system

Abstract

We provide the system. [Solution] Means for collecting users' spending history, purchasing behavior, and investment preferences, A means for analyzing the user's data and generating an individually optimized financial management plan, A means of presenting the aforementioned plan to the user and managing its progress, A means of rewarding users according to their level of achievement, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult for users with little financial knowledge to effectively learn and practice financial management. In addition, many users want to improve their daily expenses and investment behaviors, but there are no specific guidelines on how to act, and as a result, they may fall into a vague state. Therefore, there is a need to provide a financial management method that suits the user's situation while enjoying and continuously learning.

Means for Solving the Problems

[0005] This invention provides means for collecting and analyzing a user's spending history, purchasing behavior, and investment preferences to generate an individually optimized financial management plan. The generated plan is presented to the user, and its progress is managed. Furthermore, by including means for rewarding the user according to their achievement level, it enables learning financial management in a gamified manner while maintaining motivation. In addition, by learning the user's behavioral history and adaptively generating new plans, it becomes possible to provide more effective, personalized guidance.

[0006] A "user" refers to an individual or organization that uses the system to optimize their financial management and investment plans.

[0007] "Expenditure history" refers to a user's past financial payment records and trends.

[0008] "Purchasing behavior" refers to the patterns and tendencies of choices users make when purchasing goods or services.

[0009] "Investment orientation" refers to the user's mindset and approach to managing their assets.

[0010] A "financial management plan" refers to a set of actionable guidelines designed to optimize a user's spending, investing, and saving methods, and to maximize their effectiveness.

[0011] "Progress management" refers to the process of tracking and evaluating how well a user is achieving their set financial goals.

[0012] "Rewards" refer to points, badges, or other incentives given to users when they complete a mission.

[0013] "Game-like feel" refers to playful elements incorporated into the system to allow users to enjoy using it.

[0014] "Activity history" refers to a record of a series of activities a user has performed within the system in the past. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiments for Carrying out the Invention

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

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

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

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

[0020] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

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

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

[0023] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] In order to implement the invention, the user must first access the application using their own device and input information about their spending history, purchasing behavior, and investment preferences. The user fills out the details in the form provided by the application and creates an initial dataset. This data is securely transmitted to the server.

[0037] The server uses a generative AI model to analyze the received data and evaluate each user's behavioral patterns and trends. Based on this, the server generates a personalized financial management plan optimized for each user, taking into account their current financial situation and goals. This plan includes specific savings suggestions and investment plans.

[0038] Next, the server sends the generated plans to the user's device. The user reviews these plans on their device and selects the one that best suits their lifestyle and goals. Based on the selected plan, the user can then work on their savings and investment missions.

[0039] The system periodically checks the user's progress and evaluates how close they are to the goals set by the server. Progress management is important for maintaining user motivation towards achieving the goals. When a user achieves their goal, the server calculates rewards such as points or badges and notifies the user via their device.

[0040] Even more importantly, the server can continuously learn from the user's usage history and adaptively create new financial plans. This ensures that users always receive the latest and most optimal plan. At the same time, the gamified user experience design allows users to continuously improve their financial management skills while having fun.

[0041] For example, if a user selects the goal of "saving 5,000 yen on monthly food expenses," the server will provide suggestions to reduce unnecessary spending based on the user's past purchase history. These suggestions may include, for instance, refraining from purchasing certain ingredients or choosing cheaper alternatives. Once the user achieves this goal, a reward will be displayed on the device, and the next challenge will be suggested.

[0042] In this way, the invention becomes a system that provides users with a valuable financial management experience and naturally promotes skill improvement.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users log in to the application using their device and enter information about their spending history, purchasing behavior, and investment preferences. The entered data is then sent from the device to the server.

[0046] Step 2:

[0047] The server stores the received data and analyzes it using a generative AI model. Through this analysis, it extracts patterns in users' spending tendencies and investment preferences, and evaluates the current situation.

[0048] Step 3:

[0049] Based on the analysis results, the server generates a financial management plan optimized for each user. This plan includes specific guidance on saving advice and investment opportunities.

[0050] Step 4:

[0051] The server sends the generated financial management plan to the user's terminal. The user reviews the plan details on their terminal, selects one that suits their needs, and starts the process.

[0052] Step 5:

[0053] Once the user begins taking action based on their selected plan, the server monitors their progress in real time. Progress information is updated periodically, and the degree of achievement is evaluated.

[0054] Step 6:

[0055] The server sends supplementary feedback to the user's device as needed, based on the user's progress. The user uses this feedback to adjust their actions towards achieving their goals.

[0056] Step 7:

[0057] When a user completes a mission or achieves a goal, the server calculates and awards reward points or badges. The device displays information about the rewards earned, allowing the user to experience a sense of accomplishment.

[0058] Step 8:

[0059] The server continuously learns from the user's behavior history and updates the financial plan based on the user's needs and achievement patterns. This ensures that the most up-to-date, personalized plan is always provided.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] The present invention aims to provide a system that quickly and effectively delivers management plans tailored to the financial situation of individual users, thereby improving users' financial skills and increasing their motivation. In particular, it aims to achieve more precise financial management by individually analyzing users' spending and investment patterns and providing adaptively adjusted plans.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for collecting user financial information, means for analyzing the financial information using a generation AI model, and means for generating individually optimized financial plans based on the analysis results. This makes it possible to provide users with the most up-to-date and optimized financial management plans at all times.

[0065] A "user" is an individual or group that uses this system to input financial information and optimize their own actions.

[0066] "Financial information" refers to all data related to financial management, including a user's spending history, purchasing behavior, and investment preferences.

[0067] A "generative AI model" is an analytical model that utilizes artificial intelligence technology to analyze a user's financial information and generate an optimized financial plan.

[0068] A "financial plan" is a plan that includes individualized optimization strategies for spending and investment, created by a generative AI model based on analysis for each user.

[0069] "Analysis" refers to the process of analyzing users' financial information using generative AI models to evaluate behavioral patterns and directions for optimization.

[0070] "Rewards" refer to incentives designed to motivate users, such as points or badges given based on the degree to which users achieve their set financial goals.

[0071] A "game format" is a format that incorporates game elements into financial planning activities to improve user engagement.

[0072] This invention constructs a system that provides a financial management plan optimized for individual users. First, the user accesses the application using their own terminal and inputs financial information regarding spending history, purchasing behavior, and investment preferences. This information is transmitted to the server via a secure communication method.

[0073] The server analyzes the received financial information using a generating AI model. Python libraries such as TENSORFLOW® and PyTorch are used for data analysis and evaluation. Through this analysis, user behavior patterns are identified, and a personalized financial management plan is generated. This plan includes specific guidelines based on the user's spending habits and investment strategy.

[0074] The generated financial plan is sent from the server to the user's device. The user can then select the plan best suited to their lifestyle and goals on their device and take action according to that plan. Furthermore, progress is regularly evaluated, and rewards are provided based on the degree of goal achievement. This allows users to improve their financial habits while maintaining sustained motivation.

[0075] For example, if a user sets a financial goal of "saving 5,000 yen on monthly food expenses," the server will analyze past purchase history and provide specific suggestions to reduce unnecessary spending. These suggestions will include a list of ingredients to avoid purchasing and alternative products to choose. An example of a prompt might be, "Generate an AI model that proposes a savings plan based on the user's purchasing patterns."

[0076] This system ensures users always receive up-to-date and effective financial plans, enabling them to make optimal choices based on their own financial situation.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users launch a dedicated application on their devices and input financial information regarding their spending history, purchasing behavior, and investment preferences. This information is collected through the device's interface and transmitted to the server using a secure communication protocol. The entered data arrives at the server with each item properly formatted.

[0080] Step 2:

[0081] The server stores the received user's financial information in a database. Then, it begins data analysis using a generative AI model. Based on the input information, the AI ​​model evaluates the user's spending patterns and investment tendencies. Here, behavioral patterns are identified through clustering and statistical analysis, and the results of this analysis are used in subsequent processing.

[0082] Step 3:

[0083] The server generates a personalized financial management plan based on the analysis results. The generating AI model makes specific suggestions for reducing expenses and optimizing investments, based on the user's behavioral characteristics obtained from the analysis. These suggestions are then adjusted to suit each user and output in a format that can be displayed in PDF or within the app's user interface.

[0084] Step 4:

[0085] The server sends the generated financial plan back to the user's terminal. The terminal receives it and displays it on the user interface. The user reviews the presented plan and selects the one that best suits their lifestyle and goals. This selection is recorded within the application to guide subsequent actions.

[0086] Step 5:

[0087] The server periodically evaluates the user's progress based on the plan they have selected. Each time new purchase history or spending information is entered by the user, the server checks their progress toward their goals and generates a report. This report shows how close the user is to achieving their goals and provides feedback through their device.

[0088] Step 6:

[0089] When a user achieves their set goals, the server calculates and awards them a reward. This reward, in the form of points or badges, is communicated to the user through the device's notification function. This feature enhances user engagement and continuously motivates them for their next challenge. The server also continuously analyzes new data and updates its generative AI model to ensure users always receive the most up-to-date financial plans.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] Modern consumers exhibit diverse purchasing behaviors and investment preferences, and a wide range of transactions are conducted via the internet. Therefore, there is a need to present effective and optimal economic management plans to individual consumers. However, conventional systems lack sufficient support that considers specific savings strategies and rewards based on each user's spending and investment trends.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for collecting a user's transaction history, consumption behavior, and capital investment intentions; means for analyzing the user's information and generating an individually optimized economic investment plan; means for providing the plan to the user and monitoring its progress; means for rewarding the user according to their achievement level; and means for proposing saving methods and reward structures based on the user's purchasing behavior and transaction history. As a result, consumers can receive an individually optimized economic investment plan, enabling more efficient fund management and the acquisition of associated rewards.

[0095] "Transaction history" refers to a record of a user's economic activities, specifically including information such as purchased items, payment amounts, and purchase dates and times.

[0096] "Consumer behavior" refers to the patterns and tendencies of how users behave when purchasing goods or services, and includes things like purchase frequency and the selection of purchase categories.

[0097] "Capital investment intentions" represent the user's wishes and plans for how they want to invest the funds they hold.

[0098] An "economic management plan" is a plan created to optimize a user's assets and spending, and includes specific savings suggestions and investment strategies.

[0099] "Progress" refers to the monitoring and evaluation of the execution status of the economic investment plan set by the user.

[0100] "Rewards" are given to users when they achieve goals they have set, and are specifically provided in the form of points, badges, etc.

[0101] "Savings methods" refer to specific strategies for minimizing user spending and making effective use of assets.

[0102] The "reward structure" is a system of rewards provided according to the user's level of achievement, and it shows how points and rewards are calculated.

[0103] The system for implementing this invention uses the user's smartphone as the primary terminal and interacts with a server over a network. Information regarding the user's transaction history, consumption behavior, and capital investment intentions is first collected through an application installed on the user's terminal. This allows the user to input their purchase history and spending summary. The entered data is then transmitted to a cloud server using a secure protocol. For example, data such as "food expenses 10,000 yen, entertainment expenses 5,000 yen, transportation expenses 3,000 yen / month" might be entered.

[0104] The server analyzes the received data using a generative AI model. Based on each user's data, the generative AI model develops an optimized financial management plan tailored to that individual user. This plan can include examples of saving methods and reward structures based on the user's purchasing behavior. The optimized plan is then sent back to the user's device, where they can view it and confirm specific saving goals and investment plans.

[0105] Furthermore, the server periodically monitors the progress of the user's actions according to their plan, calculates rewards based on the degree of goal achievement, and notifies the user via their terminal. For example, if a user achieves an economic goal such as "reducing food expenses by 20% this month," they may be rewarded with points or badges.

[0106] To generate new suggestions, the generation AI model continuously learns from the user's behavior history. Through this learning process, the server can always provide the user with the latest economic management plan. An example of a prompt message is, "User ID: 12345, Spending Summary: Food 5000 yen, Beverages 1500 yen, Generate Plan Proposal." In this way, the system allows users to work on improving their financial situation while experiencing something similar to a game.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] Users log in to the application using their smartphones and input information about their transaction history, consumer behavior, and capital investment intentions. The entered data is saved on the device in JSON format and then sent to a server in the cloud via a secure protocol. This creates a unique data set for each user.

