system

Abstract

Provide a system. 【Solution means】 Means for acquiring personal economic information, Means for analyzing the acquired economic information and predicting the economic state, Means for generating an optimal economic plan based on the analysis result, Means for visually presenting the generated economic plan, Means for receiving opinions on the presented plan, Means for adjusting the economic plan based on the opinions, Means for analyzing the user's past expenditure data and predicting future expenditures, Means for presenting a savings plan based on the expenditure pattern, A system including

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional personal asset management method, it is difficult to integrally manage dispersed financial information, and there is insufficient support for the user to comprehensively grasp their own financial situation and formulate an appropriate financial plan. In addition, since specialized knowledge regarding asset management is required, there is a disadvantageous situation for general users. As a result, it is difficult to achieve financial goals and there is a problem that sustainable asset management cannot be realized.

Means for Solving the Problems

[0005] This invention solves the above problem by providing a means for collecting and analyzing personal financial information and predicting the user's financial status. Specifically, it constructs a means for generating an optimal financial plan based on the collected financial information and presenting it visually. Furthermore, it provides a system that includes a means for receiving feedback from the user and adjusting the financial plan based on that feedback. As a result, users can develop integrated asset management and sustainable financial plans without requiring specialized knowledge.

[0006] "Personal financial information" refers to all financial data related to an individual's income, expenses, savings, investment portfolio, insurance policies, etc.

[0007] "Means of collection" refers to the methods and processes for obtaining necessary information from users and incorporating it into the system.

[0008] "Means of analysis" refers to the process of analyzing collected financial information to evaluate the user's financial situation and future trends.

[0009] "Predictive means" refers to the process of estimating and providing forecasts for a user's future financial condition based on analysis results.

[0010] An "optimal financial plan" refers to an asset management strategy customized to the user's goals and risk tolerance.

[0011] "Means of generation" refers to methods for constructing and assembling new financial plans based on analytical information.

[0012] "Visual presentation methods" refer to ways of clearly showing the generated financial plan to the user using graphs, charts, and other visual aids.

[0013] "Means of receiving feedback" refers to methods for collecting opinions and requests from users and handling them within the system.

[0014] "Means of adjustment" refers to methods for reviewing existing financial plans based on user feedback and making modifications as necessary. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

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

[0020] In the following embodiments, a 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.

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

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system designed to effectively support personal financial management, realized through the interaction of a server, terminals, and users. The system primarily collects and analyzes users' financial information to provide an optimal financial plan.

[0037] The server centrally manages the financial data entered by users. Specifically, it securely stores details such as users' bank account information, investment assets, and insurance contract information, and analyzes their financial status based on this data. The server uses this data to apply machine learning algorithms and make future predictions tailored to each user's situation. The algorithms build models based on historical data and perform analyses that take into account each user's different risk profile and goals.

[0038] The terminal provides a user interface and plays a role in visualizing the collected information. The analyzed information is presented to the user visually through the terminal as graphs and charts. This allows users to easily understand their financial situation and make decisions based on the provided plan. The terminal also has a function to collect user feedback, which is sent to the server and used to review and adjust the plan.

[0039] Based on their financial goals, users receive instructions from their device and input the necessary financial information. This includes monthly income and expenses and savings targets. Furthermore, users can provide feedback on the presented plan and adjust its depth. This process allows users to develop the investment, savings, and insurance strategies best suited to them.

[0040] As a concrete example, consider a user in their 20s who plans to buy a home within the next few years. This user inputs their current income, expenses, and savings into the system. Based on this, the server presents a plan showing future savings potential and the funds needed to purchase a home. The user reviews the visualized proposal on their terminal and receives specific actionable guidance, such as increasing their monthly savings. The server flexibly adjusts the plan to accommodate subsequent changes, enabling the maintenance of a long-term financial plan.

[0041] In this way, the entire system works together to effectively and efficiently support individual asset management.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users enter their financial information into the system via a terminal. This includes details of salary, expenses, savings, investments, and insurance policies. The terminal provides a user-friendly interface, making it easy for users to enter the necessary information.

[0045] Step 2:

[0046] The terminal transmits the acquired financial information to the server. The server securely stores this data in a database and prepares it for subsequent processing. Data integrity and security are ensured.

[0047] Step 3:

[0048] The server analyzes the user's current financial status based on stored financial data. Here, data cleaning and normalization are performed, preparing the system to use machine learning algorithms to predict future income, expenses, and risk profiles.

[0049] Step 4:

[0050] Based on the analysis results, the server generates an optimal financial plan tailored to the user's goals and desired financial situation. This includes investment strategies, savings plans, and risk management recommendations. The generated plan is customized to the individual user's needs.

[0051] Step 5:

[0052] The terminal visually presents the financial plan received from the server to the user. Graphs and charts are used to help users intuitively understand their own financial situation and the proposed plan.

[0053] Step 6:

[0054] Users review the presented plan through their device and provide feedback as needed. This feedback is sent from the device to the server and used to adjust the plan.

[0055] Step 7:

[0056] The server receives user feedback, incorporates it into the analysis results, revises the financial plan, and regenerates it as needed. This provides the user with a more appropriate and actionable plan.

[0057] Step 8:

[0058] Throughout the entire process, the server analyzes user usage data, continuously improving algorithm accuracy and adding new features. This ensures the system always provides users with the best possible support.

[0059] (Example 1)

[0060] 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."

[0061] In modern times, personal financial management has become increasingly complex, requiring a comprehensive understanding of various factors such as income, expenses, savings, and investments, as well as the ability to predict future economic conditions. However, accurately managing these factors on one's own is difficult, and in many cases, individuals have no choice but to rely on experts. Therefore, there is a growing need for a system that can provide optimal financial plans tailored to individual circumstances at a low cost, and present them in a visually easy-to-understand manner.

[0062] 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.

[0063] In this invention, the server includes means for acquiring personal economic data, means for analyzing the acquired economic data and predicting economic conditions, and means for generating an optimal economic plan based on the analysis results. This enables accurate predictions of economic conditions tailored to each individual user and easy-to-understand suggestions through visualization.

[0064] "Personal financial data" refers to financial information owned or managed by an individual, and includes data such as income, expenses, savings, investments, and insurance policies.

[0065] "Means of acquisition" refers to the technical means of providing functions and interfaces for collecting economic data from users.

[0066] "Means of analysis" refers to a system mechanism that includes algorithms and processes used to analyze collected economic data.

[0067] "Means of predicting economic conditions" refer to methods and techniques for estimating an individual's future economic situation based on analyzed data.

[0068] "Means of generating optimal economic plans" refers to methods of designing and providing individuals with the most effective financial management plans under predicted economic conditions.

[0069] "Means of visualization and presentation" refers to technologies that display generated economic plans in visual formats such as graphs and charts, providing information in a way that users can easily understand.

[0070] "Means of receiving feedback" refers to interfaces and functions for collecting user feedback and opinions on the plan.

[0071] "Means of adjustment" refer to mechanisms or methods for modifying or optimizing existing economic plans based on feedback received.

[0072] A "data learning algorithm" is a computational method that uses machine learning techniques to learn patterns in data and supports predictions and decisions regarding new data.

[0073] This invention is a system that supports personal financial management. The system is realized through the interaction of a server, terminals, and users. The following describes in detail how this system is implemented.

[0074] The server is responsible for acquiring and centrally managing economic data entered by each user. This data includes information on users' income, expenses, savings, investments, and insurance. The server utilizes general-purpose cloud database technology to securely store this data. Specifically, it manages data on a platform widely used as a cloud service. The stored data is analyzed using data learning algorithms. Machine learning frameworks such as TENSORFLOW® and PyTorch are used to learn data patterns and build models that predict economic conditions.

[0075] The terminal receives and visualizes information analyzed from the server via a user interface. For visualization, a graphics library is used to provide information in a visually easy-to-understand format. For example, the terminal's interface displays graphs showing income and expenditure trends and progress toward savings goals. The terminal can also receive user feedback, which is sent to the server and used to adjust the financial plan.

[0076] Users interact with the system by inputting financial information. For example, users can follow the terminal's instructions to input their monthly income, expenses, and target savings. Based on the analysis, they can review the presented financial plan and adjust their lifestyle as needed. Through this process, users can efficiently manage their own finances.

[0077] As a concrete example, consider a user who plans to buy a home in the near future. The user inputs their income and current savings into the system, and the server predicts their future savings potential and suggests a savings plan aligned with their goal. Based on this plan, the user adjusts their monthly expenses and receives specific guidance to track their progress on their device.

[0078] As an example of a prompt, providing the AI ​​model with the instruction, "Please generate the optimal savings plan for purchasing a home," allows the user to obtain a specific plan that matches their objectives.

[0079] In this way, by having servers, terminals, and users work together, it becomes possible to provide economic management services optimized for each individual user.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users input economic data through their devices. Specifically, they enter monthly income, expenses, savings, investment information, and so on. This input data is temporarily stored in a database on the device.

[0083] Step 2:

[0084] The terminal sends the entered data to the server. The HTTPS protocol is used for transmission, and the data is encrypted. By securely transferring the entered data to the server, user privacy is protected. The server receives the data and stores its contents in a cloud database.

[0085] Step 3:

[0086] The server analyzes the stored data. First, it preprocesses the data, including imputing missing values ​​and normalizing it. Next, it uses a generative AI model to run a predictive algorithm to identify the user's financial situation. Based on this, it predicts their future financial condition.

[0087] Step 4:

[0088] The server generates an optimal economic plan based on the analysis results. As a result of the data learning algorithm, personalized plan suggestions are generated for each user, determining specific savings and investment guidelines.

[0089] Step 5:

[0090] The server sends the generated plan to the terminal. The terminal displays the received data as a graphical interface to visually present it to the user. For example, it may use graphs and charts to visualize the economic situation or the progress towards achieving goals.

[0091] Step 6:

[0092] Users review the proposed plan based on visual feedback and input their opinions and evaluations into the device based on their own judgment. This user input is used to refine the plan in the next step.

[0093] Step 7:

[0094] The server incorporates user feedback and readjusts the economic plan. By analyzing the feedback information and revising the plan as needed, it becomes possible to present a more effective economic strategy.

[0095] This entire process allows users to efficiently manage financial plans tailored to their individual needs and achieve achievable goals.

[0096] (Application Example 1)

[0097] 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."

[0098] Currently, many individuals are unable to manage their finances properly, and in particular, they have difficulty creating long-term financial plans. Furthermore, the lack of adequate visualization of economic information, forecasting of future spending, and suggestions for saving money makes it difficult for users to accurately understand their financial situation and make informed decisions. Therefore, it is necessary to systematically acquire individual financial information, present the analysis results visually, and dynamically provide users with optimal financial plans.

[0099] 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.

[0100] In this invention, the server includes means for acquiring personal economic information, means for analyzing the acquired economic information and predicting the economic state, and means for generating an optimal economic plan based on the analysis results. This enables users to visually grasp their economic situation, predict future spending through a dynamic and rational economic plan, and receive savings suggestions based on their spending patterns, thereby enabling effective financial management.

[0101] "Personal economic information" refers to financial data related to an individual, such as information about their economic activities, including income, expenses, assets, and liabilities.

[0102] "Analysis methods" refer to techniques for analyzing information using acquired data and extracting meaning based on a specific purpose.

[0103] An "economic plan" is a long-term set of action guidelines that includes financial targets and budget management, formulated based on an individual's economic situation.

[0104] "Visualization techniques" are technologies that display data in the form of diagrams, charts, and other visual representations to make information easier for users to understand.

[0105] "Opinions" refer to feedback and comments provided by users regarding the presented plans and predictions.

[0106] "Adjustment mechanisms" refer to systems for modifying and changing plans and forecasts based on user feedback and newly acquired data.

[0107] "Spending patterns" refer to the tendencies and regularities observed in an individual's financial consumption behavior.

[0108] A "savings plan" is a specific guideline or proposal for reducing future expenses.

[0109] This application demonstrates a system for effectively managing personal financial information. First, the user inputs their financial information using a smartphone application. This information includes income, expenses, assets, and liabilities. The device visualizes this information, displaying it in graphs and charts for intuitive understanding.

[0110] The server uses programs such as Python and Scikit-learn to analyze users' financial information. This analysis reveals individual users' spending patterns and makes it possible to predict future spending. It also uses machine learning algorithms based on historical data to create financial plans. This allows users to develop the most suitable financial strategies for themselves.

[0111] The server then generates savings plans based on numerical data analysis and presents them to the user via the terminal. The user can provide feedback, and this feedback is incorporated into the plan adjustments by the server. In this way, the economic plan is dynamically optimized according to the user's lifestyle and goals.

