System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting payment information of users who want to review household budgets; means for transmitting the payment information to a server; means for analyzing the payment information using a AI; means for generating advice for improving household budgets based on the analysis result; and means for providing the generated advice to the users.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With rising prices in recent years, many households feel the need to improve their finances. However, when reviewing their finances, they often do not know which items to adjust and how. Furthermore, consulting a traditional financial planner is expensive and takes time and effort, making it unrealistic for many households. Given this background, there is a need for a simple and efficient way to review household finances. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a means for inputting payment data of a user who wishes to review their household finances, a means for transmitting the payment data to a server, an analysis means using AI to perform comparative analysis based on the payment data with average expenditure data of comparable households, a means for generating advice for improving household finances based on the results of the analysis, and a means for providing the generated advice to the user.

[0006] Users enter payment data for rent, mortgage, utilities, communication, education, and various insurances into their device and send it to the server. The AI ​​on the server analyzes this data and compares it with the average expenditure data of similar households to identify outliers for each expenditure item and analyze the causes. Based on the analysis results, specific improvement proposals are generated and provided to the user, providing efficient support for reviewing household finances.

[0007] "Payment data" refers to information on the amounts of various expense items in a household, and specifically includes expenditures such as rent, loans, utility bills, communication costs, education costs, and various insurances.

[0008] "Server" refers to a computer system for receiving, storing, and analyzing payment data sent from a user's terminal.

[0009] "Comparable households" refer to households with similar income, family structure, residential area, and other conditions to the user's household, and are the households with which average expenditure data can be compared.

[0010] "AI" (artificial intelligence) refers to a program that learns and analyzes based on input data, and in this invention it has the function of analyzing payment data and identifying outliers and areas for improvement.

[0011] "Analysis methods" refers to the process of using AI to compare users' payment data with average expenditure data from comparable households, identify outliers and excessive expenditure items, and analyze the causes.

[0012] The "advice generation means" refers to the process of creating specific household improvement proposals that can be implemented by the user based on information on abnormal values ​​and excessive expenditure items identified by the analysis means.

[0013] The "means for providing" refers to the process of communicating and displaying the generated advice to the user, and is specifically executed through the user interface of the terminal. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

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

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system that analyzes spending in a simple and efficient way and suggests improvements for users who wish to review their household finances. The system collects users' payment data and compares them with similar households to generate and provide specific advice on improving their household finances.

[0036] System configuration

[0037] The system consists of the following components:

[0038] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication costs, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[0039] 2. Data transmission means (terminal): A means for transmitting the entered payment data to the server. This is also part of the application.

[0040] 3. Analysis method (server): A method in which the AI ​​implemented on the server performs comparative analysis with the average expenditure of comparable households.

[0041] 4. Advice generation means (server): A means for generating specific financial improvement proposals to be provided to users based on the analysis results.

[0042] 5. Provision means (terminal): A means for displaying the generated advice to the user.

[0043] A natural language description of the program's operation

[0044] User data entry

[0045] Users access a dedicated application on their smartphone or PC and enter various household expenses. For example, the items a user might enter might be rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[0046] Data transmission

[0047] The terminal temporarily stores the entered payment data before sending it to the server, which uses standard APIs and a secure connection to protect the data.

[0048] Data analysis

[0049] The server then provides the received payment data to the AI, which then compares it with the average expenditure data of comparable households stored in a database. For example, if a user's utility bill is ¥15,000 and the average for comparable households is ¥10,000, the AI ​​will identify this difference and recognize it as an anomaly.

[0050] Advice Generation

[0051] The server generates specific advice based on the analysis results provided by the AI. The advice generation module suggests, for example, "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company." This provides users with realistic and feasible improvement measures.

[0052] Providing advice

[0053] The device displays the advice received from the server to the user. The application visualizes the list of advice in an easy-to-understand manner to make it easier for the user to understand. Each improvement suggestion also includes a detailed explanation and instructions on how to implement it, allowing the user to put it into practice immediately.

[0054] Specific examples

[0055] For example, let's say a user named Yamada uses this system to review his household finances. Yamada enters the following into the dedicated application: rent (100,000 yen), utility bills (15,000 yen), communication costs (10,000 yen), education expenses (30,000 yen), and insurance (20,000 yen), and submits the data.

[0056] The server receives this data and the AI ​​begins analyzing it. When compared to the average data for similar households, it is discovered that the utility bills are abnormally high. The AI ​​identifies the cause and reflects this in the analysis method. Based on the analysis results, the server generates advice such as "changing to more energy-efficient home appliances" or "switching to a cheaper plan from the power company."

[0057] These specific improvement suggestions are displayed on Yamada's device, allowing him to immediately learn about methods he can implement. For example, links to power company plan comparison sites are also displayed, allowing Yamada to easily obtain information and put into action a review of his household finances. In this way, the present invention efficiently supports household finance improvement.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] Users input various household payment items (rent, loan, utilities, communication costs, education costs, various insurances, etc.) into a dedicated application on their device. For example, they enter detailed information such as rent of 100,000 yen, utilities of 15,000 yen, and communication costs of 10,000 yen.

[0061] Step 2:

[0062] The terminal temporarily stores the entered payment data and displays a screen for the user to confirm the entered data. The user checks the entered data and clicks the send button after confirming that it is correct.

[0063] Step 3:

[0064] The terminal sends the stored payment data to the server, where it is sent over a secure connection and an API to reach the server's receiving endpoint.

[0065] Step 4:

[0066] The server stores the received payment data in a database to maintain a chain of record, and optionally cleanses the data before providing it to the analysis means.

[0067] Step 5:

[0068] The server's analysis means calls the AI ​​and provides the stored payment data to the AI, which then retrieves the average expenditure data of comparable households in the database and performs a comparative analysis with the user's payment data.

[0069] Step 6:

[0070] AI identifies outliers and excess expenditures for each payment item and analyzes the causes. For example, if utility bills are higher (15,000 yen) than the average expenditure of comparable households (10,000 yen), it identifies the cause as being the use of inefficient or excessive energy-efficient appliances.

[0071] Step 7:

[0072] The server runs an advice generation module based on the analysis results obtained from the AI, which then creates specific improvement proposals for identified outliers and excessive spending, such as "replace your appliances with more energy-efficient ones" or "switch to a cheaper electricity plan."

[0073] Step 8:

[0074] The server stores the generated advice in a database and prepares it for sending back to the user's device, formatting the advice in a format that is easy for the user to understand.

[0075] Step 9:

[0076] The device then reflects the advice received from the server in the display UI. The user can then view the specific improvements and suggestions on the device screen. For example, a message such as "Consider using energy-efficient appliances to reduce your utility bills" may be displayed.

[0077] Step 10:

[0078] Users can take action based on the displayed advice. Following the advice, links to replace appliances and resources for changing plans are provided, helping users easily take the next step.

[0079] Example 1

[0080] 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."

[0081] In today's busy lifestyles, it is not easy to efficiently manage household finances and review spending. Traditional methods require manually recording expenditure data and conducting comparative analysis, which is time-consuming and labor-intensive and often results in inaccurate analysis. Furthermore, data for comparison with similar households is not readily available, making rational household budget reviews difficult. Therefore, there is a need for technology that can efficiently and accurately review household budgets using an automated system and provide specific suggestions for improvement.

[0082] 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.

[0083] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to an information processing device, means for analyzing the payment data using artificial intelligence to compare it with average expenditure data of comparable households, means for generating proposals for improving the household finances based on the analysis results, and means for providing the generated proposals to the user, thereby enabling the user to easily and efficiently analyze their household expenditures and receive specific proposals for improvement.

[0084] A "household budget review" is the process of reassessing a household's income and expenses, eliminating waste, and achieving efficient financial management.

[0085] A "user" is an individual or family who intends to use the system to review their household finances.

[0086] "Payment data" refers to all expenses incurred by users in their daily lives, including, for example, rent, loans, utility bills, communication fees, education expenses, and various insurances.

[0087] "Means of input" refers to the method by which users provide payment data to the system through a dedicated application or interface, such as a smartphone or computer.

[0088] "Means for transmitting to an information processing device" refers to a method for temporarily storing the entered payment data and securely transmitting the data to a server, and includes standard APIs and secure communication protocols (e.g., HTTPS).

[0089] "Artificial intelligence-based analytical means" refers to technology that performs advanced data analysis based on received payment data and conducts comparative analysis with average expenditure data of comparable households, and includes, for example, machine learning algorithms.

[0090] The "means for generating proposals" is a process for automatically generating specific advice and proposals for improving household finances for users based on the analysis results.

[0091] The "means of providing" refers to a method for presenting the generated suggestions and advice to users in an easy-to-understand manner, such as the user interface of a dedicated application.

[0092] "Average expenditure data for comparable households" is data showing the average expenditures of households with similar family structures and living environments to the user, and is based on past data analysis results and statistical data.

[0093] A "storage device" is a device for storing data required by the system, such as average expenditure data for comparable households and analysis results, for a long period of time, and examples include a database server.

[0094] The present invention is a system that analyzes spending in a simple and efficient way and suggests improvements for users who wish to review their household finances. The system collects users' payment data and compares them with similar households to generate and provide specific advice on improving their household finances.

[0095] System configuration

[0096] The system consists of the following components:

[0097] 1. Payment data input means (terminal)

[0098] 2. Data transmission means (terminal)

[0099] 3. Analysis method (server)

[0100] 4. Advice Generation Method (Server)

[0101] 5. Means of provision (terminal)

[0102] Payment data input means (terminal)

[0103] Users use a dedicated application on their smartphone or PC to input various household payment items. This input includes rent, loans, utilities, communication expenses, education expenses, and various insurances. For example, a user can input rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[0104] Data transmission means (terminal)

[0105] The terminal temporarily stores the entered payment data and then sends it to the server. The data is transmitted using standard APIs (e.g. REST API) and an HTTPS connection to ensure data security. The transmitted data is in JSON format, for example:

[0106] json

[0107] {

[0108] "Rent": 100000,

[0109] "Utilities": 15000,

[0110] "Communication Fee": 10000,

[0111] "Education expenses": 30000,

[0112] "Insurance": 20000

[0113] }

[0114] Analysis method (server)

[0115] The server then provides the received payment data to an AI analysis module. The AI ​​uses the scikit-learn library to compare it with average expenditure data for comparable households. For example, if a user's utility bill is ¥15,000 and the average for comparable households is ¥10,000, the AI ​​will identify this difference and recognize it as an outlier.

[0116] Advice generation means (server)

[0117] The server generates specific advice based on the analysis results provided by the AI. The advice generation module uses natural language generation (NLG) technology to create suggestions in an easy-to-understand format for users. For example, advice may be generated that recommends "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company."

[0118] Means of provision (terminal)

[0119] The device displays the advice received from the server to the user. The application's user interface visualizes a list of advice suggestions, each with a detailed explanation and instructions on how to implement them. For example, when a user opens the application, the following advice is displayed:

[0120] "Your current utility bills are higher than average. Consider switching to more energy-efficient appliances."

[0121] "Also, below are links to energy company plan comparison sites so you can find a cheaper plan."

[0122] Specific examples

[0123] For example, let's say a user named Yamada uses this system to review his or her household finances. Yamada enters and submits the following data into the dedicated application: rent (¥100,000), utility bills (¥15,000), communication fees (¥10,000), education expenses (¥30,000), and insurance (¥20,000). The server receives this data, and the AI ​​begins analyzing it. Comparing it to the average data for similar households reveals that utility bills are abnormally high. The AI ​​identifies the cause and incorporates it into the analysis. Based on the analysis results, the server generates advice such as "switching to more energy-efficient appliances" or "switching to a cheaper electricity provider plan." These specific improvement suggestions are displayed on Yamada's device, allowing him to immediately understand and implement them. For example, links to electricity provider plan comparison sites are also displayed, allowing Yamada to easily access information and take action to review his or her household finances.

[0124] Prompt Sentence Examples

[0125] Here are some example prompts to input to the generative AI model:

[0126] User payment data

[0127] Rent: 100,000 yen

[0128] Utilities: 15,000 yen

[0129] Communication fee: 10,000 yen

[0130] Educational expenses: 30,000 yen

[0131] Insurance: 20,000 yen

[0132] Use this data to compare with average spending, identify abnormal spending, and generate specific improvement advice. For example, if utility bills are high, suggest appropriate actions.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1: User Data Entry

[0135] Users access a dedicated application on their smartphone or PC and enter various household bills. This input includes items such as rent, utilities, communication fees, education expenses, and insurance. For example, a user enters rent of 100,000 yen, utilities of 15,000 yen, communication fees of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen. The input data is temporarily stored in the application's local storage.

[0136] Step 2: Send data

[0137] The terminal sends the stored payment data to the server using a standard API (e.g., REST API) and a secure connection protocol (HTTPS). The input data is converted to JSON format and sent to the API endpoint using the POST method. An example of the data to be sent is as follows:

[0138] json

[0139] {

[0140] "Rent": 100000,

[0141] "Utilities": 15000,

[0142] "Communication Fee": 10000,

[0143] "Education expenses": 30000,

[0144] "Insurance": 20000

[0145] }

[0146] Step 3: Data analysis

[0147] The server provides the received payment data to an AI analysis module, which uses the scikit-learn library to perform comparative analysis with average expenditure data for comparable households. For example, if a user's household's utility bill is 15,000 yen and the average for comparable households is 10,000 yen, the AI ​​will identify this difference and recognize it as an outlier. The input data is compared with the average expenditure data for comparable households, and the difference is output as the analysis result.

[0148] Step 4: Advice Generation

[0149] The server generates specific advice based on the analysis results provided by the AI. The advice generation module uses natural language generation (NLG) technology to create suggestions in an easy-to-understand format for users. For example, advice may be generated that recommends "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company." Based on the analysis results, specific improvement measures are output with detailed explanations.

[0150] Step 5: Providing advice

[0151] The device displays the advice received from the server to the user. The application's user interface visualizes a list of advice, with each suggestion including a detailed explanation and how to implement it. For example, advice such as "Your current utility bills are higher than average. Consider switching to more energy-efficient appliances" or "Below is a link to a comparison site for power company plans to help you find a cheaper plan" is displayed, allowing the user to immediately see what they can do.

[0152] (Application example 1)

[0153] 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."

[0154] In modern society, improving household finances is an important issue for many families. However, there is a lack of methods to efficiently analyze various expenditure data and provide specific advice when reviewing household finances. To solve this problem, a system is needed that allows users to easily input expenditure data themselves, analyzes the data based on that data, and provides appropriate improvement suggestions.

[0155] 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.

[0156] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, means for analyzing the payment data using AI to compare it with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, means for inputting the user's expenditure data directly on the device and automatically importing it, means for transmitting data using a standard API and protecting the data through a secure connection, and means for visualizing improvement measures so that the user can immediately put them into practice. This enables users to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[0157] "Reviewing the household budget" refers to the act of users reevaluating and optimizing household expenditure items.

[0158] "Payment data" is a record of various expenses that a user pays on a daily basis, including rent, purchase fees, utility bills, communication fees, education fees, various insurances, and the like.

[0159] "Input means" refers to the device or function that allows a user to input payment data into the system, such as a smartphone application or web interface.

[0160] "Transmission means" refers to a device or mechanism for transmitting input payment data to a server, using a secure API or network connection.

[0161] The "analysis method" is a function that uses AI to compare and analyze expenditures with those of similar households based on the received payment data and derives results.

[0162] The "advice generation means" is a device or function that generates specific suggestions for improving household finances to be provided to the user based on the analysis results.

[0163] The "means for providing" refers to a device or function for displaying the generated advice to the user in an easily understandable format.

[0164] "Auto Capture" is a feature that automatically collects user spending data without requiring manual entry.

[0165] A "standard API" is a standardized interface for exchanging data between different systems and applications.

[0166] A "secure connection" is a connection method that uses security technologies such as encryption to prevent unauthorized access and data leaks when sending and receiving data.

[0167] "Visualization" refers to displaying data and analytical results in a visually easy-to-understand format, such as graphs or charts.

[0168] The present invention provides a system for analyzing expenditures in a simple and efficient manner and suggesting improvements to users who wish to review their household finances. Specific embodiments of this system will be described below.

[0169] System configuration

[0170] The system consists of the following components:

[0171] 1. Payment data input means (terminal):

[0172] It is a means for users to input various household expenditure items. It is implemented as an application on a smartphone or PC. Users can enter their expenditure data manually or automatically import their electronic payment history.

[0173] 2. Data transmission means (terminal):

[0174] This is the means by which entered payment data is sent to the server. Data is sent using standard APIs and protected by a secure connection.

[0175] 3. Analysis method (server):

[0176] An AI implemented on the server (for example, a generative AI model implemented using Python) performs comparative analysis with the average expenditure data of comparable households. The AI ​​performs analysis using the average expenditure data of comparable households stored in a database.

[0177] 4. Advice generator (server):

[0178] It is a means of generating specific proposals for improving household finances based on the analysis results. Based on the results of the AI ​​analysis, it generates actionable proposals for improvement.

[0179] 5. Means of provision (terminal):

[0180] This is a way to provide generated advice to users. It is displayed on a smartphone or computer screen and presented in a visual form, making it easy for users to quickly understand the suggestions and put them into action.

[0181] Program processing explanation

[0182] The server first receives the user's payment data and provides it to the AI. The AI ​​then analyzes this data, comparing it with the average expenditure data of comparable households. Based on the analysis results, it generates specific improvement suggestions and sends them to the user's device. The user's device then displays the generated advice in a visually easy-to-understand format.

[0183] To give a specific example, a user inputs data such as rent, utility bills, and communication fees and sends it to a server. The server receives this data and analyzes it using an AI model (for example, a generative AI model using PyTorch or TensorFlow). If the analysis reveals that the user's utility bills are higher than the average for similar households, the AI ​​will generate improvement suggestions such as "switching to more energy-efficient appliances" or "reviewing your power company plan." The results are then sent to the user's device and visualized and displayed.

[0184] Prompt Sentence Examples

[0185] Here are some examples of prompts for generative AI models:

[0186] Generate advice on improving your household finances based on your expenditure data. Rent: 100,000 yen, utilities: 15,000 yen, communication costs: 10,000 yen, education costs: 30,000 yen, insurance: 20,000 yen. Compare your expenses with the average for similar households and suggest specific areas for improvement.

[0187] The above is a detailed description of the configuration and processing of the embodiment of the present invention. This system enables users to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[0188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0189] Step 1:

[0190] The user opens the application on their smartphone or computer and enters payment data for rent, utilities, communication fees, education fees, various insurances, etc.

[0191] Input: User payment data

[0192] Output: A list of entered payment data

[0193] What happens: The user enters the amount for each expense item and saves it in the in-app input form. If auto-capture is enabled, the data is retrieved from the associated payment app.

[0194] Step 2:

[0195] The terminal temporarily stores the entered payment data and transmits it to the server over a secure connection using a standard API.

[0196] Input: A list of entered payment data

[0197] Output: Payment data sent to the server

[0198] Specific operation: Converts data stored in the device into JSON format, constructs an API request, and sends it to the server using the HTTPS protocol.

[0199] Step 3:

[0200] The server stores the received payment data in a database for analysis and provides it to the AI ​​for comparison with average expenditure data of comparable households.

[0201] Input: Payment data sent to the server

[0202] Output: The dataset that is provided to the AI

[0203] Specific operation: The data is stored in a database on the server side, and after necessary preprocessing, it is provided to the AI ​​model.

[0204] Step 4:

[0205] The server's AI compares and analyzes the payment data received with average expenditure data from comparable households to identify outliers and areas for improvement.

[0206] Input: A dataset provided to the AI

[0207] Output: Expenditure analysis results

[0208] What it does: It uses an AI model (using, for example, PyTorch or TensorFlow) to analyze input data, perform statistical comparisons, and output results if it detects outliers or finds patterns.

[0209] Step 5:

[0210] The server uses AI to generate specific advice for improving household finances based on the analysis results.

[0211] Input: Expenditure analysis results

[0212] Output: Advice on improving your finances

[0213] How it works: Based on the analysis results, the generative AI model generates effective household improvement measures, such as "switching to more energy-efficient appliances" or "reviewing your communication plan."

