system

Abstract

We provide the system. [Solution] Means of obtaining financial information from users, A means for generating a plan to achieve the user's future asset goals based on said financial information, A means of notifying the user of the generated plan, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When an individual plans for future asset formation or pension amount, there is a problem that it is not clear how to set goals and formulate an appropriate financial plan, and as a result, it is often impossible to secure sufficient assets. Furthermore, individual financial data is diverse, and the problem is how to analyze this data to formulate an effective plan.

Means for Solving the Problems

[0005] This invention solves the above-mentioned problems by providing a system that acquires financial information from a user, generates a plan to achieve the user's future asset goals based on that information, and notifies the user of the generated plan. More specifically, by providing means for analyzing the acquired financial information to predict future pension amounts, the system can present an optimal asset formation plan for the user, and by providing means for adjusting the plan based on feedback from the user, it can flexibly respond to the user's needs.

[0006] "User" refers to an individual who uses the system and provides financial information.

[0007] "Financial information" refers to information related to asset building, such as a user's consumption history, investment history, age, and asset goals.

[0008] "Asset goals" refer to the financial targets or required asset amounts that users set for the future.

[0009] "Means for generating plans" refers to AI models and algorithms that construct optimal asset formation plans based on the user's financial information.

[0010] "Means of notification" refers to interfaces and communication methods for providing users with information about the generated asset formation plan.

[0011] "Predictive methods" refer to techniques for analyzing acquired financial information and estimating future pension amounts and asset formation through simulations and statistical methods.

[0012] "Feedback" refers to the opinions and requests that users provide regarding the presented plan.

[0013] "Means of adjustment" refers to the processes and technologies used to revise the plan in response to user feedback. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] 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.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system according to this invention has a configuration for providing an optimal asset formation plan to individual users. The main components are a terminal for collecting the user's financial information, a server for analyzing the received information, and an interface for notifying the user of the plan.

[0036] The server stores financial information received from the user's terminal in a database. This information includes monthly spending, current investment status, age, and future asset goals. The server uses machine learning models to analyze this information and predict the user's future pension amount. Furthermore, based on this prediction and the user's asset goals, it generates an optimal investment plan.

[0037] The terminal notifies the user of the generated plan and displays the detailed plan on the screen. The user can review the displayed plan and adjust the recommended investment products and monthly investment amount as needed. Furthermore, user feedback is sent back to the server, and the plan is adjusted as necessary.

[0038] For example, if a 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year, the server analyzes their current spending and investment patterns. If the analysis predicts that they will not reach their target pension amount by age 65, it suggests specific measures such as increasing their investment amount. This suggestion is communicated to the user via their device, allowing them to efficiently manage their asset building.

[0039] This system allows users to quickly create a personalized asset building plan, helping them gain financial security for the future.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users use their own devices to input their current financial information (such as spending history, investment information, age, and asset goals) and send it to the server.

[0043] Step 2:

[0044] The terminal reliably transfers the user's financial information to the server.

[0045] Step 3:

[0046] The server stores the received financial information in a database and compares and updates it with past data.

[0047] Step 4:

[0048] The server feeds stored financial information into a machine learning model to analyze consumption and investment patterns.

[0049] Step 5:

[0050] Based on the analysis results, the server predicts future pension amounts and asset growth, and generates an optimal investment plan. This plan includes monthly investment amounts, recommended investment products, and risk assessments.

[0051] Step 6:

[0052] The server sends the generated investment plan to the terminal.

[0053] Step 7:

[0054] The terminal notifies the user of the received investment plan and displays the details on the screen.

[0055] Step 8:

[0056] Users review the plan through the screen and select their investment amount and products according to the recommendations. They can also send any feedback regarding the plan to the server via their device.

[0057] Step 9:

[0058] The server receives feedback from the user and readjusts the investment plan as needed. The readjusted plan is then sent back to the user.

[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] To provide each user with an optimal asset building plan, a system is needed that creates accurate investment strategies tailored to the user's financial situation and future goals, and efficiently notifies and manages them. Furthermore, it is necessary to dynamically and flexibly adjust the plan by quickly incorporating user feedback.

[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 financial data from the user, means for generating a plan to achieve the user's asset goals using a machine learning model, means for notifying the user of the plan via their terminal for the user to review and adjust, and means for retraining the generated AI model and adjusting the plan. This makes it possible to generate and adjust investment plans tailored to each individual user, thereby improving the efficiency and flexibility of asset building.

[0064] A "user" is an individual or legal entity that utilizes the system, provides financial data, and receives a personalized asset building plan.

[0065] "Financial data" refers to information related to wealth building, such as a user's monthly spending, investment status, age, and future asset goals.

[0066] A "machine learning model" is an algorithm used to analyze data and perform predictions and optimizations, and in this invention, it is used specifically for generating asset formation plans.

[0067] "Asset goals" refer to specific financial objectives that users hope to achieve in the future, and include things like pension amounts.

[0068] A "plan" is an investment strategy generated based on the user's financial data to efficiently advance wealth creation.

[0069] A "terminal" is a device used by users to access the system, input financial data, and review plans.

[0070] A "generative AI model" is an artificial intelligence system that retrains itself based on data and feedback obtained from users to provide an optimal asset formation plan.

[0071] This invention is an information processing system for providing individual users with optimal asset formation plans. Its main components are a terminal for collecting user financial data and a server for analyzing the received information and generating plans. The operation of these components is described in detail below.

[0072] The device features an interface for users to input their financial data. This interface is provided through a web browser or mobile application. Users can input personal data such as their monthly spending, current investment status, and age.

[0073] The server receives data sent from terminals and stores it in a central database. This database uses database management systems such as MySQL® or PostgreSQL. The stored data is analyzed by machine learning models to predict the user's future pension amount and the likelihood of achieving their asset goals. This analysis utilizes machine learning libraries such as TENSORFLOW® and PyTorch.

[0074] The server uses a generative AI model to generate an optimal investment plan based on the user's data. This model takes into account the user's current asset situation and goals, and has the capability to propose an ideal long-term investment strategy.

[0075] The generated plan is sent to the device and notified to the user. The user can review the detailed plan on the device screen and adjust investment products and monthly investment amounts as needed. This feedback is then sent back to the server, and the plan is adjusted again as necessary.

[0076] As a concrete example, consider a 30-year-old user who spends 50,000 yen per month and invests 500,000 yen per year. Based on this information, the server analyzes the user's spending and investment patterns and generates an optimal investment plan to achieve the pension amount the user will need by age 65. The plan may include increasing the required investment amount or revising asset allocation. This suggestion is provided to the user via their device.

[0077] An example of a prompt for a generating AI model might be: "A 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year. They aim to have a pension of 50 million yen by age 65. Please suggest the best investment plan for this user." Based on this prompt, the AI ​​model will provide a customized investment strategy tailored to the user's lifestyle and financial goals.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The terminal receives financial data from the user as input. Specifically, the user inputs data such as monthly spending, current investment status, age, and future asset goals through a dedicated application or web interface. This input data is sent to the server and used in the next processing step.

[0081] Step 2:

[0082] The server stores the financial data received from the terminal as input into a database. Specifically, the server uses a database management system such as MySQL or PostgreSQL to record the received user data in a structured format. The stored data is then used for subsequent analysis and plan generation.

[0083] Step 3:

[0084] The server analyzes stored financial data as input to a machine learning model. To predict the user's future pension amount and the likelihood of achieving their asset goals, the server performs analysis using libraries such as TensorFlow and PyTorch. The output obtained from this analysis forms the basis for a specific investment plan for the user.

[0085] Step 4:

[0086] The server uses the analysis results to generate an optimal investment plan through a generated AI model. This process takes into account the user's financial situation and investment goals, formulating a strategy suitable for long-term wealth building. The generated plan is then communicated to the user in the next step.

[0087] Step 5:

[0088] The terminal notifies the user of the investment plan sent from the server as output. Specifically, the terminal screen displays the detailed plan. The user can review this and adjust the proposed investment products and monthly investment amounts.

[0089] Step 6:

[0090] Users send feedback on the plan back to the server as input via their terminal. This feedback includes information such as how the user evaluates the plan and what adjustments they would like to see made.

[0091] Step 7:

[0092] The server re-evaluates the plan using user feedback as input and adjusts the plan by retraining the system's generating AI model. Specifically, it analyzes the feedback, performs new data calculations as needed, and generates a more appropriate investment strategy again. The adjusted plan is then provided to the user again, resulting in dynamic optimization of asset building.

[0093] (Application Example 1)

[0094] 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."

[0095] In today's world, it is difficult for individual users to accurately understand their own financial situation and create effective asset building plans. In particular, deriving specific investment strategies to achieve future asset goals requires specialized knowledge and time. To address this problem, there is a need to provide efficient and individually tailored asset building plans.

[0096] 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.

[0097] In this invention, the server includes means for acquiring financial information from a user, means for generating a plan to achieve the user's future asset goals based on said financial information, means for notifying the user of the generated plan, and means for analyzing the user's financial information and providing an asset formation plan that operates within an electronic payment service. This enables the user to receive an optimal investment strategy without any hassle and to efficiently build their assets.

[0098] A "user" is an individual or legal entity that provides financial information and receives an asset building plan based on that information.

[0099] "Financial information" refers to financially related data such as a user's spending, investment status, age, and asset goals.

[0100] "Asset goals" refer to the financial objectives that a user hopes to achieve in the future.

[0101] A "plan" is an investment strategy generated based on the user's financial information to achieve their asset goals.

[0102] "Analysis" is the process of thoroughly examining acquired financial information, and is carried out to find patterns in the data.

[0103] An "investment strategy" is a plan that includes selecting financial products and adjusting investment amounts necessary to achieve asset goals.

[0104] "Feedback" refers to opinions and evaluations of the plan provided by the user, and is information used to revise the plan as needed.

[0105] An "electronic payment service" is an online system that manages and processes the financial information of individual users and provides them with asset building plans.

[0106] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user.

[0107] The server is a Python program responsible for collecting and analyzing financial information provided by users. It uses the Scikit-learn library to build machine learning models and develop future wealth building plans based on users' spending patterns and investment status. Specifically, it analyzes data using linear regression models to generate optimal investment strategies for achieving users' wealth goals. The server stores this information in a database and also manages feedback for adjusting the plan.

[0108] The terminal functions as a user interface and includes applications that run on a smartphone. Through this terminal, users can receive and review plan notifications from the server. At this stage, users can select investment products and adjust investment amounts. Furthermore, they can provide feedback on the plan, which is then sent back to the server.

[0109] Users can efficiently and easily build their assets based on the plans provided by the server. For example, if a 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year, and the server determines that their projected future pension amount will not reach their target, they will be offered suggestions for additional investment or a change in investment products. In this way, users can easily receive an optimal asset plan.

[0110] An example of a prompt would be, "I am 30 years old with an annual income of 8 million yen, spends 50,000 yen per month, and invests 500,000 yen per year. Please provide advice on how to achieve assets of 30 million yen by age 60 under these conditions." Using this prompt, the server generates a customized investment strategy for each user.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server collects the user's financial information. Input data such as the user's spending, investment status, age, and asset goals is required. This data is provided by the user, and the server stores it in a database. Specifically, financial information is transmitted from the user's smartphone or other input devices, and the server receives it in real time.

[0114] Step 2:

[0115] The server analyzes the collected financial information. The input here is the financial data collected in Step 1. The server uses the Scikit-learn LinearRegression model to perform the analysis and create a predictive model for the data. Specifically, the server passes the financial data through a machine learning algorithm, analyzes the data patterns, and evaluates the user's future asset attainment probability.

