system

Abstract

We provide the system. [Solution] A means of collecting user transaction information and storing it in a database, A means for analyzing the aforementioned transaction information, classifying it into categories, and calculating the progress of expenditures, A means to detect spending exceeding the set budget and generate alerts to notify users, A means of automatically purchasing financial products based on investment criteria, A means of monitoring the performance of financial products held and making adjustments as necessary, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern household management, there is a problem that it is difficult to appropriately classify individual transaction information and promptly notify the user when the expenditure exceeds the set budget. Also, in the family's investment activities, it is very difficult to provide a mechanism for automatically purchasing financial products at an appropriate timing based on the investment criteria set by the user and optimizing the operation. Such problems are increasing the need for the general user's lack of financial knowledge and the need to clarify complex household management requirements.

Means for Solving the Problems

[0005] This invention proposes a system for collecting user transaction information and storing it in a database. By analyzing the transaction information, classifying it into categories, and calculating the progress of expenditures against the budget, the system immediately alerts the user if expenditures exceed the budget. Furthermore, it provides a means to automatically purchase financial products according to the investment criteria set by the user, and to periodically monitor the investment status and make necessary adjustments. Such a system can efficiently support users' household financial management and investment activities, and optimize their individual financial situations.

[0006] "Transaction information" refers to detailed data about payments and receipts made by users, including date, amount, payee, category, etc.

[0007] A "database" is a system of information aggregation that systematically stores, manages, and allows for the retrieval of collected transaction information.

[0008] "Analysis" is the process of examining collected data and information to extract specific patterns and trends.

[0009] A "category" is a group or set of transaction information used to classify information based on common criteria or conditions.

[0010] A "budget" is a financial plan that sets limits on expenditures within a specific period, and serves as a benchmark in financial management.

[0011] "Exceeding" is a term that describes a situation that exceeds a set limit or budget.

[0012] An "alert" is a function that notifies the user of a warning or alert when certain conditions occur.

[0013] "Investment criteria" refer to the conditions and standards that users set as factors to consider when making decisions about managing their assets.

[0014] "Financial products" refer to products for fund management, such as securities, stocks, insurance, etc. as investment targets.

[0015] "Management" refers to activities for managing assets or funds and generating profits through investment and reinvestment.

[0016] "Adjustment" is a process for changing settings or policies according to plans or management situations to achieve optimization.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[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 for streamlining users' financial management, particularly supporting daily transaction management and investment activities. Specific embodiments are described below.

[0039] The server automatically collects transaction information from the user's financial institution. This information includes payment date, amount, location and service used, and category. The server stores this data in a database and manages the information centrally.

[0040] The server then analyzes the stored transaction information and categorizes it into predefined categories, such as groceries, transportation, entertainment, and housing. The server also calculates monthly spending for each category and compares the total spending to the budget. If the budget is exceeded in any category, the server immediately generates an alert and notifies the user.

[0041] The device receives this alert information and displays it to the user. The alert can include the reason for the overspending and specific advice on how to save money. For example, "Your food expenses this month exceeded your budget. You can save money next month by eating out one less time." Based on this information, the user can review their spending in the following months.

[0042] Furthermore, users can set up their own investment plans through their devices. This includes selecting the monthly investment amount and the financial products to target (such as stocks and mutual funds). The server automatically purchases the most suitable financial products on behalf of the user based on the set investment criteria. This process is repeated at intervals specified by the user.

[0043] Furthermore, the server periodically monitors the performance of financial instruments and rebalances the portfolio if necessary. These features enable users to continuously make optimal investments in response to market fluctuations. For example, if stock prices fluctuate significantly, the system can maintain portfolio performance by selling some of its holdings or purchasing new ones as needed.

[0044] By implementing this system, users can efficiently and effectively manage their household expenses and invest their assets, thereby improving their overall financial situation.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server prepares to automatically collect user transaction information from financial institutions and store it in a database. This information includes date, amount, customer, and payment category.

[0048] Step 2:

[0049] The server analyzes the collected transaction information and classifies it into pre-defined categories (e.g., food expenses, transportation expenses, entertainment). Using natural language processing technology, it automatically assigns the transaction information to the appropriate category based on the textual content.

[0050] Step 3:

[0051] The server aggregates monthly spending for each category and compares the set budget with actual spending. This allows the user to track budget progress.

[0052] Step 4:

[0053] If spending exceeds the set budget, the server generates an alert and automatically creates savings advice for the overspending category based on past data.

[0054] Step 5:

[0055] The device receives alerts and savings advice sent from the server and notifies the user. Through these notifications, the user can review their current spending and consider taking any necessary action.

[0056] Step 6:

[0057] Users input and configure their investment plans through their devices. This includes details such as the investment amount and the financial products they will invest in.

[0058] Step 7:

[0059] The server automatically purchases the specified financial products based on the investment plan set by the user. After the transaction is completed, the purchase history is recorded in the database.

[0060] Step 8:

[0061] The server periodically monitors the performance of the financial instruments held by the user and market conditions, and rebalances the portfolio as needed. This ensures that optimal management continues.

[0062] This series of processing steps enables users to efficiently manage their household finances and engage in investment activities.

[0063] (Example 1)

[0064] 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."

[0065] Many modern users find it time-consuming and cumbersome to manage complex financial transaction information and investment activities. This can lead to increased wasteful spending and insufficient investment optimization, potentially causing personal financial planning to fail. Furthermore, traditional methods struggle to effectively organize transaction information and analyze financial situations, resulting in a lack of tools to support rational decision-making based on this information.

[0066] 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.

[0067] In this invention, the server includes means for collecting and storing the user's financial information in a data storage device; means for analyzing the financial information, organizing it into categories, and calculating the expenditure status; means for detecting overspending relative to a set budget, generating warnings, and notifying the user; means for automatically acquiring financial assets based on investment conditions; and means for monitoring the operational status of held financial assets and making necessary changes. This enables the user to streamline the management of complex financial transaction information, support rational decision-making, and optimize their overall financial situation.

[0068] A "user" refers to an individual or legal entity that uses the system to manage financial information or engage in investment activities.

[0069] "Financial information" refers to all data related to a user's financial activities, including transaction data, spending details, budgets, and investment information.

[0070] A "data storage device" refers to a system built on a server that securely and efficiently stores financial information collected from users.

[0071] "Analysis" refers to the process of analyzing collected financial information to extract meaningful patterns and insights.

[0072] "Classification" refers to organizing analyzed financial information into specific categories based on established criteria.

[0073] "Expenditure status" refers to information showing the total amount and breakdown of expenditures in the user's financial activities.

[0074] A "warning" is a notification generated when a user's spending exceeds certain conditions, and refers to a message intended to draw the user's attention.

[0075] "Investment conditions" refer to the criteria and guidelines set by the user regarding the purchase and management of various financial assets.

[0076] "Financial assets" refer to assets traded in financial markets, such as stocks, bonds, and mutual funds.

[0077] "Operating status" refers to information about how the financial assets held are currently functioning and generating value.

[0078] "Change" refers to the process of making necessary adjustments and reallocations in accordance with the composition and purpose of financial assets.

[0079] This invention is a system for streamlining users' financial management, significantly reducing user time and effort by collecting, analyzing, and automatically investing financial information. The system consists of a server, a data storage device, and a user interface.

[0080] The server automatically collects users' financial information via APIs. This includes transaction data and spending details, which are stored in a database. The server also uses analytical algorithms to categorize the information and generate foundational data for tracking spending progress. This utilizes natural language processing (NLP) techniques and computational software. Specific software used includes Python and SQL.

[0081] Based on the analyzed information, the server generates a warning if spending exceeds a set threshold. This warning is sent to the user via their device. The user can receive the warning on an application on their smartphone or PC and review their spending. For example, a message like, "Your entertainment expenses this month have exceeded your budget. You can save money by cutting back on movie tickets next month by one," might be displayed.

[0082] Users can set investment criteria for their long-term financial management. The server then automatically purchases financial assets through the brokerage's API based on these criteria. The server also periodically monitors the operational status and responds immediately if any changes are needed. This allows users to maintain appropriate financial strategies in response to market fluctuations.

[0083] As a concrete example, a user can input a prompt into the AI ​​model that says, "I want to record my monthly fixed expenses and receive a warning if I exceed my budget. I want any additional funds to be automatically invested in an S&P 500 index fund." Based on this prompt, the system can then perform optimal financial management. This prompt is expected to ensure that the system operates in a way that aligns with the user's specific needs.

[0084] In this way, users can effectively manage their financial assets and daily expenses, thereby improving their overall financial situation.

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

[0086] Step 1:

[0087] The server collects user transaction information through financial institutions' APIs. User authentication information and the API endpoint are required as input, which retrieves transaction data (date, time, amount, category, etc.). The retrieved data is passed to a script via secure communication and immediately stored in a data storage device. Detailed information for each transaction is stored as input data in the database.

[0088] Step 2:

[0089] The server executes a Python script to analyze the stored transaction information. The input is unclassified transaction data from the database, and the output is data where each transaction is categorized. Utilizing natural language processing techniques, the script automatically classifies transactions into categories such as groceries or transportation expenses based on the keywords used.

[0090] Step 3:

[0091] The server calculates the total expenditure for each category from the analyzed data. The input is classified transaction data, and the output is the monthly total expenditure for each category. A script is used for the calculation, and it is compared with the user's set budget. Specifically, the expenditure for each category is compared with past history to perform trend analysis.

[0092] Step 4:

[0093] The server generates an alert if spending exceeds the set budget. The input is the amount of spending that exceeded the budget, and the output is an alert message. The alert content includes saving tips that the user should take. For example, a warning might be generated saying, "Your spending on eating out is excessive. You can save money by cooking more at home."

[0094] Step 5:

[0095] The device notifies the user of the received warning message. The input is the warning notification sent from the server, and the output is the warning content displayed on the user's device screen. Specifically, on mobile devices, a push notification is triggered to display a visually verifiable notification.

[0096] Step 6:

[0097] Users set their investment conditions using their smartphone or PC. The input consists of the user's investment instructions and desired conditions, and the output is a specific investment strategy based on this input. Through the user interface, users select which assets to invest in and how much to invest.

[0098] Step 7:

[0099] The server automatically purchases financial assets based on the configured investment conditions. Inputs are the user's investment conditions and market data, while output is information about the purchased financial assets. Specifically, a buy order is placed via a securities API, and a purchase confirmation message is generated.

[0100] Step 8:

[0101] The server periodically monitors market trends and adjusts the portfolio as needed. Inputs are market data and data on held financial assets, and output is a rebalanced portfolio. An algorithm is used to quickly buy and sell in response to market fluctuations, optimizing asset value.

[0102] (Application Example 1)

[0103] 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."

[0104] In today's world, personal financial management has become increasingly complex, making manual organization and analysis of transaction information difficult. Furthermore, while timely decision-making and management are crucial in investment activities, users face a significant burden in continuously maintaining optimal asset management. To address these challenges, automated management of transaction information and real-time monitoring of asset status are essential.

[0105] 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.

[0106] In this invention, the server includes means for collecting and storing the user's financial information on a recording medium, means for analyzing the financial information, categorizing it, and calculating the progress of expenditures, and means for monitoring the status of the financial products held and making adjustments as necessary. This enables the user to easily manage their daily expenditures while making timely and optimal decisions in their investment activities.

[0107] A "user" is an entity that uses the system to manage financial information and receive support for spending and investment activities.

[0108] "Financial information" refers to all data related to banking transactions, credit card payments, income, and expenses.

[0109] "Recording medium" refers to digital storage devices such as databases and cloud storage used to store collected financial information.

[0110] "Analysis" refers to the activity of analyzing financial information to identify the details of each transaction and classify them into relevant categories.

[0111] "Classification" refers to organizing analyzed transaction information into different categories or segments based on specific criteria.

[0112] "Progress" refers to information that shows the ratio or status of expenditures relative to the budget, and is an indicator used to understand the actual status of implementation against the plan.

[0113] "Monitoring" refers to the process of continuously checking the status of financial instruments held, which is necessary to maintain the proper management of an investment portfolio.

[0114] "Adjustment" refers to actions taken to optimize investment performance by buying and selling or restructuring assets according to the performance of financial products.

[0115] To implement this invention, a system is constructed that integrates a server, user terminals, and mobile devices. The server collects the user's financial information from various financial institutions and stores it on a recording medium. This process requires a program that automatically retrieves data using bank APIs, etc. This system eliminates the need for users to manually enter their transaction information.

[0116] Next, the server analyzes the collected financial information and categorizes each transaction. This analysis utilizes data analysis libraries such as Python to categorize transactions according to their content. For example, grocery purchases are classified as "living expenses."

[0117] Next, the server calculates the progress of spending based on the classification results and compares it to the set budget. If spending exceeds the budget, the server automatically generates a warning and notifies the user's device. This notification is displayed in the application developed using React Native, and the user can check it in real time.