[0214] Step 6:

[0215] To provide a user with improvement advice received by a terminal from a server in a visually easy-to-understand format.

[0216] Input: Advice for improving your finances

[0217] Output: Visualized advice shown to the user

[0218] Specific operation: The device obtains the advice content and displays it on the app in a format that is easy for the user to understand (graphs, lists, etc.). This allows the user to easily check the suggested improvement measures and put them into practice.

[0219] The above are the specific processing steps in the embodiment of the present invention, which allow the user to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[0220] 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.

[0221] The present invention provides a system that analyzes spending and suggests improvements in a simple and efficient way for users who wish to review their household finances. The system collects the user's payment data and compares it with similar households to generate and provide specific advice on improving their household finances. The system also incorporates an emotion engine that recognizes the user's emotions, analyzes the user's emotions when inputting information, and provides optimal advice.

[0222] System configuration

[0223] The system consists of the following components:

[0224] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication costs, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[0225] 2. Data transmission means (terminal): A means for transmitting the entered payment data to the server. This is also part of the application.

[0226] 3. Analysis method (server): A method in which the AI ​​implemented on the server performs comparative analysis with the average expenditure of comparable households.

[0227] 4. Advice generation means (server): A means for generating specific financial improvement proposals to be provided to users based on the analysis results.

[0228] 5. Provision means (terminal): A means for displaying the generated advice to the user.

[0229] 6. Emotion recognition means (terminal): A means of analyzing the user's emotions when inputting information and adjusting the content and timing of advice.

[0230] A natural language description of the program's operation

[0231] User data entry

[0232] Users access a dedicated application on their smartphone or PC and enter various household payment items. For example, the items a user might enter include rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[0233] emotion recognition

[0234] When a user enters payment data, the terminal's emotion engine analyzes the user's tone of voice, facial expression, and input speed and accuracy. Based on this, the emotion engine assesses the user's stress level and emotional state. For example, if the user enters data quickly and with a rising tone of voice, it will determine that the user is stressed.

[0235] Data transmission

[0236] The terminal temporarily stores the user's payment data and emotional state, and displays a confirmation screen. The user confirms the input and clicks the submit button to send the data to the server. The data is transmitted over a secure connection.

[0237] Data analysis

[0238] The server stores the received payment data and emotional information in a database, then runs the analysis tool. The analysis tool calls AI and performs a comparative analysis of the stored data with the average expenditure of similar households. For example, it may be determined that the user's household's utility bill is 15,000 yen, while the average for similar households is 10,000 yen. The AI ​​identifies this difference and recognizes it as an outlier.

[0239] Advice Generation

[0240] The server runs an advice generation module based on the analysis results and emotion information. The module creates specific improvement suggestions for identified outliers and excessive spending. For example, the advice might be to "use energy-efficient appliances" or "consider a cheaper plan from your electricity provider." The content and timing of the suggestions are adjusted based on the emotion recognition information. For example, a user who is feeling stressed will be offered realistic and easy-to-implement improvement measures.

[0241] Providing advice

[0242] The device reflects the advice received from the server in the display UI. The user can check the specific improvements and suggestions on the device screen. For example, a message such as "Consider using energy-efficient home appliances to reduce your utility bills" is displayed. In addition, advice is presented based on emotion recognition information at a time that suits the user's condition.

[0243] Specific examples

[0244] For example, let's say a user named Yamada uses this system to review his household finances. Yamada uses the dedicated application to input and submit the following: rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education expenses 30,000 yen, and insurance 20,000 yen.

[0245] When inputting the data, the device's emotion engine analyzes Yamada's emotional state and detects that stress is increasing. The server receives this data and compares it with the average expenditures of comparable households. It turns out that his utility bills are 15,000 yen, higher than the average of 10,000 yen for comparable households. Based on the analysis results, the server generates advice such as "use energy-efficient appliances" and "consider a cheaper plan from your power company." Taking Yamada's emotional state into consideration, simple and easy improvements are suggested.

[0246] This advice is displayed on Yamada's device, allowing him to take specific action immediately. For example, links to appropriate plan changes and information on energy-efficient home appliances are also provided. In this way, the present invention not only efficiently supports household budget improvement, but also provides optimal advice based on the user's emotional state, helping to make household budget reviews more actionable.

[0247] The processing flow will be explained below.

[0248] Step 1:

[0249] Users enter various household payment items (rent, loan, utilities, communication costs, education expenses, various insurances, etc.) into a dedicated application on their device. For example, they enter detailed information such as rent of 100,000 yen, utilities of 15,000 yen, and communication costs of 10,000 yen.

[0250] Step 2:

[0251] As users type, the device's emotion engine analyzes their voice tone, typing speed, facial expressions, etc. Based on this data, the emotion engine evaluates the user's emotional state and determines their stress level and emotional temperature.

[0252] Step 3:

[0253] The terminal temporarily stores the entered payment data and analyzed emotion data, and displays a confirmation screen to the user. The user confirms that the entered information is correct and clicks the send button.

[0254] Step 4:

[0255] The terminal transmits the stored payment data and emotion data to the server, where the data reaches the server's API endpoint over a secure connection.

[0256] Step 5:

[0257] The server stores the received payment and sentiment data in a database, maintaining a chain of record. The data is filtered and cleansed as needed.

[0258] Step 6:

[0259] The server's analysis function calls AI and performs a comparative analysis based on the stored payment data with the average expenditure data of similar households. For example, if the user's household utility bill is 15,000 yen and the average for similar households is 10,000 yen, this outlier is identified.

[0260] Step 7:

[0261] The AI ​​analyzes the causes of identified outliers and excessive spending items and performs a detailed analysis of each spending item, such as identifying the use of energy-inefficient appliances or excessive late-night electricity usage.

[0262] Step 8:

[0263] The server runs an advice generation module based on the analysis results provided by the AI. The module creates specific improvement proposals for identified outliers. For example, it could generate advice such as "replace your appliances with more energy-efficient ones" or "change your electricity plan."

[0264] Step 9:

[0265] The advice generation module also takes into account the user's emotional data and adjusts the content and timing of the advice it provides. For example, it suggests simple and easy-to-implement solutions for users with high stress levels, and provides advice in a format that is easy for the user to accept.

[0266] Step 10:

[0267] The server stores the generated advice in a database and prepares it for sending back to the user's device. The advice is formatted in a specific and easy-to-understand format.

[0268] Step 11:

[0269] The device receives advice from the server and displays it in the display UI, allowing the user to view specific improvement suggestions and action plans based on them on the device screen.

[0270] Step 12:

[0271] Users are guided by the advice they receive and can take action. The advice provides relevant links and resources (e.g., links to energy-efficient appliances or websites for changing your plan) to help users take immediate next steps.

[0272] Example 2

[0273] 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."

[0274] In today's busy lifestyles, managing household finances is an important issue for many families. However, reviewing household finances and optimizing spending requires users to collect, compare, and analyze a large amount of data, which is time-consuming. Furthermore, if users' emotional state is affected by stress or anxiety, they may find it difficult to implement appropriate advice. As a result, it becomes difficult to make feasible improvements to their household finances, and spending optimization does not progress.

[0275] 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.

[0276] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, analysis means using AI for performing comparative analysis of the payment data with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, emotion recognition means for analyzing the emotional state of the user when the payment data is input, and means for providing optimal advice using the emotion recognition results together with the analysis results. This makes it possible to provide highly accurate advice for improving household finances while taking the user's emotional state into consideration.

[0277] "Household finances" refers to the management of a household's income and expenditures, and includes the entire flow of money, such as living expenses, savings, and investments.

[0278] "Payment data" refers to data entered by the user that includes the amounts of various household expense items, such as rent, loans, utilities, communication costs, education costs, and insurance.

[0279] "Server" refers to a computer system for storing, analyzing, and processing data over a network.

[0280] "Comparable households" refers to households with similar criteria such as family structure and income, and are households that serve as reference data for comparative analysis.

[0281] "Average expenditure data" refers to data on the average expenditure amount for each expense item of comparable households.

[0282] "Analysis means" refers to functions including AI for conducting comparative analysis based on payment data and average expenditure data of comparable households.

[0283] The "advice generation means" refers to a function for generating advice for improving household finances to be provided to the user based on the analysis results.

[0284] "Providing means" refers to a function such as a UI for presenting the generated advice to the user.

[0285] "Emotion recognition means" refers to a function for analyzing the emotional state of a user when entering payment data, including the tone of the user's voice, input speed, and facial expression analysis.

[0286] The "emotion recognition result" refers to the evaluation result of the user's emotional state analyzed by the emotion recognition means.

[0287] "Providing optimal advice" refers to providing advice on improving household finances at a timing and with content that suits the user's condition based on the results of emotion recognition of the user.

[0288] This invention provides a system that efficiently and precisely analyzes and proposes improvements to users who wish to review their household finances. This system uses hardware and software to collect and analyze users' payment data, recognize their emotional state, and generate optimal advice for improving their household finances.

[0289] Hardware and software used

[0290] 1. Terminal

[0291] The terminal is a device with an interface for users to input household information, such as a smartphone or PC. A dedicated application is installed on the terminal, and users use it to input payment data. The terminal also has an emotion recognition engine built in to analyze the user's emotional state.

[0292] 2. Server

[0293] The server plays a central role in processing the received data and generating advice based on the analysis results. The server is equipped with a database, AI model, analysis module, and advice generation module.

[0294] Data collection and transmission

[0295] The user launches the application on the device and enters payment items such as rent, utilities, communication fees, education fees, and insurance. This data is temporarily stored on the device and a confirmation screen is displayed. Once the user confirms the information entered and clicks the submit button, the data is sent to the server via a secure connection.

[0296] emotion recognition

[0297] While entering data, the device analyzes the user's input speed, voice tone, and input accuracy in real time. The emotion recognition engine uses this data to evaluate the user's emotional state. For example, if the user types quickly and their voice tone is rising, the system will determine that the user is feeling stressed. This emotional state is classified as "high stress," "medium stress," or "low stress," and is used to generate advice.

[0298] Data analysis and advice generation

[0299] The server stores the received payment data in a database and uses an AI model to compare it with the average expenditure data of comparable households. As a result of the comparison analysis, outliers and excessive expenditures are identified. For example, the server detects that the user's household utility bill is 15,000 yen, while the average for comparable households is 10,000 yen. Based on the analysis results, the server runs an advice generation module. Specific improvement measures (e.g., "use energy-efficient appliances" or "consider a cheaper plan from your power company") are generated and provided to the user.

[0300] The generated advice is sent to the device at an appropriate time and in an appropriate format, taking into account the user's emotional state. For example, a user who is feeling stressed will be suggested easy-to-implement improvement measures.

[0301] Specific examples

[0302] For example, if a user were to use this system to review their finances, it would work like this:

[0303] 1. The user uses a dedicated application to enter the following amounts: rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education expenses 30,000 yen, and insurance 20,000 yen.

[0304] 2. The emotion recognition engine analyzes the user's typing speed and tone of voice and determines that the user is under high stress.

[0305] 3. The user clicks the submit button and the data is sent to the server.

[0306] 4. The server analyzes the data and detects that the utility bills are higher than the average of 10,000 yen for comparable households.

[0307] 5. The server will suggest things like "use more energy-efficient appliances" or "consider cheaper plans from your electricity provider," providing easy-to-implement improvements based on your emotional state.

[0308] Prompt Sentence Examples

[0309] "Analyze the emotional state of the user of the household budget review system based on the payment data entered and suggest optimal spending improvements. User's payment data: rent 100,000 yen, water and electricity bills 15,000 yen, communication expenses 10,000 yen, education expenses 30,000 yen, insurance 20,000 yen. The emotional state is determined to be highly stressed."

[0310] As described above, the present invention is a system that performs efficient and precise analysis for users who wish to review their household finances, and suggests improvements that take into account their emotional state.

[0311] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0312] Step 1: User enters payment data

[0313] The user launches the dedicated application and inputs household payment items. In each field, they input the expenditure amounts for rent, utilities, communication, education, insurance, etc. For example, they input 100,000 yen for rent, 15,000 yen for utilities, 10,000 yen for communication, 30,000 yen for education, and 20,000 yen for insurance.

[0314] Input: Amount data for each pay item

[0315] Output: Payment data entered

[0316] Step 2: Emotion Recognition

[0317] The terminal observes various actions of the user in real time as they enter payment data. The emotion engine analyzes the user's tone of voice, input speed, and frequency of input errors to assess the user's emotional state. For example, if the user types quickly and has a rising tone of voice, it can determine that the user is stressed.

[0318] Input: User input behavior (speed, tone of voice, frequency of input errors)

[0319] Output: Emotional state (high stress, medium stress, low stress)

[0320] Step 3: Check and submit data

[0321] Once the user has entered their payment data, the terminal temporarily stores it in memory and displays a confirmation screen. The user must confirm the data and click the submit button, which sends the data to the server over a secure connection.

[0322] Input: entered payment data, emotional state

[0323] Output: Data sent to the server

[0324] Step 4: Store and parse payment data

[0325] The server stores the received payment data and emotional state information in a database. It then uses analytical means to compare the data with average expenditure data for comparable households. For example, it detects that the user's household utility bill is 15,000 yen, while the average for comparable households is 10,000 yen.

[0326] Input: Received payment data, emotional state

[0327] Output: Analysis results (abnormal values ​​and excess expenditures for each expenditure item)

[0328] Step 5: Advice Generation

[0329] The server generates advice based on the analysis results and the user's emotional state. The advice generation module creates improvement proposals to address identified outliers and excessive spending. For example, it suggests specific improvement measures such as "using energy-efficient home appliances" or "considering cheaper plans from your power company." The content and timing of the proposals are adjusted based on the user's emotional state.

[0330] Input: Analysis results, emotional state

[0331] Output: Generated financial advice

[0332] Step 6: Providing advice

[0333] The device displays the advice received from the server to the user. The application UI displays specific suggestions for improving household finances. The user can review the advice and take action. For example, a message might say, "Use energy-efficient appliances to reduce your utility bills," along with specific links and reference materials.

[0334] Input: Generated financial advice

[0335] Output: Advice and action guide provided to the user

[0336] (Application example 2)

[0337] 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."

[0338] Conventional household management systems simply aggregate payment data and compare it with similar households, providing uniform advice without considering the user's emotional state. This creates problems when users are stressed or their emotional state is affected, making it difficult to provide practical and actionable advice. Furthermore, advice is not provided at the optimal time based on the user's emotional state, which can lead to suggestions being rejected effectively. To solve these issues, there is a need for a system that can analyze the user's emotional state and provide personalized and optimal advice for improving household finances based on that information.

[0339] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0340] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, means for analyzing the payment data by using AI to compare it with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, and emotion recognition means for analyzing the user's emotional state when entering the payment data and adjusting the content and timing of the advice. This makes it possible to provide realistic and actionable advice for improving household finances that takes into account the user's emotional state.

[0341] "Reviewing the household budget" is an activity aimed at reevaluating the flow of money within a household and optimizing spending.

[0342] "Payment data" refers to data that includes detailed information on household expenditures, such as rent, loans, utility bills, communication costs, education costs, and various insurances.

[0343] "Comparable households" refer to other households with incomes and family structures similar to that of the user's household.

[0344] "Average expenditure data" is aggregate data that indicates the standard amount for each expenditure item for similar households.

[0345] "Emotional state" refers to the psychological state a user exhibits in response to a particular input or situation, and includes feelings of stress, satisfaction, etc.

[0346] The "emotion recognition means" is a means for analyzing the user's tone of voice, facial expression, input speed, etc., to evaluate the user's emotional state.

[0347] The "analysis tool" is an AI-based program that has the ability to analyze the collected payment data by comparing it with the average expenditure data of comparable households.

[0348] The "advice generation means" is a device or program that generates specific suggestions for improving the user's household finances based on the results obtained by the analysis means.

[0349] The "advice providing means" is a means for visually or audibly providing the generated advice for improving household finances to the user.

[0350] The present invention relates to a system that analyzes spending easily and efficiently and suggests improvements for users who wish to review their household finances. This system collects payment data and uses emotion recognition technology to optimize the advice provided. Specific embodiments of the system are described below.

[0351] System Configuration

[0352] The system consists of the following components:

[0353] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication fees, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[0354] 2. Data transmission means (terminal): A means for transmitting input payment data to the server. This is also part of the application.

[0355] 3. Analysis method (server): This is the method by which AI implemented on the server performs comparative analysis with the average expenditure of comparable households.

[0356] 4. Advice generation means (server): A means for generating specific household improvement proposals to be provided to users based on the analysis results.

[0357] 5. Providing means (terminal): A means for displaying the generated advice to the user.

[0358] 6. Emotion recognition means (terminal): A means for analyzing the emotions expressed by the user when inputting information and adjusting the content and timing of the advice provided.

[0359] Hardware / Software used

[0360] EmotionRecognition Library: A library for analyzing a user's emotional state.

[0361] BudgetAnalysis library: A library that analyzes household payment data, compares it with peer households, and generates advice.

[0362] Python3: A programming language for implementing the entire program.

[0363] API Server: A communication server for collecting user payment data.

[0364] System Operation

[0365] The server collects and analyzes the user's payment data. First, the user accesses a dedicated application on their smartphone or PC and enters payment data such as rent, utilities, communication fees, education fees, and insurance. The device then analyzes the entered payment data and the user's emotional state and sends it to the server via a secure connection. The server stores the received payment data and emotional information in a database and invokes AI to perform comparative analysis with the average expenditures of comparable households. Based on the analysis results and emotional information, it generates specific advice for improving household finances, adjusting the content and timing of the suggestions based on the user's emotional state. The generated advice is displayed on the user's device.

[0366] Specific examples

[0367] For example, if a user says, "My living expenses have increased recently and I'm worried," the device's emotion recognition engine will analyze this and determine that stress is increasing. The server will compare the user with similar households based on payment data for rent, utilities, communication fees, education expenses, insurance, etc., and generate advice such as "use more energy-efficient home appliances" or "review your communication plan." This advice is provided at an appropriate time, taking into account the user's emotional state.

[0368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0369] Step 1:

[0370] The user accesses a dedicated application and enters payment data such as rent, utility bills, communication fees, education fees, and insurance. Once the user has completed the input, the device uses an emotion recognition engine to analyze the user's emotional state. The input data and analyzed emotional information are temporarily stored.

[0371] Input: Payment data (e.g. rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education fees 30,000 yen, insurance 20,000 yen)

[0372] Output: Payment data and emotional state (e.g., stress level)

[0373] Behavior: Analyzes the user's tone of voice, facial expressions, and typing speed.

[0374] Step 2:

[0375] The terminal displays the entered payment data and emotional state on a confirmation screen and prompts the user to confirm the submission. Once the user clicks the submit button, the terminal transmits the data to the server over a secure connection.

[0376] Input: Payment data and emotional state

[0377] Output: Send data to the server

[0378] Operation: Displays a confirmation screen and transmits data.

[0379] Step 3:

[0380] The server stores the received payment data and emotional state in a database and runs an analysis tool. The analysis tool compares the payment data with the average expenditure data of comparable households and identifies outliers and excessive expenditures. For example, it may discover that the utility bill is 15,000 yen, which is higher than the average of 10,000 yen for comparable households.

[0381] Input: Submitted payment data and emotional state

[0382] Output: Analysis results (identification of outliers)

[0383] Action: Save to database and call analysis method.

[0384] Step 4:

[0385] The server generates specific advice for improving household finances using an advice generation means based on the analysis results and the user's emotional state. For example, it may suggest "using energy-efficient home appliances" or "reviewing your communication plan." If the user is feeling stressed, the server prioritizes suggestions that are easy to implement.

[0386] Input: Analysis results and emotional state

[0387] Output: Specific advice for improving your household finances

[0388] Action: Triggers the advice generator and generates appropriate suggestions.

[0389] Step 5:

[0390] The server sends the generated advice to the device, which then displays it to the user. The advice is displayed at the optimal time depending on the user's emotional state. For example, the advice is displayed after stress levels have decreased.

[0391] Input: Generated advice

[0392] Output: Advice displayed to the user

[0393] Action: Send and display advice.

[0394] Through this series of steps, expenditure analysis and advice on improving household finances are provided to the user.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] [Second embodiment]

[0399] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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."

