System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to set savings goals and manage their progress; means for a user to analyze their income and expenditure patterns and suggest personalized savings plans; means for a user to participate in savings challenges and provide rewards and benefits upon completion; means for automatic savings based on a user's income and expenditure; means for analyzing a user's investment goals and risk tolerance and suggesting appropriate investment strategies; means for visualizing progress of a user's savings goals and providing rewards upon completion; and means for providing educational content to a user regarding savings and investments.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] While many people want to develop the habit of saving, setting goals, formulating plans, and tracking progress is difficult, making it difficult to continue saving. Furthermore, to popularize cashless payments, it is necessary to increase the frequency of users' use and the amount of transactions, but the current system does not adequately address these issues. Furthermore, appropriate support is not provided to users who lack knowledge and information about asset formation and investment. These issues need to be resolved comprehensively. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to set a savings goal and manage the progress toward that goal, a means for analyzing a user's income and expenditure patterns and proposing an individual savings plan, a means for a user to participate in a savings challenge and receive rewards and benefits upon achievement, a means for automatically saving based on the user's income and expenditure, a means for analyzing a user's investment goals and risk tolerance and proposing an appropriate investment strategy, a means for visualizing the user's progress toward their savings goal and providing rewards upon achievement, and a means for providing users with educational content on savings and investment. This system can increase users' motivation to save and increase the frequency of cashless payment, while also providing knowledge on asset formation and investment and realizing comprehensive financial support.

[0006] "Savings goal" refers to the specific savings amount and deadline that a user wants to achieve.

[0007] "Progress management" refers to the process of tracking your current savings status and understanding your progress toward your set savings goals.

[0008] "Income" refers to the financial benefits a user receives through labor, investment, etc.

[0009] "Expenses" means monetary payments made by the User for living expenses or other purposes.

[0010] A "savings plan" refers to a specific action plan for saving money efficiently based on the user's income and expenses.

[0011] "Savings Challenge" refers to a contest or program that challenges users to achieve a savings goal.

[0012] "Rewards" means any benefits or incentives offered to Users who complete a savings challenge or goal.

[0013] "Rewards" refers to additional benefits or services provided to users who complete a savings challenge or goal.

[0014] "Automatic savings" refers to a system that automatically saves a fixed amount on a regular basis based on conditions specified by the user.

[0015] "Investment goal" refers to the specific investment amount, profit, or other objective that a user wishes to achieve.

[0016] "Risk tolerance" means the range and degree of risk that a User can tolerate in an investment.

[0017] An "investment strategy" refers to a specific plan for efficiently managing assets based on a user's investment goals and risk tolerance.

[0018] "Visualization" refers to the visual representation of data and information, and displaying it in a way that is easy for users to understand.

[0019] "Educational content" refers to teaching materials and resources that provide users with knowledge and information about savings and investments. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a 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.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0034] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention is a system that allows users to set savings goals and effectively manage their progress. Below, we will explain the programs for realizing each function and specific examples.

[0042] Setting savings goals and tracking progress

[0043] This system provides a savings goal setting function. Users input the amount they want to save, the deadline, and the purpose, and the server stores this information in a database. The system periodically tracks the user's savings progress and analyzes the progress data to provide the user with a visual representation of their current situation.

[0044] Examples:

[0045] The user sets a goal of "saving 200,000 yen in one year." The server links the user's bank account information, automatically updates the user's savings progress each month, and notifies the user through the app.

[0046] Savings plan suggestions

[0047] The server analyzes the user's income and spending patterns and proposes an individual savings plan. Based on the user's income information and regular spending information, the generative AI model generates an optimal savings plan and provides it to the user. If the user agrees with the proposal, the plan is confirmed and savings are automatically made.

[0048] Examples:

[0049] If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, the server will suggest a savings plan that involves saving 30,000 yen each month and then starting to save an additional 10,000 yen six months later.

[0050] Savings Challenges and Rewards

[0051] Users participate in savings challenges and receive rewards and perks when they achieve their goals. The server tracks the progress of the challenges and awards rewards when they are achieved.

[0052] Examples:

[0053] If a user participates in the challenge of "saving 10,000 yen every month for three months" and meets all the conditions, the server will award the user 500 yen worth of points.

[0054] Setting up an automatic savings program

[0055] The server proposes an automatic savings program based on the user's income and spending patterns, and if the user approves, the system automatically saves the specified amount on a regular basis.

[0056] Examples:

[0057] When a user agrees to a program that automatically saves 5,000 yen on the 15th and 30th of every month, the server automatically saves the specified amount on a regular basis.

[0058] Investment advice and asset growth support

[0059] The server analyzes the user's investment goals and risk tolerance and proposes an appropriate investment strategy, allowing users to not only save money but also manage their assets efficiently.

[0060] Examples:

[0061] If a user inputs a goal of "long-term asset growth with medium risk," the server will suggest a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[0062] Visualizing savings goals and reward systems

[0063] The server visualizes the progress of the user's savings goal, using graphs and various indicators to show the progress to the user, and provides rewards when the goal is achieved.

[0064] Examples:

[0065] Users set a goal of "saving 300,000 yen in 6 months" and progress while checking their progress. When the goal is achieved, the server provides a cashback of 1,000 yen.

[0066] Providing educational content on savings

[0067] The server provides users with educational content on savings and investments to improve their financial literacy. Using a generative AI model, it automatically creates and provides the most appropriate content for each user.

[0068] Examples:

[0069] If a user requests, "I want to learn the basics of saving," the server will provide educational content such as "How to review your monthly expenses" and "Tips for reducing fixed expenses."

[0070] The processing flow will be explained below.

[0071] Flow of setting savings goals and tracking progress

[0072] Step 1:

[0073] On the device: The user opens the app and accesses the savings goal setting screen.

[0074] Step 2:

[0075] User: Enter the target amount, deadline, and purpose.

[0076] Step 3:

[0077] Terminal: Sends user input data to the server.

[0078] Step 4:

[0079] Server: Receives user input data and stores it in a database.

[0080] Step 5:

[0081] Server: Retrieves transaction history from the database to check the user's savings status on a daily or weekly basis.

[0082] Step 6:

[0083] Server: Analyzes progress towards goals through batch processing.

[0084] Step 7:

[0085] Server: Calculates the progress and sends the result to the device.

[0086] Step 8:

[0087] Terminal: Visually display the received progress data, for example using a gauge or bar graph.

[0088] Savings plan proposal process flow

[0089] Step 1:

[0090] Terminal: Provides a screen where users can enter income and expense information.

[0091] Step 2:

[0092] User: Enter your monthly income and major recurring expenses (rent, utilities, food, etc.).

[0093] Step 3:

[0094] Terminal: Sends input data to the server.

[0095] Step 4:

[0096] Server: Receives user income and expenditure data and analyzes it using a generative AI model.

[0097] Step 5:

[0098] Server: Generates a savings plan and sends it to the device.

[0099] Step 6:

[0100] Terminal: Display the suggested savings plan to the user.

[0101] Step 7:

[0102] User: Review the proposed savings plan and adjust as needed.

[0103] Savings Challenge and Reward Processing Flow

[0104] Step 1:

[0105] Server: Sets the savings challenge and sends it to the device.

[0106] Step 2:

[0107] Device: Shows the challenge to the user and asks for their confirmation.

[0108] Step 3:

[0109] User: Decides to participate in the proposed challenge and clicks the Join button.

[0110] Step 4:

[0111] Device: Sends the user's decision to the server.

[0112] Step 5:

[0113] Server: Periodically checks the user's savings progress and calculates the challenge progress.

[0114] Step 6:

[0115] Server: Awards rewards when a challenge is completed and sends the data to the device.

[0116] Step 7:

[0117] Device: Notifies the user of the challenge completion and reward details.

[0118] Process flow for setting up an automatic savings program

[0119] Step 1:

[0120] Server: Proposes an automatic savings program based on the user's income and expenditure data.

[0121] Step 2:

[0122] On the device: Display the suggestions to the user.

[0123] Step 3:

[0124] User: Approves the proposed automatic savings program.

[0125] Step 4:

[0126] Terminal: Sends authorization information to the server.

[0127] Step 5:

[0128] Server: Executes approved automatic savings programs and automatically saves according to a specified schedule.

[0129] Step 6:

[0130] Server: Notifies the user of the progress of the automatic savings.

[0131] Step 7:

[0132] On the device: Show users their savings progress.

[0133] Investment advice and asset growth support process

[0134] Step 1:

[0135] Terminal: Provides a screen where users can input their investment goals and risk tolerance.

[0136] Step 2:

[0137] User: Enter your investment goals and risk tolerance.

[0138] Step 3:

[0139] Terminal: Sends input data to the server.

[0140] Step 4:

[0141] Server: Uses generative AI models to analyze user input data and generate optimal investment strategies.

[0142] Step 5:

[0143] Server: Sends investment strategies to the terminal.

[0144] Step 6:

[0145] Terminal: displays the proposed investment strategy to the user.

[0146] Step 7:

[0147] User: Review investment strategies and implement them as needed.

[0148] Step 8:

[0149] Server: Regularly tracks users' investment progress and adjusts strategies as needed.

[0150] Step 9:

[0151] On the device: Notify the user of progress and suggested adjustments.

[0152] Visualization of savings goals and reward system processing flow

[0153] Step 1:

[0154] Device: Provides a screen where users can set their savings goals.

[0155] Step 2:

[0156] User: Set the goal amount and deadline.

[0157] Step 3:

[0158] Terminal: Sends the settings to the server.

[0159] Step 4:

[0160] Server: Stores goal settings in a database and tracks progress periodically.

[0161] Step 5:

[0162] Server: Performs the process of granting rewards when the goal is achieved.

[0163] Step 6:

[0164] Server: Analyzes the progress data and sends it to the device as visual information.

[0165] Step 7:

[0166] On the device: Show progress to the user with graphs and metrics.

[0167] Step 8:

[0168] Server: Notifies the device of the reward to be provided when the goal is achieved.

[0169] Step 9:

[0170] Terminal: Notifies the user and assists them in claiming their reward.

[0171] Process flow for providing educational content on savings

[0172] Step 1:

[0173] Server: Uses generative AI models to create educational content tailored to the user.

[0174] Step 2:

[0175] Server: Sends content to the device.

[0176] Step 3:

[0177] Device: Displays educational content to the user.

[0178] Step 4:

[0179] Users: View and learn educational content.

[0180] Step 5:

[0181] Device: Sends user feedback to the server.

[0182] Step 6:

[0183] Server: Regularly update educational content based on user feedback.

[0184] Example 1

[0185] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0186] Existing savings management systems have basic functions for setting users' savings goals and tracking progress, but they lack the ability to analyze individual income and expenditure patterns and propose optimal savings plans. They also lack the ability to provide appropriate rewards and educational content based on savings progress. Furthermore, there are challenges in how to effectively analyze collected data and present specific savings and investment strategies to users.

[0187] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0188] In this invention, the server includes: means for allowing users to set savings goals and manage their progress; means for analyzing the user's income and spending patterns and proposing personalized savings plans; means for allowing users to participate in savings challenges and offering rewards and benefits upon achievement; means for automatically saving based on the user's income and spending; means for analyzing the user's investment goals and risk tolerance and proposing appropriate investment strategies; means for visualizing the user's progress toward their savings goals and offering rewards upon achievement; means for providing the user with educational content on savings and investments; means for analyzing the user's income and spending using a generative AI model and generating an optimal savings plan; and means for inputting the user's income and spending data into the generative AI model using prompts. This allows for the proposal and implementation of an optimal savings plan tailored to the user's individual financial situation, resulting in more effective savings and investment management. Furthermore, regular progress checks and rewards can motivate users to save, and the provision of educational content can improve users' financial literacy.

[0189] "Means for analyzing a user's income and expenditure patterns" refers to a data processing device or software that analyzes the user's financial situation and creates a future savings plan based on the income and expenditure information entered by the user.

[0190] "Means to propose individual savings plans" refers to the function in which the generative AI model analyzes the collected income and expenditure data and automatically generates and presents the optimal savings plan for each user.

[0191] "Means to participate in savings challenges" refers to the functionality that allows users to challenge themselves to set savings goals through the application, track their progress, and receive rewards upon achieving their goals.

[0192] "Means of providing rewards or benefits upon achievement" refers to a function that provides rewards such as points or monetary incentives when a user achieves a set savings goal.

[0193] "Means for automatic savings" refers to a system function that automatically saves an amount according to income and spending patterns based on a savings plan set by the user.

[0194] "Means for proposing appropriate investment strategies" refers to the function of presenting optimal investment plans and portfolios to users based on their investment goals and risk tolerance.

[0195] "Means for visualizing progress toward savings goals" refers to a function that visually displays the current progress toward the savings goal set by the user using graphs and numerical data.

[0196] "Means for providing educational content related to savings and investments" refers to the function of automatically generating and presenting educational materials and information that provide knowledge about savings and investments in order to improve users' financial literacy.

[0197] A "generative AI model" refers to an artificial intelligence algorithm that inputs a user's income and expenditure data as prompts and generates optimal savings plans and investment strategies based on the data.

[0198] A "prompt" refers to a question or command that is entered to instruct a generative AI model to perform a specific analysis or generate data.

[0199] The present invention is a system that allows users to set savings goals and effectively manage their progress. Below, we will explain the programs for realizing each function and specific examples.

[0200] Hardware and software used

[0201] In this invention, we mainly use a server, a user terminal (such as a smartphone or PC), and a generative AI model.

[0202] Server: Provides key functions such as setting savings goals, collecting income and expenditure data, analyzing data, proposing savings plans, tracking progress, and providing a reward system.

[0203] User device: Using a smartphone app or web app, the user enters information and receives feedback from the server.

[0204] Generative AI model: Generates savings plans and investment strategies based on user income and expenditure data via prompts.

[0205] Setting savings goals and tracking progress

[0206] Users input the amount they want to save, the deadline, and the purpose into their device. The device sends this information to the server, which stores this data in a database. The server periodically checks the user's bank account information, updates their savings progress, and sends notifications to the user that visually display their progress.

[0207] Examples:

[0208] The user sets a goal of "saving 200,000 yen in one year." The user's device sends this information to the server, which stores it in a database. At the end of each month, the server checks the bank account information, updates the progress, and notifies the user.

[0209] Savings plan suggestions

[0210] The user enters their monthly income and fixed expenses into their device. The device sends this to the server, which prepares the collected data for input into the generative AI model. The generative AI model generates an optimal savings plan through prompts and returns the results to the server. The server sends this savings plan to the user's device and presents it to the user. If the user agrees to the plan, regular automatic savings will begin.

[0211] Examples:

[0212] The prompt sentence "If the user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, please suggest the optimal savings plan" is input into the AI ​​model, and the model generates a plan that says "Save 30,000 yen each month and start saving an additional 10,000 yen six months later." This is presented to the user, and if they agree, the server sets up automatic savings.

[0213] Savings Challenges and Rewards

[0214] If a user wants to participate in the savings challenge, they register on their device. The server stores this information in a database and periodically checks the user's savings status. When the user completes the challenge, the server automatically awards rewards.

[0215] Examples:

[0216] When a user participates in the challenge of "saving 10,000 yen every month for three months," the server checks the user's progress every month, and if the user achieves the goal after three months, the server awards the user with 500 yen worth of points.

[0217] Setting up an automatic savings program

[0218] The server proposes an automatic savings program based on the user's income and spending patterns, and if the user agrees, the server sets up a system to automatically save the specified amount on a regular basis.

[0219] Examples:

[0220] When a user agrees to a program that "automatically saves 5,000 yen on the 15th and 30th of every month," the server automatically sets up and executes the program to save 5,000 yen on the 15th and 30th of every month.

[0221] Investment advice and asset growth support

[0222] When a user inputs their investment goals and risk tolerance, the server analyzes them and inputs an appropriate investment strategy into the AI ​​model. The model generates an optimal investment strategy and returns it to the server, which then presents it to the user.

[0223] Examples:

[0224] When a user inputs a goal such as "aiming for long-term asset growth with medium risk," the server asks the AI ​​model to generate a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts" and presents it to the user.

[0225] Visualizing savings goals and reward systems

[0226] The server visually displays the progress of the savings goal set by the user using graphs and various indicators, and when the user achieves the goal, the server grants a reward.

[0227] Examples:

[0228] The user sets a goal of "saving 300,000 yen in 6 months," and the server displays the progress in a graph. When the goal is achieved, the server provides the user with a cashback of 1,000 yen.

[0229] Providing educational content on savings

[0230] The server uses a generative AI model to generate and provide educational content that best suits the user's request.

[0231] Examples:

[0232] When a user requests to "learn the basics of saving," the server generates educational content such as "how to review your monthly expenses" and "tips for reducing fixed costs" and provides it to the user.

[0233] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0234] Program processing flow

[0235] Setting savings goals and tracking progress

[0236] Step 1:

[0237] The user enters a savings goal.

[0238] Input: Amount you want to save, deadline, and purpose.

[0239] Specific behavior: A user uses a smartphone app or web app to enter the required information into a screen for setting a savings goal.

[0240] Step 2:

[0241] The terminal sends the input data to the server.

[0242] Input: Savings goal data entered by the user.

[0243] Data processing: Converting data into a format that the server can accept.

[0244] Output: The transformed data is sent to the server.

[0245] Step 3:

[0246] The server stores the received data in a database.

[0247] Input: Data sent from the terminal.

[0248] Data processing: Processing into a format that is compatible with the database.

[0249] Output: The processed data is stored in a database.

[0250] Step 4:

[0251] The server periodically checks the user's bank account and updates the progress data.

[0252] Input: Bank account information, previous savings progress data.

[0253] Data calculations: Analyze bank account balances and transaction histories.

[0254] Output: Save the updated progress data to the database.

[0255] Step 5:

[0256] The server notifies the user terminal of the progress.

[0257] Input: The updated progress data.

[0258] What it does: The server visualizes the progress data and generates notification messages.

[0259] Output: A notification message is sent to the user's terminal.

[0260] Savings plan suggestions

[0261] Step 1:

[0262] The user enters their monthly income and fixed expenses.

[0263] Input: Monthly income, monthly fixed expenses.

[0264] Specific behavior: A user enters income and expenses into a smartphone app or web app.

[0265] Step 2:

[0266] The terminal sends user input to the server.

[0267] Input: Income and expense data entered by the user.

[0268] Data processing: Convert the data format to one suitable for the server.

[0269] Output: The transformed data is sent to the server.

[0270] Step 3:

[0271] The server converts the collected data into prompt sentence format and sends it to the generative AI model.

[0272] Input: Income and expenditure data.

[0273] Data processing: Convert into prompt sentence format.

[0274] Output: "If the user's monthly income is ¥300,000 and their monthly fixed expenses are ¥150,000, please suggest the optimal savings plan."

[0275] Step 4:

[0276] The generative AI model generates an appropriate savings plan and returns it to the server.

[0277] Input: The prompt statement.

[0278] Data calculation: The AI ​​model calculates income and expenditure data to generate an optimal savings plan.

[0279] Output: Savings plan (e.g., "Save 30,000 yen each month, and save an additional 10,000 yen after six months").

[0280] Step 5:

[0281] The server transmits the generated savings plan to the user terminal and presents it.

[0282] Input: Generated savings plan data.

[0283] Specific behavior: The server generates a notification message to the user.

[0284] Output: A notification message is sent to the user's terminal.

[0285] Step 6:

[0286] If the user agrees to the proposal, the server sets up the savings plan to run periodically.

[0287] Input: User consent data.

[0288] Specific operation: The server sets up and runs an automatic savings program.

[0289] Output: Savings are made periodically.

[0290] Savings Challenges and Rewards

[0291] Step 1:

[0292] A user participates in a savings challenge.

[0293] Input: Challenge goal (e.g., "Save 10,000 yen every month for three months").

[0294] Specific actions: Register to participate in the challenge on the app.

[0295] Step 2:

[0296] The terminal sends the participation information to the server.

[0297] Input: Challenge goal data.

[0298] Data processing: Convert data into server format.

[0299] Output: The transformed data is sent to the server.

[0300] Step 3:

[0301] The server periodically checks the progress of the challenge and updates the database.

[0302] Input: Account information and savings progress data.

[0303] Data calculation: Analyze your progress based on your account information.

[0304] Output: Save the updated progress data to the database.

[0305] Step 4:

[0306] If the challenge is met, the server will reward the user.

[0307] Input: The completed challenge data.

[0308] Specific behavior: Generates reward data and adds it to the user's points account.

[0309] Output: The user's points balance is updated.

[0310] Setting up an automatic savings program

[0311] Step 1:

[0312] The server suggests automatic savings programs based on the user's income and spending patterns.

[0313] Input: Income, expenditure data.

[0314] Data calculation: Analyzes data and generates optimal automatic savings programs.

[0315] Output: Proposal for an automated savings program.

[0316] Step 2:

[0317] The user agrees to the proposed program, and the device sends the consent information to the server.

[0318] Input: User consent data.

[0319] Data processing: Convert consent data into server format.

[0320] Output: The transformed data is sent to the server.

[0321] Step 3:

[0322] The server sets an automatic savings program and automatically saves a designated amount periodically.

[0323] Input: Automatic savings program data, user's bank account information.

[0324] Specific operation: The server sets a periodic task and automatically saves money on the specified date.

[0325] Output: The savings will be made on the specified date.

[0326] Investment advice and asset growth support

[0327] Step 1:

[0328] The user enters their investment goals and risk tolerance.

[0329] Inputs: Investment goals, risk tolerance.

[0330] Specific action: The user enters required information into a smartphone app or web app.

[0331] Step 2:

[0332] The terminal sends the input data to the server.

[0333] Inputs: Investment objectives and risk tolerance data.

[0334] Data processing: Convert the data format to one suitable for the server.

[0335] Output: The transformed data is sent to the server.

[0336] Step 3:

[0337] The server converts the collected data into prompt sentence format and sends it to the generative AI model.

[0338] Inputs: Investment objectives, risk tolerance data.

[0339] Data processing: Convert into prompt sentence format.

[0340] Output: Prompt statement (e.g., "Please suggest a portfolio that offers medium risk and long-term capital growth.").

[0341] Step 4:

[0342] The generative AI model generates an appropriate investment strategy and returns it to the server.

[0343] Input: The prompt statement.

[0344] Data calculation: AI models analyze user data and generate appropriate investment strategies.

[0345] Output: Investment strategy (e.g., "50% bonds, 30% stocks, 20% real estate investment trusts").

[0346] Step 5:

[0347] The server transmits the generated investment strategy to the user terminal and presents it.

[0348] Input: Generated investment strategy data.

[0349] Specific behavior: The server generates a notification message to the user.

[0350] Output: A notification message is sent to the user's terminal.

[0351] Visualizing savings goals and reward systems

[0352] Step 1:

[0353] The user sets a savings goal.

[0354] Input: Savings goal (amount, period).

[0355] Specific behavior: The user enters their savings goal in a smartphone app or web app.

[0356] Step 2:

[0357] The device sends the savings goal to the server.

[0358] Input: Savings goal data.

[0359] Data processing: Convert the data format to one suitable for the server.

[0360] Output: The transformed data is sent to the server.

[0361] Step 3:

[0362] The server periodically checks the progress of the savings goal and displays it visually.

[0363] Input: Savings goal data and progress data.

[0364] Data calculation: Analyze progress to display it graphically and numerically.

[0365] Output: A progress visualization is generated.

[0366] Step 4:

[0367] The server notifies the user terminal of the progress.

[0368] Input: Visualized progress data.

[0369] Specific action: The server generates a notification message.

[0370] Output: A notification message is sent to the user's terminal.

[0371] Providing educational content on savings

[0372] Step 1:

[0373] A user requests educational content.

[0374] Input: A request for educational content.

[0375] Specific operation: A user enters a request into a smartphone app or web app.

[0376] Step 2:

[0377] The device sends a request to the server.

[0378] Input: Educational content request data.

[0379] Data processing: Convert the data format to one suitable for the server.

[0380] Output: The transformed data is sent to the server.

[0381] Step 3:

[0382] The server sends the request to the generative AI model to generate optimal educational content.

[0383] Input: A request for educational content.

[0384] Data calculation: AI models generate optimal content based on user requests.

[0385] Output: Optimal educational content.

[0386] Step 4:

[0387] The server transmits the generated educational content to the user terminal and provides it.

[0388] Input: Generated educational content data.

[0389] What it does: Generates formatted educational content as notification messages.

[0390] Output: A notification message is sent to the user's terminal.