[0118] Furthermore, the server also provides functionality for managing investment activities. It incorporates a process that automatically selects financial instruments based on pre-set investment criteria and acquires them as needed. It monitors the performance of held financial instruments and adjusts assets as necessary to support the management of an optimal portfolio.

[0119] For example, if a user sets a food budget of 20,000 yen, a push notification will be issued when their monthly spending exceeds that budget, stating, "Your food expenses have exceeded 20,000 yen. You can save 1,000 yen by refraining from eating out next week."

[0120] An example of a prompt to a generating AI model is, "Generate saving suggestions if I exceed my monthly food budget." This prompt allows the system to use AI to generate practical advice and improvement suggestions for the user and provide them as notifications.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The server automatically retrieves transaction information through the user's financial institution API. The input consists of the user's financial institution ID and authentication information. Based on this, financial transaction data (e.g., date, amount, trading partner, etc.) is collected and stored as output on the server's storage medium.

[0124] Step 2:

[0125] The server analyzes the stored transaction information using a Python data analysis library. The input is the transaction information stored in Step 1. The server classifies the transactions into categories using a machine learning model or rule-based method, assigning them to categories such as groceries and transportation expenses. Based on this, it calculates and outputs the expenditure progress for each category. The output is aggregated expenditure data by category.

[0126] Step 3:

[0127] The server compares the category-based spending summary with the budget set by the user. The inputs are the category-based spending summary data obtained in step 2 and the category-based budgets set by the user in advance. If the spending exceeds the budget, the server generates a warning message. The output is the generated warning message.

[0128] Step 4:

[0129] The server notifies the terminal of the generated warning message. The input is the warning message generated in step 3. It is provided to the user in real time through the terminal's React Native application. This allows the user to immediately check for overspending. The output is the warning notification displayed on the user's terminal.

[0130] Step 5:

[0131] The server uses Python to screen financial instruments based on user-defined investment criteria and initiates an automated purchase process. Inputs include financial market data and the user's investment criteria information. Output is a list of financial instruments for which the purchase process has been completed. The server continuously monitors the portfolio's performance and makes necessary adjustments in real time.

[0132] Step 6:

[0133] Users can check investment results via their device and, if necessary, input prompts into the generated AI model to receive savings and investment advice. This process involves inputting prompts, and based on the results, situation-appropriate advice is generated. The output is specific savings or investment advice presented to the user.

[0134] 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.

[0135] This invention is a system that combines a user's transaction information and emotional state to provide more personalized financial management and investment recommendations. Specific embodiments are described below.

[0136] The server collects user transaction information and stores it in a database. This information includes payment date, amount, and payment category. The transaction information is further analyzed to calculate spending progress against the budget and categorize it accordingly. If the budget is exceeded, the server generates an alert and sends a notification to the terminal.

[0137] A device equipped with an emotion engine recognizes the user's emotions by analyzing their facial expressions and voice tone through the camera and microphone. For example, if the user is feeling stressed, the system will acquire that information. The recognized emotion information is then sent to a server.

[0138] The server analyzes transaction and sentiment data to predict spending trends. For example, if feelings of joy are frequently observed during shopping, it can be inferred that there is a positive trend in consumption. Based on this information, the server can provide customized financial advice to the user, such as suggesting, "Why not find a new hobby that brings you joy to help you reduce future spending?"

[0139] Furthermore, investment activities are adjusted based on emotional information. For example, if a user is feeling anxious, the system will suggest low-risk investment products to allow them to invest with confidence. The server periodically monitors investment performance and rebalances the portfolio in accordance with changes in the user's emotions.

[0140] In this way, this system provides flexible financial management and investment support that takes into account the user's emotional state, and supports the optimization of the user's financial situation in their daily life. Through this, users can manage their money rationally while being attentive to their own emotions.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] When a user completes a daily transaction, the terminal sends transaction information to the server. This information includes the date, amount, and category of the transaction.

[0144] Step 2:

[0145] The server stores the received transaction information in a database and then classifies each transaction into a pre-defined category (e.g., food expenses, transportation expenses, entertainment). During this process, it also calculates the progress of expenditures and compares them against the set budget.

[0146] Step 3:

[0147] The device collects the user's facial expressions and voice via its camera and microphone, and analyzes the user's emotions using an emotion engine. The analysis results are sent to the server as emotional information, such as whether the user is happy or stressed.

[0148] Step 4:

[0149] The server analyzes transactional information and sentiment information in combination. For example, if spending in a certain category is increasing while the user is experiencing stress, it considers the possibility of stress-induced impulse buying.

[0150] Step 5:

[0151] The server generates personalized advice for the user based on emotional information and sends it to the terminal. For example, it might suggest, "When you feel stressed, try window shopping to avoid impulse purchases."

[0152] Step 6:

[0153] Users set up investment plans through their devices, and the server instructs the system to automatically purchase financial products based on those plans. This includes monthly investment amounts and the financial products to be purchased.

[0154] Step 7:

[0155] The server evaluates investment products based on the user's emotional state, selects low-risk products if necessary, and automatically executes purchases. For example, if the user's emotional state is one of wanting stability, it will purchase high-safety products.

[0156] Step 8:

[0157] The server periodically collects market data, monitors the performance of the user's financial products, and rebalances the portfolio, taking sentiment information into consideration.

[0158] Throughout this entire process, users can engage in emotionally-driven, flexible financial management and investment activities, receiving optimal advice tailored to their individual circumstances.

[0159] (Example 2)

[0160] 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".

[0161] Traditional financial management systems analyze users' economic activities based solely on objective numerical data, failing to consider individual user emotions and psychological factors, making it difficult to provide optimal financial advice and investment strategies. Therefore, there is a need for flexible financial management and investment optimization that takes user emotions into account.

[0162] 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.

[0163] In this invention, the server includes means for collecting information on the user's transactions, means for analyzing emotions and transmitting the information, and means for integrating the transaction information and emotion information to provide financial advice. This enables personalized financial management and investment adjustments that take into account the user's emotional state.

[0164] A "user" refers to an individual who uses this system to manage and analyze transaction information and emotional state.

[0165] "Transaction information" refers to detailed information about a user's payments and receipts, including information such as date, amount, and category.

[0166] The "information aggregation unit" refers to a database or storage system that stores transaction information collected from users in preparation for later analysis and use.

[0167] "Emotional state" refers to a psychological state obtained by analyzing the user's facial expressions, tone of voice, etc., and includes emotions such as joy and stress.

[0168] "Financial advice" refers to customized savings and investment advice generated by taking into account the user's transaction history and emotional state.

[0169] "Investment adjustment" refers to the process of optimizing the allocation and selection of financial assets held based on the user's emotional state and the status of their investment portfolio.

[0170] "Subject" refers to a category used to classify transaction-related information, and generally includes expenses such as food, transportation, and entertainment.

[0171] This invention is a system that combines a user's transaction information and emotional state to provide personalized financial management and investment recommendations. Specific embodiments are described below.

[0172] The server automatically collects users' financial transaction information from financial institutions via APIs and stores it in the information aggregation unit. This information includes payment dates, amounts, categories, etc. This allows for accurate and timely management of information related to users' transactions.

[0173] The device captures the user's facial expressions and voice tone in real time through its camera and microphone, and analyzes their emotional state. It incorporates an emotion engine that recognizes whether the user is experiencing joy or stress. This information is encrypted and sent to the server.

[0174] The server integrates the received sentiment and transaction information and analyzes the data using a generative AI model. This allows it to predict the user's spending trends and provide financial advice tailored to individual sentiments. For example, it might generate advice such as, "To prevent recent budget overruns, we recommend finding a new hobby."

[0175] For example, if a user frequently expresses joy while shopping, the server can use that information to analyze their spending habits and suggest hobbies that will have a positive impact. On the other hand, if a user expresses anxiety, the server can suggest low-risk investment options and adjust their asset allocation.

[0176] This system enables users to achieve emotionally responsive, flexible, and rational financial management and investment. Examples of prompts include, "Generate customized budget management advice based on the user's recent emotions and transaction data," and "Create low-risk investment suggestions for the user when stress is recognized."

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The server retrieves user financial transaction information via an API. The input is transaction history data from financial institutions. This data consists of elements such as date, amount, and category. The server stores this transaction information in a database, preparing it for later analysis. The output is structured transaction data stored in the database.

[0180] Step 2:

[0181] The device captures the user's facial expressions and voice in real time using its camera and microphone. It receives the user's video and audio data as input. Using an emotion engine, the device analyzes and identifies the user's emotional state (e.g., joy or stress) from this data. The analyzed emotion information is then generated as output.

[0182] Step 3:

[0183] The device sends analyzed emotional information to the server. This information includes the specific type and intensity of the emotions the user is experiencing. It receives emotional analysis data as input and sends it to the server in an appropriate format. The server receives the received emotional information as output.

[0184] Step 4:

[0185] The server integrates transaction and sentiment data and analyzes user spending trends using a generative AI model. Inputs required are transaction data from a database and sentiment data sent to the server. Data processing involves integrating each type of information chronologically and calculating the correlation between spending and sentiment. The output generates predictive and analytical data regarding user spending trends.

[0186] Step 5:

[0187] The server generates personalized financial advice based on the analyzed data. For example, it might suggest ways to improve consumer behavior based on recent emotional trends. Inputs include analytical data on spending trends and data on the user's past actions. This data is combined with a generative AI model to produce specific advice and suggestions.

[0188] Step 6:

[0189] The server generates adjustment proposals for the user's investment portfolio. Inputs include the user's emotional state and investment performance data. Based on this information, it proposes a risk-aware asset allocation. The output includes investment actions and rebalancing plans to communicate to the user.

[0190] (Application Example 2)

[0191] 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".

[0192] Traditional financial management systems are limited to analyzing user transaction data and do not consider user emotional states, making it difficult to provide personalized advice. Furthermore, they lack real-time feedback in budget management, missing opportunities to encourage user behavioral change. There is a need to improve this situation and achieve flexible and adaptive financial management that leverages emotional data.

[0193] 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.

[0194] In this invention, the server includes means for collecting and storing user transaction information in a database, means for analyzing the user's emotional state using emotion recognition technology and acquiring emotional data, and means for predicting the user's spending trends by combining transaction information and emotional data. This makes it possible to provide personalized financial advice based on emotions in real time and promote behavioral change in users.

[0195] "Transaction information" refers to data related to financial transactions conducted by the user, specifically including attributes such as payment date, amount, and payment category.

[0196] "Emotion recognition technology" is a technology that analyzes and digitizes a user's emotional state from their facial expressions and voice tone, which are captured through a camera or microphone.

[0197] "Emotional data" refers to information about a user's emotional state, analyzed using emotion recognition technology.

[0198] "Spending trends" refer to a user's future spending tendencies, predicted based on transaction information and sentiment data.

[0199] "Financial health" is an indicator that comprehensively evaluates a user's current financial status, and is calculated by taking into account spending trends and sentiment data.

[0200] "Feedback" refers to information and advice that a system provides to a user, intended to encourage changes in the user's behavior.

[0201] To realize this invention, the server collects transaction information and stores it in a database. This transaction information includes data such as the payment date, amount, and payment category made by the user. This information is analyzed to calculate the progress of the user's spending against their budget, and alerts are generated as needed.

[0202] Meanwhile, the device is equipped with a camera and microphone, and uses emotion recognition technology to analyze the user's emotional state from their facial expressions and tone of voice. The obtained emotional data is sent to a server and combined with transaction information for analysis. This analysis predicts the user's spending trends, and their financial health is displayed in real time on a dashboard.

[0203] For example, if a user's happiness level is detected while they are dining out, the system can use that data to display advice on how it might affect their next budget. The server then provides feedback to the user based on the analysis results, encouraging behavioral change.

[0204] As a concrete example, if a user frequently purchases art and craft supplies for their hobby and feels happy doing so, we can suggest specific budget adjustments that take this spending into account. This can also be done by asking the user questions using prompts. For example, a prompt such as, "Tell us about any products or services you've recently purchased that you were particularly pleased with," can help us understand the user's emotional state more deeply and provide optimal feedback.

[0205] The hardware uses mobile devices such as smartphones, and the software includes facial recognition and voice analysis technologies. Specific software used for emotion recognition includes, for example, facial recognition APIs and voice analysis APIs, and transaction information is obtained from financial institutions via these APIs. These technologies enable users to perform rational financial management that takes emotions into consideration.

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

[0207] Step 1:

[0208] The server retrieves user transaction information via external financial service APIs and stores it in a database. Inputs include user authentication information and transaction information obtained from APIs, while output is the transaction data stored in the database. During storage, the data is organized by category using the database's indexing function.

[0209] Step 2:

[0210] The device uses its camera and microphone to record the user's facial expressions and voice, and generates emotion data using emotion recognition technology. The input is raw data from the camera and microphone, and the output is emotion data indicating the analyzed emotional state. The emotion data is obtained using a facial recognition API and a voice analysis API, quantified, and sent to the server.

