system

Abstract

We provide the system. [Solution] Data collection means, An analysis means that analyzes the information obtained by the aforementioned data collection means and predicts the trends of financial assets, A display means for visualizing the predictive information obtained by the analysis means, A means for analyzing consumption patterns using electronic transaction history, A means for presenting an asset management method based on the analysis results of the aforementioned consumption patterns, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problem is that it is difficult for an individual to accurately manage their own cash flow and effectively conduct future asset formation due to human error and lack of knowledge. Also, since it is difficult for a user to select an optimal asset management plan in the field of interest, there is a need for a system that can provide information tailored to individual lifestyles and support execution in a unified manner.

Means for Solving the Problems

[0005] This invention provides a system that visualizes a user's financial assets by acquiring user financial data through data collection means and analyzing that information through analysis means to predict trends. Furthermore, it improves an individual's asset management ability by organizing information related to asset formation based on the user's areas of interest through information provision means, and by proposing and supporting the execution of a concrete investment plan based on asset goals set by the planning means.

[0006] "Data collection means" refers to a device or method that involves a process or interface for obtaining a user's financial data.

[0007] "Analysis means" refers to a device or method that uses processes or algorithms to analyze collected data and predict trends and patterns in financial assets.

[0008] "Display means" refers to devices or methods for providing users with analysis results and predictive information visually.

[0009] "Information provision means" refers to a device or method for collecting, organizing, and providing information related to asset formation, taking into consideration the user's interests.

[0010] A "planning tool" is a device or method for formulating specific investment and savings plans based on asset formation goals and supporting their implementation. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] 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.

[0015] 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.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is a system for assisting users in managing their cash flow and primarily involves a series of processes involving a server, a terminal, and a user. The following describes embodiments of the system based on each of these roles.

[0033] First, the server has data collection capabilities to aggregate users' financial data. Specifically, it obtains data from household budgeting apps, banks, and cashless payment apps using APIs. This data includes details of the user's income and expenses. Based on this information, the server uses analytical tools to analyze time-series data and predict the expected income and expenses for the following month, as well as specific spending patterns.

[0034] Next, the server sends visualized information to the terminal based on the predictions and analysis results obtained. The terminal receives this information and presents it to the user in the form of graphs and charts through its dashboard function. This allows the user to see their financial situation at a glance and understand which items they should pay attention to.

[0035] This system also has a feature that allows users to select their areas of interest. Through a dedicated interface, users can set asset-building themes based on their personal interests, such as travel, education, and investment. The server uses this information to collect useful information related to the selected area from external sources and organize it through information provision mechanisms.

[0036] Furthermore, users can set their own asset-building goals, which the server receives and uses a planning mechanism to develop a concrete asset-building plan. This plan includes regular savings amounts, investment strategies, and risk management, and is presented to the user via their terminal. Based on these suggestions, users can formulate their own asset-building path and, with support from the server, move towards its implementation.

[0037] For example, if a user in their 20s enjoys traveling and aims to buy a home in the future, the server can analyze their spending habits, suggest an ideal monthly savings amount, and provide methods for efficiently managing travel funds. In this way, the present invention realizes support for asset management tailored to the individual user's lifestyle.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server connects to budgeting apps, banking apps, and cashless payment apps via APIs to automatically collect data on users' income and expenses. This data includes details such as transaction date, amount, and category.

[0041] Step 2:

[0042] The server integrates the collected data and uses analytical tools to analyze monthly income and expenditure patterns. This analysis identifies trends, detects outliers, and generates a forecast model for the following month's expenditures.

[0043] Step 3:

[0044] Based on the analysis results and predicted financial situation, the server selects the most relevant financial information for the user and sends this information to the device to update the dashboard.

[0045] Step 4:

[0046] The device uses the received data to provide users with visual information such as graphs and charts. Through the dashboard, users can check their current financial status and future outlook.

[0047] Step 5:

[0048] Users select their areas of interest in wealth building through their device. This selection is sent to the server and used to develop wealth building plans.

[0049] Step 6:

[0050] The server collects and organizes relevant asset management plans and information based on the selected field. This includes the latest economic trends and investment proposals obtained from external sources.

[0051] Step 7:

[0052] The server develops specific savings and investment plans based on the user's asset building goals. These plans also include risk assessments, offering a variety of options tailored to the user's tolerance level.

[0053] Step 8:

[0054] The device displays the proposed asset building plan to the user and provides the necessary functions to monitor progress. This allows the user to constantly check their current status against their set goals and adjust the plan as needed.

[0055] (Example 1)

[0056] 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."

[0057] In financial management, providing optimal asset building methods tailored to each user's individual circumstances and goals is challenging. Furthermore, there is a need for effective methods to efficiently utilize financial data obtained from diverse sources and accurately predict future asset flows. Additionally, providing predictions and plans based on collected data in a format easily understandable to users is a crucial challenge.

[0058] 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.

[0059] In this invention, the server includes a data aggregation device, an analysis device, a visualization device, an information collection device, and a planning device. This makes it possible to integrally process the user's financial data, formulate a future asset formation plan tailored to individual needs, and present it in a visually easy-to-understand manner.

[0060] A "data aggregation device" is a device that enables the collection of financial data from various sources used by users and its centralized management.

[0061] An "analysis device" is a device that performs data analysis based on aggregated financial data to predict future financial trends and patterns.

[0062] A "visualization device" is a device that displays data and predictive information obtained by an analysis device as graphs and charts so that users can easily understand them.

[0063] An "information gathering device" is a device that efficiently collects and organizes useful information related to the user's areas of interest from external data sources.

[0064] A "planning device" is a device that helps users formulate specific asset formation plans based on their goals and supports their implementation.

[0065] The present invention is an integrated system for facilitating user financial management, and its embodiments are shown below.

[0066] First, the server plays a central role, using data aggregation devices to collect user financial data from various data sources such as household budgeting applications, financial institutions, and electronic payment applications. During this process, security and accuracy are ensured by using APIs and secure authentication methods to obtain the information.

[0067] Next, the server analyzes the collected data in detail using an analysis device. This analysis utilizes a generative AI model, which learns trends and patterns in time-series data, enabling highly accurate predictions of future financial trends. In particular, it extracts income and expenditure forecasts and spending patterns, which are then used to inform the next steps.

[0068] The obtained analytical information is sent to the terminal and presented to the user in an easy-to-understand manner by a visualization device. This includes various graphs and charts that are updated in real time, allowing the user to intuitively understand their own financial situation.

[0069] Furthermore, users can set their areas of interest through the information gathering device. For example, by selecting categories such as travel, education, or investment, relevant and useful information will be automatically collected and organized from external data sources.

[0070] Ultimately, the server uses a planning device to create a plan based on the user's asset building goals. This plan includes regular savings amounts, investment guidelines, and risk assessments, and is presented to the user via the terminal.

[0071] As a concrete example, let's consider a scenario where a user in their 20s enjoys traveling while aiming to purchase a home in the future. In this case, the server can analyze the user's spending habits, suggest an ideal monthly savings amount, and provide methods for efficiently managing travel funds.

[0072] An example of a prompt might be, "How should a single user in their 20s manage their cash flow if their goal is to buy a home in the future while traveling?" This prompt allows the generative AI model to provide highly accurate advice, contributing to the user's concrete action plan.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server uses a data aggregation device to collect financial data from users' personal finance apps, financial institutions, and electronic payment apps via APIs. The input here consists of the user's income and expenditure information provided by each data source. This data is transmitted to the server using a secure protocol and stored in a database. Specifically, the server accesses each API and automatically updates the data periodically.

[0076] Step 2:

[0077] The server analyzes the accumulated data using an analysis device. The input is time-series financial data aggregated on the server. Based on this data, a generative AI model is used to analyze future income and expenditure forecasts and consumption patterns. Specifically, the AI ​​model analyzes trends in the data and predicts the income and expenditure forecast for the next month and specific spending trends. The output is the predicted income and expenditure data and consumption patterns, which are then passed on to the next step.

[0078] Step 3:

[0079] The terminal receives analysis results sent from the server and presents them to the user using a visualization device. The input consists of predicted data and analysis results from the server. Specifically, the terminal generates various types of graphs and charts based on this data and displays them as a dashboard that is updated in real time. The output is a visual representation of income and expenditure status and spending patterns.

[0080] Step 4:

[0081] The user sets their areas of interest through their device. The input consists of selections based on the user's interests (e.g., travel, education, investment). This information is sent to the server and serves as a guide for collecting relevant information from external data sources. Specifically, the user operates the interface on their device and selects themes of interest.

[0082] Step 5:

[0083] The server uses information gathering devices to collect and organize external information based on the user's areas of interest and goals. The input is information about the user's areas of interest, and the output is useful information related to those areas. Specifically, the server accesses an external database, extracts, organizes, and compiles relevant information, and prepares it for the user.

[0084] Step 6:

[0085] The server uses a planning device to develop a specific plan based on the user's asset building goals. Inputs include user goals and collected external information. Based on this, the server creates a plan that includes savings amounts, investment plans, and risk management policies. Specifically, the server performs calculations and simulations to formulate the optimal asset building strategy and prepares it for presentation to the user in the next step.

[0086] Step 7:

[0087] The terminal receives the plan formulated from the server and presents it to the user. The input is the asset formation plan from the server, and the output is the presentation of the plan to the user. Specifically, the terminal visualizes each element of the plan and displays it in a clear and easy-to-understand manner for the user. Based on this information, the user can then create their own action plan.

[0088] (Application Example 1)

[0089] 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."

[0090] With the rise of modern electronic transactions, accurately understanding individual spending habits and effectively managing assets is essential. However, manually managing large amounts of transaction data is cumbersome, and developing predictive asset building plans is not easy. It is necessary to address these challenges and enable users to easily analyze their own financial situation and manage their assets optimally.

[0091] 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.

[0092] In this invention, the server includes data collection means, analysis means, display means, means for analyzing consumption patterns using electronic transaction history, and means for presenting asset management methods. This enables users to automatically collect transaction data, analyze consumption trends, and easily formulate future asset management plans.

[0093] "Data collection means" refers to a function that automatically acquires user transaction information through APIs of various services.

[0094] "Analysis method" refers to an algorithm that uses collected transaction information to analyze the trends in users' financial assets and consumption patterns.

[0095] "Display means" refers to an interface for providing users with information obtained through analysis in a visual format such as graphs or charts.

[0096] "Method for analyzing consumption patterns using electronic transaction history" refers to a function that performs analysis to identify consumption trends and patterns based on the user's electronic transaction history.

[0097] "Means of presenting asset management methods" refers to a function that suggests the optimal asset management method to the user based on analyzed consumption patterns and financial information.

[0098] In this embodiment of the invention, a system is constructed in which a server, a terminal, and a user are closely involved. The server is equipped with data collection means and retrieves user transaction data from multiple financial services via APIs and stores it in a database. This includes various cashless payment services and household budgeting applications. SQL is suitable as the database technology.

[0099] The server's analysis method analyzes the user's financial asset trends and consumption patterns based on the acquired data. By using the ARIMA model as the time-series analysis algorithm, it is possible to predict consumption trends and future income and expenses. This allows the generative AI model to generate predictive information and support user behavior.

[0100] The analysis results are sent from the server to the terminal and visualized as charts and graphs by the display device. Visualization libraries such as D3.js and Plotly are utilized in this process. Users can review the visualized information through their own terminals and intuitively understand how to manage their assets.

[0101] Furthermore, the server proposes the optimal asset management method based on the user's interests and preferences, and provides a concrete asset building plan according to the user's goals. In this process, using a generative AI model enables more personalized suggestions.

[0102] For example, if a user tends to spend more at the beginning of the month, the system will use that data to suggest a savings plan for the end of the month. In this way, users can optimize their daily spending while building long-term wealth.

[0103] An example of a prompt message would be, "Based on my monthly spending patterns, please suggest the best savings method," which would provide specific instructions to the generating AI model.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server uses data collection methods to retrieve transaction data from the user's financial services (cashless payment services and household budgeting apps) via APIs. During this process, user authentication information is used to securely collect the data. The input consists of transaction data from each service, and the output is structured data stored in a database.

[0107] Step 2:

[0108] The server processes the acquired transaction data using analytical tools to predict trends in financial assets and consumption patterns. Here, the ARIMA model is applied for time series analysis. The input is transaction data stored in a database, and the output is predicted future income and expenditure data.