[0116] Step 3:

[0117] The server generates an asset building plan based on the analysis results. The input is the result of the predictive model obtained in step 2. Based on this result, the server creates a plan to propose the optimal investment strategy for the user's asset goals. Specifically, the server uses the generated AI model to determine the optimal investment products and asset allocation, and formulates the plan.

[0118] Step 4:

[0119] The system notifies the user of the plan generated via the device. The input is the asset building plan generated in step 3. The device displays this plan to the user and provides detailed information. Specifically, a notification is sent to the user's smartphone, and the user can check the investment strategy via the app.

[0120] Step 5:

[0121] The user provides feedback on the provided plan. The input is the asset building plan received in step 4. The user adjusts the investment amount and products and sends feedback to the server via their device. Specifically, the user reviews the plan in the app, makes necessary adjustments, and sends feedback.

[0122] 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.

[0123] The system of this invention is configured to collect users' financial information and provide asset building plans that also take emotions into consideration. The system mainly consists of three main elements: an emotion engine, a terminal for acquiring users' financial information, and a server that analyzes the data and generates plans.

[0124] Users input financial information using their own devices. This information includes income, consumption, investments, age, and asset goals. Users can also provide their own emotional data simultaneously through the emotion recognition function built into their devices. The emotion engine analyzes the input voice and facial expression data to identify the user's current emotional state.

[0125] The server stores financial and emotional data received from the terminal and analyzes it using machine learning models. Based on the analysis results, it generates an asset building plan that reflects the user's emotions. For example, if the emotional data indicates anxiety, it can suggest a low-risk, stable investment plan. Conversely, if positive emotions are indicated, it can suggest a more advantageous investment plan, even if it involves slightly higher risk.

[0126] The generated plan is sent back to the device and notified to the user. The user can review the plan and customize its contents as needed. During this time, the emotion engine continuously monitors the user's emotional changes and provides feedback to the server. The plan is automatically adjusted in response to these emotional changes, resulting in the user receiving the most suitable suggestions.

[0127] For example, if a user is feeling stressed when determining suitable investment strategies or savings plans to achieve future asset goals, the plan can be simplified and modified to provide a sense of security. In this way, the present invention provides an asset building approach that takes user emotions into consideration, enabling the implementation of more personalized financial services.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] Users input financial information such as income, consumption, investment, age, and asset goals using their devices. They also input current emotional data into the devices through voice and facial expressions using emotion recognition technology.

[0131] Step 2:

[0132] The terminal transmits financial information and emotional data entered by the user to the server. It is desirable to encrypt the data before transmission to prevent leakage.

[0133] Step 3:

[0134] The server stores the received financial information and sentiment data in a database. It then compares the stored data with historical data and updates it as needed.

[0135] Step 4:

[0136] The server inputs stored data into a machine learning model to analyze user consumption patterns, investment tendencies, and sentiment data. This analysis derives appropriate scenarios for achieving future pension amounts and asset goals.

[0137] Step 5:

[0138] Based on the analysis results, the server generates an investment plan that takes into account the user's emotional state. Specifically, if the user is feeling anxious, it designs a plan with low risk; if the user is feeling secure, it designs a plan with some risk.

[0139] Step 6:

[0140] The server sends the generated investment plan back to the terminal. The plan includes a monthly target investment amount, recommended financial instruments, and a risk level tailored to your emotions.

[0141] Step 7:

[0142] The device displays the received investment plan to the user in a visually easy-to-understand format. The user reviews the plan details and decides whether to take action based on them.

[0143] Step 8:

[0144] After reviewing the investment plan, users can send feedback to the server via their device. This feedback can include changes in their feelings and opinions on the plan.

[0145] Step 9:

[0146] The server receives feedback from the user, updates sentiment data, and readjusts the investment plan as needed. The readjusted plan is sent back to the device and notified to the user as a new proposal.

[0147] (Example 2)

[0148] 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".

[0149] Traditional asset building systems only provide plans based on the user's financial information and fail to take the user's emotional state into consideration. This creates a challenge in that users may not receive appropriate investment strategies or financial plans even when they are emotionally unstable. Furthermore, existing systems lack mechanisms to dynamically update plans based on user feedback.

[0150] 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.

[0151] In this invention, the server includes means for acquiring financial and emotional information from the user, means for generating a plan based on the financial and emotional information, means for adjusting and notifying the user of the generated plan according to the user's emotional state, and means for dynamically updating the plan based on user feedback. This makes it possible to provide an asset building plan that is tailored to the user's emotional state and to realize financial services optimized for each individual user.

[0152] "User" refers to anyone who uses this system to provide financial or emotional information.

[0153] "Financial information" refers to data about a user's economic situation, such as income, expenses, assets, and liabilities.

[0154] "Emotional information" refers to data that indicates a user's emotional state, and is collected through information such as voice and facial expressions.

[0155] "Plan" refers to specific asset building proposals generated based on the user's financial and emotional information.

[0156] "Feedback" refers to information about user reactions and requests regarding the plan they have provided.

[0157] "Emotion recognition technology" refers to technology that analyzes a user's voice and facial expression data to determine their emotional state.

[0158] This invention is a system that effectively utilizes users' financial and emotional information to provide individually optimized asset building plans. The following describes specific embodiments for carrying out this invention.

[0159] Users input financial information using their own devices. These devices include electronic devices such as tablets and smartphones, into which data such as income, expenses, assets, and liabilities are entered. Furthermore, to input emotional information, the devices are equipped with cameras and microphones, and software that recognizes emotions from voice and facial expressions is installed. This allows the user's current emotional state to be captured in real time.

[0160] The device transmits this information to the server. It is desirable that encryption technologies and communication protocols (e.g., SSL / TLS) be used to protect data security during this process. The server stores the received financial and emotional information in a database. The stored information is analyzed using generative AI models such as machine learning. This generates an optimal asset building plan that combines the user's financial situation and emotional state.

[0161] The generated plan is sent back to the user's device and the user is notified. For example, if the emotional information indicates the user is "anxious," the server will make adjustments such as suggesting low-risk asset management. Another example of a prompt message could be, "Please recalculate the plan to reduce the user's anxiety." The device continuously collects the user's responses and feedback, and the server dynamically updates the plan accordingly. This cycle enables suggestions that are tailored to the user's emotional state.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] Users input financial and emotional information using a device. Specifically, they input information such as income, expenses, assets, and liabilities in text format on the device's input screen. Emotional information is provided by capturing voice and facial expressions via the camera and microphone. The entered data is temporarily stored on the device.

[0165] Step 2:

[0166] The device transmits acquired financial and emotional information to the server. The transmitted data is encrypted and securely communicated using protocols such as SSL / TLS. This data includes financial information and emotional status, along with the user ID.

[0167] Step 3:

[0168] The server stores received financial and sentiment information in a database. The stored data is then analyzed using machine learning models. This analysis includes trend analysis of financial information and evaluation of sentiment information. As an output of the analysis, a draft asset building plan tailored to the user's current situation is created.

[0169] Step 4:

[0170] The server uses a generative AI model to implement asset building plans generated based on financial and emotional data. This includes adjusting the plan to take the user's emotional state into account. For example, if the emotional data indicates "anxiety," a low-risk investment plan will be suggested. The specific details of the plan are then determined.

[0171] Step 5:

[0172] The server sends the generated asset building plan to the terminal. The plan is transmitted in a format that is easy for the user to understand (for example, in summarized text or graph format). This includes an explanation of the plan and recommended actions.

[0173] Step 6:

[0174] Users review their asset building plans on their devices and provide feedback as needed. The devices provide an interface for users to input their thoughts and requests for changes to the plan. This feedback is important because it will be reflected in the newly generated plan.

[0175] Step 7:

[0176] The device sends user feedback and new sentiment data to the server. The server re-analyzes this information and dynamically updates the plan. The new plan is sent back to the user, and the same flow continues thereafter.

[0177] (Application Example 2)

[0178] 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".

[0179] In modern society, anxiety and mental stress related to users' financial activities are major obstacles to wealth creation. While conventional systems could generate plans based on financial data, they struggled to provide wealth creation plans that considered users' emotions. Therefore, there is a need for a system that comprehensively considers financial information and users' mental state to provide more accurate feedback.

[0180] 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.

[0181] In this invention, the server includes means for acquiring financial data from a user, means for analyzing the user's emotional state using emotion recognition technology, and means for generating a plan to achieve the user's future asset goals based on the financial data and emotional state, and adjusting it according to the emotional state. This makes it possible to provide a flexible and personalized asset building plan that takes the user's emotional state into consideration.

[0182] "Financial data" refers to information about a user's income, consumption, investment status, and asset goals.

[0183] "Emotional state" refers to the user's mental or emotional condition as analyzed by emotion recognition technology.

[0184] "Emotion recognition technology" is a technology that analyzes voice and facial expression data to identify a user's emotions.

[0185] An "asset building plan" is a plan designed to achieve future asset goals based on the user's financial data and emotional state.

[0186] A "server" is an information processing device that collects and analyzes financial data and emotional states from users, and generates and adjusts asset formation plans.

[0187] This invention is a system that provides a personalized asset building plan based on the user's financial data and emotional state. The system consists of the user's terminal, an emotion engine incorporating emotion recognition technology, and a server that analyzes the data.

[0188] Users input financial and emotional data using their own devices. Financial information includes income, consumption, investment, age, and asset goals. Typically, smartphones or other digital devices are used as the devices. Emotional data is acquired through the device's camera and microphone and processed by an emotion engine. Emotion recognition technologies such as OpenCV and TensorFlow are utilized here.

[0189] The server stores financial and emotional data received from users and generates asset building plans based on this data. The generated plans are personalized, taking into account the user's emotional state, using machine learning models. Based on financial information and emotional state, it can suggest investment plans with lower or higher risk. Machine learning libraries such as scikit-learn in Python are commonly used.

[0190] As a concrete example, when a user makes a purchase at a commercial facility, the terminal could recognize the user's facial expressions and, if the user is feeling anxious, display advice on sound spending management on the screen. This would allow the user to review their financial plan in real time in accordance with their emotions. An example of an input prompt to the generating AI model could be: "Generate feedback notifications to help manage spending based on the user's financial information and emotional data."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] Users input financial information using a terminal. This input data includes income, consumption, investment information, age, and asset goals. This data is stored on the terminal as foundational information for creating future wealth-building plans. Detailed field input is performed by the user through the terminal's input interface.

[0194] Step 2:

[0195] The device uses its camera and microphone to acquire user emotion data. The acquired video and audio data is then processed by an emotion engine. Emotion recognition technologies such as OpenCV and TensorFlow are applied to analyze the user's mental state, quantify it, and send it to the server.

[0196] Step 3:

[0197] The server receives financial information and sentiment data sent from the terminal. The server stores this input data and stores it in a database. This data is analyzed by a machine learning model to derive intermediate results for generating the user's asset building plan. Data analysis is performed using scikit-learn in languages ​​such as Python.

[0198] Step 4:

[0199] The server uses a generative AI model to generate an asset building plan based on the input financial information and emotional data. If the emotional data indicates risk aversion, a low-risk investment plan is formulated. The generated plan is best suited to the user's emotional state. This plan is generated as a prompt message.

[0200] Step 5:

[0201] The server sends the generated asset formation plan to the terminal and prepares to notify the user. The terminal generates a notification based on the plan and displays it on the user's screen. At this point, the user can review the plan and send feedback as needed.