[0112] For example, if the server recognizes that a user spends a lot at cafes, it might suggest a saving plan such as, "By skipping one cafe visit per week starting next month, you can save approximately ¥5,000 per year." Through the generating AI model, the prompt could be: "Based on the user's spending history, predict monthly spending and present a planned savings plan in a graph. As a specific example, please also include advice that takes into account reducing spending at cafes."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] Users input their personal financial information (income, expenses, assets, liabilities, etc.) using a smartphone application. This information is collected by the device and sent to a server. The entered data is stored in a database that the server uses for later analysis.

[0116] Step 2:

[0117] The server retrieves collected economic information and begins data analysis using Python or Scikit-learn. Based on the input data, the server analyzes spending patterns and calculates average spending for specific items. The output includes aggregated data and analysis results for each spending category.

[0118] Step 3:

[0119] The server applies a machine learning algorithm to predict future economic conditions based on the analysis results. The input is historical spending data, and the algorithm generates predictions for future spending. The output is the predicted monthly spending amount.

[0120] Step 4:

[0121] The server uses prediction results and historical data to generate an economic plan tailored to the user's lifestyle. This plan includes monthly income and expenses, savings suggestions, and proposals for reducing specific expenses. The output consists of specific economic strategies and visualized data.

[0122] Step 5:

[0123] The terminal visually presents the generated economic plan to the user. It converts data received from the server into graphs and charts, making it easy for the user to understand the economic situation. The input is the server's output data, and the output is the visualized economic plan.

[0124] Step 6:

[0125] Users can provide their opinions on the presented economic plan. User opinions and feedback are received on the device and sent to the server. The input is user feedback, and the output is feedback data.

[0126] Step 7:

[0127] The server adjusts the economic plan based on feedback and makes the best recommendation to the user again. The algorithm incorporates the feedback data and updates the plan. The output is the updated economic plan.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] This invention combines a system that supports personal financial management with an emotion engine that recognizes user emotions and utilizes them in generating and presenting financial plans. The system functions effectively through the cooperation of a server, terminal, and user. Its main function is to analyze the user's financial situation and propose an optimal plan, but by also reflecting the user's emotions, a more personalized service becomes possible.

[0130] First, the user enters their financial information through the device. The device analyzes the user's voice, facial expressions, and input patterns, and an emotion engine recognizes the user's current emotional state. The entered financial data and emotional information are sent to a server, which uses machine learning algorithms to analyze the user's financial status.

[0131] The emotion engine flexibly adjusts how financial plans are presented based on the user's emotional state. For example, if the user is stressed, it can avoid overly detailed information and present a concise plan. Conversely, if the user is relaxed, it can provide more detailed analysis results and future simulations.

[0132] The device visually displays the generated financial plan, providing information in the most easily understandable format for the user. For example, it might use colors and fonts preferred by the user, or refer to sentiment engine data to present information in the style most receptive to the user.

[0133] Based on the information provided, users can build a financial strategy tailored to their needs. Furthermore, the server readjusts the plan as needed based on user feedback. This includes emotional feedback obtained through the emotion engine, which is then reflected in future plan generation.

[0134] For example, if the emotion engine detects a user's high stress level, the server can be configured to refrain from suggesting new financial products to that user and instead send a simple reminder of their existing plan. The device then takes care not to overload the user with information and provides a function to reaffirm this once the user has calmed down somewhat.

[0135] Thus, by utilizing an emotion engine, this system goes beyond simply providing financial information; it enables flexible responses tailored to the user's emotional state, supporting more effective individual asset management.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users input their financial information through the device. During this process, the device collects user emotional data using voice and facial recognition technology. This data is recorded simultaneously with the user's input patterns.

[0139] Step 2:

[0140] The device transmits collected financial and emotional data to the server. The server stores this data in a database for subsequent analysis. Emotional data is treated as an important indicator for understanding the user's state.

[0141] Step 3:

[0142] The server analyzes stored financial data and evaluates the user's financial status through machine learning algorithms. Simultaneously, it utilizes an emotion engine to identify the user's emotional state. Based on this information, the server generates a financial plan best suited to the user.

[0143] Step 4:

[0144] The server generates an optimal financial plan based on the analysis results. It takes the user's emotional state into account and adjusts the amount and level of detail of information accordingly. A concise plan is created if the user is stressed, while a detailed plan is created if they are relaxed.

[0145] Step 5:

[0146] The terminal presents the generated financial plan to the user. It is displayed in a visually easy-to-understand format and in a style that takes into account the user's emotional state. The user uses this as a reference to decide on their next course of action.

[0147] Step 6:

[0148] After the user reviews the presented plan, they provide feedback through their device. Along with this feedback, the device sends the user's emotional state to the server.

[0149] Step 7:

[0150] The server integrates user feedback and sentiment data, adjusting financial plans as needed. The results are then used to improve the user experience by incorporating them into future plan generation.

[0151] Step 8:

[0152] Throughout the entire process, the server accumulates new emotional data, which is used to improve the accuracy of the emotion engine and optimize personalization features. This continuous process ensures that the system is constantly evolving and continues to provide users with the best possible support.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] Traditional financial management systems only analyze the user's financial situation and propose plans, lacking planning that takes the user's emotional state into consideration. Therefore, providing flexible and individualized financial plans tailored to the user's psychological state was difficult.

[0156] 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.

[0157] In this invention, the server includes means for collecting personal financial information, means for analyzing the collected financial information and emotional data to predict the financial state, and means for generating an optimal financial plan based on the user's emotional state. This makes it possible to provide a personalized financial plan that takes the user's emotional state into account.

[0158] "Personal financial information" refers to economic data such as a user's income, expenses, savings goals, and assets.

[0159] "Emotional data" refers to information about a user's psychological state obtained from their tone of voice, facial expressions, input patterns, etc.

[0160] A "machine learning algorithm" refers to a mathematical model or statistical method that computer programs use to learn from data and perform analysis and predictions.

[0161] A "financial plan" refers to future economic policies and action plans proposed to the user based on collected and analyzed financial information.

[0162] "Presenting visually" refers to displaying information on a screen in a way that is easy for the user to understand, and includes forms such as graphs and charts.

[0163] "Feedback" refers to the opinions and evaluations that users provide regarding the proposed plan, and includes information used to improve and adjust the system.

[0164] The embodiments for carrying out this invention are described below.

[0165] Users input their financial information through their devices and provide emotional data such as voice, facial expressions, and input patterns. These devices include smartphones, tablets, and personal computers, and dedicated applications run on them. For analyzing the emotional data, speech recognition libraries (e.g., Google® Speech-to-Text) and facial expression analysis libraries (e.g., OpenCV) are used to identify emotional states.

[0166] Financial and emotional data collected on the device are sent to the server. After receiving this data, the server uses machine learning algorithms (e.g., Scikit-learn or PyTorch) to analyze the financial state. This generates an optimal financial plan, which is then adjusted according to the user's emotional state. For example, if the user is feeling stressed, a simplified plan is provided.

[0167] The generated financial plan is visualized by the terminal. The terminal presents the information in graphs and charts, using color themes and fonts tailored to the user's preferences. The server takes into account the sentiment feedback provided by the user when generating the next plan, improving the accuracy of personalization.

[0168] For example, if the emotion engine detects a high level of stress in a user, the server can provide a simple reminder of an existing plan instead of recommending new economic products. This measure allows the device to avoid over-information and deliver information in a way that is most acceptable to the user.

[0169] An example of a prompt might be, "Please tell me how to propose the optimal financial strategy while taking the user's emotions into consideration." Based on this prompt, the generating AI model will create a financial plan that is appropriate to the user's emotional state.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] Users input financial information such as monthly income, expenses, and savings goals through their device. They also provide emotional data through voice input and camera. This input includes the user's financial data as well as voice and video data. This data is initially processed on the device and becomes primary information about the current financial situation and emotional state.

[0173] Step 2:

[0174] The device uses speech recognition and facial expression analysis libraries to analyze the collected audio and video data. This analysis transcribes the user's speech into text and determines the emotional state of their facial expressions. Specifically, it converts the audio data into text and extracts emotional attributes from each point of the facial expression, then sends these outputs to the server as numerical data.

[0175] Step 3:

[0176] The server receives financial information and sentiment data sent from the terminal. The received data is analyzed using machine learning algorithms to predict the user's financial status. Based on this input, trends and anomalies are detected in the data, and then candidate financial plans that are considered optimal for the user are generated as output.

[0177] Step 4:

[0178] The server adjusts the generated plan to match the user's emotional state. For example, if it determines that the user is stressed, it simplifies the plan and provides information in a way that does not cause the user anxiety. Based on this, the final plan to be presented to the user is determined.

[0179] Step 5:

[0180] The terminal visually displays the final financial plan sent from the server. This includes actions to make the information easy to understand using graphs and charts, and also allows for color themes and layout adjustments to suit the user's preferences. The output to the user consists of the main elements of the plan and recommended actions.

[0181] Step 6:

[0182] Users provide feedback on the presented financial plan. This feedback includes evaluations and suggestions for improvement for each element of the plan. This input is sent to the server via the terminal.

[0183] Step 7:

[0184] The server receives feedback from users and readjusts the plan as needed. The data obtained from the feedback serves as learning material for future proposals, leading to improvements that more accurately meet the individual needs of users. The output is a new version of the improved financial plan.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] In today's world, personal financial management is becoming increasingly complex, while services that take into account the user's mental and emotional state are lacking. Therefore, there is a need to create and present flexible financial plans that take the user's feelings into consideration.

[0188] 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.

[0189] In this invention, the server includes means for collecting personal financial information, means for analyzing the financial information and predicting the financial state, and means for recognizing the user's emotional state and adjusting the information presented in accordance with that emotional state. This makes it possible to propose flexible and effective financial plans that are tailored to the user's emotional state.

[0190] "Personal financial information" refers to the collective financial data owned by each individual, including income, expenses, savings, and investments.

[0191] "Financial forecasting" refers to predicting future income and expenditure balances and fund management situations based on collected financial information.

[0192] "Financial plan generation" is the process of formulating specific asset management and spending plans based on the user's financial information.

[0193] "Recognizing the user's emotional state" is a technology that analyzes voice, facial expressions, and input patterns to identify the user's psychological condition.

[0194] "Adjusting information presentation" refers to the act of changing the content and presentation of data in accordance with the user's emotional state.

[0195] "Receiving feedback" is the process of collecting user reactions and opinions on the presented financial plan.

[0196] "Visual presentation of financial plans" refers to a method of displaying generated financial plans to users in an easy-to-understand manner using graphs, charts, and other visual aids.

[0197] A "machine learning algorithm" is a computational method that allows computer programs to learn regularities and patterns in data from experience and perform inference and prediction.

[0198] This system provides advanced support for managing personal financial information and functions through the cooperation of the server, terminal, and user. Users first use a terminal to input their financial data, such as income, expenses, and savings. This terminal captures the user's voice and facial expressions using voice recognition sensors and a camera, and uses emotion analysis software to determine their current psychological state from this data. This analysis utilizes an emotion recognition engine.

[0199] The server receives collected financial and sentiment data and uses machine learning algorithms to predict financial status. The Python library TextBlob is useful for this server processing. TextBlob supports natural language processing and plays a crucial role in sentiment analysis.

[0200] Next, the server generates a personalized financial plan based on the predicted financial state. This plan generation takes an approach that takes into account the user's emotional state, presenting a simple plan to stressed users and a detailed investment plan to relaxed users.

[0201] The generated financial plan is sent to the terminal and presented visually in a user-friendly interface. This includes the selection of colors and fonts, as well as graphic representations, that are considered in the user interface design.

[0202] Users can provide feedback on the presented plan, and this feedback will be reflected in future plan generation. For example, if a user states, "I feel calm today," the system will propose a detailed financial plan. This enables optimal financial management tailored to the user's emotions.

[0203] An example of a prompt for obtaining emotional feedback from a generative AI model is, "Tell me how you've been feeling lately in one word. And what kind of spending plan do you think you need?" This prompt allows for the acquisition of information based on the user's emotional state.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] Users input their financial information, such as income, expenses, and savings, through a terminal. This information is entered into the terminal in text and numerical format and transmitted from the terminal to the server. The terminal also operates sensors to recognize the user's voice and facial expressions, collecting data related to the user's psychological state.

[0207] Step 2:

[0208] The device uses emotion analysis software to convert collected voice and facial expression data into emotional states. An emotion recognition algorithm is then applied to process the data and recognize the user's stress level and relaxation level. This analysis result is often quantified as an emotion score and sent to the server.

[0209] Step 3:

[0210] The server receives financial information and sentiment scores sent from the terminal, analyzes this data using machine learning algorithms, and predicts the user's financial status. Based on the input data, the predictive model operates to assess future income and expenditure balances and investment risks. This prediction result forms the basis for plan generation in the next step.

[0211] Step 4:

[0212] The server generates an optimal financial plan by considering the user's predicted financial state and emotional state. For example, it suggests a simple savings plan to a stressed user and a detailed investment plan to a relaxed user. The generated plan is structured as visual content using text and graphical elements and sent to the terminal.

