system

Abstract

We provide the system. [Solution] A means of automatically collecting and organizing users' financial information, A method for analyzing spending data using machine learning algorithms and identifying spending patterns, A means of generating individual savings advice and presenting it to the user's terminal, A means of obtaining special offer information in real time and notifying users, A means of providing a user interface that displays expenditure data in a visually easy-to-understand format, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional financial management system, the function of automatically collecting and organizing a user's daily expenses is insufficient, and there are many situations where manual input work is required. For this reason, continuous management of financial information is likely to be a burden for users, and since the analysis of expense data is not intuitive, it has been difficult for users to efficiently manage themselves and formulate savings plans. Furthermore, it has been impossible to receive privilege information in real time, and it has been difficult to obtain effective information for reducing wasteful expenses.

Means for Solving the Problems

[0005] This invention provides a means for automatically collecting and organizing a user's financial information, and by applying machine learning algorithms to analyze spending patterns, it supports efficient spending management. Furthermore, by generating and presenting personalized saving advice to the user's terminal, it makes it easier for users to optimize their spending. In addition, by providing a means for acquiring and notifying users of reward information in real time, users can always obtain useful reward information, making it easier to reduce unnecessary spending. By integrating these functions and providing a visually easy-to-understand user interface, users can intuitively grasp their financial situation.

[0006] A "user" refers to an individual or organization that uses the system to manage financial information and optimize spending.

[0007] "Financial information" refers to data related to the financial activities of an individual or organization, such as banking transactions, credit card usage history, and purchase history.

[0008] "Means of automatic collection and organization" refers to functions that allow the system to acquire necessary financial information without user intervention, and to systematically store and organize it.

[0009] A "machine learning algorithm" refers to a type of computer program used to analyze user spending data and perform pattern recognition and trend analysis.

[0010] "Identifying spending patterns" refers to finding regularities and trends in a user's spending data and extracting their characteristics.

[0011] "Savings advice" refers to specific suggestions and guidance provided to help users streamline their spending and reduce waste.

[0012] A "terminal" refers to a digital device, such as a smartphone or computer, that a user uses to view and interact with information provided by a system.

[0013] "Benefit information" refers to economic incentives offered to users, such as discounts, campaigns, and point rewards.

[0014] "Real-time notification" refers to a function that allows users to be immediately informed the moment information is generated.

[0015] A "visually easy-to-understand" format refers to display methods such as graphs and charts that visualize data in a way that is easy for users to intuitively understand.

[0016] "User interface" refers to the visual and operational elements that allow a user to interact with a system. [Brief explanation of the drawing]

[0017] [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in 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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system that streamlines users' financial management and supports the optimization of their spending. In its implementation form, the system primarily consists of three elements: a server, a terminal, and a user.

[0039] Data collection and organization

[0040] The server integrates with the user's financial institution and automatically collects financial information. This process retrieves banking transactions and credit card usage history via APIs, organizing and storing it in a database. The information is always up-to-date without user intervention.

[0041] Data Analysis

[0042] The server uses machine learning algorithms to analyze the collected financial information. By identifying spending patterns and trends, it reveals which categories users are wasting money in. Based on this information, it is ready to suggest appropriate saving strategies.

[0043] Generating and providing advice

[0044] The server generates savings advice to optimize the user's spending based on the analysis results and sends it to the user's device. The device displays the advice in a visually easy-to-understand dashboard format to help the user understand it.

[0045] (Specific example) If a user is identified as spending too much on food, the server will advise them to "reduce their monthly food expenses by 10% and purchase alternatives." This advice will be visualized on the device along with a graph of the monthly breakdown.

[0046] Notification of special offers

[0047] The server organizes reward information obtained from partner companies in real time, selects information tailored to the user's purchase history and interests, and sends it to the device. The device then notifies the user of this information as a pop-up notification, prompting them to take the necessary action.

[0048] (Specific example) If a discount coupon becomes available for a supermarket the user previously visited, the system will instantly notify the user's device. The user can then immediately use it to reduce their spending.

[0049] Overall, this invention utilizes machine learning and real-time data processing technologies to enable users to intuitively manage their finances and help optimize their daily spending. This allows users to efficiently maintain and improve their financial health.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The server accesses the user's financial institution API and securely retrieves the user's transaction history through an authentication protocol. Here, it collects data including the amount, date, and category information for each transaction.

[0053] Step 2:

[0054] The server classifies the acquired transaction data and stores it in a database. The data is organized by category, making it easy to identify user spending trends in later analysis.

[0055] Step 3:

[0056] The server analyzes the stored data by applying machine learning algorithms. In this process, it identifies the user's spending patterns and extracts categories that show monthly spending fluctuations or wasteful spending.

[0057] Step 4:

[0058] The server generates personalized savings advice based on the analysis results. This advice includes suggestions for spending limits and warnings about unusual spending.

[0059] Step 5:

[0060] The terminal receives advice sent from the server and displays it to the user in a visually easy-to-understand format. Dashboards and graphs are used to ensure that the user can understand it immediately.

[0061] Step 6:

[0062] The server collects the latest information from partner companies' reward databases and selects the most relevant reward information based on the user's purchase history.

[0063] Step 7:

[0064] The device notifies the user in real time of selected reward information. The notification is presented in a pop-up format to attract the user's attention.

[0065] Step 8:

[0066] Users can check the display on their device, change settings as needed, and optimize their actions by following savings advice.

[0067] (Example 1)

[0068] 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."

[0069] In modern society, personal spending management has become increasingly complex, requiring vast amounts of financial data and intricate analysis due to diverse transactions and purchases. As a result, many users need support to ensure their financial health. However, traditional methods have relied on manual organization and analysis of financial information, which has been extremely time-consuming and laborious. Furthermore, it has been difficult to discover useful information about benefits for users or to provide specific and personalized savings suggestions.

[0070] 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.

[0071] This invention includes a server that automatically collects and stores user financial information in a structured format, a digital computing means for analyzing the collected information and highlighting spending trends, and a generative AI model for creating individual savings suggestions and visualizing and displaying them on the user's device. This enables users to efficiently manage their financial information and intuitively utilize personalized, practical savings strategies.

[0072] "Financial information" refers to data related to a user's economic activities, such as banking transactions, credit card usage history, and asset and liability information.

[0073] A "structured format" refers to a data format organized according to specific rules, where data is classified and organized in a way that facilitates information handling.

[0074] "Digital computing methods" refer to methods of processing and analyzing data using computers and related technologies, enabling efficient information analysis.

[0075] A "generative AI model" is an algorithm that uses artificial intelligence to learn from data and generate new information and suggestions, and is particularly used to generate personalized recommendations for users.

[0076] "Savings suggestions" refer to specific advice and plans to optimize a user's spending, encouraging them to reduce waste and make effective use of their assets.

[0077] "Means of visualization and display" refers to technologies that graphically convert data and information and display them in a format that users can intuitively understand.

[0078] This invention is a system for users to effectively manage their financial information and optimize their spending. The system consists of three elements: a server, a terminal, and a user.

[0079] The server automatically collects information about the user's financial institutions using their APIs. This includes banking transactions and credit card usage history. This information is stored in a structured format within a database. The software used includes database management systems and API integration modules.

[0080] Next, the server uses digital computing tools to analyze the collected information. Specifically, it uses programming languages ​​such as Python and machine learning libraries such as scikit-learn and TENSORFLOW® to reveal spending trends. This analysis identifies which categories of spending the user needs to review and uses this to create individual savings suggestions.

[0081] Using a generative AI model, the server generates optimal savings suggestions for each user. These generated suggestions are sent to the user's device. The device visualizes the suggestions and presents them to the user through a graphical user interface. The D3.js JavaScript® library is used for visualization.

[0082] For example, if a user's food expenses are determined to be excessive, the server will generate a suggestion such as "Reduce your monthly food expenses by 10% and purchase alternatives." An example of a prompt message would be, "Analyze the user's food spending data and suggest the best way to save money."

[0083] Furthermore, the server retrieves reward information from partner companies in real time, selects rewards that match the user's transaction history, and notifies them. This feature allows users to instantly see available promotions.

[0084] Thus, this invention is a system that provides functions to enable users to intuitively manage their financial information and improve their financial health.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server collects transaction information from financial institutions linked to by the user using APIs. The input is API request data, and the output is the user's financial transaction information. The server periodically executes this process to keep the financial data up-to-date. Here, we perform the specific action of retrieving transaction history from financial institutions using API keys and saving it to a local database.

[0088] Step 2:

[0089] The server stores and organizes collected transaction information in a structured format within a database. Input is raw transaction data, while output is transaction data organized by category. The server classifies transaction details into categories such as food expenses, transportation expenses, and utility expenses, making the data easier to use for subsequent analysis. During this process, it updates the database to enable efficient data retrieval.

[0090] Step 3:

[0091] The server utilizes organized data, employs digital computing tools, and performs analysis using machine learning algorithms. The input is categorized transaction data, and the output is an analysis showing spending trends. Using the Python programming language and scikit-learn, it performs specific actions to detect particular spending patterns and anomalies, thereby revealing wasteful spending by users.

[0092] Step 4:

[0093] The server uses a generative AI model to create personalized savings suggestions based on the analysis results. The input is the analysis results, and the output is a savings suggestion tailored to the user. The prompt message "Analyze the user's food expenditure data and suggest the best savings strategy" is input to the generative AI model, and the AI ​​model generates the suggestions.

[0094] Step 5:

[0095] The server sends the generated savings suggestions to the user's terminal, which displays them in a visually intuitive interface. The input is the savings suggestions, and the output is the visualized user screen. The terminal uses JavaScript's D3.js to perform the specific actions of displaying the suggestions on graphs and dashboards, helping the user understand the suggestions more easily.

[0096] Step 6:

[0097] The server retrieves reward information from partner companies in real time, selects relevant rewards based on the user's transaction history, and notifies the terminal. Inputs are reward information and user transaction history, while output is the reward information notified to the user. The server specifically selects rewards that match the user's interests and past transactions, and the terminal then displays a pop-up notification.

[0098] (Application Example 1)

[0099] 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."

[0100] Modern consumers face increasing challenges in efficiently managing their finances due to their diverse spending patterns. Identifying wasteful spending and practicing daily savings, in particular, requires significant time and effort. Furthermore, users often miss out on valuable savings opportunities because they are unable to access timely information about rewards and benefits related to the products and services they purchase. There is a need for solutions to these challenges, enabling consumers to optimize their spending while effectively utilizing reward information.

[0101] 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.

[0102] This invention includes a server that automatically collects and organizes users' financial information, a means for analyzing spending data using machine learning algorithms and identifying spending patterns, and a means for controlling an information-providing terminal that is integrated into an electronic payment system and assists in optimizing spending using financial transaction information. This makes it possible to streamline consumers' financial management and optimize spending through saving and utilizing reward information.

[0103] "User financial information" refers to information about transactions conducted by individuals or corporations using financial institutions or payment services, and specifically includes account balances, transaction details, and credit card usage history.

[0104] "Machine learning algorithms" refer to algorithms that analyze patterns based on large amounts of data to make predictions and decisions. They have the characteristic of learning from data and improving their accuracy.

[0105] "Expenditure data" refers to a collection of information related to a user's consumption activities, including individual transactions, purchased goods and services, and their amounts.

[0106] "Means for identifying spending patterns" refer to methods and techniques for analyzing consistency and trends in a user's spending behavior. This makes it possible to identify wasteful spending and suggest appropriate saving strategies.

[0107] An "electronic payment system" refers to a system that allows for the completion of payments for goods and services using electronic methods rather than cash. This includes online banking and digital wallets.

[0108] An "information provision terminal" refers to an electronic device used to present information to users, such as a smartphone, tablet, or personal computer. It serves as a means of delivering information visually.

[0109] This invention is a system designed to streamline users' financial management and support the optimization of their spending. The system mainly consists of a server, an information terminal, and the user.

[0110] The server automatically collects financial information from the user's financial institution via an API and organizes it in a database. This information, including transaction details and credit card usage history, is kept up-to-date without user intervention. The server uses machine learning algorithms to analyze the collected financial information and identify the user's spending patterns. This clarifies which categories of spending are wasteful and helps prepare appropriate saving strategies.

[0111] The information terminal presents users with personalized savings advice generated based on analysis results. This advice is displayed in a visually easy-to-understand dashboard format to aid user comprehension. Furthermore, the server retrieves and organizes reward information from partner companies and notifies users in real time of selected information based on their purchase history. Notifications on the terminal allow users to immediately take advantage of rewards and have the opportunity to reduce their spending.

[0112] This system utilizes machine learning platforms such as TensorFlow and PyTorch, and implements an intuitive user interface using React Native on information delivery devices such as smartphones. By integrating it into electronic payment systems, it is possible to optimize spending by effectively utilizing financial transaction information.

[0113] For example, if the analysis reveals that a user regularly spends money on "weekend cafes," the app might offer advice such as, "Reduce your weekend cafe spending by 20% and try recipes you can enjoy at home."

[0114] An example of a prompt to the generating AI model is: "Analyze the user's bank account transaction history, identify unnecessary spending, and generate effective savings advice."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server collects financial information from users' financial institutions via an API. Inputs include transaction details and credit card usage history obtained through the API. This data is organized in a database on the server and updated and managed on a per-user basis.

[0118] Step 2:

[0119] The server analyzes collected financial information using machine learning algorithms. The input is financial information stored in a database, and the output is a modeled result of spending patterns. Specifically, the algorithm identifies trends in the data and uses that to determine signs of wasteful spending.

