminal. This report provides a basis for the user to easily understand the financial situation and make appropriate decisions. Also, when the user makes an inquiry, the server can generate an answer in real time and provide educational content as needed. This content is customized according to the user's skill level.

[0281] As a specific example, when the user asks about "alternative methods of saving", the server can propose low-risk saving methods based on the latest financial information. Examples of prompt sentences include "Please tell me the optimal expenditure ratio when the monthly salary is 300,000 yen".

[0282] In this way, this system integrates a series of functions to support the user's financial management and provides a very convenient platform for the user.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user logs in to the system using a terminal. The information entered is the user ID and password, and based on this, user authentication is performed. If the authentication is successful, the user can access their personal dashboard.

[0286] Step 2:

[0287] The user inputs reward information, consumption information, and accumulation information from the terminal. These information are processed as data indicating the user's income, expenditure, and savings status, and are sent to the server. Data transfer is securely performed using an encryption protocol.

[0288] Step 3:

[0289] The server generates a user profile based on the received information. This includes the integration and organization of each data item, and finally, it is output as a profile accurately representing the user's financial situation. Information editing in the database is also performed in this process.

[0290] Step 4:

[0291] The server utilizes the generated AI model to analyze the profile data to identify consumption patterns and wasteful spending tendencies. The accumulated consumption information is used as input data, and the output information is the characteristics of the analyzed user's consumption behavior and recommended improvement points. <​​​​​​​​​​​​​​The server generates a report that expresses the overall financial situation in natural language and provides it to the user via a terminal. User profiles and analysis results are used as input, and the output is a report in a viewable format. This report also includes a summary of overall spending.

[0296] Step 7:

[0297] The server generates answers to user inquiries and provides educational content. In this process, a generative AI model analyzes user questions and prompts. The output includes educational content tailored to the user's knowledge level and specific answers.

[0298] Step 8:

[0299] The server provides real-time responses and delivers information to the user through the terminal. Here, immediate responses are provided to each inquiry, helping users resolve their problems. The output includes detailed advice and suggestions for the next steps, tailored to the situation.

[0300] (Application Example 1)

[0301] 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."

[0302] In modern society, many individuals face difficulties in financial management. Understanding the impact of daily expenses on their budget and systematically building wealth while minimizing unnecessary spending are crucial, but achieving these goals efficiently is not easy. This invention aims to improve the efficiency of financial management by supporting users in real-time expense management and appropriate budget allocation.

[0303] 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.

[0304] In this invention, the server includes means for acquiring benefit receipt information, expense information, and storage information, means for integrating the acquired information to create an information set of the user, means for analyzing the expense pattern and proposing an optimal budget allocation, and means for identifying the items to be purchased and evaluating in real time the impact of the expenses on the budget. As a result, the user can effectively manage expenditures, suppress wasteful spending, and achieve planned asset management.

[0305] "Benefit" is a general term for the rewards and income that an individual receives regularly.

[0306] "Expense" refers to the money paid when purchasing goods or services.

[0307] "Storage" means the act of saving money in preparation for future use.

[0308] "User" refers to an individual or group using this system.

[0309] "Information set" is a series of profile data created by integrating the acquired data.

[0310] "Expense pattern" is information indicating the tendency of how a user spends money.

[0311] "Budget allocation" refers to a plan for allocating available resources to specific uses.

[0312] "Identification of items" is a process of specifying and classifying goods and services. <000​​​​​The server first acquires benefit receipt information, expenditure information, and savings information through the terminal, and integrates this data to create a user information set. When the user records daily expenditures, the server updates the information each time and uses IBM Watson to analyze expenditure patterns. Based on this analysis, a generative AI model is used to propose the optimal budget allocation.

[0315] Of particular importance is the terminal's ability to identify items intended for purchase. When a user scans an item's barcode, the server evaluates in real time how that expenditure will impact the budget and provides feedback to the terminal. This system allows users to instantly understand the impact of their spending on their budget and avoid unnecessary purchases.

[0316] A concrete example would be a user receiving a message while shopping at a supermarket stating, "Purchasing this item may cause your grocery budget to exceed your budget." This would enable more planned spending. An example of a prompt for a generative AI model would be, "Considering user A's spending patterns and this month's budget, please suggest how much they can comfortably spend on eating out this weekend."

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] Users use a terminal to input information about benefit receipts, expenses, and savings. The terminal sends this data to a server. This information is collected as basic data to understand the user's current financial situation.

[0320] Step 2:

[0321] The server creates a user information set based on the data sent from the terminal. During this process, information from multiple different data streams is combined into a single, unified profile through a data integration process. Once this profile is created, subsequent analysis becomes possible.

[0322] Step 3:

[0323] The server uses IBM Watson to analyze the user's spending patterns. This analysis step examines past spending data to reveal trends in specific categories and time periods. The analysis results are useful for the following suggestions.

[0324] Step 4:

[0325] The server uses a generative AI model to suggest the optimal budget allocation to the user. In this step, prompts are generated based on the analyzed data, allowing the AI ​​to derive an appropriate budget plan. For example, a prompt such as "I want to reduce my food expenses, but how much is appropriate?" might be created.

[0326] Step 5:

[0327] The user scans the items they intend to purchase using a terminal. The terminal sends this information to a server. The server evaluates the impact of the expenditure on the budget in real time and sends the results back to the terminal. For example, barcode data is input, the impact on the budget is calculated, and a message such as "Purchasing this item may cause you to exceed your budget" is output.

[0328] Step 6:

[0329] The device provides visual feedback to the user based on information provided by the server. For example, it may alert users to reconsider their purchases, preventing unnecessary spending. Visual feedback may include specific numerical information or graphs showing budget status.

[0330] 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.

[0331] This invention provides users with more personalized feedback and suggestions by incorporating emotion recognition capabilities into a financial management system. The system operates through the interaction of a server, a terminal, and a user.

[0332] Users access the system through their terminals and provide financial data. The server retrieves information about the user's salary, expenses, and savings, integrates it, and creates a user profile. Furthermore, it analyzes the user's spending information in detail to identify waste and areas for improvement.

[0333] The emotion engine incorporates algorithms to recognize emotions from user interactions and behavior. The server uses the emotion engine to evaluate the user's emotional state and adjusts feedback and suggestions based on the results. This process enables the delivery of more effective and convincing financial plans and advice that resonate with the user's emotional state.

[0334] For example, if the server detects that a user's emotions indicate stress, it can adjust budget allocation suggestions and provide a flexible spending plan to alleviate stress. Conversely, if positive emotions are detected, it can propose a proactive investment strategy to achieve goals.

[0335] The device displays information and feedback sent from the server to the user in real time, communicating in an easy-to-understand format. Furthermore, customized educational content is adjusted based on emotions, supporting learning tailored to the user's knowledge and interests.

[0336] Thus, the system of the present invention collects and analyzes information from both the financial and emotional aspects of the user, and provides optimal support. As a result, users can improve their financial management skills and build wealth effectively while reducing stress.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The user operates the device and logs into the application. The device sends the user's authentication information to the server.

[0340] Step 2:

[0341] The server authenticates the user using the transmitted authentication information. If authentication is successful, it retrieves data about the user's salary, expenses, and savings.

[0342] Step 3:

[0343] The server creates a user profile based on the acquired data. This profile integrates income, expense details, and savings status.

[0344] Step 4:

[0345] The server analyzes user spending data to identify wasteful spending patterns. This analysis includes a process of reviewing past spending patterns and identifying inefficient spending items.

[0346] Step 5:

[0347] A server equipped with an emotion engine analyzes the user's emotional state based on their conversation history and input. This analysis helps determine whether the user is feeling stressed or in a positive mood.

[0348] Step 6:

[0349] The server adjusts budget allocation and advice based on the evaluation results of the emotion engine. For example, if a user is experiencing stress, it will suggest increasing flexibility in their spending plan.

[0350] Step 7:

[0351] The server generates tailored advice and budget allocations as natural language reports. These reports are easy for users to understand and provide guidance for deciding on their next course of action.

[0352] Step 8:

[0353] The device displays generated reports and sentiment-based feedback to the user, which then uses this information to make financial decisions.

[0354] Step 9:

[0355] The server creates educational content optimized for the user's knowledge level and emotional state, and delivers it to the user through their device. This content helps to facilitate user learning and deepen their understanding.

[0356] (Example 2)

[0357] 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".

[0358] Traditional financial management systems provide general advice and suggestions based on users' financial data, but they do not take into account feedback optimized for individual psychological states and emotions. As a result, users may find it difficult to accept suggestions readily or may experience stress. Therefore, there is a need to provide personalized advice and learning support that takes into account not only the user's financial situation but also their emotional state.