[0109] Step 3:

[0110] The server processes the predictive information obtained through analysis using a generative AI model to provide more detailed insights. The input is analyzed revenue and expenditure information, and the output is visualized suggestions for the user.

[0111] Step 4:

[0112] The analysis results are sent from the server to the terminal and visualized as graphs or charts using the terminal's display method. Visualization libraries such as D3.js and Plotly are used. The input is the analysis information sent from the server, and the output is visualized data that the user can visually confirm.

[0113] Step 5:

[0114] Users review the visualized information provided via their device and reassess their asset management methods. In addition, they develop a concrete asset building plan based on their interests and goals. During this process, they prompt a generating AI model to receive more personalized suggestions. The input consists of visualized information and user prompts, while the output is the user's asset management plan.

[0115] 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.

[0116] This invention provides a system equipped with the ability to recognize user emotions, thereby aiming to personalize and adapt users' asset building activities. Specific embodiments of the system are described below.

[0117] First, the server uses data collection tools to gather the user's financial data from budgeting apps, banks, and cashless payment apps. In addition, the device has an emotion engine built in that analyzes the user's voice, facial expressions, input speed, etc., to estimate their current emotional state in real time.

[0118] The server uses emotional data generated by the emotion engine and analytical tools based on financial data to predict the user's asset trends. This prediction also takes into account the impact of the user's emotions on asset management; if the emotions are positive, the system automatically adjusts the investment plan to allow for more risk, while if the emotions are negative, it adjusts to a more risk-averse plan.

[0119] Based on analysis results and trend predictions, the device provides users with instantly visualized information. This includes an interface that allows users to select an asset building plan that matches their emotions for the day. For example, if the system detects that the user is feeling stressed, it will recommend a prudent savings plan, supporting rational decision-making that is not influenced by emotions.

[0120] Furthermore, the server uses information provision tools to collect and organize asset building information tailored to the user's areas of interest from external sources. This information is also linked to the user's emotional state and reflected in the planning process.

[0121] When a user sets their asset-building goals, the server mobilizes planning tools to develop a long-term investment plan tailored to their individual emotional fluctuations. This plan incorporates emotional records and future emotional predictions based on those fluctuations, helping users build their assets in a manageable way.

[0122] For example, if a user in their 20s is considering starting a new investment, the server will remember that the user has had significant anxieties about investing in the past and suggest a safe, low-risk investment plan. In this way, emotion recognition is utilized to achieve more flexible and personalized asset management tailored to the user.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The server connects to budgeting apps, banks, and cashless payment apps via APIs to periodically collect users' financial transaction data. This data is stored in the server's database.

[0126] Step 2:

[0127] The device utilizes a built-in emotion engine to analyze the user's voice tone, facial expression data, and touch input speed in real time to identify the user's current emotion. The emotion data is immediately transmitted to the server.

[0128] Step 3:

[0129] The server integrates emotional and financial data and analyzes users' asset trends using analytical tools. In this process, a predictive model is generated that takes into account the impact of emotional changes on asset management.

[0130] Step 4:

[0131] The server uses a predictive model to select the optimal investment plan based on the user's emotional state and sends that plan to the device. A plan that allows for more risk is selected if the user is feeling positive, while a plan that prioritizes safety is selected if the user is feeling negative.

[0132] Step 5:

[0133] The terminal graphically displays the received asset management plan. Through the interface, the user can review the proposed plan and choose to implement it. The options may also include emotionally reassuring feedback.

[0134] Step 6:

[0135] The user uses their device to specify areas of interest in asset building. This information is sent to the server and used for future information gathering.

[0136] Step 7:

[0137] The server collects the latest asset building information related to the user's areas of interest from external sources and organizes it in conjunction with the user's emotional tendencies. This ensures that the user is always provided with the most relevant information.

[0138] Step 8:

[0139] When a user sets long-term wealth accumulation goals, the server uses a planning mechanism that takes emotional data into account to create a plan. This plan is designed to predictively respond to fluctuations in the user's emotions. The plan is sent to the user's device, and the user can review it periodically.

[0140] (Example 2)

[0141] 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 will be referred to as the "terminal."

[0142] In modern times, personal wealth creation is becoming increasingly complex due to various factors. Among these, an individual's emotional state has a particularly significant impact on investment decision-making. However, conventional systems struggle to provide investment plans that take emotional fluctuations into account. Furthermore, there is a lack of systems that can effectively integrate individual financial and emotional data to generate accurate predictions and plans. Therefore, there is a need to make user asset management more reliable and personalized.

[0143] 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.

[0144] In this invention, the server includes means for collecting data, means for analyzing the acquired data and predicting an individual's financial trends, and means for analyzing voice, facial expressions, and input speed to estimate an individual's emotional state in real time. This makes it possible to provide asset management plans that take into account an individual's emotional changes, thereby supporting more personalized asset building.

[0145] "Data collection means" refers to a mechanism for collecting personal financial information and related information.

[0146] "Analysis tools" refer to methods that analyze collected data and predict individual financial trends.

[0147] "Emotion analysis methods" refer to technologies for estimating an individual's emotional state in real time from information such as voice, facial expressions, and input speed.

[0148] An "asset management plan" is a plan for effectively managing and investing assets, created by taking into account an individual's financial trends and emotional information.

[0149] A "display means" is an interface for visually providing users with analysis results and asset management plans.

[0150] "Information provision methods" refer to the process of collecting and organizing financial information based on an individual's areas of interest and emotional state.

[0151] "Planning methods" refer to methods for formulating specific investment plans that take into account individual emotional fluctuations and align with asset formation goals.

[0152] This invention is a system that provides an asset management plan that takes into account the user's emotional state. This system mainly consists of a server and a terminal. The server collects and analyzes data, and the terminal visually presents the results to the user.

[0153] First, the server uses data collection methods to gather the user's financial information from budgeting apps, banks, and cashless payment apps. This information includes the user's income, expenses, savings, and investment history. Next, the terminal is equipped with a dedicated emotion analysis engine that analyzes the user's voice, facial expressions, and input speed to estimate their emotional state in real time. This uses hardware such as cameras and microphones, and employs emotion analysis software.

[0154] The server integrates and analyzes collected financial and emotional data, and uses a generative AI model to predict the user's asset trends. In doing so, it considers the impact of the user's emotional state on asset management and automatically generates appropriate asset management plans based on risk levels. If the user is experiencing positive emotions, a plan that allows for some risk is suggested; conversely, if the user is experiencing negative emotions, a safer plan is proposed.

[0155] For example, if a user has generally experienced positive emotions when they have made high profits from stock investments in the past, the server will find similar investment opportunities and suggest riskier plans. Conversely, if the user's emotional state has reached a stress level, the server will recommend saving or low-risk bond investments.

[0156] An example of a prompt to input into the generating AI model would be: "Based on the user's financial and emotional data, please suggest the optimal asset management plan for both positive and negative emotional states."

[0157] Finally, the device displays the visualized results and proposed plan to the user. This helps the user make rational decisions based on their emotions. In this way, personal emotions and financial data are managed comprehensively, enabling more flexible and personalized asset management.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server uses data collection methods to obtain users' financial information. This information includes income, expenses, and savings from budgeting apps, banking systems, and cashless payment platforms. Inputs to this data collection process include APIs and data feeds, and the output is a structured financial database.

[0161] Step 2:

[0162] The device activates an emotion analysis engine and analyzes voice, facial expressions, and input speed to estimate the user's real-time emotional state. This analysis input includes raw data captured through the device's camera and microphone. The emotion analysis software processes this data and generates a record of the real-time emotional state as output.

[0163] Step 3:

[0164] The server integrates collected financial data and analyzed sentiment data. Here, a generative AI model is used to predict the user's asset trends. The input for this step includes processed financial and sentiment data. Based on this, the AI ​​model performs data calculations and outputs predictions aligned with the user's risk profile.

[0165] Step 4:

[0166] The server uses the generated predictive data to design asset management plans tailored to the user's emotional state. Inputs include the user's emotional data and asset trend predictions. The server analyzes this data and outputs either a risk-minimizing, stability-oriented plan or a risk-accepting, aggressive plan.

[0167] Step 5:

[0168] The terminal displays an asset management plan created on the server to the user. This display includes a visualized plan with sentiment analysis results and predictive data. An interface is provided for the user to visually confirm this, with input being plan data from the server and output being information presented to the user.

[0169] Step 6:

[0170] Based on the information presented, the user takes action to build their assets. In this final step, the user performs operations to execute the plan they confirmed on their device, and feedback is sent back to the server via the device. This feedback is used to improve the system.

[0171] (Application Example 2)

[0172] 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 device 14 will be referred to as the "terminal."

[0173] In modern times, financial asset management is often heavily influenced by individual emotions, making it a challenge to build wealth rationally without being swayed by feelings. Furthermore, providing flexible investment plans tailored to the user's emotional state presents a challenge. Solving these issues and realizing personalized asset management is essential.

[0174] 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.

[0175] In this invention, the server includes data collection means, analysis means, emotion recognition means, and display means. This makes it possible to flexibly adjust the asset formation plan according to the user's emotional state and support asset formation that is not influenced by emotions.

[0176] "Data collection means" refers to a device or system that has the function of obtaining a user's financial information from multiple sources.

[0177] "Analysis means" refers to a device or system used to analyze acquired information and predict trends in financial resources.

[0178] "Emotion recognition means" refers to a device or system used to estimate a user's emotional state from their voice, facial expressions, etc.

[0179] "Display means" refers to a device or system that visualizes and provides information based on analysis results and estimated emotional states to the user.

[0180] "Information provision means" refers to a device or system for collecting and organizing necessary information based on the user's interests.

[0181] A "planning tool" refers to a device or system that helps users formulate and support investment plans based on their asset formation goals and emotional state.

[0182] In this invention, a system is constructed in which a server and a terminal work together to create assets based on the recognition of the user's emotions.

[0183] The server first uses data collection tools to obtain users' financial information from various sources. This includes data from home accounting apps, banking applications, and cashless payment services. The obtained information is then analyzed by analytical tools to predict trends in financial resources. This can involve implementing algorithms that utilize machine learning techniques. Specifically, it is desirable to use frameworks such as TENSORFLOW®.

[0184] The device is equipped with emotion recognition technology that estimates the user's emotional state in real time from facial video and audio data. This technology utilizes OpenFace and other voice analysis tools. By analyzing the user's emotions, the server collects the information and stores it as emotion data that is updated in real time.

[0185] Based on analyzed financial data and estimated emotional states, the server generates an asset building plan tailored to the user's current emotions. This information is then visualized and presented to the user as a selectable interface. For example, a library like D3.js can be used to present the information in an intuitive and visually easy-to-understand manner.

[0186] Users can make rational investment decisions by utilizing asset building plans based on their daily emotions. For example, a user who is stressed at work might be presented with a low-risk savings plan, while a user who is in a good mood might be presented with more challenging investment options.

[0187] An example of a prompt message for a generative AI model is: "Please build a program that presents a risk-reduced asset building plan to the user while they are in a positive state."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server acquires users' financial information using data collection methods. Transaction data from home accounting apps and bank APIs is used as input. The server records this data in a database and performs data processing to format it for later analysis.

[0191] Step 2:

[0192] The device captures the user's facial expressions and voice data, and estimates their emotional state in real time using emotion recognition technology. Input is data from the camera and microphone, and output is the estimated emotional state. Facial recognition and voice analysis are performed using OpenFace or voice analysis tools, and the resulting emotional data is sent to a server.

[0193] Step 3:

[0194] The server integrates acquired financial data and sentiment data received from terminals using analytical tools to predict trends in financial resources. The inputs are financial data and sentiment data, and the output is the predicted asset formation trend. Machine learning algorithms, such as TensorFlow models, are used to compute the data and generate a predictive model.

[0195] Step 4:

[0196] The server generates an asset building plan tailored to the user, taking into account the analysis results and emotional state. The input is the predicted trends and emotional state obtained in step 3, and the output is an asset building plan adapted to the user's emotions. The generated plan is visualized using D3.js to make it easy for the user to understand.

[0197] Step 5:

[0198] The terminal displays and provides the user with a visualized asset management plan sent from the server. This allows the user to take action to make rational investment decisions based on their emotions on that day and in the past. The input is visualized data, and the output is a user-selectable interface. Users can view this and gain motivation to improve their asset management.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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".