[0411] The present invention is a system that analyzes spending in a simple and efficient way and suggests improvements for users who wish to review their household finances. The system collects users' payment data and compares them with similar households to generate and provide specific advice on improving their household finances.

[0412] System configuration

[0413] The system consists of the following components:

[0414] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication costs, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[0415] 2. Data transmission means (terminal): A means for transmitting the entered payment data to the server. This is also part of the application.

[0416] 3. Analysis method (server): A method in which the AI ​​implemented on the server performs comparative analysis with the average expenditure of comparable households.

[0417] 4. Advice generation means (server): A means for generating specific financial improvement proposals to be provided to users based on the analysis results.

[0418] 5. Provision means (terminal): A means for displaying the generated advice to the user.

[0419] A natural language description of the program's operation

[0420] User data entry

[0421] Users access a dedicated application on their smartphone or PC and enter various household expenses. For example, the items a user might enter might be rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[0422] Data transmission

[0423] The terminal temporarily stores the entered payment data before sending it to the server, which uses standard APIs and a secure connection to protect the data.

[0424] Data analysis

[0425] The server then provides the received payment data to the AI, which then compares it with the average expenditure data of comparable households stored in a database. For example, if a user's utility bill is ¥15,000 and the average for comparable households is ¥10,000, the AI ​​will identify this difference and recognize it as an anomaly.

[0426] Advice Generation

[0427] The server generates specific advice based on the analysis results provided by the AI. The advice generation module suggests, for example, "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company." This provides users with realistic and feasible improvement measures.

[0428] Providing advice

[0429] The device displays the advice received from the server to the user. The application visualizes the list of advice in an easy-to-understand manner to make it easier for the user to understand. Each improvement suggestion also includes a detailed explanation and instructions on how to implement it, allowing the user to put it into practice immediately.

[0430] Specific examples

[0431] For example, let's say a user named Yamada uses this system to review his household finances. Yamada enters the following into the dedicated application: rent (100,000 yen), utility bills (15,000 yen), communication costs (10,000 yen), education expenses (30,000 yen), and insurance (20,000 yen), and submits the data.

[0432] The server receives this data and the AI ​​begins analyzing it. When compared to the average data for similar households, it is discovered that the utility bills are abnormally high. The AI ​​identifies the cause and reflects this in the analysis method. Based on the analysis results, the server generates advice such as "changing to more energy-efficient home appliances" or "switching to a cheaper plan from the power company."

[0433] These specific improvement suggestions are displayed on Yamada's device, allowing him to immediately learn about methods he can implement. For example, links to power company plan comparison sites are also displayed, allowing Yamada to easily obtain information and put into action a review of his household finances. In this way, the present invention efficiently supports household finance improvement.

[0434] The processing flow will be explained below.

[0435] Step 1:

[0436] Users input various household payment items (rent, loan, utilities, communication costs, education costs, various insurances, etc.) into a dedicated application on their device. For example, they enter detailed information such as rent of 100,000 yen, utilities of 15,000 yen, and communication costs of 10,000 yen.

[0437] Step 2:

[0438] The terminal temporarily stores the entered payment data and displays a screen for the user to confirm the entered data. The user checks the entered data and clicks the send button after confirming that it is correct.

[0439] Step 3:

[0440] The terminal sends the stored payment data to the server, where it is sent over a secure connection and an API to reach the server's receiving endpoint.

[0441] Step 4:

[0442] The server stores the received payment data in a database to maintain a chain of record, and optionally cleanses the data before providing it to the analysis means.

[0443] Step 5:

[0444] The server's analysis means calls the AI ​​and provides the stored payment data to the AI, which then retrieves the average expenditure data of comparable households in the database and performs a comparative analysis with the user's payment data.

[0445] Step 6:

[0446] AI identifies outliers and excess expenditures for each payment item and analyzes the causes. For example, if utility bills are higher (15,000 yen) than the average expenditure of comparable households (10,000 yen), it identifies the cause as being the use of inefficient or excessive energy-efficient appliances.

[0447] Step 7:

[0448] The server runs an advice generation module based on the analysis results obtained from the AI, which then creates specific improvement proposals for identified outliers and excessive spending, such as "replace your appliances with more energy-efficient ones" or "switch to a cheaper electricity plan."

[0449] Step 8:

[0450] The server stores the generated advice in a database and prepares it for sending back to the user's device, formatting the advice in a format that is easy for the user to understand.

[0451] Step 9:

[0452] The device then reflects the advice received from the server in the display UI. The user can then view the specific improvements and suggestions on the device screen. For example, a message such as "Consider using energy-efficient appliances to reduce your utility bills" may be displayed.

[0453] Step 10:

[0454] Users can take action based on the displayed advice. Following the advice, links to replace appliances and resources for changing plans are provided, helping users easily take the next step.

[0455] Example 1

[0456] 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."

[0457] In today's busy lifestyles, it is not easy to efficiently manage household finances and review spending. Traditional methods require manually recording expenditure data and conducting comparative analysis, which is time-consuming and labor-intensive and often results in inaccurate analysis. Furthermore, data for comparison with similar households is not readily available, making rational household budget reviews difficult. Therefore, there is a need for technology that can efficiently and accurately review household budgets using an automated system and provide specific suggestions for improvement.

[0458] 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.

[0459] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to an information processing device, means for analyzing the payment data using artificial intelligence to compare it with average expenditure data of comparable households, means for generating proposals for improving the household finances based on the analysis results, and means for providing the generated proposals to the user, thereby enabling the user to easily and efficiently analyze their household expenditures and receive specific proposals for improvement.

[0460] A "household budget review" is the process of reassessing a household's income and expenses, eliminating waste, and achieving efficient financial management.

[0461] A "user" is an individual or family who intends to use the system to review their household finances.

[0462] "Payment data" refers to all expenses incurred by users in their daily lives, including, for example, rent, loans, utility bills, communication fees, education expenses, and various insurances.

[0463] "Means of input" refers to the method by which users provide payment data to the system through a dedicated application or interface, such as a smartphone or computer.

[0464] "Means for transmitting to an information processing device" refers to a method for temporarily storing the entered payment data and securely transmitting the data to a server, and includes standard APIs and secure communication protocols (e.g., HTTPS).

[0465] "Artificial intelligence-based analytical means" refers to technology that performs advanced data analysis based on received payment data and conducts comparative analysis with average expenditure data of comparable households, and includes, for example, machine learning algorithms.

[0466] The "means for generating proposals" is a process for automatically generating specific advice and proposals for improving household finances for users based on the analysis results.

[0467] The "means of providing" refers to a method for presenting the generated suggestions and advice to users in an easy-to-understand manner, such as the user interface of a dedicated application.

[0468] "Average expenditure data for comparable households" is data showing the average expenditures of households with similar family structures and living environments to the user, and is based on past data analysis results and statistical data.

[0469] A "storage device" is a device for storing data required by the system, such as average expenditure data for comparable households and analysis results, for a long period of time, and examples include a database server.

[0470] The present invention is a system that analyzes spending in a simple and efficient way and suggests improvements for users who wish to review their household finances. The system collects users' payment data and compares them with similar households to generate and provide specific advice on improving their household finances.

[0471] System configuration

[0472] The system consists of the following components:

[0473] 1. Payment data input means (terminal)

[0474] 2. Data transmission means (terminal)

[0475] 3. Analysis method (server)

[0476] 4. Advice Generation Method (Server)

[0477] 5. Means of provision (terminal)

[0478] Payment data input means (terminal)

[0479] Users use a dedicated application on their smartphone or PC to input various household expenses, including rent, loans, utilities, communication fees, education expenses, and various insurances. For example, a user can input rent of 100,000 yen, utilities of 15,000 yen, communication fees of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[0480] Data transmission means (terminal)

[0481] The terminal temporarily stores the entered payment data and then sends it to the server. The data is transmitted using standard APIs (e.g. REST API) and an HTTPS connection to ensure data security. The transmitted data is in JSON format, for example:

[0482] json

[0483] {

[0484] "Rent": 100000,

[0485] "Utilities": 15000,

[0486] "Communication Fee": 10000,

[0487] "Education expenses": 30000,

[0488] "Insurance": 20000

[0489] }

[0490] Analysis method (server)

[0491] The server then provides the received payment data to an AI analysis module. The AI ​​uses the scikit-learn library to compare it with average expenditure data for comparable households. For example, if a user's utility bill is ¥15,000 and the average for comparable households is ¥10,000, the AI ​​will identify this difference and recognize it as an outlier.

[0492] Advice generation means (server)

[0493] The server generates specific advice based on the analysis results provided by the AI. The advice generation module uses natural language generation (NLG) technology to create suggestions in an easy-to-understand format for users. For example, advice may be generated that recommends "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company."

[0494] Means of provision (terminal)

[0495] The device displays the advice received from the server to the user. The application's user interface visualizes a list of advice suggestions, each with a detailed explanation and instructions on how to implement them. For example, when a user opens the application, the following advice is displayed:

[0496] "Your current utility bills are higher than average. Consider switching to more energy-efficient appliances."

[0497] "Also, below are links to energy company plan comparison sites so you can find a cheaper plan."

[0498] Specific examples

[0499] For example, let's say a user named Yamada uses this system to review his or her household finances. Yamada enters and submits the following data into the dedicated application: rent (¥100,000), utility bills (¥15,000), communication fees (¥10,000), education expenses (¥30,000), and insurance (¥20,000). The server receives this data, and the AI ​​begins analyzing it. Comparing it to the average data for similar households reveals that utility bills are abnormally high. The AI ​​identifies the cause and incorporates it into the analysis. Based on the analysis results, the server generates advice such as "switching to more energy-efficient appliances" or "switching to a cheaper electricity provider plan." These specific improvement suggestions are displayed on Yamada's device, allowing him to immediately understand and implement them. For example, links to electricity provider plan comparison sites are also displayed, allowing Yamada to easily access information and take action to review his or her household finances.

[0500] Prompt Sentence Examples

[0501] Here are some example prompts to input to the generative AI model:

[0502] User payment data

[0503] Rent: 100,000 yen

[0504] Utilities: 15,000 yen

[0505] Communication fee: 10,000 yen

[0506] Educational expenses: 30,000 yen

[0507] Insurance: 20,000 yen

[0508] Use this data to compare with average spending, identify abnormal spending, and generate specific improvement advice. For example, if utility bills are high, suggest appropriate actions.

[0509] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0510] Step 1: User Data Entry

[0511] Users access a dedicated application on their smartphone or PC and enter various household bills. This input includes items such as rent, utilities, communication fees, education expenses, and insurance. For example, a user enters rent of 100,000 yen, utilities of 15,000 yen, communication fees of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen. The input data is temporarily stored in the application's local storage.

[0512] Step 2: Send data

[0513] The terminal sends the stored payment data to the server using a standard API (e.g., REST API) and a secure connection protocol (HTTPS). The input data is converted to JSON format and sent to the API endpoint using the POST method. An example of the data to be sent is as follows:

[0514] json

[0515] {

[0516] "Rent": 100000,

[0517] "Utilities": 15000,

[0518] "Communication Fee": 10000,

[0519] "Education expenses": 30000,

[0520] "Insurance": 20000

[0521] }

[0522] Step 3: Data analysis

[0523] The server provides the received payment data to an AI analysis module, which uses the scikit-learn library to perform comparative analysis with average expenditure data for comparable households. For example, if a user's household's utility bill is 15,000 yen and the average for comparable households is 10,000 yen, the AI ​​will identify this difference and recognize it as an outlier. The input data is compared with the average expenditure data for comparable households, and the difference is output as the analysis result.

[0524] Step 4: Advice Generation

[0525] The server generates specific advice based on the analysis results provided by the AI. The advice generation module uses natural language generation (NLG) technology to create suggestions in an easy-to-understand format for users. For example, advice may be generated that recommends "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company." Based on the analysis results, specific improvement measures are output with detailed explanations.

[0526] Step 5: Providing advice

[0527] The device displays the advice received from the server to the user. The application's user interface visualizes a list of advice, with each suggestion including a detailed explanation and how to implement it. For example, advice such as "Your current utility bills are higher than average. Consider switching to more energy-efficient appliances" or "Below is a link to a comparison site for power company plans to help you find a cheaper plan" is displayed, allowing the user to immediately see what they can do.

[0528] (Application example 1)

[0529] 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."

[0530] In modern society, improving household finances is an important issue for many families. However, there is a lack of methods to efficiently analyze various expenditure data and provide specific advice when reviewing household finances. To solve this problem, a system is needed that allows users to easily input expenditure data themselves, analyzes the data based on that data, and provides appropriate improvement suggestions.

[0531] 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.

[0532] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, means for analyzing the payment data using AI to compare it with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, means for inputting the user's expenditure data directly on the device and automatically importing it, means for transmitting data using a standard API and protecting the data through a secure connection, and means for visualizing improvement measures so that the user can immediately put them into practice. This enables users to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[0533] "Reviewing the household budget" refers to the act of users reevaluating and optimizing household expenditure items.

[0534] "Payment data" is a record of various expenses that a user pays on a daily basis, including rent, purchase fees, utility bills, communication fees, education fees, various insurances, and the like.

[0535] "Input means" refers to the device or function that allows a user to input payment data into the system, such as a smartphone application or web interface.

[0536] "Transmission means" refers to a device or mechanism for transmitting input payment data to a server, using a secure API or network connection.

[0537] The "analysis method" is a function that uses AI to compare and analyze expenditures with those of similar households based on the received payment data and derives results.

[0538] The "advice generation means" is a device or function that generates specific suggestions for improving household finances to be provided to the user based on the analysis results.

[0539] The "means for providing" refers to a device or function for displaying the generated advice to the user in an easily understandable format.

[0540] "Auto Capture" is a feature that automatically collects user spending data without requiring manual entry.

[0541] A "standard API" is a standardized interface for exchanging data between different systems and applications.

[0542] A "secure connection" is a connection method that uses security technologies such as encryption to prevent unauthorized access and data leaks when sending and receiving data.

[0543] "Visualization" refers to displaying data and analytical results in a visually easy-to-understand format, such as graphs or charts.

[0544] The present invention provides a system for analyzing expenditures in a simple and efficient manner and suggesting improvements to users who wish to review their household finances. Specific embodiments of this system will be described below.

[0545] System configuration

[0546] The system consists of the following components:

[0547] 1. Payment data input means (terminal):

[0548] It is a means for users to input various household expenditure items. It is implemented as an application on a smartphone or PC. Users can enter their expenditure data manually or automatically import their electronic payment history.

[0549] 2. Data transmission means (terminal):

[0550] This is the means by which entered payment data is sent to the server. Data is sent using standard APIs and protected by a secure connection.

[0551] 3. Analysis method (server):

[0552] An AI implemented on the server (for example, a generative AI model implemented using Python) performs comparative analysis with the average expenditure data of comparable households. The AI ​​performs analysis using the average expenditure data of comparable households stored in a database.

[0553] 4. Advice generator (server):

[0554] It is a means of generating specific proposals for improving household finances based on the analysis results. Based on the results of the AI ​​analysis, it generates actionable proposals for improvement.

[0555] 5. Means of provision (terminal):

[0556] This is a way to provide generated advice to users. It is displayed on a smartphone or computer screen and presented in a visual form, making it easy for users to quickly understand the suggestions and put them into action.

[0557] Program processing explanation

[0558] The server first receives the user's payment data and provides it to the AI. The AI ​​then analyzes this data, comparing it with the average expenditure data of comparable households. Based on the analysis results, it generates specific improvement suggestions and sends them to the user's device. The user's device then displays the generated advice in a visually easy-to-understand format.

[0559] To give a specific example, a user inputs data such as rent, utility bills, and communication fees and sends it to a server. The server receives this data and analyzes it using an AI model (for example, a generative AI model using PyTorch or TensorFlow). If the analysis reveals that the user's utility bills are higher than the average for similar households, the AI ​​will generate improvement suggestions such as "switching to more energy-efficient appliances" or "reviewing your power company plan." The results are then sent to the user's device and visualized and displayed.

[0560] Prompt Sentence Examples

[0561] Here are some examples of prompts for generative AI models:

[0562] Generate advice on improving your household finances based on your expenditure data. Rent: 100,000 yen, utilities: 15,000 yen, communication costs: 10,000 yen, education costs: 30,000 yen, insurance: 20,000 yen. Compare your expenses with the average for similar households and suggest specific areas for improvement.

[0563] The above is a detailed description of the configuration and processing of the embodiment of the present invention. This system enables users to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[0564] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0565] Step 1:

[0566] The user opens the application on their smartphone or computer and enters payment data for rent, utilities, communication fees, education fees, various insurances, etc.

[0567] Input: User payment data

[0568] Output: A list of entered payment data

[0569] What happens: The user enters the amount for each expense item and saves it in the in-app input form. If auto-capture is enabled, the data is retrieved from the associated payment app.

[0570] Step 2:

[0571] The terminal temporarily stores the entered payment data and transmits it to the server over a secure connection using a standard API.

[0572] Input: A list of entered payment data

[0573] Output: Payment data sent to the server

[0574] Specific operation: Converts data stored in the device into JSON format, constructs an API request, and sends it to the server using the HTTPS protocol.

[0575] Step 3:

[0576] The server stores the received payment data in a database for analysis and provides it to the AI ​​for comparison with average expenditure data of comparable households.

[0577] Input: Payment data sent to the server

[0578] Output: The dataset that is provided to the AI

[0579] Specific operation: The data is stored in a database on the server side, and after necessary preprocessing, it is provided to the AI ​​model.

[0580] Step 4:

[0581] The server's AI compares and analyzes the payment data received with average expenditure data from comparable households to identify outliers and areas for improvement.

[0582] Input: A dataset provided to the AI

[0583] Output: Expenditure analysis results

[0584] What it does: It uses an AI model (using, for example, PyTorch or TensorFlow) to analyze input data, perform statistical comparisons, and output results if it detects outliers or finds patterns.

[0585] Step 5:

[0586] The server uses AI to generate specific advice for improving household finances based on the analysis results.

[0587] Input: Expenditure analysis results

[0588] Output: Advice on improving your finances

[0589] How it works: Based on the analysis results, the generative AI model generates effective household improvement measures, such as "switching to more energy-efficient appliances" or "reviewing your communication plan."

[0590] Step 6:

[0591] To provide a user with improvement advice received by a terminal from a server in a visually easy-to-understand format.

[0592] Input: Advice for improving your finances

[0593] Output: Visualized advice shown to the user

[0594] Specific operation: The device obtains the advice content and displays it on the app in a format that is easy for the user to understand (graphs, lists, etc.). This allows the user to easily check the suggested improvement measures and put them into practice.

[0595] The above are the specific processing steps in the embodiment of the present invention, which allow the user to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[0596] 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.

[0597] The present invention provides a system that analyzes spending and suggests improvements in a simple and efficient way for users who wish to review their household finances. The system collects the user's payment data and compares it with similar households to generate and provide specific advice on improving their household finances. The system also incorporates an emotion engine that recognizes the user's emotions, analyzes the user's emotions when inputting information, and provides optimal advice.

[0598] System configuration

[0599] The system consists of the following components:

[0600] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication costs, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[0601] 2. Data transmission means (terminal): A means for transmitting the entered payment data to the server. This is also part of the application.

[0602] 3. Analysis method (server): A method in which the AI ​​implemented on the server performs comparative analysis with the average expenditure of comparable households.

[0603] 4. Advice generation means (server): A means for generating specific financial improvement proposals to be provided to users based on the analysis results.

[0604] 5. Provision means (terminal): A means for displaying the generated advice to the user.

[0605] 6. Emotion recognition means (terminal): A means of analyzing the user's emotions when inputting information and adjusting the content and timing of advice.

[0606] A natural language description of the program's operation

[0607] User data entry

[0608] Users access a dedicated application on their smartphone or PC and enter various household payment items. For example, the items a user might enter include rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[0609] emotion recognition

[0610] When a user enters payment data, the terminal's emotion engine analyzes the user's tone of voice, facial expression, and input speed and accuracy. Based on this, the emotion engine assesses the user's stress level and emotional state. For example, if the user enters data quickly and with a rising tone of voice, it will determine that the user is stressed.