[0359] 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.

[0360] In this invention, the server includes means for creating a user profile, means for recognizing the user's emotional state, and means for adjusting feedback based on the emotional state. This enables the provision of effective financial plans and educational suggestions that are in tune with the user's emotions.

[0361] "Salary receipt information" refers to detailed data about the salary a user receives from their employer, including the amount, payment date, and payment frequency.

[0362] "Spending information" refers to data about the amount of money a user has spent on purchasing goods and services, and includes purchase history and spending categories.

[0363] "Savings information" refers to data about funds that users have saved for the future, including savings account balances and savings plans.

[0364] A "user profile" refers to an individual profile generated by integrating financial and emotional data about a user, reflecting the user's overall financial and emotional state.

[0365] "Optimal budget allocation" refers to the most effective and efficient way to allocate funds based on the user's income and expenses, supporting the achievement of financial goals.

[0366] "Emotional state" refers to the user's psychological or emotional condition, and is evaluated based on past actions and current input.

[0367] "Feedback" refers to the advice and suggestions that a system provides to the user, which are adjusted based on financial management and sentiment recognition.

[0368] "Educational content" refers to information and learning materials aimed at improving the user's knowledge and skills, and is customized according to the user's level and interests.

[0369] This invention incorporates emotion recognition capabilities into a financial management system, providing users with personalized feedback and suggestions. The system is realized through the interaction of a server, a terminal, and a user.

[0370] Users access the system using a terminal and enter financial information such as salary, expenses, and savings. The terminal sends this data to the server. The server registers the received information in a database and generates a unified user profile for each user. This profile reflects the user's overall financial situation.

[0371] The server uses an emotion engine to recognize the user's emotional state from past user interaction data and real-time input data. For example, if the user's input suddenly decreases or positive content declines, the server can determine that the user is experiencing stress. Based on this, the server can generate feedback tailored to the emotional state and adjust individual financial advice for the user.

[0372] For example, if the user's emotions indicate stress, the server will offer a plan to flexibly adjust spending items. Conversely, if the user's emotions are positive, it will suggest a proactive investment strategy. This feedback is presented to the user in real time via the terminal and displayed in a visually easy-to-understand format.

[0373] An example of a prompt might be, "Analyze the biggest wasteful spending category in your spending this month, and then suggest ways to improve it."

[0374] In this way, this system comprehensively analyzes users' financial and emotional data, and by providing more appropriate financial management and emotionally responsive support, it can improve users' financial management capabilities.

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The user uses a terminal to input financial data regarding salary, expenses, and savings. The terminal receives this data and prepares to send it to the server. The input here is raw data about the user's financial situation, and the output is a digital file containing that data. The terminal sends this file to the server.

[0378] Step 2:

[0379] The server receives financial data sent from the terminal and stores it in a database. The server uses this data to generate individual user profiles. The input is raw financial data, stored in the database. The output is a user-specific profile, used for subsequent analysis. The server then proceeds to the next step based on this profile.

[0380] Step 3:

[0381] The server uses an emotion engine to analyze the user's emotional state from user interactions and input data. Specifically, it analyzes patterns and rate changes in the input data to infer emotions. Inputs include stored profile data and real-time user data. The output is the estimated user emotional state, which is used for feedback adjustments.

[0382] Step 4:

[0383] The server uses a generative AI model to generate feedback tailored to the user profile and emotional state. The inputs are profile data and emotional state. The output is personalized feedback and advice, including appropriate financial management and educational content. The generated feedback is then sent to the terminal.

[0384] Step 5:

[0385] The terminal presents the user with feedback received from the server. The feedback is displayed in a visually easy-to-understand format, such as text or graphs. The input is feedback data from the server, and the output is the information displayed on the user's screen. The user reviews and understands this information.

[0386] Through this series of steps, the system becomes capable of providing personalized support based on the user's financial situation and emotions.

[0387] (Application Example 2)

[0388] 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."

[0389] Existing financial management systems have been unable to provide personalized feedback and suggestions that take into account the user's emotional state, making it difficult to offer flexible advice that addresses diverse emotional needs. Therefore, there is a need for support that comprehensively considers both the user's emotions and their financial situation.

[0390] 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.

[0391] In this invention, the server includes means for acquiring salary receipt information, expenditure information, and savings information; means for integrating the acquired information to create a user profile; means for analyzing expenditure patterns and proposing an optimal budget allocation; means for automatically allocating the remaining amount after deducting fixed expenses and budget from the salary to savings or investments; means for generating and providing financial status data as a report in natural language; means for generating answers to user questions and providing educational content; and means for recognizing the user's emotional state and adjusting financial advice based on that state. This enables optimal support that simultaneously considers the user's emotions and financial situation.

[0392] "Salary receipt information" refers to detailed information about a user's income, including data on salary, bonuses, and other sources of income.

[0393] "Expense information" refers to data on all expenses incurred by the user in their daily life, including details such as food expenses, transportation expenses, and entertainment expenses.

[0394] "Savings information" refers to data related to a user's asset building, including information such as deposit balances and investment amounts in investment products.

[0395] A "user profile" is an integrated dataset representing a user's financial situation and spending patterns, used to gain a comprehensive understanding of an individual's economic status.

[0396] "Spending patterns" refer to a set of data that represents the user's consumption behavior trends, and include the results of analyzing past purchase history and daily spending trends.

[0397] "Budget allocation" refers to the method of efficiently distributing available funds across various expenditure items, and serves as a guideline for managing consumption in a planned manner.

[0398] A "report in natural language" is a report that provides specialized financial data in a format that is easy for the general public to understand, using conversational expressions to convey information in a way that is close to human language.

[0399] "Educational content" refers to a collection of learning resources provided to users to improve their financial knowledge, including textbooks, tutorials, and advice.

[0400] "Emotional state" refers to the user's psychological state at a given moment, and is evaluated based on psychological indicators such as joy, sadness, and stress.

[0401] "Financial advice" refers to a set of recommendations provided to support users in improving their financial situation and achieving their goals, including spending control, increasing savings, and investment strategies.

[0402] The system for realizing this invention is provided as an application accessible from the user's smartphone or personal computer. The user inputs information about income, expenses, and savings through the device. This information is transmitted to a cloud server using a secure transmission protocol.

[0403] The server first retrieves the user's income, expenditure, and savings information, and integrates this data to create a user profile. This profile includes the user's past spending history, savings status, and financial goals. The server analyzes this information through data processing using the Pandas library to identify the user's spending patterns. Based on the analysis of these spending patterns, it provides a means to suggest an optimal budget allocation.

[0404] Furthermore, the server analyzes the user's emotions using natural language processing libraries such as NLTK and the Google Cloud Natural Language API. It analyzes the voice and text data entered by the user and evaluates their current emotional state. Based on this emotional state, it utilizes a generative AI model (e.g., GPT-3) to generate personalized financial advice in real time. If the emotion is positive, it can suggest proactive savings and investments; if it is negative, it can suggest a flexible spending plan.

[0405] One particularly interesting example is a feature that advises users to reconsider their spending when they are considering an expensive purchase right after payday, linking it to their emotional state that day. For example, it might send a message like, "You're happy your income increased today! However, we recommend you reconsider your big purchase and think about whether or not to buy it again in a few days."

[0406] An example of a prompt would be, "Based on the user's income and spending data, what savings strategy should be suggested if their current emotional state is positive?" This prompt is then used by the generative AI model to generate optimal advice.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The user enters their financial information using a terminal. This information includes salary, expense information, and savings information. This data is sent to the server using end-to-end encrypted communication. Based on the user's financial information as input, the server obtains basic data to form a user profile.

[0410] Step 2:

[0411] The server integrates the received financial information and creates a user profile. This involves processing the data using the Python Pandas library to integrate information about the user's income, expenses, and savings. The output is a user profile that provides an overall picture.

[0412] Step 3:

[0413] The server analyzes spending patterns based on this user profile data. This data analysis includes exploring average spending amounts and seasonal fluctuations for each spending category. This identifies wasteful spending and spending trends that need improvement, providing the basis for subsequent budget allocation suggestions.

[0414] Step 4:

[0415] The server proposes an optimal budget allocation based on the analyzed spending patterns. This involves financial recommendations that consider the balance between fixed costs, variable costs, and savings targets. The output of this process is the budget allocation proposal presented to the user.

[0416] Step 5:

[0417] The server analyzes the user's emotional state using text or voice data. It uses Google Cloud Natural Language API and NLTK to perform data calculations for sentiment analysis. The resulting sentiment analysis is output as data representing the user's current psychological state.

[0418] Step 6:

[0419] Based on the user's emotional state, a generative AI model is used to generate customized financial advice. The server receives the emotional analysis results and financial data from the user profile as prompts, and obtains specific advice as output from the model.