[0211] Step 3:

[0212] The server acquires transaction information and sentiment data, and uses a machine learning algorithm to predict the user's spending trends. The input is stored transaction data and acquired sentiment data, and the output is predictive data showing future spending trends. In this process, a generative AI model is used to analyze patterns in past sentiment and spending data.

[0213] Step 4:

[0214] The server calculates financial health based on predictive data and provides feedback to the terminal. The input is predicted spending trend data, and the output is a score representing the user's financial health and specific saving advice. The generated feedback is communicated to the user via prompt messages.

[0215] Step 5:

[0216] The system reviews feedback provided by the user through their device and adjusts budgets and spending as needed. Input is feedback information sent from the server, and output is the user's new budget settings and action plan. This step allows the user to re-evaluate their financial management and make necessary adjustments.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention is a system for streamlining users' financial management, particularly supporting daily transaction management and investment activities. Specific embodiments are described below.

[0234] The server automatically collects transaction information from the user's financial institution. This information includes payment date, amount, location and service used, and category. The server stores this data in a database and manages the information centrally.

[0235] The server then analyzes the stored transaction information and categorizes it into predefined categories, such as groceries, transportation, entertainment, and housing. The server also calculates monthly spending for each category and compares the total spending to the budget. If the budget is exceeded in any category, the server immediately generates an alert and notifies the user.

[0236] The device receives this alert information and displays it to the user. The alert can include the reason for the overspending and specific advice on how to save money. For example, "Your food expenses this month exceeded your budget. You can save money next month by eating out one less time." Based on this information, the user can review their spending in the following months.

[0237] Furthermore, users can set up their own investment plans through their devices. This includes selecting the monthly investment amount and the financial products to target (such as stocks and mutual funds). The server automatically purchases the most suitable financial products on behalf of the user based on the set investment criteria. This process is repeated at intervals specified by the user.

[0238] Furthermore, the server periodically monitors the performance of financial instruments and rebalances the portfolio if necessary. These features enable users to continuously make optimal investments in response to market fluctuations. For example, if stock prices fluctuate significantly, the system can maintain portfolio performance by selling some of its holdings or purchasing new ones as needed.

[0239] By implementing this system, users can efficiently and effectively manage their household expenses and invest their assets, thereby improving their overall financial situation.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The server prepares to automatically collect user transaction information from financial institutions and store it in a database. This information includes date, amount, customer, and payment category.

[0243] Step 2:

[0244] The server analyzes the collected transaction information and classifies it into pre-defined categories (e.g., food expenses, transportation expenses, entertainment). Using natural language processing technology, it automatically assigns the transaction information to the appropriate category based on the textual content.

[0245] Step 3:

[0246] The server aggregates monthly spending for each category and compares the set budget with actual spending. This allows the user to track budget progress.

[0247] Step 4:

[0248] If spending exceeds the set budget, the server generates an alert and automatically creates savings advice for the overspending category based on past data.

[0249] Step 5:

[0250] The device receives alerts and savings advice sent from the server and notifies the user. Through these notifications, the user can review their current spending and consider taking any necessary action.

[0251] Step 6:

[0252] Users input and configure their investment plans through their devices. This includes details such as the investment amount and the financial products they will invest in.

[0253] Step 7:

[0254] The server automatically purchases the specified financial products based on the investment plan set by the user. After the transaction is completed, the purchase history is recorded in the database.

[0255] Step 8:

[0256] The server periodically monitors the performance of the financial instruments held by the user and market conditions, and rebalances the portfolio as needed. This ensures that optimal management continues.

[0257] This series of processing steps enables users to efficiently manage their household finances and engage in investment activities.

[0258] (Example 1)

[0259] 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."

[0260] Many modern users find it time-consuming and cumbersome to manage complex financial transaction information and investment activities. This can lead to increased wasteful spending and insufficient investment optimization, potentially causing personal financial planning to fail. Furthermore, traditional methods struggle to effectively organize transaction information and analyze financial situations, resulting in a lack of tools to support rational decision-making based on this information.

[0261] 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.

[0262] In this invention, the server includes means for collecting and storing the user's financial information in a data storage device; means for analyzing the financial information, organizing it into categories, and calculating the expenditure status; means for detecting overspending relative to a set budget, generating warnings, and notifying the user; means for automatically acquiring financial assets based on investment conditions; and means for monitoring the operational status of held financial assets and making necessary changes. This enables the user to streamline the management of complex financial transaction information, support rational decision-making, and optimize their overall financial situation.

[0263] A "user" refers to an individual or legal entity that uses the system to manage financial information or engage in investment activities.

[0264] "Financial information" refers to all data related to a user's financial activities, including transaction data, spending details, budgets, and investment information.

[0265] A "data storage device" refers to a system built on a server that securely and efficiently stores financial information collected from users.

[0266] "Analysis" refers to the process of analyzing collected financial information to extract meaningful patterns and insights.

[0267] "Classification" refers to organizing analyzed financial information into specific categories based on established criteria.

[0268] "Expenditure status" refers to information showing the total amount and breakdown of expenditures in the user's financial activities.

[0269] A "warning" is a notification generated when a user's spending exceeds certain conditions, and refers to a message intended to draw the user's attention.

[0270] "Investment conditions" refer to the criteria and guidelines set by the user regarding the purchase and management of various financial assets.

[0271] "Financial assets" refer to assets traded in financial markets, such as stocks, bonds, and mutual funds.

[0272] "Operating status" refers to information about how the financial assets held are currently functioning and generating value.

[0273] "Change" refers to the process of making necessary adjustments and reallocations in accordance with the composition and purpose of financial assets.

[0274] This invention is a system for streamlining users' financial management, significantly reducing user time and effort by collecting, analyzing, and automatically investing financial information. The system consists of a server, a data storage device, and a user interface.

[0275] The server automatically collects users' financial information via APIs. This includes transaction data and spending details, which are stored in a database. The server also uses analytical algorithms to categorize the information and generate foundational data for tracking spending progress. This utilizes natural language processing (NLP) techniques and computational software. Specific software used includes Python and SQL.

[0276] Based on the analyzed information, the server generates a warning if spending exceeds a set threshold. This warning is sent to the user via their device. The user can receive the warning on an application on their smartphone or PC and review their spending. For example, a message like, "Your entertainment expenses this month have exceeded your budget. You can save money by cutting back on movie tickets next month by one," might be displayed.

[0277] Users can set investment criteria for their long-term financial management. The server then automatically purchases financial assets through the brokerage's API based on these criteria. The server also periodically monitors the operational status and responds immediately if any changes are needed. This allows users to maintain appropriate financial strategies in response to market fluctuations.

[0278] As a concrete example, a user can input a prompt into the AI ​​model that says, "I want to record my monthly fixed expenses and receive a warning if I exceed my budget. I want any additional funds to be automatically invested in an S&P 500 index fund." Based on this prompt, the system can then perform optimal financial management. This prompt is expected to ensure that the system operates in a way that aligns with the user's specific needs.

[0279] In this way, users can effectively manage their financial assets and daily expenses, thereby improving their overall financial situation.

[0280] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0281] Step 1:

[0282] The server collects user transaction information through financial institutions' APIs. User authentication information and the API endpoint are required as input, which retrieves transaction data (date, time, amount, category, etc.). The retrieved data is passed to a script via secure communication and immediately stored in a data storage device. Detailed information for each transaction is stored as input data in the database.

[0283] Step 2:

[0284] The server runs a Python script to analyze the saved transaction information. Unclassified transaction data in the database is used as input, and data where each transaction is classified into categories is obtained as output. Utilizing natural language processing technology, as a specific operation, transactions are automatically classified into categories such as groceries and transportation expenses based on the keywords used.

[0285] Step 3:

[0286] The server calculates the total expenditure for each category from the analyzed data. The input is the classified transaction data, and the output is the total monthly expenditure for each category. A script is used for the calculation, which is compared with the budget set by the user. As a specific operation, the expenditure for each category is compared with past history for trend analysis.

[0287] Step 4:

[0288] The server generates a warning when the expenditure exceeds the set budget. The input is the amount of expenditure that exceeds the budget, and there is a warning message generated as output. The alert content includes savings measures to prompt the user. For example, a warning such as "Your dining-out expenses are excessive. You can save by cooking more at home" is generated.

[0289] Step 5:

[0290] The terminal notifies the user of the received warning message. The input is the warning notification sent from the server, and the output is the warning content displayed on the user's device screen. As a specific operation, on a mobile device, a visually confirmable notification triggered by a push notification is displayed.

[0291] Step 6:

[0292] Users set their investment conditions using their smartphone or PC. The input consists of the user's investment instructions and desired conditions, and the output is a specific investment strategy based on this input. Through the user interface, users select which assets to invest in and how much to invest.

[0293] Step 7:

[0294] The server automatically purchases financial assets based on the configured investment conditions. Inputs are the user's investment conditions and market data, while output is information about the purchased financial assets. Specifically, a buy order is placed via a securities API, and a purchase confirmation message is generated.

[0295] Step 8:

[0296] The server periodically monitors market trends and adjusts the portfolio as needed. Inputs are market data and data on held financial assets, and output is a rebalanced portfolio. An algorithm is used to quickly buy and sell in response to market fluctuations, optimizing asset value.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] In today's world, personal financial management has become increasingly complex, making manual organization and analysis of transaction information difficult. Furthermore, while timely decision-making and management are crucial in investment activities, users face a significant burden in continuously maintaining optimal asset management. To address these challenges, automated management of transaction information and real-time monitoring of asset status are essential.

[0300] 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.

[0301] In this invention, the server includes means for collecting the user's financial information and storing it in a recording medium, means for analyzing the financial information, classifying it into respective categories, and calculating the progress of expenditures, and means for monitoring the status of the financial products held and making necessary adjustments. As a result, the user can easily manage daily expenditures and can timely make optimal decisions in investment activities.

[0302] A "user" is a subject that manages financial information using the system and receives support for expenditures and investment activities.

[0303] "Financial information" refers to all data related to bank transactions, credit card payments, income, and expenditures.

[0304] A "recording medium" refers to a digital storage device such as a database or cloud storage for storing the collected financial information.

[0305] "Analysis" refers to the activity of analyzing financial information to identify the content of each transaction and classify it into related categories.

[0306] "Classification" refers to organizing the analyzed transaction information into different categories or segments based on specific criteria.

[0307] "Progress" is information indicating the ratio or status of expenditures relative to the budget, and is an indicator for grasping the actual execution status relative to the plan.

[0308] "Monitoring" refers to the process of continuously checking the status of the held financial products, and is necessary to maintain the proper operation of the investment portfolio.

[0309] "Adjustment" refers to actions such as buying and selling or restructuring assets according to the operation status of financial products to optimize investment performance.

[0310] To implement this invention, a system is constructed that integrates a server, user terminals, and mobile devices. The server collects the user's financial information from various financial institutions and stores it on a recording medium. This process requires a program that automatically retrieves data using bank APIs, etc. This system eliminates the need for users to manually enter their transaction information.

[0311] Next, the server analyzes the collected financial information and categorizes each transaction. This analysis utilizes data analysis libraries such as Python to categorize transactions according to their content. For example, grocery purchases are classified as "living expenses."

[0312] Next, the server calculates the progress of spending based on the classification results and compares it to the set budget. If spending exceeds the budget, the server automatically generates a warning and notifies the user's device. This notification is displayed in the application developed using React Native, and the user can check it in real time.

[0313] Furthermore, the server also provides functionality for managing investment activities. It incorporates a process that automatically selects financial instruments based on pre-set investment criteria and acquires them as needed. It monitors the performance of held financial instruments and adjusts assets as necessary to support the management of an optimal portfolio.

[0314] For example, if a user sets a food budget of 20,000 yen, a push notification will be issued when their monthly spending exceeds that budget, stating, "Your food expenses have exceeded 20,000 yen. You can save 1,000 yen by refraining from eating out next week."

[0315] An example of a prompt to a generating AI model is, "Generate saving suggestions if I exceed my monthly food budget." This prompt allows the system to use AI to generate practical advice and improvement suggestions for the user and provide them as notifications.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The server automatically retrieves transaction information through the user's financial institution API. The input consists of the user's financial institution ID and authentication information. Based on this, financial transaction data (e.g., date, amount, trading partner, etc.) is collected and stored as output on the server's storage medium.

[0319] Step 2:

[0320] The server analyzes the stored transaction information using a Python data analysis library. The input is the transaction information stored in Step 1. The server classifies the transactions into categories using a machine learning model or rule-based method, assigning them to categories such as groceries and transportation expenses. Based on this, it calculates and outputs the expenditure progress for each category. The output is aggregated expenditure data by category.

[0321] Step 3:

[0322] The server compares the category-based spending summary with the budget set by the user. The inputs are the category-based spending summary data obtained in step 2 and the category-based budgets set by the user in advance. If the spending exceeds the budget, the server generates a warning message. The output is the generated warning message.

[0323] Step 4:

[0324] The server notifies the terminal of the generated warning message. The input is the warning message generated in step 3. It is provided to the user in real time through the terminal's React Native application. This allows the user to immediately check for overspending. The output is the warning notification displayed on the user's terminal.