[0213] Step 5:

[0214] The device presents the user with an optimized financial plan. The plan is displayed in a customized interface, with adjusted colors and fonts to aid user understanding. The user can review the plan and provide feedback as needed.

[0215] Step 6:

[0216] User feedback is sent from the device to the server and used to generate future plans. This process also re-evaluates emotion recognition data, and adjustments are made based on changes in the user's emotions in response to the feedback. This creates an evolutionary system that adapts to the user's emotions and financial situation.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

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

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention is a system designed to effectively support personal financial management, realized through the interaction of a server, terminals, and users. The system primarily collects and analyzes users' financial information to provide an optimal financial plan.

[0234] The server centrally manages the financial data entered by users. Specifically, it securely stores details such as users' bank account information, investment assets, and insurance contract information, and analyzes their financial status based on this data. The server uses this data to apply machine learning algorithms and make future predictions tailored to each user's situation. The algorithms build models based on historical data and perform analyses that take into account each user's different risk profile and goals.

[0235] The terminal provides a user interface and plays a role in visualizing the collected information. The analyzed information is presented to the user visually through the terminal as graphs and charts. This allows users to easily understand their financial situation and make decisions based on the provided plan. The terminal also has a function to collect user feedback, which is sent to the server and used to review and adjust the plan.

[0236] Based on their financial goals, users receive instructions from their device and input the necessary financial information. This includes monthly income and expenses and savings targets. Furthermore, users can provide feedback on the presented plan and adjust its depth. This process allows users to develop the investment, savings, and insurance strategies best suited to them.

[0237] As a concrete example, consider a user in their 20s who plans to buy a home within the next few years. This user inputs their current income, expenses, and savings into the system. Based on this, the server presents a plan showing future savings potential and the funds needed to purchase a home. The user reviews the visualized proposal on their terminal and receives specific actionable guidance, such as increasing their monthly savings. The server flexibly adjusts the plan to accommodate subsequent changes, enabling the maintenance of a long-term financial plan.

[0238] In this way, the entire system works together to effectively and efficiently support individual asset management.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] Users enter their financial information into the system via a terminal. This includes details of salary, expenses, savings, investments, and insurance policies. The terminal provides a user-friendly interface, making it easy for users to enter the necessary information.

[0242] Step 2:

[0243] The terminal transmits the acquired financial information to the server. The server securely stores this data in a database and prepares it for subsequent processing. Data integrity and security are ensured.

[0244] Step 3:

[0245] The server analyzes the user's current financial status based on stored financial data. Here, data cleaning and normalization are performed, preparing the system to use machine learning algorithms to predict future income, expenses, and risk profiles.

[0246] Step 4:

[0247] Based on the analysis results, the server generates an optimal financial plan tailored to the user's goals and desired financial situation. This includes investment strategies, savings plans, and risk management recommendations. The generated plan is customized to the individual user's needs.

[0248] Step 5:

[0249] The terminal visually presents the financial plan received from the server to the user. Graphs and charts are used to help users intuitively understand their own financial situation and the proposed plan.

[0250] Step 6:

[0251] Users review the presented plan through their device and provide feedback as needed. This feedback is sent from the device to the server and used to adjust the plan.

[0252] Step 7:

[0253] The server receives user feedback, incorporates it into the analysis results, revises the financial plan, and regenerates it as needed. This provides the user with a more appropriate and actionable plan.

[0254] Step 8:

[0255] Throughout the entire process, the server analyzes user usage data, continuously improving algorithm accuracy and adding new features. This ensures the system always provides users with the best possible support.

[0256] (Example 1)

[0257] 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."

[0258] In modern times, personal financial management has become increasingly complex, requiring a comprehensive understanding of various factors such as income, expenses, savings, and investments, as well as the ability to predict future economic conditions. However, accurately managing these factors on one's own is difficult, and in many cases, individuals have no choice but to rely on experts. Therefore, there is a growing need for a system that can provide optimal financial plans tailored to individual circumstances at a low cost, and present them in a visually easy-to-understand manner.

[0259] 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.

[0260] In this invention, the server includes means for acquiring personal economic data, means for analyzing the acquired economic data and predicting economic conditions, and means for generating an optimal economic plan based on the analysis results. This enables accurate predictions of economic conditions tailored to each individual user and easy-to-understand suggestions through visualization.

[0261] "Personal financial data" refers to financial information owned or managed by an individual, and includes data such as income, expenses, savings, investments, and insurance policies.

[0262] "Means of acquisition" refers to the technical means of providing functions and interfaces for collecting economic data from users.

[0263] "Means of analysis" refers to a system mechanism that includes algorithms and processes used to analyze collected economic data.

[0264] "Means of predicting economic conditions" refer to methods and techniques for estimating an individual's future economic situation based on analyzed data.

[0265] "Means of generating optimal economic plans" refers to methods of designing and providing individuals with the most effective financial management plans under predicted economic conditions.

[0266] "Means of visualization and presentation" refers to technologies that display generated economic plans in visual formats such as graphs and charts, providing information in a way that users can easily understand.

[0267] "Means of receiving feedback" refers to interfaces and functions for collecting user feedback and opinions on the plan.

[0268] "Means of adjustment" refer to mechanisms or methods for modifying or optimizing existing economic plans based on feedback received.

[0269] A "data learning algorithm" is a computational method that uses machine learning techniques to learn patterns in data and supports predictions and decisions regarding new data.

[0270] This invention is a system that supports personal financial management. The system is realized through the interaction of a server, terminals, and users. The following describes in detail how this system is implemented.

[0271] The server is responsible for acquiring and centrally managing economic data entered by each user. This data includes information on users' income, expenses, savings, investments, and insurance. The server utilizes general-purpose cloud database technology to securely store this data. Specifically, it manages data on a platform widely used as a cloud service. The stored data is analyzed using data learning algorithms. Machine learning frameworks such as TensorFlow and PyTorch are used to learn data patterns and build models that predict economic conditions.

[0272] The terminal receives and visualizes information analyzed from the server via a user interface. For visualization, a graphics library is used to provide information in a visually easy-to-understand format. For example, the terminal's interface displays graphs showing income and expenditure trends and progress toward savings goals. The terminal can also receive user feedback, which is sent to the server and used to adjust the financial plan.

[0273] Users interact with the system by inputting financial information. For example, users can follow the terminal's instructions to input their monthly income, expenses, and target savings. Based on the analysis, they can review the presented financial plan and adjust their lifestyle as needed. Through this process, users can efficiently manage their own finances.

[0274] As a concrete example, consider a user who plans to buy a home in the near future. The user inputs their income and current savings into the system, and the server predicts their future savings potential and suggests a savings plan aligned with their goal. Based on this plan, the user adjusts their monthly expenses and receives specific guidance to track their progress on their device.

[0275] As an example of a prompt, providing the AI ​​model with the instruction, "Please generate the optimal savings plan for purchasing a home," allows the user to obtain a specific plan that matches their objectives.

[0276] In this way, by having servers, terminals, and users work together, it becomes possible to provide economic management services optimized for each individual user.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] Users input economic data through their devices. Specifically, they enter monthly income, expenses, savings, investment information, and so on. This input data is temporarily stored in a database on the device.

[0280] Step 2:

[0281] The terminal sends the entered data to the server. The HTTPS protocol is used for transmission, and the data is encrypted. By securely transferring the entered data to the server, user privacy is protected. The server receives the data and stores its contents in a cloud database.

[0282] Step 3:

[0283] The server analyzes the stored data. First, it preprocesses the data, performing tasks such as filling in missing values and normalization. Next, it executes a prediction algorithm using a generative AI model to identify the user's economic situation. Based on this, it predicts the future financial state.

[0284] Step 4:

[0285] Based on the analysis results, the server generates an optimal economic plan. As a result of the data learning algorithm, a plan proposal tailored to each individual user is generated, determining specific savings and investment guidelines.

[0286] Step 5:

[0287] The server sends the generated plan to the terminal. The terminal displays it as a graphical interface to visually present the received data to the user. For example, it visualizes the economic situation and the degree of goal achievement using graphs and charts.

[0288] Step 6:

[0289] The user checks the proposed plan based on the visual feedback and inputs opinions and evaluations to the terminal based on their own judgment. The user's input is used to adjust the plan in the next step.

[0290] Step 7:

[0291] The server reflects the opinions received from the user and adjusts the economic plan again. By analyzing the feedback information and revising the plan as necessary, it becomes possible to present a more effective economic strategy.

[0292] Through this series of processes, the user can efficiently manage an economic plan tailored to their individual needs and achieve achievable goals.

[0293] (Application Example 1)

[0294] 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."

[0295] Currently, many individuals are unable to manage their finances properly, and in particular, they have difficulty creating long-term financial plans. Furthermore, the lack of adequate visualization of economic information, forecasting of future spending, and suggestions for saving money makes it difficult for users to accurately understand their financial situation and make informed decisions. Therefore, it is necessary to systematically acquire individual financial information, present the analysis results visually, and dynamically provide users with optimal financial plans.

[0296] 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.

[0297] In this invention, the server includes means for acquiring personal economic information, means for analyzing the acquired economic information and predicting the economic state, and means for generating an optimal economic plan based on the analysis results. This enables users to visually grasp their economic situation, predict future spending through a dynamic and rational economic plan, and receive savings suggestions based on their spending patterns, thereby enabling effective financial management.

[0298] "Personal economic information" refers to financial data related to an individual, such as information about their economic activities, including income, expenses, assets, and liabilities.

[0299] "Analysis methods" refer to techniques for analyzing information using acquired data and extracting meaning based on a specific purpose.

[0300] An "economic plan" is a long-term set of action guidelines that includes financial targets and budget management, formulated based on an individual's economic situation.

[0301] "Visualization techniques" are technologies that display data in the form of diagrams, charts, and other visual representations to make information easier for users to understand.

[0302] "Opinion" refers to the feedback or comments provided by users on the presented plans or predictions.

[0303] "Adjustment means" is a mechanism for modifying or changing plans or predictions based on opinions from users or newly acquired data.

[0304] "Expenditure pattern" refers to the trends and regularities observed in an individual's financial consumption behavior.

[0305] "Savings plan" is a specific guideline or proposal for suppressing future expenditures.

[0306] In this application example, a system for effectively managing an individual's economic information is realized. First, the user uses a smartphone application to input their economic information. This information includes income, expenditure, assets, liabilities, etc. The terminal visualizes this information and displays it in graphs and charts so that the user can intuitively understand it.

[0307] The server analyzes the user's economic information using programs such as Python and Scikit-learn. Through this analysis, the expenditure patterns of individual users are revealed, and it becomes possible to predict future expenditures. Also, an economic plan is created using a machine learning algorithm based on past data. This enables the user to formulate an optimal economic strategy for themselves.

[0308] The server further generates a savings plan based on the analysis of numerical data and presents it to the user via the terminal. The user can provide opinions on this, and those opinions are reflected by the server in the adjustment of the plan. As a result, the economic plan is dynamically optimized according to the user's lifestyle and goals.

[0309] For example, if the server recognizes that a user spends a lot at cafes, it might suggest a saving plan such as, "By skipping one cafe visit per week starting next month, you can save approximately ¥5,000 per year." Through the generating AI model, the prompt could be: "Based on the user's spending history, predict monthly spending and present a planned savings plan in a graph. As a specific example, please also include advice that takes into account reducing spending at cafes."

[0310] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0311] Step 1:

[0312] Users input their personal financial information (income, expenses, assets, liabilities, etc.) using a smartphone application. This information is collected by the device and sent to a server. The entered data is stored in a database that the server uses for later analysis.

[0313] Step 2:

[0314] The server retrieves collected economic information and begins data analysis using Python or Scikit-learn. Based on the input data, the server analyzes spending patterns and calculates average spending for specific items. The output includes aggregated data and analysis results for each spending category.

[0315] Step 3:

[0316] The server applies a machine learning algorithm to predict future economic conditions based on the analysis results. The input is historical spending data, and the algorithm generates predictions for future spending. The output is the predicted monthly spending amount.

[0317] Step 4:

[0318] The server uses prediction results and historical data to generate an economic plan tailored to the user's lifestyle. This plan includes monthly income and expenses, savings suggestions, and proposals for reducing specific expenses. The output consists of specific economic strategies and visualized data.

[0319] Step 5:

[0320] The terminal visually presents the generated economic plan to the user. It converts data received from the server into graphs and charts, making it easy for the user to understand the economic situation. The input is the server's output data, and the output is the visualized economic plan.

[0321] Step 6:

[0322] Users can provide their opinions on the presented economic plan. User opinions and feedback are received on the device and sent to the server. The input is user feedback, and the output is feedback data.

[0323] Step 7:

[0324] The server adjusts the economic plan based on feedback and makes the best recommendation to the user again. The algorithm incorporates the feedback data and updates the plan. The output is the updated economic plan.