[0420] Step 7:

[0421] The server sends the generated financial advice to the terminal and presents it to the user. The terminal provides real-time feedback to the user and offers further specific guidelines for the next steps. As output, the user is provided with emotionally sensitive financial advice.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] [Third Embodiment]

[0426] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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".

[0438] This invention relates to a financial management system, the embodiment of which mainly consists of the interaction between a server, a terminal, and a user. To effectively implement this invention, the following system configuration and program flow are necessary.

[0439] This system operates based on communication between the server, terminal, and user. First, the user logs in through the terminal and provides information about their salary, expenses, and savings to the server. Based on this information, the server creates a financial profile for each user. This profile creation allows for an understanding of the user's income and expenses, enabling further analysis.

[0440] The server analyzes the collected information using IBM Watson for Financial Services to understand the user's spending patterns. Based on the analysis results, it uses OpenAI GPT-4 to propose the optimal budget allocation for the user. This process helps users manage their assets efficiently.

[0441] For example, the server analyzes that "User A" spends 30,000 yen on entertainment and 20,000 yen on food, and then presents an optimized budget arrangement for these expenses. It also provides a function to automatically allocate the remaining amount after deducting necessary expenses from the user's salary to savings or investments. This automatic allocation allows the user to systematically increase their assets.

[0442] Furthermore, the server converts complex numerical data into easy-to-understand reports in natural language. These reports provide users with an overview of their financial situation and aid in understanding. It also has a function to provide real-time answers to user inquiries through the terminal. In this process, educational content can be customized and delivered according to the user's knowledge level.

[0443] For example, if a user asks about "alternative savings methods," the server will provide advice tailored to their individual circumstances based on the latest financial data. This could include information on low-risk, fee-free savings methods.

[0444] The above describes an embodiment of the present invention, which functions as a system that provides users with efficient financial management and educational support.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] The user operates the device and logs into the application. The device sends user authentication information to the server.

[0448] Step 2:

[0449] The server uses the transmitted authentication information to authenticate the user. If authentication is successful, the server retrieves data about the user's salary, expenses, and savings.

[0450] Step 3:

[0451] The server uses the acquired data to create a financial profile for each user. This profile includes amounts for each category of income and expenditure, as well as savings status.

[0452] Step 4:

[0453] The server uses IBM Watson for Financial Services to analyze the user's spending patterns and identify areas for improvement in their spending, as well as frequently used categories.

[0454] Step 5:

[0455] Based on the analysis results, the server uses OpenAI GPT-4 to propose the optimal budget allocation to the user. Specifically, it presents a plan outlining ways to save money on necessary expenditure items and the optimal spending amounts for each category.

[0456] Step 6:

[0457] The server calculates the remaining amount after subtracting fixed expenses and the proposed budget from the user's salary. It then automatically allocates part or all of this remaining amount to savings or investments.

[0458] Step 7:

[0459] The server converts the user's financial data into a natural language report. The report includes the user's current financial situation, future spending plans, savings, and investment status.

[0460] Step 8:

[0461] When a user enters a question through their device, the server uses OpenAI GPT-4 to generate an answer to the question. The answer is provided in language that is easy for the user to understand.

[0462] Step 9:

[0463] The server generates customized educational content based on the user's knowledge level and sends it to the device. The device displays the received content to the user, supporting continuous learning.

[0464] (Example 1)

[0465] 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."

[0466] In today's lifestyle, proper financial management is becoming increasingly important for individuals. However, many individuals do not have an accurate grasp of their own spending and savings, and are unable to manage their assets efficiently. Therefore, there is a need for a system that effectively manages income and expenses, prevents wasteful spending, and enables optimal budget allocation.

[0467] 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.

[0468] In this invention, the server includes means for acquiring reward information, consumption information, and storage information; means for integrating the acquired information to create a user profile; and means for analyzing consumption patterns and proposing an optimal resource allocation. This enables individuals to accurately understand their financial situation and achieve efficient asset management and planned savings.

[0469] "Reward information" refers to information about the monetary rewards an individual receives for their work or transactions.

[0470] "Consumer information" refers to spending information about goods and services that individuals purchase on a daily basis.

[0471] "Accumulated information" refers to information about an individual's accumulated assets and savings.

[0472] A "user profile" is data that shows an individual's financial situation, created by integrating financial information such as an individual's income, expenses, and savings.

[0473] "Consumption patterns" refer to analytical information about the tendencies and behaviors of how individuals spend their money.

[0474] "Resource allocation" refers to proposals and plans regarding the appropriate allocation of users' income and assets.

[0475] "Wasteful spending" refers to irrational spending patterns or unnecessary consumption behaviors.

[0476] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and makes predictions and suggestions for specific tasks.

[0477] "Real-time" refers to the immediate response and processing of user actions and inquiries.

[0478] The financial management system based on this invention is implemented through the interaction of three parties: the user, the terminal, and the server. The user first logs into the system using the terminal and inputs their reward information, spending information, and accumulated information. This information is securely transmitted to the server via the terminal.

[0479] The server collects the received information and generates user profiles. This profile creation involves data integration and organization to provide an accurate understanding of an individual's financial situation. A data analytics platform is used for data analysis, enabling the identification of consumption patterns and analysis of wasteful spending tendencies.

[0480] Using a generative AI model, the server performs a process of proposing customized resource allocations for each user. This proposal includes the automatic accumulation or allocation of remaining funds from earnings, taking into account fixed expenses and budgets. This allows users to manage their assets efficiently.

[0481] Furthermore, the server generates a report on the financial situation in natural language and provides it to the user via the terminal. This report provides a foundation for users to easily understand their financial situation and make appropriate decisions. In addition, when a user makes a query, the server can generate a response in real time and provide educational content as needed. This content is customized according to the user's skill level.

[0482] For example, if a user asks about "alternative savings methods," the server can suggest low-risk savings methods based on the latest financial information. An example of a prompt might be, "If my monthly salary is 300,000 yen, what is the optimal spending ratio?"

[0483] In this way, this system integrates a set of functions to support users' financial management, providing a very convenient platform for users.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] Users log in to the system using their device. The information entered is a user ID and password, and user authentication is performed based on this information. If authentication is successful, the user can access their personal dashboard.

[0487] Step 2:

[0488] Users input reward information, spending information, and accumulated information from their devices. This information is processed as data showing the user's income, expenses, and savings status and sent to the server. Data transfer is performed securely using encryption protocols.

[0489] Step 3:

[0490] The server generates a user profile based on the received information. This involves integrating and organizing each data item, ultimately outputting a profile that accurately represents the user's financial situation. This process also involves compiling information in the database.

[0491] Step 4:

[0492] The server utilizes a generative AI model to analyze profile data and identify consumption patterns and wasteful spending tendencies. The input data consists of accumulated consumption information, and the output information includes the characteristics of the analyzed user's consumption behavior and recommended areas for improvement.

[0493] Step 5:

[0494] Based on the analysis results, the server proposes an optimal resource allocation tailored to the identified consumption patterns. This proposal generation process includes setting spending limits and savings targets for each item. The output is a customized budget allocation plan for each user.

[0495] Step 6:

[0496] The server generates a report that expresses the overall financial situation in natural language and provides it to the user via a terminal. User profiles and analysis results are used as input, and the output is a report in a viewable format. This report also includes a summary of overall spending.

[0497] Step 7:

[0498] The server generates answers to user inquiries and provides educational content. In this process, a generative AI model analyzes user questions and prompts. The output includes educational content tailored to the user's knowledge level and specific answers.

[0499] Step 8:

[0500] The server provides real-time responses and delivers information to the user through the terminal. Here, immediate responses are provided to each inquiry, helping users resolve their problems. The output includes detailed advice and suggestions for the next steps, tailored to the situation.

[0501] (Application Example 1)

[0502] 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."

[0503] In modern society, many individuals face difficulties in financial management. Understanding the impact of daily expenses on their budget and systematically building wealth while minimizing unnecessary spending are crucial, but achieving these goals efficiently is not easy. This invention aims to improve the efficiency of financial management by supporting users in real-time expense management and appropriate budget allocation.

[0504] 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.

[0505] In this invention, the server includes means for acquiring benefit receipt information, expenditure information, and storage information; means for integrating the acquired information to create a user information set; means for analyzing expenditure patterns and proposing an optimal budget allocation; and means for identifying items to be purchased and evaluating the impact of those expenditures on the budget in real time. This enables users to effectively manage their spending, suppress unnecessary expenses, and manage their assets in a planned manner.

[0506] "Benefits" is a general term for the remuneration or income that an individual receives on a regular basis.