[0202] Step 6:

[0203] When a user enters feedback into their device, the device sends the feedback to the server. The server uses the feedback information and ongoing sentiment data to update and adapt the asset building plan. The revised plan is then sent back to the device and presented to the user as a new proposal.

[0204] 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.

[0205] 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 those described above. 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 shown 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] The system according to this invention has a configuration for providing an optimal asset formation plan to individual users. The main components are a terminal for collecting the user's financial information, a server for analyzing the received information, and an interface for notifying the user of the plan.

[0221] The server stores financial information received from the user's terminal in a database. This information includes monthly spending, current investment status, age, and future asset goals. The server uses machine learning models to analyze this information and predict the user's future pension amount. Furthermore, based on this prediction and the user's asset goals, it generates an optimal investment plan.

[0222] The terminal notifies the user of the generated plan and displays the detailed plan on the screen. The user can review the displayed plan and adjust the recommended investment products and monthly investment amount as needed. Furthermore, user feedback is sent back to the server, and the plan is adjusted as necessary.

[0223] For example, if a 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year, the server analyzes their current spending and investment patterns. If the analysis predicts that they will not reach their target pension amount by age 65, it suggests specific measures such as increasing their investment amount. This suggestion is communicated to the user via their device, allowing them to efficiently manage their asset building.

[0224] This system allows users to quickly create a personalized asset building plan, helping them gain financial security for the future.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] Users use their own devices to input their current financial information (such as spending history, investment information, age, and asset goals) and send it to the server.

[0228] Step 2:

[0229] The terminal reliably transfers the user's financial information to the server.

[0230] Step 3:

[0231] The server stores the received financial information in a database and compares and updates it with past data.

[0232] Step 4:

[0233] The server feeds stored financial information into a machine learning model to analyze consumption and investment patterns.

[0234] Step 5:

[0235] Based on the analysis results, the server predicts future pension amounts and asset growth, and generates an optimal investment plan. This plan includes monthly investment amounts, recommended investment products, and risk assessments.

[0236] Step 6:

[0237] The server sends the generated investment plan to the terminal.

[0238] Step 7:

[0239] The terminal notifies the user of the received investment plan and displays the details on the screen.

[0240] Step 8:

[0241] Users review the plan through the screen and select their investment amount and products according to the recommendations. They can also send any feedback regarding the plan to the server via their device.

[0242] Step 9:

[0243] The server receives feedback from the user and readjusts the investment plan as needed. The readjusted plan is then sent back to the user.

[0244] (Example 1)

[0245] 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."

[0246] To provide each user with an optimal asset building plan, a system is needed that creates accurate investment strategies tailored to the user's financial situation and future goals, and efficiently notifies and manages them. Furthermore, it is necessary to dynamically and flexibly adjust the plan by quickly incorporating user feedback.

[0247] 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.

[0248] In this invention, the server includes means for acquiring financial data from the user, means for generating a plan to achieve the user's asset goals using a machine learning model, means for notifying the user of the plan via their terminal for the user to review and adjust, and means for retraining the generated AI model and adjusting the plan. This makes it possible to generate and adjust investment plans tailored to each individual user, thereby improving the efficiency and flexibility of asset building.

[0249] A "user" is an individual or legal entity that utilizes the system, provides financial data, and receives a personalized asset building plan.

[0250] "Financial data" refers to information related to wealth building, such as a user's monthly spending, investment status, age, and future asset goals.

[0251] A "machine learning model" is an algorithm used to analyze data and perform predictions and optimizations, and in this invention, it is used specifically for generating asset formation plans.

[0252] "Asset goals" refer to specific financial objectives that users hope to achieve in the future, and include things like pension amounts.

[0253] A "plan" is an investment strategy generated based on the user's financial data to efficiently advance wealth creation.

[0254] A "terminal" is a device used by users to access the system, input financial data, and review plans.

[0255] A "generative AI model" is an artificial intelligence system that retrains itself based on data and feedback obtained from users to provide an optimal asset formation plan.

[0256] This invention is an information processing system for providing individual users with optimal asset formation plans. Its main components are a terminal for collecting user financial data and a server for analyzing the received information and generating plans. The operation of these components is described in detail below.

[0257] The device features an interface for users to input their financial data. This interface is provided through a web browser or mobile application. Users can input personal data such as their monthly spending, current investment status, and age.

[0258] The server receives data sent from the terminal and stores it in a central database. This database uses a database management system such as MySQL or PostgreSQL. The stored data is analyzed by machine learning models to predict the user's future pension amount and the likelihood of achieving their asset goals. This analysis utilizes machine learning libraries such as TensorFlow and PyTorch.

[0259] The server uses a generative AI model to generate an optimal investment plan based on the user's data. This model takes into account the user's current asset situation and goals, and has the capability to propose an ideal long-term investment strategy.

[0260] The generated plan is sent to the device and notified to the user. The user can review the detailed plan on the device screen and adjust investment products and monthly investment amounts as needed. This feedback is then sent back to the server, and the plan is adjusted again as necessary.

[0261] As a concrete example, consider a 30-year-old user who spends 50,000 yen per month and invests 500,000 yen per year. Based on this information, the server analyzes the user's spending and investment patterns and generates an optimal investment plan to achieve the pension amount the user will need by age 65. The plan may include increasing the required investment amount or revising asset allocation. This suggestion is provided to the user via their device.

[0262] An example of a prompt for a generating AI model might be: "A 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year. They aim to have a pension of 50 million yen by age 65. Please suggest the best investment plan for this user." Based on this prompt, the AI ​​model will provide a customized investment strategy tailored to the user's lifestyle and financial goals.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The terminal receives financial data from the user as input. Specifically, the user inputs data such as monthly spending, current investment status, age, and future asset goals through a dedicated application or web interface. This input data is sent to the server and used in the next processing step.

[0266] Step 2:

[0267] The server stores the financial data received from the terminal as input into a database. Specifically, the server uses a database management system such as MySQL or PostgreSQL to record the received user data in a structured format. The stored data is then used for subsequent analysis and plan generation.

[0268] Step 3:

[0269] The server analyzes stored financial data as input to a machine learning model. To predict the user's future pension amount and the likelihood of achieving their asset goals, the server performs analysis using libraries such as TensorFlow and PyTorch. The output obtained from this analysis forms the basis for a specific investment plan for the user.

[0270] Step 4:

[0271] The server uses the analysis results to generate an optimal investment plan through a generated AI model. This process takes into account the user's financial situation and investment goals, formulating a strategy suitable for long-term wealth building. The generated plan is then communicated to the user in the next step.

[0272] Step 5:

[0273] The terminal notifies the user of the investment plan sent from the server as output. Specifically, the terminal screen displays the detailed plan. The user can review this and adjust the proposed investment products and monthly investment amounts.

[0274] Step 6:

[0275] Users send feedback on the plan back to the server as input via their terminal. This feedback includes information such as how the user evaluates the plan and what adjustments they would like to see made.

[0276] Step 7:

[0277] The server re-evaluates the plan using user feedback as input and adjusts the plan by retraining the system's generating AI model. Specifically, it analyzes the feedback, performs new data calculations as needed, and generates a more appropriate investment strategy again. The adjusted plan is then provided to the user again, resulting in dynamic optimization of asset building.

[0278] (Application Example 1)

[0279] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0280] In modern times, it is difficult for individual users to accurately grasp their own financial situations and formulate effective asset formation plans. In particular, specialized knowledge and time are required to derive specific investment strategies for achieving future asset goals. In response to this problem, there is a demand for providing efficient and individually suitable asset formation plans.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0282] In this invention, the server includes means for acquiring financial information from a user, means for generating a plan for achieving the user's future asset goals based on the financial information, means for notifying the user of the generated plan, and means for analyzing the user's financial information and providing an asset formation plan operating within an electronic payment service. As a result, the user can receive an optimal investment strategy without much effort and can efficiently form assets.

[0283] A "user" is an individual or a corporation that provides financial information and receives an asset formation plan based on that information.

[0284] "Financial information" is data related to finance such as a user's consumption amount, investment situation, age, and asset goals.

[0285] An "asset goal" is a financial goal that a user desires to achieve in the future.

[0286] A "plan" is an investment strategy for achieving an asset goal generated based on a user's financial information.

[0287] "Analysis" is the process of thoroughly examining acquired financial information, and is carried out to find patterns in the data.

[0288] An "investment strategy" is a plan that includes selecting financial products and adjusting investment amounts necessary to achieve asset goals.

[0289] "Feedback" refers to opinions and evaluations of the plan provided by the user, and is information used to revise the plan as needed.

[0290] An "electronic payment service" is an online system that manages and processes the financial information of individual users and provides them with asset building plans.

[0291] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user.

[0292] The server is a Python program responsible for collecting and analyzing financial information provided by users. It uses the Scikit-learn library to build machine learning models and develop future wealth building plans based on users' spending patterns and investment status. Specifically, it analyzes data using linear regression models to generate optimal investment strategies for achieving users' wealth goals. The server stores this information in a database and also manages feedback for adjusting the plan.

[0293] The terminal functions as a user interface and includes applications that run on a smartphone. Through this terminal, users can receive and review plan notifications from the server. At this stage, users can select investment products and adjust investment amounts. Furthermore, they can provide feedback on the plan, which is then sent back to the server.

[0294] Users can efficiently and easily build their assets based on the plans provided by the server. For example, if a 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year, and the server determines that their projected future pension amount will not reach their target, they will be offered suggestions for additional investment or a change in investment products. In this way, users can easily receive an optimal asset plan.

[0295] An example of a prompt would be, "I am 30 years old with an annual income of 8 million yen, spends 50,000 yen per month, and invests 500,000 yen per year. Please provide advice on how to achieve assets of 30 million yen by age 60 under these conditions." Using this prompt, the server generates a customized investment strategy for each user.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server collects the user's financial information. Input data such as the user's spending, investment status, age, and asset goals is required. This data is provided by the user, and the server stores it in a database. Specifically, financial information is transmitted from the user's smartphone or other input devices, and the server receives it in real time.

[0299] Step 2:

[0300] The server analyzes the collected financial information. The input here is the financial data collected in Step 1. The server uses the Scikit-learn LinearRegression model to perform the analysis and create a predictive model for the data. Specifically, the server passes the financial data through a machine learning algorithm, analyzes the data patterns, and evaluates the user's future asset attainment probability.

[0301] Step 3:

[0302] The server generates an asset formation plan based on the analysis results. The input is the result of the prediction model obtained in step 2. Based on this result, the server creates a plan to propose an optimal investment strategy towards the user's asset goal. As a specific operation, the server uses a generation AI model to determine the optimal investment products and asset allocation, and formulates a plan.

[0303] Step 4:

[0304] Notify the user of the plan generated through the terminal. The input is the asset formation plan generated in step 3. The terminal displays this plan to the user and provides detailed information. As a specific operation, a notification is sent to the user's smartphone, and the user can check the investment strategy via the app.

[0305] Step 5:

[0306] Provide feedback on the plan provided to the user. The input is the asset formation plan received in step 4. The user makes adjustments to the investment amount and products, and sends the feedback to the server via the terminal. As a specific operation, the user reviews the plan in the app and supports the necessary adjustments to send feedback.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions. ...

[0308] The system of this invention is configured to collect the user's financial information and provide an asset formation plan that also takes emotions into account. The system mainly consists of three main elements: an emotion engine, a terminal for obtaining the user's financial information, and a server for analyzing data and generating a plan.