[0325] Step 5:

[0326] The server uses Python to screen financial instruments based on user-defined investment criteria and initiates an automated purchase process. Inputs include financial market data and the user's investment criteria information. Output is a list of financial instruments for which the purchase process has been completed. The server continuously monitors the portfolio's performance and makes necessary adjustments in real time.

[0327] Step 6:

[0328] Users can check investment results via their device and, if necessary, input prompts into the generated AI model to receive savings and investment advice. This process involves inputting prompts, and based on the results, situation-appropriate advice is generated. The output is specific savings or investment advice presented to the user.

[0329] 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.

[0330] This invention is a system that combines a user's transaction information and emotional state to provide more personalized financial management and investment recommendations. Specific embodiments are described below.

[0331] The server collects user transaction information and stores it in a database. This information includes payment date, amount, and payment category. The transaction information is further analyzed to calculate spending progress against the budget and categorize it accordingly. If the budget is exceeded, the server generates an alert and sends a notification to the terminal.

[0332] A device equipped with an emotion engine recognizes the user's emotions by analyzing their facial expressions and voice tone through the camera and microphone. For example, if the user is feeling stressed, the system will acquire that information. The recognized emotion information is then sent to a server.

[0333] The server analyzes transaction and sentiment data to predict spending trends. For example, if feelings of joy are frequently observed during shopping, it can be inferred that there is a positive trend in consumption. Based on this information, the server can provide customized financial advice to the user, such as suggesting, "Why not find a new hobby that brings you joy to help you reduce future spending?"

[0334] Furthermore, investment activities are adjusted based on emotional information. For example, if a user is feeling anxious, the system will suggest low-risk investment products to allow them to invest with confidence. The server periodically monitors investment performance and rebalances the portfolio in accordance with changes in the user's emotions.

[0335] In this way, this system provides flexible financial management and investment support that takes into account the user's emotional state, and supports the optimization of the user's financial situation in their daily life. Through this, users can manage their money rationally while being attentive to their own emotions.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] When a user completes a daily transaction, the terminal sends transaction information to the server. This information includes the date, amount, and category of the transaction.

[0339] Step 2:

[0340] The server stores the received transaction information in a database and then classifies each transaction into a pre-defined category (e.g., food expenses, transportation expenses, entertainment). During this process, it also calculates the progress of expenditures and compares them against the set budget.

[0341] Step 3:

[0342] The device collects the user's facial expressions and voice via its camera and microphone, and analyzes the user's emotions using an emotion engine. The analysis results are sent to the server as emotional information, such as whether the user is happy or stressed.

[0343] Step 4:

[0344] The server analyzes transactional information and sentiment information in combination. For example, if spending in a certain category is increasing while the user is experiencing stress, it considers the possibility of stress-induced impulse buying.

[0345] Step 5:

[0346] The server generates personalized advice for the user based on emotional information and sends it to the terminal. For example, it might suggest, "When you feel stressed, try window shopping to avoid impulse purchases."

[0347] Step 6:

[0348] Users set up investment plans through their devices, and the server instructs the system to automatically purchase financial products based on those plans. This includes monthly investment amounts and the financial products to be purchased.

[0349] Step 7:

[0350] The server evaluates investment products based on the user's emotional state, selects low-risk products if necessary, and automatically executes purchases. For example, if the user's emotional state is one of wanting stability, it will purchase high-safety products.

[0351] Step 8:

[0352] The server periodically collects market data, monitors the performance of the user's financial products, and rebalances the portfolio, taking sentiment information into consideration.

[0353] Throughout this entire process, users can engage in emotionally-driven, flexible financial management and investment activities, receiving optimal advice tailored to their individual circumstances.

[0354] (Example 2)

[0355] 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".

[0356] Traditional financial management systems analyze users' economic activities based solely on objective numerical data, failing to consider individual user emotions and psychological factors, making it difficult to provide optimal financial advice and investment strategies. Therefore, there is a need for flexible financial management and investment optimization that takes user emotions into account.

[0357] 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.

[0358] In this invention, the server includes means for collecting information on the user's transactions, means for analyzing emotions and transmitting the information, and means for integrating the transaction information and emotion information to provide financial advice. This enables personalized financial management and investment adjustments that take into account the user's emotional state.

[0359] A "user" refers to an individual who uses this system to manage and analyze transaction information and emotional state.

[0360] "Transaction information" refers to detailed information about a user's payments and receipts, including information such as date, amount, and category.

[0361] The "information aggregation unit" refers to a database or storage system that stores transaction information collected from users in preparation for later analysis and use.

[0362] "Emotional state" refers to a psychological state obtained by analyzing the user's facial expressions, tone of voice, etc., and includes emotions such as joy and stress.

[0363] "Financial advice" refers to customized savings and investment advice generated by taking into account the user's transaction history and emotional state.

[0364] "Investment adjustment" refers to the process of optimizing the allocation and selection of financial assets held based on the user's emotional state and the status of their investment portfolio.

[0365] "Subject" refers to a category used to classify transaction-related information, and generally includes expenses such as food, transportation, and entertainment.

[0366] This invention is a system that combines a user's transaction information and emotional state to provide personalized financial management and investment recommendations. Specific embodiments are described below.

[0367] The server automatically collects users' financial transaction information from financial institutions via APIs and stores it in the information aggregation unit. This information includes payment dates, amounts, categories, etc. This allows for accurate and timely management of information related to users' transactions.

[0368] The device captures the user's facial expressions and voice tone in real time through its camera and microphone, and analyzes their emotional state. It incorporates an emotion engine that recognizes whether the user is experiencing joy or stress. This information is encrypted and sent to the server.

[0369] The server integrates the received sentiment and transaction information and analyzes the data using a generative AI model. This allows it to predict the user's spending trends and provide financial advice tailored to individual sentiments. For example, it might generate advice such as, "To prevent recent budget overruns, we recommend finding a new hobby."

[0370] For example, if a user frequently expresses joy while shopping, the server can use that information to analyze their spending habits and suggest hobbies that will have a positive impact. On the other hand, if a user expresses anxiety, the server can suggest low-risk investment options and adjust their asset allocation.

[0371] This system enables users to achieve emotionally responsive, flexible, and rational financial management and investment. Examples of prompts include, "Generate customized budget management advice based on the user's recent emotions and transaction data," and "Create low-risk investment suggestions for the user when stress is recognized."

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The server retrieves user financial transaction information via an API. The input is transaction history data from financial institutions. This data consists of elements such as date, amount, and category. The server stores this transaction information in a database, preparing it for later analysis. The output is structured transaction data stored in the database.

[0375] Step 2:

[0376] The device captures the user's facial expressions and voice in real time using its camera and microphone. It receives the user's video and audio data as input. Using an emotion engine, the device analyzes and identifies the user's emotional state (e.g., joy or stress) from this data. The analyzed emotion information is then generated as output.

[0377] Step 3:

[0378] The device sends analyzed emotional information to the server. This information includes the specific type and intensity of the emotions the user is experiencing. It receives emotional analysis data as input and sends it to the server in an appropriate format. The server receives the received emotional information as output.

[0379] Step 4:

[0380] The server integrates transaction and sentiment data and analyzes user spending trends using a generative AI model. Inputs required are transaction data from a database and sentiment data sent to the server. Data processing involves integrating each type of information chronologically and calculating the correlation between spending and sentiment. The output generates predictive and analytical data regarding user spending trends.

[0381] Step 5:

[0382] The server generates personalized financial advice based on the analyzed data. For example, it might suggest ways to improve consumer behavior based on recent emotional trends. Inputs include analytical data on spending trends and data on the user's past actions. This data is combined with a generative AI model to produce specific advice and suggestions.

[0383] Step 6:

[0384] The server generates adjustment proposals for the user's investment portfolio. Inputs include the user's emotional state and investment performance data. Based on this information, it proposes a risk-aware asset allocation. The output includes investment actions and rebalancing plans to communicate to the user.

[0385] (Application Example 2)

[0386] 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."

[0387] Traditional financial management systems are limited to analyzing user transaction data and do not consider user emotional states, making it difficult to provide personalized advice. Furthermore, they lack real-time feedback in budget management, missing opportunities to encourage user behavioral change. There is a need to improve this situation and achieve flexible and adaptive financial management that leverages emotional data.

[0388] 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.

[0389] In this invention, the server includes means for collecting and storing user transaction information in a database, means for analyzing the user's emotional state using emotion recognition technology and acquiring emotional data, and means for predicting the user's spending trends by combining transaction information and emotional data. This makes it possible to provide personalized financial advice based on emotions in real time and promote behavioral change in users.

[0390] "Transaction information" refers to data related to financial transactions conducted by the user, specifically including attributes such as payment date, amount, and payment category.

[0391] "Emotion recognition technology" is a technology that analyzes and digitizes a user's emotional state from their facial expressions and voice tone, which are captured through a camera or microphone.

[0392] "Emotional data" refers to information about a user's emotional state, analyzed using emotion recognition technology.

[0393] "Spending trends" refer to a user's future spending tendencies, predicted based on transaction information and sentiment data.

[0394] "Financial health" is an indicator that comprehensively evaluates a user's current financial status, and is calculated by taking into account spending trends and sentiment data.

[0395] "Feedback" refers to information and advice that a system provides to a user, intended to encourage changes in the user's behavior.

[0396] To realize this invention, the server collects transaction information and stores it in a database. This transaction information includes data such as the payment date, amount, and payment category made by the user. This information is analyzed to calculate the progress of the user's spending against their budget, and alerts are generated as needed.

[0397] Meanwhile, the device is equipped with a camera and microphone, and uses emotion recognition technology to analyze the user's emotional state from their facial expressions and tone of voice. The obtained emotional data is sent to a server and combined with transaction information for analysis. This analysis predicts the user's spending trends, and their financial health is displayed in real time on a dashboard.

[0398] For example, if a user's happiness level is detected while they are dining out, the system can use that data to display advice on how it might affect their next budget. The server then provides feedback to the user based on the analysis results, encouraging behavioral change.

[0399] As a concrete example, if a user frequently purchases art and craft supplies for their hobby and feels happy doing so, we can suggest specific budget adjustments that take this spending into account. This can also be done by asking the user questions using prompts. For example, a prompt such as, "Tell us about any products or services you've recently purchased that you were particularly pleased with," can help us understand the user's emotional state more deeply and provide optimal feedback.

[0400] The hardware uses mobile devices such as smartphones, and the software includes facial recognition and voice analysis technologies. Specific software used for emotion recognition includes, for example, facial recognition APIs and voice analysis APIs, and transaction information is obtained from financial institutions via these APIs. These technologies enable users to perform rational financial management that takes emotions into consideration.

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The server retrieves user transaction information via external financial service APIs and stores it in a database. Inputs include user authentication information and transaction information obtained from APIs, while output is the transaction data stored in the database. During storage, the data is organized by category using the database's indexing function.

[0404] Step 2:

[0405] The device uses its camera and microphone to record the user's facial expressions and voice, and generates emotion data using emotion recognition technology. The input is raw data from the camera and microphone, and the output is emotion data indicating the analyzed emotional state. The emotion data is obtained using a facial recognition API and a voice analysis API, quantified, and sent to the server.

[0406] Step 3:

[0407] The server acquires transaction information and sentiment data, and uses a machine learning algorithm to predict the user's spending trends. The input is stored transaction data and acquired sentiment data, and the output is predictive data showing future spending trends. In this process, a generative AI model is used to analyze patterns in past sentiment and spending data.

[0408] Step 4:

[0409] The server calculates financial health based on predictive data and provides feedback to the terminal. The input is predicted spending trend data, and the output is a score representing the user's financial health and specific saving advice. The generated feedback is communicated to the user via prompt messages.

[0410] Step 5:

[0411] The system reviews feedback provided by the user through their device and adjusts budgets and spending as needed. Input is feedback information sent from the server, and output is the user's new budget settings and action plan. This step allows the user to re-evaluate their financial management and make necessary adjustments.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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".

[0428] This invention is a system for streamlining users' financial management, particularly supporting daily transaction management and investment activities. Specific embodiments are described below.

[0429] The server automatically collects transaction information from the user's financial institution. This information includes payment date, amount, location and service used, and category. The server stores this data in a database and manages the information centrally.

[0430] The server then analyzes the stored transaction information and categorizes it into predefined categories, such as groceries, transportation, entertainment, and housing. The server also calculates monthly spending for each category and compares the total spending to the budget. If the budget is exceeded in any category, the server immediately generates an alert and notifies the user.

[0431] The device receives this alert information and displays it to the user. The alert can include the reason for the overspending and specific advice on how to save money. For example, "Your food expenses this month exceeded your budget. You can save money next month by eating out one less time." Based on this information, the user can review their spending in the following months.

[0432] Furthermore, users can set up their own investment plans through their devices. This includes selecting the monthly investment amount and the financial products to target (such as stocks and mutual funds). The server automatically purchases the most suitable financial products on behalf of the user based on the set investment criteria. This process is repeated at intervals specified by the user.

