system

Abstract

Provide a system. A means for a user to input behavioral history and income and expenditure information; means for transmitting the input data to a server; A means for the server to analyze the received data using AI to generate an optimal spending plan; means for transmitting the generated spending plan to the user; means for displaying the submitted spending plan to the user; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, many people struggle with managing their living expenses. In particular, it can be difficult to effortlessly optimize income and expenditures and avoid wasteful spending due to busy daily schedules. Furthermore, manually managing complex income and expenditure patterns requires specialized knowledge and experience. As a result, wasteful spending increases, making planned savings difficult. The present invention aims to provide a system that automatically and efficiently solves these problems. [Means for solving the problem]

[0005] The present invention solves the above problems by using the following means. A system is constructed that provides a means for users to input their behavioral history and income and expenditure information, and includes a means for transmitting the input data to a server. The server analyzes the received data using AI and generates an optimal spending plan. The generated spending plan is sent from the server to the user, who can display it. The server then stores the received data in a database, and the AI ​​learns the user's past income and expenditure patterns. This system enables users to spend money comfortably and efficiently, making it easier to plan savings.

[0006] "User" refers to an individual who uses this system to input their behavioral history and income / expense information and receive a spending plan.

[0007] "Behavioral history" refers to specific actions and activities that a user performs in their daily life, and is information related to specific dates, times, and places.

[0008] "Income and expenditure information" refers to detailed information about the income and expenditures earned by the user, including the amounts and their breakdowns.

[0009] "Terminal" refers to an electronic device, such as a computer or smartphone, that a user uses to manipulate input data and communicate data with a server.

[0010] "Server" refers to the computer system that receives data sent by users, stores it, analyzes it using AI, and generates an optimal spending plan.

[0011] "AI" refers to artificial intelligence, which includes algorithms and techniques that learn a user's income and spending patterns and generate an optimal spending plan.

[0012] An "optimal spending plan" refers to a plan that suggests a reasonable and efficient way to spend money based on the user's income and expenditure information.

[0013] "Data transmission" refers to the process of transmitting information entered by a user or terminal to a server.

[0014] "Data reception" refers to the process in which the server receives data sent from the terminal.

[0015] "Database" refers to a system for managing a user's past behavioral history and income and expenditure information stored on a server.

[0016] "Data storage" refers to the act of registering data received by a server in a database and retaining it for the long term.

[0017] "Analysis" refers to the process by which the AI ​​processes the data it receives and calculates the optimal spending plan.

[0018] "Learning" refers to the process by which AI analyzes patterns based on a user's past data to improve future predictions and planning.

[0019] "Display" refers to the terminal visually presenting the optimal spending plan sent from the server to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0028] [First embodiment]

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

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0041] System Overview

[0042] This invention is a system that automatically collects a user's behavioral history and income / expenditure information and uses AI to generate an optimal spending plan. This system transmits data entered by the user to a server in real time, and the server analyzes the data to provide an efficient spending plan.

[0043] User side (terminal)

[0044] 1. Data Entry Module:

[0045] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0046] 2. Data transmission module:

[0047] The input data is sent from the terminal to the server, where it is converted to JSON format and sent to the server's API endpoint via an HTTP POST request.

[0048] Server side

[0049] 3. Data receiving module:

[0050] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0051] 4. AI analysis module:

[0052] The server passes the received data to an AI analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and spending patterns. For example, the AI ​​might suggest "savings" and set the next day's spending at 3,000 yen.

[0053] 5. Data storage and learning module:

[0054] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0055] 6. Spending Plan Submission Module:

[0056] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0057] User side (terminal)

[0058] 7. Result display module:

[0059] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. The result is displayed in a format that is easy for the user to understand, for example, "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0060] Specific examples

[0061] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[0062] 2. The device sends this input data to the server.

[0063] 3. The server receives the data and begins analysis using the AI ​​analysis module.

[0064] 4. The AI ​​will analyze and generate an optimal spending plan, such as "Save and spend 3,000 yen on 2023-10-11."

[0065] 5. The server sends the generated spending plan to the user's terminal.

[0066] 6. The user checks the spending plan received on the device and spends money as planned.

[0067] In this way, the system suggests ways for users to spend money comfortably and efficiently, enabling them to save money in a planned manner.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The user uses the terminal to input his / her behavior history and income / expense information.

[0071] The user launches an application.

[0072] The user accesses a new data entry screen.

[0073] Enter the date and time, the activity (e.g., shopping at the supermarket), the amount of income, and the amount of expenses.

[0074] Step 2:

[0075] The terminal transmits the input data to the server.

[0076] The terminal converts the user input data into JSON format.

[0077] The device sends an HTTP POST request to the server's API endpoint and transfers the data.

[0078] Step 3:

[0079] The server receives the data sent from the terminal.

[0080] The server receives an HTTP request that reaches an API endpoint.

[0081] The server parses the received data into JSON format and temporarily stores it in memory.

[0082] Step 4:

[0083] The server stores the received data in a database.

[0084] The server inserts the received data into the appropriate tables in the database.

[0085] The server checks the status of saving to the database and confirms success.

[0086] Step 5:

[0087] The server uses AI to analyze the stored data and generate an optimal spending plan.

[0088] The server passes past user data to the AI ​​analysis module.

[0089] The AI ​​analysis module learns the user's income and spending patterns.

[0090] AI generates an optimal spending plan and returns the results.

[0091] Step 6:

[0092] The server sends the optimal spending plan to the user's terminal.

[0093] The server converts the generated spending plan into JSON format.

[0094] The server identifies the appropriate endpoint based on the user ID and sends it in an HTTP POST request.

[0095] Step 7:

[0096] The terminal receives the spending plan sent from the server.

[0097] The device receives an HTTP response from the server.

[0098] The terminal analyzes the received spending plan and stores it in its internal memory.

[0099] Step 8:

[0100] The terminal displays the received spending plan to the user.

[0101] The terminal converts the spending plan into a format for display on the user interface.

[0102] Users can visually check their specific spending plans through the application.

[0103] Example: "2023-10-11: Savings - Income: 0, Expenses: 3000" will be displayed.

[0104] Step 9:

[0105] The user manages actual expenditures based on the expenditure plan displayed on the terminal.

[0106] The user spends money according to a spending plan.

[0107] If necessary, provide new inputs and repeat the cycle.

[0108] Example 1

[0109] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0110] In today's world, it takes time and effort for users to manage their income and expenses all by themselves, and it is difficult to create an efficient spending plan. In particular, advanced analysis and continuous learning are required to reduce wasteful spending and plan savings, but this is extremely difficult for users to do on their own. Therefore, there is a need for a system that allows users to automatically create an efficient spending plan without any effort.

[0111] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0112] In this invention, the server includes a means for temporarily storing received data for analysis, an AI analysis means for learning from past data and generating an optimal spending plan based on the user's income and spending patterns, and a means for transmitting the generated spending plan to the user's terminal, thereby enabling the user to automatically obtain an efficient and efficient spending plan.

[0113] A "user" is an entity that utilizes the system to input behavioral history and income / expenditure information and receive an optimal spending plan.

[0114] "Behavioral history" is information about the user's daily behavior, and is data that records specific activities such as shopping, eating, and traveling.

[0115] "Income and expenditure information" refers to detailed information such as specific amounts, dates, and items regarding the income and expenditures earned by the user.

[0116] A "terminal" refers to a device through which a user inputs their behavioral history and income / expense information and communicates with the system, specifically a device such as a smartphone or PC.

[0117] "Server" refers to a central processing unit that receives data sent from users and performs analysis and database management.

[0118] "Means for transmitting data in real time" refers to a function for instantly transmitting data entered by a user from a terminal to a server.

[0119] "AI analysis means" refers to a function that uses artificial intelligence technology built into the server to learn the user's income and expenditure patterns and generate an optimal expenditure plan.

[0120] The "optimal spending plan" refers to a planned spending method that minimizes waste and is generated based on the user's income and spending patterns.

[0121] "Database" refers to a storage device for systematically storing received and analyzed data for later use in learning and analysis.

[0122] The "means for transmitting a spending plan" refers to a function for transmitting the optimal spending plan generated by the server to the user's terminal.

[0123] The "means for displaying a spending plan" refers to a function for visually displaying the transmitted spending plan on the user's terminal.

[0124] This invention is a system that automatically collects a user's behavioral history and income / expenditure information and uses AI to generate an optimal spending plan. This system transmits data entered by the user to a server in real time, and the server analyzes the data to provide an efficient spending plan.

[0125] User side (terminal)

[0126] 1. Data Entry Module:

[0127] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0128] 2. Data transmission module:

[0129] The entered data is sent from the terminal to the server, where it is converted to JSON format and sent to the server's API endpoint via an HTTP POST request.

[0130] Server side

[0131] 3. Data receiving module:

[0132] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0133] 4. AI analysis module:

[0134] The server passes the received data to an AI analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and spending patterns. For example, the AI ​​might suggest "savings" and set the next day's spending at 3,000 yen.

[0135] 5. Data storage and learning module:

[0136] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0137] 6. Spending Plan Submission Module:

[0138] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0139] User side (terminal)

[0140] 7. Result display module:

[0141] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. The result is displayed in a format that is easy for the user to understand, for example, "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0142] Specific examples

[0143] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[0144] 2. The device sends this input data to the server.

[0145] 3. The server receives the data and begins analysis using the AI ​​analysis module.

[0146] 4. The AI ​​will analyze and generate an optimal spending plan, such as "Save and spend 3,000 yen on 2023-10-11."

[0147] 5. The server sends the generated spending plan to the user's terminal.

[0148] 6. The user checks the spending plan received on the device and spends money as planned.

[0149] Example prompts for generative AI models

[0150] "I would like to develop a system that allows users to record their daily income and expenses and automatically suggests optimal spending plans. Please explain in detail how this system works, how the user sends the input data, how the server analyzes it to generate the optimal spending plan, and how it sends it to the user's device."

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] The user inputs their daily activity history and income and expenditure information. Specifically, the user uses a device (such as a smartphone or PC) to input data into a dedicated input interface. For example, the user inputs "2023-10-10, purchases at supermarket, income: ¥50,000, expenditure: ¥5,000."

[0154] Input: User behavior history and income / expense information

[0155] Output: The input data

[0156] Step 2:

[0157] The device sends the input data to the server in real time. The data transmission module converts the data into JSON format and sends it to the server's API endpoint using an HTTP POST request. The data is encrypted during this process to maintain security.

[0158] Input: Data entered

[0159] Output: Data converted to JSON format is sent to the server

[0160] Step 3:

[0161] The server receives the data in real time and stores it temporarily. The data reception module receives the data that arrives at the server in real time and stores the data temporarily in memory. If necessary, it is also stored in a database.

[0162] Input: Data sent in JSON format

[0163] Output: Temporarily saved data

[0164] Step 4:

[0165] The server passes the received data to the AI ​​analysis module, which begins analysis. Specifically, the AI ​​analysis module, which has learned about past income and expenditure patterns, takes in the data and analyzes the user's expenditure patterns based on that data. This analysis generates a spending plan, such as "It would be optimal to save 3,000 yen the next day."

[0166] Input:Temporarily saved data

[0167] Output: Generated optimal spending plan

[0168] Step 5:

[0169] The server stores the analysis results in a database. The data storage and learning module stores the generated spending plan in a database and uses it to improve the accuracy of future forecasts.

[0170] Input: Generated optimal spending plan

[0171] Output: Analysis results stored in a database

[0172] Step 6:

[0173] The server sends the generated spending plan to the user's device. The spending plan sending module recognizes the user's ID and sends the spending plan to the appropriate endpoint. For example, a plan such as "2023-10-11: Savings - Income: 0, Expenses: ¥3,000" is sent in JSON format.

[0174] Input: Generated optimal spending plan

[0175] Output: Spending plan submitted in JSON format

[0176] Step 7:

[0177] The user checks the received spending plan on the device. The result display module visually displays the spending plan, allowing the user to check the specific spending plan at a glance. For example, the device screen will display "2023-10-11: Savings - Income: 0, Expenses: ￥3,000."

[0178] Input: Spending plan submitted in JSON format

[0179] Output: A visually displayed spending plan

[0180] (Application example 1)

[0181] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0182] In today's world, many users use electronic payments, but there is a lack of appropriate systems for effectively managing their spending. This makes it difficult for users to curb wasteful spending. Furthermore, the time and effort required to manually input income and expenditure information makes real-time spending management difficult. Furthermore, there is a lack of mechanisms for automatically generating and providing optimal spending plans to users. To solve these issues, a system is needed that automatically collects users' electronic payment information and generates optimal spending plans based on that information.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0184] In this invention, the server includes means for inputting a user's behavioral history and income / expenditure information, means for transmitting the input data to the server, means for the server to analyze the received data using AI and generate an optimal spending plan, means for automatically collecting electronic payment expenditure information, means for transmitting the generated spending plan to the user, and means for displaying the transmitted spending plan to the user. This allows the user to automatically collect and analyze electronic payment information and obtain an efficient spending plan in real time.

[0185] "User" refers to an individual who uses this system to input their own behavioral history and income / expense information and receive an optimal spending plan.

[0186] "Behavioral history" refers to a record of a user's daily activities and specific actions, and specifically includes information such as shopping, meals, and use of transportation.

[0187] "Income and expenditure information" refers to detailed data on a user's income (salary and bonuses) and expenses (shopping, meals, rent, etc.).

[0188] "Server" refers to a computer system for receiving, storing, analyzing, and transmitting data over the Internet.

[0189] "AI" stands for artificial intelligence and refers to technology that analyzes a user's behavioral history and income and expenditure information to generate an optimal spending plan.

[0190] A "spending plan" refers to a specific plan that AI generates based on the user's data, suggesting how to spend money efficiently.

[0191] "Electronic payment information" refers to data containing details of transactions made by a user using an electronic payment service, including the date and time of the transaction, the amount, and the transaction partner.

[0192] "Database" refers to a data structure that organizes and stores received data so that it can be quickly accessed when needed.

[0193] "Module" refers to an independent piece of software designed to perform a specific function.

[0194] The following describes the mode for carrying out the invention: This system collects and analyzes a user's behavioral history and income / expenditure information, and generates and presents an optimal spending plan, enabling efficient spending management.

[0195] System Overview

[0196] The system is primarily comprised of devices such as smartphones or PCs, a server, and an AI analysis module. It also incorporates a mechanism for automatically collecting users' electronic payment information, enabling real-time expenditure management.

[0197] Hardware used

[0198] Smartphones and PCs (user devices)

[0199] Cloud server (data collection, analysis, storage)

[0200] Database Server

[0201] Software used

[0202] Mobile Applications (iOS and ANDROID®)

[0203] Server-side platforms (AWS (registered trademark) and Google (registered trademark) Cloud)

[0204] Database management system (MySQL (registered trademark), PostgreSQL)

[0205] AI analysis module (TENSORFLOW (registered trademark), PyTorch)

[0206] Program processing overview

[0207] 1. Data Collection

[0208] When a user makes an electronic payment, payment information (e.g., transaction date and time, amount, customer, etc.) is automatically collected in the "Smart Spending Planner" application. Users can also manually enter their activity history and income and expenditure information.

[0209] 2. Data Transmission

[0210] The data collected on the user's device is sent to the server in real time, where it is converted into JSON format and sent to the server's API endpoint.

[0211] 3. Data Receipt and Storage

[0212] The server receives the data sent from the terminal in real time, temporarily stores it for analysis, and stores it in a database as needed.

[0213] 4. AI analysis

[0214] The data stored on the server is analyzed by an AI analysis module, which learns from past income and expenditure data and behavioral history to generate an optimal spending plan for the user.

[0215] 5. Submit spending plan

[0216] The generated optimal spending plan is then resent from the server to the user's device, where the user ID is recognized and the spending plan is sent to the appropriate endpoint.

[0217] 6. Results display

[0218] The user's terminal displays the received spending plan to the user, who can visually check the specific spending plan on the application screen.

[0219] Specific examples

[0220] If a user spends 5,000 yen at a supermarket on October 10, 2023, this spending information will be automatically collected by the app. Taking into account the next day's spending, the AI ​​will suggest "limit spending on October 11, 2023 to 3,000 yen and save 2,000 yen." This spending plan will be sent to the user's smartphone, where the user can confirm the plan on the screen.

[0221] Example prompt sentence:

[0222] User ID: 12345

[0223] Expenditure information: 2023-10-10, Supermarket, 5,000 yen

[0224] In this way, the system suggests ways for users to spend money in a way that is comfortable and efficient, enabling them to save money in a planned manner.

[0225] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0226] Step 1:

[0227] When a user makes an electronic payment, the electronic payment information is automatically collected on a device such as a smartphone or PC. Specifically, expenditure information (transaction date and time, amount, business partner, etc.) is automatically acquired, and the user may also manually enter their activity history and income and expenditure information. In this case, the input includes the electronic payment information and manually entered data, and the output is data temporarily stored on the device.

[0228] Step 2:

[0229] The input data is sent from the device to the server in real time. During this transmission process, the data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. The input is temporary data stored in the device, and the output is JSON format data received by the server.

[0230] Step 3:

[0231] The server receives data sent from the terminal in real time. The received data is processed by the data receiving module and temporarily stored in memory. The input is the JSON format data sent to the server, and the output is the temporarily stored data.

[0232] Step 4:

[0233] The server stores the received data in a database. In this process, the data is stored in the appropriate tables using a database management system (e.g., MySQL, PostgreSQL). The input is the temporarily stored data, and the output is the data stored in the database.

[0234] Step 5:

[0235] The server passes the data to the AI ​​analysis module, which begins analysis. The AI ​​analysis module is built using TensorFlow and PyTorch and generates an optimal spending plan based on a model trained on past income and expenditure data. The input is the data stored in the database, and the output is the generated spending plan.

[0236] Step 6:

[0237] The server sends the generated optimal spending plan to the user's device. During this process, the data is converted to JSON format, and the user ID is recognized and sent to the appropriate endpoint. The input is the generated spending plan (in JSON format), and the output is the spending plan received by the user's device.