[0611] Data transmission

[0612] The terminal temporarily stores the user's payment data and emotional state, and displays a confirmation screen. The user confirms the input and clicks the submit button to send the data to the server. The data is transmitted over a secure connection.

[0613] Data analysis

[0614] The server stores the received payment data and emotional information in a database, then runs the analysis tool. The analysis tool calls AI and performs a comparative analysis of the stored data with the average expenditure of similar households. For example, it may be determined that the user's household's utility bill is 15,000 yen, while the average for similar households is 10,000 yen. The AI ​​identifies this difference and recognizes it as an outlier.

[0615] Advice Generation

[0616] The server runs an advice generation module based on the analysis results and emotion information. The module creates specific improvement suggestions for identified outliers and excessive spending. For example, the advice might be to "use energy-efficient appliances" or "consider a cheaper plan from your electricity provider." The content and timing of the suggestions are adjusted based on the emotion recognition information. For example, a user who is feeling stressed will be offered realistic and easy-to-implement improvement measures.

[0617] Providing advice

[0618] The device reflects the advice received from the server in the display UI. The user can check the specific improvements and suggestions on the device screen. For example, a message such as "Consider using energy-efficient home appliances to reduce your utility bills" is displayed. In addition, advice is presented based on emotion recognition information at a time that suits the user's condition.

[0619] Specific examples

[0620] For example, let's say a user named Yamada uses this system to review his household finances. Yamada uses the dedicated application to input and submit the following: rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education expenses 30,000 yen, and insurance 20,000 yen.

[0621] When inputting the data, the device's emotion engine analyzes Yamada's emotional state and detects that stress is increasing. The server receives this data and compares it with the average expenditures of comparable households. It turns out that his utility bills are 15,000 yen, higher than the average of 10,000 yen for comparable households. Based on the analysis results, the server generates advice such as "use energy-efficient appliances" and "consider a cheaper plan from your power company." Taking Yamada's emotional state into consideration, simple and easy improvements are suggested.

[0622] This advice is displayed on Yamada's device, allowing him to take specific action immediately. For example, links to appropriate plan changes and information on energy-efficient home appliances are also provided. In this way, the present invention not only efficiently supports household budget improvement, but also provides optimal advice based on the user's emotional state, helping to make household budget reviews more actionable.

[0623] The processing flow will be explained below.

[0624] Step 1:

[0625] Users enter various household payment items (rent, loan, utilities, communication costs, education expenses, various insurances, etc.) into a dedicated application on their device. For example, they enter detailed information such as rent of 100,000 yen, utilities of 15,000 yen, and communication costs of 10,000 yen.

[0626] Step 2:

[0627] As users type, the device's emotion engine analyzes their voice tone, typing speed, facial expressions, etc. Based on this data, the emotion engine evaluates the user's emotional state and determines their stress level and emotional temperature.

[0628] Step 3:

[0629] The terminal temporarily stores the entered payment data and analyzed emotion data, and displays a confirmation screen to the user. The user confirms that the entered information is correct and clicks the send button.

[0630] Step 4:

[0631] The terminal transmits the stored payment data and emotion data to the server, where the data reaches the server's API endpoint over a secure connection.

[0632] Step 5:

[0633] The server stores the received payment and sentiment data in a database, maintaining a chain of record. The data is filtered and cleansed as needed.

[0634] Step 6:

[0635] The server's analysis function calls AI and performs a comparative analysis based on the stored payment data with the average expenditure data of similar households. For example, if the user's household utility bill is 15,000 yen and the average for similar households is 10,000 yen, this outlier is identified.

[0636] Step 7:

[0637] The AI ​​analyzes the causes of identified outliers and excessive spending items and performs a detailed analysis of each spending item, such as identifying the use of energy-inefficient appliances or excessive late-night electricity usage.

[0638] Step 8:

[0639] The server runs an advice generation module based on the analysis results provided by the AI. The module creates specific improvement proposals for identified outliers. For example, it could generate advice such as "replace your appliances with more energy-efficient ones" or "change your electricity plan."

[0640] Step 9:

[0641] The advice generation module also takes into account the user's emotional data and adjusts the content and timing of the advice it provides. For example, it suggests simple and easy-to-implement solutions for users with high stress levels, and provides advice in a format that is easy for the user to accept.

[0642] Step 10:

[0643] The server stores the generated advice in a database and prepares it for sending back to the user's device. The advice is formatted in a specific and easy-to-understand format.

[0644] Step 11:

[0645] The device receives advice from the server and displays it in the display UI, allowing the user to view specific improvement suggestions and action plans based on them on the device screen.

[0646] Step 12:

[0647] Users are guided by the advice they receive and can take action. The advice provides relevant links and resources (e.g., links to energy-efficient appliances or websites for changing your plan) to help users take immediate next steps.

[0648] Example 2

[0649] 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."

[0650] In today's busy lifestyles, managing household finances is an important issue for many families. However, reviewing household finances and optimizing spending requires users to collect, compare, and analyze a large amount of data, which is time-consuming. Furthermore, if users' emotional state is affected by stress or anxiety, they may find it difficult to implement appropriate advice. As a result, it becomes difficult to make feasible improvements to their household finances, and spending optimization does not progress.

[0651] 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.

[0652] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, analysis means using AI for performing comparative analysis of the payment data with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, emotion recognition means for analyzing the emotional state of the user when the payment data is input, and means for providing optimal advice using the emotion recognition results together with the analysis results. This makes it possible to provide highly accurate advice for improving household finances while taking the user's emotional state into consideration.

[0653] "Household finances" refers to the management of a household's income and expenditures, and includes the entire flow of money, such as living expenses, savings, and investments.

[0654] "Payment data" refers to data entered by the user that includes the amounts of various household expense items, such as rent, loans, utilities, communication costs, education costs, and insurance.

[0655] "Server" refers to a computer system for storing, analyzing, and processing data over a network.

[0656] "Comparable households" refers to households with similar criteria such as family structure and income, and are households that serve as reference data for comparative analysis.

[0657] "Average expenditure data" refers to data on the average expenditure amount for each expense item of comparable households.

[0658] "Analysis means" refers to functions including AI for conducting comparative analysis based on payment data and average expenditure data of comparable households.

[0659] The "advice generation means" refers to a function for generating advice for improving household finances to be provided to the user based on the analysis results.

[0660] "Providing means" refers to a function such as a UI for presenting the generated advice to the user.

[0661] "Emotion recognition means" refers to a function for analyzing the emotional state of a user when entering payment data, including the tone of the user's voice, input speed, and facial expression analysis.

[0662] The "emotion recognition result" refers to the evaluation result of the user's emotional state analyzed by the emotion recognition means.

[0663] "Providing optimal advice" refers to providing advice on improving household finances at a timing and with content that suits the user's condition based on the results of emotion recognition of the user.

[0664] This invention provides a system that efficiently and precisely analyzes and proposes improvements to users who wish to review their household finances. This system uses hardware and software to collect and analyze users' payment data, recognize their emotional state, and generate optimal advice for improving their household finances.

[0665] Hardware and software used

[0666] 1. Terminal

[0667] The terminal is a device with an interface for users to input household information, such as a smartphone or PC. A dedicated application is installed on the terminal, and users use it to input payment data. The terminal also has an emotion recognition engine built in to analyze the user's emotional state.

[0668] 2. Server

[0669] The server plays a central role in processing the received data and generating advice based on the analysis results. The server is equipped with a database, AI model, analysis module, and advice generation module.

[0670] Data collection and transmission

[0671] The user launches the application on the device and enters payment items such as rent, utilities, communication fees, education fees, and insurance. This data is temporarily stored on the device and a confirmation screen is displayed. Once the user confirms the information entered and clicks the submit button, the data is sent to the server via a secure connection.

[0672] emotion recognition

[0673] While entering data, the device analyzes the user's input speed, voice tone, and input accuracy in real time. The emotion recognition engine uses this data to evaluate the user's emotional state. For example, if the user types quickly and their voice tone is rising, the system will determine that the user is feeling stressed. This emotional state is classified as "high stress," "medium stress," or "low stress," and is used to generate advice.

[0674] Data analysis and advice generation

[0675] The server stores the received payment data in a database and uses an AI model to compare it with the average expenditure data of comparable households. As a result of the comparison analysis, outliers and excessive expenditures are identified. For example, the server detects that the user's household utility bill is 15,000 yen, while the average for comparable households is 10,000 yen. Based on the analysis results, the server runs an advice generation module. Specific improvement measures (e.g., "use energy-efficient appliances" or "consider a cheaper plan from your power company") are generated and provided to the user.

[0676] The generated advice is sent to the device at an appropriate time and in an appropriate format, taking into account the user's emotional state. For example, a user who is feeling stressed will be suggested easy-to-implement improvement measures.

[0677] Specific examples

[0678] For example, if a user were to use this system to review their finances, it would work like this:

[0679] 1. The user uses a dedicated application to enter the following amounts: rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education expenses 30,000 yen, and insurance 20,000 yen.

[0680] 2. The emotion recognition engine analyzes the user's typing speed and tone of voice and determines that the user is under high stress.

[0681] 3. The user clicks the submit button and the data is sent to the server.

[0682] 4. The server analyzes the data and detects that the utility bills are higher than the average of 10,000 yen for comparable households.

[0683] 5. The server will suggest things like "use more energy-efficient appliances" or "consider cheaper plans from your electricity provider," providing easy-to-implement improvements based on your emotional state.

[0684] Prompt Sentence Examples

[0685] "Analyze the emotional state of the user of the household budget review system based on the payment data entered and suggest optimal spending improvements. User's payment data: rent 100,000 yen, water and electricity bills 15,000 yen, communication expenses 10,000 yen, education expenses 30,000 yen, insurance 20,000 yen. The emotional state is determined to be highly stressed."

[0686] As described above, the present invention is a system that performs efficient and precise analysis for users who wish to review their household finances, and suggests improvements that take into account their emotional state.

[0687] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0688] Step 1: User enters payment data

[0689] The user launches the dedicated application and inputs household payment items. In each field, they input the expenditure amounts for rent, utilities, communication, education, insurance, etc. For example, they input 100,000 yen for rent, 15,000 yen for utilities, 10,000 yen for communication, 30,000 yen for education, and 20,000 yen for insurance.

[0690] Input: Amount data for each pay item

[0691] Output: Payment data entered

[0692] Step 2: Emotion Recognition

[0693] The terminal observes various actions of the user in real time as they enter payment data. The emotion engine analyzes the user's tone of voice, input speed, and frequency of input errors to assess the user's emotional state. For example, if the user types quickly and has a rising tone of voice, it can determine that the user is stressed.

[0694] Input: User input behavior (speed, tone of voice, frequency of input errors)

[0695] Output: Emotional state (high stress, medium stress, low stress)

[0696] Step 3: Check and submit data

[0697] Once the user has entered their payment data, the terminal temporarily stores it in memory and displays a confirmation screen. The user must confirm the data and click the submit button, which sends the data to the server over a secure connection.

[0698] Input: entered payment data, emotional state

[0699] Output: Data sent to the server

[0700] Step 4: Store and parse payment data

[0701] The server stores the received payment data and emotional state information in a database. It then uses analytical means to compare the data with average expenditure data for comparable households. For example, it detects that the user's household utility bill is 15,000 yen, while the average for comparable households is 10,000 yen.

[0702] Input: Received payment data, emotional state

[0703] Output: Analysis results (abnormal values ​​and excess expenditures for each expenditure item)

[0704] Step 5: Advice Generation

[0705] The server generates advice based on the analysis results and the user's emotional state. The advice generation module creates improvement proposals to address identified outliers and excessive spending. For example, it suggests specific improvement measures such as "using energy-efficient home appliances" or "considering cheaper plans from your power company." The content and timing of the proposals are adjusted based on the user's emotional state.

[0706] Input: Analysis results, emotional state

[0707] Output: Generated financial advice

[0708] Step 6: Providing advice

[0709] The device displays the advice received from the server to the user. The application UI displays specific suggestions for improving household finances. The user can review the advice and take action. For example, a message might say, "Use energy-efficient appliances to reduce your utility bills," along with specific links and reference materials.

[0710] Input: Generated financial advice

[0711] Output: Advice and action guide provided to the user

[0712] (Application example 2)

[0713] 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."

[0714] Conventional household management systems simply aggregate payment data and compare it with similar households, providing uniform advice without considering the user's emotional state. This creates problems when users are stressed or their emotional state is affected, making it difficult to provide practical and actionable advice. Furthermore, advice is not provided at the optimal time based on the user's emotional state, which can lead to suggestions being rejected effectively. To solve these issues, there is a need for a system that can analyze the user's emotional state and provide personalized and optimal advice for improving household finances based on that information.

[0715] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0716] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, means for analyzing the payment data by using AI to compare it with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, and emotion recognition means for analyzing the user's emotional state when entering the payment data and adjusting the content and timing of the advice. This makes it possible to provide realistic and actionable advice for improving household finances that takes into account the user's emotional state.

[0717] "Reviewing the household budget" is an activity aimed at reevaluating the flow of money within a household and optimizing spending.

[0718] "Payment data" refers to data that includes detailed information on household expenditures, such as rent, loans, utility bills, communication costs, education costs, and various insurances.

[0719] "Comparable households" refer to other households with incomes and family structures similar to that of the user's household.

[0720] "Average expenditure data" is aggregate data that indicates the standard amount for each expenditure item for similar households.

[0721] "Emotional state" refers to the psychological state a user exhibits in response to a particular input or situation, and includes feelings of stress, satisfaction, etc.

[0722] The "emotion recognition means" is a means for analyzing the user's tone of voice, facial expression, input speed, etc., to evaluate the user's emotional state.

[0723] The "analysis tool" is an AI-based program that has the ability to analyze the collected payment data by comparing it with the average expenditure data of comparable households.

[0724] The "advice generation means" is a device or program that generates specific suggestions for improving the user's household finances based on the results obtained by the analysis means.

[0725] The "advice providing means" is a means for visually or audibly providing the generated advice for improving household finances to the user.

[0726] The present invention relates to a system that analyzes spending easily and efficiently and suggests improvements for users who wish to review their household finances. This system collects payment data and uses emotion recognition technology to optimize the advice provided. Specific embodiments of the system are described below.

[0727] System Configuration

[0728] The system consists of the following components:

[0729] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication fees, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[0730] 2. Data transmission means (terminal): A means for transmitting input payment data to the server. This is also part of the application.

[0731] 3. Analysis method (server): This is the method by which AI implemented on the server performs comparative analysis with the average expenditure of comparable households.

[0732] 4. Advice generation means (server): A means for generating specific household improvement proposals to be provided to users based on the analysis results.

[0733] 5. Providing means (terminal): A means for displaying the generated advice to the user.

[0734] 6. Emotion recognition means (terminal): A means for analyzing the emotions expressed by the user when inputting information and adjusting the content and timing of the advice provided.

[0735] Hardware / Software used

[0736] EmotionRecognition Library: A library for analyzing a user's emotional state.

[0737] BudgetAnalysis library: A library that analyzes household payment data, compares it with peer households, and generates advice.

[0738] Python3: A programming language for implementing the entire program.

[0739] API Server: A communication server for collecting user payment data.

[0740] System Operation

[0741] The server collects and analyzes the user's payment data. First, the user accesses a dedicated application on their smartphone or PC and enters payment data such as rent, utilities, communication fees, education fees, and insurance. The device then analyzes the entered payment data and the user's emotional state and sends it to the server via a secure connection. The server stores the received payment data and emotional information in a database and invokes AI to perform comparative analysis with the average expenditures of comparable households. Based on the analysis results and emotional information, it generates specific advice for improving household finances, adjusting the content and timing of the suggestions based on the user's emotional state. The generated advice is displayed on the user's device.

[0742] Specific examples

[0743] For example, if a user says, "My living expenses have increased recently and I'm worried," the device's emotion recognition engine will analyze this and determine that stress is increasing. The server will compare the user with similar households based on payment data for rent, utilities, communication fees, education expenses, insurance, etc., and generate advice such as "use more energy-efficient home appliances" or "review your communication plan." This advice is provided at an appropriate time, taking into account the user's emotional state.

[0744] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0745] Step 1:

[0746] The user accesses a dedicated application and enters payment data such as rent, utility bills, communication fees, education fees, and insurance. Once the user has completed the input, the device uses an emotion recognition engine to analyze the user's emotional state. The input data and analyzed emotional information are temporarily stored.

[0747] Input: Payment data (e.g. rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education fees 30,000 yen, insurance 20,000 yen)

[0748] Output: Payment data and emotional state (e.g., stress level)

[0749] Behavior: Analyzes the user's tone of voice, facial expressions, and typing speed.

[0750] Step 2:

[0751] The terminal displays the entered payment data and emotional state on a confirmation screen and prompts the user to confirm the submission. Once the user clicks the submit button, the terminal transmits the data to the server over a secure connection.

[0752] Input: Payment data and emotional state

[0753] Output: Send data to the server

[0754] Operation: Displays a confirmation screen and transmits data.

[0755] Step 3:

[0756] The server stores the received payment data and emotional state in a database and runs an analysis tool. The analysis tool compares the payment data with the average expenditure data of comparable households and identifies outliers and excessive expenditures. For example, it may discover that the utility bill is 15,000 yen, which is higher than the average of 10,000 yen for comparable households.

[0757] Input: Submitted payment data and emotional state

[0758] Output: Analysis results (identification of outliers)

[0759] Action: Save to database and call analysis method.

[0760] Step 4:

[0761] The server generates specific advice for improving household finances using an advice generation means based on the analysis results and the user's emotional state. For example, it may suggest "using energy-efficient home appliances" or "reviewing your communication plan." If the user is feeling stressed, the server prioritizes suggestions that are easy to implement.

[0762] Input: Analysis results and emotional state

[0763] Output: Specific advice for improving your household finances

[0764] Action: Triggers the advice generator and generates appropriate suggestions.

[0765] Step 5:

[0766] The server sends the generated advice to the device, which then displays it to the user. The advice is displayed at the optimal time depending on the user's emotional state. For example, the advice is displayed after stress levels have decreased.

[0767] Input: Generated advice

[0768] Output: Advice displayed to the user

[0769] Action: Send and display advice.

[0770] Through this series of steps, expenditure analysis and advice on improving household finances are provided to the user.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] [Third embodiment]

[0775] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0776] 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.

[0777] 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).

[0778] 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.

[0779] 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.

[0780] 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).

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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."

[0787] The present invention is a system that analyzes spending in a simple and efficient way and suggests improvements for users who wish to review their household finances. The system collects users' payment data and compares them with similar households to generate and provide specific advice on improving their household finances.

[0788] System configuration

[0789] The system consists of the following components:

[0790] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication costs, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[0791] 2. Data transmission means (terminal): A means for transmitting the entered payment data to the server. This is also part of the application.

[0792] 3. Analysis method (server): A method in which the AI ​​implemented on the server performs comparative analysis with the average expenditure of comparable households.

[0793] 4. Advice generation means (server): A means for generating specific financial improvement proposals to be provided to users based on the analysis results.

[0794] 5. Provision means (terminal): A means for displaying the generated advice to the user.

[0795] A natural language description of the program's operation

[0796] User data entry

[0797] Users access a dedicated application on their smartphone or PC and enter various household expenses. For example, the items a user might enter might be rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[0798] Data transmission

[0799] The terminal temporarily stores the entered payment data before sending it to the server, which uses standard APIs and a secure connection to protect the data.

[0800] Data analysis

[0801] The server then provides the received payment data to the AI, which then compares it with the average expenditure data of comparable households stored in a database. For example, if a user's utility bill is ¥15,000 and the average for comparable households is ¥10,000, the AI ​​will identify this difference and recognize it as an anomaly.

[0802] Advice Generation

[0803] The server generates specific advice based on the analysis results provided by the AI. The advice generation module suggests, for example, "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company." This provides users with realistic and feasible improvement measures.

[0804] Providing advice

[0805] The device displays the advice received from the server to the user. The application visualizes the list of advice in an easy-to-understand manner to make it easier for the user to understand. Each improvement suggestion also includes a detailed explanation and instructions on how to implement it, allowing the user to put it into practice immediately.