[0120] Step 3:

[0121] The server generates personalized savings advice based on the analysis results. The input is the analysis results of spending patterns, and the output is a set of savings advice. Specifically, it constructs text that suggests more effective saving methods based on the user's spending behavior.

[0122] Step 4:

[0123] The server sends the generated savings advice to the user's information terminal. The terminal receives this and displays it in a visually easy-to-understand dashboard format. The input is the aforementioned savings advice, and the output is the visual display presented to the user. Specifically, the interface is designed using React Native.

[0124] Step 5:

[0125] The server retrieves reward information from partner companies and filters the information based on the user's purchase history. The input consists of reward information obtained from partner companies and the user's purchase data, while the output is a set of reward information optimized for the user. To notify users of the filtered reward information in real time, the server filters the input purchase data and reward information, extracting only the information relevant to the user.

[0126] Step 6:

[0127] The device displays the reward information to the user as a pop-up notification. The input is the reward information sent from the server, and the output is the pop-up notification displayed on the user's device. This step allows the device to use its notification function to prompt the user to take immediate action.

[0128] 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.

[0129] This invention provides a system that supports users' financial management by incorporating an emotion engine to offer financial advice and special offers that take into account the user's emotional state. The system mainly consists of a server, terminals, and users.

[0130] Collection and organization of financial information

[0131] The server collects transaction data using the user's financial institution API. This data is organized by category and stored in a database. Each transaction includes details such as date, amount, and category.

[0132] Data analysis and advice generation

[0133] The server uses machine learning algorithms to analyze spending data. This identifies the user's financial trends and spending patterns, and indicates potential areas of wasteful spending. Based on this information, advice for saving and optimizing spending is generated and sent to the user's terminal.

[0134] Utilizing the Emotion Engine

[0135] The server can collect user emotion data and utilize an emotion engine to recognize the user's emotional state in real time. Emotion data can be obtained, for example, by analyzing user input information and biometric data obtained from sensors.

[0136] Personalized notifications to users

[0137] Based on information received from the server, the device displays emotionally-sensitive financial advice and special offers to the user. For example, if the user is feeling stressed, it will provide information to encourage calm decision-making.

[0138] (Specific example) If the emotion engine detects a high-stress state in the user, the server takes that emotional state into consideration and generates and displays advice such as "postpone big purchases" on the device. Furthermore, as a reward for stress reduction, it provides information on discounts at relaxation facilities.

[0139] Adjusting visual information and interfaces

[0140] The device can dynamically change the tone and design of its user interface based on the analysis results of the emotion engine. This makes it possible to present information that is adapted to the user's psychological state.

[0141] Ultimately, by incorporating an emotion engine, the present invention can achieve deeper personalization that takes into account the user's emotional state, thereby enhancing the effectiveness of financial management.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The server securely retrieves financial transaction data from the user's financial institution API. The retrieved data includes the transaction amount, date, and category, and this data is stored in a database.

[0145] Step 2:

[0146] The server uses machine learning algorithms to analyze transaction data stored in the database, identifying the user's spending patterns and financial trends. This analysis reveals tendencies for wasteful spending and opportunities for saving.

[0147] Step 3:

[0148] Users provide emotional information to the system using their historical data and biosensors. Emotional data is collected through daily interactions, facial recognition, and voice analysis.

[0149] Step 4:

[0150] The server analyzes the collected emotional data using an emotion engine to recognize the user's emotional state in real time. This recognition result is used as an important element in the advice generation process.

[0151] Step 5:

[0152] Based on the analysis results, the server generates money-saving advice tailored to the user's emotional state. For users experiencing high stress levels, it recommends reconsidering non-essential purchases and taking actions that promote relaxation.

[0153] Step 6:

[0154] The device displays advice sent from the server to the user in a visually easy-to-understand format. The app's dashboard adjusts the color scheme and tone according to the user's situation.

[0155] Step 7:

[0156] The server collects special offers from partner companies and selects discounts and campaign information relevant to the user's purchase history and emotional state. It prioritizes content that enhances the user's psychological comfort and purchasing intent.

[0157] Step 8:

[0158] The device notifies users in real time of selected reward information in a format tailored to their emotional state. Notifications are delivered in a soft tone when the user is relaxed, and in a more subdued tone to help them avoid impulsive purchases.

[0159] Step 9:

[0160] Users review the information displayed on their device and choose actions based on the advice. They can adjust settings as needed and set new savings goals.

[0161] (Example 2)

[0162] 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".

[0163] This invention aims to solve the problems of conventional financial management systems, such as insufficient personalization due to providing uniform financial advice without considering the user's emotional state, and the lack of a user interface that responds to the user's psychological state.

[0164] 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.

[0165] In this invention, the server includes means for automatically collecting and organizing the user's financial information, means for analyzing consumption data using machine learning technology and identifying consumption patterns, means for generating personalized asset management advice considering the user's emotional state and displaying it on the user's terminal, means for acquiring emotional data and analyzing it using an emotional recognition engine, and means for displaying consumption data in a visually clear format and adjusting the interface according to the user's emotional state. This makes it possible to provide effective financial advice based on the user's emotions and consumption trends, and an optimal user interface that reduces psychological burden.

[0166] A "user" refers to an individual or legal entity that uses the system to manage their finances.

[0167] "Financial information" refers to data related to a user's assets, liabilities, income, expenses, etc.

[0168] "Machine learning technology" refers to data analysis methods that use algorithms to analyze large amounts of data and find patterns.

[0169] "Consumption data" refers to information about transactions conducted by users, primarily including data related to spending.

[0170] "Emotional state" refers to the user's psychological state and is an indicator of their stress and relaxation levels.

[0171] "Asset management advice" refers to suggestions for management and saving that take into account the user's financial situation and spending patterns.

[0172] An "emotion recognition engine" refers to a program or algorithm used to analyze a user's emotional state.

[0173] A "user interface" refers to the display screen and operating environment through which a user directly interacts with the system.

[0174] As a form of implementing the invention, this system provides information that takes into account the user's emotional state in order to support the user's financial management. The system mainly consists of three entities: a server, a terminal, and a user. The role of each entity and the technology used are described below.

[0175] Server roles and technical information

[0176] The server consists of hardware and software for collecting users' financial information via APIs. Specifically, a database system within the server organizes and stores the acquired transaction data. This data includes details such as date, amount, and category, and machine learning techniques are used to analyze spending patterns and generate personalized savings advice. Furthermore, sentiment recognition technology is used to collect sentiment data from the user's input speed and operation history, and this data is analyzed by an emotion engine.

[0177] Terminal roles and interfaces

[0178] The terminal is a device that receives information transmitted from the server and displays financial advice, special offers, and consumption data based on the user's emotional state. The terminal's user interface has a function that dynamically adjusts colors and tones according to the user's emotional state. This allows the user to receive information in a way that suits their psychological state.

[0179] User operation and usage methods

[0180] Users pre-authorize the sharing of information with financial institutions and allow the system to input their daily financial transaction information. Furthermore, users can input data about their emotions, or data can be automatically collected through sensors installed in their devices. Based on this data, users can understand their spending habits and receive optimal saving and investment advice tailored to their emotions.

[0181] For example, if a user is experiencing high stress, the server may take their emotional state into consideration and generate and display advice such as "postpone large purchases." It could also provide discount information on relaxation facilities as a way to help reduce stress.

[0182] An example of a prompt for a generative AI model is, "Please think of advice to give to a user who is feeling stressed." This is expected to promote the collaboration between emotion recognition technology and financial management advice, thereby improving the effectiveness of the user's financial management.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] Collection of financial data

[0186] The server connects to the API of a financial institution approved by the user and collects transaction data. The input is transaction data in JSON format obtained from the API. This data is parsed to extract information such as date, amount, and category, and stored in the database. The output is structured transaction data. Specifically, the server periodically sends requests to the API endpoint to retrieve the latest transaction information.

[0187] Step 2:

[0188] Data classification and organization

[0189] The server analyzes the stored transaction data and classifies it into categories using machine learning algorithms. The input is the transaction data stored in step 1. For data processing, natural language processing techniques are used to estimate categories based on transaction names and content. The output is transaction data organized by category. Specifically, regular expressions and rule sets are used to classify, for example, "spending at a supermarket" as "groceries."

[0190] Step 3:

[0191] Analysis of consumption patterns

[0192] The server analyzes user spending patterns based on categorized transaction data. The input is the categorized transaction data organized in step 2. Data calculations utilize time series analysis and pivot tables to identify spending trends and peaks. The output is a report showing spending patterns. For example, it visualizes the monthly fluctuations in "food expenses."

[0193] Step 4:

[0194] Collection and analysis of emotional data

[0195] The server collects user emotion data from the terminal. Inputs include user operation history and biometric data obtained from sensors. As part of the data processing, an emotion recognition engine is used to estimate emotional states such as stress and comfort levels. The output is an index indicating the user's emotional state. Specifically, stress levels are quantified by analyzing keyboard input speed and heart rate data from sensors.

[0196] Step 5:

[0197] Personalized advice and bonus information generation

[0198] The server generates advice based on consumption patterns and emotional data. The input is the data obtained from steps 3 and 4. As part of data processing, a generative AI model is used to generate optimal advice and reward information for the user. The output is recommended advice and reward information for the user. Specifically, when the user is under high stress, it will provide advice such as "avoid unnecessary purchases" and present information about rewards at relaxation facilities.

[0199] Step 6:

[0200] User interface adjustments and display

[0201] The device adjusts the user interface based on information sent from the server. Inputs are generated advice and reward information. The output presents the user with a dynamic interface based on their emotional state. Specifically, if high stress levels are detected, the screen's color scheme changes to calming colors, and appropriate advice is displayed as a pop-up.

[0202] (Application Example 2)

[0203] 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".

[0204] Traditionally, user financial management has been limited to simply analyzing spending data and providing general advice. However, approaches that do not take into account the user's emotional state ignore the psychological factors in actual financial behavior. As a result, users may be less receptive to effective advice. Therefore, there is a need for a financial management system that reflects the user's emotional state in real time and achieves deeper personalization.

[0205] 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.

[0206] In this invention, the server includes a mechanism for automatically collecting and organizing the user's financial information, a mechanism for analyzing spending data using machine learning algorithms and identifying spending patterns, and a mechanism for providing emotionally appropriate financial advice using an emotion engine that identifies the user's emotional state in real time. This enables personalized financial management that takes the user's emotional state into consideration.

[0207] A "system that automatically collects and organizes users' financial information" is a system that has the function of organizing transaction data obtained from financial institutions' APIs by category and storing it in a database.

[0208] A "mechanism that analyzes spending data using machine learning algorithms and identifies spending patterns" is a system that uses machine learning technology to analyze collected spending data and identify users' consumption trends and potential for wasteful spending.

[0209] A "system that uses an emotion engine to identify a user's emotional state in real time and provides emotionally responsive financial advice" is a system that analyzes a user's emotional data in real time and generates optimal financial advice based on that emotion.

[0210] The "mechanism for acquiring and notifying users of special offers in real time" is a system that takes into account the user's purchase history and emotional state, acquires relevant special offer information as needed, and notifies the user accordingly.

[0211] A "mechanism that displays expenditure data in a visually easy-to-understand format and provides an interface that adapts to the user's emotional state" is a system that provides a user interface that can dynamically adjust the display layout and design, taking into account the user's psychological state.

[0212] This invention is a system for improving users' financial management and is implemented as follows: A server automatically collects and organizes user transaction data using financial institution APIs. This data includes transaction details such as date, amount, and category. The server further analyzes the collected spending data using machine learning algorithms to identify the user's spending patterns. This analysis reveals spending trends, including potential wasteful spending.

[0213] Furthermore, the server uses an emotion engine to identify the user's emotional state in real time. This emotional data is obtained by analyzing user input information and biometric data acquired from sensors. To provide advice tailored to the user's emotions, the server uses a generative AI model to generate optimal financial advice and special offers, and sends them to the user's terminal.

[0214] The user terminal receives information from the server and displays it to the user. In this process, an interface is provided that visually displays the information in a format adapted to the user's emotional state. For example, if the user is stressed, the tone and design of the interface may change.

[0215] For example, when a user is considering a large purchase at a cafe, the app might offer advice such as, "Considering your current stress level, we recommend you reconsider this purchase. We also have a discount coupon you can use at a nearby relaxing cafe."

[0216] An example of a prompt to a generative AI model would be: "The user's emotional state is {emotional state}, and their most recent transaction was {transaction details}. Please generate the best financial advice for this situation."

[0217] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0218] Step 1:

[0219] The server automatically collects user transaction data using financial institution APIs. The input requires the user's financial account information, and the output is the collected transaction data. This data includes detailed information such as date, amount, and category. This transaction information is stored in a database and prepared for use in subsequent processes.

[0220] Step 2:

[0221] The server analyzes transaction data using machine learning algorithms. The input is the transaction data collected in Step 1, and the output is the result showing the user's consumption patterns and potential for wasteful spending. This analysis is performed to identify consumption trends from historical transaction data and to identify patterns of abnormal spending. The data is processed by the algorithm and made available in a form that can be visualized.

[0222] Step 3:

[0223] The server uses an emotion engine to identify the user's emotional state in real time. Inputs are user input data and biometric data, and output is the current emotional state. Emotional data obtained from sensors and user feedback is analyzed to identify the estimated emotion. This allows for understanding the emotional state and using that information for subsequent advice.