[0391] (Application example 1)

[0392] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0393] Conventional savings management systems limit the ways in which users can effectively track and manage their savings progress even when they set savings goals. They also lack the ability to suggest optimal savings plans based on the user's individual income and spending patterns. Furthermore, they lack the ability to track progress during savings challenges, provide rewards, and provide real-time notifications, making it difficult to maintain user motivation. A system that can solve these issues and improve users' savings efficiency is needed.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0395] In this invention, the server includes means for managing progress toward a savings goal set by the user in real time and automatically updating the progress, means for proposing an optimal savings plan to the user in real time based on income and expenditure patterns, and means for periodically notifying the user of the savings progress. This allows the user to check the progress toward the savings goal in real time and to save effectively by always receiving optimal savings plans.

[0396] A "savings goal" is the amount or purpose that a user sets and wants to achieve within a certain period of time.

[0397] "Progress management" means regularly tracking and checking progress towards set goals.

[0398] "Revenue" means the amount of money a User earns within a given period of time.

[0399] "Expenses" means the amount of money a User consumes or pays within a given period of time.

[0400] A "savings plan" is a plan that suggests the optimal savings amount and period based on the user's income and expenses.

[0401] A "savings challenge" is an activity in which users participate and receive rewards and benefits by achieving certain goals.

[0402] "Rewards" refer to benefits or points given to users when they achieve goals such as savings challenges.

[0403] "Real-time" means that data and information are updated almost instantly, allowing users to see the latest status.

[0404] "Notification" means a communication from the system to the user informing them of information or progress.

[0405] A "generative AI model" is an artificial intelligence that automatically generates and suggests appropriate savings plans and educational content based on large amounts of data.

[0406] "Educational Content" means information and materials that help users learn about saving and investing.

[0407] "System" refers to a series of programs and hardware that integrates the above elements and assists users in managing their savings.

[0408] One embodiment of the present invention is a system that allows users to set savings goals and effectively manage their progress. The system proposes optimal savings plans based on the user's income and spending patterns, tracks progress in real time, and automatically updates the plans. The system also includes a function that allows users to participate in savings challenges and receive rewards and benefits when they achieve their goals.

[0409] System configuration

[0410] The system consists of the following components:

[0411] 1. User device: Users set goals and check progress on devices such as smartphones.

[0412] 2. Server: Manages user data and generates savings plans and educational content using generative AI models.

[0413] 3. Database: Stores users' savings goals, progress data, income, and expenditure information.

[0414] 4. Generative AI model: An AI engine that analyzes a user's income and spending patterns and generates a personalized savings plan.

[0415] Program processing explanation

[0416] 1. Goal setting and progress tracking:

[0417] The user sets a savings goal (e.g., "save 200,000 yen in one year") using their smartphone. The server stores this goal and related information (deadline, purpose, etc.) in a database.

[0418] The server links to the user's bank account information, automatically updates the user's savings progress each month, and notifies the user through the app.

[0419] 2. Savings plan suggestions:

[0420] The server analyzes the user's income and regular expenditure information and uses a generative AI model to propose an optimal savings plan. For example, if a user's monthly income is 300,000 yen and their monthly fixed expenditure is 150,000 yen, the server will propose a plan to "save 30,000 yen per month and start saving an additional 10,000 yen six months later."

[0421] 3. Challenge and reward management:

[0422] Users can participate in challenges such as "save 10,000 yen every month for three months." The server tracks the progress of the challenge and rewards the user with 500 yen worth of points when all conditions are met.

[0423] 4. Providing educational content:

[0424] The server provides users with educational content on saving and investing. For example, if a user requests to learn the basics of saving, the server will provide content such as how to review monthly expenses and tips for reducing fixed costs.

[0425] Specific hardware and software used

[0426] Smartphone: The device through which the user interacts with the app.

[0427] Server: Manage user data using AWS or Google Cloud.

[0428] Generative AI models: Generate savings plans and educational content using AI engines such as OpenAI GPT.

[0429] Specific examples and prompts

[0430] Examples:

[0431] Using the smartphone app, users can set a goal of "saving 200,000 yen in one year" and check their progress every month. They can also participate in a challenge to "save 10,000 yen every month for three months" and instantly check their progress each month from the app.

[0432] Example prompt sentence:

[0433] "If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, please suggest the optimal savings plan."

[0434] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0435] Step 1:

[0436] Users set savings goals using their smartphones and enter them into the application.

[0437] Input: Amount you want to save, deadline, purpose

[0438] Output: Set target data

[0439] How it works: The user enters a savings goal into the app, such as "save 200,000 yen in one year." This data is sent to the server and stored in a database.

[0440] Step 2:

[0441] The server links the target data stored in the database with the user's bank account information.

[0442] Input: User goal data, bank account information

[0443] Output: Linked account information and goal data

[0444] What it does: The server accesses the user's bank account information and links it to their savings goals, allowing the user's savings progress to be automatically tracked.

[0445] Step 3:

[0446] The server periodically checks the user's bank account and updates them on their savings progress.

[0447] Input: Bank account balance information

[0448] Output: Updated savings progress data

[0449] What it does: The server retrieves the user's bank account balance every month, calculates the current progress towards the savings goal, updates the database, and notifies the user.

[0450] Step 4:

[0451] The server analyzes the user's income and expenditure data and uses a generative AI model to propose an optimal savings plan based on those patterns.

[0452] Input: User income data, expenditure data

[0453] Output: Optimal savings plan

[0454] How it works: The server collects and analyzes the user's income and expenditure data. Using a generative AI model, it proposes a savings plan, such as "save 30,000 yen per month and start saving an additional 10,000 yen six months later." This plan is then notified to the user.

[0455] Step 5:

[0456] Users participate in savings challenges and the server tracks their progress.

[0457] Input: Challenge goal, participation period

[0458] Output: Challenge progress

[0459] Specific operation: A user participates in a challenge such as "save 10,000 yen every month for three months." The server periodically checks the progress of the challenge and notifies the user.

[0460] Step 6:

[0461] The server will award rewards and perks when the challenge is completed.

[0462] Input: Challenge completion status

[0463] Output: Rewards and rewards data

[0464] Specific operation: If the challenge is completed, the server will give the user 500 yen worth of points. This information will also be saved in the database and notified to the user.

[0465] Step 7:

[0466] The server provides users with educational content on saving and investing.

[0467] Input: User request, interest data

[0468] Output: Educational content

[0469] Specific operation: The user makes a request such as "I want to learn the basics of saving." The server uses the generative AI model to generate appropriate educational content and provides it to the user.

[0470] Specific prompt examples:

[0471] "If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, please suggest the optimal savings plan."

[0472] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0473] The present invention is a system that incorporates an emotion engine for recognizing user emotions, and performs savings goal setting, progress management, savings plan proposals, savings challenges, rewards, automatic savings, investment advice, savings goal visualization, and educational content provision. Below, we will explain in detail the programs for realizing each function and specific examples.

[0474] Emotion Engine

[0475] The emotion engine analyzes users' emotions in real time, allowing it to customize savings plans, investment strategies, rewards, and educational content based on the user's emotional state.

[0476] Setting savings goals and tracking progress

[0477] Step 1: The user opens the app and accesses the savings goal setting screen. The user enters the goal amount, deadline, and purpose. This data is sent to the server and stored in the database.

[0478] Step 2: The server checks the user's savings progress on a daily or weekly basis, analyzes the progress data, and visualizes it.

[0479] Examples:

[0480] When a user sets a goal of "saving 200,000 yen in one year," the server links the user's bank account information and provides monthly updates and notifications on progress.

[0481] Savings plan suggestions

[0482] Step 1: Provide a screen where users can enter their income and expense information. The data is sent to a server for analysis.

[0483] Step 2: The generative AI model generates a savings plan and suggests it to the user.

[0484] Step 3: The emotion engine analyzes the user's emotions and adjusts the plan.

[0485] Examples:

[0486] For users with a monthly income of 300,000 yen and fixed expenses of 150,000 yen, the plan is to save 30,000 yen each month and start saving an additional 10,000 yen after six months.

[0487] Savings Challenges and Rewards

[0488] The server sets up a savings challenge and proposes it to users. If users participate in the challenge and achieve the goal, they will receive rewards and benefits. The emotion engine customizes the reward content based on the user's emotions.

[0489] Examples:

[0490] If a user participates in the challenge of "saving 10,000 yen every month for three months" and meets the conditions, the server will award them 500 yen worth of points.

[0491] Setting up an automatic savings program

[0492] The server proposes an automatic savings program based on the user's income and expenditure data. If the user approves, the system automatically saves. The emotion engine adjusts the program based on the user's emotions.

[0493] Examples:

[0494] Run a program that automatically saves 5,000 yen on the 15th and 30th of every month.

[0495] Investment advice and asset growth support

[0496] The generative AI model analyzes users' investment goals and risk tolerance to suggest appropriate investment strategies, while the sentiment engine adjusts strategies based on users' emotions.

[0497] Examples:

[0498] If a user enters information such as "I am aiming for medium-risk, long-term asset growth," the service suggests a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[0499] Visualizing savings goals and reward systems

[0500] The server analyzes and visualizes the progress of the user's savings goal. When the goal is achieved, rewards are provided. The emotion engine customizes the reward content.

[0501] Examples:

[0502] Set a goal of "saving 300,000 yen in 6 months" and progress while checking your progress. When you reach your goal, the server will provide you with a cashback of 1,000 yen.

[0503] Providing educational content on savings

[0504] The generative AI model creates and delivers educational content tailored to the user, while the emotion engine adjusts the content based on the user's emotions.

[0505] Examples:

[0506] When a user requests to "learn the basics of saving," educational content such as "how to review your monthly spending" and "tips for reducing fixed costs" will be provided.

[0507] Use of emotion engine

[0508] The emotion engine analyzes user input and other behavioral data to recognize emotions, and uses this emotional data to optimize various system features (savings plans, rewards, investment strategies, educational content, etc.) for users.

[0509] Examples:

[0510] If the emotion engine detects that the user is feeling stressed, it will suggest relaxing their savings plan as a mitigation measure.

[0511] The processing flow will be explained below.

[0512] Processing flow of a system that combines emotion engines

[0513] Flow of setting savings goals and tracking progress

[0514] Step 1:

[0515] On the device: The user opens the app and accesses the savings goal setting screen.

[0516] Step 2:

[0517] User: Enter the target amount, deadline, and purpose.

[0518] Step 3:

[0519] Terminal: Sends user input data to the server.

[0520] Step 4:

[0521] Server: Receives user input data and stores it in a database.

[0522] Step 5:

[0523] Server: The emotion engine analyzes the user's emotions and suggests goal adjustments if necessary.

[0524] Step 6:

[0525] Server: Retrieves transaction history from the database to check the user's savings status on a daily or weekly basis.

[0526] Step 7:

[0527] Server: Analyzes progress towards goals through batch processing.

[0528] Step 8:

[0529] Server: Calculates the progress and sends the result to the device.

[0530] Step 9:

[0531] Terminal: Visually display the received progress data, for example using a gauge or bar graph.

[0532] Savings plan proposal process flow

[0533] Step 1:

[0534] Terminal: Provides a screen where users can enter income and expense information.

[0535] Step 2:

[0536] User: Enter your monthly income and major recurring expenses (rent, utilities, food, etc.).

[0537] Step 3:

[0538] Terminal: Sends input data to the server.

[0539] Step 4:

[0540] Server: Receives user income and expenditure data and analyzes it using a generative AI model.

[0541] Step 5:

[0542] Server: The generative AI model generates a savings plan and sends it to the device.

[0543] Step 6:

[0544] Server: The emotion engine analyzes the user's emotions and adjusts the plan. For example, if stress is high, it suggests relaxing the plan.

[0545] Step 7:

[0546] Terminal: Display the suggested savings plan to the user.

[0547] Step 8:

[0548] User: Review the proposed savings plan and adjust as needed.

[0549] Savings Challenge and Reward Processing Flow

[0550] Step 1:

[0551] Server: Sets the savings challenge and sends it to the device.

[0552] Step 2:

[0553] Device: Shows the challenge to the user and asks for their confirmation.

[0554] Step 3:

[0555] User: Decides to participate in the proposed challenge and clicks the Join button.

[0556] Step 4:

[0557] Device: Sends the user's decision to the server.

[0558] Step 5:

[0559] Server: Periodically checks the user's savings progress and calculates the challenge progress.

[0560] Step 6:

[0561] Server: The emotion engine analyzes the user's emotional state and customizes rewards, for example, suggesting additional incentives if motivation drops.

[0562] Step 7:

[0563] Server: Awards rewards when a challenge is completed and sends the data to the device.

[0564] Step 8:

[0565] Device: Notifies the user of the challenge completion and reward details.

[0566] Process flow for setting up an automatic savings program

[0567] Step 1:

[0568] Server: Proposes an automatic savings program based on the user's income and expenditure data.

[0569] Step 2:

[0570] On the device: Display the suggestions to the user.

[0571] Step 3:

[0572] User: Approves the proposed automatic savings program.

[0573] Step 4:

[0574] Terminal: Sends authorization information to the server.

[0575] Step 5:

[0576] Server: Executes approved automatic savings programs and automatically saves according to a specified schedule.

[0577] Step 6:

[0578] Server: The emotion engine analyzes the user's emotions and adjusts the program accordingly. For example, if the user is under high stress, the savings amount is temporarily set lower.

[0579] Step 7:

[0580] Server: Notifies the user of the progress of the automatic savings.

[0581] Step 8:

[0582] On the device: Show users their savings progress.

[0583] Investment advice and asset growth support process

[0584] Step 1:

[0585] Terminal: Provides a screen where users can input their investment goals and risk tolerance.

[0586] Step 2:

[0587] User: Enter your investment goals and risk tolerance.

[0588] Step 3:

[0589] Terminal: Sends input data to the server.

[0590] Step 4:

[0591] Server: Uses generative AI models to analyze user input data and generate optimal investment strategies.

[0592] Step 5:

[0593] Server: Sends investment strategies to the terminal.

[0594] Step 6:

[0595] Server: The sentiment engine adjusts investment strategies based on user sentiment. For example, if a user feels anxious, it suggests a low-risk strategy.

[0596] Step 7:

[0597] Terminal: displays the proposed investment strategy to the user.

[0598] Step 8:

[0599] User: Review investment strategies and implement them as needed.

[0600] Step 9:

[0601] Server: Regularly tracks users' investment progress and adjusts strategies as needed.

[0602] Step 10:

[0603] On the device: Notify the user of progress and suggested adjustments.

[0604] Visualization of savings goals and reward system processing flow

[0605] Step 1:

[0606] Device: Provides a screen where users can set their savings goals.

[0607] Step 2:

[0608] User: Set the goal amount and deadline.

[0609] Step 3:

[0610] Terminal: Sends the settings to the server.

[0611] Step 4:

[0612] Server: Stores goal settings in a database and tracks progress periodically.

[0613] Step 5:

[0614] Emotion Engine: Analyzes the user's emotional state and customizes assistance to help them achieve their goals.

[0615] Step 6:

[0616] Server: When the goal achievement conditions are met, the server processes the reward.

[0617] Step 7:

[0618] Server: Analyzes the progress data and sends it to the device as visual information.

[0619] Step 8:

[0620] On the device: Show progress to the user with graphs and metrics.

[0621] Step 9:

[0622] Server: Notifies the device of the reward to be provided when the goal is achieved.

[0623] Step 10:

[0624] Terminal: Notifies the user and assists them in claiming their reward.

[0625] Process flow for providing educational content on savings

[0626] Step 1:

[0627] Server: Uses generative AI models to create educational content tailored to the user.

[0628] Step 2:

[0629] Server: Sends content to the device.

[0630] Step 3:

[0631] Device: Displays educational content to the user.

[0632] Step 4:

[0633] Users: View and learn educational content.

[0634] Step 5:

[0635] Emotion engine: Analyzes user emotions and tailors content based on understanding and interest.

[0636] Step 6:

[0637] Device: Sends user feedback to the server.

[0638] Step 7:

[0639] Server: Regularly update educational content based on user feedback.

[0640] Example 2

[0641] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0642] Conventional savings management systems were unable to fully consider users' emotions and individual circumstances, and were limited to proposing general savings plans. They also lacked the flexibility to adapt to fluctuations in users' income and spending patterns, and the ability to customize plans based on their emotional state. Furthermore, it was difficult to address individual needs when it came to tracking savings goal progress or proposing investment strategies.

[0643] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0644] In this invention, the server includes a means for allowing users to set savings goals and manage their progress, a means for analyzing the user's income and spending patterns to propose individual savings plans, and a means for allowing the user to participate in savings challenges and receive rewards and benefits upon completion. This allows for flexible proposals of savings plans and investment strategies tailored to the user's individual circumstances. Furthermore, by using an emotion engine to analyze the user's emotional state in real time, each function of the system can be customized based on emotions, creating a user-friendly environment. Furthermore, by using a generative AI model to automatically generate optimal savings plans and investment strategies based on prompts, more accurate proposals can be made.

[0645] "Savings goal setting" is the act of a user deciding on a specific amount and setting a deadline and purpose for achieving that amount.

[0646] "Progress management" is the process of monitoring the current progress towards the savings goal set by the user and updating the information accordingly.

[0647] "Income and Expense Pattern Analysis" is a means of analyzing a user's income and expense data to understand their individual financial situation.

[0648] "Savings plan suggestions" are presented to users based on the analysis of their income and expenses, showing them specific ways in which they should save money.

[0649] A "savings challenge" is a system in which users set short-term tasks or challenges to achieve a specific savings goal, and receive rewards or benefits if they complete the challenge.

[0650] "Automatic savings" is a function that automatically saves money on a specific schedule based on the user's income and expenditure data.

[0651] "Investment strategy suggestions" means providing specific guidance on what kind of investments a user should make based on their investment goals and risk tolerance.

[0652] "Savings goal visualization" is the process of visually displaying a user's progress toward their savings goal in the form of graphs and reports.

[0653] "Providing educational content" means providing users with knowledge about savings and investments and providing information and educational materials to deepen their understanding.

[0654] An "emotion engine" is a software technology that analyzes a user's emotional state in real time and reflects the results in other system functions.

[0655] A "generative AI model" is an artificial intelligence algorithm that inputs user data and automatically generates optimal savings plans and investment strategies.

[0656] A "prompt sentence" is an input sentence that gives specific instructions or questions to a generative AI model.

[0657] The present invention is a system that incorporates an emotion engine that recognizes user emotions, and performs savings goal setting, progress management, savings plan proposals, savings challenges, rewards, automatic savings, investment advice, savings goal visualization, and educational content provision. Below, we will explain in detail the programs for realizing each function and specific examples.

[0658] Emotion Engine

[0659] The server collects user input data and operation data in real time and analyzes it with an emotion engine, which identifies the user's emotional state and provides feedback to other system functions.

[0660] Examples:

[0661] When users log in and set their savings goals, an emotion engine analyzes their typing speed and sequence to detect signs of stress or excitement.

[0662] Setting savings goals and tracking progress

[0663] The user enters the goal amount, deadline, and purpose on the savings goal setting screen. The device sends this information to the server, which stores it in a database. The server periodically checks the user's progress, analyzes the progress data, generates visualization data, and notifies the user.

[0664] Examples:

[0665] When a user sets a goal of "saving 200,000 yen in one year," the server updates and notifies the user of their progress every month.

[0666] Savings plan suggestions

[0667] Users input their income and expenditure information, which is then sent to the server via their device. The server then sends prompts to the generative AI model to generate an optimal savings plan. The emotion engine then adjusts the plan and makes suggestions to the user.

[0668] Examples:

[0669] For users with a monthly income of 300,000 yen and fixed expenses of 150,000 yen, the plan is to "save 30,000 yen each month and save an additional 10,000 yen after six months."

[0670] Savings Challenges and Rewards

[0671] The server sets up savings challenges, and the emotion engine proposes challenges based on the user's emotions. When a user participates in and completes a challenge, the server grants rewards and benefits.

[0672] Examples:

[0673] If a user succeeds in the challenge of "saving 10,000 yen every month for three months," the server will award them 500 yen worth of points.

[0674] Setting up an automatic savings program

[0675] The server proposes an automatic savings program based on the user's income and expenditure data, and the emotion engine adjusts the proposal. If the user approves the proposal, the server automatically executes the savings.

[0676] Examples:

[0677] The server runs a program that automatically saves 5,000 yen on the 15th and 30th of every month.

[0678] Investment advice and asset growth support

[0679] Users input their investment goals and risk tolerance, and the device sends this to the server. The server then sends prompts to the generative AI model to generate an optimal investment strategy. The emotion engine then adjusts the strategy and presents it to the user.

[0680] Examples:

[0681] For users seeking medium-risk, long-term asset growth, we propose a portfolio of 50% bonds, 30% stocks, and 20% real estate investment trusts.

[0682] Visualizing savings goals and reward systems

[0683] The server analyzes the progress of savings goals and creates visualizations. Rewards are provided when goals are achieved, and the emotion engine customizes the rewards.

[0684] Examples:

[0685] If the goal of saving 300,000 yen in six months is achieved, the server will provide a cashback of 1,000 yen.

[0686] Providing educational content on savings

[0687] The server uses a generative AI model to create educational content appropriate for the user, and an emotion engine to tailor the content. When a user requests content, the server provides the tailored educational content.

[0688] Examples:

[0689] When a user requests to "learn the basics of saving," educational content such as "how to review your monthly spending" and "tips for reducing fixed costs" is provided.

[0690] Use of emotion engine

[0691] The server analyzes user input and behavioral data using an emotion engine and customizes other functions of the system based on the results of this analysis.

[0692] Examples:

[0693] The emotion engine recognizes when a user is feeling stressed and suggests relaxing their savings plan as a relief measure.

[0694] Prompt Sentence Examples

[0695] "Generate an optimal savings plan based on the user's monthly income and fixed expenses. Adjust the plan based on stress levels identified by the emotion engine."

[0696] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0697] Specific flow of program processing

[0698] Emotion Engine Analysis

[0699] Step 1:

[0700] A user logs in to the app.

[0701] Input: User login data, operation log

[0702] Output: The operation log is sent to the server.

[0703] Specific operation: A user logs in to the app, and the app collects user operation data and sends it to the server.

[0704] Step 2:

[0705] The server passes the operation log to the emotion engine.

[0706] Input: Operation log

[0707] Output: Emotion analysis results

[0708] Specific operation: The received operation log is passed to the emotion engine, which analyzes the user's emotional state in real time.

[0709] Step 3:

[0710] The emotion engine analyzes the user's emotions and provides feedback on the results.

[0711] Input: Operation log

[0712] Output: Emotion analysis results (e.g., stress level, excitement level)

[0713] Specific operation: The emotion engine analyzes the operation log, identifies the emotional state, and provides feedback to other functions of the system.

[0714] Setting savings goals and tracking progress

[0715] Step 1:

[0716] The user accesses the savings goal setting screen and inputs the goal amount, deadline, and purpose.

[0717] Input: target amount, deadline, purpose

[0718] Output: User input data is saved to the device.

[0719] Specific operation: The user enters a savings goal, which is temporarily saved on the device.

[0720] Step 2:

[0721] The device sends the user input data to the server.

[0722] Input: User-entered data

[0723] Output: User-entered data sent to the server

[0724] Specific operation: The terminal sends user input data to the server.

[0725] Step 3:

[0726] The server stores the received data in a database and periodically collects progress data.

[0727] Input: User-entered data

[0728] Output: Data stored in the database, progress data

[0729] What it does: The server saves the user-entered data in a database and periodically collects progress data from the bank account.

[0730] Step 4:

[0731] The server analyzes and visualizes the progress data.

[0732] Input: Progress data

[0733] Output: Visualized progress data (graphs, reports)

[0734] Specific operation: The server analyzes the progress data, visualizes it in the form of graphs and reports, and notifies the user.

[0735] Savings plan suggestions

[0736] Step 1:

[0737] The user enters income and expense information.

[0738] Input: Income and expenditure data

[0739] Output: User input data is saved to the device.

[0740] Specific operation: The user enters income and expenditure data, which is temporarily stored on the device.

[0741] Step 2:

[0742] The device sends the user input data to the server.

[0743] Input: Income and expenditure data

[0744] Output: User-entered data sent to the server

[0745] Specific operation: The terminal sends user input data to the server.

[0746] Step 3:

[0747] The server analyzes the incoming data and sends prompts to the generative AI model.

[0748] Input: Income and expenditure data

[0749] Output: The input data, including the prompt sentence, is sent to the generative AI model.

[0750] Specific operation: The server analyzes the received data and sends prompt sentences to the generative AI model to generate an optimal savings plan.