[0238] Step 7:

[0239] The user's terminal displays the received spending plan to the user, allowing the user to visually check the specific spending plan on the terminal screen. The input is the spending plan received from the server, and the output is the spending plan converted into a format that the user can visually check.

[0240] In this way, the optimal spending plan is provided to the user through each step.

[0241] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0242] System Overview

[0243] This invention is a system that automatically collects user behavior history and income / expenditure information, recognizes user emotions by combining it with an emotion engine, and generates an optimal spending plan using AI. This system sends data entered by the user to a server in real time, and the server analyzes the data and the user's emotions to provide an efficient spending plan.

[0244] User side (terminal)

[0245] 1. Data Entry Module:

[0246] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0247] 2. Emotion Recognition Module:

[0248] The device's built-in emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time and recognize the user's emotions.

[0249] 3. Data transmission module:

[0250] The input behavioral history, income and expenditure information, and emotion data are sent from the device to the server, where each piece of data is converted into JSON format and sent to the server's API endpoint via an HTTP POST request.

[0251] Server side

[0252] 4. Data receiving module:

[0253] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0254] 5. AI analysis module:

[0255] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and performs future optimization based on the user's income and expenditure patterns, as well as recognized emotions. For example, if the user is feeling stressed, it can suggest spending items that will promote relaxation.

[0256] 6. Data Storage and Learning Module:

[0257] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0258] 7. Spending Plan Submission Module:

[0259] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0260] User side (terminal)

[0261] 8. Result display module:

[0262] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. For example, it is displayed as "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0263] Specific examples

[0264] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[0265] 2. The emotion recognition module analyzes the user's facial expressions and voice and recognizes that the user is feeling stressed.

[0266] 3. The device sends the input data and emotion data to the server.

[0267] 4. The server receives the data and begins analysis using the AI ​​analysis module.

[0268] 5. The AI ​​analysis module comprehensively analyzes behavioral history, income and expenditure information, and emotional data to generate an optimal spending plan. For example, it might suggest, "Consider going to a spa the next day for relaxation and spend 10,000 yen."

[0269] 6. The server sends the generated spending plan to the user's terminal.

[0270] 7. The user checks the spending plan received on the device and spends money as planned.

[0271] In this way, the system takes the user's emotions into consideration and suggests ways to spend money comfortably and efficiently, enabling planned savings and stress reduction.

[0272] The processing flow will be explained below.

[0273] Step 1:

[0274] The user uses the terminal to input his / her behavior history and income / expense information.

[0275] The user launches an application.

[0276] The user accesses a new data entry screen.

[0277] Enter the date and time, the activity (e.g., shopping at the supermarket), the amount of income, and the amount of expenses.

[0278] Step 2:

[0279] The emotion engine installed in the device recognizes the user's emotions.

[0280] The device's camera captures the user's facial expressions, which are then analyzed by an emotion recognition algorithm.

[0281] The device's microphone records the user's voice, and the voice analysis module analyzes emotions.

[0282] The emotion recognition result is extracted and analyzed as, for example, "the user is feeling stressed."

[0283] Step 3:

[0284] The terminal transmits the behavior history, income and expenditure information, and emotion data to the server.

[0285] The terminal converts the collected data into JSON format.

[0286] The device sends an HTTP POST request to the server's API endpoint and transfers the data.

[0287] Step 4:

[0288] The server receives the data sent from the terminal.

[0289] The server receives the HTTP request that reaches the API endpoint and parses the data in JSON format.

[0290] Received data is temporarily stored in memory.

[0291] Step 5:

[0292] The server stores the received data in a database.

[0293] The server inserts the parsed data into the appropriate tables in the database.

[0294] Check the status of saving to the database and confirm that saving is successful.

[0295] Step 6:

[0296] The server uses AI to analyze the stored data and generate an optimal spending plan.

[0297] The server passes the stored user data and emotion data to the AI ​​analysis module.

[0298] The AI ​​analysis module learns and analyzes the user's past income and expenditure patterns and current emotional state.

[0299] The AI ​​generates an optimal spending plan and outputs a plan such as, "The user is feeling stressed, so we suggest a visit to a spa to relax (cost: 10,000 yen)."

[0300] Step 7:

[0301] The server transmits the generated spending plan to the user's terminal.

[0302] The server converts the generated spending plan into JSON format.

[0303] The server identifies the appropriate endpoint based on the user ID and sends it in an HTTP POST request.

[0304] Step 8:

[0305] The terminal receives the spending plan sent from the server.

[0306] The terminal receives the HTTP response from the server and parses the spending plan data.

[0307] The received spending plan data is stored in an internal memory.

[0308] Step 9:

[0309] The terminal displays the received spending plan to the user.

[0310] The terminal converts the spending plan into a display format.

[0311] Users can visually check their specific spending plans through the application.

[0312] For example, it might show "2023-10-11: Spa visit - Income: 0, Expenses: 10000".

[0313] Step 10:

[0314] The user manages actual expenditures based on the expenditure plan displayed on the terminal.

[0315] The user follows a spending plan, books a spa visit, and spends according to budget.

[0316] If necessary, enter new behavioral history and income / expense information, update your emotional state, and continue using the system.

[0317] This series of steps allows users to spend money comfortably and efficiently while taking their emotional state into account.

[0318] Example 2

[0319] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0320] In modern society, users are required to create efficient and emotionally-based spending plans while recording their daily activities, income, and expenses. However, conventional systems have difficulty providing optimal spending plans that take into account the user's emotional state. This makes users prone to wasteful spending, and presents challenges in achieving optimal savings and stress management.

[0321] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0322] In this invention, the server includes means for a user to input behavioral history and income / expenditure information, means for transmitting the input data to the server, means for an emotion engine installed in the terminal to recognize the user's emotions, means for the server to analyze the received data and emotion data using AI and generate an optimal spending plan, means for transmitting the generated spending plan to the user, and means for displaying the transmitted spending plan to the user. This makes it possible to provide an optimal spending plan that takes the user's emotions into consideration in real time.

[0323] "Behavioral history" is data that records the user's daily activities and behaviors.

[0324] "Income and expenditure information" is data in which a user specifically records his or her daily income and expenditure.

[0325] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice and recognizes their emotions.

[0326] A "server" is a computer system that receives, stores, analyzes, and transmits data.

[0327] "AI" stands for Artificial Intelligence, a technology that analyzes a user's behavioral history, income and expenditure information, and emotional data to generate an optimal spending plan.

[0328] A "spending plan" is a plan that suggests how to spend money in the future based on the user's income and expenses.

[0329] A "database" is a system for systematically storing and managing received data.

[0330] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and is a format for structurally expressing data in text format.

[0331] An "HTTP POST request" is a protocol for sending data to a web server.

[0332] The present invention is a system that automatically collects a user's behavioral history and income / expenditure information, recognizes the user's emotions using an emotion engine, and provides the user with an optimal spending plan. This system mainly consists of the following components:

[0333] User side (terminal)

[0334] 1. Data Entry Module

[0335] Users input their daily activity history and income and expenditure information using devices such as smartphones or PCs. This data input module has a user-friendly interface, allowing users to input information hassle-free. For example, a user might input data such as "I went shopping at the supermarket on 2023-10-10, and recorded an income of ¥50,000 and an expenditure of ¥5,000."

[0336] 2. Emotion Recognition Module

[0337] The emotion engine installed on the device uses the camera and microphone to analyze the user's facial expressions and voice in real time to recognize the user's emotions. Specifically, it uses the Facial Expression Recognition API and Speech Emotion Recognition API. For example, when the user is feeling stressed, it can recognize this from changes in facial expression and voice.

[0338] 3. Data transmission module

[0339] The entered behavioral history, income and expenditure information, and emotional data are sent from the device to the server. During this process, each piece of data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. For example, the data sent from the device will be {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}.

[0340] Server side

[0341] 4. Data Reception Module

[0342] The server receives data sent from the device in real time, temporarily stores the data for analysis, and then stores it in a database after analysis.

[0343] 5. AI analysis module

[0344] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module, which then generates an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and expenditure patterns and perceived emotions. For example, if the user is feeling stressed, it may suggest "consider going to the spa the next day and spend 10,000 yen" to encourage relaxation.

[0345] 6. Data storage and learning module

[0346] The server stores the analyzed data in a database and performs learning to improve future prediction accuracy, allowing the system to propose spending plans that respond to changes in the user's behavior and emotions.

[0347] 7. Spending plan sending module

[0348] The server resends the generated optimal spending plan to the user's device. The server recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0349] User side (terminal)

[0350] 8. Result display module

[0351] The device displays the received spending plan to the user, for example, "2023-10-11: Relax at the spa - Spending: ¥10,000."

[0352] Specific examples

[0353] The user opens the dedicated app and enters their daily income and expenditure information. For example, they might record "Shopping at the supermarket on 2023-10-10, income 50,000 yen, expenditure 5,000 yen." At the same time, the emotion recognition module uses the camera and microphone to analyze the user's emotions and recognizes that the user is feeling stressed. This data is converted into JSON format and sent to the server. The server receives the data, and the AI ​​analysis module generates an optimal spending plan and resends it to the user's device. The user then checks the spending plan displayed on the device screen and spends money as planned.

[0354] Prompt Sentence Examples

[0355] An example of an input prompt for the generative AI model is, "Based on daily income and expenditure information, behavioral history, and emotional data, please suggest a spending plan that will allow the user to save money comfortably."

[0356] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0357] Step 1: Data Entry Module

[0358] Users use a smartphone or computer to enter their daily activity history and income and expenditure information through a dedicated interface. This interface has an intuitive design, allowing users to enter data easily. The entered information is saved in the database in the format of date, income, and expense. For example, "I went shopping at the supermarket on 2023-10-10 and recorded an income of ¥50,000 and an expense of ¥5,000." The input data format is {"date": "2023-10-10", "income": 50000, "expense": 5000}.

[0359] Step 2: Emotion Recognition Module

[0360] The device's camera and microphone are used to analyze the user's facial expressions and voice in real time. The analysis is performed using the Facial Expression Recognition API and Speech Emotion Recognition API. For example, the device captures the user's facial expressions, such as smile or anger, and analyzes their emotional state as numerical data. For example, if the user's facial expression is recognized as "stressed," the corresponding data will be "emotion": "stress."

[0361] Step 3: Data transmission module

[0362] The device sends the information collected and analyzed in steps 1 and 2 to the server. The data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. For example, the data sent is {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}. At this point, the device receives a response from the server to confirm whether the data transmission was successful.

[0363] Step 4: Data receiving module

[0364] The server receives data sent from the device in real time. The received data is temporarily stored in memory and then stored in a database. For example, the received data is saved as {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}. This data becomes the input for the next AI analysis step.

[0365] Step 5: AI Analysis Module

[0366] The server passes the behavioral history, income and expenditure information, and emotional data collected from the database to the AI ​​analysis module. The AI ​​analysis module generates an optimal spending plan based on this data. To analyze the data, a machine learning model is used to take into account past income and expenditure patterns and emotional state. For example, if a user is feeling stressed, a spending plan such as "Consider going to a spa the next day and spend 10,000 yen" may be suggested to promote relaxation. The generated spending plan data is in the format {"plan_date": "2023-10-11", "recommendation": "spend 10000 on spa"}.

[0367] Step 6: Data storage and learning module

[0368] The server saves the generated spending plan in a database and uses it as learning data to further improve the accuracy of future predictions. For example, the saved data is in the format {"user_id": "12345", "plan": "spend 10000 on spa", "emotion": "stress"}. This data will enable more accurate suggestions in the next analysis.

[0369] Step 7: Spending Plan Submission Module

[0370] The server then sends the generated optimal spending plan back to the user's device. The server recognizes the user ID and sends the data to the corresponding device. For example, the data to be sent is {"user_id": "12345", "plan_date": "2023-10-11", "recommendation": "spend 10000 on spa"}.

[0371] Step 8: Results display module

[0372] The device displays the received spending plan to the user. The user can visually confirm and understand the proposed spending plan. For example, the device screen will display "Spend 10,000 yen to relax at the spa on 2023-10-11." This result display allows the user to spend according to the plan.

[0373] (Application example 2)

[0374] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0375] In modern society, it is difficult for users to efficiently manage their daily expenses and income. In particular, there is a need for an optimal spending plan that takes into account the impact of users' emotions on spending behavior. However, conventional systems only analyze behavioral history and income / expense information, and do not consider users' emotions, which means they are unable to create effective spending plans.

[0376] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input behavioral history and income and expenditure information; means for transmitting the input data to the server; means for the server to analyze the received data using AI and generate an optimal spending plan; means for transmitting the generated spending plan to the user; means for displaying the transmitted spending plan to the user; emotion recognition means for identifying the user's emotions; means for transmitting emotion data to the server; and means for analyzing income and expenditure patterns using the emotion data and adjusting expenses to an optimal level. This makes it possible to optimize the spending plan while taking the user's emotions into consideration and support planned financial management.

[0377] "Behavioral history" is a record of specific activities and events in a user's daily life.

[0378] "Income and expenditure information" is data relating to the user's income and expenditure.

[0379] A "server" is a computer system for receiving, storing, and processing data.

[0380] "AI analytics" is the process of using artificial intelligence to analyze data and generate insights and predictions.

[0381] A "spending plan" is a specific guideline or suggestion for effectively managing a user's future income and expenses.

[0382] "Sending to user" is the act of sending data from the server to the client device.

[0383] "Displaying to the user" means visually presenting information on the screen of a client device.

[0384] "Emotion recognition means" refers to techniques and methods for identifying a user's emotions.

[0385] "Emotion data" is digital information that indicates the user's emotional state.

[0386] "Analyzing income and expenditure patterns" refers to analyzing a user's past income and expenditure data to derive certain trends and patterns.

[0387] This invention is a system that automatically collects a user's behavioral history and income / expenditure information, identifies the user's emotions by combining emotion recognition means, and generates an optimal spending plan using AI. This system transmits data entered by the user to a server in real time, and the server analyzes the data and the user's emotions to provide an efficient spending plan.

[0388] User side (terminal)

[0389] 1. Data Entry Module

[0390] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0391] 2. Emotion Recognition Module

[0392] An emotion recognition means installed in the terminal uses a camera and a microphone to analyze the user's facial expressions and voice in real time and identify the user's emotions.

[0393] 3. Data transmission module

[0394] The input behavioral history, income and expenditure information, and emotion data are sent from the device to the server, where each piece of data is converted into JSON format and sent to the server's API endpoint via an HTTP POST request.

[0395] Server side

[0396] 4. Data Reception Module

[0397] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0398] 5. AI analysis module

[0399] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and performs future optimization based on the user's income and expenditure patterns, as well as the identified emotions. For example, if the user is feeling stressed, it can suggest spending items that will promote relaxation.

[0400] 6. Data storage and learning module

[0401] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0402] 7. Spending plan sending module

[0403] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0404] User side (terminal)

[0405] 8. Result display module

[0406] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. For example, it is displayed as "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0407] Hardware / Software used

[0408] Smartphone: iOS / Android device

[0409] Camera: Smartphone built-in camera

[0410] Microphone: Built-in smartphone microphone

[0411] Server: General-purpose server

[0412] Database: Relational Database Management System (RDBMS)

[0413] AI Analysis Module: Machine Learning Platform

[0414] Examples and prompts

[0415] 1. Example: A user inputs income information (e.g., ¥50,000) and expenditure information (e.g., ¥5,000) on a smartphone, and at the same time, the emotion recognition module recognizes the user as being in a stressful state. This information is sent to the server, and the AI ​​analysis module generates a spending plan, such as "Consider going to a spa the next day to relax, and spend ¥10,000." The generated spending plan is sent to the user's device and displayed visually.

[0416] 2. Example prompt:

[0417] Use the following data to create the best spending plan for you:

[0418] Income: 50,000 yen

[0419] Expenses: 5,000 yen

[0420] Category: Supermarket

[0421] Emotion: Stress, Trust: 0.85

[0422] In this way, the system of the present invention realizes planned and efficient expenditure management while taking into consideration the user's feelings.

[0423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0424] Step 1:

[0425] The user inputs their activity history and income / expense information. Using a smartphone or computer, the user inputs their daily income, expenses, and activity history. The input data includes amounts, categories, dates, etc. A specific example of how this works is when the user inputs "purchases at the supermarket, income ¥50,000, expenditure ¥5,000" into the application's input field. The input data is converted into JSON format and prepared.

[0426] Step 2:

[0427] The device's emotion recognition module identifies the user's emotions. It uses the device's built-in camera and microphone to analyze the user's facial expressions and voice in real time. The collected data is sent to an emotion recognition engine (for example, a facial expression analysis API or a voice analysis API) to obtain the user's emotional state. Specifically, if the user is feeling stressed, the emotion recognition engine will return data such as "Emotion: Stress, Confidence: 0.85."

[0428] Step 3:

[0429] The data transmission module sends the collected data to the server. The collected behavioral history, income and expenditure information, and emotion data are sent to the server's API endpoint. Specifically, JSON-formatted data is sent via an HTTP POST request. In this process, JSON-formatted behavioral history, income and expenditure information, and emotion data are used as input, and the data sent to the server is obtained as output.

[0430] Step 4:

[0431] The server stores the received data in a database. The server receives the transmitted data and temporarily stores it for analysis. If necessary, it is stored in a relational database management system (RDBMS). For example, the data is stored in a database in the format of "user ID, income, expenses, date, emotional state."

[0432] Step 5:

[0433] The server's AI analysis module analyzes the received data and generates an optimal spending plan. The AI ​​analysis module learns from past data and creates an optimal spending plan based on income and expenditure patterns and emotional data. For example, it may create a plan in the form of "since the user is feeling stressed, it suggests spending money on a trip to a spa to relax." The inputs are behavioral history, income and expenditure information, and emotional data, and the output is an optimal spending plan.

[0434] Step 6:

[0435] The generated spending plan is sent from the server to the user's device. The server sends this spending plan to the appropriate endpoint. The generated spending plan is converted to JSON format and sent to the user's device via an HTTP POST request. This ensures accurate transmission based on the user ID.

[0436] Step 7:

[0437] The user's device displays the received spending plan. The device's result display module visually presents the received spending plan to the user. Specifically, the application screen displays something like "Considering going to a spa for relaxation on 2023-10-12, spending 10,000 yen." In this process, the spending plan data sent from the server is used as input, and the spending plan displayed on the screen is obtained as output.