[0224] Step 4:

[0225] The server generates financial advice based on the emotional state and analysis results. It requires the results of steps 2 and 3 as input, and the output is personalized financial advice and bonus information. A generation AI model is used to generate advice sentences tailored to the user's emotions and spending patterns. Based on these prompt sentences, the optimal advice is designed.

[0226] Step 5:

[0227] The terminal visually presents the user with advice and reward information sent from the server. Input is data from the server, and output is what is displayed on the user interface. The user interface displays information with a tone and design that matches the user's emotional state, enhancing user acceptance. This process ensures that users receive the information appropriately and act accordingly.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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".

[0244] This invention is a system that streamlines users' financial management and supports the optimization of their spending. In its implementation form, the system primarily consists of three elements: a server, a terminal, and a user.

[0245] Data collection and organization

[0246] The server integrates with the user's financial institution and automatically collects financial information. This process retrieves banking transactions and credit card usage history via APIs, organizing and storing it in a database. The information is always up-to-date without user intervention.

[0247] Data Analysis

[0248] The server uses machine learning algorithms to analyze the collected financial information. By identifying spending patterns and trends, it reveals which categories users are wasting money in. Based on this information, it is ready to suggest appropriate saving strategies.

[0249] Generating and providing advice

[0250] The server generates savings advice to optimize the user's spending based on the analysis results and sends it to the user's device. The device displays the advice in a visually easy-to-understand dashboard format to help the user understand it.

[0251] (Specific example) If a user is identified as spending too much on food, the server will advise them to "reduce their monthly food expenses by 10% and purchase alternatives." This advice will be visualized on the device along with a graph of the monthly breakdown.

[0252] Notification of special offers

[0253] The server organizes reward information obtained from partner companies in real time, selects information tailored to the user's purchase history and interests, and sends it to the device. The device then notifies the user of this information as a pop-up notification, prompting them to take the necessary action.

[0254] (Specific example) If a discount coupon becomes available for a supermarket the user previously visited, the system will instantly notify the user's device. The user can then immediately use it to reduce their spending.

[0255] Overall, this invention utilizes machine learning and real-time data processing technologies to enable users to intuitively manage their finances and help optimize their daily spending. This allows users to efficiently maintain and improve their financial health.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] The server accesses the user's financial institution API and securely retrieves the user's transaction history through an authentication protocol. Here, it collects data including the amount, date, and category information for each transaction.

[0259] Step 2:

[0260] The server classifies the acquired transaction data and stores it in a database. The data is organized by category, making it easy to identify user spending trends in later analysis.

[0261] Step 3:

[0262] The server analyzes the stored data by applying machine learning algorithms. In this process, it identifies the user's spending patterns and extracts categories that show monthly spending fluctuations or wasteful spending.

[0263] Step 4:

[0264] The server generates personalized savings advice based on the analysis results. This advice includes suggestions for spending limits and warnings about unusual spending.

[0265] Step 5:

[0266] The terminal receives advice sent from the server and displays it to the user in a visually easy-to-understand format. Dashboards and graphs are used to ensure that the user can understand it immediately.

[0267] Step 6:

[0268] The server collects the latest information from partner companies' reward databases and selects the most relevant rewards based on the user's purchase history.

[0269] Step 7:

[0270] The device notifies the user in real time of selected reward information. The notification is presented in a pop-up format to attract the user's attention.

[0271] Step 8:

[0272] Users can check the display on their device, change settings as needed, and optimize their actions by following savings advice.

[0273] (Example 1)

[0274] 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."

[0275] In modern society, personal spending management has become increasingly complex, requiring vast amounts of financial data and intricate analysis due to diverse transactions and purchases. As a result, many users need support to ensure their financial health. However, traditional methods have relied on manual organization and analysis of financial information, which has been extremely time-consuming and laborious. Furthermore, it has been difficult to discover useful information about benefits for users or to provide specific and personalized savings suggestions.

[0276] 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.

[0277] This invention includes a server that automatically collects and stores user financial information in a structured format, a digital computing means for analyzing the collected information and highlighting spending trends, and a generative AI model for creating individual savings suggestions and visualizing and displaying them on the user's device. This enables users to efficiently manage their financial information and intuitively utilize personalized, practical savings strategies.

[0278] "Financial information" refers to data related to a user's economic activities, such as bank transactions, credit card usage history, asset and liability information, etc.

[0279] "Structured form" refers to a data form organized based on specific rules, indicating a state where data is classified and organized to facilitate the handling of information.

[0280] "Digital computing means" refers to a method of processing and analyzing data using computers and related technologies, enabling efficient information analysis.

[0281] "Generative AI model" refers to an algorithm that uses artificial intelligence to learn from data and generate new information and proposals, and is particularly used for the purpose of generating individual recommendations for users.

[0282] "Savings proposal" means specific advice or plans to optimize a user's expenses, promoting the reduction of wasteful spending and the effective utilization of assets.

[0283] "Means for visualizing and displaying" refers to a technology that converts data and information graphically and displays them in a form that can be intuitively understood by users.

[0284] This invention is a system for a user to effectively manage financial information and optimize expenses. The system consists of three elements: a server, a terminal, and a user.

[0285] The server automatically collects a user's financial information using the API of the financial institution used by the user. This includes bank transactions and credit card usage history. This information is stored in a structured form within a database. Software used includes a database management system and an API connection module.

[0286] Next, the server analyzes the collected information using digital calculation means. Specifically, by using a programming language such as Python and a machine learning library such as scikit-learn or TensorFlow, the spending trend is revealed. Through this analysis, it is identified which category the user needs to review their spending in, and based on this, individual savings proposals are created.

[0287] Utilizing the generative AI model, the server generates optimal savings proposals for each user. These generated proposals are sent to the user's terminal. The terminal visualizes the proposals and presents them to the user through a graphical user interface. D3.js, a JavaScript library, is used for visualization.

[0288] As a specific example, if the user is determined to have excessive food expenses, the server generates a proposal such as "Reduce monthly food expenses by 10% and purchase alternative products." An example of a prompt sentence is "Analyze the user's food expense data and propose optimal savings measures."

[0289] Furthermore, the server obtains privilege information from partner companies in real-time, selects and notifies the privileges that match the user's transaction history. With this function, users can grasp the immediately available promotions.

[0290] In this way, this invention is a system that provides functions for users to intuitively manage financial information and improve financial soundness.

[0291] The flow of the specific process in Example 1 will be described using FIG. 11.

[0292] Step 1:

[0293] The server collects transaction information from financial institutions linked to by the user using APIs. The input is API request data, and the output is the user's financial transaction information. The server periodically executes this process to keep the financial data up-to-date. Here, we perform the specific action of retrieving transaction history from financial institutions using API keys and saving it to a local database.

[0294] Step 2:

[0295] The server stores and organizes collected transaction information in a structured format within a database. Input is raw transaction data, while output is transaction data organized by category. The server classifies transaction details into categories such as food expenses, transportation expenses, and utility expenses, making the data easier to use for subsequent analysis. During this process, it updates the database to enable efficient data retrieval.

[0296] Step 3:

[0297] The server utilizes organized data, employs digital computing tools, and performs analysis using machine learning algorithms. The input is categorized transaction data, and the output is an analysis showing spending trends. Using the Python programming language and scikit-learn, it performs specific actions to detect particular spending patterns and anomalies, thereby revealing wasteful spending by users.

[0298] Step 4:

[0299] The server uses a generative AI model to create personalized savings suggestions based on the analysis results. The input is the analysis results, and the output is a savings suggestion tailored to the user. The prompt message "Analyze the user's food expenditure data and suggest the best savings strategy" is input to the generative AI model, and the AI ​​model generates the suggestions.

[0300] Step 5:

[0301] The server sends the generated savings proposal to the user's terminal, and the terminal displays it through a visually intuitive interface. The input is the savings proposal, and the output is the visualized user screen. The terminal uses D3.js of JavaScript to perform specific operations to display the proposal on graphs and dashboards, assisting the user to better understand the proposal.

[0302] Step 6:

[0303] The server obtains privilege information from partner companies in real time, selects relevant privileges based on the user's transaction history, and notifies the terminal. The inputs are the privilege information and the user transaction history, and the output is the privilege information notified to the user. The server specifically selects privileges that match the user's interests and past transactions, and the terminal performs the operation of sending a pop-up notification.

[0304] (Application Example 1)

[0305] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] Modern consumers find it increasingly difficult to efficiently manage their finances due to the ever-diversifying spending patterns. In particular, identifying wasteful spending and practicing daily savings requires a lot of time and effort. Also, users may not be able to obtain timely privilege information related to the products and services they purchase, often missing out on valuable savings opportunities. There is a need for means to solve such problems, optimize consumers' spending, and effectively utilize privilege information.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0308] This invention includes a server that automatically collects and organizes users' financial information, a means for analyzing spending data using machine learning algorithms and identifying spending patterns, and a means for controlling an information-providing terminal that is integrated into an electronic payment system and assists in optimizing spending using financial transaction information. This makes it possible to streamline consumers' financial management and optimize spending through saving and utilizing reward information.

[0309] "User financial information" refers to information about transactions conducted by individuals or corporations using financial institutions or payment services, and specifically includes account balances, transaction details, and credit card usage history.

[0310] "Machine learning algorithms" refer to algorithms that analyze patterns based on large amounts of data to make predictions and decisions. They have the characteristic of learning from data and improving their accuracy.

[0311] "Expenditure data" refers to a collection of information related to a user's consumption activities, including individual transactions, purchased goods and services, and their amounts.

[0312] "Means for identifying spending patterns" refer to methods and techniques for analyzing consistency and trends in a user's spending behavior. This makes it possible to identify wasteful spending and suggest appropriate saving strategies.

[0313] An "electronic payment system" refers to a system that allows for the completion of payments for goods and services using electronic methods rather than cash. This includes online banking and digital wallets.

[0314] An "information provision terminal" refers to an electronic device used to present information to users, such as a smartphone, tablet, or personal computer. It serves as a means of delivering information visually.

[0315] This invention is a system designed to streamline users' financial management and support the optimization of their spending. The system mainly consists of a server, an information terminal, and the user.

[0316] The server automatically collects financial information from the user's financial institution via an API and organizes it in a database. This information, including transaction details and credit card usage history, is kept up-to-date without user intervention. The server uses machine learning algorithms to analyze the collected financial information and identify the user's spending patterns. This clarifies which categories of spending are wasteful and helps prepare appropriate saving strategies.

[0317] The information terminal presents users with personalized savings advice generated based on analysis results. This advice is displayed in a visually easy-to-understand dashboard format to aid user comprehension. Furthermore, the server retrieves and organizes reward information from partner companies and notifies users in real time of selected information based on their purchase history. Notifications on the terminal allow users to immediately take advantage of rewards and have the opportunity to reduce their spending.

[0318] This system utilizes machine learning platforms such as TensorFlow and PyTorch, and implements an intuitive user interface using React Native on information delivery devices such as smartphones. By integrating it into electronic payment systems, it is possible to optimize spending by effectively utilizing financial transaction information.

[0319] For example, if the analysis reveals that a user regularly spends money on "weekend cafes," the app might offer advice such as, "Reduce your weekend cafe spending by 20% and try recipes you can enjoy at home."

[0320] An example of a prompt to the generating AI model is: "Analyze the user's bank account transaction history, identify unnecessary spending, and generate effective savings advice."

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The server collects financial information from users' financial institutions via an API. Inputs include transaction details and credit card usage history obtained through the API. This data is organized in a database on the server and updated and managed on a per-user basis.

[0324] Step 2:

[0325] The server analyzes collected financial information using machine learning algorithms. The input is financial information stored in a database, and the output is a modeled result of spending patterns. Specifically, the algorithm identifies trends in the data and uses that to determine signs of wasteful spending.

[0326] Step 3:

[0327] The server generates personalized savings advice based on the analysis results. The input is the analysis results of spending patterns, and the output is a set of savings advice. Specifically, it constructs text that suggests more effective saving methods based on the user's spending behavior.

[0328] Step 4:

[0329] The server sends the generated savings advice to the user's information terminal. The terminal receives this and displays it in a visually easy-to-understand dashboard format. The input is the aforementioned savings advice, and the output is the visual display presented to the user. Specifically, the interface is designed using React Native.

[0330] Step 5:

[0331] The server retrieves reward information from partner companies and filters the information based on the user's purchase history. The input consists of reward information obtained from partner companies and the user's purchase data, while the output is a set of reward information optimized for the user. To notify users of the filtered reward information in real time, the server filters the input purchase data and reward information, extracting only the information relevant to the user.

[0332] Step 6:

[0333] The device displays the reward information to the user as a pop-up notification. The input is the reward information sent from the server, and the output is the pop-up notification displayed on the user's device. This step allows the device to use its notification function to prompt the user to take immediate action.

[0334] 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.

[0335] This invention provides a system that supports users' financial management by incorporating an emotion engine to offer financial advice and special offers that take into account the user's emotional state. The system mainly consists of a server, terminals, and users.

[0336] Collection and organization of financial information

[0337] The server collects transaction data using the user's financial institution API. This data is organized by category and stored in a database. Each transaction includes details such as date, amount, and category.

[0338] Data analysis and advice generation

[0339] The server uses machine learning algorithms to analyze spending data. This identifies the user's financial trends and spending patterns, and indicates potential areas of wasteful spending. Based on this information, advice for saving and optimizing spending is generated and sent to the user's terminal.