[0507] "Expenses" refer to the money paid when purchasing goods or services.

[0508] "Storage" refers to the act of saving money in preparation for future use.

[0509] "User" refers to an individual or organization that uses this system.

[0510] An "information set" is a series of profile data created by integrating acquired data.

[0511] "Spending patterns" refer to information that shows the tendencies of how users spend their money.

[0512] "Budget allocation" refers to a plan for assigning available resources to specific uses.

[0513] "Identification of goods" is the process of identifying and classifying products and services.

[0514] "Real-time evaluation" means analyzing data and providing results immediately.

[0515] The server first acquires benefit receipt information, expenditure information, and savings information through the terminal, and integrates this data to create a user information set. When the user records daily expenditures, the server updates the information each time and uses IBM Watson to analyze expenditure patterns. Based on this analysis, a generative AI model is used to propose the optimal budget allocation.

[0516] Of particular importance is the terminal's ability to identify items intended for purchase. When a user scans an item's barcode, the server evaluates in real time how that expenditure will impact the budget and provides feedback to the terminal. This system allows users to instantly understand the impact of their spending on their budget and avoid unnecessary purchases.

[0517] A concrete example would be a user receiving a message while shopping at a supermarket stating, "Purchasing this item may cause your grocery budget to exceed your budget." This would enable more planned spending. An example of a prompt for a generative AI model would be, "Considering user A's spending patterns and this month's budget, please suggest how much they can comfortably spend on eating out this weekend."

[0518] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0519] Step 1:

[0520] Users use a terminal to input information about benefit receipts, expenses, and savings. The terminal sends this data to a server. This information is collected as basic data to understand the user's current financial situation.

[0521] Step 2:

[0522] The server creates a user information set based on the data sent from the terminal. During this process, information from multiple different data streams is combined into a single, unified profile through a data integration process. Once this profile is created, subsequent analysis becomes possible.

[0523] Step 3:

[0524] The server uses IBM Watson to analyze the user's spending patterns. This analysis step examines past spending data to reveal trends in specific categories and time periods. The analysis results are useful for the following suggestions.

[0525] Step 4:

[0526] The server uses a generative AI model to suggest the optimal budget allocation to the user. In this step, prompts are generated based on the analyzed data, allowing the AI ​​to derive an appropriate budget plan. For example, a prompt such as "I want to reduce my food expenses, but how much is appropriate?" might be created.

[0527] Step 5:

[0528] The user scans the items they intend to purchase using a terminal. The terminal sends this information to a server. The server evaluates the impact of the expenditure on the budget in real time and sends the results back to the terminal. For example, barcode data is input, the impact on the budget is calculated, and a message such as "Purchasing this item may cause you to exceed your budget" is output.

[0529] Step 6:

[0530] The device provides visual feedback to the user based on information provided by the server. For example, it may alert users to reconsider their purchases, preventing unnecessary spending. Visual feedback may include specific numerical information or graphs showing budget status.

[0531] 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.

[0532] This invention provides users with more personalized feedback and suggestions by incorporating emotion recognition capabilities into a financial management system. The system operates through the interaction of a server, a terminal, and a user.

[0533] Users access the system through their terminals and provide financial data. The server retrieves information about the user's salary, expenses, and savings, integrates it, and creates a user profile. Furthermore, it analyzes the user's spending information in detail to identify waste and areas for improvement.

[0534] The emotion engine incorporates algorithms to recognize emotions from user interactions and behavior. The server uses the emotion engine to evaluate the user's emotional state and adjusts feedback and suggestions based on the results. This process enables the delivery of more effective and convincing financial plans and advice that resonate with the user's emotional state.

[0535] For example, if the server detects that a user's emotions indicate stress, it can adjust budget allocation suggestions and provide a flexible spending plan to alleviate stress. Conversely, if positive emotions are detected, it can propose a proactive investment strategy to achieve goals.

[0536] The device displays information and feedback sent from the server to the user in real time, communicating in an easy-to-understand format. Furthermore, customized educational content is adjusted based on emotions, supporting learning tailored to the user's knowledge and interests.

[0537] Thus, the system of the present invention collects and analyzes information from both the financial and emotional aspects of the user, and provides optimal support. As a result, users can improve their financial management skills and build wealth effectively while reducing stress.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The user operates the device and logs into the application. The device sends the user's authentication information to the server.

[0541] Step 2:

[0542] The server authenticates the user using the transmitted authentication information. If authentication is successful, it retrieves data about the user's salary, expenses, and savings.

[0543] Step 3:

[0544] The server creates a user profile based on the acquired data. This profile integrates income, expense details, and savings status.

[0545] Step 4:

[0546] The server analyzes user spending data to identify wasteful spending patterns. This analysis includes a process of reviewing past spending patterns and identifying inefficient spending items.

[0547] Step 5:

[0548] A server equipped with an emotion engine analyzes the user's emotional state based on their conversation history and input. This analysis helps determine whether the user is feeling stressed or in a positive mood.

[0549] Step 6:

[0550] The server adjusts budget allocation and advice based on the evaluation results of the emotion engine. For example, if a user is experiencing stress, it will suggest increasing flexibility in their spending plan.

[0551] Step 7:

[0552] The server generates tailored advice and budget allocations as natural language reports. These reports are easy for users to understand and provide guidance for deciding on their next course of action.

[0553] Step 8:

[0554] The device displays generated reports and sentiment-based feedback to the user, which then uses this information to make financial decisions.

[0555] Step 9:

[0556] The server creates educational content optimized for the user's knowledge level and emotional state, and delivers it to the user through their device. This content helps to facilitate user learning and deepen their understanding.

[0557] (Example 2)

[0558] 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."

[0559] Traditional financial management systems provide general advice and suggestions based on users' financial data, but they do not take into account feedback optimized for individual psychological states and emotions. As a result, users may find it difficult to accept suggestions readily or may experience stress. Therefore, there is a need to provide personalized advice and learning support that takes into account not only the user's financial situation but also their emotional state.

[0560] 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.

[0561] In this invention, the server includes means for creating a user profile, means for recognizing the user's emotional state, and means for adjusting feedback based on the emotional state. This enables the provision of effective financial plans and educational suggestions that are in tune with the user's emotions.

[0562] "Salary receipt information" refers to detailed data about the salary a user receives from their employer, including the amount, payment date, and payment frequency.

[0563] "Spending information" refers to data about the amount of money a user has spent on purchasing goods and services, and includes purchase history and spending categories.

[0564] "Savings information" refers to data about funds that users have saved for the future, including savings account balances and savings plans.

[0565] A "user profile" refers to an individual profile generated by integrating financial and emotional data about a user, reflecting the user's overall financial and emotional state.

[0566] "Optimal budget allocation" refers to the most effective and efficient way to allocate funds based on the user's income and expenses, supporting the achievement of financial goals.

[0567] "Emotional state" refers to the user's psychological or emotional condition, and is evaluated based on past actions and current input.

[0568] "Feedback" refers to the advice and suggestions that a system provides to the user, which are adjusted based on financial management and sentiment recognition.

[0569] "Educational content" refers to information and learning materials aimed at improving the user's knowledge and skills, and is customized according to the user's level and interests.

[0570] This invention incorporates emotion recognition capabilities into a financial management system, providing users with personalized feedback and suggestions. The system is realized through the interaction of a server, a terminal, and a user.

[0571] Users access the system using a terminal and enter financial information such as salary, expenses, and savings. The terminal sends this data to the server. The server registers the received information in a database and generates a unified user profile for each user. This profile reflects the user's overall financial situation.

[0572] The server uses an emotion engine to recognize the user's emotional state from past user interaction data and real-time input data. For example, if the user's input suddenly decreases or positive content declines, the server can determine that the user is experiencing stress. Based on this, the server can generate feedback tailored to the emotional state and adjust individual financial advice for the user.

[0573] For example, if the user's emotions indicate stress, the server will offer a plan to flexibly adjust spending items. Conversely, if the user's emotions are positive, it will suggest a proactive investment strategy. This feedback is presented to the user in real time via the terminal and displayed in a visually easy-to-understand format.

[0574] An example of a prompt might be, "Analyze the biggest wasteful spending category in your spending this month, and then suggest ways to improve it."

[0575] In this way, this system comprehensively analyzes users' financial and emotional data, and by providing more appropriate financial management and emotionally responsive support, it can improve users' financial management capabilities.

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] The user uses a terminal to input financial data regarding salary, expenses, and savings. The terminal receives this data and prepares to send it to the server. The input here is raw data about the user's financial situation, and the output is a digital file containing that data. The terminal sends this file to the server.