[0438] Through the above steps, a system is realized that can more appropriately manage income and expenditure while taking into account the user's emotions.

[0439] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0440] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0441] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0442] [Second embodiment]

[0443] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0444] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0445] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0446] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0447] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0448] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0449] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0450] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0451] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0452] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0453] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0454] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0455] System Overview

[0456] This invention is a system that automatically collects a user's behavioral history and income / expenditure information and uses AI to generate an optimal spending plan. This system transmits data entered by the user to a server in real time, and the server analyzes the data to provide an efficient spending plan.

[0457] User side (terminal)

[0458] 1. Data Entry Module:

[0459] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0460] 2. Data transmission module:

[0461] The input data is sent from the terminal to the server, where it is converted to JSON format and sent to the server's API endpoint via an HTTP POST request.

[0462] Server side

[0463] 3. Data receiving module:

[0464] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0465] 4. AI analysis module:

[0466] The server passes the received data to an AI analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and spending patterns. For example, the AI ​​might suggest "savings" and set the next day's spending at 3,000 yen.

[0467] 5. Data storage and learning module:

[0468] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0469] 6. Spending Plan Submission Module:

[0470] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0471] User side (terminal)

[0472] 7. Result display module:

[0473] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. The result is displayed in a format that is easy for the user to understand, for example, "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0474] Specific examples

[0475] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[0476] 2. The device sends this input data to the server.

[0477] 3. The server receives the data and begins analysis using the AI ​​analysis module.

[0478] 4. The AI ​​will analyze and generate an optimal spending plan, such as "Save and spend 3,000 yen on 2023-10-11."

[0479] 5. The server sends the generated spending plan to the user's terminal.

[0480] 6. The user checks the spending plan received on the device and spends money as planned.

[0481] In this way, the system suggests ways for users to spend money comfortably and efficiently, enabling them to save money in a planned manner.

[0482] The processing flow will be explained below.

[0483] Step 1:

[0484] The user uses the terminal to input his / her behavior history and income / expense information.

[0485] The user launches an application.

[0486] The user accesses a new data entry screen.

[0487] Enter the date and time, the activity (e.g., shopping at the supermarket), the amount of income, and the amount of expenses.

[0488] Step 2:

[0489] The terminal transmits the input data to the server.

[0490] The terminal converts the user input data into JSON format.

[0491] The device sends an HTTP POST request to the server's API endpoint and transfers the data.

[0492] Step 3:

[0493] The server receives the data sent from the terminal.

[0494] The server receives an HTTP request that reaches an API endpoint.

[0495] The server parses the received data into JSON format and temporarily stores it in memory.

[0496] Step 4:

[0497] The server stores the received data in a database.

[0498] The server inserts the received data into the appropriate tables in the database.

[0499] The server checks the status of saving to the database and confirms success.

[0500] Step 5:

[0501] The server uses AI to analyze the stored data and generate an optimal spending plan.

[0502] The server passes past user data to the AI ​​analysis module.

[0503] The AI ​​analysis module learns the user's income and spending patterns.

[0504] AI generates an optimal spending plan and returns the results.

[0505] Step 6:

[0506] The server sends the optimal spending plan to the user's terminal.

[0507] The server converts the generated spending plan into JSON format.

[0508] The server identifies the appropriate endpoint based on the user ID and sends it in an HTTP POST request.

[0509] Step 7:

[0510] The terminal receives the spending plan sent from the server.

[0511] The device receives an HTTP response from the server.

[0512] The terminal analyzes the received spending plan and stores it in its internal memory.

[0513] Step 8:

[0514] The terminal displays the received spending plan to the user.

[0515] The terminal converts the spending plan into a format for display on the user interface.

[0516] Users can visually check their specific spending plans through the application.

[0517] Example: "2023-10-11: Savings - Income: 0, Expenses: 3000" will be displayed.

[0518] Step 9:

[0519] The user manages actual expenditures based on the expenditure plan displayed on the terminal.

[0520] The user spends money according to a spending plan.

[0521] If necessary, provide new inputs and repeat the cycle.

[0522] Example 1

[0523] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0524] In today's world, it takes time and effort for users to manage their income and expenses all by themselves, and it is difficult to create an efficient spending plan. In particular, advanced analysis and continuous learning are required to reduce wasteful spending and plan savings, but this is extremely difficult for users to do on their own. Therefore, there is a need for a system that allows users to automatically create an efficient spending plan without any effort.

[0525] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0526] In this invention, the server includes a means for temporarily storing received data for analysis, an AI analysis means for learning from past data and generating an optimal spending plan based on the user's income and spending patterns, and a means for transmitting the generated spending plan to the user's terminal, thereby enabling the user to automatically obtain an efficient and efficient spending plan.

[0527] A "user" is an entity that utilizes the system to input behavioral history and income / expenditure information and receive an optimal spending plan.

[0528] "Behavioral history" is information about the user's daily behavior, and is data that records specific activities such as shopping, eating, and traveling.

[0529] "Income and expenditure information" refers to detailed information such as specific amounts, dates, and items regarding the income and expenditures earned by the user.

[0530] A "terminal" refers to a device through which a user inputs their behavioral history and income / expense information and communicates with the system, specifically a device such as a smartphone or PC.

[0531] "Server" refers to a central processing unit that receives data sent from users and performs analysis and database management.

[0532] "Means for transmitting data in real time" refers to a function for instantly transmitting data entered by a user from a terminal to a server.

[0533] "AI analysis means" refers to a function that uses artificial intelligence technology built into the server to learn the user's income and expenditure patterns and generate an optimal expenditure plan.

[0534] The "optimal spending plan" refers to a planned spending method that minimizes waste and is generated based on the user's income and spending patterns.

[0535] "Database" refers to a storage device for systematically storing received and analyzed data for later use in learning and analysis.

[0536] The "means for transmitting a spending plan" refers to a function for transmitting the optimal spending plan generated by the server to the user's terminal.

[0537] The "means for displaying a spending plan" refers to a function for visually displaying the transmitted spending plan on the user's terminal.

[0538] This invention is a system that automatically collects a user's behavioral history and income / expenditure information and uses AI to generate an optimal spending plan. This system transmits data entered by the user to a server in real time, and the server analyzes the data to provide an efficient spending plan.

[0539] User side (terminal)

[0540] 1. Data Entry Module:

[0541] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0542] 2. Data transmission module:

[0543] The entered data is sent from the terminal to the server, where it is converted to JSON format and sent to the server's API endpoint via an HTTP POST request.

[0544] Server side

[0545] 3. Data receiving module:

[0546] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0547] 4. AI analysis module:

[0548] The server passes the received data to an AI analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and spending patterns. For example, the AI ​​might suggest "savings" and set the next day's spending at 3,000 yen.

[0549] 5. Data storage and learning module:

[0550] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0551] 6. Spending Plan Submission Module:

[0552] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0553] User side (terminal)

[0554] 7. Result display module:

[0555] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. The result is displayed in a format that is easy for the user to understand, for example, "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0556] Specific examples

[0557] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[0558] 2. The device sends this input data to the server.

[0559] 3. The server receives the data and begins analysis using the AI ​​analysis module.

[0560] 4. The AI ​​will analyze and generate an optimal spending plan, such as "Save and spend 3,000 yen on 2023-10-11."

[0561] 5. The server sends the generated spending plan to the user's terminal.

[0562] 6. The user checks the spending plan received on the device and spends money as planned.

[0563] Example prompts for generative AI models

[0564] "I would like to develop a system that allows users to record their daily income and expenses and automatically suggests optimal spending plans. Please explain in detail how this system works, how the user sends the input data, how the server analyzes it to generate the optimal spending plan, and how it sends it to the user's device."

[0565] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0566] Step 1:

[0567] The user inputs their daily activity history and income and expenditure information. Specifically, the user uses a device (such as a smartphone or PC) to input data into a dedicated input interface. For example, the user inputs "2023-10-10, purchases at supermarket, income: ¥50,000, expenditure: ¥5,000."

[0568] Input: User behavior history and income / expense information

[0569] Output: The input data

[0570] Step 2:

[0571] The device sends the input data to the server in real time. The data transmission module converts the data into JSON format and sends it to the server's API endpoint using an HTTP POST request. The data is encrypted during this process to maintain security.

[0572] Input: Data entered

[0573] Output: Data converted to JSON format is sent to the server

[0574] Step 3:

[0575] The server receives the data in real time and stores it temporarily. The data reception module receives the data that arrives at the server in real time and stores the data temporarily in memory. If necessary, it is also stored in a database.

[0576] Input: Data sent in JSON format

[0577] Output: Temporarily saved data

[0578] Step 4:

[0579] The server passes the received data to the AI ​​analysis module, which begins analysis. Specifically, the AI ​​analysis module, which has learned about past income and expenditure patterns, takes in the data and analyzes the user's expenditure patterns based on that data. This analysis generates a spending plan, such as "It would be optimal to save 3,000 yen the next day."

[0580] Input:Temporarily saved data

[0581] Output: Generated optimal spending plan

[0582] Step 5:

[0583] The server stores the analysis results in a database. The data storage and learning module stores the generated spending plan in a database and uses it to improve the accuracy of future forecasts.

[0584] Input: Generated optimal spending plan

[0585] Output: Analysis results stored in a database

[0586] Step 6:

[0587] The server sends the generated spending plan to the user's device. The spending plan sending module recognizes the user's ID and sends the spending plan to the appropriate endpoint. For example, a plan such as "2023-10-11: Savings - Income: 0, Expenses: ¥3,000" is sent in JSON format.

[0588] Input: Generated optimal spending plan

[0589] Output: Spending plan submitted in JSON format

[0590] Step 7:

[0591] The user checks the received spending plan on the device. The result display module visually displays the spending plan, allowing the user to check the specific spending plan at a glance. For example, the device screen will display "2023-10-11: Savings - Income: 0, Expenses: ￥3,000."

[0592] Input: Spending plan submitted in JSON format

[0593] Output: A visually displayed spending plan

[0594] (Application example 1)

[0595] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0596] In today's world, many users use electronic payments, but there is a lack of appropriate systems for effectively managing their spending. This makes it difficult for users to curb wasteful spending. Furthermore, the time and effort required to manually input income and expenditure information makes real-time spending management difficult. Furthermore, there is a lack of mechanisms for automatically generating and providing optimal spending plans to users. To solve these issues, a system is needed that automatically collects users' electronic payment information and generates optimal spending plans based on that information.

[0597] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0598] In this invention, the server includes means for inputting a user's behavioral history and income / expenditure information, means for transmitting the input data to the server, means for the server to analyze the received data using AI and generate an optimal spending plan, means for automatically collecting electronic payment expenditure information, means for transmitting the generated spending plan to the user, and means for displaying the transmitted spending plan to the user. This allows the user to automatically collect and analyze electronic payment information and obtain an efficient spending plan in real time.

[0599] "User" refers to an individual who uses this system to input their own behavioral history and income / expense information and receive an optimal spending plan.

[0600] "Behavioral history" refers to a record of a user's daily activities and specific actions, and specifically includes information such as shopping, meals, and use of transportation.

[0601] "Income and expenditure information" refers to detailed data on a user's income (salary and bonuses) and expenses (shopping, meals, rent, etc.).

[0602] "Server" refers to a computer system for receiving, storing, analyzing, and transmitting data over the Internet.

[0603] "AI" stands for artificial intelligence and refers to technology that analyzes a user's behavioral history and income and expenditure information to generate an optimal spending plan.

[0604] A "spending plan" refers to a specific plan that AI generates based on the user's data, suggesting how to spend money efficiently.

[0605] "Electronic payment information" refers to data containing details of transactions made by a user using an electronic payment service, including the date and time of the transaction, the amount, and the transaction partner.

[0606] "Database" refers to a data structure that organizes and stores received data so that it can be quickly accessed when needed.

[0607] "Module" refers to an independent piece of software designed to perform a specific function.

[0608] The following describes the mode for carrying out the invention: This system collects and analyzes a user's behavioral history and income / expenditure information, and generates and presents an optimal spending plan, enabling efficient spending management.

[0609] System Overview

[0610] The system is primarily comprised of devices such as smartphones or PCs, a server, and an AI analysis module. It also incorporates a mechanism for automatically collecting users' electronic payment information, enabling real-time expenditure management.

[0611] Hardware used

[0612] Smartphones and PCs (user devices)

[0613] Cloud server (data collection, analysis, storage)

[0614] Database Server

[0615] Software used

[0616] Mobile Applications (iOS and Android)

[0617] Server-side platforms (AWS and Google Cloud)

[0618] Database management systems (MySQL, PostgreSQL)

[0619] AI analysis module (TensorFlow, PyTorch)

[0620] Program processing overview

[0621] 1. Data Collection

[0622] When a user makes an electronic payment, payment information (e.g., transaction date and time, amount, customer, etc.) is automatically collected in the "Smart Spending Planner" application. Users can also manually enter their activity history and income and expenditure information.

[0623] 2. Data Transmission

[0624] The data collected on the user's device is sent to the server in real time, where it is converted into JSON format and sent to the server's API endpoint.

[0625] 3. Data Receipt and Storage

[0626] The server receives the data sent from the terminal in real time, temporarily stores it for analysis, and stores it in a database as needed.

[0627] 4. AI analysis

[0628] The data stored on the server is analyzed by an AI analysis module, which learns from past income and expenditure data and behavioral history to generate an optimal spending plan for the user.

[0629] 5. Submit spending plan

[0630] The generated optimal spending plan is then resent from the server to the user's device, where the user ID is recognized and the spending plan is sent to the appropriate endpoint.

[0631] 6. Results display

[0632] The user's terminal displays the received spending plan to the user, who can visually check the specific spending plan on the application screen.

[0633] Specific examples

[0634] If a user spends 5,000 yen at a supermarket on October 10, 2023, this spending information will be automatically collected by the app. Taking into account the next day's spending, the AI ​​will suggest "limit spending on October 11, 2023 to 3,000 yen and save 2,000 yen." This spending plan will be sent to the user's smartphone, where the user can confirm the plan on the screen.

[0635] Example prompt sentence:

[0636] User ID: 12345

[0637] Expenditure information: 2023-10-10, Supermarket, 5,000 yen

[0638] In this way, the system suggests ways for users to spend money in a way that is comfortable and efficient, enabling them to save money in a planned manner.

[0639] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0640] Step 1:

[0641] When a user makes an electronic payment, the electronic payment information is automatically collected on a device such as a smartphone or PC. Specifically, expenditure information (transaction date and time, amount, business partner, etc.) is automatically acquired, and the user may also manually enter their activity history and income and expenditure information. In this case, the input includes the electronic payment information and manually entered data, and the output is data temporarily stored on the device.

[0642] Step 2:

[0643] The input data is sent from the device to the server in real time. During this transmission process, the data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. The input is temporary data stored in the device, and the output is JSON format data received by the server.

[0644] Step 3:

[0645] The server receives data sent from the terminal in real time. The received data is processed by the data receiving module and temporarily stored in memory. The input is the JSON format data sent to the server, and the output is the temporarily stored data.

[0646] Step 4:

[0647] The server stores the received data in a database. In this process, the data is stored in the appropriate tables using a database management system (e.g., MySQL, PostgreSQL). The input is the temporarily stored data, and the output is the data stored in the database.

[0648] Step 5:

[0649] The server passes the data to the AI ​​analysis module, which begins analysis. The AI ​​analysis module is built using TensorFlow and PyTorch and generates an optimal spending plan based on a model trained on past income and expenditure data. The input is the data stored in the database, and the output is the generated spending plan.

[0650] Step 6:

[0651] The server sends the generated optimal spending plan to the user's device. During this process, the data is converted to JSON format, and the user ID is recognized and sent to the appropriate endpoint. The input is the generated spending plan (in JSON format), and the output is the spending plan received by the user's device.

[0652] Step 7:

[0653] The user's terminal displays the received spending plan to the user, allowing the user to visually check the specific spending plan on the terminal screen. The input is the spending plan received from the server, and the output is the spending plan converted into a format that the user can visually check.

[0654] In this way, the optimal spending plan is provided to the user through each step.

[0655] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0656] System Overview

[0657] This invention is a system that automatically collects user behavior history and income / expenditure information, recognizes user emotions by combining it with an emotion engine, and generates an optimal spending plan using AI. This system sends data entered by the user to a server in real time, and the server analyzes the data and the user's emotions to provide an efficient spending plan.

[0658] User side (terminal)

[0659] 1. Data Entry Module:

[0660] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0661] 2. Emotion Recognition Module:

[0662] The device's built-in emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time and recognize the user's emotions.

[0663] 3. Data transmission module:

[0664] The input behavioral history, income and expenditure information, and emotion data are sent from the device to the server, where each piece of data is converted into JSON format and sent to the server's API endpoint via an HTTP POST request.

[0665] Server side

[0666] 4. Data receiving module:

[0667] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0668] 5. AI analysis module:

[0669] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and performs future optimization based on the user's income and expenditure patterns, as well as recognized emotions. For example, if the user is feeling stressed, it can suggest spending items that will promote relaxation.

[0670] 6. Data Storage and Learning Module:

[0671] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0672] 7. Spending Plan Submission Module:

[0673] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0674] User side (terminal)

[0675] 8. Result display module:

[0676] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. For example, it is displayed as "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0677] Specific examples

[0678] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[0679] 2. The emotion recognition module analyzes the user's facial expressions and voice and recognizes that the user is feeling stressed.

[0680] 3. The device sends the input data and emotion data to the server.

[0681] 4. The server receives the data and begins analysis using the AI ​​analysis module.

[0682] 5. The AI ​​analysis module comprehensively analyzes behavioral history, income and expenditure information, and emotional data to generate an optimal spending plan. For example, it might suggest, "Consider going to a spa the next day for relaxation and spend 10,000 yen."

[0683] 6. The server sends the generated spending plan to the user's terminal.

[0684] 7. The user checks the spending plan received on the device and spends money as planned.

[0685] In this way, the system takes the user's emotions into consideration and suggests ways to spend money comfortably and efficiently, enabling planned savings and stress reduction.