[0215] This invention is a system for assisting users in managing their cash flow and primarily involves a series of processes involving a server, a terminal, and a user. The following describes embodiments of the system based on each of these roles.

[0216] First, the server has data collection capabilities to aggregate users' financial data. Specifically, it obtains data from household budgeting apps, banks, and cashless payment apps using APIs. This data includes details of the user's income and expenses. Based on this information, the server uses analytical tools to analyze time-series data and predict the expected income and expenses for the following month, as well as specific spending patterns.

[0217] Next, the server sends visualized information to the terminal based on the predictions and analysis results obtained. The terminal receives this information and presents it to the user in the form of graphs and charts through its dashboard function. This allows the user to see their financial situation at a glance and understand which items they should pay attention to.

[0218] This system also has a feature that allows users to select their areas of interest. Through a dedicated interface, users can set asset-building themes based on their personal interests, such as travel, education, and investment. The server uses this information to collect useful information related to the selected area from external sources and organize it through information provision mechanisms.

[0219] Furthermore, users can set their own asset-building goals, which the server receives and uses a planning mechanism to develop a concrete asset-building plan. This plan includes regular savings amounts, investment strategies, and risk management, and is presented to the user via their terminal. Based on these suggestions, users can formulate their own asset-building path and, with support from the server, move towards its implementation.

[0220] For example, if a user in their 20s enjoys traveling and aims to buy a home in the future, the server can analyze their spending habits, suggest an ideal monthly savings amount, and provide methods for efficiently managing travel funds. In this way, the present invention realizes support for asset management tailored to the individual user's lifestyle.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The server connects to budgeting apps, banking apps, and cashless payment apps via APIs to automatically collect data on users' income and expenses. This data includes details such as transaction date, amount, and category.

[0224] Step 2:

[0225] The server integrates the collected data and uses analytical tools to analyze monthly income and expenditure patterns. This analysis identifies trends, detects outliers, and generates a forecast model for the following month's expenditures.

[0226] Step 3:

[0227] Based on the analysis results and predicted financial situation, the server selects the most relevant financial information for the user and sends this information to the device to update the dashboard.

[0228] Step 4:

[0229] The device uses the received data to provide users with visual information such as graphs and charts. Through the dashboard, users can check their current financial status and future outlook.

[0230] Step 5:

[0231] Users select their areas of interest in wealth building through their device. This selection is sent to the server and used to develop wealth building plans.

[0232] Step 6:

[0233] The server collects and organizes relevant asset management plans and information based on the selected field. This includes the latest economic trends and investment proposals obtained from external sources.

[0234] Step 7:

[0235] The server develops specific savings and investment plans based on the user's asset building goals. These plans also include risk assessments, offering a variety of options tailored to the user's tolerance level.

[0236] Step 8:

[0237] The device displays the proposed asset building plan to the user and provides the necessary functions to monitor progress. This allows the user to constantly check their current status against their set goals and adjust the plan as needed.

[0238] (Example 1)

[0239] 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".

[0240] In financial management, providing optimal asset building methods tailored to each user's individual circumstances and goals is challenging. Furthermore, there is a need for effective methods to efficiently utilize financial data obtained from diverse sources and accurately predict future asset flows. Additionally, providing predictions and plans based on collected data in a format easily understandable to users is a crucial challenge.

[0241] 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.

[0242] In this invention, the server includes a data aggregation device, an analysis device, a visualization device, an information collection device, and a planning device. This makes it possible to integrally process the user's financial data, formulate a future asset formation plan tailored to individual needs, and present it in a visually easy-to-understand manner.

[0243] A "data aggregation device" is a device that enables the collection of financial data from various sources used by users and its centralized management.

[0244] An "analysis device" is a device that performs data analysis based on aggregated financial data to predict future financial trends and patterns.

[0245] A "visualization device" is a device that displays data and predictive information obtained by an analysis device as graphs and charts so that users can easily understand them.

[0246] An "information gathering device" is a device that efficiently collects and organizes useful information related to the user's areas of interest from external data sources.

[0247] A "planning device" is a device that helps users formulate specific asset formation plans based on their goals and supports their implementation.

[0248] The present invention is an integrated system for facilitating user financial management, and its embodiments are shown below.

[0249] First, the server plays a central role, using data aggregation devices to collect user financial data from various data sources such as household budgeting applications, financial institutions, and electronic payment applications. During this process, security and accuracy are ensured by using APIs and secure authentication methods to obtain the information.

[0250] Next, the server analyzes the collected data in detail using an analysis device. This analysis utilizes a generative AI model, which learns trends and patterns in time-series data, enabling highly accurate predictions of future financial trends. In particular, it extracts income and expenditure forecasts and spending patterns, which are then used to inform the next steps.

[0251] The obtained analytical information is sent to the terminal and presented to the user in an easy-to-understand manner by a visualization device. This includes various graphs and charts that are updated in real time, allowing the user to intuitively understand their own financial situation.

[0252] Furthermore, users can set their areas of interest through the information gathering device. For example, by selecting categories such as travel, education, or investment, relevant and useful information will be automatically collected and organized from external data sources.

[0253] Ultimately, the server uses a planning device to create a plan based on the user's asset building goals. This plan includes regular savings amounts, investment guidelines, and risk assessments, and is presented to the user via the terminal.

[0254] As a concrete example, let's consider a scenario where a user in their 20s enjoys traveling while aiming to purchase a home in the future. In this case, the server can analyze the user's spending habits, suggest an ideal monthly savings amount, and provide methods for efficiently managing travel funds.

[0255] An example of a prompt might be, "How should a single user in their 20s manage their cash flow if their goal is to buy a home in the future while traveling?" This prompt allows the generative AI model to provide highly accurate advice, contributing to the user's concrete action plan.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The server uses a data aggregation device to collect financial data from users' personal finance apps, financial institutions, and electronic payment apps via APIs. The input here consists of the user's income and expenditure information provided by each data source. This data is transmitted to the server using a secure protocol and stored in a database. Specifically, the server accesses each API and automatically updates the data periodically.

[0259] Step 2:

[0260] The server analyzes the accumulated data using an analysis device. The input is time-series financial data aggregated on the server. Based on this data, a generative AI model is used to analyze future income and expenditure forecasts and consumption patterns. Specifically, the AI ​​model analyzes trends in the data and predicts the income and expenditure forecast for the next month and specific spending trends. The output is the predicted income and expenditure data and consumption patterns, which are then passed on to the next step.

[0261] Step 3:

[0262] The terminal receives analysis results sent from the server and presents them to the user using a visualization device. The input consists of predicted data and analysis results from the server. Specifically, the terminal generates various types of graphs and charts based on this data and displays them as a dashboard that is updated in real time. The output is a visual representation of income and expenditure status and spending patterns.

[0263] Step 4:

[0264] The user sets their areas of interest through their device. The input consists of selections based on the user's interests (e.g., travel, education, investment). This information is sent to the server and serves as a guide for collecting relevant information from external data sources. Specifically, the user operates the interface on their device and selects themes of interest.

[0265] Step 5:

[0266] The server uses information gathering devices to collect and organize external information based on the user's areas of interest and goals. The input is information about the user's areas of interest, and the output is useful information related to those areas. Specifically, the server accesses an external database, extracts, organizes, and compiles relevant information, and prepares it for the user.

[0267] Step 6:

[0268] The server uses a planning device to develop a specific plan based on the user's asset building goals. Inputs include user goals and collected external information. Based on this, the server creates a plan that includes savings amounts, investment plans, and risk management policies. Specifically, the server performs calculations and simulations to formulate the optimal asset building strategy and prepares it for presentation to the user in the next step.

[0269] Step 7:

[0270] The terminal receives the plan formulated from the server and presents it to the user. The input is the asset formation plan from the server, and the output is the presentation of the plan to the user. Specifically, the terminal visualizes each element of the plan and displays it in a clear and easy-to-understand manner for the user. Based on this information, the user can then create their own action plan.

[0271] (Application Example 1)

[0272] 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."

[0273] With the rise of modern electronic transactions, accurately understanding individual spending habits and effectively managing assets is essential. However, manually managing large amounts of transaction data is cumbersome, and developing predictive asset building plans is not easy. It is necessary to address these challenges and enable users to easily analyze their own financial situation and manage their assets optimally.

[0274] 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.

[0275] In this invention, the server includes data collection means, analysis means, display means, means for analyzing consumption patterns using electronic transaction history, and means for presenting asset management methods. This enables users to automatically collect transaction data, analyze consumption trends, and easily formulate future asset management plans.

[0276] "Data collection means" refers to a function that automatically acquires user transaction information through APIs of various services.

[0277] "Analysis method" refers to an algorithm that uses collected transaction information to analyze the trends in users' financial assets and consumption patterns.

[0278] "Display means" refers to an interface for providing users with information obtained through analysis in a visual format such as graphs or charts.

[0279] "Method for analyzing consumption patterns using electronic transaction history" refers to a function that performs analysis to identify consumption trends and patterns based on the user's electronic transaction history.

[0280] "Means of presenting asset management methods" refers to a function that suggests the optimal asset management method to the user based on analyzed consumption patterns and financial information.

[0281] In this embodiment of the invention, a system is constructed in which a server, a terminal, and a user are closely involved. The server is equipped with data collection means and retrieves user transaction data from multiple financial services via APIs and stores it in a database. This includes various cashless payment services and household budgeting applications. SQL is suitable as the database technology.

[0282] Based on the acquired data, the server's analysis means analyzes the trends of the user's financial assets and consumption patterns. At this time, by using the ARIMA model as the time series analysis algorithm, it is possible to predict consumption trends and future balances. As a result, the generative AI model generates prediction information to assist the user's behavior.

[0283] The analysis results are sent from the server to the terminal and visualized as charts and graphs by the display means. Visualization libraries such as D3.js and Plotly are utilized in this process. The user can intuitively understand how to manage assets by reviewing the visualized information via their terminal.

[0284] Furthermore, based on the user's interests and concerns, the server proposes an optimal asset management method and provides a specific asset formation plan according to the user's goal setting. By using the generative AI model in this process, more personalized proposals can be made.

[0285] As a specific example, when a certain user has a tendency to have high expenditures at the beginning of the month, the system presents a savings plan towards the end of the month based on that data. In this way, the user can optimize daily expenditures while aiming for long-term asset formation.

[0286] As an example of the prompt text, it is conceivable to give specific instructions to the generative AI model in the form of "Please propose an optimal savings method based on my monthly expenditure pattern."

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server uses data collection methods to retrieve transaction data from the user's financial services (cashless payment services and household budgeting apps) via APIs. During this process, user authentication information is used to securely collect the data. The input consists of transaction data from each service, and the output is structured data stored in a database.

[0290] Step 2:

[0291] The server processes the acquired transaction data using analytical tools to predict trends in financial assets and consumption patterns. Here, the ARIMA model is applied for time series analysis. The input is transaction data stored in a database, and the output is predicted future income and expenditure data.

[0292] Step 3:

[0293] The server processes the predictive information obtained through analysis using a generative AI model to provide more detailed insights. The input is analyzed revenue and expenditure information, and the output is visualized suggestions for the user.

[0294] Step 4:

[0295] The analysis results are sent from the server to the terminal and visualized as graphs or charts using the terminal's display method. Visualization libraries such as D3.js and Plotly are used. The input is the analysis information sent from the server, and the output is visualized data that the user can visually confirm.

[0296] Step 5:

[0297] Users review the visualized information provided via their device and reassess their asset management methods. In addition, they develop a concrete asset building plan based on their interests and goals. During this process, they prompt a generating AI model to receive more personalized suggestions. The input consists of visualized information and user prompts, while the output is the user's asset management plan.

[0298] 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.

[0299] This invention provides a system equipped with the ability to recognize user emotions, thereby aiming to personalize and adapt users' asset building activities. Specific embodiments of the system are described below.

[0300] First, the server uses data collection tools to gather the user's financial data from budgeting apps, banks, and cashless payment apps. In addition, the device has an emotion engine built in that analyzes the user's voice, facial expressions, input speed, etc., to estimate their current emotional state in real time.

[0301] The server uses emotional data generated by the emotion engine and analytical tools based on financial data to predict the user's asset trends. This prediction also takes into account the impact of the user's emotions on asset management; if the emotions are positive, the system automatically adjusts the investment plan to allow for more risk, while if the emotions are negative, it adjusts to a more risk-averse plan.