[0340] Utilizing the Emotion Engine

[0341] The server can collect user emotion data and utilize an emotion engine to recognize the user's emotional state in real time. Emotion data can be obtained, for example, by analyzing user input information and biometric data obtained from sensors.

[0342] Personalized notifications to users

[0343] Based on information received from the server, the device displays emotionally-sensitive financial advice and special offers to the user. For example, if the user is feeling stressed, it will provide information to encourage calm decision-making.

[0344] (Specific example) If the emotion engine detects a high-stress state in the user, the server takes that emotional state into consideration and generates and displays advice such as "postpone big purchases" on the device. Furthermore, as a reward for stress reduction, it provides information on discounts at relaxation facilities.

[0345] Adjusting visual information and interfaces

[0346] The device can dynamically change the tone and design of its user interface based on the analysis results of the emotion engine. This makes it possible to present information that is adapted to the user's psychological state.

[0347] Ultimately, by incorporating an emotion engine, the present invention can achieve deeper personalization that takes into account the user's emotional state, thereby enhancing the effectiveness of financial management.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The server securely retrieves financial transaction data from the user's financial institution API. The retrieved data includes the transaction amount, date, and category, and this data is stored in a database.

[0351] Step 2:

[0352] The server uses machine learning algorithms to analyze transaction data stored in the database, identifying the user's spending patterns and financial trends. This analysis reveals tendencies for wasteful spending and opportunities for saving.

[0353] Step 3:

[0354] Users provide emotional information to the system using their historical data and biosensors. Emotional data is collected through daily interactions, facial recognition, and voice analysis.

[0355] Step 4:

[0356] The server analyzes the collected emotional data using an emotion engine to recognize the user's emotional state in real time. This recognition result is used as an important element in the advice generation process.

[0357] Step 5:

[0358] Based on the analysis results, the server generates money-saving advice tailored to the user's emotional state. For users experiencing high stress levels, it recommends reconsidering non-essential purchases and taking actions that promote relaxation.

[0359] Step 6:

[0360] The device displays advice sent from the server to the user in a visually easy-to-understand format. The app's dashboard adjusts the color scheme and tone according to the user's situation.

[0361] Step 7:

[0362] The server collects special offers from partner companies and selects discounts and campaign information relevant to the user's purchase history and emotional state. It prioritizes content that enhances the user's psychological comfort and purchasing intent.

[0363] Step 8:

[0364] The device notifies users in real time of selected reward information in a format tailored to their emotional state. Notifications are delivered in a soft tone when the user is relaxed, and in a more subdued tone to help them avoid impulsive purchases.

[0365] Step 9:

[0366] Users review the information displayed on their device and choose actions based on the advice. They can adjust settings as needed and set new savings goals.

[0367] (Example 2)

[0368] 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".

[0369] This invention aims to solve the problems of conventional financial management systems, such as insufficient personalization due to providing uniform financial advice without considering the user's emotional state, and the lack of a user interface that responds to the user's psychological state.

[0370] 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.

[0371] In this invention, the server includes means for automatically collecting and organizing the user's financial information, means for analyzing consumption data using machine learning technology and identifying consumption patterns, means for generating personalized asset management advice considering the user's emotional state and displaying it on the user's terminal, means for acquiring emotional data and analyzing it using an emotional recognition engine, and means for displaying consumption data in a visually clear format and adjusting the interface according to the user's emotional state. This makes it possible to provide effective financial advice based on the user's emotions and consumption trends, and an optimal user interface that reduces psychological burden.

[0372] A "user" refers to an individual or legal entity that uses the system to manage their finances.

[0373] "Financial information" refers to data related to a user's assets, liabilities, income, expenses, etc.

[0374] "Machine learning technology" refers to data analysis methods that use algorithms to analyze large amounts of data and find patterns.

[0375] "Consumption data" refers to information about transactions conducted by users, primarily including data related to spending.

[0376] "Emotional state" refers to the user's psychological state and is an indicator of their stress and relaxation levels.

[0377] "Asset management advice" refers to suggestions for management and saving that take into account the user's financial situation and spending patterns.

[0378] An "emotion recognition engine" refers to a program or algorithm used to analyze a user's emotional state.

[0379] A "user interface" refers to the display screen and operating environment through which a user directly interacts with the system.

[0380] As a form of implementing the invention, this system provides information that takes into account the user's emotional state in order to support the user's financial management. The system mainly consists of three entities: a server, a terminal, and a user. The role of each entity and the technology used are described below.

[0381] Server roles and technical information

[0382] The server consists of hardware and software for collecting users' financial information via APIs. Specifically, a database system within the server organizes and stores the acquired transaction data. This data includes details such as date, amount, and category, and machine learning techniques are used to analyze spending patterns and generate personalized savings advice. Furthermore, sentiment recognition technology is used to collect sentiment data from the user's input speed and operation history, and this data is analyzed by an emotion engine.

[0383] Terminal roles and interfaces

[0384] The terminal is a device that receives information transmitted from the server and displays financial advice, special offers, and consumption data based on the user's emotional state. The terminal's user interface has a function that dynamically adjusts colors and tones according to the user's emotional state. This allows the user to receive information in a way that suits their psychological state.

[0385] User operation and usage methods

[0386] Users pre-authorize the sharing of information with financial institutions and allow the system to input their daily financial transaction information. Furthermore, users can input data about their emotions, or data can be automatically collected through sensors installed in their devices. Based on this data, users can understand their spending habits and receive optimal saving and investment advice tailored to their emotions.

[0387] For example, if a user is experiencing high stress, the server may take their emotional state into consideration and generate and display advice such as "postpone large purchases." It could also provide discount information on relaxation facilities as a way to help reduce stress.

[0388] An example of a prompt for a generative AI model is, "Please think of advice to give to a user who is feeling stressed." This is expected to promote the collaboration between emotion recognition technology and financial management advice, thereby improving the effectiveness of the user's financial management.

[0389] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0390] Step 1:

[0391] Collection of financial data

[0392] The server connects to the API of a financial institution approved by the user and collects transaction data. The input is transaction data in JSON format obtained from the API. This data is parsed to extract information such as date, amount, and category, and stored in the database. The output is structured transaction data. Specifically, the server periodically sends requests to the API endpoint to retrieve the latest transaction information.

[0393] Step 2:

[0394] Data classification and organization

[0395] The server analyzes the stored transaction data and classifies it into categories using machine learning algorithms. The input is the transaction data stored in step 1. For data processing, natural language processing techniques are used to estimate categories based on transaction names and content. The output is transaction data organized by category. Specifically, regular expressions and rule sets are used to classify, for example, "spending at a supermarket" as "groceries."

[0396] Step 3:

[0397] Analysis of consumption patterns

[0398] The server analyzes user spending patterns based on categorized transaction data. The input is the categorized transaction data organized in step 2. Data calculations utilize time series analysis and pivot tables to identify spending trends and peaks. The output is a report showing spending patterns. For example, it visualizes the monthly fluctuations in "food expenses."

[0399] Step 4:

[0400] Collection and analysis of emotional data

[0401] The server collects user emotion data from the terminal. Inputs include user operation history and biometric data obtained from sensors. As part of the data processing, an emotion recognition engine is used to estimate emotional states such as stress and comfort levels. The output is an index indicating the user's emotional state. Specifically, stress levels are quantified by analyzing keyboard input speed and heart rate data from sensors.

[0402] Step 5:

[0403] Personalized advice and bonus information generation

[0404] The server generates advice based on consumption patterns and emotional data. The input is the data obtained from steps 3 and 4. As part of data processing, a generative AI model is used to generate optimal advice and reward information for the user. The output is recommended advice and reward information for the user. Specifically, when the user is under high stress, it will provide advice such as "avoid unnecessary purchases" and present information about rewards at relaxation facilities.

[0405] Step 6:

[0406] User interface adjustments and display

[0407] The device adjusts the user interface based on information sent from the server. Inputs are generated advice and reward information. The output presents the user with a dynamic interface based on their emotional state. Specifically, if high stress levels are detected, the screen's color scheme changes to calming colors, and appropriate advice is displayed as a pop-up.

[0408] (Application Example 2)

[0409] 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."

[0410] Traditionally, user financial management has been limited to simply analyzing spending data and providing general advice. However, approaches that do not take into account the user's emotional state ignore the psychological factors in actual financial behavior. As a result, users may be less receptive to effective advice. Therefore, there is a need for a financial management system that reflects the user's emotional state in real time and achieves deeper personalization.

[0411] 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.

[0412] In this invention, the server includes a mechanism for automatically collecting and organizing the user's financial information, a mechanism for analyzing spending data using machine learning algorithms and identifying spending patterns, and a mechanism for providing emotionally appropriate financial advice using an emotion engine that identifies the user's emotional state in real time. This enables personalized financial management that takes the user's emotional state into consideration.

[0413] A "system that automatically collects and organizes users' financial information" is a system that has the function of organizing transaction data obtained from financial institutions' APIs by category and storing it in a database.

[0414] A "mechanism that analyzes spending data using machine learning algorithms and identifies spending patterns" is a system that uses machine learning technology to analyze collected spending data and identify users' consumption trends and potential for wasteful spending.

[0415] A "system that uses an emotion engine to identify a user's emotional state in real time and provides emotionally responsive financial advice" is a system that analyzes a user's emotional data in real time and generates optimal financial advice based on that emotion.

[0416] The "mechanism for acquiring and notifying users of special offers in real time" is a system that takes into account the user's purchase history and emotional state, acquires relevant special offer information as needed, and notifies the user accordingly.

[0417] A "mechanism that displays expenditure data in a visually easy-to-understand format and provides an interface that adapts to the user's emotional state" is a system that provides a user interface that can dynamically adjust the display layout and design, taking into account the user's psychological state.

[0418] This invention is a system for improving users' financial management and is implemented as follows: A server automatically collects and organizes user transaction data using financial institution APIs. This data includes transaction details such as date, amount, and category. The server further analyzes the collected spending data using machine learning algorithms to identify the user's spending patterns. This analysis reveals spending trends, including potential wasteful spending.

[0419] Furthermore, the server uses an emotion engine to identify the user's emotional state in real time. This emotional data is obtained by analyzing user input information and biometric data acquired from sensors. To provide advice tailored to the user's emotions, the server uses a generative AI model to generate optimal financial advice and special offers, and sends them to the user's terminal.

[0420] The user terminal receives information from the server and displays it to the user. In this process, an interface is provided that visually displays the information in a format adapted to the user's emotional state. For example, if the user is stressed, the tone and design of the interface may change.

[0421] For example, when a user is considering a large purchase at a cafe, the app might offer advice such as, "Considering your current stress level, we recommend you reconsider this purchase. We also have a discount coupon you can use at a nearby relaxing cafe."

[0422] An example of a prompt to a generative AI model would be: "The user's emotional state is {emotional state}, and their most recent transaction was {transaction details}. Please generate the best financial advice for this situation."

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] The server automatically collects user transaction data using financial institution APIs. The input requires the user's financial account information, and the output is the collected transaction data. This data includes detailed information such as date, amount, and category. This transaction information is stored in a database and prepared for use in subsequent processes.

[0426] Step 2:

[0427] The server analyzes transaction data using machine learning algorithms. The input is the transaction data collected in Step 1, and the output is the result showing the user's consumption patterns and potential for wasteful spending. This analysis is performed to identify consumption trends from historical transaction data and to identify patterns of abnormal spending. The data is processed by the algorithm and made available in a form that can be visualized.

[0428] Step 3:

[0429] The server uses an emotion engine to identify the user's emotional state in real time. Inputs are user input data and biometric data, and output is the current emotional state. Emotional data obtained from sensors and user feedback is analyzed to identify the estimated emotion. This allows for understanding the emotional state and using that information for subsequent advice.

[0430] Step 4:

[0431] The server generates financial advice based on the emotional state and analysis results. It requires the results of steps 2 and 3 as input, and the output is personalized financial advice and bonus information. A generation AI model is used to generate advice sentences tailored to the user's emotions and spending patterns. Based on these prompt sentences, the optimal advice is designed.

[0432] Step 5:

[0433] The terminal visually presents the user with advice and reward information sent from the server. Input is data from the server, and output is what is displayed on the user interface. The user interface displays information with a tone and design that matches the user's emotional state, enhancing user acceptance. This process ensures that users receive the information appropriately and act accordingly.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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".

[0450] This invention is a system that streamlines users' financial management and supports the optimization of their spending. In its implementation form, the system primarily consists of three elements: a server, a terminal, and a user.

[0451] Data collection and organization

[0452] The server integrates with the user's financial institution and automatically collects financial information. This process retrieves banking transactions and credit card usage history via APIs, organizing and storing it in a database. The information is always up-to-date without user intervention.

[0453] Data Analysis

[0454] The server uses machine learning algorithms to analyze the collected financial information. By identifying spending patterns and trends, it reveals which categories users are wasting money in. Based on this information, it is ready to suggest appropriate saving strategies.

[0455] Generating and providing advice

[0456] The server generates savings advice to optimize the user's spending based on the analysis results and sends it to the user's device. The device displays the advice in a visually easy-to-understand dashboard format to help the user understand it.

[0457] (Specific example) If a user is identified as spending too much on food, the server will advise them to "reduce their monthly food expenses by 10% and purchase alternatives." This advice will be visualized on the device along with a graph of the monthly breakdown.