[0579] Step 2:

[0580] The server receives financial data sent from the terminal and stores it in a database. The server uses this data to generate individual user profiles. The input is raw financial data, stored in the database. The output is a user-specific profile, used for subsequent analysis. The server then proceeds to the next step based on this profile.

[0581] Step 3:

[0582] The server uses an emotion engine to analyze the user's emotional state from user interactions and input data. Specifically, it analyzes patterns and rate changes in the input data to infer emotions. Inputs include stored profile data and real-time user data. The output is the estimated user emotional state, which is used for feedback adjustments.

[0583] Step 4:

[0584] The server uses a generative AI model to generate feedback tailored to the user profile and emotional state. The inputs are profile data and emotional state. The output is personalized feedback and advice, including appropriate financial management and educational content. The generated feedback is then sent to the terminal.

[0585] Step 5:

[0586] The terminal presents the user with feedback received from the server. The feedback is displayed in a visually easy-to-understand format, such as text or graphs. The input is feedback data from the server, and the output is the information displayed on the user's screen. The user reviews and understands this information.

[0587] Through this series of steps, the system becomes capable of providing personalized support based on the user's financial situation and emotions.

[0588] (Application Example 2)

[0589] 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."

[0590] Existing financial management systems have been unable to provide personalized feedback and suggestions that take into account the user's emotional state, making it difficult to offer flexible advice that addresses diverse emotional needs. Therefore, there is a need for support that comprehensively considers both the user's emotions and their financial situation.

[0591] 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.

[0592] In this invention, the server includes means for acquiring salary receipt information, expenditure information, and savings information; means for integrating the acquired information to create a user profile; means for analyzing expenditure patterns and proposing an optimal budget allocation; means for automatically allocating the remaining amount after deducting fixed expenses and budget from the salary to savings or investments; means for generating and providing financial status data as a report in natural language; means for generating answers to user questions and providing educational content; and means for recognizing the user's emotional state and adjusting financial advice based on that state. This enables optimal support that simultaneously considers the user's emotions and financial situation.

[0593] "Salary receipt information" refers to detailed information about a user's income, including data on salary, bonuses, and other sources of income.

[0594] "Expense information" refers to data on all expenses incurred by the user in their daily life, including details such as food expenses, transportation expenses, and entertainment expenses.

[0595] "Savings information" refers to data related to a user's asset building, including information such as deposit balances and investment amounts in investment products.

[0596] A "user profile" is an integrated dataset representing a user's financial situation and spending patterns, used to gain a comprehensive understanding of an individual's economic status.

[0597] "Spending patterns" refer to a set of data that represents the user's consumption behavior trends, and include the results of analyzing past purchase history and daily spending trends.

[0598] "Budget allocation" refers to the method of efficiently distributing available funds across various expenditure items, and serves as a guideline for managing consumption in a planned manner.

[0599] A "report in natural language" is a report that provides specialized financial data in a format that is easy for the general public to understand, using conversational expressions to convey information in a way that is close to human language.

[0600] "Educational content" refers to a collection of learning resources provided to users to improve their financial knowledge, including textbooks, tutorials, and advice.

[0601] "Emotional state" refers to the user's psychological state at a given moment, and is evaluated based on psychological indicators such as joy, sadness, and stress.

[0602] "Financial advice" refers to a set of recommendations provided to support users in improving their financial situation and achieving their goals, including spending control, increasing savings, and investment strategies.

[0603] The system for realizing this invention is provided as an application accessible from the user's smartphone or personal computer. The user inputs information about income, expenses, and savings through the device. This information is transmitted to a cloud server using a secure transmission protocol.

[0604] The server first retrieves the user's income, expenditure, and savings information, and integrates this data to create a user profile. This profile includes the user's past spending history, savings status, and financial goals. The server analyzes this information through data processing using the Pandas library to identify the user's spending patterns. Based on the analysis of these spending patterns, it provides a means to suggest an optimal budget allocation.

[0605] Furthermore, the server analyzes the user's emotions using natural language processing libraries such as NLTK and the Google Cloud Natural Language API. It analyzes the voice and text data entered by the user and evaluates their current emotional state. Based on this emotional state, it utilizes a generative AI model (e.g., GPT-3) to generate personalized financial advice in real time. If the emotion is positive, it can suggest proactive savings and investments; if it is negative, it can suggest a flexible spending plan.

[0606] One particularly interesting example is a feature that advises users to reconsider their spending when they are considering an expensive purchase right after payday, linking it to their emotional state that day. For example, it might send a message like, "You're happy your income increased today! However, we recommend you reconsider your big purchase and think about whether or not to buy it again in a few days."

[0607] An example of a prompt would be, "Based on the user's income and spending data, what savings strategy should be suggested if their current emotional state is positive?" This prompt is then used by the generative AI model to generate optimal advice.

[0608] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0609] Step 1:

[0610] The user enters their financial information using a terminal. This information includes salary, expense information, and savings information. This data is sent to the server using end-to-end encrypted communication. Based on the user's financial information as input, the server obtains basic data to form a user profile.

[0611] Step 2:

[0612] The server integrates the received financial information and creates a user profile. This involves processing the data using the Python Pandas library to integrate information about the user's income, expenses, and savings. The output is a user profile that provides an overall picture.

[0613] Step 3:

[0614] The server analyzes spending patterns based on this user profile data. This data analysis includes exploring average spending amounts and seasonal fluctuations for each spending category. This identifies wasteful spending and spending trends that need improvement, providing the basis for subsequent budget allocation suggestions.

[0615] Step 4:

[0616] The server proposes an optimal budget allocation based on the analyzed spending patterns. This involves financial recommendations that consider the balance between fixed costs, variable costs, and savings targets. The output of this process is the budget allocation proposal presented to the user.

[0617] Step 5:

[0618] The server analyzes the user's emotional state using text or voice data. It uses Google Cloud Natural Language API and NLTK to perform data calculations for sentiment analysis. The resulting sentiment analysis is output as data representing the user's current psychological state.

[0619] Step 6:

[0620] Based on the user's emotional state, a generative AI model is used to generate customized financial advice. The server receives the emotional analysis results and financial data from the user profile as prompts, and obtains specific advice as output from the model.

[0621] Step 7:

[0622] The server sends the generated financial advice to the terminal and presents it to the user. The terminal provides real-time feedback to the user and offers further specific guidelines for the next steps. As output, the user is provided with emotionally sensitive financial advice.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] [Fourth Embodiment]

[0627] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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".

[0640] This invention relates to a financial management system, the embodiment of which mainly consists of the interaction between a server, a terminal, and a user. To effectively implement this invention, the following system configuration and program flow are necessary.

[0641] This system operates based on communication between the server, terminal, and user. First, the user logs in through the terminal and provides information about their salary, expenses, and savings to the server. Based on this information, the server creates a financial profile for each user. This profile creation allows for an understanding of the user's income and expenses, enabling further analysis.

[0642] The server analyzes the collected information using IBM Watson for Financial Services to understand the user's spending patterns. Based on the analysis results, it uses OpenAI GPT-4 to propose the optimal budget allocation for the user. This process helps users manage their assets efficiently.

[0643] For example, the server analyzes that "User A" spends 30,000 yen on entertainment and 20,000 yen on food, and then presents an optimized budget arrangement for these expenses. It also provides a function to automatically allocate the remaining amount after deducting necessary expenses from the user's salary to savings or investments. This automatic allocation allows the user to systematically increase their assets.

[0644] Furthermore, the server converts complex numerical data into easy-to-understand reports in natural language. These reports provide users with an overview of their financial situation and aid in understanding. It also has a function to provide real-time answers to user inquiries through the terminal. In this process, educational content can be customized and delivered according to the user's knowledge level.

[0645] For example, if a user asks about "alternative savings methods," the server will provide advice tailored to their individual circumstances based on the latest financial data. This could include information on low-risk, fee-free savings methods.

[0646] The above describes an embodiment of the present invention, which functions as a system that provides users with efficient financial management and educational support.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] The user operates the device and logs into the application. The device sends user authentication information to the server.

[0650] Step 2:

[0651] The server uses the transmitted authentication information to authenticate the user. If authentication is successful, the server retrieves data about the user's salary, expenses, and savings.

[0652] Step 3:

[0653] The server uses the acquired data to create a financial profile for each user. This profile includes amounts for each category of income and expenditure, as well as savings status.

[0654] Step 4:

[0655] The server uses IBM Watson for Financial Services to analyze the user's spending patterns and identify areas for improvement in their spending, as well as frequently used categories.

[0656] Step 5:

[0657] Based on the analysis results, the server uses OpenAI GPT-4 to propose the optimal budget allocation to the user. Specifically, it presents a plan outlining ways to save money on necessary expenditure items and the optimal spending amounts for each category.