[0686] The processing flow will be explained below.

[0687] Step 1:

[0688] The user uses the terminal to input his / her behavior history and income / expense information.

[0689] The user launches an application.

[0690] The user accesses a new data entry screen.

[0691] Enter the date and time, the activity (e.g., shopping at the supermarket), the amount of income, and the amount of expenses.

[0692] Step 2:

[0693] The emotion engine installed in the device recognizes the user's emotions.

[0694] The device's camera captures the user's facial expressions, which are then analyzed by an emotion recognition algorithm.

[0695] The device's microphone records the user's voice, and the voice analysis module analyzes emotions.

[0696] The emotion recognition result is extracted and analyzed as, for example, "the user is feeling stressed."

[0697] Step 3:

[0698] The terminal transmits the behavior history, income and expenditure information, and emotion data to the server.

[0699] The terminal converts the collected data into JSON format.

[0700] The device sends an HTTP POST request to the server's API endpoint and transfers the data.

[0701] Step 4:

[0702] The server receives the data sent from the terminal.

[0703] The server receives the HTTP request that reaches the API endpoint and parses the data in JSON format.

[0704] Received data is temporarily stored in memory.

[0705] Step 5:

[0706] The server stores the received data in a database.

[0707] The server inserts the parsed data into the appropriate tables in the database.

[0708] Check the status of saving to the database and confirm that saving is successful.

[0709] Step 6:

[0710] The server uses AI to analyze the stored data and generate an optimal spending plan.

[0711] The server passes the stored user data and emotion data to the AI ​​analysis module.

[0712] The AI ​​analysis module learns and analyzes the user's past income and expenditure patterns and current emotional state.

[0713] The AI ​​generates an optimal spending plan and outputs a plan such as, "The user is feeling stressed, so we suggest a visit to a spa to relax (cost: 10,000 yen)."

[0714] Step 7:

[0715] The server transmits the generated spending plan to the user's terminal.

[0716] The server converts the generated spending plan into JSON format.

[0717] The server identifies the appropriate endpoint based on the user ID and sends it in an HTTP POST request.

[0718] Step 8:

[0719] The terminal receives the spending plan sent from the server.

[0720] The terminal receives the HTTP response from the server and parses the spending plan data.

[0721] The received spending plan data is stored in an internal memory.

[0722] Step 9:

[0723] The terminal displays the received spending plan to the user.

[0724] The terminal converts the spending plan into a display format.

[0725] Users can visually check their specific spending plans through the application.

[0726] For example, it might show "2023-10-11: Spa visit - Income: 0, Expenses: 10000".

[0727] Step 10:

[0728] The user manages actual expenditures based on the expenditure plan displayed on the terminal.

[0729] The user follows a spending plan, books a spa visit, and spends according to budget.

[0730] If necessary, enter new behavioral history and income / expense information, update your emotional state, and continue using the system.

[0731] This series of steps allows users to spend money comfortably and efficiently while taking their emotional state into account.

[0732] Example 2

[0733] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0734] In modern society, users are required to create efficient and emotionally-based spending plans while recording their daily activities, income, and expenses. However, conventional systems have difficulty providing optimal spending plans that take into account the user's emotional state. This makes users prone to wasteful spending, and presents challenges in achieving optimal savings and stress management.

[0735] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0736] In this invention, the server includes means for a user to input behavioral history and income / expenditure information, means for transmitting the input data to the server, means for an emotion engine installed in the terminal to recognize the user's emotions, means for the server to analyze the received data and emotion data using AI and generate an optimal spending plan, means for transmitting the generated spending plan to the user, and means for displaying the transmitted spending plan to the user. This makes it possible to provide an optimal spending plan that takes the user's emotions into consideration in real time.

[0737] "Behavioral history" is data that records the user's daily activities and behaviors.

[0738] "Income and expenditure information" is data in which a user specifically records his or her daily income and expenditure.

[0739] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice and recognizes their emotions.

[0740] A "server" is a computer system that receives, stores, analyzes, and transmits data.

[0741] "AI" stands for Artificial Intelligence, a technology that analyzes a user's behavioral history, income and expenditure information, and emotional data to generate an optimal spending plan.

[0742] A "spending plan" is a plan that suggests how to spend money in the future based on the user's income and expenses.

[0743] A "database" is a system for systematically storing and managing received data.

[0744] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for structurally representing data in text format.

[0745] An "HTTP POST request" is a protocol for sending data to a web server.

[0746] The present invention is a system that automatically collects a user's behavioral history and income / expenditure information, recognizes the user's emotions using an emotion engine, and provides the user with an optimal spending plan. This system mainly consists of the following components:

[0747] User side (terminal)

[0748] 1. Data Entry Module

[0749] Users input their daily activity history and income and expenditure information using devices such as smartphones or PCs. This data input module has a user-friendly interface, allowing users to input information hassle-free. For example, a user might input data such as "I went shopping at the supermarket on 2023-10-10, and recorded an income of ¥50,000 and an expenditure of ¥5,000."

[0750] 2. Emotion Recognition Module

[0751] The emotion engine installed on the device uses the camera and microphone to analyze the user's facial expressions and voice in real time to recognize the user's emotions. Specifically, it uses the Facial Expression Recognition API and Speech Emotion Recognition API. For example, when the user is feeling stressed, it can recognize this from changes in facial expression and voice.

[0752] 3. Data transmission module

[0753] The entered behavioral history, income and expenditure information, and emotional data are sent from the device to the server. During this process, each piece of data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. For example, the data sent from the device will be {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}.

[0754] Server side

[0755] 4. Data Reception Module

[0756] The server receives data sent from the device in real time, temporarily stores the data for analysis, and then stores it in a database after analysis.

[0757] 5. AI analysis module

[0758] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module, which then generates an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and expenditure patterns and perceived emotions. For example, if the user is feeling stressed, it may suggest "consider going to the spa the next day and spend 10,000 yen" to encourage relaxation.

[0759] 6. Data storage and learning module

[0760] The server stores the analyzed data in a database and performs learning to improve future prediction accuracy, allowing the system to propose spending plans that respond to changes in the user's behavior and emotions.

[0761] 7. Spending plan sending module

[0762] The server resends the generated optimal spending plan to the user's device. The server recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0763] User side (terminal)

[0764] 8. Result display module

[0765] The device displays the received spending plan to the user, for example, "2023-10-11: Relax at the spa - Spending: ¥10,000."

[0766] Specific examples

[0767] The user opens the dedicated app and enters their daily income and expenditure information. For example, they might record "Shopping at the supermarket on 2023-10-10, income 50,000 yen, expenditure 5,000 yen." At the same time, the emotion recognition module uses the camera and microphone to analyze the user's emotions and recognizes that the user is feeling stressed. This data is converted into JSON format and sent to the server. The server receives the data, and the AI ​​analysis module generates an optimal spending plan and resends it to the user's device. The user then checks the spending plan displayed on the device screen and spends money as planned.

[0768] Prompt Sentence Examples

[0769] An example of an input prompt for the generative AI model is, "Based on daily income and expenditure information, behavioral history, and emotional data, please suggest a spending plan that will allow the user to save money comfortably."

[0770] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0771] Step 1: Data Entry Module

[0772] Users use a smartphone or computer to enter their daily activity history and income and expenditure information through a dedicated interface. This interface has an intuitive design, allowing users to enter data easily. The entered information is saved in the database in the format of date, income, and expense. For example, "I went shopping at the supermarket on 2023-10-10 and recorded an income of ¥50,000 and an expense of ¥5,000." The input data format is {"date": "2023-10-10", "income": 50000, "expense": 5000}.

[0773] Step 2: Emotion Recognition Module

[0774] The device's camera and microphone are used to analyze the user's facial expressions and voice in real time. The analysis is performed using the Facial Expression Recognition API and Speech Emotion Recognition API. For example, the device captures the user's facial expressions, such as smile or anger, and analyzes their emotional state as numerical data. For example, if the user's facial expression is recognized as "stressed," the corresponding data will be "emotion": "stress."

[0775] Step 3: Data transmission module

[0776] The device sends the information collected and analyzed in steps 1 and 2 to the server. The data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. For example, the data sent is {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}. At this point, the device receives a response from the server to confirm whether the data transmission was successful.

[0777] Step 4: Data receiving module

[0778] The server receives data sent from the device in real time. The received data is temporarily stored in memory and then stored in a database. For example, the received data is saved as {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}. This data becomes the input for the next AI analysis step.

[0779] Step 5: AI Analysis Module

[0780] The server passes the behavioral history, income and expenditure information, and emotional data collected from the database to the AI ​​analysis module. The AI ​​analysis module generates an optimal spending plan based on this data. To analyze the data, a machine learning model is used to take into account past income and expenditure patterns and emotional state. For example, if a user is feeling stressed, a spending plan such as "Consider going to a spa the next day and spend 10,000 yen" may be suggested to promote relaxation. The generated spending plan data is in the format {"plan_date": "2023-10-11", "recommendation": "spend 10000 on spa"}.

[0781] Step 6: Data storage and learning module

[0782] The server saves the generated spending plan in a database and uses it as learning data to further improve the accuracy of future predictions. For example, the saved data is in the format {"user_id": "12345", "plan": "spend 10000 on spa", "emotion": "stress"}. This data will enable more accurate suggestions in the next analysis.

[0783] Step 7: Spending Plan Submission Module

[0784] The server then sends the generated optimal spending plan back to the user's device. The server recognizes the user ID and sends the data to the corresponding device. For example, the data to be sent is {"user_id": "12345", "plan_date": "2023-10-11", "recommendation": "spend 10000 on spa"}.

[0785] Step 8: Results display module

[0786] The device displays the received spending plan to the user. The user can visually confirm and understand the proposed spending plan. For example, the device screen will display "Spend 10,000 yen to relax at the spa on 2023-10-11." This result display allows the user to spend according to the plan.

[0787] (Application example 2)

[0788] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0789] In modern society, it is difficult for users to efficiently manage their daily expenses and income. In particular, there is a need for an optimal spending plan that takes into account the impact of users' emotions on spending behavior. However, conventional systems only analyze behavioral history and income / expense information, and do not consider users' emotions, which means they are unable to create effective spending plans.

[0790] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input behavioral history and income and expenditure information; means for transmitting the input data to the server; means for the server to analyze the received data using AI and generate an optimal spending plan; means for transmitting the generated spending plan to the user; means for displaying the transmitted spending plan to the user; emotion recognition means for identifying the user's emotions; means for transmitting emotion data to the server; and means for analyzing income and expenditure patterns using the emotion data and adjusting expenses to an optimal level. This makes it possible to optimize the spending plan while taking the user's emotions into consideration and support planned financial management.

[0791] "Behavioral history" is a record of specific activities and events in a user's daily life.

[0792] "Income and expenditure information" is data relating to the user's income and expenditure.

[0793] A "server" is a computer system for receiving, storing, and processing data.

[0794] "AI analytics" is the process of using artificial intelligence to analyze data and generate insights and predictions.

[0795] A "spending plan" is a specific guideline or suggestion for effectively managing a user's future income and expenses.

[0796] "Sending to user" is the act of sending data from the server to the client device.

[0797] "Displaying to the user" means visually presenting information on the screen of a client device.

[0798] "Emotion recognition means" refers to techniques and methods for identifying a user's emotions.

[0799] "Emotion data" is digital information that indicates the user's emotional state.

[0800] "Analyzing income and expenditure patterns" refers to analyzing a user's past income and expenditure data to derive certain trends and patterns.

[0801] This invention is a system that automatically collects a user's behavioral history and income / expenditure information, identifies the user's emotions by combining emotion recognition means, and generates an optimal spending plan using AI. This system transmits data entered by the user to a server in real time, and the server analyzes the data and the user's emotions to provide an efficient spending plan.

[0802] User side (terminal)

[0803] 1. Data Entry Module

[0804] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0805] 2. Emotion Recognition Module

[0806] An emotion recognition means installed in the terminal uses a camera and a microphone to analyze the user's facial expressions and voice in real time and identify the user's emotions.

[0807] 3. Data transmission module

[0808] The input behavioral history, income and expenditure information, and emotion data are sent from the device to the server, where each piece of data is converted into JSON format and sent to the server's API endpoint via an HTTP POST request.

[0809] Server side

[0810] 4. Data Reception Module

[0811] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0812] 5. AI analysis module

[0813] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and performs future optimization based on the user's income and expenditure patterns, as well as the identified emotions. For example, if the user is feeling stressed, it can suggest spending items that will promote relaxation.

[0814] 6. Data storage and learning module

[0815] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0816] 7. Spending plan sending module

[0817] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0818] User side (terminal)

[0819] 8. Result display module

[0820] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. For example, it is displayed as "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0821] Hardware / Software used

[0822] Smartphone: iOS / Android device

[0823] Camera: Smartphone built-in camera

[0824] Microphone: Built-in smartphone microphone

[0825] Server: General-purpose server

[0826] Database: Relational Database Management System (RDBMS)

[0827] AI Analysis Module: Machine Learning Platform

[0828] Examples and prompts

[0829] 1. Example: A user inputs income information (e.g., ¥50,000) and expenditure information (e.g., ¥5,000) on a smartphone, and at the same time, the emotion recognition module recognizes the user as being in a stressful state. This information is sent to the server, and the AI ​​analysis module generates a spending plan, such as "Consider going to a spa the next day to relax, and spend ¥10,000." The generated spending plan is sent to the user's device and displayed visually.

[0830] 2. Example prompt:

[0831] Use the following data to create the best spending plan for you:

[0832] Income: 50,000 yen

[0833] Expenses: 5,000 yen

[0834] Category: Supermarket

[0835] Emotion: Stress, Trust: 0.85

[0836] In this way, the system of the present invention realizes planned and efficient expenditure management while taking into consideration the user's feelings.

[0837] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0838] Step 1:

[0839] The user inputs their activity history and income / expense information. Using a smartphone or computer, the user inputs their daily income, expenses, and activity history. The input data includes amounts, categories, dates, etc. A specific example of how this works is when the user inputs "purchases at the supermarket, income ¥50,000, expenditure ¥5,000" into the application's input field. The input data is converted into JSON format and prepared.

[0840] Step 2:

[0841] The device's emotion recognition module identifies the user's emotions. It uses the device's built-in camera and microphone to analyze the user's facial expressions and voice in real time. The collected data is sent to an emotion recognition engine (for example, a facial expression analysis API or a voice analysis API) to obtain the user's emotional state. Specifically, if the user is feeling stressed, the emotion recognition engine will return data such as "Emotion: Stress, Confidence: 0.85."

[0842] Step 3:

[0843] The data transmission module sends the collected data to the server. The collected behavioral history, income and expenditure information, and emotion data are sent to the server's API endpoint. Specifically, JSON-formatted data is sent via an HTTP POST request. In this process, JSON-formatted behavioral history, income and expenditure information, and emotion data are used as input, and the data sent to the server is obtained as output.

[0844] Step 4:

[0845] The server stores the received data in a database. The server receives the transmitted data and temporarily stores it for analysis. If necessary, it is stored in a relational database management system (RDBMS). For example, the data is stored in a database in the format of "user ID, income, expenses, date, emotional state."

[0846] Step 5:

[0847] The server's AI analysis module analyzes the received data and generates an optimal spending plan. The AI ​​analysis module learns from past data and creates an optimal spending plan based on income and expenditure patterns and emotional data. For example, it may create a plan in the form of "since the user is feeling stressed, it suggests spending money on a trip to a spa to relax." The inputs are behavioral history, income and expenditure information, and emotional data, and the output is an optimal spending plan.

[0848] Step 6:

[0849] The generated spending plan is sent from the server to the user's device. The server sends this spending plan to the appropriate endpoint. The generated spending plan is converted to JSON format and sent to the user's device via an HTTP POST request. This ensures accurate transmission based on the user ID.

[0850] Step 7:

[0851] The user's device displays the received spending plan. The device's result display module visually presents the received spending plan to the user. Specifically, the application screen displays something like "Considering going to a spa for relaxation on 2023-10-12, spending 10,000 yen." In this process, the spending plan data sent from the server is used as input, and the spending plan displayed on the screen is obtained as output.

[0852] Through the above steps, a system is realized that can more appropriately manage income and expenditure while taking into account the user's emotions.

[0853] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0854] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0855] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0856] [Third embodiment]

[0857] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0858] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0859] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0860] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0861] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0862] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0863] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0864] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0865] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0866] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0867] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0868] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0869] System Overview

[0870] This invention is a system that automatically collects a user's behavioral history and income / expenditure information and uses AI to generate an optimal spending plan. This system transmits data entered by the user to a server in real time, and the server analyzes the data to provide an efficient spending plan.

[0871] User side (terminal)

[0872] 1. Data Entry Module:

[0873] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0874] 2. Data transmission module:

[0875] The input data is sent from the terminal to the server, where it is converted to JSON format and sent to the server's API endpoint via an HTTP POST request.

[0876] Server side

[0877] 3. Data receiving module:

[0878] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0879] 4. AI analysis module:

[0880] The server passes the received data to an AI analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and spending patterns. For example, the AI ​​might suggest "savings" and set the next day's spending at 3,000 yen.

[0881] 5. Data storage and learning module:

[0882] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0883] 6. Spending Plan Submission Module:

[0884] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0885] User side (terminal)

[0886] 7. Result display module:

[0887] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. The result is displayed in a format that is easy for the user to understand, for example, "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0888] Specific examples

[0889] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[0890] 2. The device sends this input data to the server.

[0891] 3. The server receives the data and begins analysis using the AI ​​analysis module.

[0892] 4. The AI ​​will analyze and generate an optimal spending plan, such as "Save and spend 3,000 yen on 2023-10-11."

[0893] 5. The server sends the generated spending plan to the user's terminal.

[0894] 6. The user checks the spending plan received on the device and spends money as planned.

[0895] In this way, the system suggests ways for users to spend money comfortably and efficiently, enabling them to save money in a planned manner.

[0896] The processing flow will be explained below.

[0897] Step 1:

[0898] The user uses the terminal to input his / her behavior history and income / expense information.

[0899] The user launches an application.

[0900] The user accesses a new data entry screen.