[0751] Step 4:

[0752] A generative AI model generates an optimal savings plan.

[0753] Input: prompt statement, user input data

[0754] Output: Generated savings plan

[0755] How it works: The generative AI model generates an optimal savings plan based on the prompt.

[0756] Step 5:

[0757] The emotion engine adjusts the plan and makes suggestions to the user.

[0758] Input: Generated savings plan, sentiment analysis results

[0759] Output: Adjusted savings plan

[0760] Specific operation: The emotion engine adjusts the generated savings plan based on the user's emotional state and suggests it to the user.

[0761] Savings Challenges and Rewards

[0762] Step 1:

[0763] The server sets up a savings challenge and proposes it to the user.

[0764] Input: None

[0765] Output: Savings challenge proposal

[0766] Specific operation: The server periodically proposes a savings challenge to the user.

[0767] Step 2:

[0768] An emotion engine adjusts the challenge based on the user's emotions.

[0769] Input: Sentiment analysis results

[0770] Output: Adjusted Savings Challenge

[0771] How it works: The emotional engine takes into account the user's emotional state and adjusts the content and rewards of the savings challenge.

[0772] Step 3:

[0773] The user participates in a challenge.

[0774] Input: Press the Join button

[0775] Output: Join information is sent to the server.

[0776] Specific operation: The user presses a button to participate in the challenge, and participation information is sent to the server.

[0777] Step 4:

[0778] The server manages the progress of the challenge and grants rewards upon completion.

[0779] Input: Challenge progress data

[0780] Output: Reward

[0781] Specific operation: The server manages the progress of the challenge and grants rewards and benefits when the conditions are met.

[0782] Setting up an automatic savings program

[0783] Step 1:

[0784] The server suggests an automatic savings program based on the user's income and expenditure data.

[0785] Input: Income and expenditure data

[0786] Output: Proposal for an automatic savings program

[0787] Specific operation: The server analyzes the user's income and expenditure data and suggests the most suitable automatic savings program.

[0788] Step 2:

[0789] The emotion engine adjusts the suggestions.

[0790] Input: Sentiment analysis results, automatic savings program proposal

[0791] Output: Adjusted automatic savings program

[0792] How it works: The emotion engine adjusts the content of the automatic savings program based on the user's emotions.

[0793] Step 3:

[0794] The user approves the proposal.

[0795] Input: Press the approve button

[0796] Output: The authorization information is sent to the server.

[0797] Specific operation: The user approves the automatic savings program, and the approval information is sent to the server.

[0798] Step 4:

[0799] The server automatically executes the savings.

[0800] Enter: Approved automatic savings program

[0801] Output: Automatic savings execution data

[0802] Specific operation: The server automatically debits the user's account at regular intervals according to the approved program.

[0803] Investment advice and asset growth support

[0804] Step 1:

[0805] Users input their investment goals and risk tolerance.

[0806] Inputs: Investment goals, risk tolerance

[0807] Output: User input data is saved to the device.

[0808] Specific operation: The user enters their investment goals and risk tolerance, which are then temporarily saved on the device.

[0809] Step 2:

[0810] The device sends the user input data to the server.

[0811] Inputs: Investment goals, risk tolerance

[0812] Output: User-entered data sent to the server

[0813] Specific operation: The terminal sends user input data to the server.

[0814] Step 3:

[0815] The server analyzes the incoming data and sends prompts to the generative AI model.

[0816] Inputs: Investment goals, risk tolerance

[0817] Output: The input data, including the prompt sentence, is sent to the generative AI model.

[0818] Specific operation: The server analyzes the received data and sends prompt statements to the generative AI model to generate the optimal investment strategy.

[0819] Step 4:

[0820] A generative AI model generates optimal investment strategies.

[0821] Input: Prompt statement, investment goal, risk tolerance

[0822] Output: Generated investment strategy

[0823] How it works: The generative AI model generates an optimal investment strategy based on the prompt.

[0824] Step 5:

[0825] The emotion engine adjusts the strategy and makes suggestions to the user.

[0826] Input: Generated investment strategy, sentiment analysis results

[0827] Output: Adjusted investment strategy

[0828] Specific operation: The emotion engine adjusts the generated investment strategy based on the user's emotional state and suggests it to the user.

[0829] Visualizing savings goals and reward systems

[0830] Step 1:

[0831] The server analyzes the progress of the savings goal.

[0832] Input: Progress data

[0833] Output: Analysis results

[0834] Specific behavior: The server analyzes the savings goal progress data.

[0835] Step 2:

[0836] The server visualizes the progress data.

[0837] Input: Analysis results

[0838] Output: Visualized data (graphs, reports)

[0839] What it does: The server visualizes the progress data in the form of graphs and reports.

[0840] Step 3:

[0841] The server provides rewards when goals are achieved.

[0842] Input: Analysis results, visualization data

[0843] Output: Reward

[0844] Specific behavior: The server confirms goal achievement and provides a reward to the user.

[0845] Step 4:

[0846] The emotion engine customizes rewards.

[0847] Input: Sentiment analysis results

[0848] Output: Customized reward content

[0849] How it works: The emotion engine adjusts and customizes rewards based on the user's emotions.

[0850] Providing educational content on savings

[0851] Step 1:

[0852] The server sends prompts to the generative AI model to create educational content.

[0853] Input: User request, prompt

[0854] Output: Generated educational content

[0855] Specific operation: The server sends the user's request as a prompt to the generative AI model to create educational content.

[0856] Step 2:

[0857] An emotional engine adjusts educational content.

[0858] Input: Educational content, sentiment analysis results

[0859] Output: Tailored educational content

[0860] Specific behavior: The emotion engine adjusts educational content based on the user's emotional state.

[0861] Step 3:

[0862] A user requests educational content.

[0863] Input: Request operation

[0864] Output: The request information is sent to the server.

[0865] Specific operation: The user requests educational content and the relevant information is sent to the server.

[0866] Step 4:

[0867] The server provides tailored educational content to the user.

[0868] Input: Tailored educational content

[0869] Output: Provided educational content

[0870] Specific operation: The server provides the adjusted educational content to the user.

[0871] (Application example 2)

[0872] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0873] This invention is a system that incorporates an emotion engine that analyzes user emotions and provides functions such as setting savings goals, tracking progress, and proposing savings plans. However, current systems lack individualized responses that take into account the user's emotions and psychological state, and further personalization is required. Furthermore, the lack of effective user interaction in virtual environments poses challenges in improving user satisfaction and motivation. Therefore, it is desirable to provide a system that optimizes savings plans, investment strategies, rewards, and educational content based on the user's emotions, enabling more familiar interactions through a virtual assistant.

[0874] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for allowing a user to set a savings goal and manage the progress of the goal; means for analyzing the user's income and spending patterns and proposing an individual savings plan; means for allowing the user to participate in a savings challenge and offering rewards or benefits upon achievement; means for automatically saving based on the user's income and spending; means for analyzing the user's investment goals and risk tolerance and proposing an appropriate investment strategy; means for visualizing the user's progress toward the savings goal and offering rewards upon achievement; means for providing the user with educational content related to savings and investment; means for recognizing the user's emotions using an emotion analysis module and customizing the savings plan, investment strategy, reward details, and educational content based on the emotions; and means for using a virtual assistant to support interaction with the user and progress and set the savings goal. This enables personalized responses based on the user's individual emotions and psychological state, enabling effective interaction in a virtual environment.

[0875] "User" refers to any individual or organization that uses this system.

[0876] A "savings goal" refers to the amount of savings or purpose that a user sets and wants to achieve within a certain period of time.

[0877] "Progress management" refers to monitoring, analyzing, and visualizing progress toward savings goals set by users.

[0878] "Income and expenditure patterns" refers to the user's monthly income and expenditure trends and specific breakdowns.

[0879] A "savings plan" refers to specific methods and steps for achieving savings goals that are presented to users after taking into account their income and spending patterns.

[0880] A "savings challenge" refers to a competitive initiative in which users participate in short-term or medium- to long-term savings goals.

[0881] "Rewards and perks" refers to incentives offered to users upon achieving savings challenges and goals.

[0882] "Automatic savings" refers to a function in which the system automatically saves a certain amount based on the user's income and expenditure data.

[0883] "Investment goal" refers to the investment results or return target set by the user that they wish to achieve within a certain period of time.

[0884] "Risk tolerance" refers to the investment risk level that a user can tolerate.

[0885] An "investment strategy" refers to a specific investment policy or portfolio that is generated taking into account a user's investment goals and risk tolerance.

[0886] "Visualization" refers to the system displaying the user's savings goals and progress in visual ways, such as graphs and charts.

[0887] "Educational Content" means educational materials and opportunities related to savings and investing that are provided to Users.

[0888] "Emotion analysis module" refers to software or algorithms used to analyze a user's emotional state.

[0889] A "virtual assistant" refers to a virtual character or interface that interacts with and provides assistance to users in a digital environment.

[0890] This system incorporates an emotion analysis module that recognizes user emotions, and performs savings goal setting, progress management, savings plan proposals, rewards, automatic savings, investment advice, savings goal visualization, and educational content provision. Specifically, these functions are realized through interactions between the server, terminals, and users.

[0891] The emotion analysis module analyzes the user's emotional state in real time and recognizes emotions based on the user's input and behavioral data. This emotional data is then used by the entire system to provide the optimal response to the user.

[0892] Setting savings goals and tracking progress

[0893] Users access the savings goal setting screen through their device and enter the goal amount, deadline, purpose, etc. This data is sent to the server and stored in a database. The server checks the user's savings progress on a daily or weekly basis, and analyzes and visualizes the progress data. For example, if a user sets a goal of "saving 200,000 yen in one year," the server will link the user's bank account information and provide monthly updates and notifications on their progress.

[0894] Savings plan suggestions

[0895] The system provides a screen where users can enter income and expenditure information via their device, which is then sent to a server for analysis. A generative AI model generates a savings plan and suggests it to the user. An emotion analysis module then analyzes the user's emotions and adjusts the plan accordingly. For example, for a user with a monthly income of 300,000 yen and fixed expenses of 150,000 yen, the system suggests a plan to "save 30,000 yen each month and start saving an additional 10,000 yen six months later."

[0896] Savings Challenges and Rewards

[0897] The server sets up savings challenges and proposes them to users. If users participate in the challenge and achieve their goals, they will receive rewards and benefits. The sentiment analysis module customizes the reward content based on the user's emotions. For example, if a user participates in a challenge to "save 10,000 yen every month for three months" and meets the conditions, the server will award 500 yen worth of points.

[0898] Setting up an automatic savings program

[0899] The server proposes an automatic savings program based on the user's income and expenditure data. If the user approves, the system automatically starts saving. The emotion analysis module adjusts the program based on the user's emotions. For example, the system can implement a program that automatically saves 5,000 yen on the 15th and 30th of each month.

[0900] Investment advice and asset growth support

[0901] The generative AI model analyzes the user's investment goals and risk tolerance and proposes an appropriate investment strategy. The sentiment analysis module adjusts the strategy based on the user's sentiment. For example, if a user enters information such as "I aim for medium-risk, long-term asset growth," the system will propose a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[0902] Visualizing savings goals and reward systems

[0903] The server analyzes and visualizes the user's progress toward their savings goal. Once the goal is achieved, a reward is provided. The sentiment analysis module customizes the reward content. For example, a user can set a goal of "saving 300,000 yen in six months" and check their progress as they go along. When the goal is achieved, a cashback of 1,000 yen is provided.

[0904] Providing educational content on savings

[0905] The generative AI model creates and provides educational content appropriate for the user. The sentiment analysis module adjusts the content based on the user's emotions. For example, if a user requests to learn the basics of saving, educational content such as "How to review your monthly spending" and "Tips for reducing fixed costs" will be provided.

[0906] Using the sentiment analysis module

[0907] The sentiment analysis module analyzes user input and other behavioral data to recognize emotions. Based on this emotional data, the system's various features (savings plans, rewards, investment strategies, educational content, etc.) are optimized for the user. For example, if the sentiment analysis module detects that the user is feeling stressed, it can suggest relaxing the savings plan as a mitigation measure.

[0908] Examples of prompt statements

[0909] User A opens the app and sets a goal of "saving 200,000 yen in one year." Their income is 300,000 yen and their expenses are 150,000 yen. The emotion engine recognizes "stress." Please suggest a savings plan based on this information.

[0910] As described above, the interaction between the server, terminal, and user allows for emotion analysis and realizes a system that responds to individual user needs, enabling users to more effectively achieve their savings goals and supporting financial independence.

[0911] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0912] Step 1:

[0913] The user accesses the savings goal setting screen through the terminal and inputs the goal amount, deadline, purpose, etc. This inputs the savings goal data. The input data is sent to the server and stored in the database.

[0914] Step 2:

[0915] The server checks the user's savings progress on a daily or weekly basis. Savings progress data is collected by linking the user's bank account information. This data is analyzed by the server, and the progress is visualized and output to the terminal. For example, the progress is displayed in chart form.

[0916] Step 3:

[0917] Users have access to a screen on their device where they can input their income and expenditure information. Once the income and expenditure data is entered, it is sent to a server. The server uses a generative AI model to analyze the data, generate a savings plan, and obtain the savings plan data, which is then output back to the device.

[0918] Step 4:

[0919] The emotion analysis module analyzes the user's input data and behavioral data to recognize the user's emotional state. For example, it generates emotion data through text analysis or voice analysis. The generated emotion data is sent to the server and stored in a database.

[0920] Step 5:

[0921] Based on the emotional data, the server adjusts the savings plan. For example, if the user is feeling stressed, the generative AI model will recalculate the savings plan to ease the stress, and generate new savings plan data. This new data will be output to the device.

[0922] Step 6:

[0923] The server sets up the user to participate in the savings challenge and proposes it to the device. When the user participates in the challenge, the status is sent to the server and stored in a database. When the goal is achieved, the server generates reward and benefit data and outputs it to the device.

[0924] Step 7:

[0925] The server proposes an automatic savings program based on the user's income and expenditure data. If the user approves, the data is sent to the server and stored in a database. The program is adjusted appropriately based on the user's emotional data, and automatic savings data is generated. For example, the terminal may output a message such as, "Automatically save 5,000 yen on the 15th and 30th of each month."

[0926] Step 8:

[0927] The server analyzes the user's investment goals and risk tolerance and uses a generative AI model to propose an appropriate investment strategy. Investment strategy data is generated and output to the terminal. For example, an investment strategy such as "Aiming for long-term asset growth with medium risk" is displayed.

[0928] Step 9:

[0929] The server analyzes the user's progress toward their savings goal and generates visualization data. The progress is visualized in charts and other formats and output to the device. When the goal is achieved, the server generates reward data and displays a cashback of 1,000 yen.

[0930] Step 10:

[0931] The system uses a generative AI model to create educational content requested by the user, which is then provided by the server. The content is adjusted based on emotion data, and the educational content is output to the device. For example, content such as "How to review your monthly expenses" and "Tips for reducing fixed costs" is displayed.

[0932] Through the above processing steps, the server continuously analyzes the emotional data and provides savings plans and educational content tailored to the individual needs of the user, thereby realizing a system that encourages users to become financially independent.

[0933] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0934] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0935] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0936] [Second embodiment]

[0937] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0938] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0939] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0940] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0941] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0942] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0943] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0944] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0945] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0946] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0947] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0948] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0949] The present invention is a system that allows users to set savings goals and effectively manage their progress. Below, we will explain the programs for realizing each function and specific examples.

[0950] Setting savings goals and tracking progress

[0951] This system provides a savings goal setting function. Users input the amount they want to save, the deadline, and the purpose, and the server stores this information in a database. The system periodically tracks the user's savings progress and analyzes the progress data to provide the user with a visual representation of their current situation.

[0952] Examples:

[0953] The user sets a goal of "saving 200,000 yen in one year." The server links the user's bank account information, automatically updates the user's savings progress each month, and notifies the user through the app.

[0954] Savings plan suggestions

[0955] The server analyzes the user's income and spending patterns and proposes an individual savings plan. Based on the user's income information and regular spending information, the generative AI model generates an optimal savings plan and provides it to the user. If the user agrees with the proposal, the plan is confirmed and savings are automatically made.

[0956] Examples:

[0957] If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, the server will suggest a savings plan that involves saving 30,000 yen each month and then starting to save an additional 10,000 yen six months later.

[0958] Savings Challenges and Rewards

[0959] Users participate in savings challenges and receive rewards and perks when they achieve their goals. The server tracks the progress of the challenges and awards rewards when they are achieved.

[0960] Examples:

[0961] If a user participates in the challenge of "saving 10,000 yen every month for three months" and meets all the conditions, the server will award the user 500 yen worth of points.

[0962] Setting up an automatic savings program

[0963] The server proposes an automatic savings program based on the user's income and spending patterns, and if the user approves, the system automatically saves the specified amount on a regular basis.

[0964] Examples:

[0965] When a user agrees to a program that automatically saves 5,000 yen on the 15th and 30th of every month, the server automatically saves the specified amount on a regular basis.

[0966] Investment advice and asset growth support

[0967] The server analyzes the user's investment goals and risk tolerance and proposes an appropriate investment strategy, allowing users to not only save money but also manage their assets efficiently.

[0968] Examples:

[0969] If a user inputs a goal of "long-term asset growth with medium risk," the server will suggest a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[0970] Visualizing savings goals and reward systems

[0971] The server visualizes the progress of the user's savings goal, using graphs and various indicators to show the progress to the user, and provides rewards when the goal is achieved.

[0972] Examples:

[0973] Users set a goal of "saving 300,000 yen in 6 months" and progress while checking their progress. When the goal is achieved, the server provides a cashback of 1,000 yen.

[0974] Providing educational content on savings

[0975] The server provides users with educational content on savings and investments to improve their financial literacy. Using a generative AI model, it automatically creates and provides the most appropriate content for each user.

[0976] Examples:

[0977] If a user requests, "I want to learn the basics of saving," the server will provide educational content such as "How to review your monthly expenses" and "Tips for reducing fixed expenses."

[0978] The processing flow will be explained below.

[0979] Flow of setting savings goals and tracking progress

[0980] Step 1:

[0981] On the device: The user opens the app and accesses the savings goal setting screen.

[0982] Step 2:

[0983] User: Enter the target amount, deadline, and purpose.

[0984] Step 3:

[0985] Terminal: Sends user input data to the server.

[0986] Step 4:

[0987] Server: Receives user input data and stores it in a database.

[0988] Step 5:

[0989] Server: Retrieves transaction history from the database to check the user's savings status on a daily or weekly basis.

[0990] Step 6:

[0991] Server: Analyzes progress towards goals through batch processing.

[0992] Step 7:

[0993] Server: Calculates the progress and sends the result to the device.

[0994] Step 8:

[0995] Terminal: Visually display the received progress data, for example using a gauge or bar graph.

[0996] Savings plan proposal process flow

[0997] Step 1:

[0998] Terminal: Provides a screen where users can enter income and expense information.

[0999] Step 2:

[1000] User: Enter your monthly income and major recurring expenses (rent, utilities, food, etc.).

[1001] Step 3:

[1002] Terminal: Sends input data to the server.

[1003] Step 4:

[1004] Server: Receives user income and expenditure data and analyzes it using a generative AI model.

[1005] Step 5:

[1006] Server: Generates a savings plan and sends it to the device.

[1007] Step 6:

[1008] Terminal: Display the suggested savings plan to the user.

[1009] Step 7:

[1010] User: Review the proposed savings plan and adjust as needed.

[1011] Savings Challenge and Reward Processing Flow

[1012] Step 1:

[1013] Server: Sets the savings challenge and sends it to the device.

[1014] Step 2:

[1015] Device: Shows the challenge to the user and asks for their confirmation.

[1016] Step 3:

[1017] User: Decides to participate in the proposed challenge and clicks the Join button.

[1018] Step 4:

[1019] Device: Sends the user's decision to the server.

[1020] Step 5:

[1021] Server: Periodically checks the user's savings progress and calculates the challenge progress.

[1022] Step 6:

[1023] Server: Awards rewards when a challenge is completed and sends the data to the device.

[1024] Step 7:

[1025] Device: Notifies the user of the challenge completion and reward details.

[1026] Process flow for setting up an automatic savings program

[1027] Step 1:

[1028] Server: Proposes an automatic savings program based on the user's income and expenditure data.

[1029] Step 2:

[1030] On the device: Display the suggestions to the user.

[1031] Step 3:

[1032] User: Approves the proposed automatic savings program.

[1033] Step 4:

[1034] Terminal: Sends authorization information to the server.

[1035] Step 5:

[1036] Server: Executes approved automatic savings programs and automatically saves according to a specified schedule.

[1037] Step 6:

[1038] Server: Notifies the user of the progress of the automatic savings.

[1039] Step 7:

[1040] On the device: Show users their savings progress.

[1041] Investment advice and asset growth support process

[1042] Step 1:

[1043] Terminal: Provides a screen where users can input their investment goals and risk tolerance.

[1044] Step 2:

[1045] User: Enter your investment goals and risk tolerance.

[1046] Step 3:

[1047] Terminal: Sends input data to the server.

[1048] Step 4:

[1049] Server: Uses generative AI models to analyze user input data and generate optimal investment strategies.

[1050] Step 5:

[1051] Server: Sends investment strategies to the terminal.

[1052] Step 6:

[1053] Terminal: displays the proposed investment strategy to the user.

[1054] Step 7:

[1055] User: Review investment strategies and implement them as needed.

[1056] Step 8:

[1057] Server: Regularly tracks users' investment progress and adjusts strategies as needed.

[1058] Step 9:

[1059] On the device: Notify the user of progress and suggested adjustments.

[1060] Visualization of savings goals and reward system processing flow

[1061] Step 1:

[1062] Device: Provides a screen where users can set their savings goals.

[1063] Step 2:

[1064] User: Set the goal amount and deadline.

[1065] Step 3:

[1066] Terminal: Sends the settings to the server.

[1067] Step 4:

[1068] Server: Stores goal settings in a database and tracks progress periodically.

[1069] Step 5:

[1070] Server: Performs the process of granting rewards when the goal is achieved.

[1071] Step 6:

[1072] Server: Analyzes the progress data and sends it to the device as visual information.

[1073] Step 7:

[1074] On the device: Show progress to the user with graphs and metrics.

[1075] Step 8:

[1076] Server: Notifies the device of the reward to be provided when the goal is achieved.

[1077] Step 9:

[1078] Terminal: Notifies the user and assists them in claiming their reward.

[1079] Process flow for providing educational content on savings

[1080] Step 1:

[1081] Server: Uses generative AI models to create educational content tailored to the user.

[1082] Step 2:

[1083] Server: Sends content to the device.

[1084] Step 3:

[1085] Device: Displays educational content to the user.

[1086] Step 4:

[1087] Users: View and learn educational content.

[1088] Step 5:

[1089] Device: Sends user feedback to the server.

[1090] Step 6:

[1091] Server: Regularly update educational content based on user feedback.

[1092] Example 1

[1093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1094] Existing savings management systems have basic functions for setting users' savings goals and tracking progress, but they lack the ability to analyze individual income and expenditure patterns and propose optimal savings plans. They also lack the ability to provide appropriate rewards and educational content based on savings progress. Furthermore, there are challenges in how to effectively analyze collected data and present specific savings and investment strategies to users.

[1095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1096] In this invention, the server includes: means for allowing users to set savings goals and manage their progress; means for analyzing the user's income and spending patterns and proposing personalized savings plans; means for allowing users to participate in savings challenges and offering rewards and benefits upon achievement; means for automatically saving based on the user's income and spending; means for analyzing the user's investment goals and risk tolerance and proposing appropriate investment strategies; means for visualizing the user's progress toward their savings goals and offering rewards upon achievement; means for providing the user with educational content on savings and investments; means for analyzing the user's income and spending using a generative AI model and generating an optimal savings plan; and means for inputting the user's income and spending data into the generative AI model using prompts. This allows for the proposal and implementation of an optimal savings plan tailored to the user's individual financial situation, resulting in more effective savings and investment management. Furthermore, regular progress checks and rewards can motivate users to save, and the provision of educational content can improve users' financial literacy.

[1097] "Means for analyzing a user's income and expenditure patterns" refers to a data processing device or software that analyzes the user's financial situation and creates a future savings plan based on the income and expenditure information entered by the user.

[1098] "Means to propose individual savings plans" refers to the function in which the generative AI model analyzes the collected income and expenditure data and automatically generates and presents the optimal savings plan for each user.

[1099] "Means to participate in savings challenges" refers to the functionality that allows users to challenge themselves to set savings goals through the application, track their progress, and receive rewards upon achieving their goals.