[0325] 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.

[0326] This invention combines a system that supports personal financial management with an emotion engine that recognizes user emotions and utilizes them in generating and presenting financial plans. The system functions effectively through the cooperation of a server, terminal, and user. Its main function is to analyze the user's financial situation and propose an optimal plan, but by also reflecting the user's emotions, a more personalized service becomes possible.

[0327] First, the user enters their financial information through the device. The device analyzes the user's voice, facial expressions, and input patterns, and an emotion engine recognizes the user's current emotional state. The entered financial data and emotional information are sent to a server, which uses machine learning algorithms to analyze the user's financial status.

[0328] The emotion engine flexibly adjusts how financial plans are presented based on the user's emotional state. For example, if the user is stressed, it can avoid overly detailed information and present a concise plan. Conversely, if the user is relaxed, it can provide more detailed analysis results and future simulations.

[0329] The device visually displays the generated financial plan, providing information in the most easily understandable format for the user. For example, it might use colors and fonts preferred by the user, or refer to sentiment engine data to present information in the style most receptive to the user.

[0330] Based on the information provided, users can build a financial strategy tailored to their needs. Furthermore, the server readjusts the plan as needed based on user feedback. This includes emotional feedback obtained through the emotion engine, which is then reflected in future plan generation.

[0331] For example, if the emotion engine detects a user's high stress level, the server can be configured to refrain from suggesting new financial products to that user and instead send a simple reminder of their existing plan. The device then takes care not to overload the user with information and provides a function to reaffirm this once the user has calmed down somewhat.

[0332] Thus, by utilizing an emotion engine, this system goes beyond simply providing financial information; it enables flexible responses tailored to the user's emotional state, supporting more effective individual asset management.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] Users input their financial information through the device. During this process, the device collects user emotional data using voice and facial recognition technology. This data is recorded simultaneously with the user's input patterns.

[0336] Step 2:

[0337] The device transmits collected financial and emotional data to the server. The server stores this data in a database for subsequent analysis. Emotional data is treated as an important indicator for understanding the user's state.

[0338] Step 3:

[0339] The server analyzes stored financial data and evaluates the user's financial status through machine learning algorithms. Simultaneously, it utilizes an emotion engine to identify the user's emotional state. Based on this information, the server generates a financial plan best suited to the user.

[0340] Step 4:

[0341] The server generates an optimal financial plan based on the analysis results. It takes the user's emotional state into account and adjusts the amount and level of detail of information accordingly. A concise plan is created if the user is stressed, while a detailed plan is created if they are relaxed.

[0342] Step 5:

[0343] The terminal presents the generated financial plan to the user. It is displayed in a visually easy-to-understand format and in a style that takes into account the user's emotional state. The user uses this as a reference to decide on their next course of action.

[0344] Step 6:

[0345] After the user reviews the presented plan, they provide feedback through their device. Along with this feedback, the device sends the user's emotional state to the server.

[0346] Step 7:

[0347] The server integrates user feedback and sentiment data, adjusting financial plans as needed. The results are then used to improve the user experience by incorporating them into future plan generation.

[0348] Step 8:

[0349] Throughout the entire process, the server accumulates new emotional data, which is used to improve the accuracy of the emotion engine and optimize personalization features. This continuous process ensures that the system is constantly evolving and continues to provide users with the best possible support.

[0350] (Example 2)

[0351] 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".

[0352] Traditional financial management systems only analyze the user's financial situation and propose plans, lacking planning that takes the user's emotional state into consideration. Therefore, providing flexible and individualized financial plans tailored to the user's psychological state was difficult.

[0353] 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.

[0354] In this invention, the server includes means for collecting personal financial information, means for analyzing the collected financial information and emotional data to predict the financial state, and means for generating an optimal financial plan based on the user's emotional state. This makes it possible to provide a personalized financial plan that takes the user's emotional state into account.

[0355] "Personal financial information" refers to economic data such as a user's income, expenses, savings goals, and assets.

[0356] "Emotional data" refers to information about a user's psychological state obtained from their tone of voice, facial expressions, input patterns, etc.

[0357] A "machine learning algorithm" refers to a mathematical model or statistical method that computer programs use to learn from data and perform analysis and predictions.

[0358] A "financial plan" refers to future economic policies and action plans proposed to the user based on collected and analyzed financial information.

[0359] "Presenting visually" refers to displaying information on a screen in a way that is easy for the user to understand, and includes forms such as graphs and charts.

[0360] "Feedback" refers to the opinions and evaluations that users provide regarding the proposed plan, and includes information used to improve and adjust the system.

[0361] The embodiments for carrying out this invention are described below.

[0362] Users input their financial information through their devices and provide emotional data such as voice, facial expressions, and input patterns. These devices include smartphones, tablets, and personal computers, and dedicated applications run on them. Emotional data analysis utilizes speech recognition libraries (e.g., Google Speech-to-Text) and facial expression analysis libraries (e.g., OpenCV) to identify emotional states.

[0363] Financial and emotional data collected on the device are sent to the server. After receiving this data, the server uses machine learning algorithms (e.g., Scikit-learn or PyTorch) to analyze the financial state. This generates an optimal financial plan, which is then adjusted according to the user's emotional state. For example, if the user is feeling stressed, a simplified plan is provided.

[0364] The generated financial plan is visualized by the terminal. The terminal presents the information in graphs and charts, using color themes and fonts tailored to the user's preferences. The server takes into account the sentiment feedback provided by the user when generating the next plan, improving the accuracy of personalization.

[0365] For example, if the emotion engine detects a high level of stress in a user, the server can provide a simple reminder of an existing plan instead of recommending new economic products. This measure allows the device to avoid over-information and deliver information in a way that is most acceptable to the user.

[0366] An example of a prompt might be, "Please tell me how to propose the optimal financial strategy while taking the user's emotions into consideration." Based on this prompt, the generating AI model will create a financial plan that is appropriate to the user's emotional state.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] Users input financial information such as monthly income, expenses, and savings goals through their device. They also provide emotional data through voice input and camera. This input includes the user's financial data as well as voice and video data. This data is initially processed on the device and becomes primary information about the current financial situation and emotional state.

[0370] Step 2:

[0371] The device uses speech recognition and facial expression analysis libraries to analyze the collected audio and video data. This analysis transcribes the user's speech into text and determines the emotional state of their facial expressions. Specifically, it converts the audio data into text and extracts emotional attributes from each point of the facial expression, then sends these outputs to the server as numerical data.

[0372] Step 3:

[0373] The server receives financial information and sentiment data sent from the terminal. The received data is analyzed using machine learning algorithms to predict the user's financial status. Based on this input, trends and anomalies are detected in the data, and then candidate financial plans that are considered optimal for the user are generated as output.

[0374] Step 4:

[0375] The server adjusts the generated plan to match the user's emotional state. For example, if it determines that the user is stressed, it simplifies the plan and provides information in a way that does not cause the user anxiety. Based on this, the final plan to be presented to the user is determined.

[0376] Step 5:

[0377] The terminal visually displays the final financial plan sent from the server. This includes actions to make the information easy to understand using graphs and charts, and also allows for color themes and layout adjustments to suit the user's preferences. The output to the user consists of the main elements of the plan and recommended actions.

[0378] Step 6:

[0379] Users provide feedback on the presented financial plan. This feedback includes evaluations and suggestions for improvement for each element of the plan. This input is sent to the server via the terminal.

[0380] Step 7:

[0381] The server receives feedback from users and readjusts the plan as needed. The data obtained from the feedback serves as learning material for future proposals, leading to improvements that more accurately meet the individual needs of users. The output is a new version of the improved financial plan.

[0382] (Application Example 2)

[0383] 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 as the "terminal".

[0384] In today's world, personal financial management is becoming increasingly complex, while services that take into account the user's mental and emotional state are lacking. Therefore, there is a need to create and present flexible financial plans that take the user's feelings into consideration.

[0385] 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.

[0386] In this invention, the server includes means for collecting personal financial information, means for analyzing the financial information and predicting the financial state, and means for recognizing the user's emotional state and adjusting the information presented in accordance with that emotional state. This makes it possible to propose flexible and effective financial plans that are tailored to the user's emotional state.

[0387] "Personal financial information" refers to the collective financial data owned by each individual, including income, expenses, savings, and investments.

[0388] "Financial forecasting" refers to predicting future income and expenditure balances and fund management situations based on collected financial information.

[0389] "Financial plan generation" is the process of formulating specific asset management and spending plans based on the user's financial information.

[0390] "Recognizing the user's emotional state" is a technology that analyzes voice, facial expressions, and input patterns to identify the user's psychological condition.

[0391] "Adjusting information presentation" refers to the act of changing the content and presentation of data in accordance with the user's emotional state.

[0392] "Receiving feedback" is the process of collecting user reactions and opinions on the presented financial plan.

[0393] "Visual presentation of financial plans" refers to a method of displaying generated financial plans to users in an easy-to-understand manner using graphs, charts, and other visual aids.

[0394] A "machine learning algorithm" is a computational method that allows computer programs to learn regularities and patterns in data from experience and perform inference and prediction.

[0395] This system provides advanced support for managing personal financial information and functions through the cooperation of the server, terminal, and user. Users first use a terminal to input their financial data, such as income, expenses, and savings. This terminal captures the user's voice and facial expressions using voice recognition sensors and a camera, and uses emotion analysis software to determine their current psychological state from this data. This analysis utilizes an emotion recognition engine.

[0396] The server receives collected financial and sentiment data and uses machine learning algorithms to predict financial status. The Python library TextBlob is useful for this server processing. TextBlob supports natural language processing and plays a crucial role in sentiment analysis.

[0397] Next, the server generates a personalized financial plan based on the predicted financial state. This plan generation takes an approach that takes into account the user's emotional state, presenting a simple plan to stressed users and a detailed investment plan to relaxed users.

[0398] The generated financial plan is sent to the terminal and presented visually in a user-friendly interface. This includes the selection of colors and fonts, as well as graphic representations, that are considered in the user interface design.

[0399] Users can provide feedback on the presented plan, and this feedback will be reflected in future plan generation. For example, if a user states, "I feel calm today," the system will propose a detailed financial plan. This enables optimal financial management tailored to the user's emotions.

[0400] An example of a prompt for obtaining emotional feedback from a generative AI model is, "Tell me how you've been feeling lately in one word. And what kind of spending plan do you think you need?" This prompt allows for the acquisition of information based on the user's emotional state.

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

[0402] Step 1:

[0403] Users input their financial information, such as income, expenses, and savings, through a terminal. This information is entered into the terminal in text and numerical format and transmitted from the terminal to the server. The terminal also operates sensors to recognize the user's voice and facial expressions, collecting data related to the user's psychological state.

[0404] Step 2:

[0405] The device uses emotion analysis software to convert collected voice and facial expression data into emotional states. An emotion recognition algorithm is then applied to process the data and recognize the user's stress level and relaxation level. This analysis result is often quantified as an emotion score and sent to the server.

[0406] Step 3:

[0407] The server receives financial information and sentiment scores sent from the terminal, analyzes this data using machine learning algorithms, and predicts the user's financial status. Based on the input data, the predictive model operates to assess future income and expenditure balances and investment risks. This prediction result forms the basis for plan generation in the next step.

[0408] Step 4:

[0409] The server generates an optimal financial plan by considering the user's predicted financial state and emotional state. For example, it suggests a simple savings plan to a stressed user and a detailed investment plan to a relaxed user. The generated plan is structured as visual content using text and graphical elements and sent to the terminal.

[0410] Step 5:

[0411] The device presents the user with an optimized financial plan. The plan is displayed in a customized interface, with adjusted colors and fonts to aid user understanding. The user can review the plan and provide feedback as needed.

[0412] Step 6:

[0413] User feedback is sent from the device to the server and used to generate future plans. This process also re-evaluates emotion recognition data, and adjustments are made based on changes in the user's emotions in response to the feedback. This creates an evolutionary system that adapts to the user's emotions and financial situation.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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".

[0430] This invention is a system designed to effectively support personal financial management, realized through the interaction of a server, terminals, and users. The system primarily collects and analyzes users' financial information to provide an optimal financial plan.

[0431] The server centrally manages the financial data entered by users. Specifically, it securely stores details such as users' bank account information, investment assets, and insurance contract information, and analyzes their financial status based on this data. The server uses this data to apply machine learning algorithms and make future predictions tailored to each user's situation. The algorithms build models based on historical data and perform analyses that take into account each user's different risk profile and goals.

[0432] The terminal provides a user interface and plays a role in visualizing the collected information. The analyzed information is presented to the user visually through the terminal as graphs and charts. This allows users to easily understand their financial situation and make decisions based on the provided plan. The terminal also has a function to collect user feedback, which is sent to the server and used to review and adjust the plan.