[0309] Users input financial information using their own devices. This information includes income, consumption, investments, age, and asset goals. Users can also provide their own emotional data simultaneously through the emotion recognition function built into their devices. The emotion engine analyzes the input voice and facial expression data to identify the user's current emotional state.

[0310] The server stores financial and emotional data received from the terminal and analyzes it using machine learning models. Based on the analysis results, it generates an asset building plan that reflects the user's emotions. For example, if the emotional data indicates anxiety, it can suggest a low-risk, stable investment plan. Conversely, if positive emotions are indicated, it can suggest a more advantageous investment plan, even if it involves slightly higher risk.

[0311] The generated plan is sent back to the device and notified to the user. The user can review the plan and customize its contents as needed. During this time, the emotion engine continuously monitors the user's emotional changes and provides feedback to the server. The plan is automatically adjusted in response to these emotional changes, resulting in the user receiving the most suitable suggestions.

[0312] For example, if a user is feeling stressed when determining suitable investment strategies or savings plans to achieve future asset goals, the plan can be simplified and modified to provide a sense of security. In this way, the present invention provides an asset building approach that takes user emotions into consideration, enabling the implementation of more personalized financial services.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] Users input financial information such as income, consumption, investment, age, and asset goals using their devices. They also input current emotional data into the devices through voice and facial expressions using emotion recognition technology.

[0316] Step 2:

[0317] The terminal transmits financial information and emotional data entered by the user to the server. It is desirable to encrypt the data before transmission to prevent leakage.

[0318] Step 3:

[0319] The server stores the received financial information and sentiment data in a database. It then compares the stored data with historical data and updates it as needed.

[0320] Step 4:

[0321] The server inputs stored data into a machine learning model to analyze user consumption patterns, investment tendencies, and sentiment data. This analysis derives appropriate scenarios for achieving future pension amounts and asset goals.

[0322] Step 5:

[0323] Based on the analysis results, the server generates an investment plan that takes into account the user's emotional state. Specifically, if the user is feeling anxious, it designs a plan with low risk; if the user is feeling secure, it designs a plan with some risk.

[0324] Step 6:

[0325] The server sends the generated investment plan back to the terminal. The plan includes a monthly target investment amount, recommended financial instruments, and a risk level tailored to your emotions.

[0326] Step 7:

[0327] The device displays the received investment plan to the user in a visually easy-to-understand format. The user reviews the plan details and decides whether to take action based on them.

[0328] Step 8:

[0329] After reviewing the investment plan, users can send feedback to the server via their device. This feedback can include changes in their feelings and opinions on the plan.

[0330] Step 9:

[0331] The server receives feedback from the user, updates sentiment data, and readjusts the investment plan as needed. The readjusted plan is sent back to the device and notified to the user as a new proposal.

[0332] (Example 2)

[0333] 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".

[0334] Traditional asset building systems only provide plans based on the user's financial information and fail to take the user's emotional state into consideration. This creates a challenge in that users may not receive appropriate investment strategies or financial plans even when they are emotionally unstable. Furthermore, existing systems lack mechanisms to dynamically update plans based on user feedback.

[0335] 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.

[0336] In this invention, the server includes means for acquiring financial and emotional information from the user, means for generating a plan based on the financial and emotional information, means for adjusting and notifying the user of the generated plan according to the user's emotional state, and means for dynamically updating the plan based on user feedback. This makes it possible to provide an asset building plan that is tailored to the user's emotional state and to realize financial services optimized for each individual user.

[0337] "User" refers to anyone who uses this system to provide financial or emotional information.

[0338] "Financial information" refers to data about a user's economic situation, such as income, expenses, assets, and liabilities.

[0339] "Emotional information" refers to data that indicates a user's emotional state, and is collected through information such as voice and facial expressions.

[0340] "Plan" refers to specific asset building proposals generated based on the user's financial and emotional information.

[0341] "Feedback" refers to information about user reactions and requests regarding the plan they have provided.

[0342] "Emotion recognition technology" refers to technology that analyzes a user's voice and facial expression data to determine their emotional state.

[0343] This invention is a system that effectively utilizes users' financial and emotional information to provide individually optimized asset building plans. The following describes specific embodiments for carrying out this invention.

[0344] Users input financial information using their own devices. These devices include electronic devices such as tablets and smartphones, into which data such as income, expenses, assets, and liabilities are entered. Furthermore, to input emotional information, the devices are equipped with cameras and microphones, and software that recognizes emotions from voice and facial expressions is installed. This allows the user's current emotional state to be captured in real time.

[0345] The device transmits this information to the server. It is desirable that encryption technologies and communication protocols (e.g., SSL / TLS) be used to protect data security during this process. The server stores the received financial and emotional information in a database. The stored information is analyzed using generative AI models such as machine learning. This generates an optimal asset building plan that combines the user's financial situation and emotional state.

[0346] The generated plan is sent back to the user's device and the user is notified. For example, if the emotional information indicates the user is "anxious," the server will make adjustments such as suggesting low-risk asset management. Another example of a prompt message could be, "Please recalculate the plan to reduce the user's anxiety." The device continuously collects the user's responses and feedback, and the server dynamically updates the plan accordingly. This cycle enables suggestions that are tailored to the user's emotional state.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] Users input financial and emotional information using a device. Specifically, they input information such as income, expenses, assets, and liabilities in text format on the device's input screen. Emotional information is provided by capturing voice and facial expressions via the camera and microphone. The entered data is temporarily stored on the device.

[0350] Step 2:

[0351] The device transmits acquired financial and emotional information to the server. The transmitted data is encrypted and securely communicated using protocols such as SSL / TLS. This data includes financial information and emotional status, along with the user ID.

[0352] Step 3:

[0353] The server stores received financial and sentiment information in a database. The stored data is then analyzed using machine learning models. This analysis includes trend analysis of financial information and evaluation of sentiment information. As an output of the analysis, a draft asset building plan tailored to the user's current situation is created.

[0354] Step 4:

[0355] The server uses a generative AI model to implement asset building plans generated based on financial and emotional data. This includes adjusting the plan to take the user's emotional state into account. For example, if the emotional data indicates "anxiety," a low-risk investment plan will be suggested. The specific details of the plan are then determined.

[0356] Step 5:

[0357] The server sends the generated asset building plan to the terminal. The plan is transmitted in a format that is easy for the user to understand (for example, in summarized text or graph format). This includes an explanation of the plan and recommended actions.

[0358] Step 6:

[0359] Users review their asset building plans on their devices and provide feedback as needed. The devices provide an interface for users to input their thoughts and requests for changes to the plan. This feedback is important because it will be reflected in the newly generated plan.

[0360] Step 7:

[0361] The device sends user feedback and new sentiment data to the server. The server re-analyzes this information and dynamically updates the plan. The new plan is sent back to the user, and the same flow continues thereafter.

[0362] (Application Example 2)

[0363] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0364] In modern society, anxiety and mental stress related to users' financial activities are major obstacles to wealth creation. While conventional systems could generate plans based on financial data, they struggled to provide wealth creation plans that considered users' emotions. Therefore, there is a need for a system that comprehensively considers financial information and users' mental state to provide more accurate feedback.

[0365] 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.

[0366] In this invention, the server includes means for acquiring financial data from a user, means for analyzing the user's emotional state using emotion recognition technology, and means for generating a plan to achieve the user's future asset goals based on the financial data and emotional state, and adjusting it according to the emotional state. This makes it possible to provide a flexible and personalized asset building plan that takes the user's emotional state into consideration.

[0367] "Financial data" refers to information about a user's income, consumption, investment status, and asset goals.

[0368] "Emotional state" refers to the user's mental or emotional condition as analyzed by emotion recognition technology.

[0369] "Emotion recognition technology" is a technology that analyzes voice and facial expression data to identify a user's emotions.

[0370] An "asset building plan" is a plan designed to achieve future asset goals based on the user's financial data and emotional state.

[0371] A "server" is an information processing device that collects and analyzes financial data and emotional states from users, and generates and adjusts asset formation plans.

[0372] This invention is a system that provides a personalized asset building plan based on the user's financial data and emotional state. The system consists of the user's terminal, an emotion engine incorporating emotion recognition technology, and a server that analyzes the data.

[0373] Users input financial and emotional data using their own devices. Financial information includes income, consumption, investment, age, and asset goals. Typically, smartphones or other digital devices are used as the devices. Emotional data is acquired through the device's camera and microphone and processed by an emotion engine. Emotion recognition technologies such as OpenCV and TensorFlow are utilized here.

[0374] The server stores financial and emotional data received from users and generates asset building plans based on this data. The generated plans are personalized, taking into account the user's emotional state, using machine learning models. Based on financial information and emotional state, it can suggest investment plans with lower or higher risk. Machine learning libraries such as scikit-learn in Python are commonly used.

[0375] As a concrete example, when a user makes a purchase at a commercial facility, the terminal could recognize the user's facial expressions and, if the user is feeling anxious, display advice on sound spending management on the screen. This would allow the user to review their financial plan in real time in accordance with their emotions. An example of an input prompt to the generating AI model could be: "Generate feedback notifications to help manage spending based on the user's financial information and emotional data."

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] Users input financial information using a terminal. This input data includes income, consumption, investment information, age, and asset goals. This data is stored on the terminal as foundational information for creating future wealth-building plans. Detailed field input is performed by the user through the terminal's input interface.

[0379] Step 2:

[0380] The device uses its camera and microphone to acquire user emotion data. The acquired video and audio data is then processed by an emotion engine. Emotion recognition technologies such as OpenCV and TensorFlow are applied to analyze the user's mental state, quantify it, and send it to the server.

[0381] Step 3:

[0382] The server receives financial information and sentiment data sent from the terminal. The server stores this input data and stores it in a database. This data is analyzed by a machine learning model to derive intermediate results for generating the user's asset building plan. Data analysis is performed using scikit-learn in languages ​​such as Python.

[0383] Step 4:

[0384] The server uses a generative AI model to generate an asset building plan based on the input financial information and emotional data. If the emotional data indicates risk aversion, a low-risk investment plan is formulated. The generated plan is best suited to the user's emotional state. This plan is generated as a prompt message.

[0385] Step 5:

[0386] The server sends the generated asset formation plan to the terminal and prepares to notify the user. The terminal generates a notification based on the plan and displays it on the user's screen. At this point, the user can review the plan and send feedback as needed.

[0387] Step 6:

[0388] When a user enters feedback into their device, the device sends the feedback to the server. The server uses the feedback information and ongoing sentiment data to update and adapt the asset building plan. The revised plan is then sent back to the device and presented to the user as a new proposal.

[0389] 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.

[0390] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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 those described above. 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 shown 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.

[0391] 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.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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".

[0405] The system according to this invention has a configuration for providing an optimal asset formation plan to individual users. The main components are a terminal for collecting the user's financial information, a server for analyzing the received information, and an interface for notifying the user of the plan.

[0406] The server stores financial information received from the user's terminal in a database. This information includes monthly spending, current investment status, age, and future asset goals. The server uses machine learning models to analyze this information and predict the user's future pension amount. Furthermore, based on this prediction and the user's asset goals, it generates an optimal investment plan.

[0407] The terminal notifies the user of the generated plan and displays the detailed plan on the screen. The user can review the displayed plan and adjust the recommended investment products and monthly investment amount as needed. Furthermore, user feedback is sent back to the server, and the plan is adjusted as necessary.

[0408] For example, if a 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year, the server analyzes their current spending and investment patterns. If the analysis predicts that they will not reach their target pension amount by age 65, it suggests specific measures such as increasing their investment amount. This suggestion is communicated to the user via their device, allowing them to efficiently manage their asset building.