[0433] Furthermore, the server periodically monitors the performance of financial instruments and rebalances the portfolio if necessary. These features enable users to continuously make optimal investments in response to market fluctuations. For example, if stock prices fluctuate significantly, the system can maintain portfolio performance by selling some of its holdings or purchasing new ones as needed.

[0434] By implementing this system, users can efficiently and effectively manage their household expenses and invest their assets, thereby improving their overall financial situation.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The server prepares to automatically collect user transaction information from financial institutions and store it in a database. This information includes date, amount, customer, and payment category.

[0438] Step 2:

[0439] The server analyzes the collected transaction information and classifies it into pre-defined categories (e.g., food expenses, transportation expenses, entertainment). Using natural language processing technology, it automatically assigns the transaction information to the appropriate category based on the textual content.

[0440] Step 3:

[0441] The server aggregates monthly spending for each category and compares the set budget with actual spending. This allows the user to track budget progress.

[0442] Step 4:

[0443] If spending exceeds the set budget, the server generates an alert and automatically creates savings advice for the overspending category based on past data.

[0444] Step 5:

[0445] The device receives alerts and savings advice sent from the server and notifies the user. Through these notifications, the user can review their current spending and consider taking any necessary action.

[0446] Step 6:

[0447] Users input and configure their investment plans through their devices. This includes details such as the investment amount and the financial products they will invest in.

[0448] Step 7:

[0449] The server automatically purchases the specified financial products based on the investment plan set by the user. After the transaction is completed, the purchase history is recorded in the database.

[0450] Step 8:

[0451] The server periodically monitors the performance of the financial instruments held by the user and market conditions, and rebalances the portfolio as needed. This ensures that optimal management continues.

[0452] This series of processing steps enables users to efficiently manage their household finances and engage in investment activities.

[0453] (Example 1)

[0454] 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."

[0455] Many modern users find it time-consuming and cumbersome to manage complex financial transaction information and investment activities. This can lead to increased wasteful spending and insufficient investment optimization, potentially causing personal financial planning to fail. Furthermore, traditional methods struggle to effectively organize transaction information and analyze financial situations, resulting in a lack of tools to support rational decision-making based on this information.

[0456] 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.

[0457] In this invention, the server includes means for collecting and storing the user's financial information in a data storage device; means for analyzing the financial information, organizing it into categories, and calculating the expenditure status; means for detecting overspending relative to a set budget, generating warnings, and notifying the user; means for automatically acquiring financial assets based on investment conditions; and means for monitoring the operational status of held financial assets and making necessary changes. This enables the user to streamline the management of complex financial transaction information, support rational decision-making, and optimize their overall financial situation.

[0458] A "user" refers to an individual or legal entity that uses the system to manage financial information or engage in investment activities.

[0459] "Financial information" refers to all data related to a user's financial activities, including transaction data, spending details, budgets, and investment information.

[0460] A "data storage device" refers to a system built on a server that securely and efficiently stores financial information collected from users.

[0461] "Analysis" refers to the process of analyzing collected financial information to extract meaningful patterns and insights.

[0462] "Classification" refers to organizing analyzed financial information into specific categories based on established criteria.

[0463] "Expenditure status" refers to information showing the total amount and breakdown of expenditures in the user's financial activities.

[0464] A "warning" is a notification generated when a user's spending exceeds certain conditions, and refers to a message intended to draw the user's attention.

[0465] "Investment conditions" refer to the criteria and guidelines set by the user regarding the purchase and management of various financial assets.

[0466] "Financial assets" refer to assets traded in financial markets, such as stocks, bonds, and mutual funds.

[0467] "Operating status" refers to information about how the financial assets held are currently functioning and generating value.

[0468] "Change" refers to the process of making necessary adjustments and reallocations in accordance with the composition and purpose of financial assets.

[0469] This invention is a system for streamlining users' financial management, significantly reducing user time and effort by collecting, analyzing, and automatically investing financial information. The system consists of a server, a data storage device, and a user interface.

[0470] The server automatically collects users' financial information via APIs. This includes transaction data and spending details, which are stored in a database. The server also uses analytical algorithms to categorize the information and generate foundational data for tracking spending progress. This utilizes natural language processing (NLP) techniques and computational software. Specific software used includes Python and SQL.

[0471] Based on the analyzed information, the server generates a warning if spending exceeds a set threshold. This warning is sent to the user via their device. The user can receive the warning on an application on their smartphone or PC and review their spending. For example, a message like, "Your entertainment expenses this month have exceeded your budget. You can save money by cutting back on movie tickets next month by one," might be displayed.

[0472] Users can set investment criteria for their long-term financial management. The server then automatically purchases financial assets through the brokerage's API based on these criteria. The server also periodically monitors the operational status and responds immediately if any changes are needed. This allows users to maintain appropriate financial strategies in response to market fluctuations.

[0473] As a concrete example, a user can input a prompt into the AI ​​model that says, "I want to record my monthly fixed expenses and receive a warning if I exceed my budget. I want any additional funds to be automatically invested in an S&P 500 index fund." Based on this prompt, the system can then perform optimal financial management. This prompt is expected to ensure that the system operates in a way that aligns with the user's specific needs.

[0474] In this way, users can effectively manage their financial assets and daily expenses, thereby improving their overall financial situation.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The server collects user transaction information through financial institutions' APIs. User authentication information and the API endpoint are required as input, which retrieves transaction data (date, time, amount, category, etc.). The retrieved data is passed to a script via secure communication and immediately stored in a data storage device. Detailed information for each transaction is stored as input data in the database.

[0478] Step 2:

[0479] The server executes a Python script to analyze the stored transaction information. The input is unclassified transaction data from the database, and the output is data where each transaction is categorized. Utilizing natural language processing techniques, the script automatically classifies transactions into categories such as groceries or transportation expenses based on the keywords used.

[0480] Step 3:

[0481] The server calculates the total expenditure for each category from the analyzed data. The input is classified transaction data, and the output is the monthly total expenditure for each category. A script is used for the calculation, and it is compared with the user's set budget. Specifically, the expenditure for each category is compared with past history to perform trend analysis.

[0482] Step 4:

[0483] The server generates an alert if spending exceeds the set budget. The input is the amount of spending that exceeded the budget, and the output is an alert message. The alert content includes saving tips that the user should take. For example, a warning might be generated saying, "Your spending on eating out is excessive. You can save money by cooking more at home."

[0484] Step 5:

[0485] The device notifies the user of the received warning message. The input is the warning notification sent from the server, and the output is the warning content displayed on the user's device screen. Specifically, on mobile devices, a push notification is triggered to display a visually verifiable notification.

[0486] Step 6:

[0487] Users set their investment conditions using their smartphone or PC. The input consists of the user's investment instructions and desired conditions, and the output is a specific investment strategy based on this input. Through the user interface, users select which assets to invest in and how much to invest.

[0488] Step 7:

[0489] The server automatically purchases financial assets based on the configured investment conditions. Inputs are the user's investment conditions and market data, while output is information about the purchased financial assets. Specifically, a buy order is placed via a securities API, and a purchase confirmation message is generated.

[0490] Step 8:

[0491] The server periodically monitors market trends and adjusts the portfolio as needed. Inputs are market data and data on held financial assets, and output is a rebalanced portfolio. An algorithm is used to quickly buy and sell in response to market fluctuations, optimizing asset value.

[0492] (Application Example 1)

[0493] 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."

[0494] In today's world, personal financial management has become increasingly complex, making manual organization and analysis of transaction information difficult. Furthermore, while timely decision-making and management are crucial in investment activities, users face a significant burden in continuously maintaining optimal asset management. To address these challenges, automated management of transaction information and real-time monitoring of asset status are essential.

[0495] 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.

[0496] In this invention, the server includes means for collecting and storing the user's financial information on a recording medium, means for analyzing the financial information, categorizing it, and calculating the progress of expenditures, and means for monitoring the status of the financial products held and making adjustments as necessary. This enables the user to easily manage their daily expenditures while making timely and optimal decisions in their investment activities.

[0497] A "user" is an entity that uses the system to manage financial information and receive support for spending and investment activities.

[0498] "Financial information" refers to all data related to banking transactions, credit card payments, income, and expenses.

[0499] "Recording medium" refers to digital storage devices such as databases and cloud storage used to store collected financial information.

[0500] "Analysis" refers to the activity of analyzing financial information to identify the details of each transaction and classify them into relevant categories.

[0501] "Classification" refers to organizing analyzed transaction information into different categories or segments based on specific criteria.

[0502] "Progress" refers to information that shows the ratio or status of expenditures relative to the budget, and is an indicator used to understand the actual status of implementation against the plan.

[0503] "Monitoring" refers to the process of continuously checking the status of financial instruments held, which is necessary to maintain the proper management of an investment portfolio.

[0504] "Adjustment" refers to actions taken to optimize investment performance by buying and selling or restructuring assets according to the performance of financial products.

[0505] To implement this invention, a system is constructed that integrates a server, user terminals, and mobile devices. The server collects the user's financial information from various financial institutions and stores it on a recording medium. This process requires a program that automatically retrieves data using bank APIs, etc. This system eliminates the need for users to manually enter their transaction information.

[0506] Next, the server analyzes the collected financial information and categorizes each transaction. This analysis utilizes data analysis libraries such as Python to categorize transactions according to their content. For example, grocery purchases are classified as "living expenses."

[0507] Next, the server calculates the progress of spending based on the classification results and compares it to the set budget. If spending exceeds the budget, the server automatically generates a warning and notifies the user's device. This notification is displayed in the application developed using React Native, and the user can check it in real time.

[0508] Furthermore, the server also provides functionality for managing investment activities. It incorporates a process that automatically selects financial instruments based on pre-set investment criteria and acquires them as needed. It monitors the performance of held financial instruments and adjusts assets as necessary to support the management of an optimal portfolio.

[0509] For example, if a user sets a food budget of 20,000 yen, a push notification will be issued when their monthly spending exceeds that budget, stating, "Your food expenses have exceeded 20,000 yen. You can save 1,000 yen by refraining from eating out next week."

[0510] An example of a prompt to a generating AI model is, "Generate saving suggestions if I exceed my monthly food budget." This prompt allows the system to use AI to generate practical advice and improvement suggestions for the user and provide them as notifications.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The server automatically retrieves transaction information through the user's financial institution API. The input consists of the user's financial institution ID and authentication information. Based on this, financial transaction data (e.g., date, amount, trading partner, etc.) is collected and stored as output on the server's storage medium.

[0514] Step 2:

[0515] The server analyzes the stored transaction information using a Python data analysis library. The input is the transaction information stored in Step 1. The server classifies the transactions into categories using a machine learning model or rule-based method, assigning them to categories such as groceries and transportation expenses. Based on this, it calculates and outputs the expenditure progress for each category. The output is aggregated expenditure data by category.

[0516] Step 3:

[0517] The server compares the category-based spending summary with the budget set by the user. The inputs are the category-based spending summary data obtained in step 2 and the category-based budgets set by the user in advance. If the spending exceeds the budget, the server generates a warning message. The output is the generated warning message.

[0518] Step 4:

[0519] The server notifies the terminal of the generated warning message. The input is the warning message generated in step 3. It is provided to the user in real time through the terminal's React Native application. This allows the user to immediately check for overspending. The output is the warning notification displayed on the user's terminal.

[0520] Step 5:

[0521] The server uses Python to screen financial instruments based on user-defined investment criteria and initiates an automated purchase process. Inputs include financial market data and the user's investment criteria information. Output is a list of financial instruments for which the purchase process has been completed. The server continuously monitors the portfolio's performance and makes necessary adjustments in real time.

[0522] Step 6:

[0523] Users can check investment results via their device and, if necessary, input prompts into the generated AI model to receive savings and investment advice. This process involves inputting prompts, and based on the results, situation-appropriate advice is generated. The output is specific savings or investment advice presented to the user.

[0524] 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.

[0525] This invention is a system that combines a user's transaction information and emotional state to provide more personalized financial management and investment recommendations. Specific embodiments are described below.

[0526] The server collects user transaction information and stores it in a database. This information includes payment date, amount, and payment category. The transaction information is further analyzed to calculate spending progress against the budget and categorize it accordingly. If the budget is exceeded, the server generates an alert and sends a notification to the terminal.

[0527] A device equipped with an emotion engine recognizes the user's emotions by analyzing their facial expressions and voice tone through the camera and microphone. For example, if the user is feeling stressed, the system will acquire that information. The recognized emotion information is then sent to a server.

[0528] The server analyzes transaction and sentiment data to predict spending trends. For example, if feelings of joy are frequently observed during shopping, it can be inferred that there is a positive trend in consumption. Based on this information, the server can provide customized financial advice to the user, such as suggesting, "Why not find a new hobby that brings you joy to help you reduce future spending?"

[0529] Furthermore, investment activities are adjusted based on emotional information. For example, if a user is feeling anxious, the system will suggest low-risk investment products to allow them to invest with confidence. The server periodically monitors investment performance and rebalances the portfolio in accordance with changes in the user's emotions.