[0658] Step 6:

[0659] The server calculates the remaining amount after subtracting fixed expenses and the proposed budget from the user's salary. It then automatically allocates part or all of this remaining amount to savings or investments.

[0660] Step 7:

[0661] The server converts the user's financial data into a natural language report. The report includes the user's current financial situation, future spending plans, savings, and investment status.

[0662] Step 8:

[0663] When a user enters a question through their device, the server uses OpenAI GPT-4 to generate an answer to the question. The answer is provided in language that is easy for the user to understand.

[0664] Step 9:

[0665] The server generates customized educational content based on the user's knowledge level and sends it to the device. The device displays the received content to the user, supporting continuous learning.

[0666] (Example 1)

[0667] 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".

[0668] In today's lifestyle, proper financial management is becoming increasingly important for individuals. However, many individuals do not have an accurate grasp of their own spending and savings, and are unable to manage their assets efficiently. Therefore, there is a need for a system that effectively manages income and expenses, prevents wasteful spending, and enables optimal budget allocation.

[0669] 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.

[0670] In this invention, the server includes means for acquiring reward information, consumption information, and storage information; means for integrating the acquired information to create a user profile; and means for analyzing consumption patterns and proposing an optimal resource allocation. This enables individuals to accurately understand their financial situation and achieve efficient asset management and planned savings.

[0671] "Reward information" refers to information about the monetary rewards an individual receives for their work or transactions.

[0672] "Consumer information" refers to spending information about goods and services that individuals purchase on a daily basis.

[0673] "Accumulated information" refers to information about an individual's accumulated assets and savings.

[0674] A "user profile" is data that shows an individual's financial situation, created by integrating financial information such as an individual's income, expenses, and savings.

[0675] "Consumption patterns" refer to analytical information about the tendencies and behaviors of how individuals spend their money.

[0676] "Resource allocation" refers to proposals and plans regarding the appropriate allocation of users' income and assets.

[0677] "Wasteful spending" refers to irrational spending patterns or unnecessary consumption behaviors.

[0678] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and makes predictions and suggestions for specific tasks.

[0679] "Real-time" refers to the immediate response and processing of user actions and inquiries.

[0680] The financial management system based on this invention is implemented through the interaction of three parties: the user, the terminal, and the server. The user first logs into the system using the terminal and inputs their reward information, spending information, and accumulated information. This information is securely transmitted to the server via the terminal.

[0681] The server collects the received information and generates user profiles. This profile creation involves data integration and organization to provide an accurate understanding of an individual's financial situation. A data analytics platform is used for data analysis, enabling the identification of consumption patterns and analysis of wasteful spending tendencies.

[0682] Using a generative AI model, the server performs a process of proposing customized resource allocations for each user. This proposal includes the automatic accumulation or allocation of remaining funds from earnings, taking into account fixed expenses and budgets. This allows users to manage their assets efficiently.

[0683] Furthermore, the server generates a report on the financial situation in natural language and provides it to the user via the terminal. This report provides a foundation for users to easily understand their financial situation and make appropriate decisions. In addition, when a user makes a query, the server can generate a response in real time and provide educational content as needed. This content is customized according to the user's skill level.

[0684] For example, if a user asks about "alternative savings methods," the server can suggest low-risk savings methods based on the latest financial information. An example of a prompt might be, "If my monthly salary is 300,000 yen, what is the optimal spending ratio?"

[0685] In this way, this system integrates a set of functions to support users' financial management, providing a very convenient platform for users.

[0686] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0687] Step 1:

[0688] Users log in to the system using their device. The information entered is a user ID and password, and user authentication is performed based on this information. If authentication is successful, the user can access their personal dashboard.

[0689] Step 2:

[0690] Users input reward information, spending information, and accumulated information from their devices. This information is processed as data showing the user's income, expenses, and savings status and sent to the server. Data transfer is performed securely using encryption protocols.

[0691] Step 3:

[0692] The server generates a user profile based on the received information. This involves integrating and organizing each data item, ultimately outputting a profile that accurately represents the user's financial situation. This process also involves compiling information in the database.

[0693] Step 4:

[0694] The server utilizes a generative AI model to analyze profile data and identify consumption patterns and wasteful spending tendencies. The input data consists of accumulated consumption information, and the output information includes the characteristics of the analyzed user's consumption behavior and recommended areas for improvement.

[0695] Step 5:

[0696] Based on the analysis results, the server proposes an optimal resource allocation tailored to the identified consumption patterns. This proposal generation process includes setting spending limits and savings targets for each item. The output is a customized budget allocation plan for each user.

[0697] Step 6:

[0698] The server generates a report that expresses the overall financial situation in natural language and provides it to the user via a terminal. User profiles and analysis results are used as input, and the output is a report in a viewable format. This report also includes a summary of overall spending.

[0699] Step 7:

[0700] The server generates answers to user inquiries and provides educational content. In this process, a generative AI model analyzes user questions and prompts. The output includes educational content tailored to the user's knowledge level and specific answers.

[0701] Step 8:

[0702] The server provides real-time responses and delivers information to the user through the terminal. Here, immediate responses are provided to each inquiry, helping users resolve their problems. The output includes detailed advice and suggestions for the next steps, tailored to the situation.

[0703] (Application Example 1)

[0704] 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".

[0705] In modern society, many individuals face difficulties in financial management. Understanding the impact of daily expenses on their budget and systematically building wealth while minimizing unnecessary spending are crucial, but achieving these goals efficiently is not easy. This invention aims to improve the efficiency of financial management by supporting users in real-time expense management and appropriate budget allocation.

[0706] 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.

[0707] In this invention, the server includes means for acquiring benefit receipt information, expenditure information, and storage information; means for integrating the acquired information to create a user information set; means for analyzing expenditure patterns and proposing an optimal budget allocation; and means for identifying items to be purchased and evaluating the impact of those expenditures on the budget in real time. This enables users to effectively manage their spending, suppress unnecessary expenses, and manage their assets in a planned manner.

[0708] "Benefits" is a general term for the remuneration or income that an individual receives on a regular basis.

[0709] "Expenses" refer to the money paid when purchasing goods or services.

[0710] "Storage" refers to the act of saving money in preparation for future use.

[0711] "User" refers to an individual or organization that uses this system.

[0712] An "information set" is a series of profile data created by integrating acquired data.

[0713] "Spending patterns" refer to information that shows the tendencies of how users spend their money.

[0714] "Budget allocation" refers to a plan for assigning available resources to specific uses.

[0715] "Identification of goods" is the process of identifying and classifying products and services.

[0716] "Real-time evaluation" means analyzing data and providing results immediately.

[0717] The server first acquires benefit receipt information, expenditure information, and savings information through the terminal, and integrates this data to create a user information set. When the user records daily expenditures, the server updates the information each time and uses IBM Watson to analyze expenditure patterns. Based on this analysis, a generative AI model is used to propose the optimal budget allocation.

[0718] Of particular importance is the terminal's ability to identify items intended for purchase. When a user scans an item's barcode, the server evaluates in real time how that expenditure will impact the budget and provides feedback to the terminal. This system allows users to instantly understand the impact of their spending on their budget and avoid unnecessary purchases.

[0719] A concrete example would be a user receiving a message while shopping at a supermarket stating, "Purchasing this item may cause your grocery budget to exceed your budget." This would enable more planned spending. An example of a prompt for a generative AI model would be, "Considering user A's spending patterns and this month's budget, please suggest how much they can comfortably spend on eating out this weekend."

[0720] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0721] Step 1:

[0722] Users use a terminal to input information about benefit receipts, expenses, and savings. The terminal sends this data to a server. This information is collected as basic data to understand the user's current financial situation.

[0723] Step 2:

[0724] The server creates a user information set based on the data sent from the terminal. During this process, information from multiple different data streams is combined into a single, unified profile through a data integration process. Once this profile is created, subsequent analysis becomes possible.

[0725] Step 3:

[0726] The server uses IBM Watson to analyze the user's spending patterns. This analysis step examines past spending data to reveal trends in specific categories and time periods. The analysis results are useful for the following suggestions.

[0727] Step 4:

[0728] The server uses a generative AI model to suggest the optimal budget allocation to the user. In this step, prompts are generated based on the analyzed data, allowing the AI ​​to derive an appropriate budget plan. For example, a prompt such as "I want to reduce my food expenses, but how much is appropriate?" might be created.

[0729] Step 5:

[0730] The user scans the items they intend to purchase using a terminal. The terminal sends this information to a server. The server evaluates the impact of the expenditure on the budget in real time and sends the results back to the terminal. For example, barcode data is input, the impact on the budget is calculated, and a message such as "Purchasing this item may cause you to exceed your budget" is output.