[0806] Specific examples

[0807] For example, let's say a user named Yamada uses this system to review his household finances. Yamada enters the following into the dedicated application: rent (100,000 yen), utility bills (15,000 yen), communication costs (10,000 yen), education expenses (30,000 yen), and insurance (20,000 yen), and submits the data.

[0808] The server receives this data and the AI ​​begins analyzing it. When compared to the average data for similar households, it is discovered that the utility bills are abnormally high. The AI ​​identifies the cause and reflects this in the analysis method. Based on the analysis results, the server generates advice such as "changing to more energy-efficient home appliances" or "switching to a cheaper plan from the power company."

[0809] These specific improvement suggestions are displayed on Yamada's device, allowing him to immediately learn about methods he can implement. For example, links to power company plan comparison sites are also displayed, allowing Yamada to easily obtain information and put into action a review of his household finances. In this way, the present invention efficiently supports household finance improvement.

[0810] The processing flow will be explained below.

[0811] Step 1:

[0812] Users input various household payment items (rent, loan, utilities, communication costs, education costs, various insurances, etc.) into a dedicated application on their device. For example, they enter detailed information such as rent of 100,000 yen, utilities of 15,000 yen, and communication costs of 10,000 yen.

[0813] Step 2:

[0814] The terminal temporarily stores the entered payment data and displays a screen for the user to confirm the entered data. The user checks the entered data and clicks the send button after confirming that it is correct.

[0815] Step 3:

[0816] The terminal sends the stored payment data to the server, where it is sent over a secure connection and an API to reach the server's receiving endpoint.

[0817] Step 4:

[0818] The server stores the received payment data in a database to maintain a chain of record, and optionally cleanses the data before providing it to the analysis means.

[0819] Step 5:

[0820] The server's analysis means calls the AI ​​and provides the stored payment data to the AI, which then retrieves the average expenditure data of comparable households in the database and performs a comparative analysis with the user's payment data.

[0821] Step 6:

[0822] AI identifies outliers and excess expenditures for each payment item and analyzes the causes. For example, if utility bills are higher (15,000 yen) than the average expenditure of comparable households (10,000 yen), it identifies the cause as being the use of inefficient or excessive energy-efficient appliances.

[0823] Step 7:

[0824] The server runs an advice generation module based on the analysis results obtained from the AI, which then creates specific improvement proposals for identified outliers and excessive spending, such as "replace your appliances with more energy-efficient ones" or "switch to a cheaper electricity plan."

[0825] Step 8:

[0826] The server stores the generated advice in a database and prepares it for sending back to the user's device, formatting the advice in a format that is easy for the user to understand.

[0827] Step 9:

[0828] The device then reflects the advice received from the server in the display UI. The user can then view the specific improvements and suggestions on the device screen. For example, a message such as "Consider using energy-efficient appliances to reduce your utility bills" may be displayed.

[0829] Step 10:

[0830] Users can take action based on the displayed advice. Following the advice, links to replace appliances and resources for changing plans are provided, helping users easily take the next step.

[0831] Example 1

[0832] 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."

[0833] In today's busy lifestyles, it is not easy to efficiently manage household finances and review spending. Traditional methods require manually recording expenditure data and conducting comparative analysis, which is time-consuming and labor-intensive and often results in inaccurate analysis. Furthermore, data for comparison with similar households is not readily available, making rational household budget reviews difficult. Therefore, there is a need for technology that can efficiently and accurately review household budgets using an automated system and provide specific suggestions for improvement.

[0834] 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.

[0835] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to an information processing device, means for analyzing the payment data using artificial intelligence to compare it with average expenditure data of comparable households, means for generating proposals for improving the household finances based on the analysis results, and means for providing the generated proposals to the user, thereby enabling the user to easily and efficiently analyze their household expenditures and receive specific proposals for improvement.

[0836] A "household budget review" is the process of reassessing a household's income and expenses, eliminating waste, and achieving efficient financial management.

[0837] A "user" is an individual or family who intends to use the system to review their household finances.

[0838] "Payment data" refers to all expenses incurred by users in their daily lives, including, for example, rent, loans, utility bills, communication fees, education expenses, and various insurances.

[0839] "Means of input" refers to the method by which users provide payment data to the system through a dedicated application or interface, such as a smartphone or computer.

[0840] "Means for transmitting to an information processing device" refers to a method for temporarily storing the entered payment data and securely transmitting the data to a server, and includes standard APIs and secure communication protocols (e.g., HTTPS).

[0841] "Artificial intelligence-based analytical means" refers to technology that performs advanced data analysis based on received payment data and conducts comparative analysis with average expenditure data of comparable households, and includes, for example, machine learning algorithms.

[0842] The "means for generating proposals" is a process for automatically generating specific advice and proposals for improving household finances for users based on the analysis results.

[0843] The "means of providing" refers to a method for presenting the generated suggestions and advice to users in an easy-to-understand manner, such as the user interface of a dedicated application.

[0844] "Average expenditure data for comparable households" is data showing the average expenditures of households with similar family structures and living environments to the user, and is based on past data analysis results and statistical data.

[0845] A "storage device" is a device for storing data required by the system, such as average expenditure data for comparable households and analysis results, for a long period of time, and examples include a database server.

[0846] The present invention is a system that analyzes spending in a simple and efficient way and suggests improvements for users who wish to review their household finances. The system collects users' payment data and compares them with similar households to generate and provide specific advice on improving their household finances.

[0847] System configuration

[0848] The system consists of the following components:

[0849] 1. Payment data input means (terminal)

[0850] 2. Data transmission means (terminal)

[0851] 3. Analysis method (server)

[0852] 4. Advice Generation Method (Server)

[0853] 5. Means of provision (terminal)

[0854] Payment data input means (terminal)

[0855] Users use a dedicated application on their smartphone or PC to input various household expenses, including rent, loans, utilities, communication fees, education expenses, and various insurances. For example, a user can input rent of 100,000 yen, utilities of 15,000 yen, communication fees of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[0856] Data transmission means (terminal)

[0857] The terminal temporarily stores the entered payment data and then sends it to the server. The data is transmitted using standard APIs (e.g. REST API) and an HTTPS connection to ensure data security. The transmitted data is in JSON format, for example:

[0858] json

[0859] {

[0860] "Rent": 100000,

[0861] "Utilities": 15000,

[0862] "Communication Fee": 10000,

[0863] "Education expenses": 30000,

[0864] "Insurance": 20000

[0865] }

[0866] Analysis method (server)

[0867] The server then provides the received payment data to an AI analysis module. The AI ​​uses the scikit-learn library to compare it with average expenditure data for comparable households. For example, if a user's utility bill is ¥15,000 and the average for comparable households is ¥10,000, the AI ​​will identify this difference and recognize it as an outlier.

[0868] Advice generation means (server)

[0869] The server generates specific advice based on the analysis results provided by the AI. The advice generation module uses natural language generation (NLG) technology to create suggestions in an easy-to-understand format for users. For example, advice may be generated that recommends "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company."

[0870] Means of provision (terminal)

[0871] The device displays the advice received from the server to the user. The application's user interface visualizes a list of advice suggestions, each with a detailed explanation and instructions on how to implement them. For example, when a user opens the application, the following advice is displayed:

[0872] "Your current utility bills are higher than average. Consider switching to more energy-efficient appliances."

[0873] "Also, below are links to energy company plan comparison sites so you can find a cheaper plan."

[0874] Specific examples

[0875] For example, let's say a user named Yamada uses this system to review his or her household finances. Yamada enters and submits the following data into the dedicated application: rent (¥100,000), utility bills (¥15,000), communication fees (¥10,000), education expenses (¥30,000), and insurance (¥20,000). The server receives this data, and the AI ​​begins analyzing it. Comparing it to the average data for similar households reveals that utility bills are abnormally high. The AI ​​identifies the cause and incorporates it into the analysis. Based on the analysis results, the server generates advice such as "switching to more energy-efficient appliances" or "switching to a cheaper electricity provider plan." These specific improvement suggestions are displayed on Yamada's device, allowing him to immediately understand and implement them. For example, links to electricity provider plan comparison sites are also displayed, allowing Yamada to easily access information and take action to review his or her household finances.

[0876] Prompt Sentence Examples

[0877] Here are some example prompts to input to the generative AI model:

[0878] User payment data

[0879] Rent: 100,000 yen

[0880] Utilities: 15,000 yen

[0881] Communication fee: 10,000 yen

[0882] Educational expenses: 30,000 yen

[0883] Insurance: 20,000 yen

[0884] Use this data to compare with average spending, identify abnormal spending, and generate specific improvement advice. For example, if utility bills are high, suggest appropriate actions.

[0885] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0886] Step 1: User Data Entry

[0887] Users access a dedicated application on their smartphone or PC and enter various household bills. This input includes items such as rent, utilities, communication fees, education expenses, and insurance. For example, a user enters rent of 100,000 yen, utilities of 15,000 yen, communication fees of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen. The input data is temporarily stored in the application's local storage.

[0888] Step 2: Send data

[0889] The terminal sends the stored payment data to the server using a standard API (e.g., REST API) and a secure connection protocol (HTTPS). The input data is converted to JSON format and sent to the API endpoint using the POST method. An example of the data to be sent is as follows:

[0890] json

[0891] {

[0892] "Rent": 100000,

[0893] "Utilities": 15000,

[0894] "Communication Fee": 10000,

[0895] "Education expenses": 30000,

[0896] "Insurance": 20000

[0897] }

[0898] Step 3: Data analysis

[0899] The server provides the received payment data to an AI analysis module, which uses the scikit-learn library to perform comparative analysis with average expenditure data for comparable households. For example, if a user's household's utility bill is 15,000 yen and the average for comparable households is 10,000 yen, the AI ​​will identify this difference and recognize it as an outlier. The input data is compared with the average expenditure data for comparable households, and the difference is output as the analysis result.

[0900] Step 4: Advice Generation

[0901] The server generates specific advice based on the analysis results provided by the AI. The advice generation module uses natural language generation (NLG) technology to create suggestions in an easy-to-understand format for users. For example, advice may be generated that recommends "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company." Based on the analysis results, specific improvement measures are output with detailed explanations.

[0902] Step 5: Providing advice

[0903] The device displays the advice received from the server to the user. The application's user interface visualizes a list of advice, with each suggestion including a detailed explanation and how to implement it. For example, advice such as "Your current utility bills are higher than average. Consider switching to more energy-efficient appliances" or "Below is a link to a comparison site for power company plans to help you find a cheaper plan" is displayed, allowing the user to immediately see what they can do.

[0904] (Application example 1)

[0905] 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."

[0906] In modern society, improving household finances is an important issue for many families. However, there is a lack of methods to efficiently analyze various expenditure data and provide specific advice when reviewing household finances. To solve this problem, a system is needed that allows users to easily input expenditure data themselves, analyzes the data based on that data, and provides appropriate improvement suggestions.

[0907] 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.

[0908] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, means for analyzing the payment data using AI to compare it with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, means for inputting the user's expenditure data directly on the device and automatically importing it, means for transmitting data using a standard API and protecting the data through a secure connection, and means for visualizing improvement measures so that the user can immediately put them into practice. This enables users to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[0909] "Reviewing the household budget" refers to the act of users reevaluating and optimizing household expenditure items.

[0910] "Payment data" is a record of various expenses that a user pays on a daily basis, including rent, purchase fees, utility bills, communication fees, education fees, various insurances, and the like.

[0911] "Input means" refers to the device or function that allows a user to input payment data into the system, such as a smartphone application or web interface.

[0912] "Transmission means" refers to a device or mechanism for transmitting input payment data to a server, using a secure API or network connection.

[0913] The "analysis method" is a function that uses AI to compare and analyze expenditures with those of similar households based on the received payment data and derives results.

[0914] The "advice generation means" is a device or function that generates specific suggestions for improving household finances to be provided to the user based on the analysis results.

[0915] The "means for providing" refers to a device or function for displaying the generated advice to the user in an easily understandable format.

[0916] "Auto Capture" is a feature that automatically collects user spending data without requiring manual entry.

[0917] A "standard API" is a standardized interface for exchanging data between different systems and applications.

[0918] A "secure connection" is a connection method that uses security technologies such as encryption to prevent unauthorized access and data leaks when sending and receiving data.

[0919] "Visualization" refers to displaying data and analytical results in a visually easy-to-understand format, such as graphs or charts.

[0920] The present invention provides a system for analyzing expenditures in a simple and efficient manner and suggesting improvements to users who wish to review their household finances. Specific embodiments of this system will be described below.

[0921] System configuration

[0922] The system consists of the following components:

[0923] 1. Payment data input means (terminal):

[0924] It is a means for users to input various household expenditure items. It is implemented as an application on a smartphone or PC. Users can enter their expenditure data manually or automatically import their electronic payment history.

[0925] 2. Data transmission means (terminal):

[0926] This is the means by which entered payment data is sent to the server. Data is sent using standard APIs and protected by a secure connection.

[0927] 3. Analysis method (server):

[0928] An AI implemented on the server (for example, a generative AI model implemented using Python) performs comparative analysis with the average expenditure data of comparable households. The AI ​​performs analysis using the average expenditure data of comparable households stored in a database.

[0929] 4. Advice generator (server):

[0930] It is a means of generating specific proposals for improving household finances based on the analysis results. Based on the results of the AI ​​analysis, it generates actionable proposals for improvement.

[0931] 5. Means of provision (terminal):

[0932] This is a way to provide generated advice to users. It is displayed on a smartphone or computer screen and presented in a visual form, making it easy for users to quickly understand the suggestions and put them into action.

[0933] Program processing explanation

[0934] The server first receives the user's payment data and provides it to the AI. The AI ​​then analyzes this data, comparing it with the average expenditure data of comparable households. Based on the analysis results, it generates specific improvement suggestions and sends them to the user's device. The user's device then displays the generated advice in a visually easy-to-understand format.

[0935] To give a specific example, a user inputs data such as rent, utility bills, and communication fees and sends it to a server. The server receives this data and analyzes it using an AI model (for example, a generative AI model using PyTorch or TensorFlow). If the analysis reveals that the user's utility bills are higher than the average for similar households, the AI ​​will generate improvement suggestions such as "switching to more energy-efficient appliances" or "reviewing your power company plan." The results are then sent to the user's device and visualized and displayed.

[0936] Prompt Sentence Examples

[0937] Here are some examples of prompts for generative AI models:

[0938] Generate advice on improving your household finances based on your expenditure data. Rent: 100,000 yen, utilities: 15,000 yen, communication costs: 10,000 yen, education costs: 30,000 yen, insurance: 20,000 yen. Compare your expenses with the average for similar households and suggest specific areas for improvement.

[0939] The above is a detailed description of the configuration and processing of the embodiment of the present invention. This system enables users to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[0940] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0941] Step 1:

[0942] The user opens the application on their smartphone or computer and enters payment data for rent, utilities, communication fees, education fees, various insurances, etc.

[0943] Input: User payment data

[0944] Output: A list of entered payment data

[0945] What happens: The user enters the amount for each expense item and saves it in the in-app input form. If auto-capture is enabled, the data is retrieved from the associated payment app.

[0946] Step 2:

[0947] The terminal temporarily stores the entered payment data and transmits it to the server over a secure connection using a standard API.

[0948] Input: A list of entered payment data

[0949] Output: Payment data sent to the server

[0950] Specific operation: Converts data stored in the device into JSON format, constructs an API request, and sends it to the server using the HTTPS protocol.

[0951] Step 3:

[0952] The server stores the received payment data in a database for analysis and provides it to the AI ​​for comparison with average expenditure data of comparable households.

[0953] Input: Payment data sent to the server

[0954] Output: The dataset that is provided to the AI

[0955] Specific operation: The data is stored in a database on the server side, and after necessary preprocessing, it is provided to the AI ​​model.

[0956] Step 4:

[0957] The server's AI compares and analyzes the payment data received with average expenditure data from comparable households to identify outliers and areas for improvement.

[0958] Input: A dataset provided to the AI

[0959] Output: Expenditure analysis results

[0960] What it does: It uses an AI model (using, for example, PyTorch or TensorFlow) to analyze input data, perform statistical comparisons, and output results if it detects outliers or finds patterns.

[0961] Step 5:

[0962] The server uses AI to generate specific advice for improving household finances based on the analysis results.

[0963] Input: Expenditure analysis results

[0964] Output: Advice on improving your finances

[0965] How it works: Based on the analysis results, the generative AI model generates effective household improvement measures, such as "switching to more energy-efficient appliances" or "reviewing your communication plan."

[0966] Step 6:

[0967] To provide a user with improvement advice received by a terminal from a server in a visually easy-to-understand format.

[0968] Input: Advice for improving your finances

[0969] Output: Visualized advice shown to the user

[0970] Specific operation: The device obtains the advice content and displays it on the app in a format that is easy for the user to understand (graphs, lists, etc.). This allows the user to easily check the suggested improvement measures and put them into practice.

[0971] The above are the specific processing steps in the embodiment of the present invention, which allow the user to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[0972] 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.

[0973] The present invention provides a system that analyzes spending and suggests improvements in a simple and efficient way for users who wish to review their household finances. The system collects the user's payment data and compares it with similar households to generate and provide specific advice on improving their household finances. The system also incorporates an emotion engine that recognizes the user's emotions, analyzes the user's emotions when inputting information, and provides optimal advice.

[0974] System configuration

[0975] The system consists of the following components:

[0976] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication costs, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[0977] 2. Data transmission means (terminal): A means for transmitting the entered payment data to the server. This is also part of the application.

[0978] 3. Analysis method (server): A method in which the AI ​​implemented on the server performs comparative analysis with the average expenditure of comparable households.

[0979] 4. Advice generation means (server): A means for generating specific financial improvement proposals to be provided to users based on the analysis results.

[0980] 5. Provision means (terminal): A means for displaying the generated advice to the user.

[0981] 6. Emotion recognition means (terminal): A means of analyzing the user's emotions when inputting information and adjusting the content and timing of advice.

[0982] A natural language description of the program's operation

[0983] User data entry

[0984] Users access a dedicated application on their smartphone or PC and enter various household payment items. For example, the items a user might enter include rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[0985] emotion recognition

[0986] When a user enters payment data, the terminal's emotion engine analyzes the user's tone of voice, facial expression, and input speed and accuracy. Based on this, the emotion engine assesses the user's stress level and emotional state. For example, if the user enters data quickly and with a rising tone of voice, it will determine that the user is stressed.

[0987] Data transmission

[0988] The terminal temporarily stores the user's payment data and emotional state, and displays a confirmation screen. The user confirms the input and clicks the submit button to send the data to the server. The data is transmitted over a secure connection.

[0989] Data analysis

[0990] The server stores the received payment data and emotional information in a database, then runs the analysis tool. The analysis tool calls AI and performs a comparative analysis of the stored data with the average expenditure of similar households. For example, it may be determined that the user's household's utility bill is 15,000 yen, while the average for similar households is 10,000 yen. The AI ​​identifies this difference and recognizes it as an outlier.

[0991] Advice Generation

[0992] The server runs an advice generation module based on the analysis results and emotion information. The module creates specific improvement suggestions for identified outliers and excessive spending. For example, the advice might be to "use energy-efficient appliances" or "consider a cheaper plan from your electricity provider." The content and timing of the suggestions are adjusted based on the emotion recognition information. For example, a user who is feeling stressed will be offered realistic and easy-to-implement improvement measures.

[0993] Providing advice

[0994] The device reflects the advice received from the server in the display UI. The user can check the specific improvements and suggestions on the device screen. For example, a message such as "Consider using energy-efficient home appliances to reduce your utility bills" is displayed. In addition, advice is presented based on emotion recognition information at a time that suits the user's condition.

[0995] Specific examples

[0996] For example, let's say a user named Yamada uses this system to review his household finances. Yamada uses the dedicated application to input and submit the following: rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education expenses 30,000 yen, and insurance 20,000 yen.

[0997] When inputting the data, the device's emotion engine analyzes Yamada's emotional state and detects that stress is increasing. The server receives this data and compares it with the average expenditures of comparable households. It turns out that his utility bills are 15,000 yen, higher than the average of 10,000 yen for comparable households. Based on the analysis results, the server generates advice such as "use energy-efficient appliances" and "consider a cheaper plan from your power company." Taking Yamada's emotional state into consideration, simple and easy improvements are suggested.