[0458] Notification of special offers

[0459] The server organizes reward information obtained from partner companies in real time, selects information tailored to the user's purchase history and interests, and sends it to the device. The device then notifies the user of this information as a pop-up notification, prompting them to take the necessary action.

[0460] (Specific example) If a discount coupon becomes available for a supermarket the user previously visited, the system will instantly notify the user's device. The user can then immediately use it to reduce their spending.

[0461] Overall, this invention utilizes machine learning and real-time data processing technologies to enable users to intuitively manage their finances and help optimize their daily spending. This allows users to efficiently maintain and improve their financial health.

[0462] The following describes the processing flow.

[0463] Step 1:

[0464] The server accesses the user's financial institution API and securely retrieves the user's transaction history through an authentication protocol. Here, it collects data including the amount, date, and category information for each transaction.

[0465] Step 2:

[0466] The server classifies the acquired transaction data and stores it in a database. The data is organized by category, making it easy to identify user spending trends in later analysis.

[0467] Step 3:

[0468] The server analyzes the stored data by applying machine learning algorithms. In this process, it identifies the user's spending patterns and extracts categories that show monthly spending fluctuations or wasteful spending.

[0469] Step 4:

[0470] The server generates personalized savings advice based on the analysis results. This advice includes suggestions for spending limits and warnings about unusual spending.

[0471] Step 5:

[0472] The terminal receives advice sent from the server and displays it to the user in a visually easy-to-understand format. Dashboards and graphs are used to ensure that the user can understand it immediately.

[0473] Step 6:

[0474] The server collects the latest information from partner companies' reward databases and selects the most relevant rewards based on the user's purchase history.

[0475] Step 7:

[0476] The device notifies the user in real time of selected reward information. The notification is presented in a pop-up format to attract the user's attention.

[0477] Step 8:

[0478] Users can check the display on their device, change settings as needed, and optimize their actions by following savings advice.

[0479] (Example 1)

[0480] 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."

[0481] In modern society, personal spending management has become increasingly complex, requiring vast amounts of financial data and intricate analysis due to diverse transactions and purchases. As a result, many users need support to ensure their financial health. However, traditional methods have relied on manual organization and analysis of financial information, which has been extremely time-consuming and laborious. Furthermore, it has been difficult to discover useful information about benefits for users or to provide specific and personalized savings suggestions.

[0482] 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.

[0483] This invention includes a server that automatically collects and stores user financial information in a structured format, a digital computing means for analyzing the collected information and highlighting spending trends, and a generative AI model for creating individual savings suggestions and visualizing and displaying them on the user's device. This enables users to efficiently manage their financial information and intuitively utilize personalized, practical savings strategies.

[0484] "Financial information" refers to data related to a user's economic activities, such as banking transactions, credit card usage history, and asset and liability information.

[0485] A "structured format" refers to a data format organized according to specific rules, where data is classified and organized in a way that facilitates information handling.

[0486] "Digital computing methods" refer to methods of processing and analyzing data using computers and related technologies, enabling efficient information analysis.

[0487] A "generative AI model" is an algorithm that uses artificial intelligence to learn from data and generate new information and suggestions, and is particularly used to generate personalized recommendations for users.

[0488] "Savings suggestions" refer to specific advice and plans to optimize a user's spending, encouraging them to reduce waste and make effective use of their assets.

[0489] "Means of visualization and display" refers to technologies that graphically convert data and information and display them in a format that users can intuitively understand.

[0490] This invention is a system for users to effectively manage their financial information and optimize their spending. The system consists of three elements: a server, a terminal, and a user.

[0491] The server automatically collects information about the user's financial institutions using their APIs. This includes banking transactions and credit card usage history. This information is stored in a structured format within a database. The software used includes database management systems and API integration modules.

[0492] Next, the server uses digital computing tools to analyze the collected information. Specifically, it uses programming languages ​​such as Python and machine learning libraries like scikit-learn and TensorFlow to reveal spending trends. This analysis identifies which categories of spending the user needs to review and uses this to create individual savings suggestions.

[0493] Using a generative AI model, the server generates optimal savings suggestions for each user. These generated suggestions are sent to the user's device. The device visualizes the suggestions and presents them to the user through a graphical user interface. The JavaScript library D3.js is used for visualization.

[0494] For example, if a user's food expenses are determined to be excessive, the server will generate a suggestion such as "Reduce your monthly food expenses by 10% and purchase alternatives." An example of a prompt message would be, "Analyze the user's food spending data and suggest the best way to save money."

[0495] Furthermore, the server retrieves reward information from partner companies in real time, selects rewards that match the user's transaction history, and notifies them. This feature allows users to instantly see available promotions.

[0496] Thus, this invention is a system that provides functions to enable users to intuitively manage their financial information and improve their financial health.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] The server collects transaction information from financial institutions linked to by the user using APIs. The input is API request data, and the output is the user's financial transaction information. The server periodically executes this process to keep the financial data up-to-date. Here, we perform the specific action of retrieving transaction history from financial institutions using API keys and saving it to a local database.

[0500] Step 2:

[0501] The server stores and organizes collected transaction information in a structured format within a database. Input is raw transaction data, while output is transaction data organized by category. The server classifies transaction details into categories such as food expenses, transportation expenses, and utility expenses, making the data easier to use for subsequent analysis. During this process, it updates the database to enable efficient data retrieval.

[0502] Step 3:

[0503] The server utilizes organized data, employs digital computing tools, and performs analysis using machine learning algorithms. The input is categorized transaction data, and the output is an analysis showing spending trends. Using the Python programming language and scikit-learn, it performs specific actions to detect particular spending patterns and anomalies, thereby revealing wasteful spending by users.

[0504] Step 4:

[0505] The server uses a generative AI model to create personalized savings suggestions based on the analysis results. The input is the analysis results, and the output is a savings suggestion tailored to the user. The prompt message "Analyze the user's food expenditure data and suggest the best savings strategy" is input to the generative AI model, and the AI ​​model generates the suggestions.

[0506] Step 5:

[0507] The server sends the generated savings suggestions to the user's terminal, which displays them in a visually intuitive interface. The input is the savings suggestions, and the output is the visualized user screen. The terminal uses JavaScript's D3.js to perform the specific actions of displaying the suggestions on graphs and dashboards, helping the user understand the suggestions more easily.

[0508] Step 6:

[0509] The server retrieves reward information from partner companies in real time, selects relevant rewards based on the user's transaction history, and notifies the terminal. Inputs are reward information and user transaction history, while output is the reward information notified to the user. The server specifically selects rewards that match the user's interests and past transactions, and the terminal then displays a pop-up notification.

[0510] (Application Example 1)

[0511] 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."

[0512] Modern consumers face increasing challenges in efficiently managing their finances due to their diverse spending patterns. Identifying wasteful spending and practicing daily savings, in particular, requires significant time and effort. Furthermore, users often miss out on valuable savings opportunities because they are unable to access timely information about rewards and benefits related to the products and services they purchase. There is a need for solutions to these challenges, enabling consumers to optimize their spending while effectively utilizing reward information.

[0513] 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.

[0514] This invention includes a server that automatically collects and organizes users' financial information, a means for analyzing spending data using machine learning algorithms and identifying spending patterns, and a means for controlling an information-providing terminal that is integrated into an electronic payment system and assists in optimizing spending using financial transaction information. This makes it possible to streamline consumers' financial management and optimize spending through saving and utilizing reward information.

[0515] "User financial information" refers to information about transactions conducted by individuals or corporations using financial institutions or payment services, and specifically includes account balances, transaction details, and credit card usage history.

[0516] "Machine learning algorithms" refer to algorithms that analyze patterns based on large amounts of data to make predictions and decisions. They have the characteristic of learning from data and improving their accuracy.

[0517] "Expenditure data" refers to a collection of information related to a user's consumption activities, including individual transactions, purchased goods and services, and their amounts.

[0518] "Means for identifying spending patterns" refer to methods and techniques for analyzing consistency and trends in a user's spending behavior. This makes it possible to identify wasteful spending and suggest appropriate saving strategies.

[0519] An "electronic payment system" refers to a system that allows for the completion of payments for goods and services using electronic methods rather than cash. This includes online banking and digital wallets.

[0520] An "information provision terminal" refers to an electronic device used to present information to users, such as a smartphone, tablet, or personal computer. It serves as a means of delivering information visually.

[0521] This invention is a system designed to streamline users' financial management and support the optimization of their spending. The system mainly consists of a server, an information terminal, and the user.

[0522] The server automatically collects financial information from the user's financial institution via an API and organizes it in a database. This information, including transaction details and credit card usage history, is kept up-to-date without user intervention. The server uses machine learning algorithms to analyze the collected financial information and identify the user's spending patterns. This clarifies which categories of spending are wasteful and helps prepare appropriate saving strategies.

[0523] The information terminal presents users with personalized savings advice generated based on analysis results. This advice is displayed in a visually easy-to-understand dashboard format to aid user comprehension. Furthermore, the server retrieves and organizes reward information from partner companies and notifies users in real time of selected information based on their purchase history. Notifications on the terminal allow users to immediately take advantage of rewards and have the opportunity to reduce their spending.

[0524] This system utilizes machine learning platforms such as TensorFlow and PyTorch, and implements an intuitive user interface using React Native on information delivery devices such as smartphones. By integrating it into electronic payment systems, it is possible to optimize spending by effectively utilizing financial transaction information.

[0525] For example, if the analysis reveals that a user regularly spends money on "weekend cafes," the app might offer advice such as, "Reduce your weekend cafe spending by 20% and try recipes you can enjoy at home."

[0526] An example of a prompt to the generating AI model is: "Analyze the user's bank account transaction history, identify unnecessary spending, and generate effective savings advice."

[0527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0528] Step 1:

[0529] The server collects financial information from users' financial institutions via an API. Inputs include transaction details and credit card usage history obtained through the API. This data is organized in a database on the server and updated and managed on a per-user basis.

[0530] Step 2:

[0531] The server analyzes collected financial information using machine learning algorithms. The input is financial information stored in a database, and the output is a modeled result of spending patterns. Specifically, the algorithm identifies trends in the data and uses that to determine signs of wasteful spending.

[0532] Step 3:

[0533] The server generates personalized savings advice based on the analysis results. The input is the analysis results of spending patterns, and the output is a set of savings advice. Specifically, it constructs text that suggests more effective saving methods based on the user's spending behavior.

[0534] Step 4:

[0535] The server sends the generated savings advice to the user's information terminal. The terminal receives this and displays it in a visually easy-to-understand dashboard format. The input is the aforementioned savings advice, and the output is the visual display presented to the user. Specifically, the interface is designed using React Native.

[0536] Step 5:

[0537] The server retrieves reward information from partner companies and filters the information based on the user's purchase history. The input consists of reward information obtained from partner companies and the user's purchase data, while the output is a set of reward information optimized for the user. To notify users of the filtered reward information in real time, the server filters the input purchase data and reward information, extracting only the information relevant to the user.

[0538] Step 6:

[0539] The device displays the reward information to the user as a pop-up notification. The input is the reward information sent from the server, and the output is the pop-up notification displayed on the user's device. This step allows the device to use its notification function to prompt the user to take immediate action.

[0540] 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.

[0541] This invention provides a system that supports users' financial management by incorporating an emotion engine to offer financial advice and special offers that take into account the user's emotional state. The system mainly consists of a server, terminals, and users.

[0542] Collection and organization of financial information

[0543] The server collects transaction data using the user's financial institution API. This data is organized by category and stored in a database. Each transaction includes details such as date, amount, and category.

[0544] Data analysis and advice generation

[0545] The server uses machine learning algorithms to analyze spending data. This identifies the user's financial trends and spending patterns, and indicates potential areas of wasteful spending. Based on this information, advice for saving and optimizing spending is generated and sent to the user's terminal.

[0546] Utilizing the Emotion Engine

[0547] The server can collect user emotion data and utilize an emotion engine to recognize the user's emotional state in real time. Emotion data can be obtained, for example, by analyzing user input information and biometric data obtained from sensors.

[0548] Personalized notifications to users

[0549] Based on information received from the server, the device displays emotionally-sensitive financial advice and special offers to the user. For example, if the user is feeling stressed, it will provide information to encourage calm decision-making.

[0550] (Specific example) If the emotion engine detects a high-stress state in the user, the server takes that emotional state into consideration and generates and displays advice such as "postpone big purchases" on the device. Furthermore, as a reward for stress reduction, it provides information on discounts at relaxation facilities.

[0551] Adjusting visual information and interfaces

[0552] The device can dynamically change the tone and design of its user interface based on the analysis results of the emotion engine. This makes it possible to present information that is adapted to the user's psychological state.

[0553] Ultimately, by incorporating an emotion engine, the present invention can achieve deeper personalization that takes into account the user's emotional state, thereby enhancing the effectiveness of financial management.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The server securely retrieves financial transaction data from the user's financial institution API. The retrieved data includes the transaction amount, date, and category, and this data is stored in a database.

[0557] Step 2:

[0558] The server uses machine learning algorithms to analyze transaction data stored in the database, identifying the user's spending patterns and financial trends. This analysis reveals tendencies for wasteful spending and opportunities for saving.

[0559] Step 3:

[0560] Users provide emotional information to the system using their historical data and biosensors. Emotional data is collected through daily interactions, facial recognition, and voice analysis.

[0561] Step 4:

[0562] The server analyzes the collected emotional data using an emotion engine to recognize the user's emotional state in real time. This recognition result is used as an important element in the advice generation process.