[0530] In this way, this system provides flexible financial management and investment support that takes into account the user's emotional state, and supports the optimization of the user's financial situation in their daily life. Through this, users can manage their money rationally while being attentive to their own emotions.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] When a user completes a daily transaction, the terminal sends transaction information to the server. This information includes the date, amount, and category of the transaction.

[0534] Step 2:

[0535] The server stores the received transaction information in a database and then classifies each transaction into a pre-defined category (e.g., food expenses, transportation expenses, entertainment). During this process, it also calculates the progress of expenditures and compares them against the set budget.

[0536] Step 3:

[0537] The device collects the user's facial expressions and voice via its camera and microphone, and analyzes the user's emotions using an emotion engine. The analysis results are sent to the server as emotional information, such as whether the user is happy or stressed.

[0538] Step 4:

[0539] The server analyzes transactional information and sentiment information in combination. For example, if spending in a certain category is increasing while the user is experiencing stress, it considers the possibility of stress-induced impulse buying.

[0540] Step 5:

[0541] The server generates personalized advice for the user based on emotional information and sends it to the terminal. For example, it might suggest, "When you feel stressed, try window shopping to avoid impulse purchases."

[0542] Step 6:

[0543] Users set up investment plans through their devices, and the server instructs the system to automatically purchase financial products based on those plans. This includes monthly investment amounts and the financial products to be purchased.

[0544] Step 7:

[0545] The server evaluates investment products based on the user's emotional state, selects low-risk products if necessary, and automatically executes purchases. For example, if the user's emotional state is one of wanting stability, it will purchase high-safety products.

[0546] Step 8:

[0547] The server periodically collects market data, monitors the performance of the user's financial products, and rebalances the portfolio, taking sentiment information into consideration.

[0548] Throughout this entire process, users can engage in emotionally-driven, flexible financial management and investment activities, receiving optimal advice tailored to their individual circumstances.

[0549] (Example 2)

[0550] 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."

[0551] Traditional financial management systems analyze users' economic activities based solely on objective numerical data, failing to consider individual user emotions and psychological factors, making it difficult to provide optimal financial advice and investment strategies. Therefore, there is a need for flexible financial management and investment optimization that takes user emotions into account.

[0552] 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.

[0553] In this invention, the server includes means for collecting information on the user's transactions, means for analyzing emotions and transmitting the information, and means for integrating the transaction information and emotion information to provide financial advice. This enables personalized financial management and investment adjustments that take into account the user's emotional state.

[0554] A "user" refers to an individual who uses this system to manage and analyze transaction information and emotional state.

[0555] "Transaction information" refers to detailed information about a user's payments and receipts, including information such as date, amount, and category.

[0556] The "information aggregation unit" refers to a database or storage system that stores transaction information collected from users in preparation for later analysis and use.

[0557] "Emotional state" refers to a psychological state obtained by analyzing the user's facial expressions, tone of voice, etc., and includes emotions such as joy and stress.

[0558] "Financial advice" refers to customized savings and investment advice generated by taking into account the user's transaction history and emotional state.

[0559] "Investment adjustment" refers to the process of optimizing the allocation and selection of financial assets held based on the user's emotional state and the status of their investment portfolio.

[0560] "Subject" refers to a category used to classify transaction-related information, and generally includes expenses such as food, transportation, and entertainment.

[0561] This invention is a system that combines a user's transaction information and emotional state to provide personalized financial management and investment recommendations. Specific embodiments are described below.

[0562] The server automatically collects users' financial transaction information from financial institutions via APIs and stores it in the information aggregation unit. This information includes payment dates, amounts, categories, etc. This allows for accurate and timely management of information related to users' transactions.

[0563] The device captures the user's facial expressions and voice tone in real time through its camera and microphone, and analyzes their emotional state. It incorporates an emotion engine that recognizes whether the user is experiencing joy or stress. This information is encrypted and sent to the server.

[0564] The server integrates the received sentiment and transaction information and analyzes the data using a generative AI model. This allows it to predict the user's spending trends and provide financial advice tailored to individual sentiments. For example, it might generate advice such as, "To prevent recent budget overruns, we recommend finding a new hobby."

[0565] For example, if a user frequently expresses joy while shopping, the server can use that information to analyze their spending habits and suggest hobbies that will have a positive impact. On the other hand, if a user expresses anxiety, the server can suggest low-risk investment options and adjust their asset allocation.

[0566] This system enables users to achieve emotionally responsive, flexible, and rational financial management and investment. Examples of prompts include, "Generate customized budget management advice based on the user's recent emotions and transaction data," and "Create low-risk investment suggestions for the user when stress is recognized."

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The server retrieves user financial transaction information via an API. The input is transaction history data from financial institutions. This data consists of elements such as date, amount, and category. The server stores this transaction information in a database, preparing it for later analysis. The output is structured transaction data stored in the database.

[0570] Step 2:

[0571] The device captures the user's facial expressions and voice in real time using its camera and microphone. It receives the user's video and audio data as input. Using an emotion engine, the device analyzes and identifies the user's emotional state (e.g., joy or stress) from this data. The analyzed emotion information is then generated as output.

[0572] Step 3:

[0573] The device sends analyzed emotional information to the server. This information includes the specific type and intensity of the emotions the user is experiencing. It receives emotional analysis data as input and sends it to the server in an appropriate format. The server receives the received emotional information as output.

[0574] Step 4:

[0575] The server integrates transaction and sentiment data and analyzes user spending trends using a generative AI model. Inputs required are transaction data from a database and sentiment data sent to the server. Data processing involves integrating each type of information chronologically and calculating the correlation between spending and sentiment. The output generates predictive and analytical data regarding user spending trends.

[0576] Step 5:

[0577] The server generates personalized financial advice based on the analyzed data. For example, it might suggest ways to improve consumer behavior based on recent emotional trends. Inputs include analytical data on spending trends and data on the user's past actions. This data is combined with a generative AI model to produce specific advice and suggestions.

[0578] Step 6:

[0579] The server generates adjustment proposals for the user's investment portfolio. Inputs include the user's emotional state and investment performance data. Based on this information, it proposes a risk-aware asset allocation. The output includes investment actions and rebalancing plans to communicate to the user.

[0580] (Application Example 2)

[0581] 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."

[0582] Traditional financial management systems are limited to analyzing user transaction data and do not consider user emotional states, making it difficult to provide personalized advice. Furthermore, they lack real-time feedback in budget management, missing opportunities to encourage user behavioral change. There is a need to improve this situation and achieve flexible and adaptive financial management that leverages emotional data.

[0583] 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.

[0584] In this invention, the server includes means for collecting and storing user transaction information in a database, means for analyzing the user's emotional state using emotion recognition technology and acquiring emotional data, and means for predicting the user's spending trends by combining transaction information and emotional data. This makes it possible to provide personalized financial advice based on emotions in real time and promote behavioral change in users.

[0585] "Transaction information" refers to data related to financial transactions conducted by the user, specifically including attributes such as payment date, amount, and payment category.

[0586] "Emotion recognition technology" is a technology that analyzes and digitizes a user's emotional state from their facial expressions and voice tone, which are captured through a camera or microphone.

[0587] "Emotional data" refers to information about a user's emotional state, analyzed using emotion recognition technology.

[0588] "Spending trends" refer to a user's future spending tendencies, predicted based on transaction information and sentiment data.

[0589] "Financial health" is an indicator that comprehensively evaluates a user's current financial status, and is calculated by taking into account spending trends and sentiment data.

[0590] "Feedback" refers to information and advice that a system provides to a user, intended to encourage changes in the user's behavior.

[0591] To realize this invention, the server collects transaction information and stores it in a database. This transaction information includes data such as the payment date, amount, and payment category made by the user. This information is analyzed to calculate the progress of the user's spending against their budget, and alerts are generated as needed.

[0592] Meanwhile, the device is equipped with a camera and microphone, and uses emotion recognition technology to analyze the user's emotional state from their facial expressions and tone of voice. The obtained emotional data is sent to a server and combined with transaction information for analysis. This analysis predicts the user's spending trends, and their financial health is displayed in real time on a dashboard.

[0593] For example, if a user's happiness level is detected while they are dining out, the system can use that data to display advice on how it might affect their next budget. The server then provides feedback to the user based on the analysis results, encouraging behavioral change.

[0594] As a concrete example, if a user frequently purchases art and craft supplies for their hobby and feels happy doing so, we can suggest specific budget adjustments that take this spending into account. This can also be done by asking the user questions using prompts. For example, a prompt such as, "Tell us about any products or services you've recently purchased that you were particularly pleased with," can help us understand the user's emotional state more deeply and provide optimal feedback.

[0595] The hardware uses mobile devices such as smartphones, and the software includes facial recognition and voice analysis technologies. Specific software used for emotion recognition includes, for example, facial recognition APIs and voice analysis APIs, and transaction information is obtained from financial institutions via these APIs. These technologies enable users to perform rational financial management that takes emotions into consideration.

[0596] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0597] Step 1:

[0598] The server retrieves user transaction information via external financial service APIs and stores it in a database. Inputs include user authentication information and transaction information obtained from APIs, while output is the transaction data stored in the database. During storage, the data is organized by category using the database's indexing function.

[0599] Step 2:

[0600] The device uses its camera and microphone to record the user's facial expressions and voice, and generates emotion data using emotion recognition technology. The input is raw data from the camera and microphone, and the output is emotion data indicating the analyzed emotional state. The emotion data is obtained using a facial recognition API and a voice analysis API, quantified, and sent to the server.

[0601] Step 3:

[0602] The server acquires transaction information and sentiment data, and uses a machine learning algorithm to predict the user's spending trends. The input is stored transaction data and acquired sentiment data, and the output is predictive data showing future spending trends. In this process, a generative AI model is used to analyze patterns in past sentiment and spending data.

[0603] Step 4:

[0604] The server calculates financial health based on predictive data and provides feedback to the terminal. The input is predicted spending trend data, and the output is a score representing the user's financial health and specific saving advice. The generated feedback is communicated to the user via prompt messages.

[0605] Step 5:

[0606] The system reviews feedback provided by the user through their device and adjusts budgets and spending as needed. Input is feedback information sent from the server, and output is the user's new budget settings and action plan. This step allows the user to re-evaluate their financial management and make necessary adjustments.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] [Fourth Embodiment]

[0611] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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".

[0624] This invention is a system for streamlining users' financial management, particularly supporting daily transaction management and investment activities. Specific embodiments are described below.

[0625] The server automatically collects transaction information from the user's financial institution. This information includes payment date, amount, location and service used, and category. The server stores this data in a database and manages the information centrally.

[0626] The server then analyzes the stored transaction information and categorizes it into predefined categories, such as groceries, transportation, entertainment, and housing. The server also calculates monthly spending for each category and compares the total spending to the budget. If the budget is exceeded in any category, the server immediately generates an alert and notifies the user.

[0627] The device receives this alert information and displays it to the user. The alert can include the reason for the overspending and specific advice on how to save money. For example, "Your food expenses this month exceeded your budget. You can save money next month by eating out one less time." Based on this information, the user can review their spending in the following months.

[0628] Furthermore, users can set up their own investment plans through their devices. This includes selecting the monthly investment amount and the financial products to target (such as stocks and mutual funds). The server automatically purchases the most suitable financial products on behalf of the user based on the set investment criteria. This process is repeated at intervals specified by the user.

[0629] Furthermore, the server periodically monitors the performance of financial instruments and rebalances the portfolio if necessary. These features enable users to continuously make optimal investments in response to market fluctuations. For example, if stock prices fluctuate significantly, the system can maintain portfolio performance by selling some of its holdings or purchasing new ones as needed.

[0630] By implementing this system, users can efficiently and effectively manage their household expenses and invest their assets, thereby improving their overall financial situation.

[0631] The following describes the processing flow.

[0632] Step 1:

[0633] The server prepares to automatically collect user transaction information from financial institutions and store it in a database. This information includes date, amount, customer, and payment category.

[0634] Step 2:

[0635] The server analyzes the collected transaction information and classifies it into pre-defined categories (e.g., food expenses, transportation expenses, entertainment). Using natural language processing technology, it automatically assigns the transaction information to the appropriate category based on the textual content.

[0636] Step 3:

[0637] The server aggregates monthly spending for each category and compares the set budget with actual spending. This allows the user to track budget progress.

[0638] Step 4:

[0639] If spending exceeds the set budget, the server generates an alert and automatically creates savings advice for the overspending category based on past data.

[0640] Step 5:

[0641] The device receives alerts and savings advice sent from the server and notifies the user. Through these notifications, the user can review their current spending and consider taking any necessary action.

[0642] Step 6:

[0643] Users input and configure their investment plans through their devices. This includes details such as the investment amount and the financial products they will invest in.

[0644] Step 7:

[0645] The server automatically purchases the specified financial products based on the investment plan set by the user. After the transaction is completed, the purchase history is recorded in the database.