[0731] Step 6:

[0732] The device provides visual feedback to the user based on information provided by the server. For example, it may alert users to reconsider their purchases, preventing unnecessary spending. Visual feedback may include specific numerical information or graphs showing budget status.

[0733] 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.

[0734] This invention provides users with more personalized feedback and suggestions by incorporating emotion recognition capabilities into a financial management system. The system operates through the interaction of a server, a terminal, and a user.

[0735] Users access the system through their terminals and provide financial data. The server retrieves information about the user's salary, expenses, and savings, integrates it, and creates a user profile. Furthermore, it analyzes the user's spending information in detail to identify waste and areas for improvement.

[0736] The emotion engine incorporates algorithms to recognize emotions from user interactions and behavior. The server uses the emotion engine to evaluate the user's emotional state and adjusts feedback and suggestions based on the results. This process enables the delivery of more effective and convincing financial plans and advice that resonate with the user's emotional state.

[0737] For example, if the server detects that a user's emotions indicate stress, it can adjust budget allocation suggestions and provide a flexible spending plan to alleviate stress. Conversely, if positive emotions are detected, it can propose a proactive investment strategy to achieve goals.

[0738] The device displays information and feedback sent from the server to the user in real time, communicating in an easy-to-understand format. Furthermore, customized educational content is adjusted based on emotions, supporting learning tailored to the user's knowledge and interests.

[0739] Thus, the system of the present invention collects and analyzes information from both the financial and emotional aspects of the user, and provides optimal support. As a result, users can improve their financial management skills and build wealth effectively while reducing stress.

[0740] The following describes the processing flow.

[0741] Step 1:

[0742] The user operates the device and logs into the application. The device sends the user's authentication information to the server.

[0743] Step 2:

[0744] The server authenticates the user using the transmitted authentication information. If authentication is successful, it retrieves data about the user's salary, expenses, and savings.

[0745] Step 3:

[0746] The server creates a user profile based on the acquired data. This profile integrates income, expense details, and savings status.

[0747] Step 4:

[0748] The server analyzes user spending data to identify wasteful spending patterns. This analysis includes a process of reviewing past spending patterns and identifying inefficient spending items.

[0749] Step 5:

[0750] A server equipped with an emotion engine analyzes the user's emotional state based on their conversation history and input. This analysis helps determine whether the user is feeling stressed or in a positive mood.

[0751] Step 6:

[0752] The server adjusts budget allocation and advice based on the evaluation results of the emotion engine. For example, if a user is experiencing stress, it will suggest increasing flexibility in their spending plan.

[0753] Step 7:

[0754] The server generates tailored advice and budget allocations as natural language reports. These reports are easy for users to understand and provide guidance for deciding on their next course of action.

[0755] Step 8:

[0756] The device displays generated reports and sentiment-based feedback to the user, which then uses this information to make financial decisions.

[0757] Step 9:

[0758] The server creates educational content optimized for the user's knowledge level and emotional state, and delivers it to the user through their device. This content helps to facilitate user learning and deepen their understanding.

[0759] (Example 2)

[0760] 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".

[0761] Traditional financial management systems provide general advice and suggestions based on users' financial data, but they do not take into account feedback optimized for individual psychological states and emotions. As a result, users may find it difficult to accept suggestions readily or may experience stress. Therefore, there is a need to provide personalized advice and learning support that takes into account not only the user's financial situation but also their emotional state.

[0762] 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.

[0763] In this invention, the server includes means for creating a user profile, means for recognizing the user's emotional state, and means for adjusting feedback based on the emotional state. This enables the provision of effective financial plans and educational suggestions that are in tune with the user's emotions.

[0764] "Salary receipt information" refers to detailed data about the salary a user receives from their employer, including the amount, payment date, and payment frequency.

[0765] "Spending information" refers to data about the amount of money a user has spent on purchasing goods and services, and includes purchase history and spending categories.

[0766] "Savings information" refers to data about funds that users have saved for the future, including savings account balances and savings plans.

[0767] A "user profile" refers to an individual profile generated by integrating financial and emotional data about a user, reflecting the user's overall financial and emotional state.

[0768] "Optimal budget allocation" refers to the most effective and efficient way to allocate funds based on the user's income and expenses, supporting the achievement of financial goals.

[0769] "Emotional state" refers to the user's psychological or emotional condition, and is evaluated based on past actions and current input.

[0770] "Feedback" refers to the advice and suggestions that a system provides to the user, which are adjusted based on financial management and sentiment recognition.

[0771] "Educational content" refers to information and learning materials aimed at improving the user's knowledge and skills, and is customized according to the user's level and interests.

[0772] This invention incorporates emotion recognition capabilities into a financial management system, providing users with personalized feedback and suggestions. The system is realized through the interaction of a server, a terminal, and a user.

[0773] Users access the system using a terminal and enter financial information such as salary, expenses, and savings. The terminal sends this data to the server. The server registers the received information in a database and generates a unified user profile for each user. This profile reflects the user's overall financial situation.

[0774] The server uses an emotion engine to recognize the user's emotional state from past user interaction data and real-time input data. For example, if the user's input suddenly decreases or positive content declines, the server can determine that the user is experiencing stress. Based on this, the server can generate feedback tailored to the emotional state and adjust individual financial advice for the user.

[0775] For example, if the user's emotions indicate stress, the server will offer a plan to flexibly adjust spending items. Conversely, if the user's emotions are positive, it will suggest a proactive investment strategy. This feedback is presented to the user in real time via the terminal and displayed in a visually easy-to-understand format.

[0776] An example of a prompt might be, "Analyze the biggest wasteful spending category in your spending this month, and then suggest ways to improve it."

[0777] In this way, this system comprehensively analyzes users' financial and emotional data, and by providing more appropriate financial management and emotionally responsive support, it can improve users' financial management capabilities.

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] The user uses a terminal to input financial data regarding salary, expenses, and savings. The terminal receives this data and prepares to send it to the server. The input here is raw data about the user's financial situation, and the output is a digital file containing that data. The terminal sends this file to the server.

[0781] Step 2:

[0782] The server receives financial data sent from the terminal and stores it in a database. The server uses this data to generate individual user profiles. The input is raw financial data, stored in the database. The output is a user-specific profile, used for subsequent analysis. The server then proceeds to the next step based on this profile.

[0783] Step 3:

[0784] The server uses an emotion engine to analyze the user's emotional state from user interactions and input data. Specifically, it analyzes patterns and rate changes in the input data to infer emotions. Inputs include stored profile data and real-time user data. The output is the estimated user emotional state, which is used for feedback adjustments.

[0785] Step 4:

[0786] The server uses a generative AI model to generate feedback tailored to the user profile and emotional state. The inputs are profile data and emotional state. The output is personalized feedback and advice, including appropriate financial management and educational content. The generated feedback is then sent to the terminal.

[0787] Step 5:

[0788] The terminal presents the user with feedback received from the server. The feedback is displayed in a visually easy-to-understand format, such as text or graphs. The input is feedback data from the server, and the output is the information displayed on the user's screen. The user reviews and understands this information.

[0789] Through this series of steps, the system becomes capable of providing personalized support based on the user's financial situation and emotions.

[0790] (Application Example 2)

[0791] 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".

[0792] Existing financial management systems have been unable to provide personalized feedback and suggestions that take into account the user's emotional state, making it difficult to offer flexible advice that addresses diverse emotional needs. Therefore, there is a need for support that comprehensively considers both the user's emotions and their financial situation.

[0793] 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.

[0794] In this invention, the server includes means for acquiring salary receipt information, expenditure information, and savings information; means for integrating the acquired information to create a user profile; means for analyzing expenditure patterns and proposing an optimal budget allocation; means for automatically allocating the remaining amount after deducting fixed expenses and budget from the salary to savings or investments; means for generating and providing financial status data as a report in natural language; means for generating answers to user questions and providing educational content; and means for recognizing the user's emotional state and adjusting financial advice based on that state. This enables optimal support that simultaneously considers the user's emotions and financial situation.

[0795] "Salary receipt information" refers to detailed information about a user's income, including data on salary, bonuses, and other sources of income.

[0796] "Expense information" refers to data on all expenses incurred by the user in their daily life, including details such as food expenses, transportation expenses, and entertainment expenses.

[0797] "Savings information" refers to data related to a user's asset building, including information such as deposit balances and investment amounts in investment products.

[0798] A "user profile" is an integrated dataset representing a user's financial situation and spending patterns, used to gain a comprehensive understanding of an individual's economic status.

[0799] "Spending patterns" refer to a set of data that represents the user's consumption behavior trends, and include the results of analyzing past purchase history and daily spending trends.