[0563] Step 5:

[0564] Based on the analysis results, the server generates money-saving advice tailored to the user's emotional state. For users experiencing high stress levels, it recommends reconsidering non-essential purchases and taking actions that promote relaxation.

[0565] Step 6:

[0566] The device displays advice sent from the server to the user in a visually easy-to-understand format. The app's dashboard adjusts the color scheme and tone according to the user's situation.

[0567] Step 7:

[0568] The server collects special offers from partner companies and selects discounts and campaign information relevant to the user's purchase history and emotional state. It prioritizes content that enhances the user's psychological comfort and purchasing intent.

[0569] Step 8:

[0570] The device notifies users in real time of selected reward information in a format tailored to their emotional state. Notifications are delivered in a soft tone when the user is relaxed, and in a more subdued tone to help them avoid impulsive purchases.

[0571] Step 9:

[0572] Users review the information displayed on their device and choose actions based on the advice. They can adjust settings as needed and set new savings goals.

[0573] (Example 2)

[0574] 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."

[0575] This invention aims to solve the problems of conventional financial management systems, such as insufficient personalization due to providing uniform financial advice without considering the user's emotional state, and the lack of a user interface that responds to the user's psychological state.

[0576] 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.

[0577] In this invention, the server includes means for automatically collecting and organizing the user's financial information, means for analyzing consumption data using machine learning technology and identifying consumption patterns, means for generating personalized asset management advice considering the user's emotional state and displaying it on the user's terminal, means for acquiring emotional data and analyzing it using an emotional recognition engine, and means for displaying consumption data in a visually clear format and adjusting the interface according to the user's emotional state. This makes it possible to provide effective financial advice based on the user's emotions and consumption trends, and an optimal user interface that reduces psychological burden.

[0578] A "user" refers to an individual or legal entity that uses the system to manage their finances.

[0579] "Financial information" refers to data related to a user's assets, liabilities, income, expenses, etc.

[0580] "Machine learning technology" refers to data analysis methods that use algorithms to analyze large amounts of data and find patterns.

[0581] "Consumption data" refers to information about transactions conducted by users, primarily including data related to spending.

[0582] "Emotional state" refers to the user's psychological state and is an indicator of their stress and relaxation levels.

[0583] "Asset management advice" refers to suggestions for management and saving that take into account the user's financial situation and spending patterns.

[0584] An "emotion recognition engine" refers to a program or algorithm used to analyze a user's emotional state.

[0585] A "user interface" refers to the display screen and operating environment through which a user directly interacts with the system.

[0586] As a form of implementing the invention, this system provides information that takes into account the user's emotional state in order to support the user's financial management. The system mainly consists of three entities: a server, a terminal, and a user. The role of each entity and the technology used are described below.

[0587] Server roles and technical information

[0588] The server consists of hardware and software for collecting users' financial information via APIs. Specifically, a database system within the server organizes and stores the acquired transaction data. This data includes details such as date, amount, and category, and machine learning techniques are used to analyze spending patterns and generate personalized savings advice. Furthermore, sentiment recognition technology is used to collect sentiment data from the user's input speed and operation history, and this data is analyzed by an emotion engine.

[0589] Terminal roles and interfaces

[0590] The terminal is a device that receives information transmitted from the server and displays financial advice, special offers, and consumption data based on the user's emotional state. The terminal's user interface has a function that dynamically adjusts colors and tones according to the user's emotional state. This allows the user to receive information in a way that suits their psychological state.

[0591] User operation and usage methods

[0592] Users pre-authorize the sharing of information with financial institutions and allow the system to input their daily financial transaction information. Furthermore, users can input data about their emotions, or data can be automatically collected through sensors installed in their devices. Based on this data, users can understand their spending habits and receive optimal saving and investment advice tailored to their emotions.

[0593] For example, if a user is experiencing high stress, the server may take their emotional state into consideration and generate and display advice such as "postpone large purchases." It could also provide discount information on relaxation facilities as a way to help reduce stress.

[0594] An example of a prompt for a generative AI model is, "Please think of advice to give to a user who is feeling stressed." This is expected to promote the collaboration between emotion recognition technology and financial management advice, thereby improving the effectiveness of the user's financial management.

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] Collection of financial data

[0598] The server connects to the API of a financial institution approved by the user and collects transaction data. The input is transaction data in JSON format obtained from the API. This data is parsed to extract information such as date, amount, and category, and stored in the database. The output is structured transaction data. Specifically, the server periodically sends requests to the API endpoint to retrieve the latest transaction information.

[0599] Step 2:

[0600] Data classification and organization

[0601] The server analyzes the stored transaction data and classifies it into categories using machine learning algorithms. The input is the transaction data stored in step 1. For data processing, natural language processing techniques are used to estimate categories based on transaction names and content. The output is transaction data organized by category. Specifically, regular expressions and rule sets are used to classify, for example, "spending at a supermarket" as "groceries."

[0602] Step 3:

[0603] Analysis of consumption patterns

[0604] The server analyzes user spending patterns based on categorized transaction data. The input is the categorized transaction data organized in step 2. Data calculations utilize time series analysis and pivot tables to identify spending trends and peaks. The output is a report showing spending patterns. For example, it visualizes the monthly fluctuations in "food expenses."

[0605] Step 4:

[0606] Collection and analysis of emotional data

[0607] The server collects user emotion data from the terminal. Inputs include user operation history and biometric data obtained from sensors. As part of the data processing, an emotion recognition engine is used to estimate emotional states such as stress and comfort levels. The output is an index indicating the user's emotional state. Specifically, stress levels are quantified by analyzing keyboard input speed and heart rate data from sensors.

[0608] Step 5:

[0609] Personalized advice and bonus information generation

[0610] The server generates advice based on consumption patterns and emotional data. The input is the data obtained from steps 3 and 4. As part of data processing, a generative AI model is used to generate optimal advice and reward information for the user. The output is recommended advice and reward information for the user. Specifically, when the user is under high stress, it will provide advice such as "avoid unnecessary purchases" and present information about rewards at relaxation facilities.

[0611] Step 6:

[0612] User interface adjustments and display

[0613] The device adjusts the user interface based on information sent from the server. Inputs are generated advice and reward information. The output presents the user with a dynamic interface based on their emotional state. Specifically, if high stress levels are detected, the screen's color scheme changes to calming colors, and appropriate advice is displayed as a pop-up.

[0614] (Application Example 2)

[0615] 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."

[0616] Traditionally, user financial management has been limited to simply analyzing spending data and providing general advice. However, approaches that do not take into account the user's emotional state ignore the psychological factors in actual financial behavior. As a result, users may be less receptive to effective advice. Therefore, there is a need for a financial management system that reflects the user's emotional state in real time and achieves deeper personalization.

[0617] 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.

[0618] In this invention, the server includes a mechanism for automatically collecting and organizing the user's financial information, a mechanism for analyzing spending data using machine learning algorithms and identifying spending patterns, and a mechanism for providing emotionally appropriate financial advice using an emotion engine that identifies the user's emotional state in real time. This enables personalized financial management that takes the user's emotional state into consideration.

[0619] A "system that automatically collects and organizes users' financial information" is a system that has the function of organizing transaction data obtained from financial institutions' APIs by category and storing it in a database.

[0620] A "mechanism that analyzes spending data using machine learning algorithms and identifies spending patterns" is a system that uses machine learning technology to analyze collected spending data and identify users' consumption trends and potential for wasteful spending.

[0621] A "system that uses an emotion engine to identify a user's emotional state in real time and provides emotionally responsive financial advice" is a system that analyzes a user's emotional data in real time and generates optimal financial advice based on that emotion.

[0622] The "mechanism for acquiring and notifying users of special offers in real time" is a system that takes into account the user's purchase history and emotional state, acquires relevant special offer information as needed, and notifies the user accordingly.

[0623] A "mechanism that displays expenditure data in a visually easy-to-understand format and provides an interface that adapts to the user's emotional state" is a system that provides a user interface that can dynamically adjust the display layout and design, taking into account the user's psychological state.

[0624] This invention is a system for improving users' financial management and is implemented as follows: A server automatically collects and organizes user transaction data using financial institution APIs. This data includes transaction details such as date, amount, and category. The server further analyzes the collected spending data using machine learning algorithms to identify the user's spending patterns. This analysis reveals spending trends, including potential wasteful spending.

[0625] Furthermore, the server uses an emotion engine to identify the user's emotional state in real time. This emotional data is obtained by analyzing user input information and biometric data acquired from sensors. To provide advice tailored to the user's emotions, the server uses a generative AI model to generate optimal financial advice and special offers, and sends them to the user's terminal.

[0626] The user terminal receives information from the server and displays it to the user. In this process, an interface is provided that visually displays the information in a format adapted to the user's emotional state. For example, if the user is stressed, the tone and design of the interface may change.

[0627] For example, when a user is considering a large purchase at a cafe, the app might offer advice such as, "Considering your current stress level, we recommend you reconsider this purchase. We also have a discount coupon you can use at a nearby relaxing cafe."

[0628] An example of a prompt to a generative AI model would be: "The user's emotional state is {emotional state}, and their most recent transaction was {transaction details}. Please generate the best financial advice for this situation."

[0629] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0630] Step 1:

[0631] The server automatically collects user transaction data using financial institution APIs. The input requires the user's financial account information, and the output is the collected transaction data. This data includes detailed information such as date, amount, and category. This transaction information is stored in a database and prepared for use in subsequent processes.

[0632] Step 2:

[0633] The server analyzes transaction data using machine learning algorithms. The input is the transaction data collected in Step 1, and the output is the result showing the user's consumption patterns and potential for wasteful spending. This analysis is performed to identify consumption trends from historical transaction data and to identify patterns of abnormal spending. The data is processed by the algorithm and made available in a form that can be visualized.

[0634] Step 3:

[0635] The server uses an emotion engine to identify the user's emotional state in real time. Inputs are user input data and biometric data, and output is the current emotional state. Emotional data obtained from sensors and user feedback is analyzed to identify the estimated emotion. This allows for understanding the emotional state and using that information for subsequent advice.

[0636] Step 4:

[0637] The server generates financial advice based on the emotional state and analysis results. It requires the results of steps 2 and 3 as input, and the output is personalized financial advice and bonus information. A generation AI model is used to generate advice sentences tailored to the user's emotions and spending patterns. Based on these prompt sentences, the optimal advice is designed.

[0638] Step 5:

[0639] The terminal visually presents the user with advice and reward information sent from the server. Input is data from the server, and output is what is displayed on the user interface. The user interface displays information with a tone and design that matches the user's emotional state, enhancing user acceptance. This process ensures that users receive the information appropriately and act accordingly.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] [Fourth Embodiment]

[0644] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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".

[0657] This invention is a system that streamlines users' financial management and supports the optimization of their spending. In its implementation form, the system primarily consists of three elements: a server, a terminal, and a user.

[0658] Data collection and organization

[0659] The server integrates with the user's financial institution and automatically collects financial information. This process retrieves banking transactions and credit card usage history via APIs, organizing and storing it in a database. The information is always up-to-date without user intervention.

[0660] Data Analysis

[0661] The server uses machine learning algorithms to analyze the collected financial information. By identifying spending patterns and trends, it reveals which categories users are wasting money in. Based on this information, it is ready to suggest appropriate saving strategies.

[0662] Generating and providing advice

[0663] The server generates savings advice to optimize the user's spending based on the analysis results and sends it to the user's device. The device displays the advice in a visually easy-to-understand dashboard format to help the user understand it.

[0664] (Specific example) If a user is identified as spending too much on food, the server will advise them to "reduce their monthly food expenses by 10% and purchase alternatives." This advice will be visualized on the device along with a graph of the monthly breakdown.

[0665] Notification of special offers

[0666] The server organizes reward information obtained from partner companies in real time, selects information tailored to the user's purchase history and interests, and sends it to the device. The device then notifies the user of this information as a pop-up notification, prompting them to take the necessary action.

[0667] (Specific example) If a discount coupon becomes available for a supermarket the user previously visited, the system will instantly notify the user's device. The user can then immediately use it to reduce their spending.

[0668] Overall, this invention utilizes machine learning and real-time data processing technologies to enable users to intuitively manage their finances and help optimize their daily spending. This allows users to efficiently maintain and improve their financial health.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The server accesses the user's financial institution API and securely retrieves the user's transaction history through an authentication protocol. Here, it collects data including the amount, date, and category information for each transaction.

[0672] Step 2:

[0673] The server classifies the acquired transaction data and stores it in a database. The data is organized by category, making it easy to identify user spending trends in later analysis.

[0674] Step 3:

[0675] The server analyzes the stored data by applying machine learning algorithms. In this process, it identifies the user's spending patterns and extracts categories that show monthly spending fluctuations or wasteful spending.

[0676] Step 4:

[0677] The server generates personalized savings advice based on the analysis results. This advice includes suggestions for spending limits and warnings about unusual spending.

[0678] Step 5:

[0679] The terminal receives advice sent from the server and displays it to the user in a visually easy-to-understand format. Dashboards and graphs are used to ensure that the user can understand it immediately.

[0680] Step 6:

[0681] The server collects the latest information from partner companies' reward databases and selects the most relevant rewards based on the user's purchase history.

[0682] Step 7:

[0683] The device notifies the user in real time of selected reward information. The notification is presented in a pop-up format to attract the user's attention.