[1100] "Means of providing rewards or benefits upon achievement" refers to a function that provides rewards such as points or monetary incentives when a user achieves a set savings goal.

[1101] "Means for automatic savings" refers to a system function that automatically saves an amount according to income and spending patterns based on a savings plan set by the user.

[1102] "Means for proposing appropriate investment strategies" refers to the function of presenting optimal investment plans and portfolios to users based on their investment goals and risk tolerance.

[1103] "Means for visualizing progress toward savings goals" refers to a function that visually displays the current progress toward the savings goal set by the user using graphs and numerical data.

[1104] "Means for providing educational content related to savings and investments" refers to the function of automatically generating and presenting educational materials and information that provide knowledge about savings and investments in order to improve users' financial literacy.

[1105] A "generative AI model" refers to an artificial intelligence algorithm that inputs a user's income and expenditure data as prompts and generates optimal savings plans and investment strategies based on the data.

[1106] A "prompt" refers to a question or command that is entered to instruct a generative AI model to perform a specific analysis or generate data.

[1107] The present invention is a system that allows users to set savings goals and effectively manage their progress. Below, we will explain the programs for realizing each function and specific examples.

[1108] Hardware and software used

[1109] In this invention, we mainly use a server, a user terminal (such as a smartphone or PC), and a generative AI model.

[1110] Server: Provides key functions such as setting savings goals, collecting income and expenditure data, analyzing data, proposing savings plans, tracking progress, and providing a reward system.

[1111] User device: Using a smartphone app or web app, the user enters information and receives feedback from the server.

[1112] Generative AI model: Generates savings plans and investment strategies based on user income and expenditure data via prompts.

[1113] Setting savings goals and tracking progress

[1114] Users input the amount they want to save, the deadline, and the purpose into their device. The device sends this information to the server, which stores this data in a database. The server periodically checks the user's bank account information, updates their savings progress, and sends notifications to the user that visually display their progress.

[1115] Examples:

[1116] The user sets a goal of "saving 200,000 yen in one year." The user's device sends this information to the server, which stores it in a database. At the end of each month, the server checks the bank account information, updates the progress, and notifies the user.

[1117] Savings plan suggestions

[1118] The user enters their monthly income and fixed expenses into their device. The device sends this to the server, which prepares the collected data for input into the generative AI model. The generative AI model generates an optimal savings plan through prompts and returns the results to the server. The server sends this savings plan to the user's device and presents it to the user. If the user agrees to the plan, regular automatic savings will begin.

[1119] Examples:

[1120] The prompt sentence "If the user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, please suggest the optimal savings plan" is input into the AI ​​model, and the model generates a plan that says "Save 30,000 yen each month and start saving an additional 10,000 yen six months later." This is presented to the user, and if they agree, the server sets up automatic savings.

[1121] Savings Challenges and Rewards

[1122] If a user wants to participate in the savings challenge, they register on their device. The server stores this information in a database and periodically checks the user's savings status. When the user completes the challenge, the server automatically awards rewards.

[1123] Examples:

[1124] When a user participates in the challenge of "saving 10,000 yen every month for three months," the server checks the user's progress every month, and if the user achieves the goal after three months, the server awards the user with 500 yen worth of points.

[1125] Setting up an automatic savings program

[1126] The server proposes an automatic savings program based on the user's income and spending patterns, and if the user agrees, the server sets up a system to automatically save the specified amount on a regular basis.

[1127] Examples:

[1128] When a user agrees to a program that "automatically saves 5,000 yen on the 15th and 30th of every month," the server automatically sets up and executes the program to save 5,000 yen on the 15th and 30th of every month.

[1129] Investment advice and asset growth support

[1130] When a user inputs their investment goals and risk tolerance, the server analyzes them and inputs an appropriate investment strategy into the AI ​​model. The model generates an optimal investment strategy and returns it to the server, which then presents it to the user.

[1131] Examples:

[1132] When a user inputs a goal such as "aiming for long-term asset growth with medium risk," the server asks the AI ​​model to generate a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts" and presents it to the user.

[1133] Visualizing savings goals and reward systems

[1134] The server visually displays the progress of the savings goal set by the user using graphs and various indicators, and when the user achieves the goal, the server grants a reward.

[1135] Examples:

[1136] The user sets a goal of "saving 300,000 yen in 6 months," and the server displays the progress in a graph. When the goal is achieved, the server provides the user with a cashback of 1,000 yen.

[1137] Providing educational content on savings

[1138] The server uses a generative AI model to generate and provide educational content that best suits the user's request.

[1139] Examples:

[1140] When a user requests to "learn the basics of saving," the server generates educational content such as "how to review your monthly expenses" and "tips for reducing fixed costs" and provides it to the user.

[1141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1142] Program processing flow

[1143] Setting savings goals and tracking progress

[1144] Step 1:

[1145] The user enters a savings goal.

[1146] Input: Amount you want to save, deadline, and purpose.

[1147] Specific behavior: A user uses a smartphone app or web app to enter the required information into a screen for setting a savings goal.

[1148] Step 2:

[1149] The terminal sends the input data to the server.

[1150] Input: Savings goal data entered by the user.

[1151] Data processing: Converting data into a format that the server can accept.

[1152] Output: The transformed data is sent to the server.

[1153] Step 3:

[1154] The server stores the received data in a database.

[1155] Input: Data sent from the terminal.

[1156] Data processing: Processing into a format that is compatible with the database.

[1157] Output: The processed data is stored in a database.

[1158] Step 4:

[1159] The server periodically checks the user's bank account and updates the progress data.

[1160] Input: Bank account information, previous savings progress data.

[1161] Data calculations: Analyze bank account balances and transaction histories.

[1162] Output: Save the updated progress data to the database.

[1163] Step 5:

[1164] The server notifies the user terminal of the progress.

[1165] Input: The updated progress data.

[1166] What it does: The server visualizes the progress data and generates notification messages.

[1167] Output: A notification message is sent to the user's terminal.

[1168] Savings plan suggestions

[1169] Step 1:

[1170] The user enters their monthly income and fixed expenses.

[1171] Input: Monthly income, monthly fixed expenses.

[1172] Specific behavior: A user enters income and expenses into a smartphone app or web app.

[1173] Step 2:

[1174] The terminal sends user input to the server.

[1175] Input: Income and expense data entered by the user.

[1176] Data processing: Convert the data format to one suitable for the server.

[1177] Output: The transformed data is sent to the server.

[1178] Step 3:

[1179] The server converts the collected data into prompt sentence format and sends it to the generative AI model.

[1180] Input: Income and expenditure data.

[1181] Data processing: Convert into prompt sentence format.

[1182] Output: "If the user's monthly income is ¥300,000 and their monthly fixed expenses are ¥150,000, please suggest the optimal savings plan."

[1183] Step 4:

[1184] The generative AI model generates an appropriate savings plan and returns it to the server.

[1185] Input: The prompt statement.

[1186] Data calculation: The AI ​​model calculates income and expenditure data to generate an optimal savings plan.

[1187] Output: Savings plan (e.g., "Save 30,000 yen each month, and save an additional 10,000 yen after six months").

[1188] Step 5:

[1189] The server transmits the generated savings plan to the user terminal and presents it.

[1190] Input: Generated savings plan data.

[1191] Specific behavior: The server generates a notification message to the user.

[1192] Output: A notification message is sent to the user's terminal.

[1193] Step 6:

[1194] If the user agrees to the proposal, the server sets up the savings plan to run periodically.

[1195] Input: User consent data.

[1196] Specific operation: The server sets up and runs an automatic savings program.

[1197] Output: Savings are made periodically.

[1198] Savings Challenges and Rewards

[1199] Step 1:

[1200] A user participates in a savings challenge.

[1201] Input: Challenge goal (e.g., "Save 10,000 yen every month for three months").

[1202] Specific actions: Register to participate in the challenge on the app.

[1203] Step 2:

[1204] The terminal sends the participation information to the server.

[1205] Input: Challenge goal data.

[1206] Data processing: Convert data into server format.

[1207] Output: The transformed data is sent to the server.

[1208] Step 3:

[1209] The server periodically checks the progress of the challenge and updates the database.

[1210] Input: Account information and savings progress data.

[1211] Data calculation: Analyze your progress based on your account information.

[1212] Output: Save the updated progress data to the database.

[1213] Step 4:

[1214] If the challenge is met, the server will reward the user.

[1215] Input: The completed challenge data.

[1216] Specific behavior: Generates reward data and adds it to the user's points account.

[1217] Output: The user's points balance is updated.

[1218] Setting up an automatic savings program

[1219] Step 1:

[1220] The server suggests automatic savings programs based on the user's income and spending patterns.

[1221] Input: Income, expenditure data.

[1222] Data calculation: Analyzes data and generates optimal automatic savings programs.

[1223] Output: Proposal for an automated savings program.

[1224] Step 2:

[1225] The user agrees to the proposed program, and the device sends the consent information to the server.

[1226] Input: User consent data.

[1227] Data processing: Convert consent data into server format.

[1228] Output: The transformed data is sent to the server.

[1229] Step 3:

[1230] The server sets an automatic savings program and automatically saves a designated amount periodically.

[1231] Input: Automatic savings program data, user's bank account information.

[1232] Specific operation: The server sets a periodic task and automatically saves money on the specified date.

[1233] Output: The savings will be made on the specified date.

[1234] Investment advice and asset growth support

[1235] Step 1:

[1236] The user enters their investment goals and risk tolerance.

[1237] Inputs: Investment goals, risk tolerance.

[1238] Specific action: The user enters required information into a smartphone app or web app.

[1239] Step 2:

[1240] The terminal sends the input data to the server.

[1241] Inputs: Investment objectives and risk tolerance data.

[1242] Data processing: Convert the data format to one suitable for the server.

[1243] Output: The transformed data is sent to the server.

[1244] Step 3:

[1245] The server converts the collected data into prompt sentence format and sends it to the generative AI model.

[1246] Inputs: Investment objectives, risk tolerance data.

[1247] Data processing: Convert into prompt sentence format.

[1248] Output: Prompt statement (e.g., "Please suggest a portfolio that offers medium risk and long-term capital growth.").

[1249] Step 4:

[1250] The generative AI model generates an appropriate investment strategy and returns it to the server.

[1251] Input: The prompt statement.

[1252] Data calculation: AI models analyze user data and generate appropriate investment strategies.

[1253] Output: Investment strategy (e.g., "50% bonds, 30% stocks, 20% real estate investment trusts").

[1254] Step 5:

[1255] The server transmits the generated investment strategy to the user terminal and presents it.

[1256] Input: Generated investment strategy data.

[1257] Specific behavior: The server generates a notification message to the user.

[1258] Output: A notification message is sent to the user's terminal.

[1259] Visualizing savings goals and reward systems

[1260] Step 1:

[1261] The user sets a savings goal.

[1262] Input: Savings goal (amount, period).

[1263] Specific behavior: The user enters their savings goal in a smartphone app or web app.

[1264] Step 2:

[1265] The device sends the savings goal to the server.

[1266] Input: Savings goal data.

[1267] Data processing: Convert the data format to one suitable for the server.

[1268] Output: The transformed data is sent to the server.

[1269] Step 3:

[1270] The server periodically checks the progress of the savings goal and displays it visually.

[1271] Input: Savings goal data and progress data.

[1272] Data calculation: Analyze progress to display it graphically and numerically.

[1273] Output: A progress visualization is generated.

[1274] Step 4:

[1275] The server notifies the user terminal of the progress.

[1276] Input: Visualized progress data.

[1277] Specific action: The server generates a notification message.

[1278] Output: A notification message is sent to the user's terminal.

[1279] Providing educational content on savings

[1280] Step 1:

[1281] A user requests educational content.

[1282] Input: A request for educational content.

[1283] Specific operation: A user enters a request into a smartphone app or web app.

[1284] Step 2:

[1285] The device sends a request to the server.

[1286] Input: Educational content request data.

[1287] Data processing: Convert the data format to one suitable for the server.

[1288] Output: The transformed data is sent to the server.

[1289] Step 3:

[1290] The server sends the request to the generative AI model to generate optimal educational content.

[1291] Input: A request for educational content.

[1292] Data calculation: AI models generate optimal content based on user requests.

[1293] Output: Optimal educational content.

[1294] Step 4:

[1295] The server transmits the generated educational content to the user terminal and provides it.

[1296] Input: Generated educational content data.

[1297] What it does: Generates formatted educational content as notification messages.

[1298] Output: A notification message is sent to the user's terminal.

[1299] (Application example 1)

[1300] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1301] Conventional savings management systems limit the ways in which users can effectively track and manage their savings progress even when they set savings goals. They also lack the ability to suggest optimal savings plans based on the user's individual income and spending patterns. Furthermore, they lack the ability to track progress during savings challenges, provide rewards, and provide real-time notifications, making it difficult to maintain user motivation. A system that can solve these issues and improve users' savings efficiency is needed.

[1302] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1303] In this invention, the server includes means for managing progress toward a savings goal set by the user in real time and automatically updating the progress, means for proposing an optimal savings plan to the user in real time based on income and expenditure patterns, and means for periodically notifying the user of the savings progress. This allows the user to check the progress toward the savings goal in real time and to save effectively by always receiving optimal savings plans.

[1304] A "savings goal" is the amount or purpose that a user sets and wants to achieve within a certain period of time.

[1305] "Progress management" means regularly tracking and checking progress towards set goals.

[1306] "Revenue" means the amount of money a User earns within a given period of time.

[1307] "Expenses" means the amount of money a User consumes or pays within a given period of time.

[1308] A "savings plan" is a plan that suggests the optimal savings amount and period based on the user's income and expenses.

[1309] A "savings challenge" is an activity in which users participate and receive rewards and benefits by achieving certain goals.

[1310] "Rewards" refer to benefits or points given to users when they achieve goals such as savings challenges.

[1311] "Real-time" means that data and information are updated almost instantly, allowing users to see the latest status.

[1312] "Notification" means a communication from the system to the user informing them of information or progress.

[1313] A "generative AI model" is an artificial intelligence that automatically generates and suggests appropriate savings plans and educational content based on large amounts of data.

[1314] "Educational Content" means information and materials that help users learn about saving and investing.

[1315] "System" refers to a series of programs and hardware that integrates the above elements and assists users in managing their savings.

[1316] One embodiment of the present invention is a system that allows users to set savings goals and effectively manage their progress. The system proposes optimal savings plans based on the user's income and spending patterns, tracks progress in real time, and automatically updates the plans. The system also includes a function that allows users to participate in savings challenges and receive rewards and benefits when they achieve their goals.

[1317] System configuration

[1318] The system consists of the following components:

[1319] 1. User device: Users set goals and check progress on devices such as smartphones.

[1320] 2. Server: Manages user data and generates savings plans and educational content using generative AI models.

[1321] 3. Database: Stores users' savings goals, progress data, income, and expenditure information.

[1322] 4. Generative AI model: An AI engine that analyzes a user's income and spending patterns and generates a personalized savings plan.

[1323] Program processing explanation

[1324] 1. Goal setting and progress tracking:

[1325] The user sets a savings goal (e.g., "save 200,000 yen in one year") using their smartphone. The server stores this goal and related information (deadline, purpose, etc.) in a database.

[1326] The server links to the user's bank account information, automatically updates the user's savings progress each month, and notifies the user through the app.

[1327] 2. Savings plan suggestions:

[1328] The server analyzes the user's income and regular expenditure information and uses a generative AI model to propose an optimal savings plan. For example, if a user's monthly income is 300,000 yen and their monthly fixed expenditure is 150,000 yen, the server will propose a plan to "save 30,000 yen per month and start saving an additional 10,000 yen six months later."

[1329] 3. Challenge and reward management:

[1330] Users can participate in challenges such as "save 10,000 yen every month for three months." The server tracks the progress of the challenge and rewards the user with 500 yen worth of points when all conditions are met.

[1331] 4. Providing educational content:

[1332] The server provides users with educational content on saving and investing. For example, if a user requests to learn the basics of saving, the server will provide content such as how to review monthly expenses and tips for reducing fixed costs.

[1333] Specific hardware and software used

[1334] Smartphone: The device through which the user interacts with the app.

[1335] Server: Manage user data using AWS or Google Cloud.

[1336] Generative AI models: Generate savings plans and educational content using AI engines such as OpenAI GPT.

[1337] Specific examples and prompts

[1338] Examples:

[1339] Using the smartphone app, users can set a goal of "saving 200,000 yen in one year" and check their progress every month. They can also participate in a challenge to "save 10,000 yen every month for three months" and instantly check their progress each month from the app.

[1340] Example prompt sentence:

[1341] "If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, please suggest the optimal savings plan."

[1342] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1343] Step 1:

[1344] Users set savings goals using their smartphones and enter them into the application.

[1345] Input: Amount you want to save, deadline, purpose

[1346] Output: Set target data

[1347] How it works: The user enters a savings goal into the app, such as "save 200,000 yen in one year." This data is sent to the server and stored in a database.

[1348] Step 2:

[1349] The server links the target data stored in the database with the user's bank account information.

[1350] Input: User goal data, bank account information

[1351] Output: Linked account information and goal data

[1352] What it does: The server accesses the user's bank account information and links it to their savings goals, allowing the user's savings progress to be automatically tracked.

[1353] Step 3:

[1354] The server periodically checks the user's bank account and updates them on their savings progress.

[1355] Input: Bank account balance information

[1356] Output: Updated savings progress data

[1357] What it does: The server retrieves the user's bank account balance every month, calculates the current progress towards the savings goal, updates the database, and notifies the user.

[1358] Step 4:

[1359] The server analyzes the user's income and expenditure data and uses a generative AI model to propose an optimal savings plan based on those patterns.

[1360] Input: User income data, expenditure data

[1361] Output: Optimal savings plan

[1362] How it works: The server collects and analyzes the user's income and expenditure data. Using a generative AI model, it proposes a savings plan, such as "save 30,000 yen per month and start saving an additional 10,000 yen six months later." This plan is then notified to the user.

[1363] Step 5:

[1364] Users participate in savings challenges and the server tracks their progress.

[1365] Input: Challenge goal, participation period

[1366] Output: Challenge progress

[1367] Specific operation: A user participates in a challenge such as "save 10,000 yen every month for three months." The server periodically checks the progress of the challenge and notifies the user.

[1368] Step 6:

[1369] The server will award rewards and perks when the challenge is completed.

[1370] Input: Challenge completion status

[1371] Output: Rewards and rewards data

[1372] Specific operation: If the challenge is completed, the server will give the user 500 yen worth of points. This information will also be saved in the database and notified to the user.

[1373] Step 7:

[1374] The server provides users with educational content on saving and investing.

[1375] Input: User request, interest data

[1376] Output: Educational content

[1377] Specific operation: The user makes a request such as "I want to learn the basics of saving." The server uses the generative AI model to generate appropriate educational content and provides it to the user.

[1378] Specific prompt examples:

[1379] "If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, please suggest the optimal savings plan."

[1380] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1381] The present invention is a system that incorporates an emotion engine for recognizing user emotions, and performs savings goal setting, progress management, savings plan proposals, savings challenges, rewards, automatic savings, investment advice, savings goal visualization, and educational content provision. Below, we will explain in detail the programs for realizing each function and specific examples.

[1382] Emotion Engine

[1383] The emotion engine analyzes users' emotions in real time, allowing it to customize savings plans, investment strategies, rewards, and educational content based on the user's emotional state.

[1384] Setting savings goals and tracking progress

[1385] Step 1: The user opens the app and accesses the savings goal setting screen. The user enters the goal amount, deadline, and purpose. This data is sent to the server and stored in the database.

[1386] Step 2: The server checks the user's savings progress on a daily or weekly basis, analyzes the progress data, and visualizes it.

[1387] Examples:

[1388] When a user sets a goal of "saving 200,000 yen in one year," the server links the user's bank account information and provides monthly updates and notifications on progress.

[1389] Savings plan suggestions

[1390] Step 1: Provide a screen where users can enter their income and expense information. The data is sent to a server for analysis.

[1391] Step 2: The generative AI model generates a savings plan and suggests it to the user.

[1392] Step 3: The emotion engine analyzes the user's emotions and adjusts the plan.

[1393] Examples:

[1394] For users with a monthly income of 300,000 yen and fixed expenses of 150,000 yen, the plan is to save 30,000 yen each month and start saving an additional 10,000 yen after six months.

[1395] Savings Challenges and Rewards

[1396] The server sets up a savings challenge and proposes it to users. If users participate in the challenge and achieve the goal, they will receive rewards and benefits. The emotion engine customizes the reward content based on the user's emotions.

[1397] Examples:

[1398] If a user participates in the challenge of "saving 10,000 yen every month for three months" and meets the conditions, the server will award them 500 yen worth of points.

[1399] Setting up an automatic savings program

[1400] The server proposes an automatic savings program based on the user's income and expenditure data. If the user approves, the system automatically saves. The emotion engine adjusts the program based on the user's emotions.

[1401] Examples:

[1402] Run a program that automatically saves 5,000 yen on the 15th and 30th of every month.

[1403] Investment advice and asset growth support

[1404] The generative AI model analyzes users' investment goals and risk tolerance to suggest appropriate investment strategies, while the sentiment engine adjusts strategies based on users' emotions.

[1405] Examples:

[1406] If a user enters information such as "I am aiming for medium-risk, long-term asset growth," the service suggests a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[1407] Visualizing savings goals and reward systems

[1408] The server analyzes and visualizes the progress of the user's savings goal. When the goal is achieved, rewards are provided. The emotion engine customizes the reward content.

[1409] Examples:

[1410] Set a goal of "saving 300,000 yen in 6 months" and progress while checking your progress. When you reach your goal, the server will provide you with a cashback of 1,000 yen.

[1411] Providing educational content on savings

[1412] The generative AI model creates and delivers educational content tailored to the user, while the emotion engine adjusts the content based on the user's emotions.

[1413] Examples:

[1414] When a user requests to "learn the basics of saving," educational content such as "how to review your monthly spending" and "tips for reducing fixed costs" will be provided.

[1415] Use of emotion engine

[1416] The emotion engine analyzes user input and other behavioral data to recognize emotions, and uses this emotional data to optimize various system features (savings plans, rewards, investment strategies, educational content, etc.) for users.

[1417] Examples:

[1418] If the emotion engine detects that the user is feeling stressed, it will suggest relaxing their savings plan as a mitigation measure.

[1419] The processing flow will be explained below.

[1420] Processing flow of a system that combines emotion engines

[1421] Flow of setting savings goals and tracking progress

[1422] Step 1:

[1423] On the device: The user opens the app and accesses the savings goal setting screen.

[1424] Step 2:

[1425] User: Enter the target amount, deadline, and purpose.

[1426] Step 3:

[1427] Terminal: Sends user input data to the server.

[1428] Step 4:

[1429] Server: Receives user input data and stores it in a database.

[1430] Step 5:

[1431] Server: The emotion engine analyzes the user's emotions and suggests goal adjustments if necessary.

[1432] Step 6:

[1433] Server: Retrieves transaction history from the database to check the user's savings status on a daily or weekly basis.

[1434] Step 7:

[1435] Server: Analyzes progress towards goals through batch processing.

[1436] Step 8:

[1437] Server: Calculates the progress and sends the result to the device.

[1438] Step 9:

[1439] Terminal: Visually display the received progress data, for example using a gauge or bar graph.

[1440] Savings plan proposal process flow

[1441] Step 1:

[1442] Terminal: Provides a screen where users can enter income and expense information.

[1443] Step 2:

[1444] User: Enter your monthly income and major recurring expenses (rent, utilities, food, etc.).

[1445] Step 3:

[1446] Terminal: Sends input data to the server.

[1447] Step 4:

[1448] Server: Receives user income and expenditure data and analyzes it using a generative AI model.

[1449] Step 5:

[1450] Server: The generative AI model generates a savings plan and sends it to the device.

[1451] Step 6:

[1452] Server: The emotion engine analyzes the user's emotions and adjusts the plan. For example, if stress is high, it suggests relaxing the plan.

[1453] Step 7:

[1454] Terminal: Display the suggested savings plan to the user.

[1455] Step 8:

[1456] User: Review the proposed savings plan and adjust as needed.

[1457] Savings Challenge and Reward Processing Flow

[1458] Step 1:

[1459] Server: Sets the savings challenge and sends it to the device.

[1460] Step 2:

[1461] Device: Shows the challenge to the user and asks for their confirmation.

[1462] Step 3:

[1463] User: Decides to participate in the proposed challenge and clicks the Join button.