[0800] "Budget allocation" refers to the method of efficiently distributing available funds across various expenditure items, and serves as a guideline for managing consumption in a planned manner.

[0801] A "report in natural language" is a report that provides specialized financial data in a format that is easy for the general public to understand, using conversational expressions to convey information in a way that is close to human language.

[0802] "Educational content" refers to a collection of learning resources provided to users to improve their financial knowledge, including textbooks, tutorials, and advice.

[0803] "Emotional state" refers to the user's psychological state at a given moment, and is evaluated based on psychological indicators such as joy, sadness, and stress.

[0804] "Financial advice" refers to a set of recommendations provided to support users in improving their financial situation and achieving their goals, including spending control, increasing savings, and investment strategies.

[0805] The system for realizing this invention is provided as an application accessible from the user's smartphone or personal computer. The user inputs information about income, expenses, and savings through the device. This information is transmitted to a cloud server using a secure transmission protocol.

[0806] The server first retrieves the user's income, expenditure, and savings information, and integrates this data to create a user profile. This profile includes the user's past spending history, savings status, and financial goals. The server analyzes this information through data processing using the Pandas library to identify the user's spending patterns. Based on the analysis of these spending patterns, it provides a means to suggest an optimal budget allocation.

[0807] Furthermore, the server analyzes the user's emotions using natural language processing libraries such as NLTK and the Google Cloud Natural Language API. It analyzes the voice and text data entered by the user and evaluates their current emotional state. Based on this emotional state, it utilizes a generative AI model (e.g., GPT-3) to generate personalized financial advice in real time. If the emotion is positive, it can suggest proactive savings and investments; if it is negative, it can suggest a flexible spending plan.

[0808] One particularly interesting example is a feature that advises users to reconsider their spending when they are considering an expensive purchase right after payday, linking it to their emotional state that day. For example, it might send a message like, "You're happy your income increased today! However, we recommend you reconsider your big purchase and think about whether or not to buy it again in a few days."

[0809] An example of a prompt would be, "Based on the user's income and spending data, what savings strategy should be suggested if their current emotional state is positive?" This prompt is then used by the generative AI model to generate optimal advice.

[0810] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0811] Step 1:

[0812] The user enters their financial information using a terminal. This information includes salary, expense information, and savings information. This data is sent to the server using end-to-end encrypted communication. Based on the user's financial information as input, the server obtains basic data to form a user profile.

[0813] Step 2:

[0814] The server integrates the received financial information and creates a user profile. This involves processing the data using the Python Pandas library to integrate information about the user's income, expenses, and savings. The output is a user profile that provides an overall picture.

[0815] Step 3:

[0816] The server analyzes spending patterns based on this user profile data. This data analysis includes exploring average spending amounts and seasonal fluctuations for each spending category. This identifies wasteful spending and spending trends that need improvement, providing the basis for subsequent budget allocation suggestions.

[0817] Step 4:

[0818] The server proposes an optimal budget allocation based on the analyzed spending patterns. This involves financial recommendations that consider the balance between fixed costs, variable costs, and savings targets. The output of this process is the budget allocation proposal presented to the user.

[0819] Step 5:

[0820] The server analyzes the user's emotional state using text or voice data. It uses Google Cloud Natural Language API and NLTK to perform data calculations for sentiment analysis. The resulting sentiment analysis is output as data representing the user's current psychological state.

[0821] Step 6:

[0822] Based on the user's emotional state, a generative AI model is used to generate customized financial advice. The server receives the emotional analysis results and financial data from the user profile as prompts, and obtains specific advice as output from the model.

[0823] Step 7:

[0824] The server sends the generated financial advice to the terminal and presents it to the user. The terminal provides real-time feedback to the user and offers further specific guidelines for the next steps. As output, the user is provided with emotionally sensitive financial advice.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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."

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] The following is further disclosed regarding the embodiments described above.

[0847] (Claim 1)

[0848] Means for obtaining information on salary receipts, expenses, and savings,

[0849] A means of integrating the acquired information to create a user profile,

[0850] A means of analyzing spending patterns and proposing the optimal budget allocation,

[0851] A means of automatically allocating the remaining amount after deducting fixed expenses and budget from the aforementioned salary to savings or investments,

[0852] A means of generating and providing financial data as a report in natural language,

[0853] A means of generating answers to user questions and providing educational content,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, characterized by having means for customizing and providing educational content according to the user's knowledge level.

[0857] (Claim 3)

[0858] The system according to claim 1, characterized by comprising means for analyzing user spending information to identify tendencies for wasteful spending.

[0859] "Example 1"

[0860] (Claim 1)

[0861] Means for acquiring reward information, consumption information, and stored information,

[0862] A means of integrating the acquired information to create a user profile,

[0863] A means of analyzing consumption patterns and proposing the optimal resource allocation,

[0864] A means of automatically accumulating or allocating to capital investment the remaining amount after deducting fixed expenses and resources from the aforementioned compensation,

[0865] A means of generating and providing financial status information as a report in natural language,

[0866] A means of generating answers to user inquiries and providing educational content,

[0867] A means of analyzing user information to identify wasteful spending patterns,

[0868] A means of customizing and providing educational content according to the skill level of the users,

[0869] A means of providing a response in real time via a communication device,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, characterized by including means for optimizing budget allocation based on the user's spending trends.

[0873] (Claim 3)

[0874] The system according to claim 1, characterized in that it includes means for providing predictive information using an AI model that generates information based on collected information.

[0875] "Application Example 1"

[0876] (Claim 1)

[0877] Means for obtaining information on benefit receipts, expenditures, and savings,

[0878] A means of integrating acquired information to create a user information set,

[0879] A method for analyzing spending patterns and proposing the optimal budget allocation,

[0880] A means of automatically allocating the remaining balance, after deducting certain expenses and budget from the aforementioned benefits, to storage or financial resources,

[0881] A means of generating and providing financial data as a report in natural language,

[0882] A means of generating answers to user questions and providing educational content,

[0883] A means to identify items to be purchased and to assess the impact of those expenses on the budget in real time,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, characterized by having means for customizing and providing educational content according to the user's knowledge level.

[0887] (Claim 3)

[0888] The system according to claim 1, characterized by comprising means for analyzing user spending information to identify trends in unnecessary spending.

[0889] "Example 2 of combining an emotion engine"

[0890] (Claim 1)

[0891] Means for obtaining information on salary receipts, expenses, and savings,

[0892] A means of integrating the acquired information to create a user profile,

[0893] A means of analyzing spending patterns and proposing the optimal budget allocation,

[0894] A means of automatically allocating the remaining amount after deducting fixed expenses and budget from the aforementioned salary to savings or investments,

[0895] A means of generating and providing financial data as a report in natural language,

[0896] A means of generating answers to user questions and providing educational content,

[0897] A means of recognizing the user's emotional state,

[0898] A means of adjusting feedback based on the user's emotional state,

[0899] A means of analyzing user responses to understand their emotions and incorporating that into future suggestions,

[0900] A means of providing real-time feedback using a device,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, characterized by having means for customizing and providing educational content according to the user's knowledge level.

[0904] (Claim 3)

[0905] The system according to claim 1, characterized by comprising means for analyzing user spending information to identify tendencies for wasteful spending.

[0906] "Application example 2 when combining with an emotional engine"

[0907] (Claim 1)

[0908] Means for obtaining information on salary receipts, expenses, and savings,

[0909] A means of integrating the acquired information to create a user profile,

[0910] A means of analyzing spending patterns and proposing the optimal budget allocation,

[0911] A means of automatically allocating the remaining amount after deducting fixed expenses and budget from the aforementioned salary to savings or investments,

[0912] A means of generating and providing financial data as a report in natural language,

[0913] A means of generating answers to user questions and providing educational content,

[0914] A means of recognizing the user's emotional state and adjusting financial advice based on that state,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, characterized by having means for customizing and providing educational content according to the user's knowledge level.

[0918] (Claim 3)

[0919] The system according to claim 1, characterized by comprising means for analyzing a user's spending information to identify tendencies for wasteful spending, and means for proposing a flexible spending plan based on their emotional state. [Explanation of Symbols]

[0920] 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. Means for obtaining information on salary receipts, expenses, and savings, A means of integrating the acquired information to create a user profile, A means of analyzing spending patterns and proposing the optimal budget allocation, A means of automatically allocating the remaining amount after deducting fixed expenses and budget from the aforementioned salary to savings or investments, A means of generating and providing financial data as a report in natural language, A means of generating answers to user questions and providing educational content, A system that includes this.

2. The system according to claim 1, characterized by comprising means for customizing and providing educational content according to the user's knowledge level.

3. The system according to claim 1, characterized by comprising means for analyzing user spending information to identify tendencies for wasteful spending.

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