[0998] This advice is displayed on Yamada's device, allowing him to take specific action immediately. For example, links to appropriate plan changes and information on energy-efficient home appliances are also provided. In this way, the present invention not only efficiently supports household budget improvement, but also provides optimal advice based on the user's emotional state, helping to make household budget reviews more actionable.

[0999] The processing flow will be explained below.

[1000] Step 1:

[1001] Users enter various household payment items (rent, loan, utilities, communication costs, education expenses, various insurances, etc.) into a dedicated application on their device. For example, they enter detailed information such as rent of 100,000 yen, utilities of 15,000 yen, and communication costs of 10,000 yen.

[1002] Step 2:

[1003] As users type, the device's emotion engine analyzes their voice tone, typing speed, facial expressions, etc. Based on this data, the emotion engine evaluates the user's emotional state and determines their stress level and emotional temperature.

[1004] Step 3:

[1005] The terminal temporarily stores the entered payment data and analyzed emotion data, and displays a confirmation screen to the user. The user confirms that the entered information is correct and clicks the send button.

[1006] Step 4:

[1007] The terminal transmits the stored payment data and emotion data to the server, where the data reaches the server's API endpoint over a secure connection.

[1008] Step 5:

[1009] The server stores the received payment and sentiment data in a database, maintaining a chain of record. The data is filtered and cleansed as needed.

[1010] Step 6:

[1011] The server's analysis function calls AI and performs a comparative analysis based on the stored payment data with the average expenditure data of similar households. For example, if the user's household utility bill is 15,000 yen and the average for similar households is 10,000 yen, this outlier is identified.

[1012] Step 7:

[1013] The AI ​​analyzes the causes of identified outliers and excessive spending items and performs a detailed analysis of each spending item, such as identifying the use of energy-inefficient appliances or excessive late-night electricity usage.

[1014] Step 8:

[1015] The server runs an advice generation module based on the analysis results provided by the AI. The module creates specific improvement proposals for identified outliers. For example, it could generate advice such as "replace your appliances with more energy-efficient ones" or "change your electricity plan."

[1016] Step 9:

[1017] The advice generation module also takes into account the user's emotional data and adjusts the content and timing of the advice it provides. For example, it suggests simple and easy-to-implement solutions for users with high stress levels, and provides advice in a format that is easy for the user to accept.

[1018] Step 10:

[1019] The server stores the generated advice in a database and prepares it for sending back to the user's device. The advice is formatted in a specific and easy-to-understand format.

[1020] Step 11:

[1021] The device receives advice from the server and displays it in the display UI, allowing the user to view specific improvement suggestions and action plans based on them on the device screen.

[1022] Step 12:

[1023] Users are guided by the advice they receive and can take action. The advice provides relevant links and resources (e.g., links to energy-efficient appliances or websites for changing your plan) to help users take immediate next steps.

[1024] Example 2

[1025] 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."

[1026] In today's busy lifestyles, managing household finances is an important issue for many families. However, reviewing household finances and optimizing spending requires users to collect, compare, and analyze a large amount of data, which is time-consuming. Furthermore, if users' emotional state is affected by stress or anxiety, they may find it difficult to implement appropriate advice. As a result, it becomes difficult to make feasible improvements to their household finances, and spending optimization does not progress.

[1027] 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.

[1028] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, analysis means using AI for performing comparative analysis of the payment data with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, emotion recognition means for analyzing the emotional state of the user when the payment data is input, and means for providing optimal advice using the emotion recognition results together with the analysis results. This makes it possible to provide highly accurate advice for improving household finances while taking the user's emotional state into consideration.

[1029] "Household finances" refers to the management of a household's income and expenditures, and includes the entire flow of money, such as living expenses, savings, and investments.

[1030] "Payment data" refers to data entered by the user that includes the amounts of various household expense items, such as rent, loans, utilities, communication costs, education costs, and insurance.

[1031] "Server" refers to a computer system for storing, analyzing, and processing data over a network.

[1032] "Comparable households" refers to households with similar criteria such as family structure and income, and are households that serve as reference data for comparative analysis.

[1033] "Average expenditure data" refers to data on the average expenditure amount for each expense item of comparable households.

[1034] "Analysis means" refers to functions including AI for conducting comparative analysis based on payment data and average expenditure data of comparable households.

[1035] The "advice generation means" refers to a function for generating advice for improving household finances to be provided to the user based on the analysis results.

[1036] "Providing means" refers to a function such as a UI for presenting the generated advice to the user.

[1037] "Emotion recognition means" refers to a function for analyzing the emotional state of a user when entering payment data, including the tone of the user's voice, input speed, and facial expression analysis.

[1038] The "emotion recognition result" refers to the evaluation result of the user's emotional state analyzed by the emotion recognition means.

[1039] "Providing optimal advice" refers to providing advice on improving household finances at a timing and with content that suits the user's condition based on the results of emotion recognition of the user.

[1040] This invention provides a system that efficiently and precisely analyzes and proposes improvements to users who wish to review their household finances. This system uses hardware and software to collect and analyze users' payment data, recognize their emotional state, and generate optimal advice for improving their household finances.

[1041] Hardware and software used

[1042] 1. Terminal

[1043] The terminal is a device with an interface for users to input household information, such as a smartphone or PC. A dedicated application is installed on the terminal, and users use it to input payment data. The terminal also has an emotion recognition engine built in to analyze the user's emotional state.

[1044] 2. Server

[1045] The server plays a central role in processing the received data and generating advice based on the analysis results. The server is equipped with a database, AI model, analysis module, and advice generation module.

[1046] Data collection and transmission

[1047] The user launches the application on the device and enters payment items such as rent, utilities, communication fees, education fees, and insurance. This data is temporarily stored on the device and a confirmation screen is displayed. Once the user confirms the information entered and clicks the submit button, the data is sent to the server via a secure connection.

[1048] emotion recognition

[1049] While entering data, the device analyzes the user's input speed, voice tone, and input accuracy in real time. The emotion recognition engine uses this data to evaluate the user's emotional state. For example, if the user types quickly and their voice tone is rising, the system will determine that the user is feeling stressed. This emotional state is classified as "high stress," "medium stress," or "low stress," and is used to generate advice.

[1050] Data analysis and advice generation

[1051] The server stores the received payment data in a database and uses an AI model to compare it with the average expenditure data of comparable households. As a result of the comparison analysis, outliers and excessive expenditures are identified. For example, the server detects that the user's household utility bill is 15,000 yen, while the average for comparable households is 10,000 yen. Based on the analysis results, the server runs an advice generation module. Specific improvement measures (e.g., "use energy-efficient appliances" or "consider a cheaper plan from your power company") are generated and provided to the user.

[1052] The generated advice is sent to the device at an appropriate time and in an appropriate format, taking into account the user's emotional state. For example, a user who is feeling stressed will be suggested easy-to-implement improvement measures.

[1053] Specific examples

[1054] For example, if a user were to use this system to review their finances, it would work like this:

[1055] 1. The user uses a dedicated application to enter the following amounts: rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education expenses 30,000 yen, and insurance 20,000 yen.

[1056] 2. The emotion recognition engine analyzes the user's typing speed and tone of voice and determines that the user is under high stress.

[1057] 3. The user clicks the submit button and the data is sent to the server.

[1058] 4. The server analyzes the data and detects that the utility bills are higher than the average of 10,000 yen for comparable households.

[1059] 5. The server will suggest things like "use more energy-efficient appliances" or "consider cheaper plans from your electricity provider," providing easy-to-implement improvements based on your emotional state.

[1060] Prompt Sentence Examples

[1061] "Analyze the emotional state of the user of the household budget review system based on the payment data entered and suggest optimal spending improvements. User's payment data: rent 100,000 yen, water and electricity bills 15,000 yen, communication expenses 10,000 yen, education expenses 30,000 yen, insurance 20,000 yen. The emotional state is determined to be highly stressed."

[1062] As described above, the present invention is a system that performs efficient and precise analysis for users who wish to review their household finances, and suggests improvements that take into account their emotional state.

[1063] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1064] Step 1: User enters payment data

[1065] The user launches the dedicated application and inputs household payment items. In each field, they input the expenditure amounts for rent, utilities, communication, education, insurance, etc. For example, they input 100,000 yen for rent, 15,000 yen for utilities, 10,000 yen for communication, 30,000 yen for education, and 20,000 yen for insurance.

[1066] Input: Amount data for each pay item

[1067] Output: Payment data entered

[1068] Step 2: Emotion Recognition

[1069] The terminal observes various actions of the user in real time as they enter payment data. The emotion engine analyzes the user's tone of voice, input speed, and frequency of input errors to assess the user's emotional state. For example, if the user types quickly and has a rising tone of voice, it can determine that the user is stressed.

[1070] Input: User input behavior (speed, tone of voice, frequency of input errors)

[1071] Output: Emotional state (high stress, medium stress, low stress)

[1072] Step 3: Check and submit data

[1073] Once the user has entered their payment data, the terminal temporarily stores it in memory and displays a confirmation screen. The user must confirm the data and click the submit button, which sends the data to the server over a secure connection.

[1074] Input: entered payment data, emotional state

[1075] Output: Data sent to the server

[1076] Step 4: Store and parse payment data

[1077] The server stores the received payment data and emotional state information in a database. It then uses analytical means to compare the data with average expenditure data for comparable households. For example, it detects that the user's household utility bill is 15,000 yen, while the average for comparable households is 10,000 yen.

[1078] Input: Received payment data, emotional state

[1079] Output: Analysis results (abnormal values ​​and excess expenditures for each expenditure item)

[1080] Step 5: Advice Generation

[1081] The server generates advice based on the analysis results and the user's emotional state. The advice generation module creates improvement proposals to address identified outliers and excessive spending. For example, it suggests specific improvement measures such as "using energy-efficient home appliances" or "considering cheaper plans from your power company." The content and timing of the proposals are adjusted based on the user's emotional state.

[1082] Input: Analysis results, emotional state

[1083] Output: Generated financial advice

[1084] Step 6: Providing advice

[1085] The device displays the advice received from the server to the user. The application UI displays specific suggestions for improving household finances. The user can review the advice and take action. For example, a message might say, "Use energy-efficient appliances to reduce your utility bills," along with specific links and reference materials.

[1086] Input: Generated financial advice

[1087] Output: Advice and action guide provided to the user

[1088] (Application example 2)

[1089] 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."

[1090] Conventional household management systems simply aggregate payment data and compare it with similar households, providing uniform advice without considering the user's emotional state. This creates problems when users are stressed or their emotional state is affected, making it difficult to provide practical and actionable advice. Furthermore, advice is not provided at the optimal time based on the user's emotional state, which can lead to suggestions being rejected effectively. To solve these issues, there is a need for a system that can analyze the user's emotional state and provide personalized and optimal advice for improving household finances based on that information.

[1091] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1092] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, means for analyzing the payment data by using AI to compare it with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, and emotion recognition means for analyzing the user's emotional state when entering the payment data and adjusting the content and timing of the advice. This makes it possible to provide realistic and actionable advice for improving household finances that takes into account the user's emotional state.

[1093] "Reviewing the household budget" is an activity aimed at reevaluating the flow of money within a household and optimizing spending.

[1094] "Payment data" refers to data that includes detailed information on household expenditures, such as rent, loans, utility bills, communication costs, education costs, and various insurances.

[1095] "Comparable households" refer to other households with incomes and family structures similar to that of the user's household.

[1096] "Average expenditure data" is aggregate data that indicates the standard amount for each expenditure item for similar households.

[1097] "Emotional state" refers to the psychological state a user exhibits in response to a particular input or situation, and includes feelings of stress, satisfaction, etc.

[1098] The "emotion recognition means" is a means for analyzing the user's tone of voice, facial expression, input speed, etc., to evaluate the user's emotional state.

[1099] The "analysis tool" is an AI-based program that has the ability to analyze the collected payment data by comparing it with the average expenditure data of comparable households.

[1100] The "advice generation means" is a device or program that generates specific suggestions for improving the user's household finances based on the results obtained by the analysis means.

[1101] The "advice providing means" is a means for visually or audibly providing the generated advice for improving household finances to the user.

[1102] The present invention relates to a system that analyzes spending easily and efficiently and suggests improvements for users who wish to review their household finances. This system collects payment data and uses emotion recognition technology to optimize the advice provided. Specific embodiments of the system are described below.

[1103] System Configuration

[1104] The system consists of the following components:

[1105] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication fees, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[1106] 2. Data transmission means (terminal): A means for transmitting input payment data to the server. This is also part of the application.

[1107] 3. Analysis method (server): This is the method by which AI implemented on the server performs comparative analysis with the average expenditure of comparable households.

[1108] 4. Advice generation means (server): A means for generating specific household improvement proposals to be provided to users based on the analysis results.

[1109] 5. Providing means (terminal): A means for displaying the generated advice to the user.

[1110] 6. Emotion recognition means (terminal): A means for analyzing the emotions expressed by the user when inputting information and adjusting the content and timing of the advice provided.

[1111] Hardware / Software used

[1112] EmotionRecognition Library: A library for analyzing a user's emotional state.

[1113] BudgetAnalysis library: A library that analyzes household payment data, compares it with peer households, and generates advice.

[1114] Python3: A programming language for implementing the entire program.

[1115] API Server: A communication server for collecting user payment data.

[1116] System Operation

[1117] The server collects and analyzes the user's payment data. First, the user accesses a dedicated application on their smartphone or PC and enters payment data such as rent, utilities, communication fees, education fees, and insurance. The device then analyzes the entered payment data and the user's emotional state and sends it to the server via a secure connection. The server stores the received payment data and emotional information in a database and invokes AI to perform comparative analysis with the average expenditures of comparable households. Based on the analysis results and emotional information, it generates specific advice for improving household finances, adjusting the content and timing of the suggestions based on the user's emotional state. The generated advice is displayed on the user's device.

[1118] Specific examples

[1119] For example, if a user says, "My living expenses have increased recently and I'm worried," the device's emotion recognition engine will analyze this and determine that stress is increasing. The server will compare the user with similar households based on payment data for rent, utilities, communication fees, education expenses, insurance, etc., and generate advice such as "use more energy-efficient home appliances" or "review your communication plan." This advice is provided at an appropriate time, taking into account the user's emotional state.

[1120] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1121] Step 1:

[1122] The user accesses a dedicated application and enters payment data such as rent, utility bills, communication fees, education fees, and insurance. Once the user has completed the input, the device uses an emotion recognition engine to analyze the user's emotional state. The input data and analyzed emotional information are temporarily stored.

[1123] Input: Payment data (e.g. rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education fees 30,000 yen, insurance 20,000 yen)

[1124] Output: Payment data and emotional state (e.g., stress level)

[1125] Behavior: Analyzes the user's tone of voice, facial expressions, and typing speed.

[1126] Step 2:

[1127] The terminal displays the entered payment data and emotional state on a confirmation screen and prompts the user to confirm the submission. Once the user clicks the submit button, the terminal transmits the data to the server over a secure connection.

[1128] Input: Payment data and emotional state

[1129] Output: Send data to the server

[1130] Operation: Displays a confirmation screen and transmits data.

[1131] Step 3:

[1132] The server stores the received payment data and emotional state in a database and runs an analysis tool. The analysis tool compares the payment data with the average expenditure data of comparable households and identifies outliers and excessive expenditures. For example, it may discover that the utility bill is 15,000 yen, which is higher than the average of 10,000 yen for comparable households.

[1133] Input: Submitted payment data and emotional state

[1134] Output: Analysis results (identification of outliers)

[1135] Action: Save to database and call analysis method.

[1136] Step 4:

[1137] The server generates specific advice for improving household finances using an advice generation means based on the analysis results and the user's emotional state. For example, it may suggest "using energy-efficient home appliances" or "reviewing your communication plan." If the user is feeling stressed, the server prioritizes suggestions that are easy to implement.

[1138] Input: Analysis results and emotional state

[1139] Output: Specific advice for improving your household finances

[1140] Action: Triggers the advice generator and generates appropriate suggestions.

[1141] Step 5:

[1142] The server sends the generated advice to the device, which then displays it to the user. The advice is displayed at the optimal time depending on the user's emotional state. For example, the advice is displayed after stress levels have decreased.

[1143] Input: Generated advice

[1144] Output: Advice displayed to the user

[1145] Action: Send and display advice.

[1146] Through this series of steps, expenditure analysis and advice on improving household finances are provided to the user.

[1147] 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.

[1148] 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.

[1149] 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.

[1150] [Fourth embodiment]

[1151] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1152] 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.

[1153] 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).

[1154] 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.

[1155] 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.

[1156] 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).

[1157] 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.

[1158] 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.

[1159] 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.

[1160] 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.

[1161] 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.

[1162] 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.

[1163] 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."

[1164] The present invention is a system that analyzes spending in a simple and efficient way and suggests improvements for users who wish to review their household finances. The system collects users' payment data and compares them with similar households to generate and provide specific advice on improving their household finances.

[1165] System configuration

[1166] The system consists of the following components:

[1167] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication costs, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[1168] 2. Data transmission means (terminal): A means for transmitting the entered payment data to the server. This is also part of the application.

[1169] 3. Analysis method (server): A method in which the AI ​​implemented on the server performs comparative analysis with the average expenditure of comparable households.

[1170] 4. Advice generation means (server): A means for generating specific financial improvement proposals to be provided to users based on the analysis results.

[1171] 5. Provision means (terminal): A means for displaying the generated advice to the user.

[1172] A natural language description of the program's operation

[1173] User data entry

[1174] Users access a dedicated application on their smartphone or PC and enter various household expenses. For example, the items a user might enter might be rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[1175] Data transmission

[1176] The terminal temporarily stores the entered payment data before sending it to the server, which uses standard APIs and a secure connection to protect the data.

[1177] Data analysis

[1178] The server then provides the received payment data to the AI, which then compares it with the average expenditure data of comparable households stored in a database. For example, if a user's utility bill is ¥15,000 and the average for comparable households is ¥10,000, the AI ​​will identify this difference and recognize it as an anomaly.

[1179] Advice Generation

[1180] The server generates specific advice based on the analysis results provided by the AI. The advice generation module suggests, for example, "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company." This provides users with realistic and feasible improvement measures.

[1181] Providing advice

[1182] The device displays the advice received from the server to the user. The application visualizes the list of advice in an easy-to-understand manner to make it easier for the user to understand. Each improvement suggestion also includes a detailed explanation and instructions on how to implement it, allowing the user to put it into practice immediately.

[1183] Specific examples

[1184] For example, let's say a user named Yamada uses this system to review his household finances. Yamada enters the following into the dedicated application: rent (100,000 yen), utility bills (15,000 yen), communication costs (10,000 yen), education expenses (30,000 yen), and insurance (20,000 yen), and submits the data.

[1185] The server receives this data and the AI ​​begins analyzing it. When compared to the average data for similar households, it is discovered that the utility bills are abnormally high. The AI ​​identifies the cause and reflects this in the analysis method. Based on the analysis results, the server generates advice such as "changing to more energy-efficient home appliances" or "switching to a cheaper plan from the power company."

[1186] These specific improvement suggestions are displayed on Yamada's device, allowing him to immediately learn about methods he can implement. For example, links to power company plan comparison sites are also displayed, allowing Yamada to easily obtain information and put into action a review of his household finances. In this way, the present invention efficiently supports household finance improvement.

[1187] The processing flow will be explained below.

[1188] Step 1:

[1189] Users input various household payment items (rent, loan, utilities, communication costs, education costs, various insurances, etc.) into a dedicated application on their device. For example, they enter detailed information such as rent of 100,000 yen, utilities of 15,000 yen, and communication costs of 10,000 yen.

[1190] Step 2:

[1191] The terminal temporarily stores the entered payment data and displays a screen for the user to confirm the entered data. The user checks the entered data and clicks the send button after confirming that it is correct.

[1192] Step 3:

[1193] The terminal sends the stored payment data to the server, where it is sent over a secure connection and an API to reach the server's receiving endpoint.

[1194] Step 4:

[1195] The server stores the received payment data in a database to maintain a chain of record, and optionally cleanses the data before providing it to the analysis means.

