System

Abstract

A system is provided.SOLUTION: A system comprising: input means for receiving data of income, expenditure, and saving target input by a user; transmitting means for transmitting the data from the input means to a server; storing means for storing the data received from the transmitting means in a database in the server; analyzing means for analyzing the data stored in the storing means and generating optimum household economy management advice in the server; and notifying means for notifying the user of the household economy management advice generated by the analyzing means.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] In modern society, many families find it difficult to effectively manage their finances. The main challenges are a lack of specific advice for balancing income and expenses and a lack of planning to achieve long-term goals. Users also need a way to understand their financial situation in real time and receive appropriate advice, but current systems do not adequately provide this. Therefore, there is a need for a system that provides users with easy-to-use, accurate financial management advice. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: An input means is provided for receiving data on income, expenses, and savings goals input by a user, and a transmission means is provided for transmitting the data from the input means to a server. The server further includes a storage means for saving the data received from the transmission means in a database. The server has an analysis means for analyzing the data saved in the storage means and generating optimal household management advice, and a notification means for notifying the user of the household management advice generated by the analysis means. This system allows users to receive appropriate advice in real time. Furthermore, the analysis means generates household management advice using the user's past data and market information, thereby providing more accurate advice. The notification means displays the advice on the user's terminal, allowing the user to receive information in a format that is intuitively easy to understand.

[0006] The "input means" is an interface through which the user inputs household data such as income, expenses, and savings goals.

[0007] The "transmission means" is a device or software having a function for transmitting data obtained from the input means to the server.

[0008] The "storage means" is a mechanism for storing data received from the transmission means in a database in the server.

[0009] The "analysis means" is a function that includes algorithms and AI for generating optimal household management advice based on the data stored in the storage means on the server.

[0010] The "notification means" is a device or software for notifying the user of the household management advice generated by the analysis means.

[0011] A "database" is a system for managing and storing a user's household data and past transactions.

[0012] "Finance Tips" are specific suggestions for optimizing your household finances that are generated based on your income, expenses, and savings goals.

[0013] A "user interface" is a component that includes screens and input forms that allow a user to interact with a system.

[0014] A "server" is a computer system that performs the functions of receiving, storing, analyzing, and notifying data.

[0015] A "terminal" is a device through which a user inputs data and receives notified advice. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention relates to a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. The system aims to be easy for users to use and to provide appropriate advice in real time.

[0038] System Configuration

[0039] User Interface (UI)

[0040] The device provides an interface through a dedicated application or a web browser, where users can input data on income, expenses, and savings goals. This UI features a graphical display and input form, and is designed to be intuitive for users to use.

[0041] data communication

[0042] The terminal converts the data entered by the user into a specific format and sends it to the server via a transmission function. The transmitted data is set to arrive at the server safely and quickly.

[0043] Data storage

[0044] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[0045] Data analysis

[0046] The server's AI analysis module uses the stored data to generate optimal advice by referencing the user's current situation, past data, and market information. This analysis is performed using sophisticated machine learning algorithms and statistical models.

[0047] Generating and Sending Advice

[0048] The server generates specific household management advice based on the analysis results and formats it as response data. The generated advice data is then sent back to the device via the sending function.

[0049] User Notification

[0050] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0051] Specific examples

[0052] Suppose a user enters their monthly income of 500,000 yen, their monthly expenses of 350,000 yen, and their savings goal of 200,000 yen into an application form. This data is sent from the device to the server by pressing the send button.

[0053] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current balance and identify areas for improvement. For example, it generates advice such as, "To save 83,333 yen per month, reduce your eating out expenses by 20% and review your mobile phone plan."

[0054] This advice is then sent back to the device, and the application screen displays, "To save 83,333 yen each month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan." Based on this, users can review their household finances and take specific actions.

[0055] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby providing support to users in effectively managing their household finances.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] A user opens a household management application and enters household data such as income, expenses, and savings goals into an input form.

[0059] Step 2:

[0060] The user checks the entered data and presses the "Submit" button.

[0061] Step 3:

[0062] The terminal converts the input data into a specific format (e.g., JSON), which includes information such as income, expenses, and savings goals.

[0063] Step 4:

[0064] The terminal transmits the converted data via a network communication module to send the data to the server.

[0065] Step 5:

[0066] The server receives the data sent from the terminal.

[0067] Step 6:

[0068] The server stores the received data in a temporary buffer area.

[0069] Step 7:

[0070] The server executes a save process to save the data saved in the buffer area to the database.