[0433] Based on their financial goals, users receive instructions from their device and input the necessary financial information. This includes monthly income and expenses and savings targets. Furthermore, users can provide feedback on the presented plan and adjust its depth. This process allows users to develop the investment, savings, and insurance strategies best suited to them.

[0434] As a concrete example, consider a user in their 20s who plans to buy a home within the next few years. This user inputs their current income, expenses, and savings into the system. Based on this, the server presents a plan showing future savings potential and the funds needed to purchase a home. The user reviews the visualized proposal on their terminal and receives specific actionable guidance, such as increasing their monthly savings. The server flexibly adjusts the plan to accommodate subsequent changes, enabling the maintenance of a long-term financial plan.

[0435] In this way, the entire system works together to effectively and efficiently support individual asset management.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] Users enter their financial information into the system via a terminal. This includes details of salary, expenses, savings, investments, and insurance policies. The terminal provides a user-friendly interface, making it easy for users to enter the necessary information.

[0439] Step 2:

[0440] The terminal transmits the acquired financial information to the server. The server securely stores this data in a database and prepares it for subsequent processing. Data integrity and security are ensured.

[0441] Step 3:

[0442] The server analyzes the user's current financial status based on stored financial data. Here, data cleaning and normalization are performed, preparing the system to use machine learning algorithms to predict future income, expenses, and risk profiles.

[0443] Step 4:

[0444] Based on the analysis results, the server generates an optimal financial plan tailored to the user's goals and desired financial situation. This includes investment strategies, savings plans, and risk management recommendations. The generated plan is customized to the individual user's needs.

[0445] Step 5:

[0446] The terminal visually presents the financial plan received from the server to the user. Graphs and charts are used to help users intuitively understand their own financial situation and the proposed plan.

[0447] Step 6:

[0448] Users review the presented plan through their device and provide feedback as needed. This feedback is sent from the device to the server and used to adjust the plan.

[0449] Step 7:

[0450] The server receives user feedback, incorporates it into the analysis results, revises the financial plan, and regenerates it as needed. This provides the user with a more appropriate and actionable plan.

[0451] Step 8:

[0452] Throughout the entire process, the server analyzes user usage data, continuously improving algorithm accuracy and adding new features. This ensures the system always provides users with the best possible support.

[0453] (Example 1)

[0454] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0455] In modern times, personal financial management has become increasingly complex, requiring a comprehensive understanding of various factors such as income, expenses, savings, and investments, as well as the ability to predict future economic conditions. However, accurately managing these factors on one's own is difficult, and in many cases, individuals have no choice but to rely on experts. Therefore, there is a growing need for a system that can provide optimal financial plans tailored to individual circumstances at a low cost, and present them in a visually easy-to-understand manner.

[0456] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0457] In this invention, the server includes means for acquiring personal economic data, means for analyzing the acquired economic data and predicting economic conditions, and means for generating an optimal economic plan based on the analysis results. This enables accurate predictions of economic conditions tailored to each individual user and easy-to-understand suggestions through visualization.

[0458] "Personal financial data" refers to financial information owned or managed by an individual, and includes data such as income, expenses, savings, investments, and insurance policies.

[0459] "Means of acquisition" refers to the technical means of providing functions and interfaces for collecting economic data from users.

[0460] "Means of analysis" refers to a system mechanism that includes algorithms and processes used to analyze collected economic data.

[0461] "Means of predicting economic conditions" refer to methods and techniques for estimating an individual's future economic situation based on analyzed data.

[0462] "Means of generating optimal economic plans" refers to methods of designing and providing individuals with the most effective financial management plans under predicted economic conditions.

[0463] "Means of visualization and presentation" refers to technologies that display generated economic plans in visual formats such as graphs and charts, providing information in a way that users can easily understand.

[0464] "Means of receiving feedback" refers to interfaces and functions for collecting user feedback and opinions on the plan.

[0465] "Means of adjustment" refer to mechanisms or methods for modifying or optimizing existing economic plans based on feedback received.

[0466] A "data learning algorithm" is a computational method that uses machine learning techniques to learn patterns in data and supports predictions and decisions regarding new data.

[0467] This invention is a system that supports personal financial management. The system is realized through the interaction of a server, terminals, and users. The following describes in detail how this system is implemented.

[0468] The server is responsible for acquiring and centrally managing economic data entered by each user. This data includes information on users' income, expenses, savings, investments, and insurance. The server utilizes general-purpose cloud database technology to securely store this data. Specifically, it manages data on a platform widely used as a cloud service. The stored data is analyzed using data learning algorithms. Machine learning frameworks such as TensorFlow and PyTorch are used to learn data patterns and build models that predict economic conditions.

[0469] The terminal receives and visualizes information analyzed from the server via a user interface. For visualization, a graphics library is used to provide information in a visually easy-to-understand format. For example, the terminal's interface displays graphs showing income and expenditure trends and progress toward savings goals. The terminal can also receive user feedback, which is sent to the server and used to adjust the financial plan.

[0470] Users interact with the system by inputting financial information. For example, users can follow the terminal's instructions to input their monthly income, expenses, and target savings. Based on the analysis, they can review the presented financial plan and adjust their lifestyle as needed. Through this process, users can efficiently manage their own finances.

[0471] As a concrete example, consider a user who plans to buy a home in the near future. The user inputs their income and current savings into the system, and the server predicts their future savings potential and suggests a savings plan aligned with their goal. Based on this plan, the user adjusts their monthly expenses and receives specific guidance to track their progress on their device.

[0472] As an example of a prompt, providing the AI ​​model with the instruction, "Please generate the optimal savings plan for purchasing a home," allows the user to obtain a specific plan that matches their objectives.

[0473] In this way, by having servers, terminals, and users work together, it becomes possible to provide economic management services optimized for each individual user.

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] Users input economic data through their devices. Specifically, they enter monthly income, expenses, savings, investment information, and so on. This input data is temporarily stored in a database on the device.

[0477] Step 2:

[0478] The terminal sends the entered data to the server. The HTTPS protocol is used for transmission, and the data is encrypted. By securely transferring the entered data to the server, user privacy is protected. The server receives the data and stores its contents in a cloud database.

[0479] Step 3:

[0480] The server analyzes the stored data. First, it preprocesses the data, including imputing missing values ​​and normalizing it. Next, it uses a generative AI model to run a predictive algorithm to identify the user's financial situation. Based on this, it predicts their future financial condition.

[0481] Step 4:

[0482] The server generates an optimal economic plan based on the analysis results. As a result of the data learning algorithm, personalized plan suggestions are generated for each user, determining specific savings and investment guidelines.

[0483] Step 5:

[0484] The server sends the generated plan to the terminal. The terminal displays the received data as a graphical interface to visually present it to the user. For example, it may use graphs and charts to visualize the economic situation or the progress towards achieving goals.

[0485] Step 6:

[0486] Users review the proposed plan based on visual feedback and input their opinions and evaluations into the device based on their own judgment. This user input is used to refine the plan in the next step.

[0487] Step 7:

[0488] The server incorporates user feedback and readjusts the economic plan. By analyzing the feedback information and revising the plan as needed, it becomes possible to present a more effective economic strategy.

[0489] This entire process allows users to efficiently manage financial plans tailored to their individual needs and achieve achievable goals.

[0490] (Application Example 1)

[0491] 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."

[0492] Currently, many individuals are unable to manage their finances properly, and in particular, they have difficulty creating long-term financial plans. Furthermore, the lack of adequate visualization of economic information, forecasting of future spending, and suggestions for saving money makes it difficult for users to accurately understand their financial situation and make informed decisions. Therefore, it is necessary to systematically acquire individual financial information, present the analysis results visually, and dynamically provide users with optimal financial plans.

[0493] 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.

[0494] In this invention, the server includes means for acquiring personal economic information, means for analyzing the acquired economic information and predicting the economic state, and means for generating an optimal economic plan based on the analysis results. This enables users to visually grasp their economic situation, predict future spending through a dynamic and rational economic plan, and receive savings suggestions based on their spending patterns, thereby enabling effective financial management.

[0495] "Personal economic information" refers to financial data related to an individual, such as information about their economic activities, including income, expenses, assets, and liabilities.

[0496] "Analysis methods" refer to techniques for analyzing information using acquired data and extracting meaning based on a specific purpose.

[0497] An "economic plan" is a long-term set of action guidelines that includes financial targets and budget management, formulated based on an individual's economic situation.

[0498] "Visualization techniques" are technologies that display data in the form of diagrams, charts, and other visual representations to make information easier for users to understand.

[0499] "Opinions" refer to feedback and comments provided by users regarding the presented plans and predictions.

[0500] "Adjustment mechanisms" refer to systems for modifying and changing plans and forecasts based on user feedback and newly acquired data.

[0501] "Spending patterns" refer to the tendencies and regularities observed in an individual's financial consumption behavior.

[0502] A "savings plan" is a specific guideline or proposal for reducing future expenses.

[0503] This application demonstrates a system for effectively managing personal financial information. First, the user inputs their financial information using a smartphone application. This information includes income, expenses, assets, and liabilities. The device visualizes this information, displaying it in graphs and charts for intuitive understanding.

[0504] The server uses programs such as Python and Scikit-learn to analyze users' financial information. This analysis reveals individual users' spending patterns and makes it possible to predict future spending. It also uses machine learning algorithms based on historical data to create financial plans. This allows users to develop the most suitable financial strategies for themselves.

[0505] The server then generates savings plans based on numerical data analysis and presents them to the user via the terminal. The user can provide feedback, and this feedback is incorporated into the plan adjustments by the server. In this way, the economic plan is dynamically optimized according to the user's lifestyle and goals.

[0506] For example, if the server recognizes that a user spends a lot at cafes, it might suggest a saving plan such as, "By skipping one cafe visit per week starting next month, you can save approximately ¥5,000 per year." Through the generating AI model, the prompt could be: "Based on the user's spending history, predict monthly spending and present a planned savings plan in a graph. As a specific example, please also include advice that takes into account reducing spending at cafes."

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] Users input their personal financial information (income, expenses, assets, liabilities, etc.) using a smartphone application. This information is collected by the device and sent to a server. The entered data is stored in a database that the server uses for later analysis.

[0510] Step 2:

[0511] The server retrieves collected economic information and begins data analysis using Python or Scikit-learn. Based on the input data, the server analyzes spending patterns and calculates average spending for specific items. The output includes aggregated data and analysis results for each spending category.

[0512] Step 3:

[0513] The server applies a machine learning algorithm to predict future economic conditions based on the analysis results. The input is historical spending data, and the algorithm generates predictions for future spending. The output is the predicted monthly spending amount.

[0514] Step 4:

[0515] The server uses prediction results and historical data to generate an economic plan tailored to the user's lifestyle. This plan includes monthly income and expenses, savings suggestions, and proposals for reducing specific expenses. The output consists of specific economic strategies and visualized data.

[0516] Step 5:

[0517] The terminal visually presents the generated economic plan to the user. It converts data received from the server into graphs and charts, making it easy for the user to understand the economic situation. The input is the server's output data, and the output is the visualized economic plan.

[0518] Step 6:

[0519] Users can provide their opinions on the presented economic plan. User opinions and feedback are received on the device and sent to the server. The input is user feedback, and the output is feedback data.

[0520] Step 7:

[0521] The server adjusts the economic plan based on feedback and makes the best recommendation to the user again. The algorithm incorporates the feedback data and updates the plan. The output is the updated economic plan.

[0522] 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.

[0523] This invention combines a system that supports personal financial management with an emotion engine that recognizes user emotions and utilizes them in generating and presenting financial plans. The system functions effectively through the cooperation of a server, terminal, and user. Its main function is to analyze the user's financial situation and propose an optimal plan, but by also reflecting the user's emotions, a more personalized service becomes possible.

[0524] First, the user enters their financial information through the device. The device analyzes the user's voice, facial expressions, and input patterns, and an emotion engine recognizes the user's current emotional state. The entered financial data and emotional information are sent to a server, which uses machine learning algorithms to analyze the user's financial status.

[0525] The emotion engine flexibly adjusts how financial plans are presented based on the user's emotional state. For example, if the user is stressed, it can avoid overly detailed information and present a concise plan. Conversely, if the user is relaxed, it can provide more detailed analysis results and future simulations.

[0526] The device visually displays the generated financial plan, providing information in the most easily understandable format for the user. For example, it might use colors and fonts preferred by the user, or refer to sentiment engine data to present information in the style most receptive to the user.

[0527] Based on the information provided, users can build a financial strategy tailored to their needs. Furthermore, the server readjusts the plan as needed based on user feedback. This includes emotional feedback obtained through the emotion engine, which is then reflected in future plan generation.

[0528] For example, if the emotion engine detects a user's high stress level, the server can be configured to refrain from suggesting new financial products to that user and instead send a simple reminder of their existing plan. The device then takes care not to overload the user with information and provides a function to reaffirm this once the user has calmed down somewhat.