[0409] This system allows users to quickly create a personalized asset building plan, helping them gain financial security for the future.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] Users use their own devices to input their current financial information (such as spending history, investment information, age, and asset goals) and send it to the server.

[0413] Step 2:

[0414] The terminal reliably transfers the user's financial information to the server.

[0415] Step 3:

[0416] The server stores the received financial information in a database and compares and updates it with past data.

[0417] Step 4:

[0418] The server feeds stored financial information into a machine learning model to analyze consumption and investment patterns.

[0419] Step 5:

[0420] Based on the analysis results, the server predicts future pension amounts and asset growth, and generates an optimal investment plan. This plan includes monthly investment amounts, recommended investment products, and risk assessments.

[0421] Step 6:

[0422] The server sends the generated investment plan to the terminal.

[0423] Step 7:

[0424] The terminal notifies the user of the received investment plan and displays the details on the screen.

[0425] Step 8:

[0426] Users review the plan through the screen and select their investment amount and products according to the recommendations. They can also send any feedback regarding the plan to the server via their device.

[0427] Step 9:

[0428] The server receives feedback from the user and readjusts the investment plan as needed. The readjusted plan is then sent back to the user.

[0429] (Example 1)

[0430] 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."

[0431] To provide each user with an optimal asset building plan, a system is needed that creates accurate investment strategies tailored to the user's financial situation and future goals, and efficiently notifies and manages them. Furthermore, it is necessary to dynamically and flexibly adjust the plan by quickly incorporating user feedback.

[0432] 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.

[0433] In this invention, the server includes means for acquiring financial data from the user, means for generating a plan to achieve the user's asset goals using a machine learning model, means for notifying the user of the plan via their terminal for the user to review and adjust, and means for retraining the generated AI model and adjusting the plan. This makes it possible to generate and adjust investment plans tailored to each individual user, thereby improving the efficiency and flexibility of asset building.

[0434] A "user" is an individual or legal entity that utilizes the system, provides financial data, and receives a personalized asset building plan.

[0435] "Financial data" refers to information related to wealth building, such as a user's monthly spending, investment status, age, and future asset goals.

[0436] A "machine learning model" is an algorithm used to analyze data and perform predictions and optimizations, and in this invention, it is used specifically for generating asset formation plans.

[0437] "Asset goals" refer to specific financial objectives that users hope to achieve in the future, and include things like pension amounts.

[0438] A "plan" is an investment strategy generated based on the user's financial data to efficiently advance wealth creation.

[0439] A "terminal" is a device used by users to access the system, input financial data, and review plans.

[0440] A "generative AI model" is an artificial intelligence system that retrains itself based on data and feedback obtained from users to provide an optimal asset formation plan.

[0441] This invention is an information processing system for providing individual users with optimal asset formation plans. Its main components are a terminal for collecting user financial data and a server for analyzing the received information and generating plans. The operation of these components is described in detail below.

[0442] The device features an interface for users to input their financial data. This interface is provided through a web browser or mobile application. Users can input personal data such as their monthly spending, current investment status, and age.

[0443] The server receives data sent from the terminal and stores it in a central database. This database uses a database management system such as MySQL or PostgreSQL. The stored data is analyzed by machine learning models to predict the user's future pension amount and the likelihood of achieving their asset goals. This analysis utilizes machine learning libraries such as TensorFlow and PyTorch.

[0444] The server uses a generative AI model to generate an optimal investment plan based on the user's data. This model takes into account the user's current asset situation and goals, and has the capability to propose an ideal long-term investment strategy.

[0445] The generated plan is sent to the device and notified to the user. The user can review the detailed plan on the device screen and adjust investment products and monthly investment amounts as needed. This feedback is then sent back to the server, and the plan is adjusted again as necessary.

[0446] As a concrete example, consider a 30-year-old user who spends 50,000 yen per month and invests 500,000 yen per year. Based on this information, the server analyzes the user's spending and investment patterns and generates an optimal investment plan to achieve the pension amount the user will need by age 65. The plan may include increasing the required investment amount or revising asset allocation. This suggestion is provided to the user via their device.

[0447] An example of a prompt for a generating AI model might be: "A 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year. They aim to have a pension of 50 million yen by age 65. Please suggest the best investment plan for this user." Based on this prompt, the AI ​​model will provide a customized investment strategy tailored to the user's lifestyle and financial goals.

[0448] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0449] Step 1:

[0450] The terminal receives financial data from the user as input. Specifically, the user inputs data such as monthly spending, current investment status, age, and future asset goals through a dedicated application or web interface. This input data is sent to the server and used in the next processing step.

[0451] Step 2:

[0452] The server stores the financial data received from the terminal as input into a database. Specifically, the server uses a database management system such as MySQL or PostgreSQL to record the received user data in a structured format. The stored data is then used for subsequent analysis and plan generation.

[0453] Step 3:

[0454] The server analyzes stored financial data as input to a machine learning model. To predict the user's future pension amount and the likelihood of achieving their asset goals, the server performs analysis using libraries such as TensorFlow and PyTorch. The output obtained from this analysis forms the basis for a specific investment plan for the user.

[0455] Step 4:

[0456] The server uses the analysis results to generate an optimal investment plan through a generated AI model. This process takes into account the user's financial situation and investment goals, formulating a strategy suitable for long-term wealth building. The generated plan is then communicated to the user in the next step.

[0457] Step 5:

[0458] The terminal notifies the user of the investment plan sent from the server as output. Specifically, the terminal screen displays the detailed plan. The user can review this and adjust the proposed investment products and monthly investment amounts.

[0459] Step 6:

[0460] Users send feedback on the plan back to the server as input via their terminal. This feedback includes information such as how the user evaluates the plan and what adjustments they would like to see made.

[0461] Step 7:

[0462] The server re-evaluates the plan using user feedback as input and adjusts the plan by retraining the system's generating AI model. Specifically, it analyzes the feedback, performs new data calculations as needed, and generates a more appropriate investment strategy again. The adjusted plan is then provided to the user again, resulting in dynamic optimization of asset building.

[0463] (Application Example 1)

[0464] 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."

[0465] In today's world, it is difficult for individual users to accurately understand their own financial situation and create effective asset building plans. In particular, deriving specific investment strategies to achieve future asset goals requires specialized knowledge and time. To address this problem, there is a need to provide efficient and individually tailored asset building plans.

[0466] 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.

[0467] In this invention, the server includes means for acquiring financial information from a user, means for generating a plan to achieve the user's future asset goals based on said financial information, means for notifying the user of the generated plan, and means for analyzing the user's financial information and providing an asset formation plan that operates within an electronic payment service. This enables the user to receive an optimal investment strategy without any hassle and to efficiently build their assets.

[0468] A "user" is an individual or legal entity that provides financial information and receives an asset building plan based on that information.

[0469] "Financial information" refers to financially related data such as a user's spending, investment status, age, and asset goals.

[0470] "Asset goals" refer to the financial objectives that a user hopes to achieve in the future.

[0471] A "plan" is an investment strategy generated based on the user's financial information to achieve their asset goals.

[0472] "Analysis" is the process of thoroughly examining acquired financial information, and is carried out to find patterns in the data.

[0473] An "investment strategy" is a plan that includes selecting financial products and adjusting investment amounts necessary to achieve asset goals.

[0474] "Feedback" refers to opinions and evaluations of the plan provided by the user, and is information used to revise the plan as needed.

[0475] An "electronic payment service" is an online system that manages and processes the financial information of individual users and provides them with asset building plans.

[0476] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user.

[0477] The server is a Python program responsible for collecting and analyzing financial information provided by users. It uses the Scikit-learn library to build machine learning models and develop future wealth building plans based on users' spending patterns and investment status. Specifically, it analyzes data using linear regression models to generate optimal investment strategies for achieving users' wealth goals. The server stores this information in a database and also manages feedback for adjusting the plan.

[0478] The terminal functions as a user interface and includes applications that run on a smartphone. Through this terminal, users can receive and review plan notifications from the server. At this stage, users can select investment products and adjust investment amounts. Furthermore, they can provide feedback on the plan, which is then sent back to the server.

[0479] Users can efficiently and easily build their assets based on the plans provided by the server. For example, if a 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year, and the server determines that their projected future pension amount will not reach their target, they will be offered suggestions for additional investment or a change in investment products. In this way, users can easily receive an optimal asset plan.

[0480] An example of a prompt would be, "I am 30 years old with an annual income of 8 million yen, spends 50,000 yen per month, and invests 500,000 yen per year. Please provide advice on how to achieve assets of 30 million yen by age 60 under these conditions." Using this prompt, the server generates a customized investment strategy for each user.

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The server collects the user's financial information. Input data such as the user's spending, investment status, age, and asset goals is required. This data is provided by the user, and the server stores it in a database. Specifically, financial information is transmitted from the user's smartphone or other input devices, and the server receives it in real time.

[0484] Step 2:

[0485] The server analyzes the collected financial information. The input here is the financial data collected in Step 1. The server uses the Scikit-learn LinearRegression model to perform the analysis and create a predictive model for the data. Specifically, the server passes the financial data through a machine learning algorithm, analyzes the data patterns, and evaluates the user's future asset attainment probability.

[0486] Step 3:

[0487] The server generates an asset building plan based on the analysis results. The input is the result of the predictive model obtained in step 2. Based on this result, the server creates a plan to propose the optimal investment strategy for the user's asset goals. Specifically, the server uses the generated AI model to determine the optimal investment products and asset allocation, and formulates the plan.

[0488] Step 4:

[0489] The system notifies the user of the plan generated via the device. The input is the asset building plan generated in step 3. The device displays this plan to the user and provides detailed information. Specifically, a notification is sent to the user's smartphone, and the user can check the investment strategy via the app.

[0490] Step 5:

[0491] The user provides feedback on the provided plan. The input is the asset building plan received in step 4. The user adjusts the investment amount and products and sends feedback to the server via their device. Specifically, the user reviews the plan in the app, makes necessary adjustments, and sends feedback.

[0492] 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.

[0493] The system of this invention is configured to collect users' financial information and provide asset building plans that also take emotions into consideration. The system mainly consists of three main elements: an emotion engine, a terminal for acquiring users' financial information, and a server that analyzes the data and generates plans.

[0494] Users input financial information using their own devices. This information includes income, consumption, investments, age, and asset goals. Users can also provide their own emotional data simultaneously through the emotion recognition function built into their devices. The emotion engine analyzes the input voice and facial expression data to identify the user's current emotional state.

[0495] The server stores financial and emotional data received from the terminal and analyzes it using machine learning models. Based on the analysis results, it generates an asset building plan that reflects the user's emotions. For example, if the emotional data indicates anxiety, it can suggest a low-risk, stable investment plan. Conversely, if positive emotions are indicated, it can suggest a more advantageous investment plan, even if it involves slightly higher risk.

[0496] The generated plan is sent back to the device and notified to the user. The user can review the plan and customize its contents as needed. During this time, the emotion engine continuously monitors the user's emotional changes and provides feedback to the server. The plan is automatically adjusted in response to these emotional changes, resulting in the user receiving the most suitable suggestions.

[0497] For example, if a user is feeling stressed when determining suitable investment strategies or savings plans to achieve future asset goals, the plan can be simplified and modified to provide a sense of security. In this way, the present invention provides an asset building approach that takes user emotions into consideration, enabling the implementation of more personalized financial services.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] Users input financial information such as income, consumption, investment, age, and asset goals using their devices. They also input current emotional data into the devices through voice and facial expressions using emotion recognition technology.