[0110] Step 2:

[0111] The server inputs the received data into a generating AI model to analyze the user's purchasing patterns and consumption trends. In this data analysis process, the algorithm operates based on the user's past activities to identify demand forecasts and potential for spending reductions. As an output, an optimized economic management plan is generated for each user.

[0112] Step 3:

[0113] The server uses the generated economic management plan to construct specific savings methods and reward structures. This plan is individually rendered and sent to the user's smartphone. The user can view the content on their device and consider the proposed savings and investment goals. A visually easy-to-understand interface is generated as output.

[0114] Step 4:

[0115] Once the user begins taking action based on the plan, the device tracks progress and periodically sends data to the server. The server receives the progress data and analyzes it to evaluate the degree of goal achievement. If necessary, it determines whether the user has achieved their savings goals according to the plan.

[0116] Step 5:

[0117] The server calculates rewards based on the user's progress toward achieving goals and notifies the user in the form of points, badges, or other means. This generates output that helps maintain user motivation and encourages further participation.

[0118] Step 6:

[0119] The server continuously learns the user's behavior history and generates new economic investment plans. The generating AI model creates an optimized plan based on past results and newly input data, and outputs that include new suggestions as examples of prompts to provide to the user.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] To implement this invention, the user must first access the application using a terminal and input information about their spending history, purchasing behavior, and investment preferences. This information serves as foundational data for constructing a comprehensive picture of the user's financial situation. Once the data is entered, the terminal securely transmits it to the server.

[0122] The server uses a generative AI model to analyze users' spending and investment patterns in detail based on the collected data. From this analysis, a financial management plan optimized for each individual user is generated. Furthermore, the server incorporates an emotion engine that identifies the user's emotional state. This emotion engine extracts emotions from textual feedback and interactions as the user operates the device, and performs real-time analysis.

[0123] The analysis results from the emotion engine are used to dynamically adjust the content of the generated financial management plan based on the user's current emotional state. For example, if the user is feeling stressed, the system can incorporate a more flexible plan or elements that promote relaxation into the plan. This makes it easier for the user to accept and implement the plan comfortably.

[0124] Once a user starts a plan, the server tracks their progress and continuously evaluates changes in their emotional state using an emotion engine. Based on this information, the server provides feedback as needed and delivers it to the user through their device. If the user's emotional state is positive, the rewards may be specially adjusted.

[0125] For example, if a user selects a plan to "save on monthly food expenses," and the emotion engine identifies that the user is anxious about saving, the server will display not only specific saving techniques but also supportive information and encouraging messages to alleviate that anxiety on the device.

[0126] Through such two-way feedback and dynamic plan adjustments, the present invention realizes a system that effectively supports financial management while being attentive to the user's emotions.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users log in to the application using their device and enter data about their spending history, purchasing behavior, and investment preferences. This data is immediately sent to the server.

[0130] Step 2:

[0131] The server securely stores the collected data. Using a generative AI model, it analyzes the user's spending patterns, purchasing behavior, and investment preferences, and generates an individually optimized financial management plan based on the results.

[0132] Step 3:

[0133] The server activates an emotion engine and evaluates the user's emotions based on the input data and interaction patterns during their interactions with the device. Based on this evaluation, the server adjusts the content of the financial management plan presented to the user.

[0134] Step 4:

[0135] The adjusted financial management plan is sent to the terminal. The user reviews the plan on the terminal, selects the appropriate actions from the recommended options, and begins implementing them.

[0136] Step 5:

[0137] The server monitors the user's progress in real time and records changes in the user's emotional state. Each time progress is reported, the server uses an emotion engine to generate feedback based on that information and sends it to the terminal.

[0138] Step 6:

[0139] When a user completes a designated mission, the server evaluates their level of achievement and emotional state, and determines an appropriate reward. The reward is then communicated to the user via their device.

[0140] Step 7:

[0141] The server stores user behavior and emotional history, which is used to improve the financial management plan provided next. The new plan will be further optimized to the user's preferences and current status.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] In today's diverse financial environment, providing financial management tailored to individual users is difficult, and uniform plans often lead to decreased user satisfaction and implementation rates. Furthermore, the inability to provide feedback or dynamically adjust plans based on users' emotional states can make it difficult to achieve goals due to stress and anxiety.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for acquiring historical information about the user's life, means for analyzing the acquired information to generate an individually tailored plan, and means for evaluating the user's emotional state using an emotion analysis device and dynamically adjusting the plan. This enables the provision of financial management optimized for the user and flexible plan adjustments that are sensitive to their emotions.

[0147] A "user" is an entity that accesses a system and performs individual actions or uses specific information.

[0148] "Lifestyle history information" refers to records of a user's daily activities, such as spending, purchasing behavior, and investment preferences.

[0149] "Means of acquisition" refers to devices and methods for collecting information and storing it in a format such as a database.

[0150] "Means of analysis" refer to software and algorithms used to analyze collected data and derive insights and conclusions.

[0151] A "personally tailored plan" is a financial management plan designed to take into account the different characteristics and needs of each user.

[0152] An "emotion analysis device" is a technology or device used to analyze and evaluate a user's psychological state based on their input and behavioral data.

[0153] "Means of dynamic adjustment" refer to functions or processes that automatically and promptly change content in response to changes in circumstances or the environment.

[0154] "Means of evaluation" refers to devices or methods that evaluate user behavior or results based on certain criteria.

[0155] This system is designed so that users input historical information about their lifestyle using a terminal, and then a personalized plan is generated and implemented based on that information. Users access the application and input information such as spending and purchasing behavior. The terminal collects this data and transmits it to the server using a secure protocol.

[0156] The server uses programming languages ​​such as Python and related data science libraries (e.g., Pandas and NumPy) to perform data analysis using generative AI models. This analysis generates a financial management plan optimized for the user. Furthermore, the server utilizes an emotion analysis device and natural language processing techniques to evaluate the user's emotional state. Based on the emotional state, the plan's content is dynamically adjusted.

[0157] For example, if a user selects a plan to "save on food expenses," and the sentiment analyzer determines that the user is feeling anxious, the server will display encouraging messages and specific techniques on the device. This process allows the user to proceed with the plan flexibly while receiving emotionally supportive assistance.

[0158] An example of a prompt message would be, "Please give me advice on how to plan my spending for this month." Based on this prompt message, the server can use a generative AI model to provide the user with appropriate advice and plan suggestions.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] Users access the application from their device and input historical information about their lifestyle. This input includes spending history, purchasing behavior, and investment preferences. The device temporarily stores the data entered by the user and transmits it to the server using a secure protocol.

[0162] Step 2:

[0163] The server receives historical information sent from the terminal and stores it in the database. After receiving the data, the server uses Python and related data science libraries to preprocess the data. Specific examples of preprocessing include imputing missing values ​​and removing outliers.

[0164] Step 3:

[0165] The server uses a generative AI model to analyze pre-processed data. This generative AI model performs pattern recognition to generate a financial management plan optimized for each user. For example, it predicts future spending based on past spending patterns and proposes a savings plan based on that prediction.

[0166] Step 4:

[0167] The server uses an emotion analysis device to evaluate the user's emotional state. It analyzes user input and log data using natural language processing techniques to determine the user's emotional state (e.g., stress, anxiety, joy). This helps identify factors that may influence the plan's content.

[0168] Step 5:

[0169] Based on the sentiment analysis, the server dynamically adjusts the generated financial management plan. For example, if the user is feeling stressed, elements that promote relaxation are added to the plan. This adjustment includes leveraging feedback mechanisms to provide emotional support to the user.

[0170] Step 6:

[0171] As users implement a plan, the server monitors its progress in real time. Progress data is collected periodically and managed in conjunction with continuous evaluations based on sentiment analysis. This allows for timely feedback to be sent to users, helping to maintain their motivation.

[0172] Step 7:

[0173] The server rewards users based on their performance. If progress and emotional state meet the criteria, special rewards or additional advice are displayed on the device. This feedback helps motivate users and promotes the effective execution of their plans.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] The present invention aims to solve the problem that, in managing a user's financial situation, it is not possible to improve user acceptance and effectiveness by making dynamic adjustments that take into account the user's emotional state. Conventional financial management systems lack sufficient feedback and plan adjustments that reflect changes in the user's emotional state, making it easy for users to feel stressed and anxious, and making it difficult to continuously execute the plan.

[0177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0178] In this invention, the server includes means for collecting the user's spending history, purchasing behavior, and investment preferences; means for analyzing the user's data and generating an individually optimized financial management plan; and means for identifying and analyzing the user's emotional state. This makes it possible to provide the user with an optimal financial management plan, and by adapting that plan to the user's emotions, it becomes possible to increase the likelihood that the user will actively implement and achieve the plan.

[0179] "User spending history" refers to a record of a user's past spending activities, primarily including data on what they spent how much money on.

[0180] "Purchasing behavior" refers to the process of selection and decision-making when users purchase goods or services, and includes data on what products they buy and how often.

[0181] "Investment orientation" refers to the tendency that indicates how users intend to manage their assets, and includes information such as risk tolerance and preferences for investment targets.

[0182] "Emotional state" refers to the mental and emotional state that a user is experiencing at a particular point in time, and includes emotions such as stress and joy.

[0183] A "financial management plan" refers to a strategy or plan developed to effectively manage a user's income and expenses, and includes guidelines for long-term asset management and short-term expense reduction.

[0184] "Dynamic adjustment" refers to a process of flexibly changing plans in response to the user's changing circumstances and emotional state, and is a method aimed at real-time adaptation.

[0185] "Real-time feedback" refers to responses and information provided immediately in response to a user's actions or status, enabling them to make improvements or choices on the spot.

[0186] To realize this invention, the process begins with installing a dedicated application on a device such as a smartphone or tablet. Through this application, the user inputs data on their spending history, purchasing behavior, and investment preferences, and sends it to a server. This data is encrypted and securely stored on the server.

[0187] The server analyzes collected data using a generative AI model and generates a financial management plan tailored to the user. During this process, an emotion engine analyzes user input and interactions to identify the user's emotional state. Optimization is performed based on the emotional state, and the generated plan is dynamically adjusted.

[0188] The adjusted plan is immediately provided as feedback on the device, improving user experience. For example, if a user chooses a savings plan, and the emotion engine detects stress or anxiety, the server will display additional stress-reducing techniques and encouraging messages on the device.

[0189] This application's program is based on the Python language, utilizing OpenAI® GPT-4® for its generative AI model and the Affectiva SDK for sentiment analysis. This creates a system that provides users with flexible and responsive financial plans.

[0190] For example, if a user starts saving money for a family birthday party and becomes anxious about the expenses along the way, the app can detect that emotion and immediately provide situation-appropriate advice and comforting messages.

[0191] Example prompt: "Estimate the user's stress level based on their daily spending data and recent interaction feedback, and dynamically adjust and provide a household budgeting plan."

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The device receives data from the user regarding spending history, purchasing behavior, and investment preferences as input. This data is collected within the application and sent to the server in an encrypted format. The transmitted data is stored in the server's database.

[0195] Step 2:

[0196] The server uses a generative AI model to analyze the collected user data. This analysis reveals each user's individual spending patterns and financial tendencies. The generative AI model then processes the data to output a financial management plan tailored to each user.

[0197] Step 3:

[0198] The server uses an emotion engine, taking user interaction data as input, to identify the user's current emotional state. It determines the emotional state based on text analysis and interface interaction patterns, and then obtains processed data representing the user's emotional state.

[0199] Step 4:

[0200] The server takes the generated financial management plan as input and dynamically adjusts it, taking into account the user's emotional state obtained from the emotion engine. If the emotional state is determined to be stress or anxiety, it adjusts the plan to be more flexible, makes data changes such as including encouraging comments, and outputs the adjusted plan.