[0302] Based on analysis results and trend predictions, the device provides users with instantly visualized information. This includes an interface that allows users to select an asset building plan that matches their emotions for the day. For example, if the system detects that the user is feeling stressed, it will recommend a prudent savings plan, supporting rational decision-making that is not influenced by emotions.

[0303] Furthermore, the server uses information provision tools to collect and organize asset building information tailored to the user's areas of interest from external sources. This information is also linked to the user's emotional state and reflected in the planning process.

[0304] When the user sets an asset formation goal, the server mobilizes the planning means to formulate a long-term investment plan tailored to the individual emotional waves. This plan also incorporates the recording of emotions and future emotional predictions based on their fluctuations to assist the user in advancing asset formation smoothly.

[0305] As a specific example, when a user in their 20s is about to start a new investment, the server remembers that the user has had a high level of anxiety about investment in the past and proposes a highly secure and low-risk investment plan. In this way, by leveraging emotion recognition, more flexible and personalized asset management tailored to the user is realized.

[0306] The following explains the processing flow.

[0307] Step 1:

[0308] The server connects to the household account book app, bank, and cashless payment app via the API and periodically collects the user's financial transaction data. This data is stored in the server's database.

[0309] Step 2:

[0310] Utilizing the emotion engine incorporated in the terminal, the server analyzes the user's voice tone, facial expression data, and touch input speed in real time to identify the user's current emotion. The emotion data is immediately sent to the server.

[0311] Step 3:

[0312] The server integrates the emotion data and financial data it has collected and uses analysis means to analyze the user's asset trends. In this process, a prediction model that takes into account the impact of emotional changes on asset management is generated.

[0313] Step 4:

[0314] The server uses a predictive model to select the optimal investment plan based on the user's emotional state and sends that plan to the device. A plan that allows for more risk is selected if the user is feeling positive, while a plan that prioritizes safety is selected if the user is feeling negative.

[0315] Step 5:

[0316] The terminal graphically displays the received asset management plan. Through the interface, the user can review the proposed plan and choose to implement it. The options may also include emotionally reassuring feedback.

[0317] Step 6:

[0318] The user uses their device to specify areas of interest in asset building. This information is sent to the server and used for future information gathering.

[0319] Step 7:

[0320] The server collects the latest asset building information related to the user's areas of interest from external sources and organizes it in conjunction with the user's emotional tendencies. This ensures that the user is always provided with the most relevant information.

[0321] Step 8:

[0322] When a user sets long-term wealth accumulation goals, the server uses a planning mechanism that takes emotional data into account to create a plan. This plan is designed to predictively respond to fluctuations in the user's emotions. The plan is sent to the user's device, and the user can review it periodically.

[0323] (Example 2)

[0324] 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".

[0325] In modern times, personal wealth creation is becoming increasingly complex due to various factors. Among these, an individual's emotional state has a particularly significant impact on investment decision-making. However, conventional systems struggle to provide investment plans that take emotional fluctuations into account. Furthermore, there is a lack of systems that can effectively integrate individual financial and emotional data to generate accurate predictions and plans. Therefore, there is a need to make user asset management more reliable and personalized.

[0326] 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.

[0327] In this invention, the server includes means for collecting data, means for analyzing the acquired data and predicting an individual's financial trends, and means for analyzing voice, facial expressions, and input speed to estimate an individual's emotional state in real time. This makes it possible to provide asset management plans that take into account an individual's emotional changes, thereby supporting more personalized asset building.

[0328] "Data collection means" refers to a mechanism for collecting personal financial information and related information.

[0329] "Analysis tools" refer to methods that analyze collected data and predict individual financial trends.

[0330] "Emotion analysis methods" refer to technologies for estimating an individual's emotional state in real time from information such as voice, facial expressions, and input speed.

[0331] An "asset management plan" is a plan for effectively managing and investing assets, created by taking into account an individual's financial trends and emotional information.

[0332] A "display means" is an interface for visually providing users with analysis results and asset management plans.

[0333] "Information provision methods" refer to the process of collecting and organizing financial information based on an individual's areas of interest and emotional state.

[0334] "Planning methods" refer to methods for formulating specific investment plans that take into account individual emotional fluctuations and align with asset formation goals.

[0335] This invention is a system that provides an asset management plan that takes into account the user's emotional state. This system mainly consists of a server and a terminal. The server collects and analyzes data, and the terminal visually presents the results to the user.

[0336] First, the server uses data collection methods to gather the user's financial information from budgeting apps, banks, and cashless payment apps. This information includes the user's income, expenses, savings, and investment history. Next, the terminal is equipped with a dedicated emotion analysis engine that analyzes the user's voice, facial expressions, and input speed to estimate their emotional state in real time. This uses hardware such as cameras and microphones, and employs emotion analysis software.

[0337] The server integrates and analyzes collected financial and emotional data, and uses a generative AI model to predict the user's asset trends. In doing so, it considers the impact of the user's emotional state on asset management and automatically generates appropriate asset management plans based on risk levels. If the user is experiencing positive emotions, a plan that allows for some risk is suggested; conversely, if the user is experiencing negative emotions, a safer plan is proposed.

[0338] For example, if a user has generally experienced positive emotions when they have made high profits from stock investments in the past, the server will find similar investment opportunities and suggest riskier plans. Conversely, if the user's emotional state has reached a stress level, the server will recommend saving or low-risk bond investments.

[0339] An example of a prompt to input into the generating AI model would be: "Based on the user's financial and emotional data, please suggest the optimal asset management plan for both positive and negative emotional states."

[0340] Finally, the device displays the visualized results and proposed plan to the user. This helps the user make rational decisions based on their emotions. In this way, personal emotions and financial data are managed comprehensively, enabling more flexible and personalized asset management.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The server uses data collection methods to obtain users' financial information. This information includes income, expenses, and savings from budgeting apps, banking systems, and cashless payment platforms. Inputs to this data collection process include APIs and data feeds, and the output is a structured financial database.

[0344] Step 2:

[0345] The device activates an emotion analysis engine and analyzes voice, facial expressions, and input speed to estimate the user's real-time emotional state. This analysis input includes raw data captured through the device's camera and microphone. The emotion analysis software processes this data and generates a record of the real-time emotional state as output.

[0346] Step 3:

[0347] The server integrates collected financial data and analyzed sentiment data. Here, a generative AI model is used to predict the user's asset trends. The input for this step includes processed financial and sentiment data. Based on this, the AI ​​model performs data calculations and outputs predictions aligned with the user's risk profile.

[0348] Step 4:

[0349] The server uses the generated predictive data to design asset management plans tailored to the user's emotional state. Inputs include the user's emotional data and asset trend predictions. The server analyzes this data and outputs either a risk-minimizing, stability-oriented plan or a risk-accepting, aggressive plan.

[0350] Step 5:

[0351] The terminal displays an asset management plan created on the server to the user. This display includes a visualized plan with sentiment analysis results and predictive data. An interface is provided for the user to visually confirm this, with input being plan data from the server and output being information presented to the user.

[0352] Step 6:

[0353] Based on the information presented, the user takes action to build their assets. In this final step, the user performs operations to execute the plan they confirmed on their device, and feedback is sent back to the server via the device. This feedback is used to improve the system.

[0354] (Application Example 2)

[0355] 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."

[0356] In modern times, financial asset management is often heavily influenced by individual emotions, making it a challenge to build wealth rationally without being swayed by feelings. Furthermore, providing flexible investment plans tailored to the user's emotional state presents a challenge. Solving these issues and realizing personalized asset management is essential.

[0357] 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.

[0358] In this invention, the server includes data collection means, analysis means, emotion recognition means, and display means. This makes it possible to flexibly adjust the asset formation plan according to the user's emotional state and support asset formation that is not influenced by emotions.

[0359] "Data collection means" refers to a device or system that has the function of obtaining a user's financial information from multiple sources.

[0360] "Analysis means" refers to a device or system used to analyze acquired information and predict trends in financial resources.

[0361] "Emotion recognition means" refers to a device or system used to estimate a user's emotional state from their voice, facial expressions, etc.

[0362] "Display means" refers to a device or system that visualizes and provides information based on analysis results and estimated emotional states to the user.

[0363] "Information provision means" refers to a device or system for collecting and organizing necessary information based on the user's interests.

[0364] A "planning tool" refers to a device or system that helps users formulate and support investment plans based on their asset formation goals and emotional state.

[0365] In this invention, a system is constructed in which a server and a terminal work together to create assets based on the recognition of the user's emotions.

[0366] The server first uses data collection tools to obtain users' financial information from various sources. This includes data from home accounting apps, banking applications, and cashless payment services. The obtained information is then analyzed by analytical tools to predict trends in financial resources. This can involve implementing algorithms that utilize machine learning techniques. Specifically, it is desirable to use frameworks such as TensorFlow.

[0367] The device is equipped with emotion recognition technology that estimates the user's emotional state in real time from facial video and audio data. This technology utilizes OpenFace and other voice analysis tools. By analyzing the user's emotions, the server collects the information and stores it as emotion data that is updated in real time.

[0368] Based on analyzed financial data and estimated emotional states, the server generates an asset building plan tailored to the user's current emotions. This information is then visualized and presented to the user as a selectable interface. For example, a library like D3.js can be used to present the information in an intuitive and visually easy-to-understand manner.

[0369] Users can make rational investment decisions by utilizing asset building plans based on their daily emotions. For example, a user who is stressed at work might be presented with a low-risk savings plan, while a user who is in a good mood might be presented with more challenging investment options.

[0370] An example of a prompt message for a generative AI model is: "Please build a program that presents a risk-reduced asset building plan to the user while they are in a positive state."

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server acquires users' financial information using data collection methods. Transaction data from home accounting apps and bank APIs is used as input. The server records this data in a database and performs data processing to format it for later analysis.

[0374] Step 2:

[0375] The device captures the user's facial expressions and voice data, and estimates their emotional state in real time using emotion recognition technology. Input is data from the camera and microphone, and output is the estimated emotional state. Facial recognition and voice analysis are performed using OpenFace or voice analysis tools, and the resulting emotional data is sent to a server.

[0376] Step 3:

[0377] The server integrates acquired financial data and sentiment data received from terminals using analytical tools to predict trends in financial resources. The inputs are financial data and sentiment data, and the output is the predicted asset formation trend. Machine learning algorithms, such as TensorFlow models, are used to compute the data and generate a predictive model.

[0378] Step 4:

[0379] The server generates an asset building plan tailored to the user, taking into account the analysis results and emotional state. The input is the predicted trends and emotional state obtained in step 3, and the output is an asset building plan adapted to the user's emotions. The generated plan is visualized using D3.js to make it easy for the user to understand.

[0380] Step 5:

[0381] The terminal displays and provides the user with a visualized asset management plan sent from the server. This allows the user to take action to make rational investment decisions based on their emotions on that day and in the past. The input is visualized data, and the output is a user-selectable interface. Users can view this and gain motivation to improve their asset management.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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".

[0398] This invention is a system for assisting users in managing their cash flow and primarily involves a series of processes involving a server, a terminal, and a user. The following describes embodiments of the system based on each of these roles.

[0399] First, the server has data collection capabilities to aggregate users' financial data. Specifically, it obtains data from household budgeting apps, banks, and cashless payment apps using APIs. This data includes details of the user's income and expenses. Based on this information, the server uses analytical tools to analyze time-series data and predict the expected income and expenses for the following month, as well as specific spending patterns.

[0400] Next, the server sends visualized information to the terminal based on the predictions and analysis results obtained. The terminal receives this information and presents it to the user in the form of graphs and charts through its dashboard function. This allows the user to see their financial situation at a glance and understand which items they should pay attention to.

[0401] This system also has a feature that allows users to select their areas of interest. Through a dedicated interface, users can set asset-building themes based on their personal interests, such as travel, education, and investment. The server uses this information to collect useful information related to the selected area from external sources and organize it through information provision mechanisms.

[0402] Furthermore, users can set their own asset-building goals, which the server receives and uses a planning mechanism to develop a concrete asset-building plan. This plan includes regular savings amounts, investment strategies, and risk management, and is presented to the user via their terminal. Based on these suggestions, users can formulate their own asset-building path and, with support from the server, move towards its implementation.