[0501] Step 2:

[0502] The terminal transmits financial information and emotional data entered by the user to the server. It is desirable to encrypt the data before transmission to prevent leakage.

[0503] Step 3:

[0504] The server stores the received financial information and sentiment data in a database. It then compares the stored data with historical data and updates it as needed.

[0505] Step 4:

[0506] The server inputs stored data into a machine learning model to analyze user consumption patterns, investment tendencies, and sentiment data. This analysis derives appropriate scenarios for achieving future pension amounts and asset goals.

[0507] Step 5:

[0508] Based on the analysis results, the server generates an investment plan that takes into account the user's emotional state. Specifically, if the user is feeling anxious, it designs a plan with low risk; if the user is feeling secure, it designs a plan with some risk.

[0509] Step 6:

[0510] The server sends the generated investment plan back to the terminal. The plan includes a monthly target investment amount, recommended financial instruments, and a risk level tailored to your emotions.

[0511] Step 7:

[0512] The device displays the received investment plan to the user in a visually easy-to-understand format. The user reviews the plan details and decides whether to take action based on them.

[0513] Step 8:

[0514] After reviewing the investment plan, users can send feedback to the server via their device. This feedback can include changes in their feelings and opinions on the plan.

[0515] Step 9:

[0516] The server receives feedback from the user, updates sentiment data, and readjusts the investment plan as needed. The readjusted plan is sent back to the device and notified to the user as a new proposal.

[0517] (Example 2)

[0518] 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."

[0519] Traditional asset building systems only provide plans based on the user's financial information and fail to take the user's emotional state into consideration. This creates a challenge in that users may not receive appropriate investment strategies or financial plans even when they are emotionally unstable. Furthermore, existing systems lack mechanisms to dynamically update plans based on user feedback.

[0520] 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.

[0521] In this invention, the server includes means for acquiring financial and emotional information from the user, means for generating a plan based on the financial and emotional information, means for adjusting and notifying the user of the generated plan according to the user's emotional state, and means for dynamically updating the plan based on user feedback. This makes it possible to provide an asset building plan that is tailored to the user's emotional state and to realize financial services optimized for each individual user.

[0522] "User" refers to anyone who uses this system to provide financial or emotional information.

[0523] "Financial information" refers to data about a user's economic situation, such as income, expenses, assets, and liabilities.

[0524] "Emotional information" refers to data that indicates a user's emotional state, and is collected through information such as voice and facial expressions.

[0525] "Plan" refers to specific asset building proposals generated based on the user's financial and emotional information.

[0526] "Feedback" refers to information about user reactions and requests regarding the plan they have provided.

[0527] "Emotion recognition technology" refers to technology that analyzes a user's voice and facial expression data to determine their emotional state.

[0528] This invention is a system that effectively utilizes users' financial and emotional information to provide individually optimized asset building plans. The following describes specific embodiments for carrying out this invention.

[0529] Users input financial information using their own devices. These devices include electronic devices such as tablets and smartphones, into which data such as income, expenses, assets, and liabilities are entered. Furthermore, to input emotional information, the devices are equipped with cameras and microphones, and software that recognizes emotions from voice and facial expressions is installed. This allows the user's current emotional state to be captured in real time.

[0530] The device transmits this information to the server. It is desirable that encryption technologies and communication protocols (e.g., SSL / TLS) be used to protect data security during this process. The server stores the received financial and emotional information in a database. The stored information is analyzed using generative AI models such as machine learning. This generates an optimal asset building plan that combines the user's financial situation and emotional state.

[0531] The generated plan is sent back to the user's device and the user is notified. For example, if the emotional information indicates the user is "anxious," the server will make adjustments such as suggesting low-risk asset management. Another example of a prompt message could be, "Please recalculate the plan to reduce the user's anxiety." The device continuously collects the user's responses and feedback, and the server dynamically updates the plan accordingly. This cycle enables suggestions that are tailored to the user's emotional state.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] Users input financial and emotional information using a device. Specifically, they input information such as income, expenses, assets, and liabilities in text format on the device's input screen. Emotional information is provided by capturing voice and facial expressions via the camera and microphone. The entered data is temporarily stored on the device.

[0535] Step 2:

[0536] The device transmits acquired financial and emotional information to the server. The transmitted data is encrypted and securely communicated using protocols such as SSL / TLS. This data includes financial information and emotional status, along with the user ID.

[0537] Step 3:

[0538] The server stores received financial and sentiment information in a database. The stored data is then analyzed using machine learning models. This analysis includes trend analysis of financial information and evaluation of sentiment information. As an output of the analysis, a draft asset building plan tailored to the user's current situation is created.

[0539] Step 4:

[0540] The server uses a generative AI model to implement asset building plans generated based on financial and emotional data. This includes adjusting the plan to take the user's emotional state into account. For example, if the emotional data indicates "anxiety," a low-risk investment plan will be suggested. The specific details of the plan are then determined.

[0541] Step 5:

[0542] The server sends the generated asset building plan to the terminal. The plan is transmitted in a format that is easy for the user to understand (for example, in summarized text or graph format). This includes an explanation of the plan and recommended actions.

[0543] Step 6:

[0544] Users review their asset building plans on their devices and provide feedback as needed. The devices provide an interface for users to input their thoughts and requests for changes to the plan. This feedback is important because it will be reflected in the newly generated plan.

[0545] Step 7:

[0546] The device sends user feedback and new sentiment data to the server. The server re-analyzes this information and dynamically updates the plan. The new plan is sent back to the user, and the same flow continues thereafter.

[0547] (Application Example 2)

[0548] 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."

[0549] In modern society, anxiety and mental stress related to users' financial activities are major obstacles to wealth creation. While conventional systems could generate plans based on financial data, they struggled to provide wealth creation plans that considered users' emotions. Therefore, there is a need for a system that comprehensively considers financial information and users' mental state to provide more accurate feedback.

[0550] 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.

[0551] In this invention, the server includes means for acquiring financial data from a user, means for analyzing the user's emotional state using emotion recognition technology, and means for generating a plan to achieve the user's future asset goals based on the financial data and emotional state, and adjusting it according to the emotional state. This makes it possible to provide a flexible and personalized asset building plan that takes the user's emotional state into consideration.

[0552] "Financial data" refers to information about a user's income, consumption, investment status, and asset goals.

[0553] "Emotional state" refers to the user's mental or emotional condition as analyzed by emotion recognition technology.

[0554] "Emotion recognition technology" is a technology that analyzes voice and facial expression data to identify a user's emotions.

[0555] An "asset building plan" is a plan designed to achieve future asset goals based on the user's financial data and emotional state.

[0556] A "server" is an information processing device that collects and analyzes financial data and emotional states from users, and generates and adjusts asset formation plans.

[0557] This invention is a system that provides a personalized asset building plan based on the user's financial data and emotional state. The system consists of the user's terminal, an emotion engine incorporating emotion recognition technology, and a server that analyzes the data.

[0558] Users input financial and emotional data using their own devices. Financial information includes income, consumption, investment, age, and asset goals. Typically, smartphones or other digital devices are used as the devices. Emotional data is acquired through the device's camera and microphone and processed by an emotion engine. Emotion recognition technologies such as OpenCV and TensorFlow are utilized here.

[0559] The server stores financial and emotional data received from users and generates asset building plans based on this data. The generated plans are personalized, taking into account the user's emotional state, using machine learning models. Based on financial information and emotional state, it can suggest investment plans with lower or higher risk. Machine learning libraries such as scikit-learn in Python are commonly used.

[0560] As a concrete example, when a user makes a purchase at a commercial facility, the terminal could recognize the user's facial expressions and, if the user is feeling anxious, display advice on sound spending management on the screen. This would allow the user to review their financial plan in real time in accordance with their emotions. An example of an input prompt to the generating AI model could be: "Generate feedback notifications to help manage spending based on the user's financial information and emotional data."

[0561] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0562] Step 1:

[0563] Users input financial information using a terminal. This input data includes income, consumption, investment information, age, and asset goals. This data is stored on the terminal as foundational information for creating future wealth-building plans. Detailed field input is performed by the user through the terminal's input interface.

[0564] Step 2:

[0565] The device uses its camera and microphone to acquire user emotion data. The acquired video and audio data is then processed by an emotion engine. Emotion recognition technologies such as OpenCV and TensorFlow are applied to analyze the user's mental state, quantify it, and send it to the server.

[0566] Step 3:

[0567] The server receives financial information and sentiment data sent from the terminal. The server stores this input data and stores it in a database. This data is analyzed by a machine learning model to derive intermediate results for generating the user's asset building plan. Data analysis is performed using scikit-learn in languages ​​such as Python.

[0568] Step 4:

[0569] The server uses a generative AI model to generate an asset building plan based on the input financial information and emotional data. If the emotional data indicates risk aversion, a low-risk investment plan is formulated. The generated plan is best suited to the user's emotional state. This plan is generated as a prompt message.

[0570] Step 5:

[0571] The server sends the generated asset formation plan to the terminal and prepares to notify the user. The terminal generates a notification based on the plan and displays it on the user's screen. At this point, the user can review the plan and send feedback as needed.

[0572] Step 6:

[0573] When a user enters feedback into their device, the device sends the feedback to the server. The server uses the feedback information and ongoing sentiment data to update and adapt the asset building plan. The revised plan is then sent back to the device and presented to the user as a new proposal.

[0574] 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.

[0575] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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 those described above. 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 shown 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.

[0576] 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.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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".

[0591] The system according to this invention has a configuration for providing an optimal asset formation plan to individual users. The main components are a terminal for collecting the user's financial information, a server for analyzing the received information, and an interface for notifying the user of the plan.

[0592] The server stores financial information received from the user's terminal in a database. This information includes monthly spending, current investment status, age, and future asset goals. The server uses machine learning models to analyze this information and predict the user's future pension amount. Furthermore, based on this prediction and the user's asset goals, it generates an optimal investment plan.

[0593] The terminal notifies the user of the generated plan and displays the detailed plan on the screen. The user can review the displayed plan and adjust the recommended investment products and monthly investment amount as needed. Furthermore, user feedback is sent back to the server, and the plan is adjusted as necessary.

[0594] For example, if a 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year, the server analyzes their current spending and investment patterns. If the analysis predicts that they will not reach their target pension amount by age 65, it suggests specific measures such as increasing their investment amount. This suggestion is communicated to the user via their device, allowing them to efficiently manage their asset building.

[0595] This system allows users to quickly create a personalized asset building plan, helping them gain financial security for the future.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] Users use their own devices to input their current financial information (such as spending history, investment information, age, and asset goals) and send it to the server.

[0599] Step 2:

[0600] The terminal reliably transfers the user's financial information to the server.

[0601] Step 3:

[0602] The server stores the received financial information in a database and compares and updates it with past data.

[0603] Step 4:

[0604] The server feeds stored financial information into a machine learning model to analyze consumption and investment patterns.

[0605] Step 5:

[0606] Based on the analysis results, the server predicts future pension amounts and asset growth, and generates an optimal investment plan. This plan includes monthly investment amounts, recommended investment products, and risk assessments.

[0607] Step 6:

[0608] The server sends the generated investment plan to the terminal.

[0609] Step 7:

[0610] The terminal notifies the user of the received investment plan and displays the details on the screen.

[0611] Step 8:

[0612] Users review the plan through the screen and select their investment amount and products according to the recommendations. They can also send any feedback regarding the plan to the server via their device.