[0901] Enter the date and time, the activity (e.g., shopping at the supermarket), the amount of income, and the amount of expenses.

[0902] Step 2:

[0903] The terminal transmits the input data to the server.

[0904] The terminal converts the user input data into JSON format.

[0905] The device sends an HTTP POST request to the server's API endpoint and transfers the data.

[0906] Step 3:

[0907] The server receives the data sent from the terminal.

[0908] The server receives an HTTP request that reaches an API endpoint.

[0909] The server parses the received data into JSON format and temporarily stores it in memory.

[0910] Step 4:

[0911] The server stores the received data in a database.

[0912] The server inserts the received data into the appropriate tables in the database.

[0913] The server checks the status of saving to the database and confirms success.

[0914] Step 5:

[0915] The server uses AI to analyze the stored data and generate an optimal spending plan.

[0916] The server passes past user data to the AI ​​analysis module.

[0917] The AI ​​analysis module learns the user's income and spending patterns.

[0918] AI generates an optimal spending plan and returns the results.

[0919] Step 6:

[0920] The server sends the optimal spending plan to the user's terminal.

[0921] The server converts the generated spending plan into JSON format.

[0922] The server identifies the appropriate endpoint based on the user ID and sends it in an HTTP POST request.

[0923] Step 7:

[0924] The terminal receives the spending plan sent from the server.

[0925] The device receives an HTTP response from the server.

[0926] The terminal analyzes the received spending plan and stores it in its internal memory.

[0927] Step 8:

[0928] The terminal displays the received spending plan to the user.

[0929] The terminal converts the spending plan into a format for display on the user interface.

[0930] Users can visually check their specific spending plans through the application.

[0931] Example: "2023-10-11: Savings - Income: 0, Expenses: 3000" will be displayed.

[0932] Step 9:

[0933] The user manages actual expenditures based on the expenditure plan displayed on the terminal.

[0934] The user spends money according to a spending plan.

[0935] If necessary, provide new inputs and repeat the cycle.

[0936] Example 1

[0937] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0938] In today's world, it takes time and effort for users to manage their income and expenses all by themselves, and it is difficult to create an efficient spending plan. In particular, advanced analysis and continuous learning are required to reduce wasteful spending and plan savings, but this is extremely difficult for users to do on their own. Therefore, there is a need for a system that allows users to automatically create an efficient spending plan without any effort.

[0939] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0940] In this invention, the server includes a means for temporarily storing received data for analysis, an AI analysis means for learning from past data and generating an optimal spending plan based on the user's income and spending patterns, and a means for transmitting the generated spending plan to the user's terminal, thereby enabling the user to automatically obtain an efficient and efficient spending plan.

[0941] A "user" is an entity that utilizes the system to input behavioral history and income / expenditure information and receive an optimal spending plan.

[0942] "Behavioral history" is information about the user's daily behavior, and is data that records specific activities such as shopping, eating, and traveling.

[0943] "Income and expenditure information" refers to detailed information such as specific amounts, dates, and items regarding the income and expenditures earned by the user.

[0944] A "terminal" refers to a device through which a user inputs their behavioral history and income / expense information and communicates with the system, specifically a device such as a smartphone or PC.

[0945] "Server" refers to a central processing unit that receives data sent from users and performs analysis and database management.

[0946] "Means for transmitting data in real time" refers to a function for instantly transmitting data entered by a user from a terminal to a server.

[0947] "AI analysis means" refers to a function that uses artificial intelligence technology built into the server to learn the user's income and expenditure patterns and generate an optimal expenditure plan.

[0948] The "optimal spending plan" refers to a planned spending method that minimizes waste and is generated based on the user's income and spending patterns.

[0949] "Database" refers to a storage device for systematically storing received and analyzed data for later use in learning and analysis.

[0950] The "means for transmitting a spending plan" refers to a function for transmitting the optimal spending plan generated by the server to the user's terminal.

[0951] The "means for displaying a spending plan" refers to a function for visually displaying the transmitted spending plan on the user's terminal.

[0952] This invention is a system that automatically collects a user's behavioral history and income / expenditure information and uses AI to generate an optimal spending plan. This system transmits data entered by the user to a server in real time, and the server analyzes the data to provide an efficient spending plan.

[0953] User side (terminal)

[0954] 1. Data Entry Module:

[0955] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[0956] 2. Data transmission module:

[0957] The entered data is sent from the terminal to the server, where it is converted to JSON format and sent to the server's API endpoint via an HTTP POST request.

[0958] Server side

[0959] 3. Data receiving module:

[0960] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[0961] 4. AI analysis module:

[0962] The server passes the received data to an AI analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and spending patterns. For example, the AI ​​might suggest "savings" and set the next day's spending at 3,000 yen.

[0963] 5. Data storage and learning module:

[0964] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[0965] 6. Spending Plan Submission Module:

[0966] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[0967] User side (terminal)

[0968] 7. Result display module:

[0969] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. The result is displayed in a format that is easy for the user to understand, for example, "2023-10-11: Savings - Income: 0, Expenses: 3000".

[0970] Specific examples

[0971] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[0972] 2. The device sends this input data to the server.

[0973] 3. The server receives the data and begins analysis using the AI ​​analysis module.

[0974] 4. The AI ​​will analyze and generate an optimal spending plan, such as "Save and spend 3,000 yen on 2023-10-11."

[0975] 5. The server sends the generated spending plan to the user's terminal.

[0976] 6. The user checks the spending plan received on the device and spends money as planned.

[0977] Example prompts for generative AI models

[0978] "I would like to develop a system that allows users to record their daily income and expenses and automatically suggests optimal spending plans. Please explain in detail how this system works, how the user sends the input data, how the server analyzes it to generate the optimal spending plan, and how it sends it to the user's device."

[0979] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0980] Step 1:

[0981] The user inputs their daily activity history and income and expenditure information. Specifically, the user uses a device (such as a smartphone or PC) to input data into a dedicated input interface. For example, the user inputs "2023-10-10, purchases at supermarket, income: ¥50,000, expenditure: ¥5,000."

[0982] Input: User behavior history and income / expense information

[0983] Output: The input data

[0984] Step 2:

[0985] The device sends the input data to the server in real time. The data transmission module converts the data into JSON format and sends it to the server's API endpoint using an HTTP POST request. The data is encrypted during this process to maintain security.

[0986] Input: Data entered

[0987] Output: Data converted to JSON format is sent to the server

[0988] Step 3:

[0989] The server receives the data in real time and stores it temporarily. The data reception module receives the data that arrives at the server in real time and stores the data temporarily in memory. If necessary, it is also stored in a database.

[0990] Input: Data sent in JSON format

[0991] Output: Temporarily saved data

[0992] Step 4:

[0993] The server passes the received data to the AI ​​analysis module, which begins analysis. Specifically, the AI ​​analysis module, which has learned about past income and expenditure patterns, takes in the data and analyzes the user's expenditure patterns based on that data. This analysis generates a spending plan, such as "It would be optimal to save 3,000 yen the next day."

[0994] Input:Temporarily saved data

[0995] Output: Generated optimal spending plan

[0996] Step 5:

[0997] The server stores the analysis results in a database. The data storage and learning module stores the generated spending plan in a database and uses it to improve the accuracy of future forecasts.

[0998] Input: Generated optimal spending plan

[0999] Output: Analysis results stored in a database

[1000] Step 6:

[1001] The server sends the generated spending plan to the user's device. The spending plan sending module recognizes the user's ID and sends the spending plan to the appropriate endpoint. For example, a plan such as "2023-10-11: Savings - Income: 0, Expenses: ¥3,000" is sent in JSON format.

[1002] Input: Generated optimal spending plan

[1003] Output: Spending plan submitted in JSON format

[1004] Step 7:

[1005] The user checks the received spending plan on the device. The result display module visually displays the spending plan, allowing the user to check the specific spending plan at a glance. For example, the device screen will display "2023-10-11: Savings - Income: 0, Expenses: ￥3,000."

[1006] Input: Spending plan submitted in JSON format

[1007] Output: A visually displayed spending plan

[1008] (Application example 1)

[1009] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1010] In today's world, many users use electronic payments, but there is a lack of appropriate systems for effectively managing their spending. This makes it difficult for users to curb wasteful spending. Furthermore, the time and effort required to manually input income and expenditure information makes real-time spending management difficult. Furthermore, there is a lack of mechanisms for automatically generating and providing optimal spending plans to users. To solve these issues, a system is needed that automatically collects users' electronic payment information and generates optimal spending plans based on that information.

[1011] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1012] In this invention, the server includes means for inputting a user's behavioral history and income / expenditure information, means for transmitting the input data to the server, means for the server to analyze the received data using AI and generate an optimal spending plan, means for automatically collecting electronic payment expenditure information, means for transmitting the generated spending plan to the user, and means for displaying the transmitted spending plan to the user. This allows the user to automatically collect and analyze electronic payment information and obtain an efficient spending plan in real time.

[1013] "User" refers to an individual who uses this system to input their own behavioral history and income / expense information and receive an optimal spending plan.

[1014] "Behavioral history" refers to a record of a user's daily activities and specific actions, and specifically includes information such as shopping, meals, and use of transportation.

[1015] "Income and expenditure information" refers to detailed data on a user's income (salary and bonuses) and expenses (shopping, meals, rent, etc.).

[1016] "Server" refers to a computer system for receiving, storing, analyzing, and transmitting data over the Internet.

[1017] "AI" stands for artificial intelligence and refers to technology that analyzes a user's behavioral history and income and expenditure information to generate an optimal spending plan.

[1018] A "spending plan" refers to a specific plan that AI generates based on the user's data, suggesting how to spend money efficiently.

[1019] "Electronic payment information" refers to data containing details of transactions made by a user using an electronic payment service, including the date and time of the transaction, the amount, and the transaction partner.

[1020] "Database" refers to a data structure that organizes and stores received data so that it can be quickly accessed when needed.

[1021] "Module" refers to an independent piece of software designed to perform a specific function.

[1022] The following describes the mode for carrying out the invention: This system collects and analyzes a user's behavioral history and income / expenditure information, and generates and presents an optimal spending plan, enabling efficient spending management.

[1023] System Overview

[1024] The system is primarily comprised of devices such as smartphones or PCs, a server, and an AI analysis module. It also incorporates a mechanism for automatically collecting users' electronic payment information, enabling real-time expenditure management.

[1025] Hardware used

[1026] Smartphones and PCs (user devices)

[1027] Cloud server (data collection, analysis, storage)

[1028] Database Server

[1029] Software used

[1030] Mobile Applications (iOS and Android)

[1031] Server-side platforms (AWS and Google Cloud)

[1032] Database management systems (MySQL, PostgreSQL)

[1033] AI analysis module (TensorFlow, PyTorch)

[1034] Program processing overview

[1035] 1. Data Collection

[1036] When a user makes an electronic payment, payment information (e.g., transaction date and time, amount, customer, etc.) is automatically collected in the "Smart Spending Planner" application. Users can also manually enter their activity history and income and expenditure information.

[1037] 2. Data Transmission

[1038] The data collected on the user's device is sent to the server in real time, where it is converted into JSON format and sent to the server's API endpoint.

[1039] 3. Data Receipt and Storage

[1040] The server receives the data sent from the terminal in real time, temporarily stores it for analysis, and stores it in a database as needed.

[1041] 4. AI analysis

[1042] The data stored on the server is analyzed by an AI analysis module, which learns from past income and expenditure data and behavioral history to generate an optimal spending plan for the user.

[1043] 5. Submit spending plan

[1044] The generated optimal spending plan is then resent from the server to the user's device, where the user ID is recognized and the spending plan is sent to the appropriate endpoint.

[1045] 6. Results display

[1046] The user's terminal displays the received spending plan to the user, who can visually check the specific spending plan on the application screen.

[1047] Specific examples

[1048] If a user spends 5,000 yen at a supermarket on October 10, 2023, this spending information will be automatically collected by the app. Taking into account the next day's spending, the AI ​​will suggest "limit spending on October 11, 2023 to 3,000 yen and save 2,000 yen." This spending plan will be sent to the user's smartphone, where the user can confirm the plan on the screen.

[1049] Example prompt sentence:

[1050] User ID: 12345

[1051] Expenditure information: 2023-10-10, Supermarket, 5,000 yen

[1052] In this way, the system suggests ways for users to spend money in a way that is comfortable and efficient, enabling them to save money in a planned manner.

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] When a user makes an electronic payment, the electronic payment information is automatically collected on a device such as a smartphone or PC. Specifically, expenditure information (transaction date and time, amount, business partner, etc.) is automatically acquired, and the user may also manually enter their activity history and income and expenditure information. In this case, the input includes the electronic payment information and manually entered data, and the output is data temporarily stored on the device.

[1056] Step 2:

[1057] The input data is sent from the device to the server in real time. During this transmission process, the data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. The input is temporary data stored in the device, and the output is JSON format data received by the server.

[1058] Step 3:

[1059] The server receives data sent from the terminal in real time. The received data is processed by the data receiving module and temporarily stored in memory. The input is the JSON format data sent to the server, and the output is the temporarily stored data.

[1060] Step 4:

[1061] The server stores the received data in a database. In this process, the data is stored in the appropriate tables using a database management system (e.g., MySQL, PostgreSQL). The input is the temporarily stored data, and the output is the data stored in the database.

[1062] Step 5:

[1063] The server passes the data to the AI ​​analysis module, which begins analysis. The AI ​​analysis module is built using TensorFlow and PyTorch and generates an optimal spending plan based on a model trained on past income and expenditure data. The input is the data stored in the database, and the output is the generated spending plan.

[1064] Step 6:

[1065] The server sends the generated optimal spending plan to the user's device. During this process, the data is converted to JSON format, and the user ID is recognized and sent to the appropriate endpoint. The input is the generated spending plan (in JSON format), and the output is the spending plan received by the user's device.

[1066] Step 7:

[1067] The user's terminal displays the received spending plan to the user, allowing the user to visually check the specific spending plan on the terminal screen. The input is the spending plan received from the server, and the output is the spending plan converted into a format that the user can visually check.

[1068] In this way, the optimal spending plan is provided to the user through each step.

[1069] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1070] System Overview

[1071] This invention is a system that automatically collects user behavior history and income / expenditure information, recognizes user emotions by combining it with an emotion engine, and generates an optimal spending plan using AI. This system sends data entered by the user to a server in real time, and the server analyzes the data and the user's emotions to provide an efficient spending plan.

[1072] User side (terminal)

[1073] 1. Data Entry Module:

[1074] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[1075] 2. Emotion Recognition Module:

[1076] The device's built-in emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time and recognize the user's emotions.

[1077] 3. Data transmission module:

[1078] The input behavioral history, income and expenditure information, and emotion data are sent from the device to the server, where each piece of data is converted into JSON format and sent to the server's API endpoint via an HTTP POST request.

[1079] Server side

[1080] 4. Data receiving module:

[1081] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[1082] 5. AI analysis module:

[1083] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and performs future optimization based on the user's income and expenditure patterns, as well as recognized emotions. For example, if the user is feeling stressed, it can suggest spending items that will promote relaxation.

[1084] 6. Data Storage and Learning Module:

[1085] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[1086] 7. Spending Plan Submission Module:

[1087] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[1088] User side (terminal)

[1089] 8. Result display module:

[1090] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. For example, it is displayed as "2023-10-11: Savings - Income: 0, Expenses: 3000".

[1091] Specific examples

[1092] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[1093] 2. The emotion recognition module analyzes the user's facial expressions and voice and recognizes that the user is feeling stressed.

[1094] 3. The device sends the input data and emotion data to the server.

[1095] 4. The server receives the data and begins analysis using the AI ​​analysis module.

[1096] 5. The AI ​​analysis module comprehensively analyzes behavioral history, income and expenditure information, and emotional data to generate an optimal spending plan. For example, it might suggest, "Consider going to a spa the next day for relaxation and spend 10,000 yen."

[1097] 6. The server sends the generated spending plan to the user's terminal.

[1098] 7. The user checks the spending plan received on the device and spends money as planned.

[1099] In this way, the system takes the user's emotions into consideration and suggests ways to spend money comfortably and efficiently, enabling planned savings and stress reduction.

[1100] The processing flow will be explained below.

[1101] Step 1:

[1102] The user uses the terminal to input his / her behavior history and income / expense information.

[1103] The user launches an application.

[1104] The user accesses a new data entry screen.

[1105] Enter the date and time, the activity (e.g., shopping at the supermarket), the amount of income, and the amount of expenses.

[1106] Step 2:

[1107] The emotion engine installed in the device recognizes the user's emotions.

[1108] The device's camera captures the user's facial expressions, which are then analyzed by an emotion recognition algorithm.

[1109] The device's microphone records the user's voice, and the voice analysis module analyzes emotions.

[1110] The emotion recognition result is extracted and analyzed as, for example, "the user is feeling stressed."

[1111] Step 3:

[1112] The terminal transmits the behavior history, income and expenditure information, and emotion data to the server.

[1113] The terminal converts the collected data into JSON format.

[1114] The device sends an HTTP POST request to the server's API endpoint and transfers the data.

[1115] Step 4:

[1116] The server receives the data sent from the terminal.

[1117] The server receives the HTTP request that reaches the API endpoint and parses the data in JSON format.

[1118] Received data is temporarily stored in memory.

[1119] Step 5:

[1120] The server stores the received data in a database.

[1121] The server inserts the parsed data into the appropriate tables in the database.

[1122] Check the status of saving to the database and confirm that saving is successful.

[1123] Step 6:

[1124] The server uses AI to analyze the stored data and generate an optimal spending plan.

[1125] The server passes the stored user data and emotion data to the AI ​​analysis module.

[1126] The AI ​​analysis module learns and analyzes the user's past income and expenditure patterns and current emotional state.

[1127] The AI ​​generates an optimal spending plan and outputs a plan such as, "The user is feeling stressed, so we suggest a visit to a spa to relax (cost: 10,000 yen)."

[1128] Step 7:

[1129] The server transmits the generated spending plan to the user's terminal.

[1130] The server converts the generated spending plan into JSON format.

[1131] The server identifies the appropriate endpoint based on the user ID and sends it in an HTTP POST request.