[0403] For example, if a user in their 20s enjoys traveling and aims to buy a home in the future, the server can analyze their spending habits, suggest an ideal monthly savings amount, and provide methods for efficiently managing travel funds. In this way, the present invention realizes support for asset management tailored to the individual user's lifestyle.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The server connects to budgeting apps, banking apps, and cashless payment apps via APIs to automatically collect data on users' income and expenses. This data includes details such as transaction date, amount, and category.

[0407] Step 2:

[0408] The server integrates the collected data and uses analytical tools to analyze monthly income and expenditure patterns. This analysis identifies trends, detects outliers, and generates a forecast model for the following month's expenditures.

[0409] Step 3:

[0410] Based on the analysis results and predicted financial situation, the server selects the most relevant financial information for the user and sends this information to the device to update the dashboard.

[0411] Step 4:

[0412] The device uses the received data to provide users with visual information such as graphs and charts. Through the dashboard, users can check their current financial status and future outlook.

[0413] Step 5:

[0414] Users select their areas of interest in wealth building through their device. This selection is sent to the server and used to develop wealth building plans.

[0415] Step 6:

[0416] The server collects and organizes relevant asset management plans and information based on the selected field. This includes the latest economic trends and investment proposals obtained from external sources.

[0417] Step 7:

[0418] The server develops specific savings and investment plans based on the user's asset building goals. These plans also include risk assessments, offering a variety of options tailored to the user's tolerance level.

[0419] Step 8:

[0420] The device displays the proposed asset building plan to the user and provides the necessary functions to monitor progress. This allows the user to constantly check their current status against their set goals and adjust the plan as needed.

[0421] (Example 1)

[0422] 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."

[0423] In financial management, providing optimal asset building methods tailored to each user's individual circumstances and goals is challenging. Furthermore, there is a need for effective methods to efficiently utilize financial data obtained from diverse sources and accurately predict future asset flows. Additionally, providing predictions and plans based on collected data in a format easily understandable to users is a crucial challenge.

[0424] 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.

[0425] In this invention, the server includes a data aggregation device, an analysis device, a visualization device, an information collection device, and a planning device. This makes it possible to integrally process the user's financial data, formulate a future asset formation plan tailored to individual needs, and present it in a visually easy-to-understand manner.

[0426] A "data aggregation device" is a device that enables the collection of financial data from various sources used by users and its centralized management.

[0427] An "analysis device" is a device that performs data analysis based on aggregated financial data to predict future financial trends and patterns.

[0428] A "visualization device" is a device that displays data and predictive information obtained by an analysis device as graphs and charts so that users can easily understand them.

[0429] An "information gathering device" is a device that efficiently collects and organizes useful information related to the user's areas of interest from external data sources.

[0430] A "planning device" is a device that helps users formulate specific asset formation plans based on their goals and supports their implementation.

[0431] The present invention is an integrated system for facilitating user financial management, and its embodiments are shown below.

[0432] First, the server plays a central role, using data aggregation devices to collect user financial data from various data sources such as household budgeting applications, financial institutions, and electronic payment applications. During this process, security and accuracy are ensured by using APIs and secure authentication methods to obtain the information.

[0433] Next, the server analyzes the collected data in detail using an analysis device. This analysis utilizes a generative AI model, which learns trends and patterns in time-series data, enabling highly accurate predictions of future financial trends. In particular, it extracts income and expenditure forecasts and spending patterns, which are then used to inform the next steps.

[0434] The obtained analytical information is sent to the terminal and presented to the user in an easy-to-understand manner by a visualization device. This includes various graphs and charts that are updated in real time, allowing the user to intuitively understand their own financial situation.

[0435] Furthermore, users can set their areas of interest through the information gathering device. For example, by selecting categories such as travel, education, or investment, relevant and useful information will be automatically collected and organized from external data sources.

[0436] Ultimately, the server uses a planning device to create a plan based on the user's asset building goals. This plan includes regular savings amounts, investment guidelines, and risk assessments, and is presented to the user via the terminal.

[0437] As a concrete example, let's consider a scenario where a user in their 20s enjoys traveling while aiming to purchase a home in the future. In this case, the server can analyze the user's spending habits, suggest an ideal monthly savings amount, and provide methods for efficiently managing travel funds.

[0438] An example of a prompt might be, "How should a single user in their 20s manage their cash flow if their goal is to buy a home in the future while traveling?" This prompt allows the generative AI model to provide highly accurate advice, contributing to the user's concrete action plan.

[0439] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0440] Step 1:

[0441] The server uses a data aggregation device to collect financial data from users' personal finance apps, financial institutions, and electronic payment apps via APIs. The input here consists of the user's income and expenditure information provided by each data source. This data is transmitted to the server using a secure protocol and stored in a database. Specifically, the server accesses each API and automatically updates the data periodically.

[0442] Step 2:

[0443] The server analyzes the accumulated data using an analysis device. The input is time-series financial data aggregated on the server. Based on this data, a generative AI model is used to analyze future income and expenditure forecasts and consumption patterns. Specifically, the AI ​​model analyzes trends in the data and predicts the income and expenditure forecast for the next month and specific spending trends. The output is the predicted income and expenditure data and consumption patterns, which are then passed on to the next step.

[0444] Step 3:

[0445] The terminal receives analysis results sent from the server and presents them to the user using a visualization device. The input consists of predicted data and analysis results from the server. Specifically, the terminal generates various types of graphs and charts based on this data and displays them as a dashboard that is updated in real time. The output is a visual representation of income and expenditure status and spending patterns.

[0446] Step 4:

[0447] The user sets their areas of interest through their device. The input consists of selections based on the user's interests (e.g., travel, education, investment). This information is sent to the server and serves as a guide for collecting relevant information from external data sources. Specifically, the user operates the interface on their device and selects themes of interest.

[0448] Step 5:

[0449] The server uses information gathering devices to collect and organize external information based on the user's areas of interest and goals. The input is information about the user's areas of interest, and the output is useful information related to those areas. Specifically, the server accesses an external database, extracts, organizes, and compiles relevant information, and prepares it for the user.

[0450] Step 6:

[0451] The server uses a planning device to develop a specific plan based on the user's asset building goals. Inputs include user goals and collected external information. Based on this, the server creates a plan that includes savings amounts, investment plans, and risk management policies. Specifically, the server performs calculations and simulations to formulate the optimal asset building strategy and prepares it for presentation to the user in the next step.

[0452] Step 7:

[0453] The terminal receives the plan formulated from the server and presents it to the user. The input is the asset formation plan from the server, and the output is the presentation of the plan to the user. Specifically, the terminal visualizes each element of the plan and displays it in a clear and easy-to-understand manner for the user. Based on this information, the user can then create their own action plan.

[0454] (Application Example 1)

[0455] 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."

[0456] With the rise of modern electronic transactions, accurately understanding individual spending habits and effectively managing assets is essential. However, manually managing large amounts of transaction data is cumbersome, and developing predictive asset building plans is not easy. It is necessary to address these challenges and enable users to easily analyze their own financial situation and manage their assets optimally.

[0457] 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.

[0458] In this invention, the server includes data collection means, analysis means, display means, means for analyzing consumption patterns using electronic transaction history, and means for presenting asset management methods. This enables users to automatically collect transaction data, analyze consumption trends, and easily formulate future asset management plans.

[0459] "Data collection means" refers to a function that automatically acquires user transaction information through APIs of various services.

[0460] "Analysis method" refers to an algorithm that uses collected transaction information to analyze the trends in users' financial assets and consumption patterns.

[0461] "Display means" refers to an interface for providing users with information obtained through analysis in a visual format such as graphs or charts.

[0462] "Method for analyzing consumption patterns using electronic transaction history" refers to a function that performs analysis to identify consumption trends and patterns based on the user's electronic transaction history.

[0463] "Means of presenting asset management methods" refers to a function that suggests the optimal asset management method to the user based on analyzed consumption patterns and financial information.

[0464] In this embodiment of the invention, a system is constructed in which a server, a terminal, and a user are closely involved. The server is equipped with data collection means and retrieves user transaction data from multiple financial services via APIs and stores it in a database. This includes various cashless payment services and household budgeting applications. SQL is suitable as the database technology.

[0465] The server's analysis method analyzes the user's financial asset trends and consumption patterns based on the acquired data. By using the ARIMA model as the time-series analysis algorithm, it is possible to predict consumption trends and future income and expenses. This allows the generative AI model to generate predictive information and support user behavior.

[0466] The analysis results are sent from the server to the terminal and visualized as charts and graphs by the display device. Visualization libraries such as D3.js and Plotly are utilized in this process. Users can review the visualized information through their own terminals and intuitively understand how to manage their assets.

[0467] Furthermore, the server proposes the optimal asset management method based on the user's interests and preferences, and provides a concrete asset building plan according to the user's goals. In this process, using a generative AI model enables more personalized suggestions.

[0468] For example, if a user tends to spend more at the beginning of the month, the system will use that data to suggest a savings plan for the end of the month. In this way, users can optimize their daily spending while building long-term wealth.

[0469] An example of a prompt message would be, "Based on my monthly spending patterns, please suggest the best savings method," which would provide specific instructions to the generating AI model.

[0470] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0471] Step 1:

[0472] The server uses data collection methods to retrieve transaction data from the user's financial services (cashless payment services and household budgeting apps) via APIs. During this process, user authentication information is used to securely collect the data. The input consists of transaction data from each service, and the output is structured data stored in a database.

[0473] Step 2:

[0474] The server processes the acquired transaction data using analytical tools to predict trends in financial assets and consumption patterns. Here, the ARIMA model is applied for time series analysis. The input is transaction data stored in a database, and the output is predicted future income and expenditure data.

[0475] Step 3:

[0476] The server processes the predictive information obtained through analysis using a generative AI model to provide more detailed insights. The input is analyzed revenue and expenditure information, and the output is visualized suggestions for the user.

[0477] Step 4:

[0478] The analysis results are sent from the server to the terminal and visualized as graphs or charts using the terminal's display method. Visualization libraries such as D3.js and Plotly are used. The input is the analysis information sent from the server, and the output is visualized data that the user can visually confirm.

[0479] Step 5:

[0480] Users review the visualized information provided via their device and reassess their asset management methods. In addition, they develop a concrete asset building plan based on their interests and goals. During this process, they prompt a generating AI model to receive more personalized suggestions. The input consists of visualized information and user prompts, while the output is the user's asset management plan.

[0481] 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.

[0482] This invention provides a system equipped with the ability to recognize user emotions, thereby aiming to personalize and adapt users' asset building activities. Specific embodiments of the system are described below.

[0483] First, the server uses data collection tools to gather the user's financial data from budgeting apps, banks, and cashless payment apps. In addition, the device has an emotion engine built in that analyzes the user's voice, facial expressions, input speed, etc., to estimate their current emotional state in real time.

[0484] The server uses emotional data generated by the emotion engine and analytical tools based on financial data to predict the user's asset trends. This prediction also takes into account the impact of the user's emotions on asset management; if the emotions are positive, the system automatically adjusts the investment plan to allow for more risk, while if the emotions are negative, it adjusts to a more risk-averse plan.

[0485] Based on analysis results and trend predictions, the device provides users with instantly visualized information. This includes an interface that allows users to select an asset building plan that matches their emotions for the day. For example, if the system detects that the user is feeling stressed, it will recommend a prudent savings plan, supporting rational decision-making that is not influenced by emotions.

[0486] Furthermore, the server uses information provision tools to collect and organize asset building information tailored to the user's areas of interest from external sources. This information is also linked to the user's emotional state and reflected in the planning process.

[0487] When a user sets their asset-building goals, the server mobilizes planning tools to develop a long-term investment plan tailored to their individual emotional fluctuations. This plan incorporates emotional records and future emotional predictions based on those fluctuations, helping users build their assets in a manageable way.

[0488] For example, if a user in their 20s is considering starting a new investment, the server will remember that the user has had significant anxieties about investing in the past and suggest a safe, low-risk investment plan. In this way, emotion recognition is utilized to achieve more flexible and personalized asset management tailored to the user.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The server connects to budgeting apps, banks, and cashless payment apps via APIs to periodically collect users' financial transaction data. This data is stored in the server's database.

[0492] Step 2:

[0493] The device utilizes a built-in emotion engine to analyze the user's voice tone, facial expression data, and touch input speed in real time to identify the user's current emotion. The emotion data is immediately transmitted to the server.