[1464] Step 4:

[1465] Device: Sends the user's decision to the server.

[1466] Step 5:

[1467] Server: Periodically checks the user's savings progress and calculates the challenge progress.

[1468] Step 6:

[1469] Server: The emotion engine analyzes the user's emotional state and customizes rewards, for example, suggesting additional incentives if motivation drops.

[1470] Step 7:

[1471] Server: Awards rewards when a challenge is completed and sends the data to the device.

[1472] Step 8:

[1473] Device: Notifies the user of the challenge completion and reward details.

[1474] Process flow for setting up an automatic savings program

[1475] Step 1:

[1476] Server: Proposes an automatic savings program based on the user's income and expenditure data.

[1477] Step 2:

[1478] On the device: Display the suggestions to the user.

[1479] Step 3:

[1480] User: Approves the proposed automatic savings program.

[1481] Step 4:

[1482] Terminal: Sends authorization information to the server.

[1483] Step 5:

[1484] Server: Executes approved automatic savings programs and automatically saves according to a specified schedule.

[1485] Step 6:

[1486] Server: The emotion engine analyzes the user's emotions and adjusts the program accordingly. For example, if the user is under high stress, the savings amount is temporarily set lower.

[1487] Step 7:

[1488] Server: Notifies the user of the progress of the automatic savings.

[1489] Step 8:

[1490] On the device: Show users their savings progress.

[1491] Investment advice and asset growth support process

[1492] Step 1:

[1493] Terminal: Provides a screen where users can input their investment goals and risk tolerance.

[1494] Step 2:

[1495] User: Enter your investment goals and risk tolerance.

[1496] Step 3:

[1497] Terminal: Sends input data to the server.

[1498] Step 4:

[1499] Server: Uses generative AI models to analyze user input data and generate optimal investment strategies.

[1500] Step 5:

[1501] Server: Sends investment strategies to the terminal.

[1502] Step 6:

[1503] Server: The sentiment engine adjusts investment strategies based on user sentiment. For example, if a user feels anxious, it suggests a low-risk strategy.

[1504] Step 7:

[1505] Terminal: displays the proposed investment strategy to the user.

[1506] Step 8:

[1507] User: Review investment strategies and implement them as needed.

[1508] Step 9:

[1509] Server: Regularly tracks users' investment progress and adjusts strategies as needed.

[1510] Step 10:

[1511] On the device: Notify the user of progress and suggested adjustments.

[1512] Visualization of savings goals and reward system processing flow

[1513] Step 1:

[1514] Device: Provides a screen where users can set their savings goals.

[1515] Step 2:

[1516] User: Set the goal amount and deadline.

[1517] Step 3:

[1518] Terminal: Sends the settings to the server.

[1519] Step 4:

[1520] Server: Stores goal settings in a database and tracks progress periodically.

[1521] Step 5:

[1522] Emotion Engine: Analyzes the user's emotional state and customizes assistance to help them achieve their goals.

[1523] Step 6:

[1524] Server: When the goal achievement conditions are met, the server processes the reward.

[1525] Step 7:

[1526] Server: Analyzes the progress data and sends it to the device as visual information.

[1527] Step 8:

[1528] On the device: Show progress to the user with graphs and metrics.

[1529] Step 9:

[1530] Server: Notifies the device of the reward to be provided when the goal is achieved.

[1531] Step 10:

[1532] Terminal: Notifies the user and assists them in claiming their reward.

[1533] Process flow for providing educational content on savings

[1534] Step 1:

[1535] Server: Uses generative AI models to create educational content tailored to the user.

[1536] Step 2:

[1537] Server: Sends content to the device.

[1538] Step 3:

[1539] Device: Displays educational content to the user.

[1540] Step 4:

[1541] Users: View and learn educational content.

[1542] Step 5:

[1543] Emotion engine: Analyzes user emotions and tailors content based on understanding and interest.

[1544] Step 6:

[1545] Device: Sends user feedback to the server.

[1546] Step 7:

[1547] Server: Regularly update educational content based on user feedback.

[1548] Example 2

[1549] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1550] Conventional savings management systems were unable to fully consider users' emotions and individual circumstances, and were limited to proposing general savings plans. They also lacked the flexibility to adapt to fluctuations in users' income and spending patterns, and the ability to customize plans based on their emotional state. Furthermore, it was difficult to address individual needs when it came to tracking savings goal progress or proposing investment strategies.

[1551] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1552] In this invention, the server includes a means for allowing users to set savings goals and manage their progress, a means for analyzing the user's income and spending patterns to propose individual savings plans, and a means for allowing the user to participate in savings challenges and receive rewards and benefits upon completion. This allows for flexible proposals of savings plans and investment strategies tailored to the user's individual circumstances. Furthermore, by using an emotion engine to analyze the user's emotional state in real time, each function of the system can be customized based on emotions, creating a user-friendly environment. Furthermore, by using a generative AI model to automatically generate optimal savings plans and investment strategies based on prompts, more accurate proposals can be made.

[1553] "Savings goal setting" is the act of a user deciding on a specific amount and setting a deadline and purpose for achieving that amount.

[1554] "Progress management" is the process of monitoring the current progress towards the savings goal set by the user and updating the information accordingly.

[1555] "Income and Expense Pattern Analysis" is a means of analyzing a user's income and expense data to understand their individual financial situation.

[1556] "Savings plan suggestions" are presented to users based on the analysis of their income and expenses, showing them specific ways in which they should save money.

[1557] A "savings challenge" is a system in which users set short-term tasks or challenges to achieve a specific savings goal, and receive rewards or benefits if they complete the challenge.

[1558] "Automatic savings" is a function that automatically saves money on a specific schedule based on the user's income and expenditure data.

[1559] "Investment strategy suggestions" means providing specific guidance on what kind of investments a user should make based on their investment goals and risk tolerance.

[1560] "Savings goal visualization" is the process of visually displaying a user's progress toward their savings goal in the form of graphs and reports.

[1561] "Providing educational content" means providing users with knowledge about savings and investments and providing information and educational materials to deepen their understanding.

[1562] An "emotion engine" is a software technology that analyzes a user's emotional state in real time and reflects the results in other system functions.

[1563] A "generative AI model" is an artificial intelligence algorithm that inputs user data and automatically generates optimal savings plans and investment strategies.

[1564] A "prompt sentence" is an input sentence that gives specific instructions or questions to a generative AI model.

[1565] The present invention is a system that incorporates an emotion engine that recognizes user emotions, and performs savings goal setting, progress management, savings plan proposals, savings challenges, rewards, automatic savings, investment advice, savings goal visualization, and educational content provision. Below, we will explain in detail the programs for realizing each function and specific examples.

[1566] Emotion Engine

[1567] The server collects user input data and operation data in real time and analyzes it with an emotion engine, which identifies the user's emotional state and provides feedback to other system functions.

[1568] Examples:

[1569] When users log in and set their savings goals, an emotion engine analyzes their typing speed and sequence to detect signs of stress or excitement.

[1570] Setting savings goals and tracking progress

[1571] The user enters the goal amount, deadline, and purpose on the savings goal setting screen. The device sends this information to the server, which stores it in a database. The server periodically checks the user's progress, analyzes the progress data, generates visualization data, and notifies the user.

[1572] Examples:

[1573] When a user sets a goal of "saving 200,000 yen in one year," the server updates and notifies the user of their progress every month.

[1574] Savings plan suggestions

[1575] Users input their income and expenditure information, which is then sent to the server via their device. The server then sends prompts to the generative AI model to generate an optimal savings plan. The emotion engine then adjusts the plan and makes suggestions to the user.

[1576] Examples:

[1577] For users with a monthly income of 300,000 yen and fixed expenses of 150,000 yen, the plan is to "save 30,000 yen each month and save an additional 10,000 yen after six months."

[1578] Savings Challenges and Rewards

[1579] The server sets up savings challenges, and the emotion engine proposes challenges based on the user's emotions. When a user participates in and completes a challenge, the server grants rewards and benefits.

[1580] Examples:

[1581] If a user succeeds in the challenge of "saving 10,000 yen every month for three months," the server will award them 500 yen worth of points.

[1582] Setting up an automatic savings program

[1583] The server proposes an automatic savings program based on the user's income and expenditure data, and the emotion engine adjusts the proposal. If the user approves the proposal, the server automatically executes the savings.

[1584] Examples:

[1585] The server runs a program that automatically saves 5,000 yen on the 15th and 30th of every month.

[1586] Investment advice and asset growth support

[1587] Users input their investment goals and risk tolerance, and the device sends this to the server. The server then sends prompts to the generative AI model to generate an optimal investment strategy. The emotion engine then adjusts the strategy and presents it to the user.

[1588] Examples:

[1589] For users seeking medium-risk, long-term asset growth, we propose a portfolio of 50% bonds, 30% stocks, and 20% real estate investment trusts.

[1590] Visualizing savings goals and reward systems

[1591] The server analyzes the progress of savings goals and creates visualizations. Rewards are provided when goals are achieved, and the emotion engine customizes the rewards.

[1592] Examples:

[1593] If the goal of saving 300,000 yen in six months is achieved, the server will provide a cashback of 1,000 yen.

[1594] Providing educational content on savings

[1595] The server uses a generative AI model to create educational content appropriate for the user, and an emotion engine to tailor the content. When a user requests content, the server provides the tailored educational content.

[1596] Examples:

[1597] When a user requests to "learn the basics of saving," educational content such as "how to review your monthly spending" and "tips for reducing fixed costs" is provided.

[1598] Use of emotion engine

[1599] The server analyzes user input and behavioral data using an emotion engine and customizes other functions of the system based on the results of this analysis.

[1600] Examples:

[1601] The emotion engine recognizes when a user is feeling stressed and suggests relaxing their savings plan as a relief measure.

[1602] Prompt Sentence Examples

[1603] "Generate an optimal savings plan based on the user's monthly income and fixed expenses. Adjust the plan based on stress levels identified by the emotion engine."

[1604] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1605] Specific flow of program processing

[1606] Emotion Engine Analysis

[1607] Step 1:

[1608] A user logs in to the app.

[1609] Input: User login data, operation log

[1610] Output: The operation log is sent to the server.

[1611] Specific operation: A user logs in to the app, and the app collects user operation data and sends it to the server.

[1612] Step 2:

[1613] The server passes the operation log to the emotion engine.

[1614] Input: Operation log

[1615] Output: Emotion analysis results

[1616] Specific operation: The received operation log is passed to the emotion engine, which analyzes the user's emotional state in real time.

[1617] Step 3:

[1618] The emotion engine analyzes the user's emotions and provides feedback on the results.

[1619] Input: Operation log

[1620] Output: Emotion analysis results (e.g., stress level, excitement level)

[1621] Specific operation: The emotion engine analyzes the operation log, identifies the emotional state, and provides feedback to other functions of the system.

[1622] Setting savings goals and tracking progress

[1623] Step 1:

[1624] The user accesses the savings goal setting screen and inputs the goal amount, deadline, and purpose.

[1625] Input: target amount, deadline, purpose

[1626] Output: User input data is saved to the device.

[1627] Specific operation: The user enters a savings goal, which is temporarily saved on the device.

[1628] Step 2:

[1629] The device sends the user input data to the server.

[1630] Input: User-entered data

[1631] Output: User-entered data sent to the server

[1632] Specific operation: The terminal sends user input data to the server.

[1633] Step 3:

[1634] The server stores the received data in a database and periodically collects progress data.

[1635] Input: User-entered data

[1636] Output: Data stored in the database, progress data

[1637] What it does: The server saves the user-entered data in a database and periodically collects progress data from the bank account.

[1638] Step 4:

[1639] The server analyzes and visualizes the progress data.

[1640] Input: Progress data

[1641] Output: Visualized progress data (graphs, reports)

[1642] Specific operation: The server analyzes the progress data, visualizes it in the form of graphs and reports, and notifies the user.

[1643] Savings plan suggestions

[1644] Step 1:

[1645] The user enters income and expense information.

[1646] Input: Income and expenditure data

[1647] Output: User input data is saved to the device.

[1648] Specific operation: The user enters income and expenditure data, which is temporarily stored on the device.

[1649] Step 2:

[1650] The device sends the user input data to the server.

[1651] Input: Income and expenditure data

[1652] Output: User-entered data sent to the server

[1653] Specific operation: The terminal sends user input data to the server.

[1654] Step 3:

[1655] The server analyzes the incoming data and sends prompts to the generative AI model.

[1656] Input: Income and expenditure data

[1657] Output: The input data, including the prompt sentence, is sent to the generative AI model.

[1658] Specific operation: The server analyzes the received data and sends prompt sentences to the generative AI model to generate an optimal savings plan.

[1659] Step 4:

[1660] A generative AI model generates an optimal savings plan.

[1661] Input: prompt statement, user input data

[1662] Output: Generated savings plan

[1663] How it works: The generative AI model generates an optimal savings plan based on the prompt.

[1664] Step 5:

[1665] The emotion engine adjusts the plan and makes suggestions to the user.

[1666] Input: Generated savings plan, sentiment analysis results

[1667] Output: Adjusted savings plan

[1668] Specific operation: The emotion engine adjusts the generated savings plan based on the user's emotional state and suggests it to the user.

[1669] Savings Challenges and Rewards

[1670] Step 1:

[1671] The server sets up a savings challenge and proposes it to the user.

[1672] Input: None

[1673] Output: Savings challenge proposal

[1674] Specific operation: The server periodically proposes a savings challenge to the user.

[1675] Step 2:

[1676] An emotion engine adjusts the challenge based on the user's emotions.

[1677] Input: Sentiment analysis results

[1678] Output: Adjusted Savings Challenge

[1679] How it works: The emotional engine takes into account the user's emotional state and adjusts the content and rewards of the savings challenge.

[1680] Step 3:

[1681] The user participates in a challenge.

[1682] Input: Press the Join button

[1683] Output: Join information is sent to the server.

[1684] Specific operation: The user presses a button to participate in the challenge, and participation information is sent to the server.

[1685] Step 4:

[1686] The server manages the progress of the challenge and grants rewards upon completion.

[1687] Input: Challenge progress data

[1688] Output: Reward

[1689] Specific operation: The server manages the progress of the challenge and grants rewards and benefits when the conditions are met.

[1690] Setting up an automatic savings program

[1691] Step 1:

[1692] The server suggests an automatic savings program based on the user's income and expenditure data.

[1693] Input: Income and expenditure data

[1694] Output: Proposal for an automatic savings program

[1695] Specific operation: The server analyzes the user's income and expenditure data and suggests the most suitable automatic savings program.

[1696] Step 2:

[1697] The emotion engine adjusts the suggestions.

[1698] Input: Sentiment analysis results, automatic savings program proposal

[1699] Output: Adjusted automatic savings program

[1700] How it works: The emotion engine adjusts the content of the automatic savings program based on the user's emotions.

[1701] Step 3:

[1702] The user approves the proposal.

[1703] Input: Press the approve button

[1704] Output: The authorization information is sent to the server.

[1705] Specific operation: The user approves the automatic savings program, and the approval information is sent to the server.

[1706] Step 4:

[1707] The server automatically executes the savings.

[1708] Enter: Approved automatic savings program

[1709] Output: Automatic savings execution data

[1710] Specific operation: The server automatically debits the user's account at regular intervals according to the approved program.

[1711] Investment advice and asset growth support

[1712] Step 1:

[1713] Users input their investment goals and risk tolerance.

[1714] Inputs: Investment goals, risk tolerance

[1715] Output: User input data is saved to the device.

[1716] Specific operation: The user enters their investment goals and risk tolerance, which are then temporarily saved on the device.

[1717] Step 2:

[1718] The device sends the user input data to the server.

[1719] Inputs: Investment goals, risk tolerance

[1720] Output: User-entered data sent to the server

[1721] Specific operation: The terminal sends user input data to the server.

[1722] Step 3:

[1723] The server analyzes the incoming data and sends prompts to the generative AI model.

[1724] Inputs: Investment goals, risk tolerance

[1725] Output: The input data, including the prompt sentence, is sent to the generative AI model.

[1726] Specific operation: The server analyzes the received data and sends prompt statements to the generative AI model to generate the optimal investment strategy.

[1727] Step 4:

[1728] A generative AI model generates optimal investment strategies.

[1729] Input: Prompt statement, investment goal, risk tolerance

[1730] Output: Generated investment strategy

[1731] How it works: The generative AI model generates an optimal investment strategy based on the prompt.

[1732] Step 5:

[1733] The emotion engine adjusts the strategy and makes suggestions to the user.

[1734] Input: Generated investment strategy, sentiment analysis results

[1735] Output: Adjusted investment strategy

[1736] Specific operation: The emotion engine adjusts the generated investment strategy based on the user's emotional state and suggests it to the user.

[1737] Visualizing savings goals and reward systems

[1738] Step 1:

[1739] The server analyzes the progress of the savings goal.

[1740] Input: Progress data

[1741] Output: Analysis results

[1742] Specific behavior: The server analyzes the savings goal progress data.

[1743] Step 2:

[1744] The server visualizes the progress data.

[1745] Input: Analysis results

[1746] Output: Visualized data (graphs, reports)

[1747] What it does: The server visualizes the progress data in the form of graphs and reports.

[1748] Step 3:

[1749] The server provides rewards when goals are achieved.

[1750] Input: Analysis results, visualization data

[1751] Output: Reward

[1752] Specific behavior: The server confirms goal achievement and provides a reward to the user.

[1753] Step 4:

[1754] The emotion engine customizes rewards.

[1755] Input: Sentiment analysis results

[1756] Output: Customized reward content

[1757] How it works: The emotion engine adjusts and customizes rewards based on the user's emotions.

[1758] Providing educational content on savings

[1759] Step 1:

[1760] The server sends prompts to the generative AI model to create educational content.

[1761] Input: User request, prompt

[1762] Output: Generated educational content

[1763] Specific operation: The server sends the user's request as a prompt to the generative AI model to create educational content.

[1764] Step 2:

[1765] An emotional engine adjusts educational content.

[1766] Input: Educational content, sentiment analysis results

[1767] Output: Tailored educational content

[1768] Specific behavior: The emotion engine adjusts educational content based on the user's emotional state.

[1769] Step 3:

[1770] A user requests educational content.

[1771] Input: Request operation

[1772] Output: The request information is sent to the server.

[1773] Specific operation: The user requests educational content and the relevant information is sent to the server.

[1774] Step 4:

[1775] The server provides tailored educational content to the user.

[1776] Input: Tailored educational content

[1777] Output: Provided educational content

[1778] Specific operation: The server provides the adjusted educational content to the user.

[1779] (Application example 2)

[1780] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1781] This invention is a system that incorporates an emotion engine that analyzes user emotions and provides functions such as setting savings goals, tracking progress, and proposing savings plans. However, current systems lack individualized responses that take into account the user's emotions and psychological state, and further personalization is required. Furthermore, the lack of effective user interaction in virtual environments poses challenges in improving user satisfaction and motivation. Therefore, it is desirable to provide a system that optimizes savings plans, investment strategies, rewards, and educational content based on the user's emotions, enabling more familiar interactions through a virtual assistant.

[1782] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for allowing a user to set a savings goal and manage the progress of the goal; means for analyzing the user's income and spending patterns and proposing an individual savings plan; means for allowing the user to participate in a savings challenge and offering rewards or benefits upon achievement; means for automatically saving based on the user's income and spending; means for analyzing the user's investment goals and risk tolerance and proposing an appropriate investment strategy; means for visualizing the user's progress toward the savings goal and offering rewards upon achievement; means for providing the user with educational content related to savings and investment; means for recognizing the user's emotions using an emotion analysis module and customizing the savings plan, investment strategy, reward details, and educational content based on the emotions; and means for using a virtual assistant to support interaction with the user and progress and set the savings goal. This enables personalized responses based on the user's individual emotions and psychological state, enabling effective interaction in a virtual environment.

[1783] "User" refers to any individual or organization that uses this system.

[1784] A "savings goal" refers to the amount of savings or purpose that a user sets and wants to achieve within a certain period of time.

[1785] "Progress management" refers to monitoring, analyzing, and visualizing progress toward savings goals set by users.

[1786] "Income and expenditure patterns" refers to the user's monthly income and expenditure trends and specific breakdowns.

[1787] A "savings plan" refers to specific methods and steps for achieving savings goals that are presented to users after taking into account their income and spending patterns.

[1788] A "savings challenge" refers to a competitive initiative in which users participate in short-term or medium- to long-term savings goals.

[1789] "Rewards and perks" refers to incentives offered to users upon achieving savings challenges and goals.

[1790] "Automatic savings" refers to a function in which the system automatically saves a certain amount based on the user's income and expenditure data.

[1791] "Investment goal" refers to the investment results or return target set by the user that they wish to achieve within a certain period of time.

[1792] "Risk tolerance" refers to the investment risk level that a user can tolerate.

[1793] An "investment strategy" refers to a specific investment policy or portfolio that is generated taking into account a user's investment goals and risk tolerance.

[1794] "Visualization" refers to the system displaying the user's savings goals and progress in visual ways, such as graphs and charts.

[1795] "Educational Content" means educational materials and opportunities related to savings and investing that are provided to Users.

[1796] "Emotion analysis module" refers to software or algorithms used to analyze a user's emotional state.

[1797] A "virtual assistant" refers to a virtual character or interface that interacts with and provides assistance to users in a digital environment.

[1798] This system incorporates an emotion analysis module that recognizes user emotions, and performs savings goal setting, progress management, savings plan proposals, rewards, automatic savings, investment advice, savings goal visualization, and educational content provision. Specifically, these functions are realized through interactions between the server, terminals, and users.

[1799] The emotion analysis module analyzes the user's emotional state in real time and recognizes emotions based on the user's input and behavioral data. This emotional data is then used by the entire system to provide the optimal response to the user.

[1800] Setting savings goals and tracking progress

[1801] Users access the savings goal setting screen through their device and enter the goal amount, deadline, purpose, etc. This data is sent to the server and stored in a database. The server checks the user's savings progress on a daily or weekly basis, and analyzes and visualizes the progress data. For example, if a user sets a goal of "saving 200,000 yen in one year," the server will link the user's bank account information and provide monthly updates and notifications on their progress.

[1802] Savings plan suggestions

[1803] The system provides a screen where users can enter income and expenditure information via their device, which is then sent to a server for analysis. A generative AI model generates a savings plan and suggests it to the user. An emotion analysis module then analyzes the user's emotions and adjusts the plan accordingly. For example, for a user with a monthly income of 300,000 yen and fixed expenses of 150,000 yen, the system suggests a plan to "save 30,000 yen each month and start saving an additional 10,000 yen six months later."

[1804] Savings Challenges and Rewards

[1805] The server sets up savings challenges and proposes them to users. If users participate in the challenge and achieve their goals, they will receive rewards and benefits. The sentiment analysis module customizes the reward content based on the user's emotions. For example, if a user participates in a challenge to "save 10,000 yen every month for three months" and meets the conditions, the server will award 500 yen worth of points.

[1806] Setting up an automatic savings program

[1807] The server proposes an automatic savings program based on the user's income and expenditure data. If the user approves, the system automatically starts saving. The emotion analysis module adjusts the program based on the user's emotions. For example, the system can implement a program that automatically saves 5,000 yen on the 15th and 30th of each month.

[1808] Investment advice and asset growth support

[1809] The generative AI model analyzes the user's investment goals and risk tolerance and proposes an appropriate investment strategy. The sentiment analysis module adjusts the strategy based on the user's sentiment. For example, if a user enters information such as "I aim for medium-risk, long-term asset growth," the system will propose a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[1810] Visualizing savings goals and reward systems

[1811] The server analyzes and visualizes the user's progress toward their savings goal. Once the goal is achieved, a reward is provided. The sentiment analysis module customizes the reward content. For example, a user can set a goal of "saving 300,000 yen in six months" and check their progress as they go along. When the goal is achieved, a cashback of 1,000 yen is provided.

[1812] Providing educational content on savings

[1813] The generative AI model creates and provides educational content appropriate for the user. The sentiment analysis module adjusts the content based on the user's emotions. For example, if a user requests to learn the basics of saving, educational content such as "How to review your monthly spending" and "Tips for reducing fixed costs" will be provided.

[1814] Using the sentiment analysis module

[1815] The sentiment analysis module analyzes user input and other behavioral data to recognize emotions. Based on this emotional data, the system's various features (savings plans, rewards, investment strategies, educational content, etc.) are optimized for the user. For example, if the sentiment analysis module detects that the user is feeling stressed, it can suggest relaxing the savings plan as a mitigation measure.