[1132] Step 8:

[1133] The terminal receives the spending plan sent from the server.

[1134] The terminal receives the HTTP response from the server and parses the spending plan data.

[1135] The received spending plan data is stored in an internal memory.

[1136] Step 9:

[1137] The terminal displays the received spending plan to the user.

[1138] The terminal converts the spending plan into a display format.

[1139] Users can visually check their specific spending plans through the application.

[1140] For example, it might show "2023-10-11: Spa visit - Income: 0, Expenses: 10000".

[1141] Step 10:

[1142] The user manages actual expenditures based on the expenditure plan displayed on the terminal.

[1143] The user follows a spending plan, books a spa visit, and spends according to budget.

[1144] If necessary, enter new behavioral history and income / expense information, update your emotional state, and continue using the system.

[1145] This series of steps allows users to spend money comfortably and efficiently while taking their emotional state into account.

[1146] Example 2

[1147] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1148] In modern society, users are required to create efficient and emotionally-based spending plans while recording their daily activities, income, and expenses. However, conventional systems have difficulty providing optimal spending plans that take into account the user's emotional state. This makes users prone to wasteful spending, and presents challenges in achieving optimal savings and stress management.

[1149] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1150] In this invention, the server includes means for a user to input behavioral history and income / expenditure information, means for transmitting the input data to the server, means for an emotion engine installed in the terminal to recognize the user's emotions, means for the server to analyze the received data and emotion data using AI and generate an optimal spending plan, means for transmitting the generated spending plan to the user, and means for displaying the transmitted spending plan to the user. This makes it possible to provide an optimal spending plan that takes the user's emotions into consideration in real time.

[1151] "Behavioral history" is data that records the user's daily activities and behaviors.

[1152] "Income and expenditure information" is data in which a user specifically records his or her daily income and expenditure.

[1153] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice and recognizes their emotions.

[1154] A "server" is a computer system that receives, stores, analyzes, and transmits data.

[1155] "AI" stands for Artificial Intelligence, a technology that analyzes a user's behavioral history, income and expenditure information, and emotional data to generate an optimal spending plan.

[1156] A "spending plan" is a plan that suggests how to spend money in the future based on the user's income and expenses.

[1157] A "database" is a system for systematically storing and managing received data.

[1158] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for structurally representing data in text format.

[1159] An "HTTP POST request" is a protocol for sending data to a web server.

[1160] The present invention is a system that automatically collects a user's behavioral history and income / expenditure information, recognizes the user's emotions using an emotion engine, and provides the user with an optimal spending plan. This system mainly consists of the following components:

[1161] User side (terminal)

[1162] 1. Data Entry Module

[1163] Users input their daily activity history and income and expenditure information using devices such as smartphones or PCs. This data input module has a user-friendly interface, allowing users to input information hassle-free. For example, a user might input data such as "I went shopping at the supermarket on 2023-10-10, and recorded an income of ¥50,000 and an expenditure of ¥5,000."

[1164] 2. Emotion Recognition Module

[1165] The emotion engine installed on the device uses the camera and microphone to analyze the user's facial expressions and voice in real time to recognize the user's emotions. Specifically, it uses the Facial Expression Recognition API and Speech Emotion Recognition API. For example, when the user is feeling stressed, it can recognize this from changes in facial expression and voice.

[1166] 3. Data transmission module

[1167] The entered behavioral history, income and expenditure information, and emotional data are sent from the device to the server. During this process, each piece of data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. For example, the data sent from the device will be {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}.

[1168] Server side

[1169] 4. Data Reception Module

[1170] The server receives data sent from the device in real time, temporarily stores the data for analysis, and then stores it in a database after analysis.

[1171] 5. AI analysis module

[1172] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module, which then generates an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and expenditure patterns and perceived emotions. For example, if the user is feeling stressed, it may suggest "consider going to the spa the next day and spend 10,000 yen" to encourage relaxation.

[1173] 6. Data storage and learning module

[1174] The server stores the analyzed data in a database and performs learning to improve future prediction accuracy, allowing the system to propose spending plans that respond to changes in the user's behavior and emotions.

[1175] 7. Spending plan sending module

[1176] The server resends the generated optimal spending plan to the user's device. The server recognizes the user ID and sends the spending plan to the appropriate endpoint.

[1177] User side (terminal)

[1178] 8. Result display module

[1179] The device displays the received spending plan to the user, for example, "2023-10-11: Relax at the spa - Spending: ¥10,000."

[1180] Specific examples

[1181] The user opens the dedicated app and enters their daily income and expenditure information. For example, they might record "Shopping at the supermarket on 2023-10-10, income 50,000 yen, expenditure 5,000 yen." At the same time, the emotion recognition module uses the camera and microphone to analyze the user's emotions and recognizes that the user is feeling stressed. This data is converted into JSON format and sent to the server. The server receives the data, and the AI ​​analysis module generates an optimal spending plan and resends it to the user's device. The user then checks the spending plan displayed on the device screen and spends money as planned.

[1182] Prompt Sentence Examples

[1183] An example of an input prompt for the generative AI model is, "Based on daily income and expenditure information, behavioral history, and emotional data, please suggest a spending plan that will allow the user to save money comfortably."

[1184] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1185] Step 1: Data Entry Module

[1186] Users use a smartphone or computer to enter their daily activity history and income and expenditure information through a dedicated interface. This interface has an intuitive design, allowing users to enter data easily. The entered information is saved in the database in the format of date, income, and expense. For example, "I went shopping at the supermarket on 2023-10-10 and recorded an income of ¥50,000 and an expense of ¥5,000." The input data format is {"date": "2023-10-10", "income": 50000, "expense": 5000}.

[1187] Step 2: Emotion Recognition Module

[1188] The device's camera and microphone are used to analyze the user's facial expressions and voice in real time. The analysis is performed using the Facial Expression Recognition API and Speech Emotion Recognition API. For example, the device captures the user's facial expressions, such as smile or anger, and analyzes their emotional state as numerical data. For example, if the user's facial expression is recognized as "stressed," the corresponding data will be "emotion": "stress."

[1189] Step 3: Data transmission module

[1190] The device sends the information collected and analyzed in steps 1 and 2 to the server. The data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. For example, the data sent is {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}. At this point, the device receives a response from the server to confirm whether the data transmission was successful.

[1191] Step 4: Data receiving module

[1192] The server receives data sent from the device in real time. The received data is temporarily stored in memory and then stored in a database. For example, the received data is saved as {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}. This data becomes the input for the next AI analysis step.

[1193] Step 5: AI Analysis Module

[1194] The server passes the behavioral history, income and expenditure information, and emotional data collected from the database to the AI ​​analysis module. The AI ​​analysis module generates an optimal spending plan based on this data. To analyze the data, a machine learning model is used to take into account past income and expenditure patterns and emotional state. For example, if a user is feeling stressed, a spending plan such as "Consider going to a spa the next day and spend 10,000 yen" may be suggested to promote relaxation. The generated spending plan data is in the format {"plan_date": "2023-10-11", "recommendation": "spend 10000 on spa"}.

[1195] Step 6: Data storage and learning module

[1196] The server saves the generated spending plan in a database and uses it as learning data to further improve the accuracy of future predictions. For example, the saved data is in the format {"user_id": "12345", "plan": "spend 10000 on spa", "emotion": "stress"}. This data will enable more accurate suggestions in the next analysis.

[1197] Step 7: Spending Plan Submission Module

[1198] The server then sends the generated optimal spending plan back to the user's device. The server recognizes the user ID and sends the data to the corresponding device. For example, the data to be sent is {"user_id": "12345", "plan_date": "2023-10-11", "recommendation": "spend 10000 on spa"}.

[1199] Step 8: Results display module

[1200] The device displays the received spending plan to the user. The user can visually confirm and understand the proposed spending plan. For example, the device screen will display "Spend 10,000 yen to relax at the spa on 2023-10-11." This result display allows the user to spend according to the plan.

[1201] (Application example 2)

[1202] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1203] In modern society, it is difficult for users to efficiently manage their daily expenses and income. In particular, there is a need for an optimal spending plan that takes into account the impact of users' emotions on spending behavior. However, conventional systems only analyze behavioral history and income / expense information, and do not consider users' emotions, which means they are unable to create effective spending plans.

[1204] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input behavioral history and income and expenditure information; means for transmitting the input data to the server; means for the server to analyze the received data using AI and generate an optimal spending plan; means for transmitting the generated spending plan to the user; means for displaying the transmitted spending plan to the user; emotion recognition means for identifying the user's emotions; means for transmitting emotion data to the server; and means for analyzing income and expenditure patterns using the emotion data and adjusting expenses to an optimal level. This makes it possible to optimize the spending plan while taking the user's emotions into consideration and support planned financial management.

[1205] "Behavioral history" is a record of specific activities and events in a user's daily life.

[1206] "Income and expenditure information" is data relating to the user's income and expenditure.

[1207] A "server" is a computer system for receiving, storing, and processing data.

[1208] "AI analytics" is the process of using artificial intelligence to analyze data and generate insights and predictions.

[1209] A "spending plan" is a specific guideline or suggestion for effectively managing a user's future income and expenses.

[1210] "Sending to user" is the act of sending data from the server to the client device.

[1211] "Displaying to the user" means visually presenting information on the screen of a client device.

[1212] "Emotion recognition means" refers to techniques and methods for identifying a user's emotions.

[1213] "Emotion data" is digital information that indicates the user's emotional state.

[1214] "Analyzing income and expenditure patterns" refers to analyzing a user's past income and expenditure data to derive certain trends and patterns.

[1215] This invention is a system that automatically collects a user's behavioral history and income / expenditure information, identifies the user's emotions by combining emotion recognition means, and generates an optimal spending plan using AI. This system transmits data entered by the user to a server in real time, and the server analyzes the data and the user's emotions to provide an efficient spending plan.

[1216] User side (terminal)

[1217] 1. Data Entry Module

[1218] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[1219] 2. Emotion Recognition Module

[1220] An emotion recognition means installed in the terminal uses a camera and a microphone to analyze the user's facial expressions and voice in real time and identify the user's emotions.

[1221] 3. Data transmission module

[1222] The input behavioral history, income and expenditure information, and emotion data are sent from the device to the server, where each piece of data is converted into JSON format and sent to the server's API endpoint via an HTTP POST request.

[1223] Server side

[1224] 4. Data Reception Module

[1225] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[1226] 5. AI analysis module

[1227] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and performs future optimization based on the user's income and expenditure patterns, as well as the identified emotions. For example, if the user is feeling stressed, it can suggest spending items that will promote relaxation.

[1228] 6. Data storage and learning module

[1229] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[1230] 7. Spending plan sending module

[1231] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[1232] User side (terminal)

[1233] 8. Result display module

[1234] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. For example, it is displayed as "2023-10-11: Savings - Income: 0, Expenses: 3000".

[1235] Hardware / Software used

[1236] Smartphone: iOS / Android device

[1237] Camera: Smartphone built-in camera

[1238] Microphone: Built-in smartphone microphone

[1239] Server: General-purpose server

[1240] Database: Relational Database Management System (RDBMS)

[1241] AI Analysis Module: Machine Learning Platform

[1242] Examples and prompts

[1243] 1. Example: A user inputs income information (e.g., ¥50,000) and expenditure information (e.g., ¥5,000) on a smartphone, and at the same time, the emotion recognition module recognizes the user as being in a stressful state. This information is sent to the server, and the AI ​​analysis module generates a spending plan, such as "Consider going to a spa the next day to relax, and spend ¥10,000." The generated spending plan is sent to the user's device and displayed visually.

[1244] 2. Example prompt:

[1245] Use the following data to create the best spending plan for you:

[1246] Income: 50,000 yen

[1247] Expenses: 5,000 yen

[1248] Category: Supermarket

[1249] Emotion: Stress, Trust: 0.85

[1250] In this way, the system of the present invention realizes planned and efficient expenditure management while taking into consideration the user's feelings.

[1251] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1252] Step 1:

[1253] The user inputs their activity history and income / expense information. Using a smartphone or computer, the user inputs their daily income, expenses, and activity history. The input data includes amounts, categories, dates, etc. A specific example of how this works is when the user inputs "purchases at the supermarket, income ¥50,000, expenditure ¥5,000" into the application's input field. The input data is converted into JSON format and prepared.

[1254] Step 2:

[1255] The device's emotion recognition module identifies the user's emotions. It uses the device's built-in camera and microphone to analyze the user's facial expressions and voice in real time. The collected data is sent to an emotion recognition engine (for example, a facial expression analysis API or a voice analysis API) to obtain the user's emotional state. Specifically, if the user is feeling stressed, the emotion recognition engine will return data such as "Emotion: Stress, Confidence: 0.85."

[1256] Step 3:

[1257] The data transmission module sends the collected data to the server. The collected behavioral history, income and expenditure information, and emotion data are sent to the server's API endpoint. Specifically, JSON-formatted data is sent via an HTTP POST request. In this process, JSON-formatted behavioral history, income and expenditure information, and emotion data are used as input, and the data sent to the server is obtained as output.

[1258] Step 4:

[1259] The server stores the received data in a database. The server receives the transmitted data and temporarily stores it for analysis. If necessary, it is stored in a relational database management system (RDBMS). For example, the data is stored in a database in the format of "user ID, income, expenses, date, emotional state."

[1260] Step 5:

[1261] The server's AI analysis module analyzes the received data and generates an optimal spending plan. The AI ​​analysis module learns from past data and creates an optimal spending plan based on income and expenditure patterns and emotional data. For example, it may create a plan in the form of "since the user is feeling stressed, it suggests spending money on a trip to a spa to relax." The inputs are behavioral history, income and expenditure information, and emotional data, and the output is an optimal spending plan.

[1262] Step 6:

[1263] The generated spending plan is sent from the server to the user's device. The server sends this spending plan to the appropriate endpoint. The generated spending plan is converted to JSON format and sent to the user's device via an HTTP POST request. This ensures accurate transmission based on the user ID.

[1264] Step 7:

[1265] The user's device displays the received spending plan. The device's result display module visually presents the received spending plan to the user. Specifically, the application screen displays something like "Considering going to a spa for relaxation on 2023-10-12, spending 10,000 yen." In this process, the spending plan data sent from the server is used as input, and the spending plan displayed on the screen is obtained as output.

[1266] Through the above steps, a system is realized that can more appropriately manage income and expenditure while taking into account the user's emotions.

[1267] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1268] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1269] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1270] [Fourth embodiment]

[1271] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1272] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1273] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1274] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1275] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1276] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1277] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1278] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1279] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1280] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1281] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1282] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1283] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1284] System Overview

[1285] This invention is a system that automatically collects a user's behavioral history and income / expenditure information and uses AI to generate an optimal spending plan. This system transmits data entered by the user to a server in real time, and the server analyzes the data to provide an efficient spending plan.

[1286] User side (terminal)

[1287] 1. Data Entry Module:

[1288] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[1289] 2. Data transmission module:

[1290] The input data is sent from the terminal to the server, where it is converted to JSON format and sent to the server's API endpoint via an HTTP POST request.

[1291] Server side

[1292] 3. Data receiving module:

[1293] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[1294] 4. AI analysis module:

[1295] The server passes the received data to an AI analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and spending patterns. For example, the AI ​​might suggest "savings" and set the next day's spending at 3,000 yen.

[1296] 5. Data storage and learning module:

[1297] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[1298] 6. Spending Plan Submission Module:

[1299] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[1300] User side (terminal)

[1301] 7. Result display module:

[1302] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. The result is displayed in a format that is easy for the user to understand, for example, "2023-10-11: Savings - Income: 0, Expenses: 3000".

[1303] Specific examples

[1304] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[1305] 2. The device sends this input data to the server.

[1306] 3. The server receives the data and begins analysis using the AI ​​analysis module.

[1307] 4. The AI ​​will analyze and generate an optimal spending plan, such as "Save and spend 3,000 yen on 2023-10-11."

[1308] 5. The server sends the generated spending plan to the user's terminal.

[1309] 6. The user checks the spending plan received on the device and spends money as planned.

[1310] In this way, the system suggests ways for users to spend money comfortably and efficiently, enabling them to save money in a planned manner.

[1311] The processing flow will be explained below.

[1312] Step 1:

[1313] The user uses the terminal to input his / her behavior history and income / expense information.

[1314] The user launches an application.

[1315] The user accesses a new data entry screen.

[1316] Enter the date and time, the activity (e.g., shopping at the supermarket), the amount of income, and the amount of expenses.

[1317] Step 2:

[1318] The terminal transmits the input data to the server.

[1319] The terminal converts the user input data into JSON format.

[1320] The device sends an HTTP POST request to the server's API endpoint and transfers the data.

[1321] Step 3:

[1322] The server receives the data sent from the terminal.

[1323] The server receives an HTTP request that reaches an API endpoint.

[1324] The server parses the received data into JSON format and temporarily stores it in memory.

[1325] Step 4:

[1326] The server stores the received data in a database.

[1327] The server inserts the received data into the appropriate tables in the database.

[1328] The server checks the status of saving to the database and confirms success.

[1329] Step 5:

[1330] The server uses AI to analyze the stored data and generate an optimal spending plan.

[1331] The server passes past user data to the AI ​​analysis module.

[1332] The AI ​​analysis module learns the user's income and spending patterns.

[1333] AI generates an optimal spending plan and returns the results.

[1334] Step 6:

[1335] The server sends the optimal spending plan to the user's terminal.

[1336] The server converts the generated spending plan into JSON format.

[1337] The server identifies the appropriate endpoint based on the user ID and sends it in an HTTP POST request.

[1338] Step 7:

[1339] The terminal receives the spending plan sent from the server.

[1340] The device receives an HTTP response from the server.

[1341] The terminal analyzes the received spending plan and stores it in its internal memory.

[1342] Step 8:

[1343] The terminal displays the received spending plan to the user.

[1344] The terminal converts the spending plan into a format for display on the user interface.

[1345] Users can visually check their specific spending plans through the application.

[1346] Example: "2023-10-11: Savings - Income: 0, Expenses: 3000" will be displayed.

[1347] Step 9:

[1348] The user manages actual expenditures based on the expenditure plan displayed on the terminal.