[0646] Step 8:

[0647] The server periodically monitors the performance of the financial instruments held by the user and market conditions, and rebalances the portfolio as needed. This ensures that optimal management continues.

[0648] This series of processing steps enables users to efficiently manage their household finances and engage in investment activities.

[0649] (Example 1)

[0650] 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".

[0651] Many modern users find it time-consuming and cumbersome to manage complex financial transaction information and investment activities. This can lead to increased wasteful spending and insufficient investment optimization, potentially causing personal financial planning to fail. Furthermore, traditional methods struggle to effectively organize transaction information and analyze financial situations, resulting in a lack of tools to support rational decision-making based on this information.

[0652] 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.

[0653] In this invention, the server includes means for collecting and storing the user's financial information in a data storage device; means for analyzing the financial information, organizing it into categories, and calculating the expenditure status; means for detecting overspending relative to a set budget, generating warnings, and notifying the user; means for automatically acquiring financial assets based on investment conditions; and means for monitoring the operational status of held financial assets and making necessary changes. This enables the user to streamline the management of complex financial transaction information, support rational decision-making, and optimize their overall financial situation.

[0654] A "user" refers to an individual or legal entity that uses the system to manage financial information or engage in investment activities.

[0655] "Financial information" refers to all data related to a user's financial activities, including transaction data, spending details, budgets, and investment information.

[0656] A "data storage device" refers to a system built on a server that securely and efficiently stores financial information collected from users.

[0657] "Analysis" refers to the process of analyzing collected financial information to extract meaningful patterns and insights.

[0658] "Classification" refers to organizing analyzed financial information into specific categories based on established criteria.

[0659] "Expenditure status" refers to information showing the total amount and breakdown of expenditures in the user's financial activities.

[0660] A "warning" is a notification generated when a user's spending exceeds certain conditions, and refers to a message intended to draw the user's attention.

[0661] "Investment conditions" refer to the criteria and guidelines set by the user regarding the purchase and management of various financial assets.

[0662] "Financial assets" refer to assets traded in financial markets, such as stocks, bonds, and mutual funds.

[0663] "Operating status" refers to information about how the financial assets held are currently functioning and generating value.

[0664] "Change" refers to the process of making necessary adjustments and reallocations in accordance with the composition and purpose of financial assets.

[0665] This invention is a system for streamlining users' financial management, significantly reducing user time and effort by collecting, analyzing, and automatically investing financial information. The system consists of a server, a data storage device, and a user interface.

[0666] The server automatically collects users' financial information via APIs. This includes transaction data and spending details, which are stored in a database. The server also uses analytical algorithms to categorize the information and generate foundational data for tracking spending progress. This utilizes natural language processing (NLP) techniques and computational software. Specific software used includes Python and SQL.

[0667] Based on the analyzed information, the server generates a warning if spending exceeds a set threshold. This warning is sent to the user via their device. The user can receive the warning on an application on their smartphone or PC and review their spending. For example, a message like, "Your entertainment expenses this month have exceeded your budget. You can save money by cutting back on movie tickets next month by one," might be displayed.

[0668] Users can set investment criteria for their long-term financial management. The server then automatically purchases financial assets through the brokerage's API based on these criteria. The server also periodically monitors the operational status and responds immediately if any changes are needed. This allows users to maintain appropriate financial strategies in response to market fluctuations.

[0669] As a concrete example, a user can input a prompt into the AI ​​model that says, "I want to record my monthly fixed expenses and receive a warning if I exceed my budget. I want any additional funds to be automatically invested in an S&P 500 index fund." Based on this prompt, the system can then perform optimal financial management. This prompt is expected to ensure that the system operates in a way that aligns with the user's specific needs.

[0670] In this way, users can effectively manage their financial assets and daily expenses, thereby improving their overall financial situation.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The server collects user transaction information through financial institutions' APIs. User authentication information and the API endpoint are required as input, which retrieves transaction data (date, time, amount, category, etc.). The retrieved data is passed to a script via secure communication and immediately stored in a data storage device. Detailed information for each transaction is stored as input data in the database.

[0674] Step 2:

[0675] The server executes a Python script to analyze the stored transaction information. The input is unclassified transaction data from the database, and the output is data where each transaction is categorized. Utilizing natural language processing techniques, the script automatically classifies transactions into categories such as groceries or transportation expenses based on the keywords used.

[0676] Step 3:

[0677] The server calculates the total expenditure for each category from the analyzed data. The input is classified transaction data, and the output is the monthly total expenditure for each category. A script is used for the calculation, and it is compared with the user's set budget. Specifically, the expenditure for each category is compared with past history to perform trend analysis.

[0678] Step 4:

[0679] The server generates an alert if spending exceeds the set budget. The input is the amount of spending that exceeded the budget, and the output is an alert message. The alert content includes saving tips that the user should take. For example, a warning might be generated saying, "Your spending on eating out is excessive. You can save money by cooking more at home."

[0680] Step 5:

[0681] The device notifies the user of the received warning message. The input is the warning notification sent from the server, and the output is the warning content displayed on the user's device screen. Specifically, on mobile devices, a push notification is triggered to display a visually verifiable notification.

[0682] Step 6:

[0683] Users set their investment conditions using their smartphone or PC. The input consists of the user's investment instructions and desired conditions, and the output is a specific investment strategy based on this input. Through the user interface, users select which assets to invest in and how much to invest.

[0684] Step 7:

[0685] The server automatically purchases financial assets based on the configured investment conditions. Inputs are the user's investment conditions and market data, while output is information about the purchased financial assets. Specifically, a buy order is placed via a securities API, and a purchase confirmation message is generated.

[0686] Step 8:

[0687] The server periodically monitors market trends and adjusts the portfolio as needed. Inputs are market data and data on held financial assets, and output is a rebalanced portfolio. An algorithm is used to quickly buy and sell in response to market fluctuations, optimizing asset value.

[0688] (Application Example 1)

[0689] 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".

[0690] In today's world, personal financial management has become increasingly complex, making manual organization and analysis of transaction information difficult. Furthermore, while timely decision-making and management are crucial in investment activities, users face a significant burden in continuously maintaining optimal asset management. To address these challenges, automated management of transaction information and real-time monitoring of asset status are essential.

[0691] 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.

[0692] In this invention, the server includes means for collecting and storing the user's financial information on a recording medium, means for analyzing the financial information, categorizing it, and calculating the progress of expenditures, and means for monitoring the status of the financial products held and making adjustments as necessary. This enables the user to easily manage their daily expenditures while making timely and optimal decisions in their investment activities.

[0693] A "user" is an entity that uses the system to manage financial information and receive support for spending and investment activities.

[0694] "Financial information" refers to all data related to banking transactions, credit card payments, income, and expenses.

[0695] "Recording medium" refers to digital storage devices such as databases and cloud storage used to store collected financial information.

[0696] "Analysis" refers to the activity of analyzing financial information to identify the details of each transaction and classify them into relevant categories.

[0697] "Classification" refers to organizing analyzed transaction information into different categories or segments based on specific criteria.

[0698] "Progress" refers to information that shows the ratio or status of expenditures relative to the budget, and is an indicator used to understand the actual status of implementation against the plan.

[0699] "Monitoring" refers to the process of continuously checking the status of financial instruments held, which is necessary to maintain the proper management of an investment portfolio.

[0700] "Adjustment" refers to actions taken to optimize investment performance by buying and selling or restructuring assets according to the performance of financial products.

[0701] To implement this invention, a system is constructed that integrates a server, user terminals, and mobile devices. The server collects the user's financial information from various financial institutions and stores it on a recording medium. This process requires a program that automatically retrieves data using bank APIs, etc. This system eliminates the need for users to manually enter their transaction information.

[0702] Next, the server analyzes the collected financial information and categorizes each transaction. This analysis utilizes data analysis libraries such as Python to categorize transactions according to their content. For example, grocery purchases are classified as "living expenses."

[0703] Next, the server calculates the progress of spending based on the classification results and compares it to the set budget. If spending exceeds the budget, the server automatically generates a warning and notifies the user's device. This notification is displayed in the application developed using React Native, and the user can check it in real time.

[0704] Furthermore, the server also provides functionality for managing investment activities. It incorporates a process that automatically selects financial instruments based on pre-set investment criteria and acquires them as needed. It monitors the performance of held financial instruments and adjusts assets as necessary to support the management of an optimal portfolio.

[0705] For example, if a user sets a food budget of 20,000 yen, a push notification will be issued when their monthly spending exceeds that budget, stating, "Your food expenses have exceeded 20,000 yen. You can save 1,000 yen by refraining from eating out next week."

[0706] An example of a prompt to a generating AI model is, "Generate saving suggestions if I exceed my monthly food budget." This prompt allows the system to use AI to generate practical advice and improvement suggestions for the user and provide them as notifications.

[0707] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0708] Step 1:

[0709] The server automatically retrieves transaction information through the user's financial institution API. The input consists of the user's financial institution ID and authentication information. Based on this, financial transaction data (e.g., date, amount, trading partner, etc.) is collected and stored as output on the server's storage medium.

[0710] Step 2:

[0711] The server analyzes the stored transaction information using a Python data analysis library. The input is the transaction information stored in Step 1. The server classifies the transactions into categories using a machine learning model or rule-based method, assigning them to categories such as groceries and transportation expenses. Based on this, it calculates and outputs the expenditure progress for each category. The output is aggregated expenditure data by category.

[0712] Step 3:

[0713] The server compares the category-based spending summary with the budget set by the user. The inputs are the category-based spending summary data obtained in step 2 and the category-based budgets set by the user in advance. If the spending exceeds the budget, the server generates a warning message. The output is the generated warning message.

[0714] Step 4:

[0715] The server notifies the terminal of the generated warning message. The input is the warning message generated in step 3. It is provided to the user in real time through the terminal's React Native application. This allows the user to immediately check for overspending. The output is the warning notification displayed on the user's terminal.

[0716] Step 5:

[0717] The server uses Python to screen financial instruments based on user-defined investment criteria and initiates an automated purchase process. Inputs include financial market data and the user's investment criteria information. Output is a list of financial instruments for which the purchase process has been completed. The server continuously monitors the portfolio's performance and makes necessary adjustments in real time.

[0718] Step 6:

[0719] Users can check investment results via their device and, if necessary, input prompts into the generated AI model to receive savings and investment advice. This process involves inputting prompts, and based on the results, situation-appropriate advice is generated. The output is specific savings or investment advice presented to the user.

[0720] 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.

[0721] This invention is a system that combines a user's transaction information and emotional state to provide more personalized financial management and investment recommendations. Specific embodiments are described below.

[0722] The server collects user transaction information and stores it in a database. This information includes payment date, amount, and payment category. The transaction information is further analyzed to calculate spending progress against the budget and categorize it accordingly. If the budget is exceeded, the server generates an alert and sends a notification to the terminal.

[0723] A device equipped with an emotion engine recognizes the user's emotions by analyzing their facial expressions and voice tone through the camera and microphone. For example, if the user is feeling stressed, the system will acquire that information. The recognized emotion information is then sent to a server.

[0724] The server analyzes transaction and sentiment data to predict spending trends. For example, if feelings of joy are frequently observed during shopping, it can be inferred that there is a positive trend in consumption. Based on this information, the server can provide customized financial advice to the user, such as suggesting, "Why not find a new hobby that brings you joy to help you reduce future spending?"

[0725] Furthermore, investment activities are adjusted based on emotional information. For example, if a user is feeling anxious, the system will suggest low-risk investment products to allow them to invest with confidence. The server periodically monitors investment performance and rebalances the portfolio in accordance with changes in the user's emotions.

[0726] In this way, this system provides flexible financial management and investment support that takes into account the user's emotional state, and supports the optimization of the user's financial situation in their daily life. Through this, users can manage their money rationally while being attentive to their own emotions.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] When a user completes a daily transaction, the terminal sends transaction information to the server. This information includes the date, amount, and category of the transaction.

[0730] Step 2:

[0731] The server stores the received transaction information in a database and then classifies each transaction into a pre-defined category (e.g., food expenses, transportation expenses, entertainment). During this process, it also calculates the progress of expenditures and compares them against the set budget.

[0732] Step 3:

[0733] The device collects the user's facial expressions and voice via its camera and microphone, and analyzes the user's emotions using an emotion engine. The analysis results are sent to the server as emotional information, such as whether the user is happy or stressed.

[0734] Step 4:

[0735] The server analyzes transactional information and sentiment information in combination. For example, if spending in a certain category is increasing while the user is experiencing stress, it considers the possibility of stress-induced impulse buying.

[0736] Step 5:

[0737] The server generates personalized advice for the user based on emotional information and sends it to the terminal. For example, it might suggest, "When you feel stressed, try window shopping to avoid impulse purchases."

[0738] Step 6:

[0739] Users set up investment plans through their devices, and the server instructs the system to automatically purchase financial products based on those plans. This includes monthly investment amounts and the financial products to be purchased.