[0529] Thus, by utilizing an emotion engine, this system goes beyond simply providing financial information; it enables flexible responses tailored to the user's emotional state, supporting more effective individual asset management.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] Users input their financial information through the device. During this process, the device collects user emotional data using voice and facial recognition technology. This data is recorded simultaneously with the user's input patterns.

[0533] Step 2:

[0534] The device transmits collected financial and emotional data to the server. The server stores this data in a database for subsequent analysis. Emotional data is treated as an important indicator for understanding the user's state.

[0535] Step 3:

[0536] The server analyzes stored financial data and evaluates the user's financial status through machine learning algorithms. Simultaneously, it utilizes an emotion engine to identify the user's emotional state. Based on this information, the server generates a financial plan best suited to the user.

[0537] Step 4:

[0538] The server generates an optimal financial plan based on the analysis results. It takes the user's emotional state into account and adjusts the amount and level of detail of information accordingly. A concise plan is created if the user is stressed, while a detailed plan is created if they are relaxed.

[0539] Step 5:

[0540] The terminal presents the generated financial plan to the user. It is displayed in a visually easy-to-understand format and in a style that takes into account the user's emotional state. The user uses this as a reference to decide on their next course of action.

[0541] Step 6:

[0542] After the user reviews the presented plan, they provide feedback through their device. Along with this feedback, the device sends the user's emotional state to the server.

[0543] Step 7:

[0544] The server integrates user feedback and sentiment data, adjusting financial plans as needed. The results are then used to improve the user experience by incorporating them into future plan generation.

[0545] Step 8:

[0546] Throughout the entire process, the server accumulates new emotional data, which is used to improve the accuracy of the emotion engine and optimize personalization features. This continuous process ensures that the system is constantly evolving and continues to provide users with the best possible support.

[0547] (Example 2)

[0548] 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."

[0549] Traditional financial management systems only analyze the user's financial situation and propose plans, lacking planning that takes the user's emotional state into consideration. Therefore, providing flexible and individualized financial plans tailored to the user's psychological state was difficult.

[0550] 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.

[0551] In this invention, the server includes means for collecting personal financial information, means for analyzing the collected financial information and emotional data to predict the financial state, and means for generating an optimal financial plan based on the user's emotional state. This makes it possible to provide a personalized financial plan that takes the user's emotional state into account.

[0552] "Personal financial information" refers to economic data such as a user's income, expenses, savings goals, and assets.

[0553] "Emotional data" refers to information about a user's psychological state obtained from their tone of voice, facial expressions, input patterns, etc.

[0554] A "machine learning algorithm" refers to a mathematical model or statistical method that computer programs use to learn from data and perform analysis and predictions.

[0555] A "financial plan" refers to future economic policies and action plans proposed to the user based on collected and analyzed financial information.

[0556] "Presenting visually" refers to displaying information on a screen in a way that is easy for the user to understand, and includes forms such as graphs and charts.

[0557] "Feedback" refers to the opinions and evaluations that users provide regarding the proposed plan, and includes information used to improve and adjust the system.

[0558] The embodiments for carrying out this invention are described below.

[0559] Users input their financial information through their devices and provide emotional data such as voice, facial expressions, and input patterns. These devices include smartphones, tablets, and personal computers, and dedicated applications run on them. Emotional data analysis utilizes speech recognition libraries (e.g., Google Speech-to-Text) and facial expression analysis libraries (e.g., OpenCV) to identify emotional states.

[0560] Financial and emotional data collected on the device are sent to the server. After receiving this data, the server uses machine learning algorithms (e.g., Scikit-learn or PyTorch) to analyze the financial state. This generates an optimal financial plan, which is then adjusted according to the user's emotional state. For example, if the user is feeling stressed, a simplified plan is provided.

[0561] The generated financial plan is visualized by the terminal. The terminal presents the information in graphs and charts, using color themes and fonts tailored to the user's preferences. The server takes into account the sentiment feedback provided by the user when generating the next plan, improving the accuracy of personalization.

[0562] For example, if the emotion engine detects a high level of stress in a user, the server can provide a simple reminder of an existing plan instead of recommending new economic products. This measure allows the device to avoid over-information and deliver information in a way that is most acceptable to the user.

[0563] An example of a prompt might be, "Please tell me how to propose the optimal financial strategy while taking the user's emotions into consideration." Based on this prompt, the generating AI model will create a financial plan that is appropriate to the user's emotional state.

[0564] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0565] Step 1:

[0566] Users input financial information such as monthly income, expenses, and savings goals through their device. They also provide emotional data through voice input and camera. This input includes the user's financial data as well as voice and video data. This data is initially processed on the device and becomes primary information about the current financial situation and emotional state.

[0567] Step 2:

[0568] The device uses speech recognition and facial expression analysis libraries to analyze the collected audio and video data. This analysis transcribes the user's speech into text and determines the emotional state of their facial expressions. Specifically, it converts the audio data into text and extracts emotional attributes from each point of the facial expression, then sends these outputs to the server as numerical data.

[0569] Step 3:

[0570] The server receives financial information and sentiment data sent from the terminal. The received data is analyzed using machine learning algorithms to predict the user's financial status. Based on this input, trends and anomalies are detected in the data, and then candidate financial plans that are considered optimal for the user are generated as output.

[0571] Step 4:

[0572] The server adjusts the generated plan to match the user's emotional state. For example, if it determines that the user is stressed, it simplifies the plan and provides information in a way that does not cause the user anxiety. Based on this, the final plan to be presented to the user is determined.

[0573] Step 5:

[0574] The terminal visually displays the final financial plan sent from the server. This includes actions to make the information easy to understand using graphs and charts, and also allows for color themes and layout adjustments to suit the user's preferences. The output to the user consists of the main elements of the plan and recommended actions.

[0575] Step 6:

[0576] Users provide feedback on the presented financial plan. This feedback includes evaluations and suggestions for improvement for each element of the plan. This input is sent to the server via the terminal.

[0577] Step 7:

[0578] The server receives feedback from users and readjusts the plan as needed. The data obtained from the feedback serves as learning material for future proposals, leading to improvements that more accurately meet the individual needs of users. The output is a new version of the improved financial plan.

[0579] (Application Example 2)

[0580] 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."

[0581] In today's world, personal financial management is becoming increasingly complex, while services that take into account the user's mental and emotional state are lacking. Therefore, there is a need to create and present flexible financial plans that take the user's feelings into consideration.

[0582] 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.

[0583] In this invention, the server includes means for collecting personal financial information, means for analyzing the financial information and predicting the financial state, and means for recognizing the user's emotional state and adjusting the information presented in accordance with that emotional state. This makes it possible to propose flexible and effective financial plans that are tailored to the user's emotional state.

[0584] "Personal financial information" refers to the collective financial data owned by each individual, including income, expenses, savings, and investments.

[0585] "Financial forecasting" refers to predicting future income and expenditure balances and fund management situations based on collected financial information.

[0586] "Financial plan generation" is the process of formulating specific asset management and spending plans based on the user's financial information.

[0587] "Recognizing the user's emotional state" is a technology that analyzes voice, facial expressions, and input patterns to identify the user's psychological condition.

[0588] "Adjusting information presentation" refers to the act of changing the content and presentation of data in accordance with the user's emotional state.

[0589] "Receiving feedback" is the process of collecting user reactions and opinions on the presented financial plan.

[0590] "Visual presentation of financial plans" refers to a method of displaying generated financial plans to users in an easy-to-understand manner using graphs, charts, and other visual aids.

[0591] A "machine learning algorithm" is a computational method that allows computer programs to learn regularities and patterns in data from experience and perform inference and prediction.

[0592] This system provides advanced support for managing personal financial information and functions through the cooperation of the server, terminal, and user. Users first use a terminal to input their financial data, such as income, expenses, and savings. This terminal captures the user's voice and facial expressions using voice recognition sensors and a camera, and uses emotion analysis software to determine their current psychological state from this data. This analysis utilizes an emotion recognition engine.

[0593] The server receives collected financial and sentiment data and uses machine learning algorithms to predict financial status. The Python library TextBlob is useful for this server processing. TextBlob supports natural language processing and plays a crucial role in sentiment analysis.

[0594] Next, the server generates a personalized financial plan based on the predicted financial state. This plan generation takes an approach that takes into account the user's emotional state, presenting a simple plan to stressed users and a detailed investment plan to relaxed users.

[0595] The generated financial plan is sent to the terminal and presented visually in a user-friendly interface. This includes the selection of colors and fonts, as well as graphic representations, that are considered in the user interface design.

[0596] Users can provide feedback on the presented plan, and this feedback will be reflected in future plan generation. For example, if a user states, "I feel calm today," the system will propose a detailed financial plan. This enables optimal financial management tailored to the user's emotions.

[0597] An example of a prompt for obtaining emotional feedback from a generative AI model is, "Tell me how you've been feeling lately in one word. And what kind of spending plan do you think you need?" This prompt allows for the acquisition of information based on the user's emotional state.

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] Users input their financial information, such as income, expenses, and savings, through a terminal. This information is entered into the terminal in text and numerical format and transmitted from the terminal to the server. The terminal also operates sensors to recognize the user's voice and facial expressions, collecting data related to the user's psychological state.

[0601] Step 2:

[0602] The device uses emotion analysis software to convert collected voice and facial expression data into emotional states. An emotion recognition algorithm is then applied to process the data and recognize the user's stress level and relaxation level. This analysis result is often quantified as an emotion score and sent to the server.

[0603] Step 3:

[0604] The server receives financial information and sentiment scores sent from the terminal, analyzes this data using machine learning algorithms, and predicts the user's financial status. Based on the input data, the predictive model operates to assess future income and expenditure balances and investment risks. This prediction result forms the basis for plan generation in the next step.

[0605] Step 4:

[0606] The server generates an optimal financial plan by considering the user's predicted financial state and emotional state. For example, it suggests a simple savings plan to a stressed user and a detailed investment plan to a relaxed user. The generated plan is structured as visual content using text and graphical elements and sent to the terminal.

[0607] Step 5:

[0608] The device presents the user with an optimized financial plan. The plan is displayed in a customized interface, with adjusted colors and fonts to aid user understanding. The user can review the plan and provide feedback as needed.

[0609] Step 6:

[0610] User feedback is sent from the device to the server and used to generate future plans. This process also re-evaluates emotion recognition data, and adjustments are made based on changes in the user's emotions in response to the feedback. This creates an evolutionary system that adapts to the user's emotions and financial situation.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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".

[0628] This invention is a system designed to effectively support personal financial management, realized through the interaction of a server, terminals, and users. The system primarily collects and analyzes users' financial information to provide an optimal financial plan.

[0629] The server centrally manages the financial data entered by users. Specifically, it securely stores details such as users' bank account information, investment assets, and insurance contract information, and analyzes their financial status based on this data. The server uses this data to apply machine learning algorithms and make future predictions tailored to each user's situation. The algorithms build models based on historical data and perform analyses that take into account each user's different risk profile and goals.

[0630] The terminal provides a user interface and plays a role in visualizing the collected information. The analyzed information is presented to the user visually through the terminal as graphs and charts. This allows users to easily understand their financial situation and make decisions based on the provided plan. The terminal also has a function to collect user feedback, which is sent to the server and used to review and adjust the plan.

[0631] Based on their financial goals, users receive instructions from their device and input the necessary financial information. This includes monthly income and expenses and savings targets. Furthermore, users can provide feedback on the presented plan and adjust its depth. This process allows users to develop the investment, savings, and insurance strategies best suited to them.

[0632] As a concrete example, consider a user in their 20s who plans to buy a home within the next few years. This user inputs their current income, expenses, and savings into the system. Based on this, the server presents a plan showing future savings potential and the funds needed to purchase a home. The user reviews the visualized proposal on their terminal and receives specific actionable guidance, such as increasing their monthly savings. The server flexibly adjusts the plan to accommodate subsequent changes, enabling the maintenance of a long-term financial plan.

[0633] In this way, the entire system works together to effectively and efficiently support individual asset management.

[0634] The following describes the processing flow.

[0635] Step 1:

[0636] Users enter their financial information into the system via a terminal. This includes details of salary, expenses, savings, investments, and insurance policies. The terminal provides a user-friendly interface, making it easy for users to enter the necessary information.

[0637] Step 2:

[0638] The terminal transmits the acquired financial information to the server. The server securely stores this data in a database and prepares it for subsequent processing. Data integrity and security are ensured.

[0639] Step 3:

[0640] The server analyzes the user's current financial status based on stored financial data. Here, data cleaning and normalization are performed, preparing the system to use machine learning algorithms to predict future income, expenses, and risk profiles.

[0641] Step 4:

[0642] Based on the analysis results, the server generates an optimal financial plan tailored to the user's goals and desired financial situation. This includes investment strategies, savings plans, and risk management recommendations. The generated plan is customized to the individual user's needs.