[0494] Step 3:

[0495] The server integrates emotional and financial data and analyzes users' asset trends using analytical tools. In this process, a predictive model is generated that takes into account the impact of emotional changes on asset management.

[0496] Step 4:

[0497] The server uses a predictive model to select the optimal investment plan based on the user's emotional state and sends that plan to the device. A plan that allows for more risk is selected if the user is feeling positive, while a plan that prioritizes safety is selected if the user is feeling negative.

[0498] Step 5:

[0499] The terminal graphically displays the received asset management plan. Through the interface, the user can review the proposed plan and choose to implement it. The options may also include emotionally reassuring feedback.

[0500] Step 6:

[0501] The user uses their device to specify areas of interest in asset building. This information is sent to the server and used for future information gathering.

[0502] Step 7:

[0503] The server collects the latest asset building information related to the user's areas of interest from external sources and organizes it in conjunction with the user's emotional tendencies. This ensures that the user is always provided with the most relevant information.

[0504] Step 8:

[0505] When a user sets long-term wealth accumulation goals, the server uses a planning mechanism that takes emotional data into account to create a plan. This plan is designed to predictively respond to fluctuations in the user's emotions. The plan is sent to the user's device, and the user can review it periodically.

[0506] (Example 2)

[0507] 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."

[0508] In modern times, personal wealth creation is becoming increasingly complex due to various factors. Among these, an individual's emotional state has a particularly significant impact on investment decision-making. However, conventional systems struggle to provide investment plans that take emotional fluctuations into account. Furthermore, there is a lack of systems that can effectively integrate individual financial and emotional data to generate accurate predictions and plans. Therefore, there is a need to make user asset management more reliable and personalized.

[0509] 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.

[0510] In this invention, the server includes means for collecting data, means for analyzing the acquired data and predicting an individual's financial trends, and means for analyzing voice, facial expressions, and input speed to estimate an individual's emotional state in real time. This makes it possible to provide asset management plans that take into account an individual's emotional changes, thereby supporting more personalized asset building.

[0511] "Data collection means" refers to a mechanism for collecting personal financial information and related information.

[0512] "Analysis tools" refer to methods that analyze collected data and predict individual financial trends.

[0513] "Emotion analysis methods" refer to technologies for estimating an individual's emotional state in real time from information such as voice, facial expressions, and input speed.

[0514] An "asset management plan" is a plan for effectively managing and investing assets, created by taking into account an individual's financial trends and emotional information.

[0515] A "display means" is an interface for visually providing users with analysis results and asset management plans.

[0516] "Information provision methods" refer to the process of collecting and organizing financial information based on an individual's areas of interest and emotional state.

[0517] "Planning methods" refer to methods for formulating specific investment plans that take into account individual emotional fluctuations and align with asset formation goals.

[0518] This invention is a system that provides an asset management plan that takes into account the user's emotional state. This system mainly consists of a server and a terminal. The server collects and analyzes data, and the terminal visually presents the results to the user.

[0519] First, the server uses data collection methods to gather the user's financial information from budgeting apps, banks, and cashless payment apps. This information includes the user's income, expenses, savings, and investment history. Next, the terminal is equipped with a dedicated emotion analysis engine that analyzes the user's voice, facial expressions, and input speed to estimate their emotional state in real time. This uses hardware such as cameras and microphones, and employs emotion analysis software.

[0520] The server integrates and analyzes collected financial and emotional data, and uses a generative AI model to predict the user's asset trends. In doing so, it considers the impact of the user's emotional state on asset management and automatically generates appropriate asset management plans based on risk levels. If the user is experiencing positive emotions, a plan that allows for some risk is suggested; conversely, if the user is experiencing negative emotions, a safer plan is proposed.

[0521] For example, if a user has generally experienced positive emotions when they have made high profits from stock investments in the past, the server will find similar investment opportunities and suggest riskier plans. Conversely, if the user's emotional state has reached a stress level, the server will recommend saving or low-risk bond investments.

[0522] An example of a prompt to input into the generating AI model would be: "Based on the user's financial and emotional data, please suggest the optimal asset management plan for both positive and negative emotional states."

[0523] Finally, the device displays the visualized results and proposed plan to the user. This helps the user make rational decisions based on their emotions. In this way, personal emotions and financial data are managed comprehensively, enabling more flexible and personalized asset management.

[0524] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0525] Step 1:

[0526] The server uses data collection methods to obtain users' financial information. This information includes income, expenses, and savings from budgeting apps, banking systems, and cashless payment platforms. Inputs to this data collection process include APIs and data feeds, and the output is a structured financial database.

[0527] Step 2:

[0528] The device activates an emotion analysis engine and analyzes voice, facial expressions, and input speed to estimate the user's real-time emotional state. This analysis input includes raw data captured through the device's camera and microphone. The emotion analysis software processes this data and generates a record of the real-time emotional state as output.

[0529] Step 3:

[0530] The server integrates collected financial data and analyzed sentiment data. Here, a generative AI model is used to predict the user's asset trends. The input for this step includes processed financial and sentiment data. Based on this, the AI ​​model performs data calculations and outputs predictions aligned with the user's risk profile.

[0531] Step 4:

[0532] The server uses the generated predictive data to design asset management plans tailored to the user's emotional state. Inputs include the user's emotional data and asset trend predictions. The server analyzes this data and outputs either a risk-minimizing, stability-oriented plan or a risk-accepting, aggressive plan.

[0533] Step 5:

[0534] The terminal displays an asset management plan created on the server to the user. This display includes a visualized plan with sentiment analysis results and predictive data. An interface is provided for the user to visually confirm this, with input being plan data from the server and output being information presented to the user.

[0535] Step 6:

[0536] Based on the information presented, the user takes action to build their assets. In this final step, the user performs operations to execute the plan they confirmed on their device, and feedback is sent back to the server via the device. This feedback is used to improve the system.

[0537] (Application Example 2)

[0538] 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."

[0539] In modern times, financial asset management is often heavily influenced by individual emotions, making it a challenge to build wealth rationally without being swayed by feelings. Furthermore, providing flexible investment plans tailored to the user's emotional state presents a challenge. Solving these issues and realizing personalized asset management is essential.

[0540] 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.

[0541] In this invention, the server includes data collection means, analysis means, emotion recognition means, and display means. This makes it possible to flexibly adjust the asset formation plan according to the user's emotional state and support asset formation that is not influenced by emotions.

[0542] "Data collection means" refers to a device or system that has the function of obtaining a user's financial information from multiple sources.

[0543] "Analysis means" refers to a device or system used to analyze acquired information and predict trends in financial resources.

[0544] "Emotion recognition means" refers to a device or system used to estimate a user's emotional state from their voice, facial expressions, etc.

[0545] "Display means" refers to a device or system that visualizes and provides information based on analysis results and estimated emotional states to the user.

[0546] "Information provision means" refers to a device or system for collecting and organizing necessary information based on the user's interests.

[0547] A "planning tool" refers to a device or system that helps users formulate and support investment plans based on their asset formation goals and emotional state.

[0548] In this invention, a system is constructed in which a server and a terminal work together to create assets based on the recognition of the user's emotions.

[0549] The server first uses data collection tools to obtain users' financial information from various sources. This includes data from home accounting apps, banking applications, and cashless payment services. The obtained information is then analyzed by analytical tools to predict trends in financial resources. This can involve implementing algorithms that utilize machine learning techniques. Specifically, it is desirable to use frameworks such as TensorFlow.

[0550] The device is equipped with emotion recognition technology that estimates the user's emotional state in real time from facial video and audio data. This technology utilizes OpenFace and other voice analysis tools. By analyzing the user's emotions, the server collects the information and stores it as emotion data that is updated in real time.

[0551] Based on analyzed financial data and estimated emotional states, the server generates an asset building plan tailored to the user's current emotions. This information is then visualized and presented to the user as a selectable interface. For example, a library like D3.js can be used to present the information in an intuitive and visually easy-to-understand manner.

[0552] Users can make rational investment decisions by utilizing asset building plans based on their daily emotions. For example, a user who is stressed at work might be presented with a low-risk savings plan, while a user who is in a good mood might be presented with more challenging investment options.

[0553] An example of a prompt message for a generative AI model is: "Please build a program that presents a risk-reduced asset building plan to the user while they are in a positive state."

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] The server acquires users' financial information using data collection methods. Transaction data from home accounting apps and bank APIs is used as input. The server records this data in a database and performs data processing to format it for later analysis.

[0557] Step 2:

[0558] The device captures the user's facial expressions and voice data, and estimates their emotional state in real time using emotion recognition technology. Input is data from the camera and microphone, and output is the estimated emotional state. Facial recognition and voice analysis are performed using OpenFace or voice analysis tools, and the resulting emotional data is sent to a server.

[0559] Step 3:

[0560] The server integrates acquired financial data and sentiment data received from terminals using analytical tools to predict trends in financial resources. The inputs are financial data and sentiment data, and the output is the predicted asset formation trend. Machine learning algorithms, such as TensorFlow models, are used to compute the data and generate a predictive model.

[0561] Step 4:

[0562] The server generates an asset building plan tailored to the user, taking into account the analysis results and emotional state. The input is the predicted trends and emotional state obtained in step 3, and the output is an asset building plan adapted to the user's emotions. The generated plan is visualized using D3.js to make it easy for the user to understand.

[0563] Step 5:

[0564] The terminal displays and provides the user with a visualized asset management plan sent from the server. This allows the user to take action to make rational investment decisions based on their emotions on that day and in the past. The input is visualized data, and the output is a user-selectable interface. Users can view this and gain motivation to improve their asset management.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] [Fourth Embodiment]

[0569] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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".

[0582] This invention is a system for assisting users in managing their cash flow and primarily involves a series of processes involving a server, a terminal, and a user. The following describes embodiments of the system based on each of these roles.

[0583] First, the server has data collection capabilities to aggregate users' financial data. Specifically, it obtains data from household budgeting apps, banks, and cashless payment apps using APIs. This data includes details of the user's income and expenses. Based on this information, the server uses analytical tools to analyze time-series data and predict the expected income and expenses for the following month, as well as specific spending patterns.

[0584] Next, the server sends visualized information to the terminal based on the predictions and analysis results obtained. The terminal receives this information and presents it to the user in the form of graphs and charts through its dashboard function. This allows the user to see their financial situation at a glance and understand which items they should pay attention to.

[0585] This system also has a feature that allows users to select their areas of interest. Through a dedicated interface, users can set asset-building themes based on their personal interests, such as travel, education, and investment. The server uses this information to collect useful information related to the selected area from external sources and organize it through information provision mechanisms.

[0586] Furthermore, users can set their own asset-building goals, which the server receives and uses a planning mechanism to develop a concrete asset-building plan. This plan includes regular savings amounts, investment strategies, and risk management, and is presented to the user via their terminal. Based on these suggestions, users can formulate their own asset-building path and, with support from the server, move towards its implementation.

[0587] For example, if a user in their 20s enjoys traveling and aims to buy a home in the future, the server can analyze their spending habits, suggest an ideal monthly savings amount, and provide methods for efficiently managing travel funds. In this way, the present invention realizes support for asset management tailored to the individual user's lifestyle.

[0588] The following describes the processing flow.

[0589] Step 1:

[0590] The server connects to budgeting apps, banking apps, and cashless payment apps via APIs to automatically collect data on users' income and expenses. This data includes details such as transaction date, amount, and category.

[0591] Step 2:

[0592] The server integrates the collected data and uses analytical tools to analyze monthly income and expenditure patterns. This analysis identifies trends, detects outliers, and generates a forecast model for the following month's expenditures.

[0593] Step 3:

[0594] Based on the analysis results and predicted financial situation, the server selects the most relevant financial information for the user and sends this information to the device to update the dashboard.

[0595] Step 4:

[0596] The device uses the received data to provide users with visual information such as graphs and charts. Through the dashboard, users can check their current financial status and future outlook.

[0597] Step 5:

[0598] Users select their areas of interest in wealth building through their device. This selection is sent to the server and used to develop wealth building plans.

[0599] Step 6:

[0600] The server collects and organizes relevant asset management plans and information based on the selected field. This includes the latest economic trends and investment proposals obtained from external sources.