[1196] Step 5:

[1197] The server's analysis means calls the AI ​​and provides the stored payment data to the AI, which then retrieves the average expenditure data of comparable households in the database and performs a comparative analysis with the user's payment data.

[1198] Step 6:

[1199] AI identifies outliers and excess expenditures for each payment item and analyzes the causes. For example, if utility bills are higher (15,000 yen) than the average expenditure of comparable households (10,000 yen), it identifies the cause as being the use of inefficient or excessive energy-efficient appliances.

[1200] Step 7:

[1201] The server runs an advice generation module based on the analysis results obtained from the AI, which then creates specific improvement proposals for identified outliers and excessive spending, such as "replace your appliances with more energy-efficient ones" or "switch to a cheaper electricity plan."

[1202] Step 8:

[1203] The server stores the generated advice in a database and prepares it for sending back to the user's device, formatting the advice in a format that is easy for the user to understand.

[1204] Step 9:

[1205] The device then reflects the advice received from the server in the display UI. The user can then view the specific improvements and suggestions on the device screen. For example, a message such as "Consider using energy-efficient appliances to reduce your utility bills" may be displayed.

[1206] Step 10:

[1207] Users can take action based on the displayed advice. Following the advice, links to replace appliances and resources for changing plans are provided, helping users easily take the next step.

[1208] Example 1

[1209] 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."

[1210] In today's busy lifestyles, it is not easy to efficiently manage household finances and review spending. Traditional methods require manually recording expenditure data and conducting comparative analysis, which is time-consuming and labor-intensive and often results in inaccurate analysis. Furthermore, data for comparison with similar households is not readily available, making rational household budget reviews difficult. Therefore, there is a need for technology that can efficiently and accurately review household budgets using an automated system and provide specific suggestions for improvement.

[1211] 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.

[1212] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to an information processing device, means for analyzing the payment data using artificial intelligence to compare it with average expenditure data of comparable households, means for generating proposals for improving the household finances based on the analysis results, and means for providing the generated proposals to the user, thereby enabling the user to easily and efficiently analyze their household expenditures and receive specific proposals for improvement.

[1213] A "household budget review" is the process of reassessing a household's income and expenses, eliminating waste, and achieving efficient financial management.

[1214] A "user" is an individual or family who intends to use the system to review their household finances.

[1215] "Payment data" refers to all expenses incurred by users in their daily lives, including, for example, rent, loans, utility bills, communication fees, education expenses, and various insurances.

[1216] "Means of input" refers to the method by which users provide payment data to the system through a dedicated application or interface, such as a smartphone or computer.

[1217] "Means for transmitting to an information processing device" refers to a method for temporarily storing the entered payment data and securely transmitting the data to a server, and includes standard APIs and secure communication protocols (e.g., HTTPS).

[1218] "Artificial intelligence-based analytical means" refers to technology that performs advanced data analysis based on received payment data and conducts comparative analysis with average expenditure data of comparable households, and includes, for example, machine learning algorithms.

[1219] The "means for generating proposals" is a process for automatically generating specific advice and proposals for improving household finances for users based on the analysis results.

[1220] The "means of providing" refers to a method for presenting the generated suggestions and advice to users in an easy-to-understand manner, such as the user interface of a dedicated application.

[1221] "Average expenditure data for comparable households" is data showing the average expenditures of households with similar family structures and living environments to the user, and is based on past data analysis results and statistical data.

[1222] A "storage device" is a device for storing data required by the system, such as average expenditure data for comparable households and analysis results, for a long period of time, and examples include a database server.

[1223] The present invention is a system that analyzes spending in a simple and efficient way and suggests improvements for users who wish to review their household finances. The system collects users' payment data and compares them with similar households to generate and provide specific advice on improving their household finances.

[1224] System configuration

[1225] The system consists of the following components:

[1226] 1. Payment data input means (terminal)

[1227] 2. Data transmission means (terminal)

[1228] 3. Analysis method (server)

[1229] 4. Advice Generation Method (Server)

[1230] 5. Means of provision (terminal)

[1231] Payment data input means (terminal)

[1232] Users use a dedicated application on their smartphone or PC to input various household payment items. This input includes rent, loans, utilities, communication expenses, education expenses, and various insurances. For example, a user can input rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[1233] Data transmission means (terminal)

[1234] The terminal temporarily stores the entered payment data and then sends it to the server. The data is transmitted using standard APIs (e.g. REST API) and an HTTPS connection to ensure data security. The transmitted data is in JSON format, for example:

[1235] json

[1236] {

[1237] "Rent": 100000,

[1238] "Utilities": 15000,

[1239] "Communication Fee": 10000,

[1240] "Education expenses": 30000,

[1241] "Insurance": 20000

[1242] }

[1243] Analysis method (server)

[1244] The server then provides the received payment data to an AI analysis module. The AI ​​uses the scikit-learn library to compare it with average expenditure data for comparable households. For example, if a user's utility bill is ¥15,000 and the average for comparable households is ¥10,000, the AI ​​will identify this difference and recognize it as an outlier.

[1245] Advice generation means (server)

[1246] The server generates specific advice based on the analysis results provided by the AI. The advice generation module uses natural language generation (NLG) technology to create suggestions in an easy-to-understand format for users. For example, advice may be generated that recommends "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company."

[1247] Means of provision (terminal)

[1248] The device displays the advice received from the server to the user. The application's user interface visualizes a list of advice suggestions, each with a detailed explanation and instructions on how to implement them. For example, when a user opens the application, the following advice is displayed:

[1249] "Your current utility bills are higher than average. Consider switching to more energy-efficient appliances."

[1250] "Also, below are links to energy company plan comparison sites so you can find a cheaper plan."

[1251] Specific examples

[1252] For example, let's say a user named Yamada uses this system to review his or her household finances. Yamada enters and submits the following data into the dedicated application: rent (¥100,000), utility bills (¥15,000), communication fees (¥10,000), education expenses (¥30,000), and insurance (¥20,000). The server receives this data, and the AI ​​begins analyzing it. Comparing it to the average data for similar households reveals that utility bills are abnormally high. The AI ​​identifies the cause and incorporates it into the analysis. Based on the analysis results, the server generates advice such as "switching to more energy-efficient appliances" or "switching to a cheaper electricity provider plan." These specific improvement suggestions are displayed on Yamada's device, allowing him to immediately understand and implement them. For example, links to electricity provider plan comparison sites are also displayed, allowing Yamada to easily access information and take action to review his or her household finances.

[1253] Prompt Sentence Examples

[1254] Here are some example prompts to input to the generative AI model:

[1255] User payment data

[1256] Rent: 100,000 yen

[1257] Utilities: 15,000 yen

[1258] Communication fee: 10,000 yen

[1259] Educational expenses: 30,000 yen

[1260] Insurance: 20,000 yen

[1261] Use this data to compare with average spending, identify abnormal spending, and generate specific improvement advice. For example, if utility bills are high, suggest appropriate actions.

[1262] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1263] Step 1: User Data Entry

[1264] Users access a dedicated application on their smartphone or PC and enter various household bills. This input includes items such as rent, utilities, communication fees, education expenses, and insurance. For example, a user enters rent of 100,000 yen, utilities of 15,000 yen, communication fees of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen. The input data is temporarily stored in the application's local storage.

[1265] Step 2: Send data

[1266] The terminal sends the stored payment data to the server using a standard API (e.g., REST API) and a secure connection protocol (HTTPS). The input data is converted to JSON format and sent to the API endpoint using the POST method. An example of the data to be sent is as follows:

[1267] json

[1268] {

[1269] "Rent": 100000,

[1270] "Utilities": 15000,

[1271] "Communication Fee": 10000,

[1272] "Education expenses": 30000,

[1273] "Insurance": 20000

[1274] }

[1275] Step 3: Data analysis

[1276] The server provides the received payment data to an AI analysis module, which uses the scikit-learn library to perform comparative analysis with average expenditure data for comparable households. For example, if a user's household's utility bill is 15,000 yen and the average for comparable households is 10,000 yen, the AI ​​will identify this difference and recognize it as an outlier. The input data is compared with the average expenditure data for comparable households, and the difference is output as the analysis result.

[1277] Step 4: Advice Generation

[1278] The server generates specific advice based on the analysis results provided by the AI. The advice generation module uses natural language generation (NLG) technology to create suggestions in an easy-to-understand format for users. For example, advice may be generated that recommends "changing to more energy-efficient home appliances" or "switching to a cheaper plan from your power company." Based on the analysis results, specific improvement measures are output with detailed explanations.

[1279] Step 5: Providing advice

[1280] The device displays the advice received from the server to the user. The application's user interface visualizes a list of advice, with each suggestion including a detailed explanation and how to implement it. For example, advice such as "Your current utility bills are higher than average. Consider switching to more energy-efficient appliances" or "Below is a link to a comparison site for power company plans to help you find a cheaper plan" is displayed, allowing the user to immediately see what they can do.

[1281] (Application example 1)

[1282] 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."

[1283] In modern society, improving household finances is an important issue for many families. However, there is a lack of methods to efficiently analyze various expenditure data and provide specific advice when reviewing household finances. To solve this problem, a system is needed that allows users to easily input expenditure data themselves, analyzes the data based on that data, and provides appropriate improvement suggestions.

[1284] 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.

[1285] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, means for analyzing the payment data using AI to compare it with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, means for inputting the user's expenditure data directly on the device and automatically importing it, means for transmitting data using a standard API and protecting the data through a secure connection, and means for visualizing improvement measures so that the user can immediately put them into practice. This enables users to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[1286] "Reviewing the household budget" refers to the act of users reevaluating and optimizing household expenditure items.

[1287] "Payment data" is a record of various expenses that a user pays on a daily basis, including rent, purchase fees, utility bills, communication fees, education fees, various insurances, and the like.

[1288] "Input means" refers to the device or function that allows a user to input payment data into the system, such as a smartphone application or web interface.

[1289] "Transmission means" refers to a device or mechanism for transmitting input payment data to a server, using a secure API or network connection.

[1290] The "analysis method" is a function that uses AI to compare and analyze expenditures with those of similar households based on the received payment data and derives results.

[1291] The "advice generation means" is a device or function that generates specific suggestions for improving household finances to be provided to the user based on the analysis results.

[1292] The "means for providing" refers to a device or function for displaying the generated advice to the user in an easily understandable format.

[1293] "Auto Capture" is a feature that automatically collects user spending data without requiring manual entry.

[1294] A "standard API" is a standardized interface for exchanging data between different systems and applications.

[1295] A "secure connection" is a connection method that uses security technologies such as encryption to prevent unauthorized access and data leaks when sending and receiving data.

[1296] "Visualization" refers to displaying data and analytical results in a visually easy-to-understand format, such as graphs or charts.

[1297] The present invention provides a system for analyzing expenditures in a simple and efficient manner and suggesting improvements to users who wish to review their household finances. Specific embodiments of this system will be described below.

[1298] System configuration

[1299] The system consists of the following components:

[1300] 1. Payment data input means (terminal):

[1301] It is a means for users to input various household expenditure items. It is implemented as an application on a smartphone or PC. Users can enter their expenditure data manually or automatically import their electronic payment history.

[1302] 2. Data transmission means (terminal):

[1303] This is the means by which entered payment data is sent to the server. Data is sent using standard APIs and protected by a secure connection.

[1304] 3. Analysis method (server):

[1305] An AI implemented on the server (for example, a generative AI model implemented using Python) performs comparative analysis with the average expenditure data of comparable households. The AI ​​performs analysis using the average expenditure data of comparable households stored in a database.

[1306] 4. Advice generator (server):

[1307] It is a means of generating specific proposals for improving household finances based on the analysis results. Based on the results of the AI ​​analysis, it generates actionable proposals for improvement.

[1308] 5. Means of provision (terminal):

[1309] This is a way to provide generated advice to users. It is displayed on a smartphone or computer screen and presented in a visual form, making it easy for users to quickly understand the suggestions and put them into action.

[1310] Program processing explanation

[1311] The server first receives the user's payment data and provides it to the AI. The AI ​​then analyzes this data, comparing it with the average expenditure data of comparable households. Based on the analysis results, it generates specific improvement suggestions and sends them to the user's device. The user's device then displays the generated advice in a visually easy-to-understand format.

[1312] To give a specific example, a user inputs data such as rent, utility bills, and communication fees and sends it to a server. The server receives this data and analyzes it using an AI model (for example, a generative AI model using PyTorch or TensorFlow). If the analysis reveals that the user's utility bills are higher than the average for similar households, the AI ​​will generate improvement suggestions such as "switching to more energy-efficient appliances" or "reviewing your power company plan." The results are then sent to the user's device and visualized and displayed.

[1313] Prompt Sentence Examples

[1314] Here are some examples of prompts for generative AI models:

[1315] Generate advice on improving your household finances based on your expenditure data. Rent: 100,000 yen, utilities: 15,000 yen, communication costs: 10,000 yen, education costs: 30,000 yen, insurance: 20,000 yen. Compare your expenses with the average for similar households and suggest specific areas for improvement.

[1316] The above is a detailed description of the configuration and processing of the embodiment of the present invention. This system enables users to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[1317] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1318] Step 1:

[1319] The user opens the application on their smartphone or computer and enters payment data for rent, utilities, communication fees, education fees, various insurances, etc.

[1320] Input: User payment data

[1321] Output: A list of entered payment data

[1322] What happens: The user enters the amount for each expense item and saves it in the in-app input form. If auto-capture is enabled, the data is retrieved from the associated payment app.

[1323] Step 2:

[1324] The terminal temporarily stores the entered payment data and transmits it to the server over a secure connection using a standard API.

[1325] Input: A list of entered payment data

[1326] Output: Payment data sent to the server

[1327] Specific operation: Converts data stored in the device into JSON format, constructs an API request, and sends it to the server using the HTTPS protocol.

[1328] Step 3:

[1329] The server stores the received payment data in a database for analysis and provides it to the AI ​​for comparison with average expenditure data of comparable households.

[1330] Input: Payment data sent to the server

[1331] Output: The dataset that is provided to the AI

[1332] Specific operation: The data is stored in a database on the server side, and after necessary preprocessing, it is provided to the AI ​​model.

[1333] Step 4:

[1334] The server's AI compares and analyzes the payment data received with average expenditure data from comparable households to identify outliers and areas for improvement.

[1335] Input: A dataset provided to the AI

[1336] Output: Expenditure analysis results

[1337] What it does: It uses an AI model (using, for example, PyTorch or TensorFlow) to analyze input data, perform statistical comparisons, and output results if it detects outliers or finds patterns.

[1338] Step 5:

[1339] The server uses AI to generate specific advice for improving household finances based on the analysis results.

[1340] Input: Expenditure analysis results

[1341] Output: Advice on improving your finances

[1342] How it works: Based on the analysis results, the generative AI model generates effective household improvement measures, such as "switching to more energy-efficient appliances" or "reviewing your communication plan."

[1343] Step 6:

[1344] To provide a user with improvement advice received by a terminal from a server in a visually easy-to-understand format.

[1345] Input: Advice for improving your finances

[1346] Output: Visualized advice shown to the user

[1347] Specific operation: The device obtains the advice content and displays it on the app in a format that is easy for the user to understand (graphs, lists, etc.). This allows the user to easily check the suggested improvement measures and put them into practice.

[1348] The above are the specific processing steps in the embodiment of the present invention, which allow the user to easily and efficiently review their household finances and implement improvement measures based on specific advice.

[1349] 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.

[1350] The present invention provides a system that analyzes spending and suggests improvements in a simple and efficient way for users who wish to review their household finances. The system collects the user's payment data and compares it with similar households to generate and provide specific advice on improving their household finances. The system also incorporates an emotion engine that recognizes the user's emotions, analyzes the user's emotions when inputting information, and provides optimal advice.

[1351] System configuration

[1352] The system consists of the following components:

[1353] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication costs, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[1354] 2. Data transmission means (terminal): A means for transmitting the entered payment data to the server. This is also part of the application.

[1355] 3. Analysis method (server): A method in which the AI ​​implemented on the server performs comparative analysis with the average expenditure of comparable households.

[1356] 4. Advice generation means (server): A means for generating specific financial improvement proposals to be provided to users based on the analysis results.

[1357] 5. Provision means (terminal): A means for displaying the generated advice to the user.

[1358] 6. Emotion recognition means (terminal): A means of analyzing the user's emotions when inputting information and adjusting the content and timing of advice.

[1359] A natural language description of the program's operation

[1360] User data entry

[1361] Users access a dedicated application on their smartphone or PC and enter various household payment items. For example, the items a user might enter include rent of 100,000 yen, utilities of 15,000 yen, communication expenses of 10,000 yen, education expenses of 30,000 yen, and insurance of 20,000 yen.

[1362] emotion recognition

[1363] When a user enters payment data, the terminal's emotion engine analyzes the user's tone of voice, facial expression, and input speed and accuracy. Based on this, the emotion engine assesses the user's stress level and emotional state. For example, if the user enters data quickly and with a rising tone of voice, it will determine that the user is stressed.

[1364] Data transmission

[1365] The terminal temporarily stores the user's payment data and emotional state, and displays a confirmation screen. The user confirms the input and clicks the submit button to send the data to the server. The data is transmitted over a secure connection.

[1366] Data analysis

[1367] The server stores the received payment data and emotional information in a database, then runs the analysis tool. The analysis tool calls AI and performs a comparative analysis of the stored data with the average expenditure of similar households. For example, it may be determined that the user's household's utility bill is 15,000 yen, while the average for similar households is 10,000 yen. The AI ​​identifies this difference and recognizes it as an outlier.

[1368] Advice Generation

[1369] The server runs an advice generation module based on the analysis results and emotion information. The module creates specific improvement suggestions for identified outliers and excessive spending. For example, the advice might be to "use energy-efficient appliances" or "consider a cheaper plan from your electricity provider." The content and timing of the suggestions are adjusted based on the emotion recognition information. For example, a user who is feeling stressed will be offered realistic and easy-to-implement improvement measures.

[1370] Providing advice

[1371] The device reflects the advice received from the server in the display UI. The user can check the specific improvements and suggestions on the device screen. For example, a message such as "Consider using energy-efficient home appliances to reduce your utility bills" is displayed. In addition, advice is presented based on emotion recognition information at a time that suits the user's condition.

[1372] Specific examples

[1373] For example, let's say a user named Yamada uses this system to review his household finances. Yamada uses the dedicated application to input and submit the following: rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education expenses 30,000 yen, and insurance 20,000 yen.

[1374] When inputting the data, the device's emotion engine analyzes Yamada's emotional state and detects that stress is increasing. The server receives this data and compares it with the average expenditures of comparable households. It turns out that his utility bills are 15,000 yen, higher than the average of 10,000 yen for comparable households. Based on the analysis results, the server generates advice such as "use energy-efficient appliances" and "consider a cheaper plan from your power company." Taking Yamada's emotional state into consideration, simple and easy improvements are suggested.

[1375] This advice is displayed on Yamada's device, allowing him to take specific action immediately. For example, links to appropriate plan changes and information on energy-efficient home appliances are also provided. In this way, the present invention not only efficiently supports household budget improvement, but also provides optimal advice based on the user's emotional state, helping to make household budget reviews more actionable.

[1376] The processing flow will be explained below.

[1377] Step 1:

[1378] Users enter various household payment items (rent, loan, utilities, communication costs, education expenses, various insurances, etc.) into a dedicated application on their device. For example, they enter detailed information such as rent of 100,000 yen, utilities of 15,000 yen, and communication costs of 10,000 yen.

[1379] Step 2:

[1380] As users type, the device's emotion engine analyzes their voice tone, typing speed, facial expressions, etc. Based on this data, the emotion engine evaluates the user's emotional state and determines their stress level and emotional temperature.

[1381] Step 3:

[1382] The terminal temporarily stores the entered payment data and analyzed emotion data, and displays a confirmation screen to the user. The user confirms that the entered information is correct and clicks the send button.

[1383] Step 4:

[1384] The terminal transmits the stored payment data and emotion data to the server, where the data reaches the server's API endpoint over a secure connection.

[1385] Step 5:

[1386] The server stores the received payment and sentiment data in a database, maintaining a chain of record. The data is filtered and cleansed as needed.