[0613] Step 9:

[0614] The server receives feedback from the user and readjusts the investment plan as needed. The readjusted plan is then sent back to the user.

[0615] (Example 1)

[0616] 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".

[0617] To provide each user with an optimal asset building plan, a system is needed that creates accurate investment strategies tailored to the user's financial situation and future goals, and efficiently notifies and manages them. Furthermore, it is necessary to dynamically and flexibly adjust the plan by quickly incorporating user feedback.

[0618] 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.

[0619] In this invention, the server includes means for acquiring financial data from the user, means for generating a plan to achieve the user's asset goals using a machine learning model, means for notifying the user of the plan via their terminal for the user to review and adjust, and means for retraining the generated AI model and adjusting the plan. This makes it possible to generate and adjust investment plans tailored to each individual user, thereby improving the efficiency and flexibility of asset building.

[0620] A "user" is an individual or legal entity that utilizes the system, provides financial data, and receives a personalized asset building plan.

[0621] "Financial data" refers to information related to wealth building, such as a user's monthly spending, investment status, age, and future asset goals.

[0622] A "machine learning model" is an algorithm used to analyze data and perform predictions and optimizations, and in this invention, it is used specifically for generating asset formation plans.

[0623] "Asset goals" refer to specific financial objectives that users hope to achieve in the future, and include things like pension amounts.

[0624] A "plan" is an investment strategy generated based on the user's financial data to efficiently advance wealth creation.

[0625] A "terminal" is a device used by users to access the system, input financial data, and review plans.

[0626] A "generative AI model" is an artificial intelligence system that retrains itself based on data and feedback obtained from users to provide an optimal asset formation plan.

[0627] This invention is an information processing system for providing individual users with optimal asset formation plans. Its main components are a terminal for collecting user financial data and a server for analyzing the received information and generating plans. The operation of these components is described in detail below.

[0628] The device features an interface for users to input their financial data. This interface is provided through a web browser or mobile application. Users can input personal data such as their monthly spending, current investment status, and age.

[0629] The server receives data sent from the terminal and stores it in a central database. This database uses a database management system such as MySQL or PostgreSQL. The stored data is analyzed by machine learning models to predict the user's future pension amount and the likelihood of achieving their asset goals. This analysis utilizes machine learning libraries such as TensorFlow and PyTorch.

[0630] The server uses a generative AI model to generate an optimal investment plan based on the user's data. This model takes into account the user's current asset situation and goals, and has the capability to propose an ideal long-term investment strategy.

[0631] The generated plan is sent to the device and notified to the user. The user can review the detailed plan on the device screen and adjust investment products and monthly investment amounts as needed. This feedback is then sent back to the server, and the plan is adjusted again as necessary.

[0632] As a concrete example, consider a 30-year-old user who spends 50,000 yen per month and invests 500,000 yen per year. Based on this information, the server analyzes the user's spending and investment patterns and generates an optimal investment plan to achieve the pension amount the user will need by age 65. The plan may include increasing the required investment amount or revising asset allocation. This suggestion is provided to the user via their device.

[0633] An example of a prompt for a generating AI model might be: "A 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year. They aim to have a pension of 50 million yen by age 65. Please suggest the best investment plan for this user." Based on this prompt, the AI ​​model will provide a customized investment strategy tailored to the user's lifestyle and financial goals.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The terminal receives financial data from the user as input. Specifically, the user inputs data such as monthly spending, current investment status, age, and future asset goals through a dedicated application or web interface. This input data is sent to the server and used in the next processing step.

[0637] Step 2:

[0638] The server stores the financial data received from the terminal as input into a database. Specifically, the server uses a database management system such as MySQL or PostgreSQL to record the received user data in a structured format. The stored data is then used for subsequent analysis and plan generation.

[0639] Step 3:

[0640] The server analyzes stored financial data as input to a machine learning model. To predict the user's future pension amount and the likelihood of achieving their asset goals, the server performs analysis using libraries such as TensorFlow and PyTorch. The output obtained from this analysis forms the basis for a specific investment plan for the user.

[0641] Step 4:

[0642] The server uses the analysis results to generate an optimal investment plan through a generated AI model. This process takes into account the user's financial situation and investment goals, formulating a strategy suitable for long-term wealth building. The generated plan is then communicated to the user in the next step.

[0643] Step 5:

[0644] The terminal notifies the user of the investment plan sent from the server as output. Specifically, the terminal screen displays the detailed plan. The user can review this and adjust the proposed investment products and monthly investment amounts.

[0645] Step 6:

[0646] Users send feedback on the plan back to the server as input via their terminal. This feedback includes information such as how the user evaluates the plan and what adjustments they would like to see made.

[0647] Step 7:

[0648] The server re-evaluates the plan using user feedback as input and adjusts the plan by retraining the system's generating AI model. Specifically, it analyzes the feedback, performs new data calculations as needed, and generates a more appropriate investment strategy again. The adjusted plan is then provided to the user again, resulting in dynamic optimization of asset building.

[0649] (Application Example 1)

[0650] 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".

[0651] In today's world, it is difficult for individual users to accurately understand their own financial situation and create effective asset building plans. In particular, deriving specific investment strategies to achieve future asset goals requires specialized knowledge and time. To address this problem, there is a need to provide efficient and individually tailored asset building plans.

[0652] 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.

[0653] In this invention, the server includes means for acquiring financial information from a user, means for generating a plan to achieve the user's future asset goals based on said financial information, means for notifying the user of the generated plan, and means for analyzing the user's financial information and providing an asset formation plan that operates within an electronic payment service. This enables the user to receive an optimal investment strategy without any hassle and to efficiently build their assets.

[0654] A "user" is an individual or legal entity that provides financial information and receives an asset building plan based on that information.

[0655] "Financial information" refers to financially related data such as a user's spending, investment status, age, and asset goals.

[0656] "Asset goals" refer to the financial objectives that a user hopes to achieve in the future.

[0657] A "plan" is an investment strategy generated based on the user's financial information to achieve their asset goals.

[0658] "Analysis" is the process of thoroughly examining acquired financial information, and is carried out to find patterns in the data.

[0659] An "investment strategy" is a plan that includes selecting financial products and adjusting investment amounts necessary to achieve asset goals.

[0660] "Feedback" refers to opinions and evaluations of the plan provided by the user, and is information used to revise the plan as needed.

[0661] An "electronic payment service" is an online system that manages and processes the financial information of individual users and provides them with asset building plans.

[0662] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user.

[0663] The server is a Python program responsible for collecting and analyzing financial information provided by users. It uses the Scikit-learn library to build machine learning models and develop future wealth building plans based on users' spending patterns and investment status. Specifically, it analyzes data using linear regression models to generate optimal investment strategies for achieving users' wealth goals. The server stores this information in a database and also manages feedback for adjusting the plan.

[0664] The terminal functions as a user interface and includes applications that run on a smartphone. Through this terminal, users can receive and review plan notifications from the server. At this stage, users can select investment products and adjust investment amounts. Furthermore, they can provide feedback on the plan, which is then sent back to the server.

[0665] Users can efficiently and easily build their assets based on the plans provided by the server. For example, if a 30-year-old user spends 50,000 yen per month and invests 500,000 yen per year, and the server determines that their projected future pension amount will not reach their target, they will be offered suggestions for additional investment or a change in investment products. In this way, users can easily receive an optimal asset plan.

[0666] An example of a prompt would be, "I am 30 years old with an annual income of 8 million yen, spends 50,000 yen per month, and invests 500,000 yen per year. Please provide advice on how to achieve assets of 30 million yen by age 60 under these conditions." Using this prompt, the server generates a customized investment strategy for each user.

[0667] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0668] Step 1:

[0669] The server collects the user's financial information. Input data such as the user's spending, investment status, age, and asset goals is required. This data is provided by the user, and the server stores it in a database. Specifically, financial information is transmitted from the user's smartphone or other input devices, and the server receives it in real time.

[0670] Step 2:

[0671] The server analyzes the collected financial information. The input here is the financial data collected in Step 1. The server uses the Scikit-learn LinearRegression model to perform the analysis and create a predictive model for the data. Specifically, the server passes the financial data through a machine learning algorithm, analyzes the data patterns, and evaluates the user's future asset attainment probability.

[0672] Step 3:

[0673] The server generates an asset building plan based on the analysis results. The input is the result of the predictive model obtained in step 2. Based on this result, the server creates a plan to propose the optimal investment strategy for the user's asset goals. Specifically, the server uses the generated AI model to determine the optimal investment products and asset allocation, and formulates the plan.

[0674] Step 4:

[0675] The system notifies the user of the plan generated via the device. The input is the asset building plan generated in step 3. The device displays this plan to the user and provides detailed information. Specifically, a notification is sent to the user's smartphone, and the user can check the investment strategy via the app.

[0676] Step 5:

[0677] The user provides feedback on the provided plan. The input is the asset building plan received in step 4. The user adjusts the investment amount and products and sends feedback to the server via their device. Specifically, the user reviews the plan in the app, makes necessary adjustments, and sends feedback.

[0678] 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.

[0679] The system of this invention is configured to collect users' financial information and provide asset building plans that also take emotions into consideration. The system mainly consists of three main elements: an emotion engine, a terminal for acquiring users' financial information, and a server that analyzes the data and generates plans.

[0680] Users input financial information using their own devices. This information includes income, consumption, investments, age, and asset goals. Users can also provide their own emotional data simultaneously through the emotion recognition function built into their devices. The emotion engine analyzes the input voice and facial expression data to identify the user's current emotional state.

[0681] The server stores financial and emotional data received from the terminal and analyzes it using machine learning models. Based on the analysis results, it generates an asset building plan that reflects the user's emotions. For example, if the emotional data indicates anxiety, it can suggest a low-risk, stable investment plan. Conversely, if positive emotions are indicated, it can suggest a more advantageous investment plan, even if it involves slightly higher risk.

[0682] The generated plan is sent back to the device and notified to the user. The user can review the plan and customize its contents as needed. During this time, the emotion engine continuously monitors the user's emotional changes and provides feedback to the server. The plan is automatically adjusted in response to these emotional changes, resulting in the user receiving the most suitable suggestions.

[0683] For example, if a user is feeling stressed when determining suitable investment strategies or savings plans to achieve future asset goals, the plan can be simplified and modified to provide a sense of security. In this way, the present invention provides an asset building approach that takes user emotions into consideration, enabling the implementation of more personalized financial services.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] Users input financial information such as income, consumption, investment, age, and asset goals using their devices. They also input current emotional data into the devices through voice and facial expressions using emotion recognition technology.

[0687] Step 2:

[0688] The terminal transmits financial information and emotional data entered by the user to the server. It is desirable to encrypt the data before transmission to prevent leakage.

[0689] Step 3:

[0690] The server stores the received financial information and sentiment data in a database. It then compares the stored data with historical data and updates it as needed.

[0691] Step 4:

[0692] The server inputs stored data into a machine learning model to analyze user consumption patterns, investment tendencies, and sentiment data. This analysis derives appropriate scenarios for achieving future pension amounts and asset goals.

[0693] Step 5:

[0694] Based on the analysis results, the server generates an investment plan that takes into account the user's emotional state. Specifically, if the user is feeling anxious, it designs a plan with low risk; if the user is feeling secure, it designs a plan with some risk.

[0695] Step 6:

[0696] The server sends the generated investment plan back to the terminal. The plan includes a monthly target investment amount, recommended financial instruments, and a risk level tailored to your emotions.

[0697] Step 7:

[0698] The device displays the received investment plan to the user in a visually easy-to-understand format. The user reviews the plan details and decides whether to take action based on them.