[0601] Step 7:

[0602] The server develops specific savings and investment plans based on the user's asset building goals. These plans also include risk assessments, offering a variety of options tailored to the user's tolerance level.

[0603] Step 8:

[0604] The device displays the proposed asset building plan to the user and provides the necessary functions to monitor progress. This allows the user to constantly check their current status against their set goals and adjust the plan as needed.

[0605] (Example 1)

[0606] 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".

[0607] In financial management, providing optimal asset building methods tailored to each user's individual circumstances and goals is challenging. Furthermore, there is a need for effective methods to efficiently utilize financial data obtained from diverse sources and accurately predict future asset flows. Additionally, providing predictions and plans based on collected data in a format easily understandable to users is a crucial challenge.

[0608] 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.

[0609] In this invention, the server includes a data aggregation device, an analysis device, a visualization device, an information collection device, and a planning device. This makes it possible to integrally process the user's financial data, formulate a future asset formation plan tailored to individual needs, and present it in a visually easy-to-understand manner.

[0610] A "data aggregation device" is a device that enables the collection of financial data from various sources used by users and its centralized management.

[0611] An "analysis device" is a device that performs data analysis based on aggregated financial data to predict future financial trends and patterns.

[0612] A "visualization device" is a device that displays data and predictive information obtained by an analysis device as graphs and charts so that users can easily understand them.

[0613] An "information gathering device" is a device that efficiently collects and organizes useful information related to the user's areas of interest from external data sources.

[0614] A "planning device" is a device that helps users formulate specific asset formation plans based on their goals and supports their implementation.

[0615] The present invention is an integrated system for facilitating user financial management, and its embodiments are shown below.

[0616] First, the server plays a central role, using data aggregation devices to collect user financial data from various data sources such as household budgeting applications, financial institutions, and electronic payment applications. During this process, security and accuracy are ensured by using APIs and secure authentication methods to obtain the information.

[0617] Next, the server analyzes the collected data in detail using an analysis device. This analysis utilizes a generative AI model, which learns trends and patterns in time-series data, enabling highly accurate predictions of future financial trends. In particular, it extracts income and expenditure forecasts and spending patterns, which are then used to inform the next steps.

[0618] The obtained analytical information is sent to the terminal and presented to the user in an easy-to-understand manner by a visualization device. This includes various graphs and charts that are updated in real time, allowing the user to intuitively understand their own financial situation.

[0619] Furthermore, users can set their areas of interest through the information gathering device. For example, by selecting categories such as travel, education, or investment, relevant and useful information will be automatically collected and organized from external data sources.

[0620] Ultimately, the server uses a planning device to create a plan based on the user's asset building goals. This plan includes regular savings amounts, investment guidelines, and risk assessments, and is presented to the user via the terminal.

[0621] As a concrete example, let's consider a scenario where a user in their 20s enjoys traveling while aiming to purchase a home in the future. In this case, the server can analyze the user's spending habits, suggest an ideal monthly savings amount, and provide methods for efficiently managing travel funds.

[0622] An example of a prompt might be, "How should a single user in their 20s manage their cash flow if their goal is to buy a home in the future while traveling?" This prompt allows the generative AI model to provide highly accurate advice, contributing to the user's concrete action plan.

[0623] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0624] Step 1:

[0625] The server uses a data aggregation device to collect financial data from users' personal finance apps, financial institutions, and electronic payment apps via APIs. The input here consists of the user's income and expenditure information provided by each data source. This data is transmitted to the server using a secure protocol and stored in a database. Specifically, the server accesses each API and automatically updates the data periodically.

[0626] Step 2:

[0627] The server analyzes the accumulated data using an analysis device. The input is time-series financial data aggregated on the server. Based on this data, a generative AI model is used to analyze future income and expenditure forecasts and consumption patterns. Specifically, the AI ​​model analyzes trends in the data and predicts the income and expenditure forecast for the next month and specific spending trends. The output is the predicted income and expenditure data and consumption patterns, which are then passed on to the next step.

[0628] Step 3:

[0629] The terminal receives analysis results sent from the server and presents them to the user using a visualization device. The input consists of predicted data and analysis results from the server. Specifically, the terminal generates various types of graphs and charts based on this data and displays them as a dashboard that is updated in real time. The output is a visual representation of income and expenditure status and spending patterns.

[0630] Step 4:

[0631] The user sets their areas of interest through their device. The input consists of selections based on the user's interests (e.g., travel, education, investment). This information is sent to the server and serves as a guide for collecting relevant information from external data sources. Specifically, the user operates the interface on their device and selects themes of interest.

[0632] Step 5:

[0633] The server uses information gathering devices to collect and organize external information based on the user's areas of interest and goals. The input is information about the user's areas of interest, and the output is useful information related to those areas. Specifically, the server accesses an external database, extracts, organizes, and compiles relevant information, and prepares it for the user.

[0634] Step 6:

[0635] The server uses a planning device to develop a specific plan based on the user's asset building goals. Inputs include user goals and collected external information. Based on this, the server creates a plan that includes savings amounts, investment plans, and risk management policies. Specifically, the server performs calculations and simulations to formulate the optimal asset building strategy and prepares it for presentation to the user in the next step.

[0636] Step 7:

[0637] The terminal receives the plan formulated from the server and presents it to the user. The input is the asset formation plan from the server, and the output is the presentation of the plan to the user. Specifically, the terminal visualizes each element of the plan and displays it in a clear and easy-to-understand manner for the user. Based on this information, the user can then create their own action plan.

[0638] (Application Example 1)

[0639] 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".

[0640] With the rise of modern electronic transactions, accurately understanding individual spending habits and effectively managing assets is essential. However, manually managing large amounts of transaction data is cumbersome, and developing predictive asset building plans is not easy. It is necessary to address these challenges and enable users to easily analyze their own financial situation and manage their assets optimally.

[0641] 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.

[0642] In this invention, the server includes data collection means, analysis means, display means, means for analyzing consumption patterns using electronic transaction history, and means for presenting asset management methods. This enables users to automatically collect transaction data, analyze consumption trends, and easily formulate future asset management plans.

[0643] "Data collection means" refers to a function that automatically acquires user transaction information through APIs of various services.

[0644] "Analysis method" refers to an algorithm that uses collected transaction information to analyze the trends in users' financial assets and consumption patterns.

[0645] "Display means" refers to an interface for providing users with information obtained through analysis in a visual format such as graphs or charts.

[0646] "Method for analyzing consumption patterns using electronic transaction history" refers to a function that performs analysis to identify consumption trends and patterns based on the user's electronic transaction history.

[0647] "Means of presenting asset management methods" refers to a function that suggests the optimal asset management method to the user based on analyzed consumption patterns and financial information.

[0648] In this embodiment of the invention, a system is constructed in which a server, a terminal, and a user are closely involved. The server is equipped with data collection means and retrieves user transaction data from multiple financial services via APIs and stores it in a database. This includes various cashless payment services and household budgeting applications. SQL is suitable as the database technology.

[0649] The server's analysis method analyzes the user's financial asset trends and consumption patterns based on the acquired data. By using the ARIMA model as the time-series analysis algorithm, it is possible to predict consumption trends and future income and expenses. This allows the generative AI model to generate predictive information and support user behavior.

[0650] The analysis results are sent from the server to the terminal and visualized as charts and graphs by the display device. Visualization libraries such as D3.js and Plotly are utilized in this process. Users can review the visualized information through their own terminals and intuitively understand how to manage their assets.

[0651] Furthermore, the server proposes the optimal asset management method based on the user's interests and preferences, and provides a concrete asset building plan according to the user's goals. In this process, using a generative AI model enables more personalized suggestions.

[0652] For example, if a user tends to spend more at the beginning of the month, the system will use that data to suggest a savings plan for the end of the month. In this way, users can optimize their daily spending while building long-term wealth.

[0653] An example of a prompt message would be, "Based on my monthly spending patterns, please suggest the best savings method," which would provide specific instructions to the generating AI model.

[0654] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0655] Step 1:

[0656] The server uses data collection methods to retrieve transaction data from the user's financial services (cashless payment services and household budgeting apps) via APIs. During this process, user authentication information is used to securely collect the data. The input consists of transaction data from each service, and the output is structured data stored in a database.

[0657] Step 2:

[0658] The server processes the acquired transaction data using analytical tools to predict trends in financial assets and consumption patterns. Here, the ARIMA model is applied for time series analysis. The input is transaction data stored in a database, and the output is predicted future income and expenditure data.

[0659] Step 3:

[0660] The server processes the predictive information obtained through analysis using a generative AI model to provide more detailed insights. The input is analyzed revenue and expenditure information, and the output is visualized suggestions for the user.

[0661] Step 4:

[0662] The analysis results are sent from the server to the terminal and visualized as graphs or charts using the terminal's display method. Visualization libraries such as D3.js and Plotly are used. The input is the analysis information sent from the server, and the output is visualized data that the user can visually confirm.

[0663] Step 5:

[0664] Users review the visualized information provided via their device and reassess their asset management methods. In addition, they develop a concrete asset building plan based on their interests and goals. During this process, they prompt a generating AI model to receive more personalized suggestions. The input consists of visualized information and user prompts, while the output is the user's asset management plan.

[0665] 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.

[0666] This invention provides a system equipped with the ability to recognize user emotions, thereby aiming to personalize and adapt users' asset building activities. Specific embodiments of the system are described below.

[0667] First, the server uses data collection tools to gather the user's financial data from budgeting apps, banks, and cashless payment apps. In addition, the device has an emotion engine built in that analyzes the user's voice, facial expressions, input speed, etc., to estimate their current emotional state in real time.

[0668] The server uses emotional data generated by the emotion engine and analytical tools based on financial data to predict the user's asset trends. This prediction also takes into account the impact of the user's emotions on asset management; if the emotions are positive, the system automatically adjusts the investment plan to allow for more risk, while if the emotions are negative, it adjusts to a more risk-averse plan.

[0669] Based on analysis results and trend predictions, the device provides users with instantly visualized information. This includes an interface that allows users to select an asset building plan that matches their emotions for the day. For example, if the system detects that the user is feeling stressed, it will recommend a prudent savings plan, supporting rational decision-making that is not influenced by emotions.

[0670] Furthermore, the server uses information provision tools to collect and organize asset building information tailored to the user's areas of interest from external sources. This information is also linked to the user's emotional state and reflected in the planning process.

[0671] When a user sets their asset-building goals, the server mobilizes planning tools to develop a long-term investment plan tailored to their individual emotional fluctuations. This plan incorporates emotional records and future emotional predictions based on those fluctuations, helping users build their assets in a manageable way.

[0672] For example, if a user in their 20s is considering starting a new investment, the server will remember that the user has had significant anxieties about investing in the past and suggest a safe, low-risk investment plan. In this way, emotion recognition is utilized to achieve more flexible and personalized asset management tailored to the user.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] The server connects to budgeting apps, banks, and cashless payment apps via APIs to periodically collect users' financial transaction data. This data is stored in the server's database.

[0676] Step 2:

[0677] The device utilizes a built-in emotion engine to analyze the user's voice tone, facial expression data, and touch input speed in real time to identify the user's current emotion. The emotion data is immediately transmitted to the server.

[0678] Step 3:

[0679] The server integrates emotional and financial data and analyzes users' asset trends using analytical tools. In this process, a predictive model is generated that takes into account the impact of emotional changes on asset management.

[0680] Step 4:

[0681] The server uses a predictive model to select the optimal investment plan based on the user's emotional state and sends that plan to the device. A plan that allows for more risk is selected if the user is feeling positive, while a plan that prioritizes safety is selected if the user is feeling negative.

[0682] Step 5:

[0683] The terminal graphically displays the received asset management plan. Through the interface, the user can review the proposed plan and choose to implement it. The options may also include emotionally reassuring feedback.

[0684] Step 6:

[0685] The user uses their device to specify areas of interest in asset building. This information is sent to the server and used for future information gathering.

[0686] Step 7:

[0687] The server collects the latest asset building information related to the user's areas of interest from external sources and organizes it in conjunction with the user's emotional tendencies. This ensures that the user is always provided with the most relevant information.

[0688] Step 8:

[0689] When a user sets long-term wealth accumulation goals, the server uses a planning mechanism that takes emotional data into account to create a plan. This plan is designed to predictively respond to fluctuations in the user's emotions. The plan is sent to the user's device, and the user can review it periodically.

[0690] (Example 2)

[0691] 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".