[1816] Examples of prompt statements

[1817] User A opens the app and sets a goal of "saving 200,000 yen in one year." Their income is 300,000 yen and their expenses are 150,000 yen. The emotion engine recognizes "stress." Please suggest a savings plan based on this information.

[1818] As described above, the interaction between the server, terminal, and user allows for emotion analysis and realizes a system that responds to individual user needs, enabling users to more effectively achieve their savings goals and supporting financial independence.

[1819] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1820] Step 1:

[1821] The user accesses the savings goal setting screen through the terminal and inputs the goal amount, deadline, purpose, etc. This inputs the savings goal data. The input data is sent to the server and stored in the database.

[1822] Step 2:

[1823] The server checks the user's savings progress on a daily or weekly basis. Savings progress data is collected by linking the user's bank account information. This data is analyzed by the server, and the progress is visualized and output to the terminal. For example, the progress is displayed in chart form.

[1824] Step 3:

[1825] Users have access to a screen on their device where they can input their income and expenditure information. Once the income and expenditure data is entered, it is sent to a server. The server uses a generative AI model to analyze the data, generate a savings plan, and obtain the savings plan data, which is then output back to the device.

[1826] Step 4:

[1827] The emotion analysis module analyzes the user's input data and behavioral data to recognize the user's emotional state. For example, it generates emotion data through text analysis or voice analysis. The generated emotion data is sent to the server and stored in a database.

[1828] Step 5:

[1829] Based on the emotional data, the server adjusts the savings plan. For example, if the user is feeling stressed, the generative AI model will recalculate the savings plan to ease the stress, and generate new savings plan data. This new data will be output to the device.

[1830] Step 6:

[1831] The server sets up the user to participate in the savings challenge and proposes it to the device. When the user participates in the challenge, the status is sent to the server and stored in a database. When the goal is achieved, the server generates reward and benefit data and outputs it to the device.

[1832] Step 7:

[1833] The server proposes an automatic savings program based on the user's income and expenditure data. If the user approves, the data is sent to the server and stored in a database. The program is adjusted appropriately based on the user's emotional data, and automatic savings data is generated. For example, the terminal may output a message such as, "Automatically save 5,000 yen on the 15th and 30th of each month."

[1834] Step 8:

[1835] The server analyzes the user's investment goals and risk tolerance and uses a generative AI model to propose an appropriate investment strategy. Investment strategy data is generated and output to the terminal. For example, an investment strategy such as "Aiming for long-term asset growth with medium risk" is displayed.

[1836] Step 9:

[1837] The server analyzes the user's progress toward their savings goal and generates visualization data. The progress is visualized in charts and other formats and output to the device. When the goal is achieved, the server generates reward data and displays a cashback of 1,000 yen.

[1838] Step 10:

[1839] The system uses a generative AI model to create educational content requested by the user, which is then provided by the server. The content is adjusted based on emotion data, and the educational content is output to the device. For example, content such as "How to review your monthly expenses" and "Tips for reducing fixed costs" is displayed.

[1840] Through the above processing steps, the server continuously analyzes the emotional data and provides savings plans and educational content tailored to the individual needs of the user, thereby realizing a system that encourages users to become financially independent.

[1841] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1842] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1843] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1844] [Third embodiment]

[1845] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1846] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1847] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1848] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1849] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1850] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1851] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1852] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1853] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1854] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1855] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1856] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1857] The present invention is a system that allows users to set savings goals and effectively manage their progress. Below, we will explain the programs for realizing each function and specific examples.

[1858] Setting savings goals and tracking progress

[1859] This system provides a savings goal setting function. Users input the amount they want to save, the deadline, and the purpose, and the server stores this information in a database. The system periodically tracks the user's savings progress and analyzes the progress data to provide the user with a visual representation of their current situation.

[1860] Examples:

[1861] The user sets a goal of "saving 200,000 yen in one year." The server links the user's bank account information, automatically updates the user's savings progress each month, and notifies the user through the app.

[1862] Savings plan suggestions

[1863] The server analyzes the user's income and spending patterns and proposes an individual savings plan. Based on the user's income information and regular spending information, the generative AI model generates an optimal savings plan and provides it to the user. If the user agrees with the proposal, the plan is confirmed and savings are automatically made.

[1864] Examples:

[1865] If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, the server will suggest a savings plan that involves saving 30,000 yen each month and then starting to save an additional 10,000 yen six months later.

[1866] Savings Challenges and Rewards

[1867] Users participate in savings challenges and receive rewards and perks when they achieve their goals. The server tracks the progress of the challenges and awards rewards when they are achieved.

[1868] Examples:

[1869] If a user participates in the challenge of "saving 10,000 yen every month for three months" and meets all the conditions, the server will award the user 500 yen worth of points.

[1870] Setting up an automatic savings program

[1871] The server proposes an automatic savings program based on the user's income and spending patterns, and if the user approves, the system automatically saves the specified amount on a regular basis.

[1872] Examples:

[1873] When a user agrees to a program that automatically saves 5,000 yen on the 15th and 30th of every month, the server automatically saves the specified amount on a regular basis.

[1874] Investment advice and asset growth support

[1875] The server analyzes the user's investment goals and risk tolerance and proposes an appropriate investment strategy, allowing users to not only save money but also manage their assets efficiently.

[1876] Examples:

[1877] If a user inputs a goal of "long-term asset growth with medium risk," the server will suggest a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[1878] Visualizing savings goals and reward systems

[1879] The server visualizes the progress of the user's savings goal, using graphs and various indicators to show the progress to the user, and provides rewards when the goal is achieved.

[1880] Examples:

[1881] Users set a goal of "saving 300,000 yen in 6 months" and progress while checking their progress. When the goal is achieved, the server provides a cashback of 1,000 yen.

[1882] Providing educational content on savings

[1883] The server provides users with educational content on savings and investments to improve their financial literacy. Using a generative AI model, it automatically creates and provides the most appropriate content for each user.

[1884] Examples:

[1885] If a user requests, "I want to learn the basics of saving," the server will provide educational content such as "How to review your monthly expenses" and "Tips for reducing fixed expenses."

[1886] The processing flow will be explained below.

[1887] Flow of setting savings goals and tracking progress

[1888] Step 1:

[1889] On the device: The user opens the app and accesses the savings goal setting screen.

[1890] Step 2:

[1891] User: Enter the target amount, deadline, and purpose.

[1892] Step 3:

[1893] Terminal: Sends user input data to the server.

[1894] Step 4:

[1895] Server: Receives user input data and stores it in a database.

[1896] Step 5:

[1897] Server: Retrieves transaction history from the database to check the user's savings status on a daily or weekly basis.

[1898] Step 6:

[1899] Server: Analyzes progress towards goals through batch processing.

[1900] Step 7:

[1901] Server: Calculates the progress and sends the result to the device.

[1902] Step 8:

[1903] Terminal: Visually display the received progress data, for example using a gauge or bar graph.

[1904] Savings plan proposal process flow

[1905] Step 1:

[1906] Terminal: Provides a screen where users can enter income and expense information.

[1907] Step 2:

[1908] User: Enter your monthly income and major recurring expenses (rent, utilities, food, etc.).

[1909] Step 3:

[1910] Terminal: Sends input data to the server.

[1911] Step 4:

[1912] Server: Receives user income and expenditure data and analyzes it using a generative AI model.

[1913] Step 5:

[1914] Server: Generates a savings plan and sends it to the device.

[1915] Step 6:

[1916] Terminal: Display the suggested savings plan to the user.

[1917] Step 7:

[1918] User: Review the proposed savings plan and adjust as needed.

[1919] Savings Challenge and Reward Processing Flow

[1920] Step 1:

[1921] Server: Sets the savings challenge and sends it to the device.

[1922] Step 2:

[1923] Device: Shows the challenge to the user and asks for their confirmation.

[1924] Step 3:

[1925] User: Decides to participate in the proposed challenge and clicks the Join button.

[1926] Step 4:

[1927] Device: Sends the user's decision to the server.

[1928] Step 5:

[1929] Server: Periodically checks the user's savings progress and calculates the challenge progress.

[1930] Step 6:

[1931] Server: Awards rewards when a challenge is completed and sends the data to the device.

[1932] Step 7:

[1933] Device: Notifies the user of the challenge completion and reward details.

[1934] Process flow for setting up an automatic savings program

[1935] Step 1:

[1936] Server: Proposes an automatic savings program based on the user's income and expenditure data.

[1937] Step 2:

[1938] On the device: Display the suggestions to the user.

[1939] Step 3:

[1940] User: Approves the proposed automatic savings program.

[1941] Step 4:

[1942] Terminal: Sends authorization information to the server.

[1943] Step 5:

[1944] Server: Executes approved automatic savings programs and automatically saves according to a specified schedule.

[1945] Step 6:

[1946] Server: Notifies the user of the progress of the automatic savings.

[1947] Step 7:

[1948] On the device: Show users their savings progress.

[1949] Investment advice and asset growth support process

[1950] Step 1:

[1951] Terminal: Provides a screen where users can input their investment goals and risk tolerance.

[1952] Step 2:

[1953] User: Enter your investment goals and risk tolerance.

[1954] Step 3:

[1955] Terminal: Sends input data to the server.

[1956] Step 4:

[1957] Server: Uses generative AI models to analyze user input data and generate optimal investment strategies.

[1958] Step 5:

[1959] Server: Sends investment strategies to the terminal.

[1960] Step 6:

[1961] Terminal: displays the proposed investment strategy to the user.

[1962] Step 7:

[1963] User: Review investment strategies and implement them as needed.

[1964] Step 8:

[1965] Server: Regularly tracks users' investment progress and adjusts strategies as needed.

[1966] Step 9:

[1967] On the device: Notify the user of progress and suggested adjustments.

[1968] Visualization of savings goals and reward system processing flow

[1969] Step 1:

[1970] Device: Provides a screen where users can set their savings goals.

[1971] Step 2:

[1972] User: Set the goal amount and deadline.

[1973] Step 3:

[1974] Terminal: Sends the settings to the server.

[1975] Step 4:

[1976] Server: Stores goal settings in a database and tracks progress periodically.

[1977] Step 5:

[1978] Server: Performs the process of granting rewards when the goal is achieved.

[1979] Step 6:

[1980] Server: Analyzes the progress data and sends it to the device as visual information.

[1981] Step 7:

[1982] On the device: Show progress to the user with graphs and metrics.

[1983] Step 8:

[1984] Server: Notifies the device of the reward to be provided when the goal is achieved.

[1985] Step 9:

[1986] Terminal: Notifies the user and assists them in claiming their reward.

[1987] Process flow for providing educational content on savings

[1988] Step 1:

[1989] Server: Uses generative AI models to create educational content tailored to the user.

[1990] Step 2:

[1991] Server: Sends content to the device.

[1992] Step 3:

[1993] Device: Displays educational content to the user.

[1994] Step 4:

[1995] Users: View and learn educational content.

[1996] Step 5:

[1997] Device: Sends user feedback to the server.

[1998] Step 6:

[1999] Server: Regularly update educational content based on user feedback.

[2000] Example 1

[2001] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2002] Existing savings management systems have basic functions for setting users' savings goals and tracking progress, but they lack the ability to analyze individual income and expenditure patterns and propose optimal savings plans. They also lack the ability to provide appropriate rewards and educational content based on savings progress. Furthermore, there are challenges in how to effectively analyze collected data and present specific savings and investment strategies to users.

[2003] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2004] In this invention, the server includes: means for allowing users to set savings goals and manage their progress; means for analyzing the user's income and spending patterns and proposing personalized savings plans; means for allowing users to participate in savings challenges and offering rewards and benefits upon achievement; means for automatically saving based on the user's income and spending; means for analyzing the user's investment goals and risk tolerance and proposing appropriate investment strategies; means for visualizing the user's progress toward their savings goals and offering rewards upon achievement; means for providing the user with educational content on savings and investments; means for analyzing the user's income and spending using a generative AI model and generating an optimal savings plan; and means for inputting the user's income and spending data into the generative AI model using prompts. This allows for the proposal and implementation of an optimal savings plan tailored to the user's individual financial situation, resulting in more effective savings and investment management. Furthermore, regular progress checks and rewards can motivate users to save, and the provision of educational content can improve users' financial literacy.

[2005] "Means for analyzing a user's income and expenditure patterns" refers to a data processing device or software that analyzes the user's financial situation and creates a future savings plan based on the income and expenditure information entered by the user.

[2006] "Means to propose individual savings plans" refers to the function in which the generative AI model analyzes the collected income and expenditure data and automatically generates and presents the optimal savings plan for each user.

[2007] "Means to participate in savings challenges" refers to the functionality that allows users to challenge themselves to set savings goals through the application, track their progress, and receive rewards upon achieving their goals.

[2008] "Means of providing rewards or benefits upon achievement" refers to a function that provides rewards such as points or monetary incentives when a user achieves a set savings goal.

[2009] "Means for automatic savings" refers to a system function that automatically saves an amount according to income and spending patterns based on a savings plan set by the user.

[2010] "Means for proposing appropriate investment strategies" refers to the function of presenting optimal investment plans and portfolios to users based on their investment goals and risk tolerance.

[2011] "Means for visualizing progress toward savings goals" refers to a function that visually displays the current progress toward the savings goal set by the user using graphs and numerical data.

[2012] "Means for providing educational content related to savings and investments" refers to the function of automatically generating and presenting educational materials and information that provide knowledge about savings and investments in order to improve users' financial literacy.

[2013] A "generative AI model" refers to an artificial intelligence algorithm that inputs a user's income and expenditure data as prompts and generates optimal savings plans and investment strategies based on the data.

[2014] A "prompt" refers to a question or command that is entered to instruct a generative AI model to perform a specific analysis or generate data.

[2015] The present invention is a system that allows users to set savings goals and effectively manage their progress. Below, we will explain the programs for realizing each function and specific examples.

[2016] Hardware and software used

[2017] In this invention, we mainly use a server, a user terminal (such as a smartphone or PC), and a generative AI model.

[2018] Server: Provides key functions such as setting savings goals, collecting income and expenditure data, analyzing data, proposing savings plans, tracking progress, and providing a reward system.

[2019] User device: Using a smartphone app or web app, the user enters information and receives feedback from the server.

[2020] Generative AI model: Generates savings plans and investment strategies based on user income and expenditure data via prompts.

[2021] Setting savings goals and tracking progress

[2022] Users input the amount they want to save, the deadline, and the purpose into their device. The device sends this information to the server, which stores this data in a database. The server periodically checks the user's bank account information, updates their savings progress, and sends notifications to the user that visually display their progress.

[2023] Examples:

[2024] The user sets a goal of "saving 200,000 yen in one year." The user's device sends this information to the server, which stores it in a database. At the end of each month, the server checks the bank account information, updates the progress, and notifies the user.

[2025] Savings plan suggestions

[2026] The user enters their monthly income and fixed expenses into their device. The device sends this to the server, which prepares the collected data for input into the generative AI model. The generative AI model generates an optimal savings plan through prompts and returns the results to the server. The server sends this savings plan to the user's device and presents it to the user. If the user agrees to the plan, regular automatic savings will begin.

[2027] Examples:

[2028] The prompt sentence "If the user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, please suggest the optimal savings plan" is input into the AI ​​model, and the model generates a plan that says "Save 30,000 yen each month and start saving an additional 10,000 yen six months later." This is presented to the user, and if they agree, the server sets up automatic savings.

[2029] Savings Challenges and Rewards

[2030] If a user wants to participate in the savings challenge, they register on their device. The server stores this information in a database and periodically checks the user's savings status. When the user completes the challenge, the server automatically awards rewards.

[2031] Examples:

[2032] When a user participates in the challenge of "saving 10,000 yen every month for three months," the server checks the user's progress every month, and if the user achieves the goal after three months, the server awards the user with 500 yen worth of points.

[2033] Setting up an automatic savings program

[2034] The server proposes an automatic savings program based on the user's income and spending patterns, and if the user agrees, the server sets up a system to automatically save the specified amount on a regular basis.

[2035] Examples:

[2036] When a user agrees to a program that "automatically saves 5,000 yen on the 15th and 30th of every month," the server automatically sets up and executes the program to save 5,000 yen on the 15th and 30th of every month.

[2037] Investment advice and asset growth support

[2038] When a user inputs their investment goals and risk tolerance, the server analyzes them and inputs an appropriate investment strategy into the AI ​​model. The model generates an optimal investment strategy and returns it to the server, which then presents it to the user.

[2039] Examples:

[2040] When a user inputs a goal such as "aiming for long-term asset growth with medium risk," the server asks the AI ​​model to generate a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts" and presents it to the user.

[2041] Visualizing savings goals and reward systems

[2042] The server visually displays the progress of the savings goal set by the user using graphs and various indicators, and when the user achieves the goal, the server grants a reward.

[2043] Examples:

[2044] The user sets a goal of "saving 300,000 yen in 6 months," and the server displays the progress in a graph. When the goal is achieved, the server provides the user with a cashback of 1,000 yen.

[2045] Providing educational content on savings

[2046] The server uses a generative AI model to generate and provide educational content that best suits the user's request.

[2047] Examples:

[2048] When a user requests to "learn the basics of saving," the server generates educational content such as "how to review your monthly expenses" and "tips for reducing fixed costs" and provides it to the user.

[2049] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2050] Program processing flow

[2051] Setting savings goals and tracking progress

[2052] Step 1:

[2053] The user enters a savings goal.

[2054] Input: Amount you want to save, deadline, and purpose.

[2055] Specific behavior: A user uses a smartphone app or web app to enter the required information into a screen for setting a savings goal.

[2056] Step 2:

[2057] The terminal sends the input data to the server.

[2058] Input: Savings goal data entered by the user.

[2059] Data processing: Converting data into a format that the server can accept.

[2060] Output: The transformed data is sent to the server.

[2061] Step 3:

[2062] The server stores the received data in a database.

[2063] Input: Data sent from the terminal.

[2064] Data processing: Processing into a format that is compatible with the database.

[2065] Output: The processed data is stored in a database.

[2066] Step 4:

[2067] The server periodically checks the user's bank account and updates the progress data.

[2068] Input: Bank account information, previous savings progress data.

[2069] Data calculations: Analyze bank account balances and transaction histories.

[2070] Output: Save the updated progress data to the database.

[2071] Step 5:

[2072] The server notifies the user terminal of the progress.

[2073] Input: The updated progress data.

[2074] What it does: The server visualizes the progress data and generates notification messages.

[2075] Output: A notification message is sent to the user's terminal.

[2076] Savings plan suggestions

[2077] Step 1:

[2078] The user enters their monthly income and fixed expenses.

[2079] Input: Monthly income, monthly fixed expenses.

[2080] Specific behavior: A user enters income and expenses into a smartphone app or web app.

[2081] Step 2:

[2082] The terminal sends user input to the server.

[2083] Input: Income and expense data entered by the user.

[2084] Data processing: Convert the data format to one suitable for the server.

[2085] Output: The transformed data is sent to the server.

[2086] Step 3:

[2087] The server converts the collected data into prompt sentence format and sends it to the generative AI model.

[2088] Input: Income and expenditure data.

[2089] Data processing: Convert into prompt sentence format.

[2090] Output: "If the user's monthly income is ¥300,000 and their monthly fixed expenses are ¥150,000, please suggest the optimal savings plan."

[2091] Step 4:

[2092] The generative AI model generates an appropriate savings plan and returns it to the server.

[2093] Input: The prompt statement.

[2094] Data calculation: The AI ​​model calculates income and expenditure data to generate an optimal savings plan.

[2095] Output: Savings plan (e.g., "Save 30,000 yen each month, and save an additional 10,000 yen after six months").

[2096] Step 5:

[2097] The server transmits the generated savings plan to the user terminal and presents it.

[2098] Input: Generated savings plan data.

[2099] Specific behavior: The server generates a notification message to the user.

[2100] Output: A notification message is sent to the user's terminal.

[2101] Step 6:

[2102] If the user agrees to the proposal, the server sets up the savings plan to run periodically.

[2103] Input: User consent data.

[2104] Specific operation: The server sets up and runs an automatic savings program.

[2105] Output: Savings are made periodically.

[2106] Savings Challenges and Rewards

[2107] Step 1:

[2108] A user participates in a savings challenge.

[2109] Input: Challenge goal (e.g., "Save 10,000 yen every month for three months").

[2110] Specific actions: Register to participate in the challenge on the app.

[2111] Step 2:

[2112] The terminal sends the participation information to the server.

[2113] Input: Challenge goal data.

[2114] Data processing: Convert data into server format.

[2115] Output: The transformed data is sent to the server.

[2116] Step 3:

[2117] The server periodically checks the progress of the challenge and updates the database.

[2118] Input: Account information and savings progress data.

[2119] Data calculation: Analyze your progress based on your account information.

[2120] Output: Save the updated progress data to the database.

[2121] Step 4:

[2122] If the challenge is met, the server will reward the user.

[2123] Input: The completed challenge data.

[2124] Specific behavior: Generates reward data and adds it to the user's points account.

[2125] Output: The user's points balance is updated.

[2126] Setting up an automatic savings program

[2127] Step 1:

[2128] The server suggests automatic savings programs based on the user's income and spending patterns.

[2129] Input: Income, expenditure data.

[2130] Data calculation: Analyzes data and generates optimal automatic savings programs.

[2131] Output: Proposal for an automated savings program.

[2132] Step 2:

[2133] The user agrees to the proposed program, and the device sends the consent information to the server.

[2134] Input: User consent data.

[2135] Data processing: Convert consent data into server format.

[2136] Output: The transformed data is sent to the server.

[2137] Step 3:

[2138] The server sets an automatic savings program and automatically saves a designated amount periodically.

[2139] Input: Automatic savings program data, user's bank account information.

[2140] Specific operation: The server sets a periodic task and automatically saves money on the specified date.

[2141] Output: The savings will be made on the specified date.

[2142] Investment advice and asset growth support

[2143] Step 1:

[2144] The user enters their investment goals and risk tolerance.

[2145] Inputs: Investment goals, risk tolerance.

[2146] Specific action: The user enters required information into a smartphone app or web app.

[2147] Step 2:

[2148] The terminal sends the input data to the server.

[2149] Inputs: Investment objectives and risk tolerance data.

[2150] Data processing: Convert the data format to one suitable for the server.

[2151] Output: The transformed data is sent to the server.

[2152] Step 3:

[2153] The server converts the collected data into prompt sentence format and sends it to the generative AI model.

[2154] Inputs: Investment objectives, risk tolerance data.

[2155] Data processing: Convert into prompt sentence format.

[2156] Output: Prompt statement (e.g., "Please suggest a portfolio that offers medium risk and long-term capital growth.").

[2157] Step 4:

[2158] The generative AI model generates an appropriate investment strategy and returns it to the server.

[2159] Input: The prompt statement.

[2160] Data calculation: AI models analyze user data and generate appropriate investment strategies.

[2161] Output: Investment strategy (e.g., "50% bonds, 30% stocks, 20% real estate investment trusts").

[2162] Step 5:

[2163] The server transmits the generated investment strategy to the user terminal and presents it.

[2164] Input: Generated investment strategy data.

[2165] Specific behavior: The server generates a notification message to the user.

[2166] Output: A notification message is sent to the user's terminal.

[2167] Visualizing savings goals and reward systems

[2168] Step 1:

[2169] The user sets a savings goal.

[2170] Input: Savings goal (amount, period).

[2171] Specific behavior: The user enters their savings goal in a smartphone app or web app.

[2172] Step 2:

[2173] The device sends the savings goal to the server.

[2174] Input: Savings goal data.

[2175] Data processing: Convert the data format to one suitable for the server.

[2176] Output: The transformed data is sent to the server.

[2177] Step 3:

[2178] The server periodically checks the progress of the savings goal and displays it visually.

[2179] Input: Savings goal data and progress data.

[2180] Data calculation: Analyze progress to display it graphically and numerically.

[2181] Output: A progress visualization is generated.

[2182] Step 4:

[2183] The server notifies the user terminal of the progress.

[2184] Input: Visualized progress data.

[2185] Specific action: The server generates a notification message.

[2186] Output: A notification message is sent to the user's terminal.

[2187] Providing educational content on savings

[2188] Step 1:

[2189] A user requests educational content.

[2190] Input: A request for educational content.

[2191] Specific operation: A user enters a request into a smartphone app or web app.

[2192] Step 2:

[2193] The device sends a request to the server.

[2194] Input: Educational content request data.

[2195] Data processing: Convert the data format to one suitable for the server.

[2196] Output: The transformed data is sent to the server.

[2197] Step 3:

[2198] The server sends the request to the generative AI model to generate optimal educational content.