[0071] Step 8:

[0072] The server launches an AI analysis module to analyze the data stored in the database.

[0073] Step 9:

[0074] The server's AI analysis module uses the stored data to generate optimal household management advice, referring to the user's income and expenditure balance, past data, and even market information.

[0075] Step 10:

[0076] The server acquires the generated household management advice and formats it into response data for notifying the user.

[0077] Step 11:

[0078] The server executes a transmission process to transmit the formatted advice data to the terminal.

[0079] Step 12:

[0080] The terminal receives the advice data transmitted from the server.

[0081] Step 13:

[0082] The terminal analyzes the received advice data and converts it into a format for display on the user interface.

[0083] Step 14:

[0084] The device displays advice on the application screen such as, "To save 83,333 yen each month, we suggest reducing your eating out expenses by 20% and reviewing your mobile phone plan."

[0085] Step 15:

[0086] The user checks the displayed advice and adjusts household management and saves money based on it.

[0087] In this way, at each processing step of the present system, the user, terminal, and server work together to collect data on income, expenses, and savings goals, generate optimal financial advice, and notify the user.

[0088] Example 1

[0089] 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."

[0090] The present invention relates to a system that provides optimal household management advice based on a user's income, expenses, and savings goals, and is particularly required to provide accurate advice in real time. However, conventional household management systems do not effectively utilize the user's past data or market information, and analysis results are often vague and lack specificity, making it difficult for users to intuitively understand and take concrete action.

[0091] 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.

[0092] In this invention, the server includes input means for receiving data on income, expenses, and savings goals input by a user, transmission means for transmitting the data from the input means to the server, storage means in the server for storing the data received from the transmission means in a database, analysis means in the server using a machine learning algorithm for analyzing the data stored in the storage means and generating optimal household management advice, notification means for notifying the user of the household management advice generated by the analysis means, display means by which the notification means displays the household management advice on the user's terminal, and an encryption protocol. As a result, the data input by the user is securely transmitted and stored in a database, allowing the data to be analyzed in detail and intuitive, specific household management advice to be provided to the user in real time.

[0093] The "input means" is an interface that allows a user to input data on income, expenses, and savings goals, and is constructed on a dedicated application or a web browser.

[0094] The "transmission means" refers to a function for converting data acquired from the input means into a specific format and transmitting the data to the server, and includes a data encryption protocol.

[0095] The "storage means" refers to a function that stores data sent from the transmission means to the server in a temporary buffer area, and transfers it to a database for management.

[0096] "Analysis means" refers to a function that uses a machine learning algorithm to generate optimal household management advice based on the data stored in the storage means.

[0097] A "machine learning algorithm" is an algorithm that uses a user's past data and market information to analyze data and make predictions and optimizations.

[0098] The "notification means" refers to a function for notifying the user of the household management advice generated by the analysis means.

[0099] The "display means" refers to a function for displaying the advice provided by the notification means on the screen of the user's terminal.

[0100] "Encryption protocols" are methods of encrypting communications to transmit data securely, and include SSL and TLS.

[0101] The present invention is a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. The system aims to be easy for users to use and to provide appropriate advice in real time.

[0102] System Configuration

[0103] User Interface (UI)

[0104] The device provides an interface through a dedicated application or a web browser, where users can input data on income, expenses, and savings goals. This UI features a graphical display and input form, and is designed to be intuitive for users to use.

[0105] data communication

[0106] The terminal converts the data entered by the user into a specific format and sends it to the server via the transmission function. The transmitted data is set to reach the server safely and quickly using encryption protocols such as SSL and TLS.

[0107] Data storage

[0108] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[0109] Data analysis

[0110] The server's AI analysis module uses the stored data to generate optimal advice by referencing the user's current situation, past data, and market information. This analysis is performed using Python-based machine learning libraries (e.g., scikit-learn, TensorFlow, Keras, etc.).

[0111] Generating and Sending Advice

[0112] The server generates specific household management advice based on the analysis results and formats it as response data. The generated advice data is then sent back to the device via the sending function.

[0113] User Notification

[0114] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0115] Specific examples

[0116] Suppose a user enters their monthly income of 500,000 yen, their monthly expenses of 350,000 yen, and their savings goal of 200,000 yen into an application form. This data is sent from the device to the server by pressing the send button.

[0117] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current income and expenditure balance and identify areas for improvement. For example, it generates advice such as, "To save 83,333 yen per month, reduce eating out expenses by 20% and review your mobile phone plan."