[0201] Step 5:

[0202] The device receives a customized financial plan from the server and presents it to the user in real time. Through the plan, displayed as immediate feedback, the user receives specific spending advice tailored to their current situation. This feedback improves the user's understanding and motivation.

[0203] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] In order to implement the invention, the user must first access the application using their own device and input information about their spending history, purchasing behavior, and investment preferences. The user fills out the details in the form provided by the application and creates an initial dataset. This data is securely transmitted to the server.

[0220] The server uses a generative AI model to analyze the received data and evaluate each user's behavioral patterns and trends. Based on this, the server generates a personalized financial management plan optimized for each user, taking into account their current financial situation and goals. This plan includes specific savings suggestions and investment plans.

[0221] Next, the server sends the generated plans to the user's device. The user reviews these plans on their device and selects the one that best suits their lifestyle and goals. Based on the selected plan, the user can then work on their savings and investment missions.

[0222] The system periodically checks the user's progress and evaluates how close they are to the goals set by the server. Progress management is important for maintaining user motivation towards achieving the goals. When a user achieves their goal, the server calculates rewards such as points or badges and notifies the user via their device.

[0223] Even more importantly, the server can continuously learn from the user's usage history and adaptively create new financial plans. This ensures that users always receive the latest and most optimal plan. At the same time, the gamified user experience design allows users to continuously improve their financial management skills while having fun.

[0224] For example, if a user selects the goal of "saving 5,000 yen on monthly food expenses," the server will provide suggestions to reduce unnecessary spending based on the user's past purchase history. These suggestions may include, for instance, refraining from purchasing certain ingredients or choosing cheaper alternatives. Once the user achieves this goal, a reward will be displayed on the device, and the next challenge will be suggested.

[0225] In this way, the invention becomes a system that provides users with a valuable financial management experience and naturally promotes skill improvement.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] Users log in to the application using their device and enter information about their spending history, purchasing behavior, and investment preferences. The entered data is then sent from the device to the server.

[0229] Step 2:

[0230] The server stores the received data and analyzes it using a generative AI model. Through this analysis, it extracts patterns in users' spending tendencies and investment preferences, and evaluates the current situation.

[0231] Step 3:

[0232] Based on the analysis results, the server generates a financial management plan optimized for each user. This plan includes specific guidance on saving advice and investment opportunities.

[0233] Step 4:

[0234] The server sends the generated financial management plan to the user's terminal. The user reviews the plan details on their terminal, selects one that suits their needs, and starts the process.

[0235] Step 5:

[0236] Once the user begins taking action based on their selected plan, the server monitors their progress in real time. Progress information is updated periodically, and the degree of achievement is evaluated.

[0237] Step 6:

[0238] The server sends supplementary feedback to the user's device as needed, based on the user's progress. The user uses this feedback to adjust their actions towards achieving their goals.

[0239] Step 7:

[0240] When a user completes a mission or achieves a goal, the server calculates and awards reward points or badges. The device displays information about the rewards earned, allowing the user to experience a sense of accomplishment.

[0241] Step 8:

[0242] The server continuously learns from the user's behavior history and updates the financial plan based on the user's needs and achievement patterns. This ensures that the most up-to-date, personalized plan is always provided.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] The present invention aims to provide a system that quickly and effectively delivers management plans tailored to the financial situation of individual users, thereby improving users' financial skills and increasing their motivation. In particular, it aims to achieve more precise financial management by individually analyzing users' spending and investment patterns and providing adaptively adjusted plans.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes means for collecting user financial information, means for analyzing the financial information using a generation AI model, and means for generating individually optimized financial plans based on the analysis results. This makes it possible to provide users with the most up-to-date and optimized financial management plans at all times.

[0248] A "user" is an individual or group that uses this system to input financial information and optimize their own actions.

[0249] "Financial information" refers to all data related to financial management, including a user's spending history, purchasing behavior, and investment preferences.

[0250] A "generative AI model" is an analytical model that utilizes artificial intelligence technology to analyze a user's financial information and generate an optimized financial plan.

[0251] A "financial plan" is a plan that includes individualized optimization strategies for spending and investment, created by a generative AI model based on analysis for each user.

[0252] "Analysis" refers to the process of analyzing users' financial information using generative AI models to evaluate behavioral patterns and directions for optimization.

[0253] "Rewards" refer to incentives designed to motivate users, such as points or badges given based on the degree to which users achieve their set financial goals.

[0254] A "game format" is a format that incorporates game elements into financial planning activities to improve user engagement.

[0255] This invention constructs a system that provides a financial management plan optimized for individual users. First, the user accesses the application using their own terminal and inputs financial information regarding spending history, purchasing behavior, and investment preferences. This information is transmitted to the server via a secure communication method.

[0256] The server analyzes the received financial information using a generating AI model. Python libraries such as TensorFlow and PyTorch are used for data analysis and evaluation. Through this analysis, user behavior patterns are identified, and a personalized financial management plan is generated. This plan includes specific guidelines based on the user's spending habits and investment strategy.

[0257] The generated financial plan is sent from the server to the user's device. The user can then select the plan best suited to their lifestyle and goals on their device and take action according to that plan. Furthermore, progress is regularly evaluated, and rewards are provided based on the degree of goal achievement. This allows users to improve their financial habits while maintaining sustained motivation.

[0258] For example, if a user sets a financial goal of "saving 5,000 yen on monthly food expenses," the server will analyze past purchase history and provide specific suggestions to reduce unnecessary spending. These suggestions will include a list of ingredients to avoid purchasing and alternative products to choose. An example of a prompt might be, "Generate an AI model that proposes a savings plan based on the user's purchasing patterns."

[0259] This system ensures users always receive up-to-date and effective financial plans, enabling them to make optimal choices based on their own financial situation.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] Users launch a dedicated application on their devices and input financial information regarding their spending history, purchasing behavior, and investment preferences. This information is collected through the device's interface and transmitted to the server using a secure communication protocol. The entered data arrives at the server with each item properly formatted.

[0263] Step 2:

[0264] The server stores the received user's financial information in a database. Then, it begins data analysis using a generative AI model. Based on the input information, the AI ​​model evaluates the user's spending patterns and investment tendencies. Here, behavioral patterns are identified through clustering and statistical analysis, and the results of this analysis are used in subsequent processing.

[0265] Step 3:

[0266] The server generates a personalized financial management plan based on the analysis results. The generating AI model makes specific suggestions for reducing expenses and optimizing investments, based on the user's behavioral characteristics obtained from the analysis. These suggestions are then adjusted to suit each user and output in a format that can be displayed in PDF or within the app's user interface.

[0267] Step 4:

[0268] The server sends the generated financial plan back to the user's terminal. The terminal receives it and displays it on the user interface. The user reviews the presented plan and selects the one that best suits their lifestyle and goals. This selection is recorded within the application to guide subsequent actions.

[0269] Step 5:

[0270] The server periodically evaluates the user's progress based on the plan they have selected. Each time new purchase history or spending information is entered by the user, the server checks their progress toward their goals and generates a report. This report shows how close the user is to achieving their goals and provides feedback through their device.

[0271] Step 6:

[0272] When a user achieves their set goals, the server calculates and awards them a reward. This reward, in the form of points or badges, is communicated to the user through the device's notification function. This feature enhances user engagement and continuously motivates them for their next challenge. The server also continuously analyzes new data and updates its generative AI model to ensure users always receive the most up-to-date financial plans.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] Modern consumers exhibit diverse purchasing behaviors and investment preferences, and a wide range of transactions are conducted via the internet. Therefore, there is a need to present effective and optimal economic management plans to individual consumers. However, conventional systems lack sufficient support that considers specific savings strategies and rewards based on each user's spending and investment trends.

[0276] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0277] In this invention, the server includes means for collecting a user's transaction history, consumption behavior, and capital investment intentions; means for analyzing the user's information and generating an individually optimized economic investment plan; means for providing the plan to the user and monitoring its progress; means for rewarding the user according to their achievement level; and means for proposing saving methods and reward structures based on the user's purchasing behavior and transaction history. As a result, consumers can receive an individually optimized economic investment plan, enabling more efficient fund management and the acquisition of associated rewards.

[0278] "Transaction history" refers to a record of a user's economic activities, specifically including information such as purchased items, payment amounts, and purchase dates and times.

[0279] "Consumer behavior" refers to the behavioral patterns and tendencies when users purchase goods or services, including purchase frequency and selection of purchase categories, etc.

[0280] "Capital utilization intention" refers to the hopes and plans of how users want to invest the funds they hold.

[0281] "Economic operation plan" is a plan created to optimize users' assets and expenditures, including specific savings proposals and investment strategies.

[0282] "Progress" is to monitor and evaluate the implementation status of the economic operation plan set by users.

[0283] "Reward" is what is given when users achieve the goals they set, and is specifically provided in the form of points, badges, etc.

[0284] "Saving method" is a specific strategy for minimizing users' expenditures and effectively utilizing assets.

[0285] "Reward structure" is the system of rewards provided according to users' achievement levels, indicating how points and rewards are calculated.

[0286] The system for implementing this invention takes the form of using the user's smartphone as the main terminal and communicating with the server via the network. Information regarding the user's transaction history, consumer behavior, and capital utilization intention is first collected through an application installed on the user terminal. As a result, users can input their own purchase history and expenditure summary. The input data is transmitted to the cloud server using a secure protocol. At this time, data such as "10,000 yen for food, 5,000 yen for entertainment, 3,000 yen for transportation per month" may be input as a specific example.

[0287] The server analyzes the received data using a generative AI model. Based on each user's data, the generative AI model develops an optimized financial management plan tailored to that individual user. This plan can include examples of saving methods and reward structures based on the user's purchasing behavior. The optimized plan is then sent back to the user's device, where they can view it and confirm specific saving goals and investment plans.

[0288] Furthermore, the server periodically monitors the progress of the user's actions according to their plan, calculates rewards based on the degree of goal achievement, and notifies the user via their terminal. For example, if a user achieves an economic goal such as "reducing food expenses by 20% this month," they may be rewarded with points or badges.

[0289] To generate new suggestions, the generation AI model continuously learns from the user's behavior history. Through this learning process, the server can always provide the user with the latest economic management plan. An example of a prompt message is, "User ID: 12345, Spending Summary: Food 5000 yen, Beverages 1500 yen, Generate Plan Proposal." In this way, the system allows users to work on improving their financial situation while experiencing something similar to a game.

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] Users log in to the application using their smartphones and input information about their transaction history, consumer behavior, and capital investment intentions. The entered data is saved on the device in JSON format and then sent to a server in the cloud via a secure protocol. This creates a unique data set for each user.

[0293] Step 2:

[0294] The server inputs the received data into a generating AI model to analyze the user's purchasing patterns and consumption trends. In this data analysis process, the algorithm operates based on the user's past activities to identify demand forecasts and potential for spending reductions. As an output, an optimized economic management plan is generated for each user.

[0295] Step 3:

[0296] The server uses the generated economic management plan to construct specific savings methods and reward structures. This plan is individually rendered and sent to the user's smartphone. The user can view the content on their device and consider the proposed savings and investment goals. A visually easy-to-understand interface is generated as output.

[0297] Step 4:

[0298] Once the user begins taking action based on the plan, the device tracks progress and periodically sends data to the server. The server receives the progress data and analyzes it to evaluate the degree of goal achievement. If necessary, it determines whether the user has achieved their savings goals according to the plan.

[0299] Step 5:

[0300] The server calculates rewards based on the user's progress toward achieving goals and notifies the user in the form of points, badges, or other means. This generates output that helps maintain user motivation and encourages further participation.

[0301] Step 6:

[0302] The server continuously learns the user's behavior history and generates new economic investment plans. The generating AI model creates an optimized plan based on past results and newly input data, and outputs that include new suggestions as examples of prompts to provide to the user.

[0303] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0304] To implement this invention, first, the user needs to access the application using the terminal and input information regarding their expenditure history, purchase behavior, and investment orientation. This information serves as the basic data for constructing an overall picture of the user's financial situation. Once the data is input, the terminal securely transmits it to the server.