[1387] Step 6:

[1388] The server's analysis function calls AI and performs a comparative analysis based on the stored payment data with the average expenditure data of similar households. For example, if the user's household utility bill is 15,000 yen and the average for similar households is 10,000 yen, this outlier is identified.

[1389] Step 7:

[1390] The AI ​​analyzes the causes of identified outliers and excessive spending items and performs a detailed analysis of each spending item, such as identifying the use of energy-inefficient appliances or excessive late-night electricity usage.

[1391] Step 8:

[1392] The server runs an advice generation module based on the analysis results provided by the AI. The module creates specific improvement proposals for identified outliers. For example, it could generate advice such as "replace your appliances with more energy-efficient ones" or "change your electricity plan."

[1393] Step 9:

[1394] The advice generation module also takes into account the user's emotional data and adjusts the content and timing of the advice it provides. For example, it suggests simple and easy-to-implement solutions for users with high stress levels, and provides advice in a format that is easy for the user to accept.

[1395] Step 10:

[1396] The server stores the generated advice in a database and prepares it for sending back to the user's device. The advice is formatted in a specific and easy-to-understand format.

[1397] Step 11:

[1398] The device receives advice from the server and displays it in the display UI, allowing the user to view specific improvement suggestions and action plans based on them on the device screen.

[1399] Step 12:

[1400] Users are guided by the advice they receive and can take action. The advice provides relevant links and resources (e.g., links to energy-efficient appliances or websites for changing your plan) to help users take immediate next steps.

[1401] Example 2

[1402] 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."

[1403] In today's busy lifestyles, managing household finances is an important issue for many families. However, reviewing household finances and optimizing spending requires users to collect, compare, and analyze a large amount of data, which is time-consuming. Furthermore, if users' emotional state is affected by stress or anxiety, they may find it difficult to implement appropriate advice. As a result, it becomes difficult to make feasible improvements to their household finances, and spending optimization does not progress.

[1404] 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.

[1405] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, analysis means using AI for performing comparative analysis of the payment data with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, emotion recognition means for analyzing the emotional state of the user when the payment data is input, and means for providing optimal advice using the emotion recognition results together with the analysis results. This makes it possible to provide highly accurate advice for improving household finances while taking the user's emotional state into consideration.

[1406] "Household finances" refers to the management of a household's income and expenditures, and includes the entire flow of money, such as living expenses, savings, and investments.

[1407] "Payment data" refers to data entered by the user that includes the amounts of various household expense items, such as rent, loans, utilities, communication costs, education costs, and insurance.

[1408] "Server" refers to a computer system for storing, analyzing, and processing data over a network.

[1409] "Comparable households" refers to households with similar criteria such as family structure and income, and are households that serve as reference data for comparative analysis.

[1410] "Average expenditure data" refers to data on the average expenditure amount for each expense item of comparable households.

[1411] "Analysis means" refers to functions including AI for conducting comparative analysis based on payment data and average expenditure data of comparable households.

[1412] The "advice generation means" refers to a function for generating advice for improving household finances to be provided to the user based on the analysis results.

[1413] "Providing means" refers to a function such as a UI for presenting the generated advice to the user.

[1414] "Emotion recognition means" refers to a function for analyzing the emotional state of a user when entering payment data, including the tone of the user's voice, input speed, and facial expression analysis.

[1415] The "emotion recognition result" refers to the evaluation result of the user's emotional state analyzed by the emotion recognition means.

[1416] "Providing optimal advice" refers to providing advice on improving household finances at a timing and with content that suits the user's condition based on the results of emotion recognition of the user.

[1417] This invention provides a system that efficiently and precisely analyzes and proposes improvements to users who wish to review their household finances. This system uses hardware and software to collect and analyze users' payment data, recognize their emotional state, and generate optimal advice for improving their household finances.

[1418] Hardware and software used

[1419] 1. Terminal

[1420] The terminal is a device with an interface for users to input household information, such as a smartphone or PC. A dedicated application is installed on the terminal, and users use it to input payment data. The terminal also has an emotion recognition engine built in to analyze the user's emotional state.

[1421] 2. Server

[1422] The server plays a central role in processing the received data and generating advice based on the analysis results. The server is equipped with a database, AI model, analysis module, and advice generation module.

[1423] Data collection and transmission

[1424] The user launches the application on the device and enters payment items such as rent, utilities, communication fees, education fees, and insurance. This data is temporarily stored on the device and a confirmation screen is displayed. Once the user confirms the information entered and clicks the submit button, the data is sent to the server via a secure connection.

[1425] emotion recognition

[1426] While entering data, the device analyzes the user's input speed, voice tone, and input accuracy in real time. The emotion recognition engine uses this data to evaluate the user's emotional state. For example, if the user types quickly and their voice tone is rising, the system will determine that the user is feeling stressed. This emotional state is classified as "high stress," "medium stress," or "low stress," and is used to generate advice.

[1427] Data analysis and advice generation

[1428] The server stores the received payment data in a database and uses an AI model to compare it with the average expenditure data of comparable households. As a result of the comparison analysis, outliers and excessive expenditures are identified. For example, the server detects that the user's household utility bill is 15,000 yen, while the average for comparable households is 10,000 yen. Based on the analysis results, the server runs an advice generation module. Specific improvement measures (e.g., "use energy-efficient appliances" or "consider a cheaper plan from your power company") are generated and provided to the user.

[1429] The generated advice is sent to the device at an appropriate time and in an appropriate format, taking into account the user's emotional state. For example, a user who is feeling stressed will be suggested easy-to-implement improvement measures.

[1430] Specific examples

[1431] For example, if a user were to use this system to review their finances, it would work like this:

[1432] 1. The user uses a dedicated application to enter the following amounts: rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education expenses 30,000 yen, and insurance 20,000 yen.

[1433] 2. The emotion recognition engine analyzes the user's typing speed and tone of voice and determines that the user is under high stress.

[1434] 3. The user clicks the submit button and the data is sent to the server.

[1435] 4. The server analyzes the data and detects that the utility bills are higher than the average of 10,000 yen for comparable households.

[1436] 5. The server will suggest things like "use more energy-efficient appliances" or "consider cheaper plans from your electricity provider," providing easy-to-implement improvements based on your emotional state.

[1437] Prompt Sentence Examples

[1438] "Analyze the emotional state of the user of the household budget review system based on the payment data entered and suggest optimal spending improvements. User's payment data: rent 100,000 yen, water and electricity bills 15,000 yen, communication expenses 10,000 yen, education expenses 30,000 yen, insurance 20,000 yen. The emotional state is determined to be highly stressed."

[1439] As described above, the present invention is a system that performs efficient and precise analysis for users who wish to review their household finances, and suggests improvements that take into account their emotional state.

[1440] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1441] Step 1: User enters payment data

[1442] The user launches the dedicated application and inputs household payment items. In each field, they input the expenditure amounts for rent, utilities, communication, education, insurance, etc. For example, they input 100,000 yen for rent, 15,000 yen for utilities, 10,000 yen for communication, 30,000 yen for education, and 20,000 yen for insurance.

[1443] Input: Amount data for each pay item

[1444] Output: Payment data entered

[1445] Step 2: Emotion Recognition

[1446] The terminal observes various actions of the user in real time as they enter payment data. The emotion engine analyzes the user's tone of voice, input speed, and frequency of input errors to assess the user's emotional state. For example, if the user types quickly and has a rising tone of voice, it can determine that the user is stressed.

[1447] Input: User input behavior (speed, tone of voice, frequency of input errors)

[1448] Output: Emotional state (high stress, medium stress, low stress)

[1449] Step 3: Check and submit data

[1450] Once the user has entered their payment data, the terminal temporarily stores it in memory and displays a confirmation screen. The user must confirm the data and click the submit button, which sends the data to the server over a secure connection.

[1451] Input: entered payment data, emotional state

[1452] Output: Data sent to the server

[1453] Step 4: Store and parse payment data

[1454] The server stores the received payment data and emotional state information in a database. It then uses analytical means to compare the data with average expenditure data for comparable households. For example, it detects that the user's household utility bill is 15,000 yen, while the average for comparable households is 10,000 yen.

[1455] Input: Received payment data, emotional state

[1456] Output: Analysis results (abnormal values ​​and excess expenditures for each expenditure item)

[1457] Step 5: Advice Generation

[1458] The server generates advice based on the analysis results and the user's emotional state. The advice generation module creates improvement proposals to address identified outliers and excessive spending. For example, it suggests specific improvement measures such as "using energy-efficient home appliances" or "considering cheaper plans from your power company." The content and timing of the proposals are adjusted based on the user's emotional state.

[1459] Input: Analysis results, emotional state

[1460] Output: Generated financial advice

[1461] Step 6: Providing advice

[1462] The device displays the advice received from the server to the user. The application UI displays specific suggestions for improving household finances. The user can review the advice and take action. For example, a message might say, "Use energy-efficient appliances to reduce your utility bills," along with specific links and reference materials.

[1463] Input: Generated financial advice

[1464] Output: Advice and action guide provided to the user

[1465] (Application example 2)

[1466] 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."

[1467] Conventional household management systems simply aggregate payment data and compare it with similar households, providing uniform advice without considering the user's emotional state. This creates problems when users are stressed or their emotional state is affected, making it difficult to provide practical and actionable advice. Furthermore, advice is not provided at the optimal time based on the user's emotional state, which can lead to suggestions being rejected effectively. To solve these issues, there is a need for a system that can analyze the user's emotional state and provide personalized and optimal advice for improving household finances based on that information.

[1468] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1469] In this invention, the server includes means for inputting payment data of a user who wishes to review their household finances, means for transmitting the payment data to the server, means for analyzing the payment data by using AI to compare it with average expenditure data of comparable households, means for generating advice for improving household finances based on the analysis results, means for providing the generated advice to the user, and emotion recognition means for analyzing the user's emotional state when entering the payment data and adjusting the content and timing of the advice. This makes it possible to provide realistic and actionable advice for improving household finances that takes into account the user's emotional state.

[1470] "Reviewing the household budget" is an activity aimed at reevaluating the flow of money within a household and optimizing spending.

[1471] "Payment data" refers to data that includes detailed information on household expenditures, such as rent, loans, utility bills, communication costs, education costs, and various insurances.

[1472] "Comparable households" refer to other households with incomes and family structures similar to that of the user's household.

[1473] "Average expenditure data" is aggregate data that indicates the standard amount for each expenditure item for similar households.

[1474] "Emotional state" refers to the psychological state a user exhibits in response to a particular input or situation, and includes feelings of stress, satisfaction, etc.

[1475] The "emotion recognition means" is a means for analyzing the user's tone of voice, facial expression, input speed, etc., to evaluate the user's emotional state.

[1476] The "analysis tool" is an AI-based program that has the ability to analyze the collected payment data by comparing it with the average expenditure data of comparable households.

[1477] The "advice generation means" is a device or program that generates specific suggestions for improving the user's household finances based on the results obtained by the analysis means.

[1478] The "advice providing means" is a means for visually or audibly providing the generated advice for improving household finances to the user.

[1479] The present invention relates to a system that analyzes spending easily and efficiently and suggests improvements for users who wish to review their household finances. This system collects payment data and uses emotion recognition technology to optimize the advice provided. Specific embodiments of the system are described below.

[1480] System Configuration

[1481] The system consists of the following components:

[1482] 1. Payment data input means (terminal): A means for users to input various household payment items (e.g., rent, loan, utility bills, communication fees, education expenses, insurance, etc.). This is implemented as a dedicated application on a smartphone or PC.

[1483] 2. Data transmission means (terminal): A means for transmitting input payment data to the server. This is also part of the application.

[1484] 3. Analysis method (server): This is the method by which AI implemented on the server performs comparative analysis with the average expenditure of comparable households.

[1485] 4. Advice generation means (server): A means for generating specific household improvement proposals to be provided to users based on the analysis results.

[1486] 5. Providing means (terminal): A means for displaying the generated advice to the user.

[1487] 6. Emotion recognition means (terminal): A means for analyzing the emotions expressed by the user when inputting information and adjusting the content and timing of the advice provided.

[1488] Hardware / Software used

[1489] EmotionRecognition Library: A library for analyzing a user's emotional state.

[1490] BudgetAnalysis library: A library that analyzes household payment data, compares it with peer households, and generates advice.

[1491] Python3: A programming language for implementing the entire program.

[1492] API Server: A communication server for collecting user payment data.

[1493] System Operation

[1494] The server collects and analyzes the user's payment data. First, the user accesses a dedicated application on their smartphone or PC and enters payment data such as rent, utilities, communication fees, education fees, and insurance. The device then analyzes the entered payment data and the user's emotional state and sends it to the server via a secure connection. The server stores the received payment data and emotional information in a database and invokes AI to perform comparative analysis with the average expenditures of comparable households. Based on the analysis results and emotional information, it generates specific advice for improving household finances, adjusting the content and timing of the suggestions based on the user's emotional state. The generated advice is displayed on the user's device.

[1495] Specific examples

[1496] For example, if a user says, "My living expenses have increased recently and I'm worried," the device's emotion recognition engine will analyze this and determine that stress is increasing. The server will compare the user with similar households based on payment data for rent, utilities, communication fees, education expenses, insurance, etc., and generate advice such as "use more energy-efficient home appliances" or "review your communication plan." This advice is provided at an appropriate time, taking into account the user's emotional state.

[1497] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1498] Step 1:

[1499] The user accesses a dedicated application and enters payment data such as rent, utility bills, communication fees, education fees, and insurance. Once the user has completed the input, the device uses an emotion recognition engine to analyze the user's emotional state. The input data and analyzed emotional information are temporarily stored.

[1500] Input: Payment data (e.g. rent 100,000 yen, utility bills 15,000 yen, communication fees 10,000 yen, education fees 30,000 yen, insurance 20,000 yen)

[1501] Output: Payment data and emotional state (e.g., stress level)

[1502] Behavior: Analyzes the user's tone of voice, facial expressions, and typing speed.

[1503] Step 2:

[1504] The terminal displays the entered payment data and emotional state on a confirmation screen and prompts the user to confirm the submission. Once the user clicks the submit button, the terminal transmits the data to the server over a secure connection.

[1505] Input: Payment data and emotional state

[1506] Output: Send data to the server

[1507] Operation: Displays a confirmation screen and transmits data.

[1508] Step 3:

[1509] The server stores the received payment data and emotional state in a database and runs an analysis tool. The analysis tool compares the payment data with the average expenditure data of comparable households and identifies outliers and excessive expenditures. For example, it may discover that the utility bill is 15,000 yen, which is higher than the average of 10,000 yen for comparable households.

[1510] Input: Submitted payment data and emotional state

[1511] Output: Analysis results (identification of outliers)

[1512] Action: Save to database and call analysis method.

[1513] Step 4:

[1514] The server generates specific advice for improving household finances using an advice generation means based on the analysis results and the user's emotional state. For example, it may suggest "using energy-efficient home appliances" or "reviewing your communication plan." If the user is feeling stressed, the server prioritizes suggestions that are easy to implement.

[1515] Input: Analysis results and emotional state

[1516] Output: Specific advice for improving your household finances

[1517] Action: Triggers the advice generator and generates appropriate suggestions.

[1518] Step 5:

[1519] The server sends the generated advice to the device, which then displays it to the user. The advice is displayed at the optimal time depending on the user's emotional state. For example, the advice is displayed after stress levels have decreased.

[1520] Input: Generated advice

[1521] Output: Advice displayed to the user

[1522] Action: Send and display advice.

[1523] Through this series of steps, expenditure analysis and advice on improving household finances are provided to the user.

[1524] 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.

[1525] 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.

[1526] 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 robot 414.

[1527] 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.

[1528] FIG. 9 is a diagram illustrating 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 actions 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.

[1529] 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.

[1530] 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).

[1531] 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.

[1532] 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."

[1533] 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.

[1534] 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).

[1535] 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.

[1536] 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.

[1537] 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.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] The following is further disclosed regarding the above embodiment.

[1546] (Claim 1)

[1547] a means for inputting payment data of users who wish to review their household finances;

[1548] means for transmitting said payment data to a server;

[1549] An analysis means using AI to perform comparative analysis based on the payment data with average expenditure data of comparable households;

[1550] means for generating advice for improving household finances based on the analysis results;

[1551] The system includes a means for providing the generated advice to a user.

[1552] (Claim 2)

[1553] 2. The system according to claim 1, wherein the payment data includes rent, loans, utility bills, communication fees, education fees, and various insurances.

[1554] (Claim 3)

[1555] The system of claim 1, wherein the average expenditure data for comparable households is stored in a database.

[1556] "Example 1"

[1557] (Claim 1)

[1558] a means for inputting payment data of users who wish to review their household finances;

[1559] means for transmitting the payment data to an information processing device;

[1560] An analysis means using artificial intelligence to perform a comparative analysis based on the payment data with average expenditure data of comparable households;

[1561] means for generating proposals for improving household finances based on the analysis results;

[1562] means for providing the generated suggestions to a user.

[1563] (Claim 2)

[1564] 2. The system according to claim 1, wherein the payment data includes rent, loans, utility bills, communication fees, education fees, and various insurances.

[1565] (Claim 3)

[1566] The system of claim 1, wherein the average expenditure data of comparable households is stored in a storage device.

[1567] "Application Example 1"

[1568] (Claim 1)

[1569] a means for inputting payment data of users who wish to review their household finances;

[1570] means for transmitting said payment data to a server;

[1571] An analysis means using AI to perform comparative analysis based on the payment data with average expenditure data of comparable households;

[1572] means for generating advice for improving household finances based on the analysis results;

[1573] means for providing the generated advice to a user;

[1574] A means to input and automatically capture user spending data directly on the device,

[1575] A means to transmit data using standard APIs and protect it through secure connections;

[1576] A system that includes a means for users to visualize improvements so that they can be implemented immediately.

[1577] (Claim 2)

[1578] 2. The system according to claim 1, wherein the payment data includes rent, purchase fees, utility bills, communication fees, education fees, and various insurance fees.

[1579] (Claim 3)

[1580] The system of claim 1, wherein the average expenditure data for comparable households is stored in a database.

[1581] "Example 2: Combining Emotion Engines"

[1582] (Claim 1)

[1583] a means for inputting payment data of users who wish to review their household finances;

[1584] means for transmitting said payment data to a server;

[1585] An analysis means using AI to perform comparative analysis based on the payment data with average expenditure data of comparable households;

[1586] means for generating advice for improving household finances based on the analysis results;

[1587] means for providing the generated advice to a user;

[1588] emotion recognition means for analyzing the emotional state of the user when entering the payment data;

[1589] means for providing optimal advice by utilizing the emotion recognition result together with the analysis result;

[1590] A system including:

[1591] (Claim 2)

[1592] 2. The system according to claim 1, wherein the payment data includes rent, loans, utility bills, communication fees, education fees, and various insurances.

[1593] (Claim 3)

[1594] The system of claim 1, wherein the average expenditure data for comparable households is stored in a database.

[1595] "Application example 2 when combining emotion engines"

[1596] (Claim 1)

[1597] a means for inputting payment data of users who wish to review their household finances;

[1598] means for transmitting said payment data to a server;

[1599] An analysis means using AI to perform comparative analysis based on the payment data with average expenditure data of comparable households;

[1600] means for generating advice for improving household finances based on the analysis results;

[1601] means for providing the generated advice to a user;

[1602] The system includes emotion recognition means for analyzing the user's emotional state when entering payment data and adjusting the content and timing of the advice.

[1603] (Claim 2)

[1604] 2. The system according to claim 1, wherein the payment data includes rent, loans, utility bills, communication fees, education fees, and various insurances.

[1605] (Claim 3)

[1606] The system of claim 1, wherein the average expenditure data for comparable households is stored in a database. [Explanation of symbols]

[1607] 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 inputting payment data of users who wish to review their household finances; means for transmitting said payment data to a server; An analysis means using AI to perform comparative analysis based on the payment data with average expenditure data of comparable households; means for generating advice for improving household finances based on the analysis results; The system includes a means for providing the generated advice to a user.

2. 2. The system according to claim 1, wherein the payment data includes rent, loans, utility bills, communication fees, education fees, and various insurances.

3. The system of claim 1 , wherein the average expenditure data for comparable households is stored in a database.

Images