[0699] Step 8:

[0700] After reviewing the investment plan, users can send feedback to the server via their device. This feedback can include changes in their feelings and opinions on the plan.

[0701] Step 9:

[0702] The server receives feedback from the user, updates sentiment data, and readjusts the investment plan as needed. The readjusted plan is sent back to the device and notified to the user as a new proposal.

[0703] (Example 2)

[0704] 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".

[0705] Traditional asset building systems only provide plans based on the user's financial information and fail to take the user's emotional state into consideration. This creates a challenge in that users may not receive appropriate investment strategies or financial plans even when they are emotionally unstable. Furthermore, existing systems lack mechanisms to dynamically update plans based on user feedback.

[0706] 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.

[0707] In this invention, the server includes means for acquiring financial and emotional information from the user, means for generating a plan based on the financial and emotional information, means for adjusting and notifying the user of the generated plan according to the user's emotional state, and means for dynamically updating the plan based on user feedback. This makes it possible to provide an asset building plan that is tailored to the user's emotional state and to realize financial services optimized for each individual user.

[0708] "User" refers to anyone who uses this system to provide financial or emotional information.

[0709] "Financial information" refers to data about a user's economic situation, such as income, expenses, assets, and liabilities.

[0710] "Emotional information" refers to data that indicates a user's emotional state, and is collected through information such as voice and facial expressions.

[0711] "Plan" refers to specific asset building proposals generated based on the user's financial and emotional information.

[0712] "Feedback" refers to information about user reactions and requests regarding the plan they have provided.

[0713] "Emotion recognition technology" refers to technology that analyzes a user's voice and facial expression data to determine their emotional state.

[0714] This invention is a system that effectively utilizes users' financial and emotional information to provide individually optimized asset building plans. The following describes specific embodiments for carrying out this invention.

[0715] Users input financial information using their own devices. These devices include electronic devices such as tablets and smartphones, into which data such as income, expenses, assets, and liabilities are entered. Furthermore, to input emotional information, the devices are equipped with cameras and microphones, and software that recognizes emotions from voice and facial expressions is installed. This allows the user's current emotional state to be captured in real time.

[0716] The device transmits this information to the server. It is desirable that encryption technologies and communication protocols (e.g., SSL / TLS) be used to protect data security during this process. The server stores the received financial and emotional information in a database. The stored information is analyzed using generative AI models such as machine learning. This generates an optimal asset building plan that combines the user's financial situation and emotional state.

[0717] The generated plan is sent back to the user's device and the user is notified. For example, if the emotional information indicates the user is "anxious," the server will make adjustments such as suggesting low-risk asset management. Another example of a prompt message could be, "Please recalculate the plan to reduce the user's anxiety." The device continuously collects the user's responses and feedback, and the server dynamically updates the plan accordingly. This cycle enables suggestions that are tailored to the user's emotional state.

[0718] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0719] Step 1:

[0720] Users input financial and emotional information using a device. Specifically, they input information such as income, expenses, assets, and liabilities in text format on the device's input screen. Emotional information is provided by capturing voice and facial expressions via the camera and microphone. The entered data is temporarily stored on the device.

[0721] Step 2:

[0722] The device transmits acquired financial and emotional information to the server. The transmitted data is encrypted and securely communicated using protocols such as SSL / TLS. This data includes financial information and emotional status, along with the user ID.

[0723] Step 3:

[0724] The server stores received financial and sentiment information in a database. The stored data is then analyzed using machine learning models. This analysis includes trend analysis of financial information and evaluation of sentiment information. As an output of the analysis, a draft asset building plan tailored to the user's current situation is created.

[0725] Step 4:

[0726] The server uses a generative AI model to implement asset building plans generated based on financial and emotional data. This includes adjusting the plan to take the user's emotional state into account. For example, if the emotional data indicates "anxiety," a low-risk investment plan will be suggested. The specific details of the plan are then determined.

[0727] Step 5:

[0728] The server sends the generated asset building plan to the terminal. The plan is transmitted in a format that is easy for the user to understand (for example, in summarized text or graph format). This includes an explanation of the plan and recommended actions.

[0729] Step 6:

[0730] Users review their asset building plans on their devices and provide feedback as needed. The devices provide an interface for users to input their thoughts and requests for changes to the plan. This feedback is important because it will be reflected in the newly generated plan.

[0731] Step 7:

[0732] The device sends user feedback and new sentiment data to the server. The server re-analyzes this information and dynamically updates the plan. The new plan is sent back to the user, and the same flow continues thereafter.

[0733] (Application Example 2)

[0734] 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".

[0735] In modern society, anxiety and mental stress related to users' financial activities are major obstacles to wealth creation. While conventional systems could generate plans based on financial data, they struggled to provide wealth creation plans that considered users' emotions. Therefore, there is a need for a system that comprehensively considers financial information and users' mental state to provide more accurate feedback.

[0736] 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.

[0737] In this invention, the server includes means for acquiring financial data from a user, means for analyzing the user's emotional state using emotion recognition technology, and means for generating a plan to achieve the user's future asset goals based on the financial data and emotional state, and adjusting it according to the emotional state. This makes it possible to provide a flexible and personalized asset building plan that takes the user's emotional state into consideration.

[0738] "Financial data" refers to information about a user's income, consumption, investment status, and asset goals.

[0739] "Emotional state" refers to the user's mental or emotional condition as analyzed by emotion recognition technology.

[0740] "Emotion recognition technology" is a technology that analyzes voice and facial expression data to identify a user's emotions.

[0741] An "asset building plan" is a plan designed to achieve future asset goals based on the user's financial data and emotional state.

[0742] A "server" is an information processing device that collects and analyzes financial data and emotional states from users, and generates and adjusts asset formation plans.

[0743] This invention is a system that provides a personalized asset building plan based on the user's financial data and emotional state. The system consists of the user's terminal, an emotion engine incorporating emotion recognition technology, and a server that analyzes the data.

[0744] Users input financial and emotional data using their own devices. Financial information includes income, consumption, investment, age, and asset goals. Typically, smartphones or other digital devices are used as the devices. Emotional data is acquired through the device's camera and microphone and processed by an emotion engine. Emotion recognition technologies such as OpenCV and TensorFlow are utilized here.

[0745] The server stores financial and emotional data received from users and generates asset building plans based on this data. The generated plans are personalized, taking into account the user's emotional state, using machine learning models. Based on financial information and emotional state, it can suggest investment plans with lower or higher risk. Machine learning libraries such as scikit-learn in Python are commonly used.

[0746] As a concrete example, when a user makes a purchase at a commercial facility, the terminal could recognize the user's facial expressions and, if the user is feeling anxious, display advice on sound spending management on the screen. This would allow the user to review their financial plan in real time in accordance with their emotions. An example of an input prompt to the generating AI model could be: "Generate feedback notifications to help manage spending based on the user's financial information and emotional data."

[0747] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0748] Step 1:

[0749] Users input financial information using a terminal. This input data includes income, consumption, investment information, age, and asset goals. This data is stored on the terminal as foundational information for creating future wealth-building plans. Detailed field input is performed by the user through the terminal's input interface.

[0750] Step 2:

[0751] The device uses its camera and microphone to acquire user emotion data. The acquired video and audio data is then processed by an emotion engine. Emotion recognition technologies such as OpenCV and TensorFlow are applied to analyze the user's mental state, quantify it, and send it to the server.

[0752] Step 3:

[0753] The server receives financial information and sentiment data sent from the terminal. The server stores this input data and stores it in a database. This data is analyzed by a machine learning model to derive intermediate results for generating the user's asset building plan. Data analysis is performed using scikit-learn in languages ​​such as Python.

[0754] Step 4:

[0755] The server uses a generative AI model to generate an asset building plan based on the input financial information and emotional data. If the emotional data indicates risk aversion, a low-risk investment plan is formulated. The generated plan is best suited to the user's emotional state. This plan is generated as a prompt message.

[0756] Step 5:

[0757] The server sends the generated asset formation plan to the terminal and prepares to notify the user. The terminal generates a notification based on the plan and displays it on the user's screen. At this point, the user can review the plan and send feedback as needed.

[0758] Step 6:

[0759] When a user enters feedback into their device, the device sends the feedback to the server. The server uses the feedback information and ongoing sentiment data to update and adapt the asset building plan. The revised plan is then sent back to the device and presented to the user as a new proposal.

[0760] 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.

[0761] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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 those described above. 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 shown 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.

[0762] 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 robot 414.

[0763] 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.

[0764] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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."

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0781] The following is further disclosed regarding the embodiments described above.

[0782] (Claim 1)

[0783] Means of obtaining financial information from users,

[0784] A means for generating a plan to achieve the user's future asset goals based on said financial information,

[0785] A means of notifying the user of the generated plan,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, further comprising means for analyzing acquired financial information to predict future pension amounts.

[0789] (Claim 3)

[0790] The system according to claim 1, further comprising means for adjusting the plan based on user feedback.

[0791] "Example 1"

[0792] (Claim 1)

[0793] Means of obtaining financial data from users,

[0794] A means for generating a plan to achieve the user's future asset goals using a machine learning model based on the financial data,

[0795] A means of notifying the user of the generated plan via their device, and allowing the user to review and adjust the displayed content,

[0796] A means of retraining the generated AI model and adjusting the plan based on user feedback and data,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, comprising the function of analyzing acquired financial data, storing it in a database, and making future pension predictions.

[0800] (Claim 3)

[0801] The system according to claim 1, which collects user feedback information and adjusts asset allocation and investment strategies based on the analysis results.

[0802] "Application Example 1"

[0803] (Claim 1)

[0804] Means of obtaining financial information from users,

[0805] A means for generating a plan to achieve the user's future asset goals based on said financial information,

[0806] A means of notifying the user of the generated plan,

[0807] A means of analyzing a user's financial information and providing an asset building plan that operates within an electronic payment service,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, further comprising means for analyzing acquired financial information to predict future pension amounts and provide an optimal investment strategy.

[0811] (Claim 3)

[0812] The system according to claim 1, further comprising means for adjusting the plan based on user feedback and proposing individual investment plans.

[0813] "Example 2 of combining an emotion engine"

[0814] (Claim 1)

[0815] Means for obtaining financial and emotional information from users,

[0816] A means for generating a plan to achieve the user's future asset goals based on said financial information and sentiment information,

[0817] A means of adjusting the generated plan according to the user's emotional state and notifying them,

[0818] A means of dynamically updating the plan based on user feedback,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, further comprising means for analyzing acquired financial information and emotional information to predict future economic benefits.

[0822] (Claim 3)

[0823] The system according to claim 1, further comprising means for evaluating the user's emotional state using emotion recognition technology and optimizing the proposed plan.

[0824] "Application example 2 when combining with an emotional engine"

[0825] (Claim 1)

[0826] Means of obtaining financial data from users,

[0827] A means for generating a plan to achieve the user's future asset goals based on the financial data and emotional state,

[0828] A means of notifying the user of the generated plan,

[0829] A means of analyzing a user's emotional state using emotion recognition technology,

[0830] Means for adjusting a plan generated based on the user's emotional state,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for analyzing acquired financial data to predict future pension amounts.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for adjusting the plan based on user feedback and emotional changes. [Explanation of symbols]

[0836] 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 financial information from users, A means for generating a plan to achieve the user's future asset goals based on said financial information, A means of notifying the user of the generated plan, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing acquired financial information to predict future pension amounts.

3. The system according to claim 1, further comprising means for adjusting the plan based on user feedback.

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