[0643] Step 5:

[0644] The terminal visually presents the financial plan received from the server to the user. Graphs and charts are used to help users intuitively understand their own financial situation and the proposed plan.

[0645] Step 6:

[0646] Users review the presented plan through their device and provide feedback as needed. This feedback is sent from the device to the server and used to adjust the plan.

[0647] Step 7:

[0648] The server receives user feedback, incorporates it into the analysis results, revises the financial plan, and regenerates it as needed. This provides the user with a more appropriate and actionable plan.

[0649] Step 8:

[0650] Throughout the entire process, the server analyzes user usage data, continuously improving algorithm accuracy and adding new features. This ensures the system always provides users with the best possible support.

[0651] (Example 1)

[0652] 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".

[0653] In modern times, personal financial management has become increasingly complex, requiring a comprehensive understanding of various factors such as income, expenses, savings, and investments, as well as the ability to predict future economic conditions. However, accurately managing these factors on one's own is difficult, and in many cases, individuals have no choice but to rely on experts. Therefore, there is a growing need for a system that can provide optimal financial plans tailored to individual circumstances at a low cost, and present them in a visually easy-to-understand manner.

[0654] 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.

[0655] In this invention, the server includes means for acquiring personal economic data, means for analyzing the acquired economic data and predicting economic conditions, and means for generating an optimal economic plan based on the analysis results. This enables accurate predictions of economic conditions tailored to each individual user and easy-to-understand suggestions through visualization.

[0656] "Personal financial data" refers to financial information owned or managed by an individual, and includes data such as income, expenses, savings, investments, and insurance policies.

[0657] "Means of acquisition" refers to the technical means of providing functions and interfaces for collecting economic data from users.

[0658] "Means of analysis" refers to a system mechanism that includes algorithms and processes used to analyze collected economic data.

[0659] "Means of predicting economic conditions" refer to methods and techniques for estimating an individual's future economic situation based on analyzed data.

[0660] "Means of generating optimal economic plans" refers to methods of designing and providing individuals with the most effective financial management plans under predicted economic conditions.

[0661] "Means of visualization and presentation" refers to technologies that display generated economic plans in visual formats such as graphs and charts, providing information in a way that users can easily understand.

[0662] "Means of receiving feedback" refers to interfaces and functions for collecting user feedback and opinions on the plan.

[0663] "Means of adjustment" refer to mechanisms or methods for modifying or optimizing existing economic plans based on feedback received.

[0664] A "data learning algorithm" is a computational method that uses machine learning techniques to learn patterns in data and supports predictions and decisions regarding new data.

[0665] This invention is a system that supports personal financial management. The system is realized through the interaction of a server, terminals, and users. The following describes in detail how this system is implemented.

[0666] The server is responsible for acquiring and centrally managing economic data entered by each user. This data includes information on users' income, expenses, savings, investments, and insurance. The server utilizes general-purpose cloud database technology to securely store this data. Specifically, it manages data on a platform widely used as a cloud service. The stored data is analyzed using data learning algorithms. Machine learning frameworks such as TensorFlow and PyTorch are used to learn data patterns and build models that predict economic conditions.

[0667] The terminal receives and visualizes information analyzed from the server via a user interface. For visualization, a graphics library is used to provide information in a visually easy-to-understand format. For example, the terminal's interface displays graphs showing income and expenditure trends and progress toward savings goals. The terminal can also receive user feedback, which is sent to the server and used to adjust the financial plan.

[0668] Users interact with the system by inputting financial information. For example, users can follow the terminal's instructions to input their monthly income, expenses, and target savings. Based on the analysis, they can review the presented financial plan and adjust their lifestyle as needed. Through this process, users can efficiently manage their own finances.

[0669] As a concrete example, consider a user who plans to buy a home in the near future. The user inputs their income and current savings into the system, and the server predicts their future savings potential and suggests a savings plan aligned with their goal. Based on this plan, the user adjusts their monthly expenses and receives specific guidance to track their progress on their device.

[0670] As an example of a prompt, providing the AI ​​model with the instruction, "Please generate the optimal savings plan for purchasing a home," allows the user to obtain a specific plan that matches their objectives.

[0671] In this way, by having servers, terminals, and users work together, it becomes possible to provide economic management services optimized for each individual user.

[0672] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0673] Step 1:

[0674] Users input economic data through their devices. Specifically, they enter monthly income, expenses, savings, investment information, and so on. This input data is temporarily stored in a database on the device.

[0675] Step 2:

[0676] The terminal sends the entered data to the server. The HTTPS protocol is used for transmission, and the data is encrypted. By securely transferring the entered data to the server, user privacy is protected. The server receives the data and stores its contents in a cloud database.

[0677] Step 3:

[0678] The server analyzes the stored data. First, it preprocesses the data, including imputing missing values ​​and normalizing it. Next, it uses a generative AI model to run a predictive algorithm to identify the user's financial situation. Based on this, it predicts their future financial condition.

[0679] Step 4:

[0680] The server generates an optimal economic plan based on the analysis results. As a result of the data learning algorithm, personalized plan suggestions are generated for each user, determining specific savings and investment guidelines.

[0681] Step 5:

[0682] The server sends the generated plan to the terminal. The terminal displays the received data as a graphical interface to visually present it to the user. For example, it may use graphs and charts to visualize the economic situation or the progress towards achieving goals.

[0683] Step 6:

[0684] Users review the proposed plan based on visual feedback and input their opinions and evaluations into the device based on their own judgment. This user input is used to refine the plan in the next step.

[0685] Step 7:

[0686] The server incorporates user feedback and readjusts the economic plan. By analyzing the feedback information and revising the plan as needed, it becomes possible to present a more effective economic strategy.

[0687] This entire process allows users to efficiently manage financial plans tailored to their individual needs and achieve achievable goals.

[0688] (Application Example 1)

[0689] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] Currently, many individuals are unable to manage their finances properly, and in particular, they have difficulty creating long-term financial plans. Furthermore, the lack of adequate visualization of economic information, forecasting of future spending, and suggestions for saving money makes it difficult for users to accurately understand their financial situation and make informed decisions. Therefore, it is necessary to systematically acquire individual financial information, present the analysis results visually, and dynamically provide users with optimal financial plans.

[0691] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0692] In this invention, the server includes means for acquiring personal economic information, means for analyzing the acquired economic information and predicting the economic state, and means for generating an optimal economic plan based on the analysis results. This enables users to visually grasp their economic situation, predict future spending through a dynamic and rational economic plan, and receive savings suggestions based on their spending patterns, thereby enabling effective financial management.

[0693] "Personal economic information" refers to financial data related to an individual, such as information about their economic activities, including income, expenses, assets, and liabilities.

[0694] "Analysis methods" refer to techniques for analyzing information using acquired data and extracting meaning based on a specific purpose.

[0695] An "economic plan" is a long-term set of action guidelines that includes financial targets and budget management, formulated based on an individual's economic situation.

[0696] "Visualization techniques" are technologies that display data in the form of diagrams, charts, and other visual representations to make information easier for users to understand.

[0697] "Opinions" refer to feedback and comments provided by users regarding the presented plans and predictions.

[0698] "Adjustment mechanisms" refer to systems for modifying and changing plans and forecasts based on user feedback and newly acquired data.

[0699] "Spending patterns" refer to the tendencies and regularities observed in an individual's financial consumption behavior.

[0700] A "savings plan" is a specific guideline or proposal for reducing future expenses.

[0701] This application demonstrates a system for effectively managing personal financial information. First, the user inputs their financial information using a smartphone application. This information includes income, expenses, assets, and liabilities. The device visualizes this information, displaying it in graphs and charts for intuitive understanding.

[0702] The server uses programs such as Python and Scikit-learn to analyze users' financial information. This analysis reveals individual users' spending patterns and makes it possible to predict future spending. It also uses machine learning algorithms based on historical data to create financial plans. This allows users to develop the most suitable financial strategies for themselves.

[0703] The server then generates savings plans based on numerical data analysis and presents them to the user via the terminal. The user can provide feedback, and this feedback is incorporated into the plan adjustments by the server. In this way, the economic plan is dynamically optimized according to the user's lifestyle and goals.

[0704] For example, if the server recognizes that a user spends a lot at cafes, it might suggest a saving plan such as, "By skipping one cafe visit per week starting next month, you can save approximately ¥5,000 per year." Through the generating AI model, the prompt could be: "Based on the user's spending history, predict monthly spending and present a planned savings plan in a graph. As a specific example, please also include advice that takes into account reducing spending at cafes."

[0705] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0706] Step 1:

[0707] Users input their personal financial information (income, expenses, assets, liabilities, etc.) using a smartphone application. This information is collected by the device and sent to a server. The entered data is stored in a database that the server uses for later analysis.

[0708] Step 2:

[0709] The server retrieves collected economic information and begins data analysis using Python or Scikit-learn. Based on the input data, the server analyzes spending patterns and calculates average spending for specific items. The output includes aggregated data and analysis results for each spending category.

[0710] Step 3:

[0711] The server applies a machine learning algorithm to predict future economic conditions based on the analysis results. The input is historical spending data, and the algorithm generates predictions for future spending. The output is the predicted monthly spending amount.

[0712] Step 4:

[0713] The server uses prediction results and historical data to generate an economic plan tailored to the user's lifestyle. This plan includes monthly income and expenses, savings suggestions, and proposals for reducing specific expenses. The output consists of specific economic strategies and visualized data.

[0714] Step 5:

[0715] The terminal visually presents the generated economic plan to the user. It converts data received from the server into graphs and charts, making it easy for the user to understand the economic situation. The input is the server's output data, and the output is the visualized economic plan.

[0716] Step 6:

[0717] Users can provide their opinions on the presented economic plan. User opinions and feedback are received on the device and sent to the server. The input is user feedback, and the output is feedback data.

[0718] Step 7:

[0719] The server adjusts the economic plan based on feedback and makes the best recommendation to the user again. The algorithm incorporates the feedback data and updates the plan. The output is the updated economic plan.

[0720] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0721] This invention combines a system that supports personal financial management with an emotion engine that recognizes user emotions and utilizes them in generating and presenting financial plans. The system functions effectively through the cooperation of a server, terminal, and user. Its main function is to analyze the user's financial situation and propose an optimal plan, but by also reflecting the user's emotions, a more personalized service becomes possible.

[0722] First, the user enters their financial information through the device. The device analyzes the user's voice, facial expressions, and input patterns, and an emotion engine recognizes the user's current emotional state. The entered financial data and emotional information are sent to a server, which uses machine learning algorithms to analyze the user's financial status.

[0723] The emotion engine flexibly adjusts how financial plans are presented based on the user's emotional state. For example, if the user is stressed, it can avoid overly detailed information and present a concise plan. Conversely, if the user is relaxed, it can provide more detailed analysis results and future simulations.

[0724] The device visually displays the generated financial plan, providing information in the most easily understandable format for the user. For example, it might use colors and fonts preferred by the user, or refer to sentiment engine data to present information in the style most receptive to the user.

[0725] Based on the information provided, users can build a financial strategy tailored to their needs. Furthermore, the server readjusts the plan as needed based on user feedback. This includes emotional feedback obtained through the emotion engine, which is then reflected in future plan generation.

[0726] For example, if the emotion engine detects a user's high stress level, the server can be configured to refrain from suggesting new financial products to that user and instead send a simple reminder of their existing plan. The device then takes care not to overload the user with information and provides a function to reaffirm this once the user has calmed down somewhat.

[0727] Thus, by utilizing an emotion engine, this system goes beyond simply providing financial information; it enables flexible responses tailored to the user's emotional state, supporting more effective individual asset management.

[0728] The following describes the processing flow.

[0729] Step 1:

[0730] Users input their financial information through the device. During this process, the device collects user emotional data using voice and facial recognition technology. This data is recorded simultaneously with the user's input patterns.

[0731] Step 2:

[0732] The device transmits collected financial and emotional data to the server. The server stores this data in a database for subsequent analysis. Emotional data is treated as an important indicator for understanding the user's state.

[0733] Step 3:

[0734] The server analyzes stored financial data and evaluates the user's financial status through machine learning algorithms. Simultaneously, it utilizes an emotion engine to identify the user's emotional state. Based on this information, the server generates a financial plan best suited to the user.

[0735] Step 4:

[0736] The server generates an optimal financial plan based on the analysis results. It takes the user's emotional state into account and adjusts the amount and level of detail of information accordingly. A concise plan is created if the user is stressed, while a detailed plan is created if they are relaxed.

[0737] Step 5:

[0738] The terminal presents the generated financial plan to the user. It is displayed in a visually easy-to-understand format and in a style that takes into account the user's emotional state. The user uses this as a reference to decide on their next course of action.

[0739] Step 6:

[0740] After the user reviews the presented plan, they provide feedback through their device. Along with this feedback, the device sends the user's emotional state to the server.