[0692] In modern times, personal wealth creation is becoming increasingly complex due to various factors. Among these, an individual's emotional state has a particularly significant impact on investment decision-making. However, conventional systems struggle to provide investment plans that take emotional fluctuations into account. Furthermore, there is a lack of systems that can effectively integrate individual financial and emotional data to generate accurate predictions and plans. Therefore, there is a need to make user asset management more reliable and personalized.

[0693] 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.

[0694] In this invention, the server includes means for collecting data, means for analyzing the acquired data and predicting an individual's financial trends, and means for analyzing voice, facial expressions, and input speed to estimate an individual's emotional state in real time. This makes it possible to provide asset management plans that take into account an individual's emotional changes, thereby supporting more personalized asset building.

[0695] "Data collection means" refers to a mechanism for collecting personal financial information and related information.

[0696] "Analysis tools" refer to methods that analyze collected data and predict individual financial trends.

[0697] "Emotion analysis methods" refer to technologies for estimating an individual's emotional state in real time from information such as voice, facial expressions, and input speed.

[0698] An "asset management plan" is a plan for effectively managing and investing assets, created by taking into account an individual's financial trends and emotional information.

[0699] A "display means" is an interface for visually providing users with analysis results and asset management plans.

[0700] "Information provision methods" refer to the process of collecting and organizing financial information based on an individual's areas of interest and emotional state.

[0701] "Planning methods" refer to methods for formulating specific investment plans that take into account individual emotional fluctuations and align with asset formation goals.

[0702] This invention is a system that provides an asset management plan that takes into account the user's emotional state. This system mainly consists of a server and a terminal. The server collects and analyzes data, and the terminal visually presents the results to the user.

[0703] First, the server uses data collection methods to gather the user's financial information from budgeting apps, banks, and cashless payment apps. This information includes the user's income, expenses, savings, and investment history. Next, the terminal is equipped with a dedicated emotion analysis engine that analyzes the user's voice, facial expressions, and input speed to estimate their emotional state in real time. This uses hardware such as cameras and microphones, and employs emotion analysis software.

[0704] The server integrates and analyzes collected financial and emotional data, and uses a generative AI model to predict the user's asset trends. In doing so, it considers the impact of the user's emotional state on asset management and automatically generates appropriate asset management plans based on risk levels. If the user is experiencing positive emotions, a plan that allows for some risk is suggested; conversely, if the user is experiencing negative emotions, a safer plan is proposed.

[0705] For example, if a user has generally experienced positive emotions when they have made high profits from stock investments in the past, the server will find similar investment opportunities and suggest riskier plans. Conversely, if the user's emotional state has reached a stress level, the server will recommend saving or low-risk bond investments.

[0706] An example of a prompt to input into the generating AI model would be: "Based on the user's financial and emotional data, please suggest the optimal asset management plan for both positive and negative emotional states."

[0707] Finally, the device displays the visualized results and proposed plan to the user. This helps the user make rational decisions based on their emotions. In this way, personal emotions and financial data are managed comprehensively, enabling more flexible and personalized asset management.

[0708] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0709] Step 1:

[0710] The server uses data collection methods to obtain users' financial information. This information includes income, expenses, and savings from budgeting apps, banking systems, and cashless payment platforms. Inputs to this data collection process include APIs and data feeds, and the output is a structured financial database.

[0711] Step 2:

[0712] The device activates an emotion analysis engine and analyzes voice, facial expressions, and input speed to estimate the user's real-time emotional state. This analysis input includes raw data captured through the device's camera and microphone. The emotion analysis software processes this data and generates a record of the real-time emotional state as output.

[0713] Step 3:

[0714] The server integrates collected financial data and analyzed sentiment data. Here, a generative AI model is used to predict the user's asset trends. The input for this step includes processed financial and sentiment data. Based on this, the AI ​​model performs data calculations and outputs predictions aligned with the user's risk profile.

[0715] Step 4:

[0716] The server uses the generated predictive data to design asset management plans tailored to the user's emotional state. Inputs include the user's emotional data and asset trend predictions. The server analyzes this data and outputs either a risk-minimizing, stability-oriented plan or a risk-accepting, aggressive plan.

[0717] Step 5:

[0718] The terminal displays an asset management plan created on the server to the user. This display includes a visualized plan with sentiment analysis results and predictive data. An interface is provided for the user to visually confirm this, with input being plan data from the server and output being information presented to the user.

[0719] Step 6:

[0720] Based on the information presented, the user takes action to build their assets. In this final step, the user performs operations to execute the plan they confirmed on their device, and feedback is sent back to the server via the device. This feedback is used to improve the system.

[0721] (Application Example 2)

[0722] 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".

[0723] In modern times, financial asset management is often heavily influenced by individual emotions, making it a challenge to build wealth rationally without being swayed by feelings. Furthermore, providing flexible investment plans tailored to the user's emotional state presents a challenge. Solving these issues and realizing personalized asset management is essential.

[0724] 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.

[0725] In this invention, the server includes data collection means, analysis means, emotion recognition means, and display means. This makes it possible to flexibly adjust the asset formation plan according to the user's emotional state and support asset formation that is not influenced by emotions.

[0726] "Data collection means" refers to a device or system that has the function of obtaining a user's financial information from multiple sources.

[0727] "Analysis means" refers to a device or system used to analyze acquired information and predict trends in financial resources.

[0728] "Emotion recognition means" refers to a device or system used to estimate a user's emotional state from their voice, facial expressions, etc.

[0729] "Display means" refers to a device or system that visualizes and provides information based on analysis results and estimated emotional states to the user.

[0730] "Information provision means" refers to a device or system for collecting and organizing necessary information based on the user's interests.

[0731] A "planning tool" refers to a device or system that helps users formulate and support investment plans based on their asset formation goals and emotional state.

[0732] In this invention, a system is constructed in which a server and a terminal work together to create assets based on the recognition of the user's emotions.

[0733] The server first uses data collection tools to obtain users' financial information from various sources. This includes data from home accounting apps, banking applications, and cashless payment services. The obtained information is then analyzed by analytical tools to predict trends in financial resources. This can involve implementing algorithms that utilize machine learning techniques. Specifically, it is desirable to use frameworks such as TensorFlow.

[0734] The device is equipped with emotion recognition technology that estimates the user's emotional state in real time from facial video and audio data. This technology utilizes OpenFace and other voice analysis tools. By analyzing the user's emotions, the server collects the information and stores it as emotion data that is updated in real time.

[0735] Based on analyzed financial data and estimated emotional states, the server generates an asset building plan tailored to the user's current emotions. This information is then visualized and presented to the user as a selectable interface. For example, a library like D3.js can be used to present the information in an intuitive and visually easy-to-understand manner.

[0736] Users can make rational investment decisions by utilizing asset building plans based on their daily emotions. For example, a user who is stressed at work might be presented with a low-risk savings plan, while a user who is in a good mood might be presented with more challenging investment options.

[0737] An example of a prompt message for a generative AI model is: "Please build a program that presents a risk-reduced asset building plan to the user while they are in a positive state."

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] The server acquires users' financial information using data collection methods. Transaction data from home accounting apps and bank APIs is used as input. The server records this data in a database and performs data processing to format it for later analysis.

[0741] Step 2:

[0742] The device captures the user's facial expressions and voice data, and estimates their emotional state in real time using emotion recognition technology. Input is data from the camera and microphone, and output is the estimated emotional state. Facial recognition and voice analysis are performed using OpenFace or voice analysis tools, and the resulting emotional data is sent to a server.

[0743] Step 3:

[0744] The server integrates acquired financial data and sentiment data received from terminals using analytical tools to predict trends in financial resources. The inputs are financial data and sentiment data, and the output is the predicted asset formation trend. Machine learning algorithms, such as TensorFlow models, are used to compute the data and generate a predictive model.

[0745] Step 4:

[0746] The server generates an asset building plan tailored to the user, taking into account the analysis results and emotional state. The input is the predicted trends and emotional state obtained in step 3, and the output is an asset building plan adapted to the user's emotions. The generated plan is visualized using D3.js to make it easy for the user to understand.

[0747] Step 5:

[0748] The terminal displays and provides the user with a visualized asset management plan sent from the server. This allows the user to take action to make rational investment decisions based on their emotions on that day and in the past. The input is visualized data, and the output is a user-selectable interface. Users can view this and gain motivation to improve their asset management.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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."

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0770] The following is further disclosed regarding the embodiments described above.

[0771] (Claim 1)

[0772] Data collection means,

[0773] An analysis means that analyzes the information obtained by the aforementioned data collection means and predicts the trends of financial assets,

[0774] A display means for visualizing the predictive information obtained by the analysis means,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, comprising information provision means for collecting and organizing information related to asset formation based on the user's areas of interest.

[0778] (Claim 3)

[0779] The system according to claim 1, comprising a planning means for formulating a specific investment plan and supporting its execution based on the asset formation goals set by the user.

[0780] "Example 1"

[0781] (Claim 1)

[0782] Data aggregation device,

[0783] An analysis device that analyzes the information acquired by the aforementioned data aggregation device and predicts financial flows,

[0784] A visualization device that displays the prediction information obtained by the analysis device,

[0785] An information gathering device that collects and organizes information based on the user's areas of interest,

[0786] A planning device that develops and supports asset formation plans based on the user's goals,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, which processes the collection and provision of useful information related to the user's areas of interest from external sources.

[0790] (Claim 3)

[0791] The system according to claim 1, which formulates a specific economic plan in accordance with the asset formation goals set by the user and presents it through a device.

[0792] "Application Example 1"

[0793] (Claim 1)

[0794] Data collection means,

[0795] An analysis means that analyzes the information obtained by the aforementioned data collection means and predicts the trends of financial assets,

[0796] A display means for visualizing the predictive information obtained by the analysis means,

[0797] A means for analyzing consumption patterns using electronic transaction history,

[0798] A means for presenting an asset management method based on the analysis results of the aforementioned consumption patterns,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, comprising information provision means for collecting and organizing information related to asset formation based on the user's areas of interest.

[0802] (Claim 3)

[0803] The system according to claim 1, comprising a planning means for formulating a concrete plan based on user-set goals and supporting its execution.

[0804] "Example 2 of combining an emotion engine"

[0805] (Claim 1)

[0806] Means of collecting data,

[0807] Analytical means for analyzing acquired data and predicting individual financial trends,

[0808] To estimate an individual's emotional state in real time, an emotion analysis method is used that analyzes voice, facial expressions, and input speed.

[0809] A means for integrating analyzed financial trends and sentiment information to generate an asset management plan that takes into account the impact of emotions on the management of financial assets,

[0810] A display means for visualizing and displaying the aforementioned analysis information and operational plan,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, comprising means for providing information that collects financial information based on an individual's areas of interest and organizes it in relation to the individual's emotional state.

[0814] (Claim 3)

[0815] The system according to claim 1, comprising a planning means for formulating and supporting the execution of an investment plan adapted to emotional fluctuations based on an individual's set asset formation goals.

[0816] "Application example 2 when combining with an emotional engine"

[0817] (Claim 1)

[0818] Data collection means,

[0819] An analytical means for analyzing information obtained by the aforementioned data collection means and predicting trends in financial resources,

[0820] An emotion recognition method for estimating the user's emotional state,

[0821] Based on the aforementioned emotion recognition means, means for adjusting the asset formation plan according to the user's emotional state,

[0822] A display means for visualizing the information obtained by the analysis means and the emotion recognition means,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, comprising means for providing information that collects and organizes information related to asset formation based on the user's areas of interest.

[0826] (Claim 3)

[0827] The system according to claim 1, comprising a planning means for formulating specific investment strategies based on asset formation goals set by the user and supporting long-term investment plans that are in line with the user's emotional fluctuations. [Explanation of Symbols]

[0828] 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. Data collection means, An analysis means that analyzes the information obtained by the aforementioned data collection means and predicts the trends of financial assets, A display means for visualizing the predictive information obtained by the analysis means, A means for analyzing consumption patterns using electronic transaction history, A means for presenting an asset management method based on the analysis results of the aforementioned consumption patterns, A system that includes this.

2. The system according to claim 1, comprising information provision means for collecting and organizing information related to asset formation based on the user's areas of interest.

3. The system according to claim 1, comprising a planning means for formulating a concrete plan based on user-set goals and supporting its execution.

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