[0684] Step 8:

[0685] Users can check the display on their device, change settings as needed, and optimize their actions by following savings advice.

[0686] (Example 1)

[0687] 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".

[0688] In modern society, personal spending management has become increasingly complex, requiring vast amounts of financial data and intricate analysis due to diverse transactions and purchases. As a result, many users need support to ensure their financial health. However, traditional methods have relied on manual organization and analysis of financial information, which has been extremely time-consuming and laborious. Furthermore, it has been difficult to discover useful information about benefits for users or to provide specific and personalized savings suggestions.

[0689] 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.

[0690] This invention includes a server that automatically collects and stores user financial information in a structured format, a digital computing means for analyzing the collected information and highlighting spending trends, and a generative AI model for creating individual savings suggestions and visualizing and displaying them on the user's device. This enables users to efficiently manage their financial information and intuitively utilize personalized, practical savings strategies.

[0691] "Financial information" refers to data related to a user's economic activities, such as banking transactions, credit card usage history, and asset and liability information.

[0692] A "structured format" refers to a data format organized according to specific rules, where data is classified and organized in a way that facilitates information handling.

[0693] "Digital computing methods" refer to methods of processing and analyzing data using computers and related technologies, enabling efficient information analysis.

[0694] A "generative AI model" is an algorithm that uses artificial intelligence to learn from data and generate new information and suggestions, and is particularly used to generate personalized recommendations for users.

[0695] "Savings suggestions" refer to specific advice and plans to optimize a user's spending, encouraging them to reduce waste and make effective use of their assets.

[0696] "Means of visualization and display" refers to technologies that graphically convert data and information and display them in a format that users can intuitively understand.

[0697] This invention is a system for users to effectively manage their financial information and optimize their spending. The system consists of three elements: a server, a terminal, and a user.

[0698] The server automatically collects information about the user's financial institutions using their APIs. This includes banking transactions and credit card usage history. This information is stored in a structured format within a database. The software used includes database management systems and API integration modules.

[0699] Next, the server uses digital computing tools to analyze the collected information. Specifically, it uses programming languages ​​such as Python and machine learning libraries like scikit-learn and TensorFlow to reveal spending trends. This analysis identifies which categories of spending the user needs to review and uses this to create individual savings suggestions.

[0700] Using a generative AI model, the server generates optimal savings suggestions for each user. These generated suggestions are sent to the user's device. The device visualizes the suggestions and presents them to the user through a graphical user interface. The JavaScript library D3.js is used for visualization.

[0701] For example, if a user's food expenses are determined to be excessive, the server will generate a suggestion such as "Reduce your monthly food expenses by 10% and purchase alternatives." An example of a prompt message would be, "Analyze the user's food spending data and suggest the best way to save money."

[0702] Furthermore, the server retrieves reward information from partner companies in real time, selects rewards that match the user's transaction history, and notifies them. This feature allows users to instantly see available promotions.

[0703] Thus, this invention is a system that provides functions to enable users to intuitively manage their financial information and improve their financial health.

[0704] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0705] Step 1:

[0706] The server collects transaction information from financial institutions linked to by the user using APIs. The input is API request data, and the output is the user's financial transaction information. The server periodically executes this process to keep the financial data up-to-date. Here, we perform the specific action of retrieving transaction history from financial institutions using API keys and saving it to a local database.

[0707] Step 2:

[0708] The server stores and organizes collected transaction information in a structured format within a database. Input is raw transaction data, while output is transaction data organized by category. The server classifies transaction details into categories such as food expenses, transportation expenses, and utility expenses, making the data easier to use for subsequent analysis. During this process, it updates the database to enable efficient data retrieval.

[0709] Step 3:

[0710] The server utilizes organized data, employs digital computing tools, and performs analysis using machine learning algorithms. The input is categorized transaction data, and the output is an analysis showing spending trends. Using the Python programming language and scikit-learn, it performs specific actions to detect particular spending patterns and anomalies, thereby revealing wasteful spending by users.

[0711] Step 4:

[0712] The server uses a generative AI model to create personalized savings suggestions based on the analysis results. The input is the analysis results, and the output is a savings suggestion tailored to the user. The prompt message "Analyze the user's food expenditure data and suggest the best savings strategy" is input to the generative AI model, and the AI ​​model generates the suggestions.

[0713] Step 5:

[0714] The server sends the generated savings suggestions to the user's terminal, which displays them in a visually intuitive interface. The input is the savings suggestions, and the output is the visualized user screen. The terminal uses JavaScript's D3.js to perform the specific actions of displaying the suggestions on graphs and dashboards, helping the user understand the suggestions more easily.

[0715] Step 6:

[0716] The server retrieves reward information from partner companies in real time, selects relevant rewards based on the user's transaction history, and notifies the terminal. Inputs are reward information and user transaction history, while output is the reward information notified to the user. The server specifically selects rewards that match the user's interests and past transactions, and the terminal then displays a pop-up notification.

[0717] (Application Example 1)

[0718] 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".

[0719] Modern consumers face increasing challenges in efficiently managing their finances due to their diverse spending patterns. Identifying wasteful spending and practicing daily savings, in particular, requires significant time and effort. Furthermore, users often miss out on valuable savings opportunities because they are unable to access timely information about rewards and benefits related to the products and services they purchase. There is a need for solutions to these challenges, enabling consumers to optimize their spending while effectively utilizing reward information.

[0720] 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.

[0721] This invention includes a server that automatically collects and organizes users' financial information, a means for analyzing spending data using machine learning algorithms and identifying spending patterns, and a means for controlling an information-providing terminal that is integrated into an electronic payment system and assists in optimizing spending using financial transaction information. This makes it possible to streamline consumers' financial management and optimize spending through saving and utilizing reward information.

[0722] "User financial information" refers to information about transactions conducted by individuals or corporations using financial institutions or payment services, and specifically includes account balances, transaction details, and credit card usage history.

[0723] "Machine learning algorithms" refer to algorithms that analyze patterns based on large amounts of data to make predictions and decisions. They have the characteristic of learning from data and improving their accuracy.

[0724] "Expenditure data" refers to a collection of information related to a user's consumption activities, including individual transactions, purchased goods and services, and their amounts.

[0725] "Means for identifying spending patterns" refer to methods and techniques for analyzing consistency and trends in a user's spending behavior. This makes it possible to identify wasteful spending and suggest appropriate saving strategies.

[0726] An "electronic payment system" refers to a system that allows for the completion of payments for goods and services using electronic methods rather than cash. This includes online banking and digital wallets.

[0727] An "information provision terminal" refers to an electronic device used to present information to users, such as a smartphone, tablet, or personal computer. It serves as a means of delivering information visually.

[0728] This invention is a system designed to streamline users' financial management and support the optimization of their spending. The system mainly consists of a server, an information terminal, and the user.

[0729] The server automatically collects financial information from the user's financial institution via an API and organizes it in a database. This information, including transaction details and credit card usage history, is kept up-to-date without user intervention. The server uses machine learning algorithms to analyze the collected financial information and identify the user's spending patterns. This clarifies which categories of spending are wasteful and helps prepare appropriate saving strategies.

[0730] The information terminal presents users with personalized savings advice generated based on analysis results. This advice is displayed in a visually easy-to-understand dashboard format to aid user comprehension. Furthermore, the server retrieves and organizes reward information from partner companies and notifies users in real time of selected information based on their purchase history. Notifications on the terminal allow users to immediately take advantage of rewards and have the opportunity to reduce their spending.

[0731] This system utilizes machine learning platforms such as TensorFlow and PyTorch, and implements an intuitive user interface using React Native on information delivery devices such as smartphones. By integrating it into electronic payment systems, it is possible to optimize spending by effectively utilizing financial transaction information.

[0732] For example, if the analysis reveals that a user regularly spends money on "weekend cafes," the app might offer advice such as, "Reduce your weekend cafe spending by 20% and try recipes you can enjoy at home."

[0733] An example of a prompt to the generating AI model is: "Analyze the user's bank account transaction history, identify unnecessary spending, and generate effective savings advice."

[0734] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0735] Step 1:

[0736] The server collects financial information from users' financial institutions via an API. Inputs include transaction details and credit card usage history obtained through the API. This data is organized in a database on the server and updated and managed on a per-user basis.

[0737] Step 2:

[0738] The server analyzes collected financial information using machine learning algorithms. The input is financial information stored in a database, and the output is a modeled result of spending patterns. Specifically, the algorithm identifies trends in the data and uses that to determine signs of wasteful spending.

[0739] Step 3:

[0740] The server generates personalized savings advice based on the analysis results. The input is the analysis results of spending patterns, and the output is a set of savings advice. Specifically, it constructs text that suggests more effective saving methods based on the user's spending behavior.

[0741] Step 4:

[0742] The server sends the generated savings advice to the user's information terminal. The terminal receives this and displays it in a visually easy-to-understand dashboard format. The input is the aforementioned savings advice, and the output is the visual display presented to the user. Specifically, the interface is designed using React Native.

[0743] Step 5:

[0744] The server retrieves reward information from partner companies and filters the information based on the user's purchase history. The input consists of reward information obtained from partner companies and the user's purchase data, while the output is a set of reward information optimized for the user. To notify users of the filtered reward information in real time, the server filters the input purchase data and reward information, extracting only the information relevant to the user.

[0745] Step 6:

[0746] The device displays the reward information to the user as a pop-up notification. The input is the reward information sent from the server, and the output is the pop-up notification displayed on the user's device. This step allows the device to use its notification function to prompt the user to take immediate action.

[0747] 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.

[0748] This invention provides a system that supports users' financial management by incorporating an emotion engine to offer financial advice and special offers that take into account the user's emotional state. The system mainly consists of a server, terminals, and users.

[0749] Collection and organization of financial information

[0750] The server collects transaction data using the user's financial institution API. This data is organized by category and stored in a database. Each transaction includes details such as date, amount, and category.

[0751] Data analysis and advice generation

[0752] The server uses machine learning algorithms to analyze spending data. This identifies the user's financial trends and spending patterns, and indicates potential areas of wasteful spending. Based on this information, advice for saving and optimizing spending is generated and sent to the user's terminal.

[0753] Utilizing the Emotion Engine

[0754] The server can collect user emotion data and utilize an emotion engine to recognize the user's emotional state in real time. Emotion data can be obtained, for example, by analyzing user input information and biometric data obtained from sensors.

[0755] Personalized notifications to users

[0756] Based on information received from the server, the device displays emotionally-sensitive financial advice and special offers to the user. For example, if the user is feeling stressed, it will provide information to encourage calm decision-making.

[0757] (Specific example) If the emotion engine detects a high-stress state in the user, the server takes that emotional state into consideration and generates and displays advice such as "postpone big purchases" on the device. Furthermore, as a reward for stress reduction, it provides information on discounts at relaxation facilities.

[0758] Adjusting visual information and interfaces

[0759] The device can dynamically change the tone and design of its user interface based on the analysis results of the emotion engine. This makes it possible to present information that is adapted to the user's psychological state.

[0760] Ultimately, by incorporating an emotion engine, the present invention can achieve deeper personalization that takes into account the user's emotional state, thereby enhancing the effectiveness of financial management.

[0761] The following describes the processing flow.

[0762] Step 1:

[0763] The server securely retrieves financial transaction data from the user's financial institution API. The retrieved data includes the transaction amount, date, and category, and this data is stored in a database.

[0764] Step 2:

[0765] The server uses machine learning algorithms to analyze transaction data stored in the database, identifying the user's spending patterns and financial trends. This analysis reveals tendencies for wasteful spending and opportunities for saving.

[0766] Step 3:

[0767] Users provide emotional information to the system using their historical data and biosensors. Emotional data is collected through daily interactions, facial recognition, and voice analysis.

[0768] Step 4:

[0769] The server analyzes the collected emotional data using an emotion engine to recognize the user's emotional state in real time. This recognition result is used as an important element in the advice generation process.

[0770] Step 5:

[0771] Based on the analysis results, the server generates money-saving advice tailored to the user's emotional state. For users experiencing high stress levels, it recommends reconsidering non-essential purchases and taking actions that promote relaxation.

[0772] Step 6:

[0773] The device displays advice sent from the server to the user in a visually easy-to-understand format. The app's dashboard adjusts the color scheme and tone according to the user's situation.

[0774] Step 7:

[0775] The server collects special offers from partner companies and selects discounts and campaign information relevant to the user's purchase history and emotional state. It prioritizes content that enhances the user's psychological comfort and purchasing intent.

[0776] Step 8:

[0777] The device notifies users in real time of selected reward information in a format tailored to their emotional state. Notifications are delivered in a soft tone when the user is relaxed, and in a more subdued tone to help them avoid impulsive purchases.

[0778] Step 9:

[0779] Users review the information displayed on their device and choose actions based on the advice. They can adjust settings as needed and set new savings goals.

[0780] (Example 2)

[0781] 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".

[0782] This invention aims to solve the problems of conventional financial management systems, such as insufficient personalization due to providing uniform financial advice without considering the user's emotional state, and the lack of a user interface that responds to the user's psychological state.

[0783] 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.

[0784] In this invention, the server includes means for automatically collecting and organizing the user's financial information, means for analyzing consumption data using machine learning technology and identifying consumption patterns, means for generating personalized asset management advice considering the user's emotional state and displaying it on the user's terminal, means for acquiring emotional data and analyzing it using an emotional recognition engine, and means for displaying consumption data in a visually clear format and adjusting the interface according to the user's emotional state. This makes it possible to provide effective financial advice based on the user's emotions and consumption trends, and an optimal user interface that reduces psychological burden.