[1349] The user spends money according to a spending plan.

[1350] If necessary, provide new inputs and repeat the cycle.

[1351] Example 1

[1352] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1353] In today's world, it takes time and effort for users to manage their income and expenses all by themselves, and it is difficult to create an efficient spending plan. In particular, advanced analysis and continuous learning are required to reduce wasteful spending and plan savings, but this is extremely difficult for users to do on their own. Therefore, there is a need for a system that allows users to automatically create an efficient spending plan without any effort.

[1354] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1355] In this invention, the server includes a means for temporarily storing received data for analysis, an AI analysis means for learning from past data and generating an optimal spending plan based on the user's income and spending patterns, and a means for transmitting the generated spending plan to the user's terminal, thereby enabling the user to automatically obtain an efficient and efficient spending plan.

[1356] A "user" is an entity that utilizes the system to input behavioral history and income / expenditure information and receive an optimal spending plan.

[1357] "Behavioral history" is information about the user's daily behavior, and is data that records specific activities such as shopping, eating, and traveling.

[1358] "Income and expenditure information" refers to detailed information such as specific amounts, dates, and items regarding the income and expenditures earned by the user.

[1359] A "terminal" refers to a device through which a user inputs their behavioral history and income / expense information and communicates with the system, specifically a device such as a smartphone or PC.

[1360] "Server" refers to a central processing unit that receives data sent from users and performs analysis and database management.

[1361] "Means for transmitting data in real time" refers to a function for instantly transmitting data entered by a user from a terminal to a server.

[1362] "AI analysis means" refers to a function that uses artificial intelligence technology built into the server to learn the user's income and expenditure patterns and generate an optimal expenditure plan.

[1363] The "optimal spending plan" refers to a planned spending method that minimizes waste and is generated based on the user's income and spending patterns.

[1364] "Database" refers to a storage device for systematically storing received and analyzed data for later use in learning and analysis.

[1365] The "means for transmitting a spending plan" refers to a function for transmitting the optimal spending plan generated by the server to the user's terminal.

[1366] The "means for displaying a spending plan" refers to a function for visually displaying the transmitted spending plan on the user's terminal.

[1367] This invention is a system that automatically collects a user's behavioral history and income / expenditure information and uses AI to generate an optimal spending plan. This system transmits data entered by the user to a server in real time, and the server analyzes the data to provide an efficient spending plan.

[1368] User side (terminal)

[1369] 1. Data Entry Module:

[1370] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[1371] 2. Data transmission module:

[1372] The entered data is sent from the terminal to the server, where it is converted to JSON format and sent to the server's API endpoint via an HTTP POST request.

[1373] Server side

[1374] 3. Data receiving module:

[1375] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[1376] 4. AI analysis module:

[1377] The server passes the received data to an AI analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and spending patterns. For example, the AI ​​might suggest "savings" and set the next day's spending at 3,000 yen.

[1378] 5. Data storage and learning module:

[1379] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[1380] 6. Spending Plan Submission Module:

[1381] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[1382] User side (terminal)

[1383] 7. Result display module:

[1384] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. The result is displayed in a format that is easy for the user to understand, for example, "2023-10-11: Savings - Income: 0, Expenses: 3000".

[1385] Specific examples

[1386] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[1387] 2. The device sends this input data to the server.

[1388] 3. The server receives the data and begins analysis using the AI ​​analysis module.

[1389] 4. The AI ​​will analyze and generate an optimal spending plan, such as "Save and spend 3,000 yen on 2023-10-11."

[1390] 5. The server sends the generated spending plan to the user's terminal.

[1391] 6. The user checks the spending plan received on the device and spends money as planned.

[1392] Example prompts for generative AI models

[1393] "I would like to develop a system that allows users to record their daily income and expenses and automatically suggests optimal spending plans. Please explain in detail how this system works, how the user sends the input data, how the server analyzes it to generate the optimal spending plan, and how it sends it to the user's device."

[1394] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1395] Step 1:

[1396] The user inputs their daily activity history and income and expenditure information. Specifically, the user uses a device (such as a smartphone or PC) to input data into a dedicated input interface. For example, the user inputs "2023-10-10, purchases at supermarket, income: ¥50,000, expenditure: ¥5,000."

[1397] Input: User behavior history and income / expense information

[1398] Output: The input data

[1399] Step 2:

[1400] The device sends the input data to the server in real time. The data transmission module converts the data into JSON format and sends it to the server's API endpoint using an HTTP POST request. The data is encrypted during this process to maintain security.

[1401] Input: Data entered

[1402] Output: Data converted to JSON format is sent to the server

[1403] Step 3:

[1404] The server receives the data in real time and stores it temporarily. The data reception module receives the data that arrives at the server in real time and stores the data temporarily in memory. If necessary, it is also stored in a database.

[1405] Input: Data sent in JSON format

[1406] Output: Temporarily saved data

[1407] Step 4:

[1408] The server passes the received data to the AI ​​analysis module, which begins analysis. Specifically, the AI ​​analysis module, which has learned about past income and expenditure patterns, takes in the data and analyzes the user's expenditure patterns based on that data. This analysis generates a spending plan, such as "It would be optimal to save 3,000 yen the next day."

[1409] Input:Temporarily saved data

[1410] Output: Generated optimal spending plan

[1411] Step 5:

[1412] The server stores the analysis results in a database. The data storage and learning module stores the generated spending plan in a database and uses it to improve the accuracy of future forecasts.

[1413] Input: Generated optimal spending plan

[1414] Output: Analysis results stored in a database

[1415] Step 6:

[1416] The server sends the generated spending plan to the user's device. The spending plan sending module recognizes the user's ID and sends the spending plan to the appropriate endpoint. For example, a plan such as "2023-10-11: Savings - Income: 0, Expenses: ¥3,000" is sent in JSON format.

[1417] Input: Generated optimal spending plan

[1418] Output: Spending plan submitted in JSON format

[1419] Step 7:

[1420] The user checks the received spending plan on the device. The result display module visually displays the spending plan, allowing the user to check the specific spending plan at a glance. For example, the device screen will display "2023-10-11: Savings - Income: 0, Expenses: ￥3,000."

[1421] Input: Spending plan submitted in JSON format

[1422] Output: A visually displayed spending plan

[1423] (Application example 1)

[1424] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1425] In today's world, many users use electronic payments, but there is a lack of appropriate systems for effectively managing their spending. This makes it difficult for users to curb wasteful spending. Furthermore, the time and effort required to manually input income and expenditure information makes real-time spending management difficult. Furthermore, there is a lack of mechanisms for automatically generating and providing optimal spending plans to users. To solve these issues, a system is needed that automatically collects users' electronic payment information and generates optimal spending plans based on that information.

[1426] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1427] In this invention, the server includes means for inputting a user's behavioral history and income / expenditure information, means for transmitting the input data to the server, means for the server to analyze the received data using AI and generate an optimal spending plan, means for automatically collecting electronic payment expenditure information, means for transmitting the generated spending plan to the user, and means for displaying the transmitted spending plan to the user. This allows the user to automatically collect and analyze electronic payment information and obtain an efficient spending plan in real time.

[1428] "User" refers to an individual who uses this system to input their own behavioral history and income / expense information and receive an optimal spending plan.

[1429] "Behavioral history" refers to a record of a user's daily activities and specific actions, and specifically includes information such as shopping, meals, and use of transportation.

[1430] "Income and expenditure information" refers to detailed data on a user's income (salary and bonuses) and expenses (shopping, meals, rent, etc.).

[1431] "Server" refers to a computer system for receiving, storing, analyzing, and transmitting data over the Internet.

[1432] "AI" stands for artificial intelligence and refers to technology that analyzes a user's behavioral history and income and expenditure information to generate an optimal spending plan.

[1433] A "spending plan" refers to a specific plan that AI generates based on the user's data, suggesting how to spend money efficiently.

[1434] "Electronic payment information" refers to data containing details of transactions made by a user using an electronic payment service, including the date and time of the transaction, the amount, and the transaction partner.

[1435] "Database" refers to a data structure that organizes and stores received data so that it can be quickly accessed when needed.

[1436] "Module" refers to an independent piece of software designed to perform a specific function.

[1437] The following describes the mode for carrying out the invention: This system collects and analyzes a user's behavioral history and income / expenditure information, and generates and presents an optimal spending plan, enabling efficient spending management.

[1438] System Overview

[1439] The system is primarily comprised of devices such as smartphones or PCs, a server, and an AI analysis module. It also incorporates a mechanism for automatically collecting users' electronic payment information, enabling real-time expenditure management.

[1440] Hardware used

[1441] Smartphones and PCs (user devices)

[1442] Cloud server (data collection, analysis, storage)

[1443] Database Server

[1444] Software used

[1445] Mobile Applications (iOS and Android)

[1446] Server-side platforms (AWS and Google Cloud)

[1447] Database management systems (MySQL, PostgreSQL)

[1448] AI analysis module (TensorFlow, PyTorch)

[1449] Program processing overview

[1450] 1. Data Collection

[1451] When a user makes an electronic payment, payment information (e.g., transaction date and time, amount, customer, etc.) is automatically collected in the "Smart Spending Planner" application. Users can also manually enter their activity history and income and expenditure information.

[1452] 2. Data Transmission

[1453] The data collected on the user's device is sent to the server in real time, where it is converted into JSON format and sent to the server's API endpoint.

[1454] 3. Data Receipt and Storage

[1455] The server receives the data sent from the terminal in real time, temporarily stores it for analysis, and stores it in a database as needed.

[1456] 4. AI analysis

[1457] The data stored on the server is analyzed by an AI analysis module, which learns from past income and expenditure data and behavioral history to generate an optimal spending plan for the user.

[1458] 5. Submit spending plan

[1459] The generated optimal spending plan is then resent from the server to the user's device, where the user ID is recognized and the spending plan is sent to the appropriate endpoint.

[1460] 6. Results display

[1461] The user's terminal displays the received spending plan to the user, who can visually check the specific spending plan on the application screen.

[1462] Specific examples

[1463] If a user spends 5,000 yen at a supermarket on October 10, 2023, this spending information will be automatically collected by the app. Taking into account the next day's spending, the AI ​​will suggest "limit spending on October 11, 2023 to 3,000 yen and save 2,000 yen." This spending plan will be sent to the user's smartphone, where the user can confirm the plan on the screen.

[1464] Example prompt sentence:

[1465] User ID: 12345

[1466] Expenditure information: 2023-10-10, Supermarket, 5,000 yen

[1467] In this way, the system suggests ways for users to spend money in a way that is comfortable and efficient, enabling them to save money in a planned manner.

[1468] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1469] Step 1:

[1470] When a user makes an electronic payment, the electronic payment information is automatically collected on a device such as a smartphone or PC. Specifically, expenditure information (transaction date and time, amount, business partner, etc.) is automatically acquired, and the user may also manually enter their activity history and income and expenditure information. In this case, the input includes the electronic payment information and manually entered data, and the output is data temporarily stored on the device.

[1471] Step 2:

[1472] The input data is sent from the device to the server in real time. During this transmission process, the data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. The input is temporary data stored in the device, and the output is JSON format data received by the server.

[1473] Step 3:

[1474] The server receives data sent from the terminal in real time. The received data is processed by the data receiving module and temporarily stored in memory. The input is the JSON format data sent to the server, and the output is the temporarily stored data.

[1475] Step 4:

[1476] The server stores the received data in a database. In this process, the data is stored in the appropriate tables using a database management system (e.g., MySQL, PostgreSQL). The input is the temporarily stored data, and the output is the data stored in the database.

[1477] Step 5:

[1478] The server passes the data to the AI ​​analysis module, which begins analysis. The AI ​​analysis module is built using TensorFlow and PyTorch and generates an optimal spending plan based on a model trained on past income and expenditure data. The input is the data stored in the database, and the output is the generated spending plan.

[1479] Step 6:

[1480] The server sends the generated optimal spending plan to the user's device. During this process, the data is converted to JSON format, and the user ID is recognized and sent to the appropriate endpoint. The input is the generated spending plan (in JSON format), and the output is the spending plan received by the user's device.

[1481] Step 7:

[1482] The user's terminal displays the received spending plan to the user, allowing the user to visually check the specific spending plan on the terminal screen. The input is the spending plan received from the server, and the output is the spending plan converted into a format that the user can visually check.

[1483] In this way, the optimal spending plan is provided to the user through each step.

[1484] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1485] System Overview

[1486] This invention is a system that automatically collects user behavior history and income / expenditure information, recognizes user emotions by combining it with an emotion engine, and generates an optimal spending plan using AI. This system sends data entered by the user to a server in real time, and the server analyzes the data and the user's emotions to provide an efficient spending plan.

[1487] User side (terminal)

[1488] 1. Data Entry Module:

[1489] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[1490] 2. Emotion Recognition Module:

[1491] The device's built-in emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time and recognize the user's emotions.

[1492] 3. Data transmission module:

[1493] The input behavioral history, income and expenditure information, and emotion data are sent from the device to the server, where each piece of data is converted into JSON format and sent to the server's API endpoint via an HTTP POST request.

[1494] Server side

[1495] 4. Data receiving module:

[1496] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[1497] 5. AI analysis module:

[1498] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and performs future optimization based on the user's income and expenditure patterns, as well as recognized emotions. For example, if the user is feeling stressed, it can suggest spending items that will promote relaxation.

[1499] 6. Data Storage and Learning Module:

[1500] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[1501] 7. Spending Plan Submission Module:

[1502] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[1503] User side (terminal)

[1504] 8. Result display module:

[1505] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. For example, it is displayed as "2023-10-11: Savings - Income: 0, Expenses: 3000".

[1506] Specific examples

[1507] 1. The user enters their daily activity history and income / expense information into the device. For example, they enter "2023-10-10, purchases at the supermarket, income ¥50,000, expenditure ¥5,000."

[1508] 2. The emotion recognition module analyzes the user's facial expressions and voice and recognizes that the user is feeling stressed.

[1509] 3. The device sends the input data and emotion data to the server.

[1510] 4. The server receives the data and begins analysis using the AI ​​analysis module.

[1511] 5. The AI ​​analysis module comprehensively analyzes behavioral history, income and expenditure information, and emotional data to generate an optimal spending plan. For example, it might suggest, "Consider going to a spa the next day for relaxation and spend 10,000 yen."

[1512] 6. The server sends the generated spending plan to the user's terminal.

[1513] 7. The user checks the spending plan received on the device and spends money as planned.

[1514] In this way, the system takes the user's emotions into consideration and suggests ways to spend money comfortably and efficiently, enabling planned savings and stress reduction.

[1515] The processing flow will be explained below.

[1516] Step 1:

[1517] The user uses the terminal to input his / her behavior history and income / expense information.

[1518] The user launches an application.

[1519] The user accesses a new data entry screen.

[1520] Enter the date and time, the activity (e.g., shopping at the supermarket), the amount of income, and the amount of expenses.

[1521] Step 2:

[1522] The emotion engine installed in the device recognizes the user's emotions.

[1523] The device's camera captures the user's facial expressions, which are then analyzed by an emotion recognition algorithm.

[1524] The device's microphone records the user's voice, and the voice analysis module analyzes emotions.

[1525] The emotion recognition result is extracted and analyzed as, for example, "the user is feeling stressed."

[1526] Step 3:

[1527] The terminal transmits the behavior history, income and expenditure information, and emotion data to the server.

[1528] The terminal converts the collected data into JSON format.

[1529] The device sends an HTTP POST request to the server's API endpoint and transfers the data.

[1530] Step 4:

[1531] The server receives the data sent from the terminal.

[1532] The server receives the HTTP request that reaches the API endpoint and parses the data in JSON format.

[1533] Received data is temporarily stored in memory.

[1534] Step 5:

[1535] The server stores the received data in a database.

[1536] The server inserts the parsed data into the appropriate tables in the database.

[1537] Check the status of saving to the database and confirm that saving is successful.

[1538] Step 6:

[1539] The server uses AI to analyze the stored data and generate an optimal spending plan.

[1540] The server passes the stored user data and emotion data to the AI ​​analysis module.

[1541] The AI ​​analysis module learns and analyzes the user's past income and expenditure patterns and current emotional state.

[1542] The AI ​​generates an optimal spending plan and outputs a plan such as, "The user is feeling stressed, so we suggest a visit to a spa to relax (cost: 10,000 yen)."

[1543] Step 7:

[1544] The server transmits the generated spending plan to the user's terminal.

[1545] The server converts the generated spending plan into JSON format.

[1546] The server identifies the appropriate endpoint based on the user ID and sends it in an HTTP POST request.

[1547] Step 8:

[1548] The terminal receives the spending plan sent from the server.

[1549] The terminal receives the HTTP response from the server and parses the spending plan data.

[1550] The received spending plan data is stored in an internal memory.

[1551] Step 9:

[1552] The terminal displays the received spending plan to the user.

[1553] The terminal converts the spending plan into a display format.

[1554] Users can visually check their specific spending plans through the application.

[1555] For example, it might show "2023-10-11: Spa visit - Income: 0, Expenses: 10000".

[1556] Step 10:

[1557] The user manages actual expenditures based on the expenditure plan displayed on the terminal.

[1558] The user follows a spending plan, books a spa visit, and spends according to budget.

[1559] If necessary, enter new behavioral history and income / expense information, update your emotional state, and continue using the system.

[1560] This series of steps allows users to spend money comfortably and efficiently while taking their emotional state into account.

[1561] Example 2

[1562] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1563] In modern society, users are required to create efficient and emotionally-based spending plans while recording their daily activities, income, and expenses. However, conventional systems have difficulty providing optimal spending plans that take into account the user's emotional state. This makes users prone to wasteful spending, and presents challenges in achieving optimal savings and stress management.

[1564] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1565] In this invention, the server includes means for a user to input behavioral history and income / expenditure information, means for transmitting the input data to the server, means for an emotion engine installed in the terminal to recognize the user's emotions, means for the server to analyze the received data and emotion data using AI and generate an optimal spending plan, means for transmitting the generated spending plan to the user, and means for displaying the transmitted spending plan to the user. This makes it possible to provide an optimal spending plan that takes the user's emotions into consideration in real time.