[2199] Input: A request for educational content.

[2200] Data calculation: AI models generate optimal content based on user requests.

[2201] Output: Optimal educational content.

[2202] Step 4:

[2203] The server transmits the generated educational content to the user terminal and provides it.

[2204] Input: Generated educational content data.

[2205] What it does: Generates formatted educational content as notification messages.

[2206] Output: A notification message is sent to the user's terminal.

[2207] (Application example 1)

[2208] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2209] Conventional savings management systems limit the ways in which users can effectively track and manage their savings progress even when they set savings goals. They also lack the ability to suggest optimal savings plans based on the user's individual income and spending patterns. Furthermore, they lack the ability to track progress during savings challenges, provide rewards, and provide real-time notifications, making it difficult to maintain user motivation. A system that can solve these issues and improve users' savings efficiency is needed.

[2210] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2211] In this invention, the server includes means for managing progress toward a savings goal set by the user in real time and automatically updating the progress, means for proposing an optimal savings plan to the user in real time based on income and expenditure patterns, and means for periodically notifying the user of the savings progress. This allows the user to check the progress toward the savings goal in real time and to save effectively by always receiving optimal savings plans.

[2212] A "savings goal" is the amount or purpose that a user sets and wants to achieve within a certain period of time.

[2213] "Progress management" means regularly tracking and checking progress towards set goals.

[2214] "Revenue" means the amount of money a User earns within a given period of time.

[2215] "Expenses" means the amount of money a User consumes or pays within a given period of time.

[2216] A "savings plan" is a plan that suggests the optimal savings amount and period based on the user's income and expenses.

[2217] A "savings challenge" is an activity in which users participate and receive rewards and benefits by achieving certain goals.

[2218] "Rewards" refer to benefits or points given to users when they achieve goals such as savings challenges.

[2219] "Real-time" means that data and information are updated almost instantly, allowing users to see the latest status.

[2220] "Notification" means a communication from the system to the user informing them of information or progress.

[2221] A "generative AI model" is an artificial intelligence that automatically generates and suggests appropriate savings plans and educational content based on large amounts of data.

[2222] "Educational Content" means information and materials that help users learn about saving and investing.

[2223] "System" refers to a series of programs and hardware that integrates the above elements and assists users in managing their savings.

[2224] One embodiment of the present invention is a system that allows users to set savings goals and effectively manage their progress. The system proposes optimal savings plans based on the user's income and spending patterns, tracks progress in real time, and automatically updates the plans. The system also includes a function that allows users to participate in savings challenges and receive rewards and benefits when they achieve their goals.

[2225] System configuration

[2226] The system consists of the following components:

[2227] 1. User device: Users set goals and check progress on devices such as smartphones.

[2228] 2. Server: Manages user data and generates savings plans and educational content using generative AI models.

[2229] 3. Database: Stores users' savings goals, progress data, income, and expenditure information.

[2230] 4. Generative AI model: An AI engine that analyzes a user's income and spending patterns and generates a personalized savings plan.

[2231] Program processing explanation

[2232] 1. Goal setting and progress tracking:

[2233] The user sets a savings goal (e.g., "save 200,000 yen in one year") using their smartphone. The server stores this goal and related information (deadline, purpose, etc.) in a database.

[2234] The server links to the user's bank account information, automatically updates the user's savings progress each month, and notifies the user through the app.

[2235] 2. Savings plan suggestions:

[2236] The server analyzes the user's income and regular expenditure information and uses a generative AI model to propose an optimal savings plan. For example, if a user's monthly income is 300,000 yen and their monthly fixed expenditure is 150,000 yen, the server will propose a plan to "save 30,000 yen per month and start saving an additional 10,000 yen six months later."

[2237] 3. Challenge and reward management:

[2238] Users can participate in challenges such as "save 10,000 yen every month for three months." The server tracks the progress of the challenge and rewards the user with 500 yen worth of points when all conditions are met.

[2239] 4. Providing educational content:

[2240] The server provides users with educational content on saving and investing. For example, if a user requests to learn the basics of saving, the server will provide content such as how to review monthly expenses and tips for reducing fixed costs.

[2241] Specific hardware and software used

[2242] Smartphone: The device through which the user interacts with the app.

[2243] Server: Manage user data using AWS or Google Cloud.

[2244] Generative AI models: Generate savings plans and educational content using AI engines such as OpenAI GPT.

[2245] Specific examples and prompts

[2246] Examples:

[2247] Using the smartphone app, users can set a goal of "saving 200,000 yen in one year" and check their progress every month. They can also participate in a challenge to "save 10,000 yen every month for three months" and instantly check their progress each month from the app.

[2248] Example prompt sentence:

[2249] "If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, please suggest the optimal savings plan."

[2250] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2251] Step 1:

[2252] Users set savings goals using their smartphones and enter them into the application.

[2253] Input: Amount you want to save, deadline, purpose

[2254] Output: Set target data

[2255] How it works: The user enters a savings goal into the app, such as "save 200,000 yen in one year." This data is sent to the server and stored in a database.

[2256] Step 2:

[2257] The server links the target data stored in the database with the user's bank account information.

[2258] Input: User goal data, bank account information

[2259] Output: Linked account information and goal data

[2260] What it does: The server accesses the user's bank account information and links it to their savings goals, allowing the user's savings progress to be automatically tracked.

[2261] Step 3:

[2262] The server periodically checks the user's bank account and updates them on their savings progress.

[2263] Input: Bank account balance information

[2264] Output: Updated savings progress data

[2265] What it does: The server retrieves the user's bank account balance every month, calculates the current progress towards the savings goal, updates the database, and notifies the user.

[2266] Step 4:

[2267] The server analyzes the user's income and expenditure data and uses a generative AI model to propose an optimal savings plan based on those patterns.

[2268] Input: User income data, expenditure data

[2269] Output: Optimal savings plan

[2270] How it works: The server collects and analyzes the user's income and expenditure data. Using a generative AI model, it proposes a savings plan, such as "save 30,000 yen per month and start saving an additional 10,000 yen six months later." This plan is then notified to the user.

[2271] Step 5:

[2272] Users participate in savings challenges and the server tracks their progress.

[2273] Input: Challenge goal, participation period

[2274] Output: Challenge progress

[2275] Specific operation: A user participates in a challenge such as "save 10,000 yen every month for three months." The server periodically checks the progress of the challenge and notifies the user.

[2276] Step 6:

[2277] The server will award rewards and perks when the challenge is completed.

[2278] Input: Challenge completion status

[2279] Output: Rewards and rewards data

[2280] Specific operation: If the challenge is completed, the server will give the user 500 yen worth of points. This information will also be saved in the database and notified to the user.

[2281] Step 7:

[2282] The server provides users with educational content on saving and investing.

[2283] Input: User request, interest data

[2284] Output: Educational content

[2285] Specific operation: The user makes a request such as "I want to learn the basics of saving." The server uses the generative AI model to generate appropriate educational content and provides it to the user.

[2286] Specific prompt examples:

[2287] "If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, please suggest the optimal savings plan."

[2288] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2289] The present invention is a system that incorporates an emotion engine for recognizing user emotions, and performs savings goal setting, progress management, savings plan proposals, savings challenges, rewards, automatic savings, investment advice, savings goal visualization, and educational content provision. Below, we will explain in detail the programs for realizing each function and specific examples.

[2290] Emotion Engine

[2291] The emotion engine analyzes users' emotions in real time, allowing it to customize savings plans, investment strategies, rewards, and educational content based on the user's emotional state.

[2292] Setting savings goals and tracking progress

[2293] Step 1: The user opens the app and accesses the savings goal setting screen. The user enters the goal amount, deadline, and purpose. This data is sent to the server and stored in the database.

[2294] Step 2: The server checks the user's savings progress on a daily or weekly basis, analyzes the progress data, and visualizes it.

[2295] Examples:

[2296] When a user sets a goal of "saving 200,000 yen in one year," the server links the user's bank account information and provides monthly updates and notifications on progress.

[2297] Savings plan suggestions

[2298] Step 1: Provide a screen where users can enter their income and expense information. The data is sent to a server for analysis.

[2299] Step 2: The generative AI model generates a savings plan and suggests it to the user.

[2300] Step 3: The emotion engine analyzes the user's emotions and adjusts the plan.

[2301] Examples:

[2302] For users with a monthly income of 300,000 yen and fixed expenses of 150,000 yen, the plan is to save 30,000 yen each month and start saving an additional 10,000 yen after six months.

[2303] Savings Challenges and Rewards

[2304] The server sets up a savings challenge and proposes it to users. If users participate in the challenge and achieve the goal, they will receive rewards and benefits. The emotion engine customizes the reward content based on the user's emotions.

[2305] Examples:

[2306] If a user participates in the challenge of "saving 10,000 yen every month for three months" and meets the conditions, the server will award them 500 yen worth of points.

[2307] Setting up an automatic savings program

[2308] The server proposes an automatic savings program based on the user's income and expenditure data. If the user approves, the system automatically saves. The emotion engine adjusts the program based on the user's emotions.

[2309] Examples:

[2310] Run a program that automatically saves 5,000 yen on the 15th and 30th of every month.

[2311] Investment advice and asset growth support

[2312] The generative AI model analyzes users' investment goals and risk tolerance to suggest appropriate investment strategies, while the sentiment engine adjusts strategies based on users' emotions.

[2313] Examples:

[2314] If a user enters information such as "I am aiming for medium-risk, long-term asset growth," the service suggests a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[2315] Visualizing savings goals and reward systems

[2316] The server analyzes and visualizes the progress of the user's savings goal. When the goal is achieved, rewards are provided. The emotion engine customizes the reward content.

[2317] Examples:

[2318] Set a goal of "saving 300,000 yen in 6 months" and progress while checking your progress. When you reach your goal, the server will provide you with a cashback of 1,000 yen.

[2319] Providing educational content on savings

[2320] The generative AI model creates and delivers educational content tailored to the user, while the emotion engine adjusts the content based on the user's emotions.

[2321] Examples:

[2322] When a user requests to "learn the basics of saving," educational content such as "how to review your monthly spending" and "tips for reducing fixed costs" will be provided.

[2323] Use of emotion engine

[2324] The emotion engine analyzes user input and other behavioral data to recognize emotions, and uses this emotional data to optimize various system features (savings plans, rewards, investment strategies, educational content, etc.) for users.

[2325] Examples:

[2326] If the emotion engine detects that the user is feeling stressed, it will suggest relaxing their savings plan as a mitigation measure.

[2327] The processing flow will be explained below.

[2328] Processing flow of a system that combines emotion engines

[2329] Flow of setting savings goals and tracking progress

[2330] Step 1:

[2331] On the device: The user opens the app and accesses the savings goal setting screen.

[2332] Step 2:

[2333] User: Enter the target amount, deadline, and purpose.

[2334] Step 3:

[2335] Terminal: Sends user input data to the server.

[2336] Step 4:

[2337] Server: Receives user input data and stores it in a database.

[2338] Step 5:

[2339] Server: The emotion engine analyzes the user's emotions and suggests goal adjustments if necessary.

[2340] Step 6:

[2341] Server: Retrieves transaction history from the database to check the user's savings status on a daily or weekly basis.

[2342] Step 7:

[2343] Server: Analyzes progress towards goals through batch processing.

[2344] Step 8:

[2345] Server: Calculates the progress and sends the result to the device.

[2346] Step 9:

[2347] Terminal: Visually display the received progress data, for example using a gauge or bar graph.

[2348] Savings plan proposal process flow

[2349] Step 1:

[2350] Terminal: Provides a screen where users can enter income and expense information.

[2351] Step 2:

[2352] User: Enter your monthly income and major recurring expenses (rent, utilities, food, etc.).

[2353] Step 3:

[2354] Terminal: Sends input data to the server.

[2355] Step 4:

[2356] Server: Receives user income and expenditure data and analyzes it using a generative AI model.

[2357] Step 5:

[2358] Server: The generative AI model generates a savings plan and sends it to the device.

[2359] Step 6:

[2360] Server: The emotion engine analyzes the user's emotions and adjusts the plan. For example, if stress is high, it suggests relaxing the plan.

[2361] Step 7:

[2362] Terminal: Display the suggested savings plan to the user.

[2363] Step 8:

[2364] User: Review the proposed savings plan and adjust as needed.

[2365] Savings Challenge and Reward Processing Flow

[2366] Step 1:

[2367] Server: Sets the savings challenge and sends it to the device.

[2368] Step 2:

[2369] Device: Shows the challenge to the user and asks for their confirmation.

[2370] Step 3:

[2371] User: Decides to participate in the proposed challenge and clicks the Join button.

[2372] Step 4:

[2373] Device: Sends the user's decision to the server.

[2374] Step 5:

[2375] Server: Periodically checks the user's savings progress and calculates the challenge progress.

[2376] Step 6:

[2377] Server: The emotion engine analyzes the user's emotional state and customizes rewards, for example, suggesting additional incentives if motivation drops.

[2378] Step 7:

[2379] Server: Awards rewards when a challenge is completed and sends the data to the device.

[2380] Step 8:

[2381] Device: Notifies the user of the challenge completion and reward details.

[2382] Process flow for setting up an automatic savings program

[2383] Step 1:

[2384] Server: Proposes an automatic savings program based on the user's income and expenditure data.

[2385] Step 2:

[2386] On the device: Display the suggestions to the user.

[2387] Step 3:

[2388] User: Approves the proposed automatic savings program.

[2389] Step 4:

[2390] Terminal: Sends authorization information to the server.

[2391] Step 5:

[2392] Server: Executes approved automatic savings programs and automatically saves according to a specified schedule.

[2393] Step 6:

[2394] Server: The emotion engine analyzes the user's emotions and adjusts the program accordingly. For example, if the user is under high stress, the savings amount is temporarily set lower.

[2395] Step 7:

[2396] Server: Notifies the user of the progress of the automatic savings.

[2397] Step 8:

[2398] On the device: Show users their savings progress.

[2399] Investment advice and asset growth support process

[2400] Step 1:

[2401] Terminal: Provides a screen where users can input their investment goals and risk tolerance.

[2402] Step 2:

[2403] User: Enter your investment goals and risk tolerance.

[2404] Step 3:

[2405] Terminal: Sends input data to the server.

[2406] Step 4:

[2407] Server: Uses generative AI models to analyze user input data and generate optimal investment strategies.

[2408] Step 5:

[2409] Server: Sends investment strategies to the terminal.

[2410] Step 6:

[2411] Server: The sentiment engine adjusts investment strategies based on user sentiment. For example, if a user feels anxious, it suggests a low-risk strategy.

[2412] Step 7:

[2413] Terminal: displays the proposed investment strategy to the user.

[2414] Step 8:

[2415] User: Review investment strategies and implement them as needed.

[2416] Step 9:

[2417] Server: Regularly tracks users' investment progress and adjusts strategies as needed.

[2418] Step 10:

[2419] On the device: Notify the user of progress and suggested adjustments.

[2420] Visualization of savings goals and reward system processing flow

[2421] Step 1:

[2422] Device: Provides a screen where users can set their savings goals.

[2423] Step 2:

[2424] User: Set the goal amount and deadline.

[2425] Step 3:

[2426] Terminal: Sends the settings to the server.

[2427] Step 4:

[2428] Server: Stores goal settings in a database and tracks progress periodically.

[2429] Step 5:

[2430] Emotion Engine: Analyzes the user's emotional state and customizes assistance to help them achieve their goals.

[2431] Step 6:

[2432] Server: When the goal achievement conditions are met, the server processes the reward.

[2433] Step 7:

[2434] Server: Analyzes the progress data and sends it to the device as visual information.

[2435] Step 8:

[2436] On the device: Show progress to the user with graphs and metrics.

[2437] Step 9:

[2438] Server: Notifies the device of the reward to be provided when the goal is achieved.

[2439] Step 10:

[2440] Terminal: Notifies the user and assists them in claiming their reward.

[2441] Process flow for providing educational content on savings

[2442] Step 1:

[2443] Server: Uses generative AI models to create educational content tailored to the user.

[2444] Step 2:

[2445] Server: Sends content to the device.

[2446] Step 3:

[2447] Device: Displays educational content to the user.

[2448] Step 4:

[2449] Users: View and learn educational content.

[2450] Step 5:

[2451] Emotion engine: Analyzes user emotions and tailors content based on understanding and interest.

[2452] Step 6:

[2453] Device: Sends user feedback to the server.

[2454] Step 7:

[2455] Server: Regularly update educational content based on user feedback.

[2456] Example 2

[2457] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2458] Conventional savings management systems were unable to fully consider users' emotions and individual circumstances, and were limited to proposing general savings plans. They also lacked the flexibility to adapt to fluctuations in users' income and spending patterns, and the ability to customize plans based on their emotional state. Furthermore, it was difficult to address individual needs when it came to tracking savings goal progress or proposing investment strategies.

[2459] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2460] In this invention, the server includes a means for allowing users to set savings goals and manage their progress, a means for analyzing the user's income and spending patterns to propose individual savings plans, and a means for allowing the user to participate in savings challenges and receive rewards and benefits upon completion. This allows for flexible proposals of savings plans and investment strategies tailored to the user's individual circumstances. Furthermore, by using an emotion engine to analyze the user's emotional state in real time, each function of the system can be customized based on emotions, creating a user-friendly environment. Furthermore, by using a generative AI model to automatically generate optimal savings plans and investment strategies based on prompts, more accurate proposals can be made.

[2461] "Savings goal setting" is the act of a user deciding on a specific amount and setting a deadline and purpose for achieving that amount.

[2462] "Progress management" is the process of monitoring the current progress towards the savings goal set by the user and updating the information accordingly.

[2463] "Income and Expense Pattern Analysis" is a means of analyzing a user's income and expense data to understand their individual financial situation.

[2464] "Savings plan suggestions" are presented to users based on the analysis of their income and expenses, showing them specific ways in which they should save money.

[2465] A "savings challenge" is a system in which users set short-term tasks or challenges to achieve a specific savings goal, and receive rewards or benefits if they complete the challenge.

[2466] "Automatic savings" is a function that automatically saves money on a specific schedule based on the user's income and expenditure data.

[2467] "Investment strategy suggestions" means providing specific guidance on what kind of investments a user should make based on their investment goals and risk tolerance.

[2468] "Savings goal visualization" is the process of visually displaying a user's progress toward their savings goal in the form of graphs and reports.

[2469] "Providing educational content" means providing users with knowledge about savings and investments and providing information and educational materials to deepen their understanding.

[2470] An "emotion engine" is a software technology that analyzes a user's emotional state in real time and reflects the results in other system functions.

[2471] A "generative AI model" is an artificial intelligence algorithm that inputs user data and automatically generates optimal savings plans and investment strategies.

[2472] A "prompt sentence" is an input sentence that gives specific instructions or questions to a generative AI model.

[2473] The present invention is a system that incorporates an emotion engine that recognizes user emotions, and performs savings goal setting, progress management, savings plan proposals, savings challenges, rewards, automatic savings, investment advice, savings goal visualization, and educational content provision. Below, we will explain in detail the programs for realizing each function and specific examples.

[2474] Emotion Engine

[2475] The server collects user input data and operation data in real time and analyzes it with an emotion engine, which identifies the user's emotional state and provides feedback to other system functions.

[2476] Examples:

[2477] When users log in and set their savings goals, an emotion engine analyzes their typing speed and sequence to detect signs of stress or excitement.

[2478] Setting savings goals and tracking progress

[2479] The user enters the goal amount, deadline, and purpose on the savings goal setting screen. The device sends this information to the server, which stores it in a database. The server periodically checks the user's progress, analyzes the progress data, generates visualization data, and notifies the user.

[2480] Examples:

[2481] When a user sets a goal of "saving 200,000 yen in one year," the server updates and notifies the user of their progress every month.

[2482] Savings plan suggestions

[2483] Users input their income and expenditure information, which is then sent to the server via their device. The server then sends prompts to the generative AI model to generate an optimal savings plan. The emotion engine then adjusts the plan and makes suggestions to the user.

[2484] Examples:

[2485] For users with a monthly income of 300,000 yen and fixed expenses of 150,000 yen, the plan is to "save 30,000 yen each month and save an additional 10,000 yen after six months."

[2486] Savings Challenges and Rewards

[2487] The server sets up savings challenges, and the emotion engine proposes challenges based on the user's emotions. When a user participates in and completes a challenge, the server grants rewards and benefits.

[2488] Examples:

[2489] If a user succeeds in the challenge of "saving 10,000 yen every month for three months," the server will award them 500 yen worth of points.

[2490] Setting up an automatic savings program

[2491] The server proposes an automatic savings program based on the user's income and expenditure data, and the emotion engine adjusts the proposal. If the user approves the proposal, the server automatically executes the savings.

[2492] Examples:

[2493] The server runs a program that automatically saves 5,000 yen on the 15th and 30th of every month.

[2494] Investment advice and asset growth support

[2495] Users input their investment goals and risk tolerance, and the device sends this to the server. The server then sends prompts to the generative AI model to generate an optimal investment strategy. The emotion engine then adjusts the strategy and presents it to the user.

[2496] Examples:

[2497] For users seeking medium-risk, long-term asset growth, we propose a portfolio of 50% bonds, 30% stocks, and 20% real estate investment trusts.

[2498] Visualizing savings goals and reward systems

[2499] The server analyzes the progress of savings goals and creates visualizations. Rewards are provided when goals are achieved, and the emotion engine customizes the rewards.

[2500] Examples:

[2501] If the goal of saving 300,000 yen in six months is achieved, the server will provide a cashback of 1,000 yen.

[2502] Providing educational content on savings

[2503] The server uses a generative AI model to create educational content appropriate for the user, and an emotion engine to tailor the content. When a user requests content, the server provides the tailored educational content.

[2504] Examples:

[2505] When a user requests to "learn the basics of saving," educational content such as "how to review your monthly spending" and "tips for reducing fixed costs" is provided.

[2506] Use of emotion engine

[2507] The server analyzes user input and behavioral data using an emotion engine and customizes other functions of the system based on the results of this analysis.

[2508] Examples:

[2509] The emotion engine recognizes when a user is feeling stressed and suggests relaxing their savings plan as a relief measure.

[2510] Prompt Sentence Examples

[2511] "Generate an optimal savings plan based on the user's monthly income and fixed expenses. Adjust the plan based on stress levels identified by the emotion engine."

[2512] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2513] Specific flow of program processing

[2514] Emotion Engine Analysis

[2515] Step 1:

[2516] A user logs in to the app.

[2517] Input: User login data, operation log

[2518] Output: The operation log is sent to the server.

[2519] Specific operation: A user logs in to the app, and the app collects user operation data and sends it to the server.

[2520] Step 2:

[2521] The server passes the operation log to the emotion engine.

[2522] Input: Operation log

[2523] Output: Emotion analysis results

[2524] Specific operation: The received operation log is passed to the emotion engine, which analyzes the user's emotional state in real time.

[2525] Step 3:

[2526] The emotion engine analyzes the user's emotions and provides feedback on the results.

[2527] Input: Operation log

[2528] Output: Emotion analysis results (e.g., stress level, excitement level)

[2529] Specific operation: The emotion engine analyzes the operation log, identifies the emotional state, and provides feedback to other functions of the system.

[2530] Setting savings goals and tracking progress

[2531] Step 1:

[2532] The user accesses the savings goal setting screen and inputs the goal amount, deadline, and purpose.

[2533] Input: target amount, deadline, purpose

[2534] Output: User input data is saved to the device.

[2535] Specific operation: The user enters a savings goal, which is temporarily saved on the device.

[2536] Step 2:

[2537] The device sends the user input data to the server.

[2538] Input: User-entered data

[2539] Output: User-entered data sent to the server

[2540] Specific operation: The terminal sends user input data to the server.

[2541] Step 3:

[2542] The server stores the received data in a database and periodically collects progress data.

[2543] Input: User-entered data

[2544] Output: Data stored in the database, progress data

[2545] What it does: The server saves the user-entered data in a database and periodically collects progress data from the bank account.

[2546] Step 4:

[2547] The server analyzes and visualizes the progress data.

[2548] Input: Progress data

[2549] Output: Visualized progress data (graphs, reports)

[2550] Specific operation: The server analyzes the progress data, visualizes it in the form of graphs and reports, and notifies the user.

[2551] Savings plan suggestions

[2552] Step 1:

[2553] The user enters income and expense information.

[2554] Input: Income and expenditure data

[2555] Output: User input data is saved to the device.

[2556] Specific operation: The user enters income and expenditure data, which is temporarily stored on the device.

[2557] Step 2:

[2558] The device sends the user input data to the server.