[0305] Based on the collected data, the server uses the generated AI model to analyze the user's expenditure and investment patterns in detail. From the analysis results, a financial management plan optimized for each individual user is generated. Furthermore, an emotion engine is implemented in the server, and a function for identifying the user's emotional state is incorporated. This emotion engine extracts emotions from textual feedback and interactions when the user operates the terminal and performs real-time analysis.

[0306] The analysis results of the emotion engine are used to dynamically adjust the content of the generated financial management plan based on the user's current emotional state. For example, when the user is feeling stressed, the system can incorporate a highly flexible plan or elements that promote relaxation into the plan. This enables the user to more comfortably accept and execute the plan.

[0307] When the user starts the plan, the server tracks the user's progress and continuously evaluates changes in the emotional state using the emotion engine. Based on this information, the server provides appropriate feedback as needed and delivers it to the user through the terminal. When the user's emotional state is positive, the content of the reward may be adjusted to something special.

[0308] For example, if a user selects a plan to "save on monthly food expenses," and the emotion engine identifies that the user is anxious about saving, the server will display not only specific saving techniques but also supportive information and encouraging messages to alleviate that anxiety on the device.

[0309] Through such two-way feedback and dynamic plan adjustments, the present invention realizes a system that effectively supports financial management while being attentive to the user's emotions.

[0310] The following describes the processing flow.

[0311] Step 1:

[0312] Users log in to the application using their device and enter data about their spending history, purchasing behavior, and investment preferences. This data is immediately sent to the server.

[0313] Step 2:

[0314] The server securely stores the collected data. Using a generative AI model, it analyzes the user's spending patterns, purchasing behavior, and investment preferences, and generates an individually optimized financial management plan based on the results.

[0315] Step 3:

[0316] The server activates an emotion engine and evaluates the user's emotions based on the input data and interaction patterns during their interactions with the device. Based on this evaluation, the server adjusts the content of the financial management plan presented to the user.

[0317] Step 4:

[0318] The adjusted financial management plan is sent to the terminal. The user reviews the plan on the terminal, selects the appropriate actions from the recommended options, and begins implementing them.

[0319] Step 5:

[0320] The server monitors the user's progress in real time and records changes in the user's emotional state. Each time progress is reported, the server uses an emotion engine to generate feedback based on that information and sends it to the terminal.

[0321] Step 6:

[0322] When a user completes a designated mission, the server evaluates their level of achievement and emotional state, and determines an appropriate reward. The reward is then communicated to the user via their device.

[0323] Step 7:

[0324] The server stores user behavior and emotional history, which is used to improve the financial management plan provided next. The new plan will be further optimized to the user's preferences and current status.

[0325] (Example 2)

[0326] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0327] In today's diverse financial environment, providing financial management tailored to individual users is difficult, and uniform plans often lead to decreased user satisfaction and implementation rates. Furthermore, the inability to provide feedback or dynamically adjust plans based on users' emotional states can make it difficult to achieve goals due to stress and anxiety.

[0328] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0329] In this invention, the server includes means for acquiring historical information about the user's life, means for analyzing the acquired information to generate an individually tailored plan, and means for evaluating the user's emotional state using an emotion analysis device and dynamically adjusting the plan. This enables the provision of financial management optimized for the user and flexible plan adjustments that are sensitive to their emotions.

[0330] A "user" is an entity that accesses a system and performs individual actions or uses specific information.

[0331] "Lifestyle history information" refers to records of a user's daily activities, such as spending, purchasing behavior, and investment preferences.

[0332] "Means of acquisition" refers to devices and methods for collecting information and storing it in a format such as a database.

[0333] "Means of analysis" refer to software and algorithms used to analyze collected data and derive insights and conclusions.

[0334] A "personally tailored plan" is a financial management plan designed to take into account the different characteristics and needs of each user.

[0335] An "emotion analysis device" is a technology or device used to analyze and evaluate a user's psychological state based on their input and behavioral data.

[0336] "Means of dynamic adjustment" refer to functions or processes that automatically and promptly change content in response to changes in circumstances or the environment.

[0337] "Means of evaluation" refers to devices or methods that evaluate user behavior or results based on certain criteria.

[0338] This system is designed so that users input historical information about their lifestyle using a terminal, and then a personalized plan is generated and implemented based on that information. Users access the application and input information such as spending and purchasing behavior. The terminal collects this data and transmits it to the server using a secure protocol.

[0339] The server uses programming languages ​​such as Python and related data science libraries (e.g., Pandas and NumPy) to perform data analysis using generative AI models. This analysis generates a financial management plan optimized for the user. Furthermore, the server utilizes an emotion analysis device and natural language processing techniques to evaluate the user's emotional state. Based on the emotional state, the plan's content is dynamically adjusted.

[0340] For example, if a user selects a plan to "save on food expenses," and the sentiment analyzer determines that the user is feeling anxious, the server will display encouraging messages and specific techniques on the device. This process allows the user to proceed with the plan flexibly while receiving emotionally supportive assistance.

[0341] An example of a prompt message would be, "Please give me advice on how to plan my spending for this month." Based on this prompt message, the server can use a generative AI model to provide the user with appropriate advice and plan suggestions.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] Users access the application from their device and input historical information about their lifestyle. This input includes spending history, purchasing behavior, and investment preferences. The device temporarily stores the data entered by the user and transmits it to the server using a secure protocol.

[0345] Step 2:

[0346] The server receives historical information sent from the terminal and stores it in the database. After receiving the data, the server uses Python and related data science libraries to preprocess the data. Specific examples of preprocessing include imputing missing values ​​and removing outliers.

[0347] Step 3:

[0348] The server uses a generative AI model to analyze pre-processed data. This generative AI model performs pattern recognition to generate a financial management plan optimized for each user. For example, it predicts future spending based on past spending patterns and proposes a savings plan based on that prediction.

[0349] Step 4:

[0350] The server uses an emotion analysis device to evaluate the user's emotional state. It analyzes user input and log data using natural language processing techniques to determine the user's emotional state (e.g., stress, anxiety, joy). This helps identify factors that may influence the plan's content.

[0351] Step 5:

[0352] Based on the sentiment analysis, the server dynamically adjusts the generated financial management plan. For example, if the user is feeling stressed, elements that promote relaxation are added to the plan. This adjustment includes leveraging feedback mechanisms to provide emotional support to the user.

[0353] Step 6:

[0354] As users implement a plan, the server monitors its progress in real time. Progress data is collected periodically and managed in conjunction with continuous evaluations based on sentiment analysis. This allows for timely feedback to be sent to users, helping to maintain their motivation.

[0355] Step 7:

[0356] The server rewards users based on their performance. If progress and emotional state meet the criteria, special rewards or additional advice are displayed on the device. This feedback helps motivate users and promotes the effective execution of their plans.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0359] The present invention aims to solve the problem that, in managing a user's financial situation, it is not possible to improve user acceptance and effectiveness by making dynamic adjustments that take into account the user's emotional state. Conventional financial management systems lack sufficient feedback and plan adjustments that reflect changes in the user's emotional state, making it easy for users to feel stressed and anxious, and making it difficult to continuously execute the plan.

[0360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0361] In this invention, the server includes means for collecting the user's spending history, purchasing behavior, and investment preferences; means for analyzing the user's data and generating an individually optimized financial management plan; and means for identifying and analyzing the user's emotional state. This makes it possible to provide the user with an optimal financial management plan, and by adapting that plan to the user's emotions, it becomes possible to increase the likelihood that the user will actively implement and achieve the plan.

[0362] "User spending history" refers to a record of a user's past spending activities, primarily including data on what they spent how much money on.

[0363] "Purchasing behavior" refers to the process of selection and decision-making when users purchase goods or services, and includes data on what products they buy and how often.

[0364] "Investment orientation" refers to the tendency that indicates how users intend to manage their assets, and includes information such as risk tolerance and preferences for investment targets.

[0365] "Emotional state" refers to the mental and emotional state that a user is experiencing at a particular point in time, and includes emotions such as stress and joy.

[0366] A "financial management plan" refers to a strategy or plan developed to effectively manage a user's income and expenses, and includes guidelines for long-term asset management and short-term expense reduction.

[0367] "Dynamic adjustment" refers to a process of flexibly changing plans in response to the user's changing circumstances and emotional state, and is a method aimed at real-time adaptation.

[0368] "Real-time feedback" refers to responses and information provided immediately in response to a user's actions or status, enabling them to make improvements or choices on the spot.

[0369] To realize this invention, the process begins with installing a dedicated application on a device such as a smartphone or tablet. Through this application, the user inputs data on their spending history, purchasing behavior, and investment preferences, and sends it to a server. This data is encrypted and securely stored on the server.

[0370] The server analyzes collected data using a generative AI model and generates a financial management plan tailored to the user. During this process, an emotion engine analyzes user input and interactions to identify the user's emotional state. Optimization is performed based on the emotional state, and the generated plan is dynamically adjusted.

[0371] The adjusted plan is immediately provided as feedback on the device, improving user experience. For example, if a user chooses a savings plan, and the emotion engine detects stress or anxiety, the server will display additional stress-reducing techniques and encouraging messages on the device.

[0372] This application's program is based on the Python language, utilizing OpenAI GPT-4 for its generative AI model and the Affectiva SDK for sentiment analysis. This creates a system that provides users with flexible and responsive financial plans.

[0373] For example, if a user starts saving money for a family birthday party and becomes anxious about the expenses along the way, the app can detect that emotion and immediately provide situation-appropriate advice and comforting messages.

[0374] Example prompt: "Estimate the user's stress level based on their daily spending data and recent interaction feedback, and dynamically adjust and provide a household budgeting plan."

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The device receives data from the user regarding spending history, purchasing behavior, and investment preferences as input. This data is collected within the application and sent to the server in an encrypted format. The transmitted data is stored in the server's database.

[0378] Step 2:

[0379] The server uses a generative AI model to analyze the collected user data. This analysis reveals each user's individual spending patterns and financial tendencies. The generative AI model then processes the data to output a financial management plan tailored to each user.

[0380] Step 3:

[0381] The server uses an emotion engine, taking user interaction data as input, to identify the user's current emotional state. It determines the emotional state based on text analysis and interface interaction patterns, and then obtains processed data representing the user's emotional state.

[0382] Step 4:

[0383] The server takes the generated financial management plan as input and dynamically adjusts it, taking into account the user's emotional state obtained from the emotion engine. If the emotional state is determined to be stress or anxiety, it adjusts the plan to be more flexible, makes data changes such as including encouraging comments, and outputs the adjusted plan.

[0384] Step 5:

[0385] The device receives a customized financial plan from the server and presents it to the user in real time. Through the plan, displayed as immediate feedback, the user receives specific spending advice tailored to their current situation. This feedback improves the user's understanding and motivation.

[0386] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0387] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0388] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0392] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0393] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0394] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0395] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0396] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0397] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0398] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0399] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0400] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0401] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0402] In order to implement the invention, the user must first access the application using their own device and input information about their spending history, purchasing behavior, and investment preferences. The user fills out the details in the form provided by the application and creates an initial dataset. This data is securely transmitted to the server.

[0403] The server uses a generative AI model to analyze the received data and evaluate each user's behavioral patterns and trends. Based on this, the server generates a personalized financial management plan optimized for each user, taking into account their current financial situation and goals. This plan includes specific savings suggestions and investment plans.

[0404] Next, the server sends the generated plans to the user's device. The user reviews these plans on their device and selects the one that best suits their lifestyle and goals. Based on the selected plan, the user can then work on their savings and investment missions.

[0405] The system periodically checks the user's progress and evaluates how close they are to the goals set by the server. Progress management is important for maintaining user motivation towards achieving the goals. When a user achieves their goal, the server calculates rewards such as points or badges and notifies the user via their device.

[0406] Even more importantly, the server can continuously learn from the user's usage history and adaptively create new financial plans. This ensures that users always receive the latest and most optimal plan. At the same time, the gamified user experience design allows users to continuously improve their financial management skills while having fun.