[0740] Step 7:

[0741] The server evaluates investment products based on the user's emotional state, selects low-risk products if necessary, and automatically executes purchases. For example, if the user's emotional state is one of wanting stability, it will purchase high-safety products.

[0742] Step 8:

[0743] The server periodically collects market data, monitors the performance of the user's financial products, and rebalances the portfolio, taking sentiment information into consideration.

[0744] Throughout this entire process, users can engage in emotionally-driven, flexible financial management and investment activities, receiving optimal advice tailored to their individual circumstances.

[0745] (Example 2)

[0746] 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".

[0747] Traditional financial management systems analyze users' economic activities based solely on objective numerical data, failing to consider individual user emotions and psychological factors, making it difficult to provide optimal financial advice and investment strategies. Therefore, there is a need for flexible financial management and investment optimization that takes user emotions into account.

[0748] 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.

[0749] In this invention, the server includes means for collecting information on the user's transactions, means for analyzing emotions and transmitting the information, and means for integrating the transaction information and emotion information to provide financial advice. This enables personalized financial management and investment adjustments that take into account the user's emotional state.

[0750] A "user" refers to an individual who uses this system to manage and analyze transaction information and emotional state.

[0751] "Transaction information" refers to detailed information about a user's payments and receipts, including information such as date, amount, and category.

[0752] The "information aggregation unit" refers to a database or storage system that stores transaction information collected from users in preparation for later analysis and use.

[0753] "Emotional state" refers to a psychological state obtained by analyzing the user's facial expressions, tone of voice, etc., and includes emotions such as joy and stress.

[0754] "Financial advice" refers to customized savings and investment advice generated by taking into account the user's transaction history and emotional state.

[0755] "Investment adjustment" refers to the process of optimizing the allocation and selection of financial assets held based on the user's emotional state and the status of their investment portfolio.

[0756] "Subject" refers to a category used to classify transaction-related information, and generally includes expenses such as food, transportation, and entertainment.

[0757] This invention is a system that combines a user's transaction information and emotional state to provide personalized financial management and investment recommendations. Specific embodiments are described below.

[0758] The server automatically collects users' financial transaction information from financial institutions via APIs and stores it in the information aggregation unit. This information includes payment dates, amounts, categories, etc. This allows for accurate and timely management of information related to users' transactions.

[0759] The device captures the user's facial expressions and voice tone in real time through its camera and microphone, and analyzes their emotional state. It incorporates an emotion engine that recognizes whether the user is experiencing joy or stress. This information is encrypted and sent to the server.

[0760] The server integrates the received sentiment and transaction information and analyzes the data using a generative AI model. This allows it to predict the user's spending trends and provide financial advice tailored to individual sentiments. For example, it might generate advice such as, "To prevent recent budget overruns, we recommend finding a new hobby."

[0761] For example, if a user frequently expresses joy while shopping, the server can use that information to analyze their spending habits and suggest hobbies that will have a positive impact. On the other hand, if a user expresses anxiety, the server can suggest low-risk investment options and adjust their asset allocation.

[0762] This system enables users to achieve emotionally responsive, flexible, and rational financial management and investment. Examples of prompts include, "Generate customized budget management advice based on the user's recent emotions and transaction data," and "Create low-risk investment suggestions for the user when stress is recognized."

[0763] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0764] Step 1:

[0765] The server retrieves user financial transaction information via an API. The input is transaction history data from financial institutions. This data consists of elements such as date, amount, and category. The server stores this transaction information in a database, preparing it for later analysis. The output is structured transaction data stored in the database.

[0766] Step 2:

[0767] The device captures the user's facial expressions and voice in real time using its camera and microphone. It receives the user's video and audio data as input. Using an emotion engine, the device analyzes and identifies the user's emotional state (e.g., joy or stress) from this data. The analyzed emotion information is then generated as output.

[0768] Step 3:

[0769] The device sends analyzed emotional information to the server. This information includes the specific type and intensity of the emotions the user is experiencing. It receives emotional analysis data as input and sends it to the server in an appropriate format. The server receives the received emotional information as output.

[0770] Step 4:

[0771] The server integrates transaction and sentiment data and analyzes user spending trends using a generative AI model. Inputs required are transaction data from a database and sentiment data sent to the server. Data processing involves integrating each type of information chronologically and calculating the correlation between spending and sentiment. The output generates predictive and analytical data regarding user spending trends.

[0772] Step 5:

[0773] The server generates personalized financial advice based on the analyzed data. For example, it might suggest ways to improve consumer behavior based on recent emotional trends. Inputs include analytical data on spending trends and data on the user's past actions. This data is combined with a generative AI model to produce specific advice and suggestions.

[0774] Step 6:

[0775] The server generates adjustment proposals for the user's investment portfolio. Inputs include the user's emotional state and investment performance data. Based on this information, it proposes a risk-aware asset allocation. The output includes investment actions and rebalancing plans to communicate to the user.

[0776] (Application Example 2)

[0777] 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".

[0778] Traditional financial management systems are limited to analyzing user transaction data and do not consider user emotional states, making it difficult to provide personalized advice. Furthermore, they lack real-time feedback in budget management, missing opportunities to encourage user behavioral change. There is a need to improve this situation and achieve flexible and adaptive financial management that leverages emotional data.

[0779] 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.

[0780] In this invention, the server includes means for collecting and storing user transaction information in a database, means for analyzing the user's emotional state using emotion recognition technology and acquiring emotional data, and means for predicting the user's spending trends by combining transaction information and emotional data. This makes it possible to provide personalized financial advice based on emotions in real time and promote behavioral change in users.

[0781] "Transaction information" refers to data related to financial transactions conducted by the user, specifically including attributes such as payment date, amount, and payment category.

[0782] "Emotion recognition technology" is a technology that analyzes and digitizes a user's emotional state from their facial expressions and voice tone, which are captured through a camera or microphone.

[0783] "Emotional data" refers to information about a user's emotional state, analyzed using emotion recognition technology.

[0784] "Spending trends" refer to a user's future spending tendencies, predicted based on transaction information and sentiment data.

[0785] "Financial health" is an indicator that comprehensively evaluates a user's current financial status, and is calculated by taking into account spending trends and sentiment data.

[0786] "Feedback" refers to information and advice that a system provides to a user, intended to encourage changes in the user's behavior.

[0787] To realize this invention, the server collects transaction information and stores it in a database. This transaction information includes data such as the payment date, amount, and payment category made by the user. This information is analyzed to calculate the progress of the user's spending against their budget, and alerts are generated as needed.

[0788] Meanwhile, the device is equipped with a camera and microphone, and uses emotion recognition technology to analyze the user's emotional state from their facial expressions and tone of voice. The obtained emotional data is sent to a server and combined with transaction information for analysis. This analysis predicts the user's spending trends, and their financial health is displayed in real time on a dashboard.

[0789] For example, if a user's happiness level is detected while they are dining out, the system can use that data to display advice on how it might affect their next budget. The server then provides feedback to the user based on the analysis results, encouraging behavioral change.

[0790] As a concrete example, if a user frequently purchases art and craft supplies for their hobby and feels happy doing so, we can suggest specific budget adjustments that take this spending into account. This can also be done by asking the user questions using prompts. For example, a prompt such as, "Tell us about any products or services you've recently purchased that you were particularly pleased with," can help us understand the user's emotional state more deeply and provide optimal feedback.

[0791] The hardware uses mobile devices such as smartphones, and the software includes facial recognition and voice analysis technologies. Specific software used for emotion recognition includes, for example, facial recognition APIs and voice analysis APIs, and transaction information is obtained from financial institutions via these APIs. These technologies enable users to perform rational financial management that takes emotions into consideration.

[0792] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0793] Step 1:

[0794] The server retrieves user transaction information via external financial service APIs and stores it in a database. Inputs include user authentication information and transaction information obtained from APIs, while output is the transaction data stored in the database. During storage, the data is organized by category using the database's indexing function.

[0795] Step 2:

[0796] The device uses its camera and microphone to record the user's facial expressions and voice, and generates emotion data using emotion recognition technology. The input is raw data from the camera and microphone, and the output is emotion data indicating the analyzed emotional state. The emotion data is obtained using a facial recognition API and a voice analysis API, quantified, and sent to the server.

[0797] Step 3:

[0798] The server acquires transaction information and sentiment data, and uses a machine learning algorithm to predict the user's spending trends. The input is stored transaction data and acquired sentiment data, and the output is predictive data showing future spending trends. In this process, a generative AI model is used to analyze patterns in past sentiment and spending data.

[0799] Step 4:

[0800] The server calculates financial health based on predictive data and provides feedback to the terminal. The input is predicted spending trend data, and the output is a score representing the user's financial health and specific saving advice. The generated feedback is communicated to the user via prompt messages.

[0801] Step 5:

[0802] The system reviews feedback provided by the user through their device and adjusts budgets and spending as needed. Input is feedback information sent from the server, and output is the user's new budget settings and action plan. This step allows the user to re-evaluate their financial management and make necessary adjustments.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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."

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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 as being incorporated by reference.

[0824] The following is further disclosed regarding the embodiments described above.

[0825] (Claim 1)

[0826] A means of collecting user transaction information and storing it in a database,

[0827] A means for analyzing the aforementioned transaction information, classifying it into categories, and calculating the progress of expenditures,

[0828] A means to detect spending exceeding the set budget and generate alerts to notify users,

[0829] A means of automatically purchasing financial products based on investment criteria,

[0830] A means of monitoring the performance of financial products held and making adjustments as necessary,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for providing users with savings advice for each transaction category.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for setting a monthly automated investment amount and optimizing the portfolio based on the user's investment plan.

[0836] "Example 1"

[0837] (Claim 1)

[0838] A means for collecting users' financial information and storing it in a data storage device,

[0839] A means for analyzing the aforementioned financial information, organizing it into categories, and calculating the status of expenditures,

[0840] A means of detecting overspending against a set budget, generating warnings, and providing notifications.

[0841] A means of automatically acquiring financial assets based on investment conditions,

[0842] A means of monitoring the performance of financial assets held and making necessary changes,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for providing users with advice on savings for each transaction category.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for setting regular automatic investment amounts and optimizing asset allocation based on the user's asset formation plan.

[0848] "Application Example 1"

[0849] (Claim 1)

[0850] A means of collecting and storing users' financial information on a recording medium,

[0851] A means for analyzing the aforementioned financial information, categorizing it into each category, and calculating the progress of expenditures,

[0852] A means for detecting spending exceeding the set budget, generating a warning, and providing notification.

[0853] A means of automatically selecting and acquiring financial products based on investment criteria,

[0854] A means of monitoring the status of financial instruments held and making adjustments as necessary,

[0855] A means of displaying transaction information in real time on the user's mobile device,

[0856] ...

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, further comprising means for providing users with savings guidance for each transaction category.

[0860] (Claim 3)

[0861] The system according to claim 1, further comprising means for setting regular investment amounts and optimizing asset composition based on the user's investment plan.

[0862] "Example 2 of combining an emotion engine"

[0863] (Claim 1)

[0864] A means for collecting and storing information related to user transactions in an information storage unit,

[0865] A means for analyzing information related to the aforementioned transactions, classifying it into each account, and calculating the progress of expenditures,

[0866] A means for detecting spending exceeding the set budget, generating a warning, and providing notification.

[0867] A means for recognizing a user's emotional state using a device for analyzing emotions and transmitting that information,

[0868] A means of integrating and analyzing transactional information and sentimental information to predict spending trends and provide personalized financial advice,

[0869] A means of adjusting investments while considering the user's emotional state, and monitoring the status of held financial assets to make necessary adjustments,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, further comprising means for providing the user with advice on saving on each item of a transaction.

[0873] (Claim 3)

[0874] The system according to claim 1, further comprising means for setting regular automatic investment amounts and optimizing asset allocation based on the user's investment plan.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A means of collecting user transaction information and storing it in a database,

[0878] A means for analyzing the aforementioned transaction information, classifying it into categories, and calculating the progress of expenditures,

[0879] A means to detect spending exceeding the set budget and generate alerts to notify users,

[0880] A means of analyzing a user's emotional state using emotion recognition technology and acquiring emotional data,

[0881] A method for predicting user spending trends by combining transaction information and sentiment data,

[0882] A means of displaying financial health in real time based on predictions and providing feedback to the user,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, further comprising means for suggesting low-risk financial products based on emotional data.

[0886] (Claim 3)

[0887] The system according to claim 1, further comprising means for linking transaction information with emotion recognition to generate personalized financial advice. [Explanation of symbols]

[0888] 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 collecting user transaction information and storing it in a database, A means for analyzing the aforementioned transaction information, classifying it into categories, and calculating the progress of expenditures, A means to detect spending exceeding the set budget and generate alerts to notify users, A means of automatically purchasing financial products based on investment criteria, A means of monitoring the performance of financial products held and making adjustments as necessary, A system that includes this.

2. The system according to claim 1, further comprising means for providing users with savings advice for each transaction category.

3. The system according to claim 1, further comprising means for setting a monthly automatic investment amount and optimizing the portfolio based on the user's investment plan.

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