[2559] Input: Income and expenditure data

[2560] Output: User-entered data sent to the server

[2561] Specific operation: The terminal sends user input data to the server.

[2562] Step 3:

[2563] The server analyzes the incoming data and sends prompts to the generative AI model.

[2564] Input: Income and expenditure data

[2565] Output: The input data, including the prompt sentence, is sent to the generative AI model.

[2566] Specific operation: The server analyzes the received data and sends prompt sentences to the generative AI model to generate an optimal savings plan.

[2567] Step 4:

[2568] A generative AI model generates an optimal savings plan.

[2569] Input: prompt statement, user input data

[2570] Output: Generated savings plan

[2571] How it works: The generative AI model generates an optimal savings plan based on the prompt.

[2572] Step 5:

[2573] The emotion engine adjusts the plan and makes suggestions to the user.

[2574] Input: Generated savings plan, sentiment analysis results

[2575] Output: Adjusted savings plan

[2576] Specific operation: The emotion engine adjusts the generated savings plan based on the user's emotional state and suggests it to the user.

[2577] Savings Challenges and Rewards

[2578] Step 1:

[2579] The server sets up a savings challenge and proposes it to the user.

[2580] Input: None

[2581] Output: Savings challenge proposal

[2582] Specific operation: The server periodically proposes a savings challenge to the user.

[2583] Step 2:

[2584] An emotion engine adjusts the challenge based on the user's emotions.

[2585] Input: Sentiment analysis results

[2586] Output: Adjusted Savings Challenge

[2587] How it works: The emotional engine takes into account the user's emotional state and adjusts the content and rewards of the savings challenge.

[2588] Step 3:

[2589] The user participates in a challenge.

[2590] Input: Press the Join button

[2591] Output: Join information is sent to the server.

[2592] Specific operation: The user presses a button to participate in the challenge, and participation information is sent to the server.

[2593] Step 4:

[2594] The server manages the progress of the challenge and grants rewards upon completion.

[2595] Input: Challenge progress data

[2596] Output: Reward

[2597] Specific operation: The server manages the progress of the challenge and grants rewards and benefits when the conditions are met.

[2598] Setting up an automatic savings program

[2599] Step 1:

[2600] The server suggests an automatic savings program based on the user's income and expenditure data.

[2601] Input: Income and expenditure data

[2602] Output: Proposal for an automatic savings program

[2603] Specific operation: The server analyzes the user's income and expenditure data and suggests the most suitable automatic savings program.

[2604] Step 2:

[2605] The emotion engine adjusts the suggestions.

[2606] Input: Sentiment analysis results, automatic savings program proposal

[2607] Output: Adjusted automatic savings program

[2608] How it works: The emotion engine adjusts the content of the automatic savings program based on the user's emotions.

[2609] Step 3:

[2610] The user approves the proposal.

[2611] Input: Press the approve button

[2612] Output: The authorization information is sent to the server.

[2613] Specific operation: The user approves the automatic savings program, and the approval information is sent to the server.

[2614] Step 4:

[2615] The server automatically executes the savings.

[2616] Enter: Approved automatic savings program

[2617] Output: Automatic savings execution data

[2618] Specific operation: The server automatically debits the user's account at regular intervals according to the approved program.

[2619] Investment advice and asset growth support

[2620] Step 1:

[2621] Users input their investment goals and risk tolerance.

[2622] Inputs: Investment goals, risk tolerance

[2623] Output: User input data is saved to the device.

[2624] Specific operation: The user enters their investment goals and risk tolerance, which are then temporarily saved on the device.

[2625] Step 2:

[2626] The device sends the user input data to the server.

[2627] Inputs: Investment goals, risk tolerance

[2628] Output: User-entered data sent to the server

[2629] Specific operation: The terminal sends user input data to the server.

[2630] Step 3:

[2631] The server analyzes the incoming data and sends prompts to the generative AI model.

[2632] Inputs: Investment goals, risk tolerance

[2633] Output: The input data, including the prompt sentence, is sent to the generative AI model.

[2634] Specific operation: The server analyzes the received data and sends prompt statements to the generative AI model to generate the optimal investment strategy.

[2635] Step 4:

[2636] A generative AI model generates optimal investment strategies.

[2637] Input: Prompt statement, investment goal, risk tolerance

[2638] Output: Generated investment strategy

[2639] How it works: The generative AI model generates an optimal investment strategy based on the prompt.

[2640] Step 5:

[2641] The emotion engine adjusts the strategy and makes suggestions to the user.

[2642] Input: Generated investment strategy, sentiment analysis results

[2643] Output: Adjusted investment strategy

[2644] Specific operation: The emotion engine adjusts the generated investment strategy based on the user's emotional state and suggests it to the user.

[2645] Visualizing savings goals and reward systems

[2646] Step 1:

[2647] The server analyzes the progress of the savings goal.

[2648] Input: Progress data

[2649] Output: Analysis results

[2650] Specific behavior: The server analyzes the savings goal progress data.

[2651] Step 2:

[2652] The server visualizes the progress data.

[2653] Input: Analysis results

[2654] Output: Visualized data (graphs, reports)

[2655] What it does: The server visualizes the progress data in the form of graphs and reports.

[2656] Step 3:

[2657] The server provides rewards when goals are achieved.

[2658] Input: Analysis results, visualization data

[2659] Output: Reward

[2660] Specific behavior: The server confirms goal achievement and provides a reward to the user.

[2661] Step 4:

[2662] The emotion engine customizes rewards.

[2663] Input: Sentiment analysis results

[2664] Output: Customized reward content

[2665] How it works: The emotion engine adjusts and customizes rewards based on the user's emotions.

[2666] Providing educational content on savings

[2667] Step 1:

[2668] The server sends prompts to the generative AI model to create educational content.

[2669] Input: User request, prompt

[2670] Output: Generated educational content

[2671] Specific operation: The server sends the user's request as a prompt to the generative AI model to create educational content.

[2672] Step 2:

[2673] An emotional engine adjusts educational content.

[2674] Input: Educational content, sentiment analysis results

[2675] Output: Tailored educational content

[2676] Specific behavior: The emotion engine adjusts educational content based on the user's emotional state.

[2677] Step 3:

[2678] A user requests educational content.

[2679] Input: Request operation

[2680] Output: The request information is sent to the server.

[2681] Specific operation: The user requests educational content and the relevant information is sent to the server.

[2682] Step 4:

[2683] The server provides tailored educational content to the user.

[2684] Input: Tailored educational content

[2685] Output: Provided educational content

[2686] Specific operation: The server provides the adjusted educational content to the user.

[2687] (Application example 2)

[2688] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2689] This invention is a system that incorporates an emotion engine that analyzes user emotions and provides functions such as setting savings goals, tracking progress, and proposing savings plans. However, current systems lack individualized responses that take into account the user's emotions and psychological state, and further personalization is required. Furthermore, the lack of effective user interaction in virtual environments poses challenges in improving user satisfaction and motivation. Therefore, it is desirable to provide a system that optimizes savings plans, investment strategies, rewards, and educational content based on the user's emotions, enabling more familiar interactions through a virtual assistant.

[2690] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for allowing a user to set a savings goal and manage the progress of the goal; means for analyzing the user's income and spending patterns and proposing an individual savings plan; means for allowing the user to participate in a savings challenge and offering rewards or benefits upon achievement; means for automatically saving based on the user's income and spending; means for analyzing the user's investment goals and risk tolerance and proposing an appropriate investment strategy; means for visualizing the user's progress toward the savings goal and offering rewards upon achievement; means for providing the user with educational content related to savings and investment; means for recognizing the user's emotions using an emotion analysis module and customizing the savings plan, investment strategy, reward details, and educational content based on the emotions; and means for using a virtual assistant to support interaction with the user and progress and set the savings goal. This enables personalized responses based on the user's individual emotions and psychological state, enabling effective interaction in a virtual environment.

[2691] "User" refers to any individual or organization that uses this system.

[2692] A "savings goal" refers to the amount of savings or purpose that a user sets and wants to achieve within a certain period of time.

[2693] "Progress management" refers to monitoring, analyzing, and visualizing progress toward savings goals set by users.

[2694] "Income and expenditure patterns" refers to the user's monthly income and expenditure trends and specific breakdowns.

[2695] A "savings plan" refers to specific methods and steps for achieving savings goals that are presented to users after taking into account their income and spending patterns.

[2696] A "savings challenge" refers to a competitive initiative in which users participate in short-term or medium- to long-term savings goals.

[2697] "Rewards and perks" refers to incentives offered to users upon achieving savings challenges and goals.

[2698] "Automatic savings" refers to a function in which the system automatically saves a certain amount based on the user's income and expenditure data.

[2699] "Investment goal" refers to the investment results or return target set by the user that they wish to achieve within a certain period of time.

[2700] "Risk tolerance" refers to the investment risk level that a user can tolerate.

[2701] An "investment strategy" refers to a specific investment policy or portfolio that is generated taking into account a user's investment goals and risk tolerance.

[2702] "Visualization" refers to the system displaying the user's savings goals and progress in visual ways, such as graphs and charts.

[2703] "Educational Content" means educational materials and opportunities related to savings and investing that are provided to Users.

[2704] "Emotion analysis module" refers to software or algorithms used to analyze a user's emotional state.

[2705] A "virtual assistant" refers to a virtual character or interface that interacts with and provides assistance to users in a digital environment.

[2706] This system incorporates an emotion analysis module that recognizes user emotions, and performs savings goal setting, progress management, savings plan proposals, rewards, automatic savings, investment advice, savings goal visualization, and educational content provision. Specifically, these functions are realized through interactions between the server, terminals, and users.

[2707] The emotion analysis module analyzes the user's emotional state in real time and recognizes emotions based on the user's input and behavioral data. This emotional data is then used by the entire system to provide the optimal response to the user.

[2708] Setting savings goals and tracking progress

[2709] Users access the savings goal setting screen through their device and enter the goal amount, deadline, purpose, etc. This data is sent to the server and stored in a database. The server checks the user's savings progress on a daily or weekly basis, and analyzes and visualizes the progress data. For example, if a user sets a goal of "saving 200,000 yen in one year," the server will link the user's bank account information and provide monthly updates and notifications on their progress.

[2710] Savings plan suggestions

[2711] The system provides a screen where users can enter income and expenditure information via their device, which is then sent to a server for analysis. A generative AI model generates a savings plan and suggests it to the user. An emotion analysis module then analyzes the user's emotions and adjusts the plan accordingly. For example, for a user with a monthly income of 300,000 yen and fixed expenses of 150,000 yen, the system suggests a plan to "save 30,000 yen each month and start saving an additional 10,000 yen six months later."

[2712] Savings Challenges and Rewards

[2713] The server sets up savings challenges and proposes them to users. If users participate in the challenge and achieve their goals, they will receive rewards and benefits. The sentiment analysis module customizes the reward content based on the user's emotions. For example, if a user participates in a challenge to "save 10,000 yen every month for three months" and meets the conditions, the server will award 500 yen worth of points.

[2714] Setting up an automatic savings program

[2715] The server proposes an automatic savings program based on the user's income and expenditure data. If the user approves, the system automatically starts saving. The emotion analysis module adjusts the program based on the user's emotions. For example, the system can implement a program that automatically saves 5,000 yen on the 15th and 30th of each month.

[2716] Investment advice and asset growth support

[2717] The generative AI model analyzes the user's investment goals and risk tolerance and proposes an appropriate investment strategy. The sentiment analysis module adjusts the strategy based on the user's sentiment. For example, if a user enters information such as "I aim for medium-risk, long-term asset growth," the system will propose a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[2718] Visualizing savings goals and reward systems

[2719] The server analyzes and visualizes the user's progress toward their savings goal. Once the goal is achieved, a reward is provided. The sentiment analysis module customizes the reward content. For example, a user can set a goal of "saving 300,000 yen in six months" and check their progress as they go along. When the goal is achieved, a cashback of 1,000 yen is provided.

[2720] Providing educational content on savings

[2721] The generative AI model creates and provides educational content appropriate for the user. The sentiment analysis module adjusts the content based on the user's emotions. For example, if a user requests to learn the basics of saving, educational content such as "How to review your monthly spending" and "Tips for reducing fixed costs" will be provided.

[2722] Using the sentiment analysis module

[2723] The sentiment analysis module analyzes user input and other behavioral data to recognize emotions. Based on this emotional data, the system's various features (savings plans, rewards, investment strategies, educational content, etc.) are optimized for the user. For example, if the sentiment analysis module detects that the user is feeling stressed, it can suggest relaxing the savings plan as a mitigation measure.

[2724] Examples of prompt statements

[2725] User A opens the app and sets a goal of "saving 200,000 yen in one year." Their income is 300,000 yen and their expenses are 150,000 yen. The emotion engine recognizes "stress." Please suggest a savings plan based on this information.

[2726] As described above, the interaction between the server, terminal, and user allows for emotion analysis and realizes a system that responds to individual user needs, enabling users to more effectively achieve their savings goals and supporting financial independence.

[2727] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2728] Step 1:

[2729] The user accesses the savings goal setting screen through the terminal and inputs the goal amount, deadline, purpose, etc. This inputs the savings goal data. The input data is sent to the server and stored in the database.

[2730] Step 2:

[2731] The server checks the user's savings progress on a daily or weekly basis. Savings progress data is collected by linking the user's bank account information. This data is analyzed by the server, and the progress is visualized and output to the terminal. For example, the progress is displayed in chart form.

[2732] Step 3:

[2733] Users have access to a screen on their device where they can input their income and expenditure information. Once the income and expenditure data is entered, it is sent to a server. The server uses a generative AI model to analyze the data, generate a savings plan, and obtain the savings plan data, which is then output back to the device.

[2734] Step 4:

[2735] The emotion analysis module analyzes the user's input data and behavioral data to recognize the user's emotional state. For example, it generates emotion data through text analysis or voice analysis. The generated emotion data is sent to the server and stored in a database.

[2736] Step 5:

[2737] Based on the emotional data, the server adjusts the savings plan. For example, if the user is feeling stressed, the generative AI model will recalculate the savings plan to ease the stress, and generate new savings plan data. This new data will be output to the device.

[2738] Step 6:

[2739] The server sets up the user to participate in the savings challenge and proposes it to the device. When the user participates in the challenge, the status is sent to the server and stored in a database. When the goal is achieved, the server generates reward and benefit data and outputs it to the device.

[2740] Step 7:

[2741] The server proposes an automatic savings program based on the user's income and expenditure data. If the user approves, the data is sent to the server and stored in a database. The program is adjusted appropriately based on the user's emotional data, and automatic savings data is generated. For example, the terminal may output a message such as, "Automatically save 5,000 yen on the 15th and 30th of each month."

[2742] Step 8:

[2743] The server analyzes the user's investment goals and risk tolerance and uses a generative AI model to propose an appropriate investment strategy. Investment strategy data is generated and output to the terminal. For example, an investment strategy such as "Aiming for long-term asset growth with medium risk" is displayed.

[2744] Step 9:

[2745] The server analyzes the user's progress toward their savings goal and generates visualization data. The progress is visualized in charts and other formats and output to the device. When the goal is achieved, the server generates reward data and displays a cashback of 1,000 yen.

[2746] Step 10:

[2747] The system uses a generative AI model to create educational content requested by the user, which is then provided by the server. The content is adjusted based on emotion data, and the educational content is output to the device. For example, content such as "How to review your monthly expenses" and "Tips for reducing fixed costs" is displayed.

[2748] Through the above processing steps, the server continuously analyzes the emotional data and provides savings plans and educational content tailored to the individual needs of the user, thereby realizing a system that encourages users to become financially independent.

[2749] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2750] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2751] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2752] [Fourth embodiment]

[2753] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2754] 7, a 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.

[2755] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2756] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2757] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2758] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2759] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2760] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2761] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2762] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[2763] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2764] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2765] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2766] The present invention is a system that allows users to set savings goals and effectively manage their progress. Below, we will explain the programs for realizing each function and specific examples.

[2767] Setting savings goals and tracking progress

[2768] This system provides a savings goal setting function. Users input the amount they want to save, the deadline, and the purpose, and the server stores this information in a database. The system periodically tracks the user's savings progress and analyzes the progress data to provide the user with a visual representation of their current situation.

[2769] Examples:

[2770] The user sets a goal of "saving 200,000 yen in one year." The server links the user's bank account information, automatically updates the user's savings progress each month, and notifies the user through the app.

[2771] Savings plan suggestions

[2772] The server analyzes the user's income and spending patterns and proposes an individual savings plan. Based on the user's income information and regular spending information, the generative AI model generates an optimal savings plan and provides it to the user. If the user agrees with the proposal, the plan is confirmed and savings are automatically made.

[2773] Examples:

[2774] If a user's monthly income is 300,000 yen and their monthly fixed expenses are 150,000 yen, the server will suggest a savings plan that involves saving 30,000 yen each month and then starting to save an additional 10,000 yen six months later.

[2775] Savings Challenges and Rewards

[2776] Users participate in savings challenges and receive rewards and perks when they achieve their goals. The server tracks the progress of the challenges and awards rewards when they are achieved.

[2777] Examples:

[2778] If a user participates in the challenge of "saving 10,000 yen every month for three months" and meets all the conditions, the server will award the user 500 yen worth of points.

[2779] Setting up an automatic savings program

[2780] The server proposes an automatic savings program based on the user's income and spending patterns, and if the user approves, the system automatically saves the specified amount on a regular basis.

[2781] Examples:

[2782] When a user agrees to a program that automatically saves 5,000 yen on the 15th and 30th of every month, the server automatically saves the specified amount on a regular basis.

[2783] Investment advice and asset growth support

[2784] The server analyzes the user's investment goals and risk tolerance and proposes an appropriate investment strategy, allowing users to not only save money but also manage their assets efficiently.

[2785] Examples:

[2786] If a user inputs a goal of "long-term asset growth with medium risk," the server will suggest a portfolio of "50% bonds, 30% stocks, and 20% real estate investment trusts."

[2787] Visualizing savings goals and reward systems

[2788] The server visualizes the progress of the user's savings goal, using graphs and various indicators to show the progress to the user, and provides rewards when the goal is achieved.

[2789] Examples:

[2790] Users set a goal of "saving 300,000 yen in 6 months" and progress while checking their progress. When the goal is achieved, the server provides a cashback of 1,000 yen.

[2791] Providing educational content on savings

[2792] The server provides users with educational content on savings and investments to improve their financial literacy. Using a generative AI model, it automatically creates and provides the most appropriate content for each user.

[2793] Examples:

[2794] If a user requests, "I want to learn the basics of saving," the server will provide educational content such as "How to review your monthly expenses" and "Tips for reducing fixed expenses."

[2795] The processing flow will be explained below.

[2796] Flow of setting savings goals and tracking progress

[2797] Step 1:

[2798] On the device: The user opens the app and accesses the savings goal setting screen.

[2799] Step 2:

[2800] User: Enter the target amount, deadline, and purpose.

[2801] Step 3:

[2802] Terminal: Sends user input data to the server.

[2803] Step 4:

[2804] Server: Receives user input data and stores it in a database.

[2805] Step 5:

[2806] Server: Retrieves transaction history from the database to check the user's savings status on a daily or weekly basis.

[2807] Step 6:

[2808] Server: Analyzes progress towards goals through batch processing.

[2809] Step 7:

[2810] Server: Calculates the progress and sends the result to the device.

[2811] Step 8:

[2812] Terminal: Visually display the received progress data, for example using a gauge or bar graph.

[2813] Savings plan proposal process flow

[2814] Step 1:

[2815] Terminal: Provides a screen where users can enter income and expense information.

[2816] Step 2:

[2817] User: Enter your monthly income and major recurring expenses (rent, utilities, food, etc.).

[2818] Step 3:

[2819] Terminal: Sends input data to the server.

[2820] Step 4:

[2821] Server: Receives user income and expenditure data and analyzes it using a generative AI model.

[2822] Step 5:

[2823] Server: Generates a savings plan and sends it to the device.

[2824] Step 6:

[2825] Terminal: Display the suggested savings plan to the user.

[2826] Step 7:

[2827] User: Review the proposed savings plan and adjust as needed.

[2828] Savings Challenge and Reward Processing Flow

[2829] Step 1:

[2830] Server: Sets the savings challenge and sends it to the device.

[2831] Step 2:

[2832] Device: Shows the challenge to the user and asks for their confirmation.

[2833] Step 3:

[2834] User: Decides to participate in the proposed challenge and clicks the Join button.

[2835] Step 4:

[2836] Device: Sends the user's decision to the server.

[2837] Step 5:

[2838] Server: Periodically checks the user's savings progress and calculates the challenge progress.

[2839] Step 6:

[2840] Server: Awards rewards when a challenge is completed and sends the data to the device.

[2841] Step 7:

[2842] Device: Notifies the user of the challenge completion and reward details.

[2843] Process flow for setting up an automatic savings program

[2844] Step 1:

[2845] Server: Proposes an automatic savings program based on the user's income and expenditure data.

[2846] Step 2:

[2847] On the device: Display the suggestions to the user.

[2848] Step 3:

[2849] User: Approves the proposed automatic savings program.

[2850] Step 4:

[2851] Terminal: Sends authorization information to the server.

[2852] Step 5:

[2853] Server: Executes approved automatic savings programs and automatically saves according to a specified schedule.

[2854] Step 6:

[2855] Server: Notifies the user of the progress of the automatic savings.

[2856] Step 7:

[2857] On the device: Show users their savings progress.

[2858] Investment advice and asset growth support process

[2859] Step 1:

[2860] Terminal: Provides a screen where users can input their investment goals and risk tolerance.

[2861] Step 2:

[2862] User: Enter your investment goals and risk tolerance.

[2863] Step 3:

[2864] Terminal: Sends input data to the server.

[2865] Step 4:

[2866] Server: Uses generative AI models to analyze user input data and generate optimal investment strategies.

[2867] Step 5:

[2868] Server: Sends investment strategies to the terminal.

[2869] Step 6:

[2870] Terminal: displays the proposed investment strategy to the user.

[2871] Step 7:

[2872] User: Review investment strategies and implement them as needed.

[2873] Step 8:

[2874] Server: Regularly tracks users' investment progress and adjusts strategies as needed.

[2875] Step 9:

[2876] On the device: Notify the user of progress and suggested adjustments.

[2877] Visualization of savings goals and reward system processing flow

[2878] Step 1:

[2879] Device: Provides a screen where users can set their savings goals.

[2880] Step 2:

[2881] User: Set the goal amount and deadline.

[2882] Step 3:

[2883] Terminal: Sends the settings to the server.

[2884] Step 4:

[2885] Server: Stores goal settings in a database and tracks progress periodically.

[2886] Step 5:

[2887] Server: Performs the process of granting rewards when the goal is achieved.

[2888] Step 6:

[2889] Server: Analyzes the progress data and sends it to the device as visual information.

[2890] Step 7:

[2891] On the device: Show progress to the user with graphs and metrics.

[2892] Step 8:

[2893] Server: Notifies the device of the reward to be provided when the goal is achieved.

[2894] Step 9:

[2895] Terminal: Notifies the user and assists them in claiming their reward.

[2896] Process flow for providing educational content on savings

[2897] Step 1:

[2898] Server: Uses generative AI models to create educational content tailored to the user.

[2899] Step 2:

[2900] Server: Sends content to the device.

[2901] Step 3:

[2902] Device: Displays educational content to the user.

[2903] Step 4:

[2904] Users: View and learn educational content.

[2905] Step 5:

[2906] Device: Sends user feedback to the server.

[2907] Step 6:

[2908] Server: Regularly update educational content based on user feedback.

[2909] Example 1

[2910] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2911] Existing savings management systems have basic functions for setting users' savings goals and tracking progress, but they lack the ability to analyze individual income and expenditure patterns and propose optimal savings plans. They also lack the ability to provide appropriate rewards and educational content based on savings progress. Furthermore, there are challenges in how to effectively analyze collected data and present specific savings a...

Claims

1. A way for users to set savings goals and track their progress; A means of analyzing users' income and spending patterns and proposing individual savings plans; A way for users to participate in savings challenges and receive rewards and perks upon completion; A way to automatically save money based on the user's income and expenses, and A means to analyze users' investment goals and risk tolerance and propose appropriate investment strategies; A way to visualize users' progress toward their savings goals and reward them when they achieve them. A means to provide users with educational content on savings and investments, and A system including:

2. 2. The system according to claim 1, further comprising a database for setting a user's savings goal and managing the progress of the goal.

3. 10. The system of claim 1, including a generative AI model that collects and analyzes user income and expense data.

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