[0407] For example, if a user selects the goal of "saving 5,000 yen on monthly food expenses," the server will provide suggestions to reduce unnecessary spending based on the user's past purchase history. These suggestions may include, for instance, refraining from purchasing certain ingredients or choosing cheaper alternatives. Once the user achieves this goal, a reward will be displayed on the device, and the next challenge will be suggested.

[0408] In this way, the invention becomes a system that provides users with a valuable financial management experience and naturally promotes skill improvement.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] Users log in to the application using their device and enter information about their spending history, purchasing behavior, and investment preferences. The entered data is then sent from the device to the server.

[0412] Step 2:

[0413] The server stores the received data and analyzes it using a generative AI model. Through this analysis, it extracts patterns in users' spending tendencies and investment preferences, and evaluates the current situation.

[0414] Step 3:

[0415] Based on the analysis results, the server generates a financial management plan optimized for each user. This plan includes specific guidance on saving advice and investment opportunities.

[0416] Step 4:

[0417] The server sends the generated financial management plan to the user's terminal. The user reviews the plan details on their terminal, selects one that suits their needs, and starts the process.

[0418] Step 5:

[0419] Once the user begins taking action based on their selected plan, the server monitors their progress in real time. Progress information is updated periodically, and the degree of achievement is evaluated.

[0420] Step 6:

[0421] The server sends supplementary feedback to the user's device as needed, based on the user's progress. The user uses this feedback to adjust their actions towards achieving their goals.

[0422] Step 7:

[0423] When a user completes a mission or achieves a goal, the server calculates and awards reward points or badges. The device displays information about the rewards earned, allowing the user to experience a sense of accomplishment.

[0424] Step 8:

[0425] The server continuously learns from the user's behavior history and updates the financial plan based on the user's needs and achievement patterns. This ensures that the most up-to-date, personalized plan is always provided.

[0426] (Example 1)

[0427] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0428] The present invention aims to provide a system that quickly and effectively delivers management plans tailored to the financial situation of individual users, thereby improving users' financial skills and increasing their motivation. In particular, it aims to achieve more precise financial management by individually analyzing users' spending and investment patterns and providing adaptively adjusted plans.

[0429] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0430] In this invention, the server includes means for collecting user financial information, means for analyzing the financial information using a generation AI model, and means for generating individually optimized financial plans based on the analysis results. This makes it possible to provide users with the most up-to-date and optimized financial management plans at all times.

[0431] A "user" is an individual or group that uses this system to input financial information and optimize their own actions.

[0432] "Financial information" refers to all data related to financial management, including a user's spending history, purchasing behavior, and investment preferences.

[0433] A "generative AI model" is an analytical model that utilizes artificial intelligence technology to analyze a user's financial information and generate an optimized financial plan.

[0434] A "financial plan" is a plan that includes individualized optimization strategies for spending and investment, created by a generative AI model based on analysis for each user.

[0435] "Analysis" refers to the process of analyzing users' financial information using generative AI models to evaluate behavioral patterns and directions for optimization.

[0436] "Rewards" refer to incentives designed to motivate users, such as points or badges given based on the degree to which users achieve their set financial goals.

[0437] A "game format" is a format that incorporates game elements into financial planning activities to improve user engagement.

[0438] This invention constructs a system that provides a financial management plan optimized for individual users. First, the user accesses the application using their own terminal and inputs financial information regarding spending history, purchasing behavior, and investment preferences. This information is transmitted to the server via a secure communication method.

[0439] The server analyzes the received financial information using a generating AI model. Python libraries such as TensorFlow and PyTorch are used for data analysis and evaluation. Through this analysis, user behavior patterns are identified, and a personalized financial management plan is generated. This plan includes specific guidelines based on the user's spending habits and investment strategy.

[0440] The generated financial plan is sent from the server to the user's device. The user can then select the plan best suited to their lifestyle and goals on their device and take action according to that plan. Furthermore, progress is regularly evaluated, and rewards are provided based on the degree of goal achievement. This allows users to improve their financial habits while maintaining sustained motivation.

[0441] For example, if a user sets a financial goal of "saving 5,000 yen on monthly food expenses," the server will analyze past purchase history and provide specific suggestions to reduce unnecessary spending. These suggestions will include a list of ingredients to avoid purchasing and alternative products to choose. An example of a prompt might be, "Generate an AI model that proposes a savings plan based on the user's purchasing patterns."

[0442] This system ensures users always receive up-to-date and effective financial plans, enabling them to make optimal choices based on their own financial situation.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] Users launch a dedicated application on their devices and input financial information regarding their spending history, purchasing behavior, and investment preferences. This information is collected through the device's interface and transmitted to the server using a secure communication protocol. The entered data arrives at the server with each item properly formatted.

[0446] Step 2:

[0447] The server stores the received user's financial information in a database. Then, it begins data analysis using a generative AI model. Based on the input information, the AI ​​model evaluates the user's spending patterns and investment tendencies. Here, behavioral patterns are identified through clustering and statistical analysis, and the results of this analysis are used in subsequent processing.

[0448] Step 3:

[0449] The server generates a personalized financial management plan based on the analysis results. The generating AI model makes specific suggestions for reducing expenses and optimizing investments, based on the user's behavioral characteristics obtained from the analysis. These suggestions are then adjusted to suit each user and output in a format that can be displayed in PDF or within the app's user interface.

[0450] Step 4:

[0451] The server sends the generated financial plan back to the user's terminal. The terminal receives it and displays it on the user interface. The user reviews the presented plan and selects the one that best suits their lifestyle and goals. This selection is recorded within the application to guide subsequent actions.

[0452] Step 5:

[0453] The server periodically evaluates the user's progress based on the plan they have selected. Each time new purchase history or spending information is entered by the user, the server checks their progress toward their goals and generates a report. This report shows how close the user is to achieving their goals and provides feedback through their device.

[0454] Step 6:

[0455] When a user achieves their set goals, the server calculates and awards them a reward. This reward, in the form of points or badges, is communicated to the user through the device's notification function. This feature enhances user engagement and continuously motivates them for their next challenge. The server also continuously analyzes new data and updates its generative AI model to ensure users always receive the most up-to-date financial plans.

[0456] (Application Example 1)

[0457] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0458] Modern consumers exhibit diverse purchasing behaviors and investment preferences, and a wide range of transactions are conducted via the internet. Therefore, there is a need to present effective and optimal economic management plans to individual consumers. However, conventional systems lack sufficient support that considers specific savings strategies and rewards based on each user's spending and investment trends.

[0459] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0460] In this invention, the server includes means for collecting a user's transaction history, consumption behavior, and capital investment intentions; means for analyzing the user's information and generating an individually optimized economic investment plan; means for providing the plan to the user and monitoring its progress; means for rewarding the user according to their achievement level; and means for proposing saving methods and reward structures based on the user's purchasing behavior and transaction history. As a result, consumers can receive an individually optimized economic investment plan, enabling more efficient fund management and the acquisition of associated rewards.

[0461] "Transaction history" refers to a record of a user's economic activities, specifically including information such as purchased items, payment amounts, and purchase dates and times.

[0462] "Consumer behavior" refers to the patterns and tendencies of how users behave when purchasing goods or services, and includes things like purchase frequency and the selection of purchase categories.

[0463] "Capital investment intentions" represent the user's wishes and plans for how they want to invest the funds they hold.

[0464] An "economic management plan" is a plan created to optimize a user's assets and spending, and includes specific savings suggestions and investment strategies.

[0465] "Progress" refers to the monitoring and evaluation of the execution status of the economic investment plan set by the user.

[0466] "Rewards" are given to users when they achieve goals they have set, and are specifically provided in the form of points, badges, etc.

[0467] "Savings methods" refer to specific strategies for minimizing user spending and making effective use of assets.

[0468] The "reward structure" is a system of rewards provided according to the user's level of achievement, and it shows how points and rewards are calculated.

[0469] The system for implementing this invention uses the user's smartphone as the primary terminal and interacts with a server over a network. Information regarding the user's transaction history, consumption behavior, and capital investment intentions is first collected through an application installed on the user's terminal. This allows the user to input their purchase history and spending summary. The entered data is then transmitted to a cloud server using a secure protocol. For example, data such as "food expenses 10,000 yen, entertainment expenses 5,000 yen, transportation expenses 3,000 yen / month" might be entered.

[0470] The server analyzes the received data using a generative AI model. Based on each user's data, the generative AI model develops an optimized financial management plan tailored to that individual user. This plan can include examples of saving methods and reward structures based on the user's purchasing behavior. The optimized plan is then sent back to the user's device, where they can view it and confirm specific saving goals and investment plans.

[0471] Furthermore, the server periodically monitors the progress of the user's actions according to their plan, calculates rewards based on the degree of goal achievement, and notifies the user via their terminal. For example, if a user achieves an economic goal such as "reducing food expenses by 20% this month," they may be rewarded with points or badges.

[0472] To generate new suggestions, the generation AI model continuously learns from the user's behavior history. Through this learning process, the server can always provide the user with the latest economic management plan. An example of a prompt message is, "User ID: 12345, Spending Summary: Food 5000 yen, Beverages 1500 yen, Generate Plan Proposal." In this way, the system allows users to work on improving their financial situation while experiencing something similar to a game.

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] Users log in to the application using their smartphones and input information about their transaction history, consumer behavior, and capital investment intentions. The entered data is saved on the device in JSON format and then sent to a server in the cloud via a secure protocol. This creates a unique data set for each user.

[0476] Step 2:

[0477] The server inputs the received data into a generating AI model to analyze the user's purchasing patterns and consumption trends. In this data analysis process, the algorithm operates based on the user's past activities to identify demand forecasts and potential for spending reductions. As an output, an optimized economic management plan is generated for each user.

[0478] Step 3:

[0479] The server uses the generated economic management plan to construct specific savings methods and reward structures. This plan is individually rendered and sent to the user's smartphone. The user can view the content on their device and consider the proposed savings and investment goals. A visually easy-to-understand interface is generated as output.

[0480] Step 4:

[0481] Once the user begins taking action based on the plan, the device tracks progress and periodically sends data to the server. The server receives the progress data and analyzes it to evaluate the degree of goal achievement. If necessary, it determines whether the user has achieved their savings goals according to the plan.

[0482] Step 5:

[0483] The server calculates rewards based on the user's progress toward achieving goals and notifies the user in the form of points, badges, or other means. This generates output that helps maintain user motivation and encourages further participation.

[0484] Step 6:

[0485] The server continuously learns the user's behavior history and generates new economic investment plans. The generating AI model creates an optimized plan based on past results and newly input data, and outputs that include new suggestions as examples of prompts to provide to the user.

[0486] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0487] To implement this invention, the user must first access the application using a terminal and input information about their spending history, purchasing behavior, and investment preferences. This information serves as foundational data for constructing a comprehensive picture of the user's financial situation. Once the data is entered, the terminal securely transmits it to the server.

[0488] The server uses a generative AI model to analyze users' spending and investment patterns in detail based on the collected data. From this analysis, a financial management plan optimized for each individual user is generated. Furthermore, the server incorporates an emotion engine that identifies the user's emotional state. This emotion engine extracts emotions from textual feedback and interactions as the user operates the device, and performs real-time analysis.

[0489] The analysis results from the emotion engine are used to dynamically adjust the content of the generated financial management plan based on the user's current emotional state. For example, if the user is feeling stressed, the system can incorporate a more flexible plan or elements that promote relaxation into the plan. This makes it easier for the user to accept and implement the plan comfortably.

[0490] Once a user starts a plan, the server tracks their progress and continuously evaluates changes in their emotional state using an emotion engine. Based on this information, the server provides feedback as needed and delivers it to the user through their device. If the user's emotional state is positive, the rewards may be specially adjusted.

[0491] For example, if a user selects a plan to "save on monthly food expenses," and the emotion engine identifies that the user is anxious about saving, the server will display not only specific saving techniques but also supportive information and encouraging messages to alleviate that anxiety on the device.