[1566] "Behavioral history" is data that records the user's daily activities and behaviors.

[1567] "Income and expenditure information" is data in which a user specifically records his or her daily income and expenditure.

[1568] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice and recognizes their emotions.

[1569] A "server" is a computer system that receives, stores, analyzes, and transmits data.

[1570] "AI" stands for Artificial Intelligence, a technology that analyzes a user's behavioral history, income and expenditure information, and emotional data to generate an optimal spending plan.

[1571] A "spending plan" is a plan that suggests how to spend money in the future based on the user's income and expenses.

[1572] A "database" is a system for systematically storing and managing received data.

[1573] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format for structurally representing data in text format.

[1574] An "HTTP POST request" is a protocol for sending data to a web server.

[1575] The present invention is a system that automatically collects a user's behavioral history and income / expenditure information, recognizes the user's emotions using an emotion engine, and provides the user with an optimal spending plan. This system mainly consists of the following components:

[1576] User side (terminal)

[1577] 1. Data Entry Module

[1578] Users input their daily activity history and income and expenditure information using devices such as smartphones or PCs. This data input module has a user-friendly interface, allowing users to input information hassle-free. For example, a user might input data such as "I went shopping at the supermarket on 2023-10-10, and recorded an income of ¥50,000 and an expenditure of ¥5,000."

[1579] 2. Emotion Recognition Module

[1580] The emotion engine installed on the device uses the camera and microphone to analyze the user's facial expressions and voice in real time to recognize the user's emotions. Specifically, it uses the Facial Expression Recognition API and Speech Emotion Recognition API. For example, when the user is feeling stressed, it can recognize this from changes in facial expression and voice.

[1581] 3. Data transmission module

[1582] The entered behavioral history, income and expenditure information, and emotional data are sent from the device to the server. During this process, each piece of data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. For example, the data sent from the device will be {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}.

[1583] Server side

[1584] 4. Data Reception Module

[1585] The server receives data sent from the device in real time, temporarily stores the data for analysis, and then stores it in a database after analysis.

[1586] 5. AI analysis module

[1587] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module, which then generates an optimal spending plan. The AI ​​analysis module learns from past data and optimizes future spending based on the user's income and expenditure patterns and perceived emotions. For example, if the user is feeling stressed, it may suggest "consider going to the spa the next day and spend 10,000 yen" to encourage relaxation.

[1588] 6. Data storage and learning module

[1589] The server stores the analyzed data in a database and performs learning to improve future prediction accuracy, allowing the system to propose spending plans that respond to changes in the user's behavior and emotions.

[1590] 7. Spending plan sending module

[1591] The server resends the generated optimal spending plan to the user's device. The server recognizes the user ID and sends the spending plan to the appropriate endpoint.

[1592] User side (terminal)

[1593] 8. Result display module

[1594] The device displays the received spending plan to the user, for example, "2023-10-11: Relax at the spa - Spending: ¥10,000."

[1595] Specific examples

[1596] The user opens the dedicated app and enters their daily income and expenditure information. For example, they might record "Shopping at the supermarket on 2023-10-10, income 50,000 yen, expenditure 5,000 yen." At the same time, the emotion recognition module uses the camera and microphone to analyze the user's emotions and recognizes that the user is feeling stressed. This data is converted into JSON format and sent to the server. The server receives the data, and the AI ​​analysis module generates an optimal spending plan and resends it to the user's device. The user then checks the spending plan displayed on the device screen and spends money as planned.

[1597] Prompt Sentence Examples

[1598] An example of an input prompt for the generative AI model is, "Based on daily income and expenditure information, behavioral history, and emotional data, please suggest a spending plan that will allow the user to save money comfortably."

[1599] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1600] Step 1: Data Entry Module

[1601] Users use a smartphone or computer to enter their daily activity history and income and expenditure information through a dedicated interface. This interface has an intuitive design, allowing users to enter data easily. The entered information is saved in the database in the format of date, income, and expense. For example, "I went shopping at the supermarket on 2023-10-10 and recorded an income of ¥50,000 and an expense of ¥5,000." The input data format is {"date": "2023-10-10", "income": 50000, "expense": 5000}.

[1602] Step 2: Emotion Recognition Module

[1603] The device's camera and microphone are used to analyze the user's facial expressions and voice in real time. The analysis is performed using the Facial Expression Recognition API and Speech Emotion Recognition API. For example, the device captures the user's facial expressions, such as smile or anger, and analyzes their emotional state as numerical data. For example, if the user's facial expression is recognized as "stressed," the corresponding data will be "emotion": "stress."

[1604] Step 3: Data transmission module

[1605] The device sends the information collected and analyzed in steps 1 and 2 to the server. The data is converted to JSON format and sent to the server's API endpoint via an HTTP POST request. For example, the data sent is {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}. At this point, the device receives a response from the server to confirm whether the data transmission was successful.

[1606] Step 4: Data receiving module

[1607] The server receives data sent from the device in real time. The received data is temporarily stored in memory and then stored in a database. For example, the received data is saved as {"date": "2023-10-10", "income": 50000, "expense": 5000, "emotion": "stress"}. This data becomes the input for the next AI analysis step.

[1608] Step 5: AI Analysis Module

[1609] The server passes the behavioral history, income and expenditure information, and emotional data collected from the database to the AI ​​analysis module. The AI ​​analysis module generates an optimal spending plan based on this data. To analyze the data, a machine learning model is used to take into account past income and expenditure patterns and emotional state. For example, if a user is feeling stressed, a spending plan such as "Consider going to a spa the next day and spend 10,000 yen" may be suggested to promote relaxation. The generated spending plan data is in the format {"plan_date": "2023-10-11", "recommendation": "spend 10000 on spa"}.

[1610] Step 6: Data storage and learning module

[1611] The server saves the generated spending plan in a database and uses it as learning data to further improve the accuracy of future predictions. For example, the saved data is in the format {"user_id": "12345", "plan": "spend 10000 on spa", "emotion": "stress"}. This data will enable more accurate suggestions in the next analysis.

[1612] Step 7: Spending Plan Submission Module

[1613] The server then sends the generated optimal spending plan back to the user's device. The server recognizes the user ID and sends the data to the corresponding device. For example, the data to be sent is {"user_id": "12345", "plan_date": "2023-10-11", "recommendation": "spend 10000 on spa"}.

[1614] Step 8: Results display module

[1615] The device displays the received spending plan to the user. The user can visually confirm and understand the proposed spending plan. For example, the device screen will display "Spend 10,000 yen to relax at the spa on 2023-10-11." This result display allows the user to spend according to the plan.

[1616] (Application example 2)

[1617] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1618] In modern society, it is difficult for users to efficiently manage their daily expenses and income. In particular, there is a need for an optimal spending plan that takes into account the impact of users' emotions on spending behavior. However, conventional systems only analyze behavioral history and income / expense information, and do not consider users' emotions, which means they are unable to create effective spending plans.

[1619] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input behavioral history and income and expenditure information; means for transmitting the input data to the server; means for the server to analyze the received data using AI and generate an optimal spending plan; means for transmitting the generated spending plan to the user; means for displaying the transmitted spending plan to the user; emotion recognition means for identifying the user's emotions; means for transmitting emotion data to the server; and means for analyzing income and expenditure patterns using the emotion data and adjusting expenses to an optimal level. This makes it possible to optimize the spending plan while taking the user's emotions into consideration and support planned financial management.

[1620] "Behavioral history" is a record of specific activities and events in a user's daily life.

[1621] "Income and expenditure information" is data relating to the user's income and expenditure.

[1622] A "server" is a computer system for receiving, storing, and processing data.

[1623] "AI analytics" is the process of using artificial intelligence to analyze data and generate insights and predictions.

[1624] A "spending plan" is a specific guideline or suggestion for effectively managing a user's future income and expenses.

[1625] "Sending to user" is the act of sending data from the server to the client device.

[1626] "Displaying to the user" means visually presenting information on the screen of a client device.

[1627] "Emotion recognition means" refers to techniques and methods for identifying a user's emotions.

[1628] "Emotion data" is digital information that indicates the user's emotional state.

[1629] "Analyzing income and expenditure patterns" refers to analyzing a user's past income and expenditure data to derive certain trends and patterns.

[1630] This invention is a system that automatically collects a user's behavioral history and income / expenditure information, identifies the user's emotions by combining emotion recognition means, and generates an optimal spending plan using AI. This system transmits data entered by the user to a server in real time, and the server analyzes the data and the user's emotions to provide an efficient spending plan.

[1631] User side (terminal)

[1632] 1. Data Entry Module

[1633] Users use devices such as smartphones or PCs to enter their daily activity history and income and expenditure information. The input interface has a simple, user-friendly design, allowing users to enter data without hassle. For example, a user enters an income of ¥50,000 and an expenditure of ¥5,000 for "supermarket shopping."

[1634] 2. Emotion Recognition Module

[1635] An emotion recognition means installed in the terminal uses a camera and a microphone to analyze the user's facial expressions and voice in real time and identify the user's emotions.

[1636] 3. Data transmission module

[1637] The input behavioral history, income and expenditure information, and emotion data are sent from the device to the server, where each piece of data is converted into JSON format and sent to the server's API endpoint via an HTTP POST request.

[1638] Server side

[1639] 4. Data Reception Module

[1640] The server receives data sent from the device in real time, temporarily stores the data for analysis, and stores it in a database as needed.

[1641] 5. AI analysis module

[1642] The server passes the received behavioral history, income and expenditure information, and emotional data to the AI ​​analysis module to generate an optimal spending plan. The AI ​​analysis module learns from past data and performs future optimization based on the user's income and expenditure patterns, as well as the identified emotions. For example, if the user is feeling stressed, it can suggest spending items that will promote relaxation.

[1643] 6. Data storage and learning module

[1644] The server stores the analyzed data in a database, which allows the AI ​​to learn in order to improve the accuracy of future predictions.

[1645] 7. Spending plan sending module

[1646] The generated optimal spending plan is then resent to the user's device by the server, which recognizes the user ID and sends the spending plan to the appropriate endpoint.

[1647] User side (terminal)

[1648] 8. Result display module

[1649] The device displays the received spending plan to the user. The user can visually check the specific spending plan on the device screen. For example, it is displayed as "2023-10-11: Savings - Income: 0, Expenses: 3000".

[1650] Hardware / Software used

[1651] Smartphone: iOS / Android device

[1652] Camera: Smartphone built-in camera

[1653] Microphone: Built-in smartphone microphone

[1654] Server: General-purpose server

[1655] Database: Relational Database Management System (RDBMS)

[1656] AI Analysis Module: Machine Learning Platform

[1657] Examples and prompts

[1658] 1. Example: A user inputs income information (e.g., ¥50,000) and expenditure information (e.g., ¥5,000) on a smartphone, and at the same time, the emotion recognition module recognizes the user as being in a stressful state. This information is sent to the server, and the AI ​​analysis module generates a spending plan, such as "Consider going to a spa the next day to relax, and spend ¥10,000." The generated spending plan is sent to the user's device and displayed visually.

[1659] 2. Example prompt:

[1660] Use the following data to create the best spending plan for you:

[1661] Income: 50,000 yen

[1662] Expenses: 5,000 yen

[1663] Category: Supermarket

[1664] Emotion: Stress, Trust: 0.85

[1665] In this way, the system of the present invention realizes planned and efficient expenditure management while taking into consideration the user's feelings.

[1666] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1667] Step 1:

[1668] The user inputs their activity history and income / expense information. Using a smartphone or computer, the user inputs their daily income, expenses, and activity history. The input data includes amounts, categories, dates, etc. A specific example of how this works is when the user inputs "purchases at the supermarket, income ¥50,000, expenditure ¥5,000" into the application's input field. The input data is converted into JSON format and prepared.

[1669] Step 2:

[1670] The device's emotion recognition module identifies the user's emotions. It uses the device's built-in camera and microphone to analyze the user's facial expressions and voice in real time. The collected data is sent to an emotion recognition engine (for example, a facial expression analysis API or a voice analysis API) to obtain the user's emotional state. Specifically, if the user is feeling stressed, the emotion recognition engine will return data such as "Emotion: Stress, Confidence: 0.85."

[1671] Step 3:

[1672] The data transmission module sends the collected data to the server. The collected behavioral history, income and expenditure information, and emotion data are sent to the server's API endpoint. Specifically, JSON-formatted data is sent via an HTTP POST request. In this process, JSON-formatted behavioral history, income and expenditure information, and emotion data are used as input, and the data sent to the server is obtained as output.

[1673] Step 4:

[1674] The server stores the received data in a database. The server receives the transmitted data and temporarily stores it for analysis. If necessary, it is stored in a relational database management system (RDBMS). For example, the data is stored in a database in the format of "user ID, income, expenses, date, emotional state."

[1675] Step 5:

[1676] The server's AI analysis module analyzes the received data and generates an optimal spending plan. The AI ​​analysis module learns from past data and creates an optimal spending plan based on income and expenditure patterns and emotional data. For example, it may create a plan in the form of "since the user is feeling stressed, it suggests spending money on a trip to a spa to relax." The inputs are behavioral history, income and expenditure information, and emotional data, and the output is an optimal spending plan.

[1677] Step 6:

[1678] The generated spending plan is sent from the server to the user's device. The server sends this spending plan to the appropriate endpoint. The generated spending plan is converted to JSON format and sent to the user's device via an HTTP POST request. This ensures accurate transmission based on the user ID.

[1679] Step 7:

[1680] The user's device displays the received spending plan. The device's result display module visually presents the received spending plan to the user. Specifically, the application screen displays something like "Considering going to a spa for relaxation on 2023-10-12, spending 10,000 yen." In this process, the spending plan data sent from the server is used as input, and the spending plan displayed on the screen is obtained as output.

[1681] Through the above steps, a system is realized that can more appropriately manage income and expenditure while taking into account the user's emotions.

[1682] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1683] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1684] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1685] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1686] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1687] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1688] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1689] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1690] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1691] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1692] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1693] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1694] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1695] 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.

[1696] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1697] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1698] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1699] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1700] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1701] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1702] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1703] The following is further disclosed regarding the above embodiment.

[1704] (Claim 1)

[1705] A means for a user to input behavior history and income and expenditure information;

[1706] means for transmitting the input data to a server;

[1707] A means for the server to analyze the received data using AI to generate an optimal spending plan;

[1708] means for transmitting the generated spending plan to the user;

[1709] means for displaying the submitted spending plan to the user;

[1710] A system including:

[1711] (Claim 2)

[1712] The system of claim 1 , wherein the server further comprises means for storing the received data in a database.

[1713] (Claim 3)

[1714] 10. The system of claim 1, wherein the AI ​​further comprises means for learning a user's past income and spending patterns.

[1715] "Example 1"

[1716] (Claim 1)

[1717] A means for a user to input behavior history and income / expense information;

[1718] means for transmitting input data to a server in real time;

[1719] A means for temporarily storing the data received by the server for analysis;

[1720] The server has an AI analysis means for learning from past data and generating an optimal spending plan based on the user's income and spending patterns;

[1721] means for transmitting the generated spending plan to a user terminal;

[1722] means for displaying the transmitted spending plan on a user terminal;

[1723] A system including:

[1724] (Claim 2)

[1725] 10. The system of claim 1, wherein the server further comprises means for storing the received data in a database.

[1726] (Claim 3)

[1727] 10. The system of claim 1, wherein the AI ​​analysis means includes learning to predict upcoming spending based on income and spending patterns.

[1728] "Application Example 1"

[1729] (Claim 1)

[1730] A means for a user to input behavior history and income and expenditure information;

[1731] means for transmitting the input data to a server;

[1732] A means for the server to analyze the received data using AI to generate an optimal spending plan;

[1733] means for automatically collecting electronic payment expenditure information;

[1734] means for transmitting the generated spending plan to the user;

[1735] means for displaying the submitted spending plan to the user;

[1736] A system including:

[1737] (Claim 2)

[1738] 10. The system of claim 1, wherein the server further comprises means for storing the received data in a database.

[1739] (Claim 3)

[1740] 10. The system of claim 1, wherein the AI ​​further comprises means for learning a user's past income and spending patterns.

[1741] "Example 2: Combining Emotion Engines"

[1742] (Claim 1)

[1743] A means for a user to input behavior history and income and expenditure information;

[1744] means for transmitting the input data to a server;

[1745] A means for an emotion engine installed in the device to recognize the user's emotion;

[1746] A means for analyzing the data and emotion data received by the server using AI to generate an optimal spending plan;

[1747] means for transmitting the generated spending plan to the user;

[1748] means for displaying the submitted spending plan to the user;

[1749] A system including:

[1750] (Claim 2)

[1751] The system of claim 1 , wherein the server further comprises means for storing the received data in a database.

[1752] (Claim 3)

[1753] 10. The system of claim 1, wherein the AI ​​further comprises means for learning a user's past income and expenditure patterns and recognized emotion data.

[1754] "Application example 2 when combining emotion engines"

[1755] (Claim 1)

[1756] A means for a user to input behavior history and income and expenditure information;

[1757] means for transmitting the input data to a server;

[1758] A means for the server to analyze the received data using AI to generate an optimal spending plan;

[1759] means for transmitting the generated spending plan to the user;

[1760] means for displaying the submitted spending plan to the user;

[1761] emotion recognition means for identifying an emotion of a user;

[1762] means for transmitting emotion data to a server;

[1763] A means for analyzing income and expenditure patterns using emotion data and adjusting optimal expenditures;

[1764] A system including:

[1765] (Claim 2)

[1766] The system of claim 1 , wherein the server further comprises means for storing the received data in a database.

[1767] (Claim 3)

[1768] 10. The system of claim 1, wherein the AI ​​further comprises means for learning a user's past income and spending patterns. [Explanation of symbols]

[1769] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user to input behavior history and income and expenditure information; means for transmitting the input data to a server; A means for the server to analyze the received data using AI to generate an optimal spending plan; means for transmitting the generated spending plan to the user; means for displaying the submitted spending plan to the user; A system including:

2. The system of claim 1 , wherein the server further comprises means for storing the received data in a database.

3. The system of claim 1 , wherein the AI ​​further comprises means for learning a user's past income and spending patterns.

Images