[0741] Step 7:

[0742] The server integrates user feedback and sentiment data, adjusting financial plans as needed. The results are then used to improve the user experience by incorporating them into future plan generation.

[0743] Step 8:

[0744] Throughout the entire process, the server accumulates new emotional data, which is used to improve the accuracy of the emotion engine and optimize personalization features. This continuous process ensures that the system is constantly evolving and continues to provide users with the best possible support.

[0745] (Example 2)

[0746] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0747] Traditional financial management systems only analyze the user's financial situation and propose plans, lacking planning that takes the user's emotional state into consideration. Therefore, providing flexible and individualized financial plans tailored to the user's psychological state was difficult.

[0748] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0749] In this invention, the server includes means for collecting personal financial information, means for analyzing the collected financial information and emotional data to predict the financial state, and means for generating an optimal financial plan based on the user's emotional state. This makes it possible to provide a personalized financial plan that takes the user's emotional state into account.

[0750] "Personal financial information" refers to economic data such as a user's income, expenses, savings goals, and assets.

[0751] "Emotional data" refers to information about a user's psychological state obtained from their tone of voice, facial expressions, input patterns, etc.

[0752] A "machine learning algorithm" refers to a mathematical model or statistical method that computer programs use to learn from data and perform analysis and predictions.

[0753] A "financial plan" refers to future economic policies and action plans proposed to the user based on collected and analyzed financial information.

[0754] "Presenting visually" refers to displaying information on a screen in a way that is easy for the user to understand, and includes forms such as graphs and charts.

[0755] "Feedback" refers to the opinions and evaluations that users provide regarding the proposed plan, and includes information used to improve and adjust the system.

[0756] The embodiments for carrying out this invention are described below.

[0757] Users input their financial information through their devices and provide emotional data such as voice, facial expressions, and input patterns. These devices include smartphones, tablets, and personal computers, and dedicated applications run on them. Emotional data analysis utilizes speech recognition libraries (e.g., Google Speech-to-Text) and facial expression analysis libraries (e.g., OpenCV) to identify emotional states.

[0758] Financial and emotional data collected on the device are sent to the server. After receiving this data, the server uses machine learning algorithms (e.g., Scikit-learn or PyTorch) to analyze the financial state. This generates an optimal financial plan, which is then adjusted according to the user's emotional state. For example, if the user is feeling stressed, a simplified plan is provided.

[0759] The generated financial plan is visualized by the terminal. The terminal presents the information in graphs and charts, using color themes and fonts tailored to the user's preferences. The server takes into account the sentiment feedback provided by the user when generating the next plan, improving the accuracy of personalization.

[0760] For example, if the emotion engine detects a high level of stress in a user, the server can provide a simple reminder of an existing plan instead of recommending new economic products. This measure allows the device to avoid over-information and deliver information in a way that is most acceptable to the user.

[0761] An example of a prompt might be, "Please tell me how to propose the optimal financial strategy while taking the user's emotions into consideration." Based on this prompt, the generating AI model will create a financial plan that is appropriate to the user's emotional state.

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] Users input financial information such as monthly income, expenses, and savings goals through their device. They also provide emotional data through voice input and camera. This input includes the user's financial data as well as voice and video data. This data is initially processed on the device and becomes primary information about the current financial situation and emotional state.

[0765] Step 2:

[0766] The device uses speech recognition and facial expression analysis libraries to analyze the collected audio and video data. This analysis transcribes the user's speech into text and determines the emotional state of their facial expressions. Specifically, it converts the audio data into text and extracts emotional attributes from each point of the facial expression, then sends these outputs to the server as numerical data.

[0767] Step 3:

[0768] The server receives financial information and sentiment data sent from the terminal. The received data is analyzed using machine learning algorithms to predict the user's financial status. Based on this input, trends and anomalies are detected in the data, and then candidate financial plans that are considered optimal for the user are generated as output.

[0769] Step 4:

[0770] The server adjusts the generated plan to match the user's emotional state. For example, if it determines that the user is stressed, it simplifies the plan and provides information in a way that does not cause the user anxiety. Based on this, the final plan to be presented to the user is determined.

[0771] Step 5:

[0772] The terminal visually displays the final financial plan sent from the server. This includes actions to make the information easy to understand using graphs and charts, and also allows for color themes and layout adjustments to suit the user's preferences. The output to the user consists of the main elements of the plan and recommended actions.

[0773] Step 6:

[0774] Users provide feedback on the presented financial plan. This feedback includes evaluations and suggestions for improvement for each element of the plan. This input is sent to the server via the terminal.

[0775] Step 7:

[0776] The server receives feedback from users and readjusts the plan as needed. The data obtained from the feedback serves as learning material for future proposals, leading to improvements that more accurately meet the individual needs of users. The output is a new version of the improved financial plan.

[0777] (Application Example 2)

[0778] 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".

[0779] In today's world, personal financial management is becoming increasingly complex, while services that take into account the user's mental and emotional state are lacking. Therefore, there is a need to create and present flexible financial plans that take the user's feelings into consideration.

[0780] 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.

[0781] In this invention, the server includes means for collecting personal financial information, means for analyzing the financial information and predicting the financial state, and means for recognizing the user's emotional state and adjusting the information presented in accordance with that emotional state. This makes it possible to propose flexible and effective financial plans that are tailored to the user's emotional state.

[0782] "Personal financial information" refers to the collective financial data owned by each individual, including income, expenses, savings, and investments.

[0783] "Financial forecasting" refers to predicting future income and expenditure balances and fund management situations based on collected financial information.

[0784] "Financial plan generation" is the process of formulating specific asset management and spending plans based on the user's financial information.

[0785] "Recognizing the user's emotional state" is a technology that analyzes voice, facial expressions, and input patterns to identify the user's psychological condition.

[0786] "Adjusting information presentation" refers to the act of changing the content and presentation of data in accordance with the user's emotional state.

[0787] "Receiving feedback" is the process of collecting user reactions and opinions on the presented financial plan.

[0788] "Visual presentation of financial plans" refers to a method of displaying generated financial plans to users in an easy-to-understand manner using graphs, charts, and other visual aids.

[0789] A "machine learning algorithm" is a computational method that allows computer programs to learn regularities and patterns in data from experience and perform inference and prediction.

[0790] This system provides advanced support for managing personal financial information and functions through the cooperation of the server, terminal, and user. Users first use a terminal to input their financial data, such as income, expenses, and savings. This terminal captures the user's voice and facial expressions using voice recognition sensors and a camera, and uses emotion analysis software to determine their current psychological state from this data. This analysis utilizes an emotion recognition engine.

[0791] The server receives collected financial and sentiment data and uses machine learning algorithms to predict financial status. The Python library TextBlob is useful for this server processing. TextBlob supports natural language processing and plays a crucial role in sentiment analysis.

[0792] Next, the server generates a personalized financial plan based on the predicted financial state. This plan generation takes an approach that takes into account the user's emotional state, presenting a simple plan to stressed users and a detailed investment plan to relaxed users.

[0793] The generated financial plan is sent to the terminal and presented visually in a user-friendly interface. This includes the selection of colors and fonts, as well as graphic representations, that are considered in the user interface design.

[0794] Users can provide feedback on the presented plan, and this feedback will be reflected in future plan generation. For example, if a user states, "I feel calm today," the system will propose a detailed financial plan. This enables optimal financial management tailored to the user's emotions.

[0795] An example of a prompt for obtaining emotional feedback from a generative AI model is, "Tell me how you've been feeling lately in one word. And what kind of spending plan do you think you need?" This prompt allows for the acquisition of information based on the user's emotional state.

[0796] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0797] Step 1:

[0798] Users input their financial information, such as income, expenses, and savings, through a terminal. This information is entered into the terminal in text and numerical format and transmitted from the terminal to the server. The terminal also operates sensors to recognize the user's voice and facial expressions, collecting data related to the user's psychological state.

[0799] Step 2:

[0800] The device uses emotion analysis software to convert collected voice and facial expression data into emotional states. An emotion recognition algorithm is then applied to process the data and recognize the user's stress level and relaxation level. This analysis result is often quantified as an emotion score and sent to the server.

[0801] Step 3:

[0802] The server receives financial information and sentiment scores sent from the terminal, analyzes this data using machine learning algorithms, and predicts the user's financial status. Based on the input data, the predictive model operates to assess future income and expenditure balances and investment risks. This prediction result forms the basis for plan generation in the next step.

[0803] Step 4:

[0804] The server generates an optimal financial plan by considering the user's predicted financial state and emotional state. For example, it suggests a simple savings plan to a stressed user and a detailed investment plan to a relaxed user. The generated plan is structured as visual content using text and graphical elements and sent to the terminal.

[0805] Step 5:

[0806] The device presents the user with an optimized financial plan. The plan is displayed in a customized interface, with adjusted colors and fonts to aid user understanding. The user can review the plan and provide feedback as needed.

[0807] Step 6:

[0808] User feedback is sent from the device to the server and used to generate future plans. This process also re-evaluates emotion recognition data, and adjustments are made based on changes in the user's emotions in response to the feedback. This creates an evolutionary system that adapts to the user's emotions and financial situation.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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."

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] The following is further disclosed regarding the embodiments described above.

[0831] (Claim 1)

[0832] Means of collecting personal financial information,

[0833] A means of analyzing collected financial information and predicting the financial condition,

[0834] A means for generating an optimal financial plan based on the analysis results,

[0835] A means of visually presenting the generated financial plan,

[0836] A means of receiving feedback on the proposed plan,

[0837] Means to adjust financial plans based on feedback,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, characterized in that it uses a machine learning algorithm for analyzing financial information.

[0841] (Claim 3)

[0842] The system according to claim 1, characterized in that it presents the generated financial plan using graphs and charts.

[0843] "Example 1"

[0844] (Claim 1)

[0845] Means of obtaining personal economic data,

[0846] A means of analyzing acquired economic data and predicting economic conditions,

[0847] A means for generating an optimal economic plan based on the analysis results,

[0848] A means of visualizing and presenting the generated economic plan,

[0849] Means of receiving feedback on the proposed plan,

[0850] A means of adjusting economic plans based on opinions,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, characterized in that it uses a data learning algorithm for analyzing economic data.

[0854] (Claim 3)

[0855] The system according to claim 1, characterized in that it presents the generated economic plan using visual elements.

[0856] "Application Example 1"

[0857] (Claim 1)

[0858] Means of obtaining personal economic information,

[0859] A means of analyzing acquired economic information and predicting the state of the economy,

[0860] A means for generating an optimal economic plan based on the analysis results,

[0861] A means of visually presenting the generated economic plan,

[0862] Means of receiving feedback on the proposed plan,

[0863] A means of adjusting economic plans based on opinions,

[0864] A means of analyzing users' past spending data and predicting future spending,

[0865] A means of presenting savings plans based on spending patterns,

[0866] ...

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, characterized in that it uses a machine learning algorithm for analyzing economic information.

[0870] (Claim 3)

[0871] The system according to claim 1, characterized in that it presents the generated economic plan using charts and graphs.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] Means of collecting personal financial information,

[0875] A means of analyzing collected financial information and sentiment data to predict financial status,

[0876] A means for generating an optimal financial plan based on the user's emotional state,

[0877] A means of visually presenting the generated financial plan,

[0878] A means of receiving feedback on the proposed plan,

[0879] Means for adjusting financial plans based on feedback and emotional feedback,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, characterized in that it uses machine learning algorithms for analyzing financial information and recognizing emotional states.

[0883] (Claim 3)

[0884] The system according to claim 1, characterized in that it presents the generated financial plan in an acceptable format that corresponds to the user's emotional state.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] Means of collecting personal financial information,

[0888] A means of analyzing collected financial information and predicting the financial condition,

[0889] A means for generating an optimal financial plan based on the analysis results,

[0890] A means of recognizing the user's emotional state and adjusting the information presented in accordance with that emotion,

[0891] A means of visually presenting the generated financial plan,

[0892] A means of receiving feedback on the proposed plan,

[0893] Means to adjust financial plans based on feedback,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, characterized in that it uses a machine learning algorithm for analyzing financial information.

[0897] (Claim 3)

[0898] The system according to claim 1, characterized in that it presents the generated financial plan using a graphical representation. [Explanation of Symbols]

[0899] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining personal economic information, A means of analyzing acquired economic information and predicting the state of the economy, A means for generating an optimal economic plan based on the analysis results, A means of visually presenting the generated economic plan, Means of receiving feedback on the proposed plan, A means of adjusting economic plans based on opinions, A means of analyzing users' past spending data and predicting future spending, A means of presenting savings plans based on spending patterns, A system that includes this.

2. The system according to claim 1, characterized in that it uses a machine learning algorithm for analyzing economic information.

3. The system according to claim 1, characterized in that it presents the generated economic plan using charts and graphs.

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