[0492] Through such two-way feedback and dynamic plan adjustments, the present invention realizes a system that effectively supports financial management while being attentive to the user's emotions.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] Users log in to the application using their device and enter data about their spending history, purchasing behavior, and investment preferences. This data is immediately sent to the server.

[0496] Step 2:

[0497] The server securely stores the collected data. Using a generative AI model, it analyzes the user's spending patterns, purchasing behavior, and investment preferences, and generates an individually optimized financial management plan based on the results.

[0498] Step 3:

[0499] The server activates an emotion engine and evaluates the user's emotions based on the input data and interaction patterns during their interactions with the device. Based on this evaluation, the server adjusts the content of the financial management plan presented to the user.

[0500] Step 4:

[0501] The adjusted financial management plan is sent to the terminal. The user reviews the plan on the terminal, selects the appropriate actions from the recommended options, and begins implementing them.

[0502] Step 5:

[0503] The server monitors the user's progress in real time and records changes in the user's emotional state. Each time progress is reported, the server uses an emotion engine to generate feedback based on that information and sends it to the terminal.

[0504] Step 6:

[0505] When a user completes a designated mission, the server evaluates their level of achievement and emotional state, and determines an appropriate reward. The reward is then communicated to the user via their device.

[0506] Step 7:

[0507] The server stores user behavior and emotional history, which is used to improve the financial management plan provided next. The new plan will be further optimized to the user's preferences and current status.

[0508] (Example 2)

[0509] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0510] In today's diverse financial environment, providing financial management tailored to individual users is difficult, and uniform plans often lead to decreased user satisfaction and implementation rates. Furthermore, the inability to provide feedback or dynamically adjust plans based on users' emotional states can make it difficult to achieve goals due to stress and anxiety.

[0511] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0512] In this invention, the server includes means for acquiring historical information about the user's life, means for analyzing the acquired information to generate an individually tailored plan, and means for evaluating the user's emotional state using an emotion analysis device and dynamically adjusting the plan. This enables the provision of financial management optimized for the user and flexible plan adjustments that are sensitive to their emotions.

[0513] A "user" is an entity that accesses a system and performs individual actions or uses specific information.

[0514] "Lifestyle history information" refers to records of a user's daily activities, such as spending, purchasing behavior, and investment preferences.

[0515] "Means of acquisition" refers to devices and methods for collecting information and storing it in a format such as a database.

[0516] "Means of analysis" refer to software and algorithms used to analyze collected data and derive insights and conclusions.

[0517] A "personally tailored plan" is a financial management plan designed to take into account the different characteristics and needs of each user.

[0518] An "emotion analysis device" is a technology or device used to analyze and evaluate a user's psychological state based on their input and behavioral data.

[0519] "Means of dynamic adjustment" refer to functions or processes that automatically and promptly change content in response to changes in circumstances or the environment.

[0520] "Means of evaluation" refers to devices or methods that evaluate user behavior or results based on certain criteria.

[0521] This system is designed so that users input historical information about their lifestyle using a terminal, and then a personalized plan is generated and implemented based on that information. Users access the application and input information such as spending and purchasing behavior. The terminal collects this data and transmits it to the server using a secure protocol.

[0522] The server uses programming languages ​​such as Python and related data science libraries (e.g., Pandas and NumPy) to perform data analysis using generative AI models. This analysis generates a financial management plan optimized for the user. Furthermore, the server utilizes an emotion analysis device and natural language processing techniques to evaluate the user's emotional state. Based on the emotional state, the plan's content is dynamically adjusted.

[0523] For example, if a user selects a plan to "save on food expenses," and the sentiment analyzer determines that the user is feeling anxious, the server will display encouraging messages and specific techniques on the device. This process allows the user to proceed with the plan flexibly while receiving emotionally supportive assistance.

[0524] An example of a prompt message would be, "Please give me advice on how to plan my spending for this month." Based on this prompt message, the server can use a generative AI model to provide the user with appropriate advice and plan suggestions.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] Users access the application from their device and input historical information about their lifestyle. This input includes spending history, purchasing behavior, and investment preferences. The device temporarily stores the data entered by the user and transmits it to the server using a secure protocol.

[0528] Step 2:

[0529] The server receives historical information sent from the terminal and stores it in the database. After receiving the data, the server uses Python and related data science libraries to preprocess the data. Specific examples of preprocessing include imputing missing values ​​and removing outliers.

[0530] Step 3:

[0531] The server uses a generative AI model to analyze pre-processed data. This generative AI model performs pattern recognition to generate a financial management plan optimized for each user. For example, it predicts future spending based on past spending patterns and proposes a savings plan based on that prediction.

[0532] Step 4:

[0533] The server uses an emotion analysis device to evaluate the user's emotional state. It analyzes user input and log data using natural language processing techniques to determine the user's emotional state (e.g., stress, anxiety, joy). This helps identify factors that may influence the plan's content.

[0534] Step 5:

[0535] Based on the sentiment analysis, the server dynamically adjusts the generated financial management plan. For example, if the user is feeling stressed, elements that promote relaxation are added to the plan. This adjustment includes leveraging feedback mechanisms to provide emotional support to the user.

[0536] Step 6:

[0537] As users implement a plan, the server monitors its progress in real time. Progress data is collected periodically and managed in conjunction with continuous evaluations based on sentiment analysis. This allows for timely feedback to be sent to users, helping to maintain their motivation.

[0538] Step 7:

[0539] The server rewards users based on their performance. If progress and emotional state meet the criteria, special rewards or additional advice are displayed on the device. This feedback helps motivate users and promotes the effective execution of their plans.

[0540] (Application Example 2)

[0541] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0542] The present invention aims to solve the problem that, in managing a user's financial situation, it is not possible to improve user acceptance and effectiveness by making dynamic adjustments that take into account the user's emotional state. Conventional financial management systems lack sufficient feedback and plan adjustments that reflect changes in the user's emotional state, making it easy for users to feel stressed and anxious, and making it difficult to continuously execute the plan.

[0543] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0544] In this invention, the server includes means for collecting the user's spending history, purchasing behavior, and investment preferences; means for analyzing the user's data and generating an individually optimized financial management plan; and means for identifying and analyzing the user's emotional state. This makes it possible to provide the user with an optimal financial management plan, and by adapting that plan to the user's emotions, it becomes possible to increase the likelihood that the user will actively implement and achieve the plan.

[0545] "User spending history" refers to a record of a user's past spending activities, primarily including data on what they spent how much money on.

[0546] "Purchasing behavior" refers to the process of selection and decision-making when users purchase goods or services, and includes data on what products they buy and how often.

[0547] "Investment orientation" refers to the tendency that indicates how users intend to manage their assets, and includes information such as risk tolerance and preferences for investment targets.

[0548] "Emotional state" refers to the mental and emotional state that a user is experiencing at a particular point in time, and includes emotions such as stress and joy.

[0549] A "financial management plan" refers to a strategy or plan developed to effectively manage a user's income and expenses, and includes guidelines for long-term asset management and short-term expense reduction.

[0550] "Dynamic adjustment" refers to a process of flexibly changing plans in response to the user's changing circumstances and emotional state, and is a method aimed at real-time adaptation.

[0551] "Real-time feedback" refers to responses and information provided immediately in response to a user's actions or status, enabling them to make improvements or choices on the spot.

[0552] To realize this invention, the process begins with installing a dedicated application on a device such as a smartphone or tablet. Through this application, the user inputs data on their spending history, purchasing behavior, and investment preferences, and sends it to a server. This data is encrypted and securely stored on the server.

[0553] The server analyzes collected data using a generative AI model and generates a financial management plan tailored to the user. During this process, an emotion engine analyzes user input and interactions to identify the user's emotional state. Optimization is performed based on the emotional state, and the generated plan is dynamically adjusted.

[0554] The adjusted plan is immediately provided as feedback on the device, improving user experience. For example, if a user chooses a savings plan, and the emotion engine detects stress or anxiety, the server will display additional stress-reducing techniques and encouraging messages on the device.

[0555] This application's program is based on the Python language, utilizing OpenAI GPT-4 for its generative AI model and the Affectiva SDK for sentiment analysis. This creates a system that provides users with flexible and responsive financial plans.

[0556] For example, if a user starts saving money for a family birthday party and becomes anxious about the expenses along the way, the app can detect that emotion and immediately provide situation-appropriate advice and comforting messages.

[0557] Example prompt: "Estimate the user's stress level based on their daily spending data and recent interaction feedback, and dynamically adjust and provide a household budgeting plan."

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] The device receives data from the user regarding spending history, purchasing behavior, and investment preferences as input. This data is collected within the application and sent to the server in an encrypted format. The transmitted data is stored in the server's database.

[0561] Step 2:

[0562] The server uses a generative AI model to analyze the collected user data. This analysis reveals each user's individual spending patterns and financial tendencies. The generative AI model then processes the data to output a financial management plan tailored to each user.

[0563] Step 3:

[0564] The server uses an emotion engine, taking user interaction data as input, to identify the user's current emotional state. It determines the emotional state based on text analysis and interface interaction patterns, and then obtains processed data representing the user's emotional state.

[0565] Step 4:

[0566] The server takes the generated financial management plan as input and dynamically adjusts it, taking into account the user's emotional state obtained from the emotion engine. If the emotional state is determined to be stress or anxiety, it adjusts the plan to be more flexible, makes data changes such as including encouraging comments, and outputs the adjusted plan.

[0567] Step 5:

[0568] The device receives a customized financial plan from the server and presents it to the user in real time. Through the plan, displayed as immediate feedback, the user receives specific spending advice tailored to their current situation. This feedback improves the user's understanding and motivation.

[0569] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0570] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0571] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0572] [Fourth Embodiment]

[0573] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0574] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0575] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0576] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0577] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0578] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0579] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0580] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0581] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0582] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0583] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0584] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0585] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0586] In order to implement the invention, the user must first access the application using their own device and input information about their spending history, purchasing behavior, and investment preferences. The user fills out the details in the form provided by the application and creates an initial dataset. This data is securely transmitted to the server.

[0587] The server uses a generative AI model to analyze the received data and evaluate each user's behavioral patterns and trends. Based on this, the server generates a personalized financial management plan optimized for each user, taking into account their current financial situation and goals. This plan includes specific savings suggestions and investment plans.

[0588] Next, the server sends the generated plans to the user's device. The user reviews these plans on their device and selects the one that best suits their lifestyle and goals. Based on the selected plan, the user can then work on their savings and investment missions.

[0589] The system periodically checks the user's progress and evaluates how close they are to the goals set by the server. Progress management is important for maintaining user motivation towards achieving the goals. When a user achieves their goal, the server calculates rewards such as points or badges and notifies the user via their device.

[0590] Even more importantly, the server can continuously learn from the user's usage history and adaptively create new financial plans. This ensures that users always receive the latest and most optimal plan. At the same time, the gamified user experience design allows users to continuously improve their financial management skills while having fun.

[0591] For example, if a user selects the goal of "saving 5,000 yen on monthly food expenses," the server will provide suggestions to reduce unnecessary spending based on the user's past purchase history. These suggestions may include, for instance, refraining from purchasing certain ingredients or choosing cheaper alternatives. Once the user achieves this goal, a reward will be displayed on the device, and the next challenge will be suggested.

[0592] In this way, the invention becomes a system that provides users with a valuable financial management experience and naturally promotes skill improvement.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] Users log in to the application using their device and enter information about their spending history, purchasing behavior, and investment preferences. The entered data is then sent from the device to the server.

[0596] Step 2:

[0597] The server stores the received data and analyzes it using a generative AI model. Through this analysis, it extracts patterns in users' spending tendencies and investment preferences, and evaluates the current situation.

[0598] Step 3:

[0599] Based on the analysis results, the server generates a financial management plan optimized for each user. This plan includes specific guidance on saving advice and investment opportunities.

[0600] Step 4:

[0601] The server sends the generated financial management plan to the user's terminal. The user reviews the plan details on their terminal, selects one that suits their needs, and starts the process.

[0602] Step 5:

[0603] Once the user begins taking action based on their selected plan, the server monitors their progress in real time. Progress information is updated periodically, and the degree of achievement is evaluated.