[0785] A "user" refers to an individual or legal entity that uses the system to manage their finances.

[0786] "Financial information" refers to data related to a user's assets, liabilities, income, expenses, etc.

[0787] "Machine learning technology" refers to data analysis methods that use algorithms to analyze large amounts of data and find patterns.

[0788] "Consumption data" refers to information about transactions conducted by users, primarily including data related to spending.

[0789] "Emotional state" refers to the user's psychological state and is an indicator of their stress and relaxation levels.

[0790] "Asset management advice" refers to suggestions for management and saving that take into account the user's financial situation and spending patterns.

[0791] An "emotion recognition engine" refers to a program or algorithm used to analyze a user's emotional state.

[0792] A "user interface" refers to the display screen and operating environment through which a user directly interacts with the system.

[0793] As a form of implementing the invention, this system provides information that takes into account the user's emotional state in order to support the user's financial management. The system mainly consists of three entities: a server, a terminal, and a user. The role of each entity and the technology used are described below.

[0794] Server roles and technical information

[0795] The server consists of hardware and software for collecting users' financial information via APIs. Specifically, a database system within the server organizes and stores the acquired transaction data. This data includes details such as date, amount, and category, and machine learning techniques are used to analyze spending patterns and generate personalized savings advice. Furthermore, sentiment recognition technology is used to collect sentiment data from the user's input speed and operation history, and this data is analyzed by an emotion engine.

[0796] Terminal roles and interfaces

[0797] The terminal is a device that receives information transmitted from the server and displays financial advice, special offers, and consumption data based on the user's emotional state. The terminal's user interface has a function that dynamically adjusts colors and tones according to the user's emotional state. This allows the user to receive information in a way that suits their psychological state.

[0798] User operation and usage methods

[0799] Users pre-authorize the sharing of information with financial institutions and allow the system to input their daily financial transaction information. Furthermore, users can input data about their emotions, or data can be automatically collected through sensors installed in their devices. Based on this data, users can understand their spending habits and receive optimal saving and investment advice tailored to their emotions.

[0800] For example, if a user is experiencing high stress, the server may take their emotional state into consideration and generate and display advice such as "postpone large purchases." It could also provide discount information on relaxation facilities as a way to help reduce stress.

[0801] An example of a prompt for a generative AI model is, "Please think of advice to give to a user who is feeling stressed." This is expected to promote the collaboration between emotion recognition technology and financial management advice, thereby improving the effectiveness of the user's financial management.

[0802] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0803] Step 1:

[0804] Collection of financial data

[0805] The server connects to the API of a financial institution approved by the user and collects transaction data. The input is transaction data in JSON format obtained from the API. This data is parsed to extract information such as date, amount, and category, and stored in the database. The output is structured transaction data. Specifically, the server periodically sends requests to the API endpoint to retrieve the latest transaction information.

[0806] Step 2:

[0807] Data classification and organization

[0808] The server analyzes the stored transaction data and classifies it into categories using machine learning algorithms. The input is the transaction data stored in step 1. For data processing, natural language processing techniques are used to estimate categories based on transaction names and content. The output is transaction data organized by category. Specifically, regular expressions and rule sets are used to classify, for example, "spending at a supermarket" as "groceries."

[0809] Step 3:

[0810] Analysis of consumption patterns

[0811] The server analyzes user spending patterns based on categorized transaction data. The input is the categorized transaction data organized in step 2. Data calculations utilize time series analysis and pivot tables to identify spending trends and peaks. The output is a report showing spending patterns. For example, it visualizes the monthly fluctuations in "food expenses."

[0812] Step 4:

[0813] Collection and analysis of emotional data

[0814] The server collects user emotion data from the terminal. Inputs include user operation history and biometric data obtained from sensors. As part of the data processing, an emotion recognition engine is used to estimate emotional states such as stress and comfort levels. The output is an index indicating the user's emotional state. Specifically, stress levels are quantified by analyzing keyboard input speed and heart rate data from sensors.

[0815] Step 5:

[0816] Personalized advice and bonus information generation

[0817] The server generates advice based on consumption patterns and emotional data. The input is the data obtained from steps 3 and 4. As part of data processing, a generative AI model is used to generate optimal advice and reward information for the user. The output is recommended advice and reward information for the user. Specifically, when the user is under high stress, it will provide advice such as "avoid unnecessary purchases" and present information about rewards at relaxation facilities.

[0818] Step 6:

[0819] User interface adjustments and display

[0820] The device adjusts the user interface based on information sent from the server. Inputs are generated advice and reward information. The output presents the user with a dynamic interface based on their emotional state. Specifically, if high stress levels are detected, the screen's color scheme changes to calming colors, and appropriate advice is displayed as a pop-up.

[0821] (Application Example 2)

[0822] 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".

[0823] Traditionally, user financial management has been limited to simply analyzing spending data and providing general advice. However, approaches that do not take into account the user's emotional state ignore the psychological factors in actual financial behavior. As a result, users may be less receptive to effective advice. Therefore, there is a need for a financial management system that reflects the user's emotional state in real time and achieves deeper personalization.

[0824] 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.

[0825] In this invention, the server includes a mechanism for automatically collecting and organizing the user's financial information, a mechanism for analyzing spending data using machine learning algorithms and identifying spending patterns, and a mechanism for providing emotionally appropriate financial advice using an emotion engine that identifies the user's emotional state in real time. This enables personalized financial management that takes the user's emotional state into consideration.

[0826] A "system that automatically collects and organizes users' financial information" is a system that has the function of organizing transaction data obtained from financial institutions' APIs by category and storing it in a database.

[0827] A "mechanism that analyzes spending data using machine learning algorithms and identifies spending patterns" is a system that uses machine learning technology to analyze collected spending data and identify users' consumption trends and potential for wasteful spending.

[0828] A "system that uses an emotion engine to identify a user's emotional state in real time and provides emotionally responsive financial advice" is a system that analyzes a user's emotional data in real time and generates optimal financial advice based on that emotion.

[0829] The "mechanism for acquiring and notifying users of special offers in real time" is a system that takes into account the user's purchase history and emotional state, acquires relevant special offer information as needed, and notifies the user accordingly.

[0830] A "mechanism that displays expenditure data in a visually easy-to-understand format and provides an interface that adapts to the user's emotional state" is a system that provides a user interface that can dynamically adjust the display layout and design, taking into account the user's psychological state.

[0831] This invention is a system for improving users' financial management and is implemented as follows: A server automatically collects and organizes user transaction data using financial institution APIs. This data includes transaction details such as date, amount, and category. The server further analyzes the collected spending data using machine learning algorithms to identify the user's spending patterns. This analysis reveals spending trends, including potential wasteful spending.

[0832] Furthermore, the server uses an emotion engine to identify the user's emotional state in real time. This emotional data is obtained by analyzing user input information and biometric data acquired from sensors. To provide advice tailored to the user's emotions, the server uses a generative AI model to generate optimal financial advice and special offers, and sends them to the user's terminal.

[0833] The user terminal receives information from the server and displays it to the user. In this process, an interface is provided that visually displays the information in a format adapted to the user's emotional state. For example, if the user is stressed, the tone and design of the interface may change.

[0834] For example, when a user is considering a large purchase at a cafe, the app might offer advice such as, "Considering your current stress level, we recommend you reconsider this purchase. We also have a discount coupon you can use at a nearby relaxing cafe."

[0835] An example of a prompt to a generative AI model would be: "The user's emotional state is {emotional state}, and their most recent transaction was {transaction details}. Please generate the best financial advice for this situation."

[0836] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0837] Step 1:

[0838] The server automatically collects user transaction data using financial institution APIs. The input requires the user's financial account information, and the output is the collected transaction data. This data includes detailed information such as date, amount, and category. This transaction information is stored in a database and prepared for use in subsequent processes.

[0839] Step 2:

[0840] The server analyzes transaction data using machine learning algorithms. The input is the transaction data collected in Step 1, and the output is the result showing the user's consumption patterns and potential for wasteful spending. This analysis is performed to identify consumption trends from historical transaction data and to identify patterns of abnormal spending. The data is processed by the algorithm and made available in a form that can be visualized.

[0841] Step 3:

[0842] The server uses an emotion engine to identify the user's emotional state in real time. Inputs are user input data and biometric data, and output is the current emotional state. Emotional data obtained from sensors and user feedback is analyzed to identify the estimated emotion. This allows for understanding the emotional state and using that information for subsequent advice.

[0843] Step 4:

[0844] The server generates financial advice based on the emotional state and analysis results. It requires the results of steps 2 and 3 as input, and the output is personalized financial advice and bonus information. A generation AI model is used to generate advice sentences tailored to the user's emotions and spending patterns. Based on these prompt sentences, the optimal advice is designed.

[0845] Step 5:

[0846] The terminal visually presents the user with advice and reward information sent from the server. Input is data from the server, and output is what is displayed on the user interface. The user interface displays information with a tone and design that matches the user's emotional state, enhancing user acceptance. This process ensures that users receive the information appropriately and act accordingly.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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."

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] The following is further disclosed regarding the embodiments described above.

[0869] (Claim 1)

[0870] A means of automatically collecting and organizing users' financial information,

[0871] A method for analyzing spending data using machine learning algorithms and identifying spending patterns,

[0872] A means of generating individual savings advice and presenting it to the user's terminal,

[0873] A means of obtaining special offer information in real time and notifying users,

[0874] A means of providing a user interface that displays expenditure data in a visually easy-to-understand format,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] It includes a means for filtering reward information based on the user's purchase history.

[0878] The system according to claim 1.

[0879] (Claim 3)

[0880] It includes a means to notify users of budget alerts based on their settings.

[0881] The system according to claim 1.

[0882] "Example 1"

[0883] (Claim 1)

[0884] A means of automatically collecting users' financial information and storing it in a structured format,

[0885] A means of analyzing information collected using digital computing methods to clarify spending trends,

[0886] A means of creating individual savings suggestions using a generative AI model and visualizing and displaying them on the user's device,

[0887] A means of selecting reward information based on transaction records and notifying the user in real time,

[0888] A means of providing an interface that delivers financial information to users in a visually easy-to-understand manner,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] It includes a means of selecting reward information based on the user's past transaction information.

[0892] The system according to claim 1.

[0893] (Claim 3)

[0894] It has a means to manage the budget based on user-set goals and to notify alerts.

[0895] The system according to claim 1.

[0896] "Application Example 1"

[0897] (Claim 1)

[0898] A means of automatically collecting and organizing users' financial information,

[0899] A method for analyzing spending data using machine learning algorithms and identifying spending patterns,

[0900] A means of generating individual savings advice and displaying it on an information display terminal,

[0901] A means of obtaining special offer information in real time and notifying information users,

[0902] A means of providing a user interface that displays expenditure data in a visually easy-to-understand format,

[0903] A means for controlling an information-providing terminal that is integrated into an electronic payment system and uses financial transaction information to support the optimization of spending,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] It includes a means for selecting reward information based on the user's purchase history.

[0907] The system according to claim 1.

[0908] (Claim 3)

[0909] It is equipped with a means to notify users of savings advice and special offers in real time on the information provision terminal.

[0910] The system according to claim 1.

[0911] "Example 2 of combining an emotion engine"

[0912] (Claim 1)

[0913] A means of automatically collecting and organizing users' financial information,

[0914] A means of analyzing consumption data using machine learning techniques and identifying consumption patterns,

[0915] A means for generating individual asset management advice that takes into account the user's emotional state and displaying it on the user's terminal,

[0916] A means of acquiring emotional data and analyzing it using an emotional recognition engine,

[0917] A means of displaying consumption data in a visually clear format and adjusting the interface according to the user's emotional state,

[0918] A system that includes this.

[0919] (Claim 2)

[0920] The system according to claim 1, comprising means for selecting reward information based on the user's purchasing behavior.

[0921] (Claim 3)

[0922] The system according to claim 1, comprising means for notifying the user of spending warnings based on user settings.

[0923] "Application example 2 when combining with an emotional engine"

[0924] (Claim 1)

[0925] A mechanism that automatically collects and organizes users' financial information,

[0926] A mechanism that uses machine learning algorithms to analyze spending data and identify spending patterns,

[0927] A mechanism that generates individual savings advice and presents it to the user's terminal,

[0928] A mechanism that acquires special offer information in real time and notifies users,

[0929] A system that uses an emotion engine to identify the user's emotional state in real time and provides emotionally tailored financial advice,

[0930] A mechanism that displays expenditure data in a visually easy-to-understand format and provides an interface that adapts to emotional states,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] It includes a mechanism to filter reward information based on the user's purchase history and emotional state.

[0934] The system according to claim 1.

[0935] (Claim 3)

[0936] It features a mechanism that notifies users of budget alerts based on their settings and adjusts the tone of the alerts based on the user's emotional state.

[0937] The system according to claim 1. [Explanation of Symbols]

[0938] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically collecting and organizing users' financial information, A method for analyzing spending data using machine learning algorithms and identifying spending patterns, A means of generating individual savings advice and presenting it to the user's terminal, A means of obtaining special offer information in real time and notifying users, A means of providing a user interface that displays expenditure data in a visually easy-to-understand format, A system that includes this.

2. It includes a means for filtering reward information based on the user's purchase history. The system according to claim 1.

3. It includes a means to notify users of budget alerts based on their settings. The system according to claim 1.

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