[0604] Step 6:

[0605] The server sends supplementary feedback to the user's device as needed, based on the user's progress. The user uses this feedback to adjust their actions towards achieving their goals.

[0606] Step 7:

[0607] When a user completes a mission or achieves a goal, the server calculates and awards reward points or badges. The device displays information about the rewards earned, allowing the user to experience a sense of accomplishment.

[0608] Step 8:

[0609] The server continuously learns from the user's behavior history and updates the financial plan based on the user's needs and achievement patterns. This ensures that the most up-to-date, personalized plan is always provided.

[0610] (Example 1)

[0611] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] The present invention aims to provide a system that quickly and effectively delivers management plans tailored to the financial situation of individual users, thereby improving users' financial skills and increasing their motivation. In particular, it aims to achieve more precise financial management by individually analyzing users' spending and investment patterns and providing adaptively adjusted plans.

[0613] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0614] In this invention, the server includes means for collecting user financial information, means for analyzing the financial information using a generation AI model, and means for generating individually optimized financial plans based on the analysis results. This makes it possible to provide users with the most up-to-date and optimized financial management plans at all times.

[0615] A "user" is an individual or group that uses this system to input financial information and optimize their own actions.

[0616] "Financial information" refers to all data related to financial management, including a user's spending history, purchasing behavior, and investment preferences.

[0617] A "generative AI model" is an analytical model that utilizes artificial intelligence technology to analyze a user's financial information and generate an optimized financial plan.

[0618] A "financial plan" is a plan that includes individualized optimization strategies for spending and investment, created by a generative AI model based on analysis for each user.

[0619] "Analysis" refers to the process of analyzing users' financial information using generative AI models to evaluate behavioral patterns and directions for optimization.

[0620] "Rewards" refer to incentives designed to motivate users, such as points or badges given based on the degree to which users achieve their set financial goals.

[0621] A "game format" is a format that incorporates game elements into financial planning activities to improve user engagement.

[0622] This invention constructs a system that provides a financial management plan optimized for individual users. First, the user accesses the application using their own terminal and inputs financial information regarding spending history, purchasing behavior, and investment preferences. This information is transmitted to the server via a secure communication method.

[0623] The server analyzes the received financial information using a generating AI model. Python libraries such as TensorFlow and PyTorch are used for data analysis and evaluation. Through this analysis, user behavior patterns are identified, and a personalized financial management plan is generated. This plan includes specific guidelines based on the user's spending habits and investment strategy.

[0624] The generated financial plan is sent from the server to the user's device. The user can then select the plan best suited to their lifestyle and goals on their device and take action according to that plan. Furthermore, progress is regularly evaluated, and rewards are provided based on the degree of goal achievement. This allows users to improve their financial habits while maintaining sustained motivation.

[0625] For example, if a user sets a financial goal of "saving 5,000 yen on monthly food expenses," the server will analyze past purchase history and provide specific suggestions to reduce unnecessary spending. These suggestions will include a list of ingredients to avoid purchasing and alternative products to choose. An example of a prompt might be, "Generate an AI model that proposes a savings plan based on the user's purchasing patterns."

[0626] This system ensures users always receive up-to-date and effective financial plans, enabling them to make optimal choices based on their own financial situation.

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] Users launch a dedicated application on their devices and input financial information regarding their spending history, purchasing behavior, and investment preferences. This information is collected through the device's interface and transmitted to the server using a secure communication protocol. The entered data arrives at the server with each item properly formatted.

[0630] Step 2:

[0631] The server stores the received user's financial information in a database. Then, it begins data analysis using a generative AI model. Based on the input information, the AI ​​model evaluates the user's spending patterns and investment tendencies. Here, behavioral patterns are identified through clustering and statistical analysis, and the results of this analysis are used in subsequent processing.

[0632] Step 3:

[0633] The server generates a personalized financial management plan based on the analysis results. The generating AI model makes specific suggestions for reducing expenses and optimizing investments, based on the user's behavioral characteristics obtained from the analysis. These suggestions are then adjusted to suit each user and output in a format that can be displayed in PDF or within the app's user interface.

[0634] Step 4:

[0635] The server sends the generated financial plan back to the user's terminal. The terminal receives it and displays it on the user interface. The user reviews the presented plan and selects the one that best suits their lifestyle and goals. This selection is recorded within the application to guide subsequent actions.

[0636] Step 5:

[0637] The server periodically evaluates the user's progress based on the plan they have selected. Each time new purchase history or spending information is entered by the user, the server checks their progress toward their goals and generates a report. This report shows how close the user is to achieving their goals and provides feedback through their device.

[0638] Step 6:

[0639] When a user achieves their set goals, the server calculates and awards them a reward. This reward, in the form of points or badges, is communicated to the user through the device's notification function. This feature enhances user engagement and continuously motivates them for their next challenge. The server also continuously analyzes new data and updates its generative AI model to ensure users always receive the most up-to-date financial plans.

[0640] (Application Example 1)

[0641] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0642] Modern consumers exhibit diverse purchasing behaviors and investment preferences, and a wide range of transactions are conducted via the internet. Therefore, there is a need to present effective and optimal economic management plans to individual consumers. However, conventional systems lack sufficient support that considers specific savings strategies and rewards based on each user's spending and investment trends.

[0643] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0644] In this invention, the server includes means for collecting a user's transaction history, consumption behavior, and capital investment intentions; means for analyzing the user's information and generating an individually optimized economic investment plan; means for providing the plan to the user and monitoring its progress; means for rewarding the user according to their achievement level; and means for proposing saving methods and reward structures based on the user's purchasing behavior and transaction history. As a result, consumers can receive an individually optimized economic investment plan, enabling more efficient fund management and the acquisition of associated rewards.

[0645] "Transaction history" refers to a record of a user's economic activities, specifically including information such as purchased items, payment amounts, and purchase dates and times.

[0646] "Consumer behavior" refers to the patterns and tendencies of how users behave when purchasing goods or services, and includes things like purchase frequency and the selection of purchase categories.

[0647] "Capital investment intentions" represent the user's wishes and plans for how they want to invest the funds they hold.

[0648] An "economic management plan" is a plan created to optimize a user's assets and spending, and includes specific savings suggestions and investment strategies.

[0649] "Progress" refers to the monitoring and evaluation of the execution status of the economic investment plan set by the user.

[0650] "Rewards" are given to users when they achieve goals they have set, and are specifically provided in the form of points, badges, etc.

[0651] "Savings methods" refer to specific strategies for minimizing user spending and making effective use of assets.

[0652] The "reward structure" is a system of rewards provided according to the user's level of achievement, and it shows how points and rewards are calculated.

[0653] The system for implementing this invention uses the user's smartphone as the primary terminal and interacts with a server over a network. Information regarding the user's transaction history, consumption behavior, and capital investment intentions is first collected through an application installed on the user's terminal. This allows the user to input their purchase history and spending summary. The entered data is then transmitted to a cloud server using a secure protocol. For example, data such as "food expenses 10,000 yen, entertainment expenses 5,000 yen, transportation expenses 3,000 yen / month" might be entered.

[0654] The server analyzes the received data using a generative AI model. Based on each user's data, the generative AI model develops an optimized financial management plan tailored to that individual user. This plan can include examples of saving methods and reward structures based on the user's purchasing behavior. The optimized plan is then sent back to the user's device, where they can view it and confirm specific saving goals and investment plans.

[0655] Furthermore, the server periodically monitors the progress of the user's actions according to their plan, calculates rewards based on the degree of goal achievement, and notifies the user via their terminal. For example, if a user achieves an economic goal such as "reducing food expenses by 20% this month," they may be rewarded with points or badges.

[0656] To generate new suggestions, the generation AI model continuously learns from the user's behavior history. Through this learning process, the server can always provide the user with the latest economic management plan. An example of a prompt message is, "User ID: 12345, Spending Summary: Food 5000 yen, Beverages 1500 yen, Generate Plan Proposal." In this way, the system allows users to work on improving their financial situation while experiencing something similar to a game.

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] Users log in to the application using their smartphones and input information about their transaction history, consumer behavior, and capital investment intentions. The entered data is saved on the device in JSON format and then sent to a server in the cloud via a secure protocol. This creates a unique data set for each user.

[0660] Step 2:

[0661] The server inputs the received data into a generating AI model to analyze the user's purchasing patterns and consumption trends. In this data analysis process, the algorithm operates based on the user's past activities to identify demand forecasts and potential for spending reductions. As an output, an optimized economic management plan is generated for each user.

[0662] Step 3:

[0663] The server uses the generated economic management plan to construct specific savings methods and reward structures. This plan is individually rendered and sent to the user's smartphone. The user can view the content on their device and consider the proposed savings and investment goals. A visually easy-to-understand interface is generated as output.

[0664] Step 4:

[0665] Once the user begins taking action based on the plan, the device tracks progress and periodically sends data to the server. The server receives the progress data and analyzes it to evaluate the degree of goal achievement. If necessary, it determines whether the user has achieved their savings goals according to the plan.

[0666] Step 5:

[0667] The server calculates rewards based on the user's progress toward achieving goals and notifies the user in the form of points, badges, or other means. This generates output that helps maintain user motivation and encourages further participation.

[0668] Step 6:

[0669] The server continuously learns the user's behavior history and generates new economic investment plans. The generating AI model creates an optimized plan based on past results and newly input data, and outputs that include new suggestions as examples of prompts to provide to the user.

[0670] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0671] To implement this invention, the user must first access the application using a terminal and input information about their spending history, purchasing behavior, and investment preferences. This information serves as foundational data for constructing a comprehensive picture of the user's financial situation. Once the data is entered, the terminal securely transmits it to the server.

[0672] The server uses a generative AI model to analyze users' spending and investment patterns in detail based on the collected data. From this analysis, a financial management plan optimized for each individual user is generated. Furthermore, the server incorporates an emotion engine that identifies the user's emotional state. This emotion engine extracts emotions from textual feedback and interactions as the user operates the device, and performs real-time analysis.

[0673] The analysis results from the emotion engine are used to dynamically adjust the content of the generated financial management plan based on the user's current emotional state. For example, if the user is feeling stressed, the system can incorporate a more flexible plan or elements that promote relaxation into the plan. This makes it easier for the user to accept and implement the plan comfortably.

[0674] Once a user starts a plan, the server tracks their progress and continuously evaluates changes in their emotional state using an emotion engine. Based on this information, the server provides feedback as needed and delivers it to the user through their device. If the user's emotional state is positive, the rewards may be specially adjusted.

[0675] For example, if a user selects a plan to "save on monthly food expenses," and the emotion engine identifies that the user is anxious about saving, the server will display not only specific saving techniques but also supportive information and encouraging messages to alleviate that anxiety on the device.

[0676] Through such two-way feedback and dynamic plan adjustments, the present invention realizes a system that effectively supports financial management while being attentive to the user's emotions.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] Users log in to the application using their device and enter data about their spending history, purchasing behavior, and investment preferences. This data is immediately sent to the server.

[0680] Step 2:

[0681] The server securely stores the collected data. Using a generative AI model, it analyzes the user's spending patterns, purchasing behavior, and investment preferences, and generates an individually optimized financial management plan based on the results.

[0682] Step 3:

[0683] The server activates an emotion engine and evaluates the user's emotions based on the input data and interaction patterns during their interactions with the device. Based on this evaluation, the server adjusts the content of the financial management plan presented to the user.

[0684] Step 4:

[0685] The adjusted financial management plan is sent to the terminal. The user reviews the plan on the terminal, selects the appropriate actions from the recommended options, and begins implementing them.

[0686] Step 5:

[0687] The server monitors the user's progress in real time and records changes in the user's emotional state. Each time progress is reported, the server uses an emotion engine to generate feedback based on that information and sends it to the terminal.

[0688] Step 6:

[0689] When a user completes a designated mission, the server evaluates their level of achievement and emotional state, and determines an appropriate reward. The reward is then communicated to the user via their device.

[0690] Step 7:

[0691] The server stores user behavior and emotional history, which is used to improve the financial management plan provided next. The new plan will be further optimized to the user's preferences and current status.

[0692] (Example 2)

[0693] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0694] In today's diverse financial environment, providing financial management tailored to individual users is difficult, and uniform plans often lead to decreased user satisfaction and implementation rates. Furthermore, the inability to provide feedback or dynamically adjust plans based on users' emotional states can make it difficult to achieve goals due to stress and anxiety.

[0695] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0696] In this invention, the server includes means for acquiring historical information about the user's life, means for analyzing the acquired information to generate an individually tailored plan, and means for evaluating the user's emotional state using an emotion analysis device and dynamically adjusting the plan. This enables the provision of financial management optimized for the user and flexible plan adjustments that are sensitive to their emotions.

[0697] A "user" is an entity that accesses a system and performs individual actions or uses specific information.

[0698] "Lifestyle history information" refers to records of a user's daily activities, such as spending, purchasing behavior, and investment preferences.

[0699] "Means of acquisition" refers to devices and methods for collecting information and storing it in a format such as a database.

[0700] "Means of analysis" refer to software and algorithms used to analyze collected data and derive insights and conclusions.

[0701] A "personally tailored plan" is a financial management plan designed to take into account the different characteristics and needs of each user.

[0702] An "emotion analysis device" is a technology or device used to analyze and evaluate a user's psychological state based on their input and behavioral data.

[0703] "Means of dynamic adjustment" refer to functions or processes that automatically and promptly change content in response to changes in circumstances or the environment.

[0704] "Means of evaluation" refers to devices or methods that evaluate user behavior or results based on certain criteria.

[0705] This system is designed so that users input historical information about their lifestyle using a terminal, and then a personalized plan is generated and implemented based on that information. Users access the application and input information such as spending and purchasing behavior. The terminal collects this data and transmits it to the server using a secure protocol.

[0706] The server uses programming languages ​​such as Python and related data science libraries (e.g., Pandas and NumPy) to perform data analysis using generative AI models. This analysis generates a financial management plan optimized for the user. Furthermore, the server utilizes an emotion analysis device and natural language processing techniques to evaluate the user's emotional state. Based on the emotional state, the plan's content is dynamically adjusted.

[0707] For example, if a user selects a plan to "save on food expenses," and the sentiment analyzer determines that the user is feeling anxious, the server will display encouraging messages and specific techniques on the device. This process allows the user to proceed with the plan flexibly while receiving emotionally supportive assistance.

[0708] An example of a prompt message would be, "Please give me advice on how to plan my spending for this month." Based on this prompt message, the server can use a generative AI model to provide the user with appropriate advice and plan suggestions.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] Step 1:

[0711] Users access the application from their device and input historical information about their lifestyle. This input includes spending history, purchasing behavior, and investment preferences. The device temporarily stores the data entered by the user and transmits it to the server using a secure protocol.

[0712] Step 2:

[0713] The server receives historical information sent from the terminal and stores it in the database. After receiving the data, the server uses Python and related data science libraries to preprocess the data. Specific examples of preprocessing include imputing missing values ​​and removing outliers.

[0714] Step 3:

[0715] The server uses a generative AI model to analyze pre-processed data. This generative AI model performs pattern recognition to generate a financial management plan optimized for each user. For example, it predicts future spending based on past spending patterns and proposes a savings plan based on that prediction.

[0716] Step 4:

[0717] The server uses an emotion analysis device to evaluate the user's emotional state. It analyzes user input and log data using natural language processing techniques to determine the user's emotional state (e.g., stress, anxiety, joy). This helps identify factors that may influence the plan's content.

[0718] Step 5:

[0719] Based on the sentiment analysis, the server dynamically adjusts the generated financial management plan. For example, if the user is feeling stressed, elements that promote relaxation are added to the plan. This adjustment includes leveraging feedback mechanisms to provide emotional support to the user.

[0720] Step 6:

[0721] As users implement a plan, the server monitors its progress in real time. Progress data is collected periodically and managed in conjunction with continuous evaluations based on sentiment analysis. This allows for timely feedback to be sent to users, helping to maintain their motivation.

[0722] Step 7:

[0723] The server rewards users based on their performance. If progress and emotional state meet the criteria, special rewards or additional advice are displayed on the device. This feedback helps motivate users and promotes the effective execution of their plans.

[0724] (Application Example 2)

[0725] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0726] The present invention aims to solve the problem that, in managing a user's financial situation, it is not possible to improve user acceptance and effectiveness by making dynamic adjustments that take into account the user's emotional state. Conventional financial management systems lack sufficient feedback and plan adjustments that reflect changes in the user's emotional state, making it easy for users to feel stressed and anxious, and making it difficult to continuously execute the plan.

[0727] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0728] In this invention, the server includes means for collecting the user's spending history, purchasing behavior, and investment preferences; means for analyzing the user's data and generating an individually optimized financial management plan; and means for identifying and analyzing the user's emotional state. This makes it possible to provide the user with an optimal financial management plan, and by adapting that plan to the user's emotions, it becomes possible to increase the likelihood that the user will actively implement and achieve the plan.

[0729] "User spending history" refers to a record of a user's past spending activities, primarily including data on what they spent how much money on.

[0730] "Purchasing behavior" refers to the process of selection and decision-making when users purchase goods or services, and includes data on what products they buy and how often.

[0731] "Investment orientation" refers to the tendency that indicates how users intend to manage their assets, and includes information such as risk tolerance and preferences for investment targets.

[0732] "Emotional state" refers to the mental and emotional state that a user is experiencing at a particular point in time, and includes emotions such as stress and joy.

[0733] A "financial management plan" refers to a strategy or plan developed to effectively manage a user's income and expenses, and includes guidelines for long-term asset management and short-term expense reduction.

[0734] "Dynamic adjustment" refers to a process of flexibly changing plans in response to the user's changing circumstances and emotional state, and is a method aimed at real-time adaptation.

[0735] "Real-time feedback" refers to responses and information provided immediately in response to a user's actions or status, enabling them to make improvements or choices on the spot.

[0736] To realize this invention, the process begins with installing a dedicated application on a device such as a smartphone or tablet. Through this application, the user inputs data on their spending history, purchasing behavior, and investment preferences, and sends it to a server. This data is encrypted and securely stored on the server.

[0737] The server analyzes collected data using a generative AI model and generates a financial management plan tailored to the user. During this process, an emotion engine analyzes user input and interactions to identify the user's emotional state. Optimization is performed based on the emotional state, and the generated plan is dynamically adjusted.

[0738] The adjusted plan is immediately provided as feedback on the device, improving user experience. For example, if a user chooses a savings plan, and the emotion engine detects stress or anxiety, the server will display additional stress-reducing techniques and encouraging messages on the device.

[0739] This application's program is based on the Python language, utilizing OpenAI GPT-4 for its generative AI model and the Affectiva SDK for sentiment analysis. This creates a system that provides users with flexible and responsive financial plans.

[0740] For example, if a user starts saving money for a family birthday party and becomes anxious about the expenses along the way, the app can detect that emotion and immediately provide situation-appropriate advice and comforting messages.

[0741] Example prompt: "Estimate the user's stress level based on their daily spending data and recent interaction feedback, and dynamically adjust and provide a household budgeting plan."

[0742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0743] Step 1:

[0744] The device receives data from the user regarding spending history, purchasing behavior, and investment preferences as input. This data is collected within the application and sent to the server in an encrypted format. The transmitted data is stored in the server's database.

[0745] Step 2:

[0746] The server uses a generative AI model to analyze the collected user data. This analysis reveals each user's individual spending patterns and financial tendencies. The generative AI model then processes the data to output a financial management plan tailored to each user.

[0747] Step 3:

[0748] The server uses an emotion engine, taking user interaction data as input, to identify the user's current emotional state. It determines the emotional state based on text analysis and interface interaction patterns, and then obtains processed data representing the user's emotional state.

[0749] Step 4:

[0750] The server takes the generated financial management plan as input and dynamically adjusts it, taking into account the user's emotional state obtained from the emotion engine. If the emotional state is determined to be stress or anxiety, it adjusts the plan to be more flexible, makes data changes such as including encouraging comments, and outputs the adjusted plan.

[0751] Step 5:

[0752] The device receives a customized financial plan from the server and presents it to the user in real time. Through the plan, displayed as immediate feedback, the user receives specific spending advice tailored to their current situation. This feedback improves the user's understanding and motivation.

[0753] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0754] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0755] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0756] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0757] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0758] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0759] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0760] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0761] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0762] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0763] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0764] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0765] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0766] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0767] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0768] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0769] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0770] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0771] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0772] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0773] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0774] The following is further disclosed regarding the embodiments described above.

[0775] (Claim 1)

[0776] Means for collecting users' spending history, purchasing behavior, and investment preferences,

[0777] A means for analyzing the user's data and generating an individually optimized financial management plan,

[0778] A means of presenting the aforementioned plan to the user and managing its progress,

[0779] A means of rewarding users according to their level of achievement,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, further comprising means for learning the user's behavior history and adaptively generating a new plan.

[0783] (Claim 3)

[0784] The system according to claim 1, further comprising means for providing missions or quests related to the aforementioned plan in a game-like manner.

[0785] "Example 1"

[0786] (Claim 1)

[0787] Means for collecting users' financial information,

[0788] A means of using a generative AI model for analyzing the aforementioned financial information,

[0789] A means for generating individually optimized financial plans based on analysis,

[0790] A means for presenting the aforementioned plan to the user and evaluating its progress,

[0791] A means of providing rewards to users based on their level of goal achievement,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, further comprising means for continuously learning user behavioral trends and adaptively generating updated financial plans.

[0795] (Claim 3)

[0796] The system according to claim 1, further comprising means for providing activities related to the aforementioned plan in a game format to improve user engagement.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] Means for collecting users' transaction history, consumption behavior, and capital investment intentions,

[0800] Means for analyzing the user's information and generating an individually optimized economic investment plan,

[0801] A means of providing the aforementioned plan to the user and supervising its progress,

[0802] A means of providing rewards to users according to their level of achievement,

[0803] A means of proposing savings methods and reward structures based on the user's purchasing behavior and transaction history,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for learning the user's activity history and adaptively generating a new plan.

[0807] (Claim 3)

[0808] The system according to claim 1, further comprising means for providing tasks and challenges related to the aforementioned plan in a game-like manner.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] A means of obtaining historical information about the user's life,

[0812] A means for analyzing acquired information and generating individually tailored plans,

[0813] A means of presenting and monitoring progress and plans to users,

[0814] A means of evaluating the user's emotional state using an emotion analysis device and dynamically adjusting the plan,

[0815] A means of providing evaluations based on the level of achievement,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising a device for dynamically generating new plans based on a record of user behavior.

[0819] (Claim 3)

[0820] The system according to claim 1, further comprising a device that provides a purpose related to the plan as an entertainment element.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] Means for collecting users' spending history, purchasing behavior, and investment preferences,

[0824] A means for analyzing the user's data and generating an individually optimized financial management plan,

[0825] A means of presenting the aforementioned plan to the user and managing its progress,

[0826] A means of rewarding users according to their level of achievement,

[0827] A means of identifying and analyzing the emotional state of a user,

[0828] A means for dynamically adjusting the financial management plan based on the aforementioned emotional state,

[0829] When providing the aforementioned adjusted plan, a means of providing real-time feedback that responds to emotions,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising means for learning the user's behavior history and adaptively generating a new plan.

[0833] (Claim 3)

[0834] The system according to claim 1, further comprising means for providing missions or quests related to the aforementioned plan in a game-like manner. [Explanation of symbols]

[0835] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting users' spending history, purchasing behavior, and investment preferences, A means for analyzing the user's data and generating an individually optimized financial management plan, A means of presenting the aforementioned plan to the user and managing its progress, A means of rewarding users according to their level of achievement, A system that includes this.

2. The system according to claim 1, further comprising means for learning the user's behavior history and adaptively generating a new plan.

3. The system according to claim 1, further comprising means for providing missions and quests related to the aforementioned plan in a game-like manner.

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