[0118] This advice is then sent back to the device, and displayed on the application screen as follows: "To save 83,333 yen per month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan." Based on this, users can review their household finances and take specific actions.

[0119] Prompt Sentence Examples

[0120] "Please tell me the specific method to provide optimal savings advice to a user who has a monthly income of 500,000 yen, monthly expenses of 350,000 yen, and a savings goal of 200,000 yen."

[0121] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby providing assistance to users in effectively managing their household finances.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Program processing flow

[0124] Step 1:

[0125] Users enter data about their income, expenses, and savings goals into a dedicated application or an input form in a web browser. The data is displayed in real time in the application to help users enter data without errors. The data is then converted into a specific format and prepared for submission.

[0126] input:

[0127] Monthly income, monthly expenses, savings goals

[0128] Specific behavior:

[0129] A user enters data into a form in an application.

[0130] The terminal displays the input data on the screen in real time.

[0131] Step 2:

[0132] The device converts the input data into a specific format (e.g., JSON format) and sends it to the server using a secure encryption protocol (SSL / TLS). Once the data is sent, a message indicating that data transmission is complete is displayed on the device.

[0133] input:

[0134] Format-converted input data

[0135] output:

[0136] Securely encrypted data packets

[0137] Specific behavior:

[0138] The device displays a send button and the user clicks it.

[0139] The device encrypts the data and sends it to the server via the HTTPS protocol.

[0140] Step 3:

[0141] The server stores the received data in a temporary buffer area, acknowledges receipt, and then transfers the data to a database for secure recording. The database also contains the user's past transactions and household data.

[0142] input:

[0143] Decrypted input data

[0144] output:

[0145] Data stored in the temporary buffer area

[0146] Data recorded in the database

[0147] Specific behavior:

[0148] The server stores the received data in a buffer.

[0149] The server transfers the data to a database and organizes it into the appropriate fields.

[0150] Step 4:

[0151] The server's AI analysis module analyzes the stored data and uses machine learning algorithms (e.g., scikit-learn, TensorFlow) to generate optimal financial management advice based on the user's income and expenditure balance and past data.

[0152] input:

[0153] User data stored in a database

[0154] output:

[0155] Analysis data for advice generation

[0156] Specific behavior:

[0157] The server loads the user's data from the database.

[0158] The server analyzes the data using machine learning algorithms to calculate the balance and areas for improvement.

[0159] Step 5:

[0160] The server generates specific financial advice based on the analysis results, including the details the user needs to take specific actions, and formats the advice in a specific way.

[0161] input:

[0162] Analysis data

[0163] output:

[0164] Formatted advice data

[0165] Specific behavior:

[0166] The server generates advice recommending, "To save 83,333 yen per month, reduce your eating out expenses by 20% and review your mobile phone plan."

[0167] Step 6:

[0168] The server then converts the generated advice data back into a specific format and sends it back to the device, again securely using an encryption protocol.

[0169] input:

[0170] Formatted advice data

[0171] output:

[0172] Encrypted Advice Data Packet

[0173] Specific behavior:

[0174] The server converts the advice data into JSON format.

[0175] The server sends it to the terminal via the HTTPS protocol.

[0176] Step 7:

[0177] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0178] input:

[0179] Decrypted advice data

[0180] output:

[0181] Financial advice displayed on the user interface

[0182] Specific behavior:

[0183] The device displays the advice on the application screen.

[0184] The user checks the advice displayed, which reads, "To save 83,333 yen per month, we suggest reducing your eating out expenses by 20% and reviewing your mobile phone plan," and takes specific action.

[0185] (Application example 1)

[0186] 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."

[0187] In modern household management, it is important to provide optimal advice using data such as income, expenses, and savings goals. However, conventional systems have difficulty providing specific, practical advice tailored to individual situations in real time based on the data entered by the user. In addition, they lack a mechanism to clearly present recommended actions to encourage users to manage their household finances.

[0188] 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.

[0189] In this invention, the server includes an input means for receiving data on income, expenses, and savings goals input by a user, a transmission means for transmitting the data from the input means to the server, a storage means in the server for storing the data received from the transmission means in a database, an analysis means in the server for analyzing the data stored in the storage means and generating optimal household management advice, and a notification means for displaying the household management advice generated by the analysis means on the user's terminal and notifying the user of recommended spending reduction methods and points to review in spending. This enables the user to receive specific advice in real time based on their income, expenses, and savings goals, and clearly show actionable actions, enabling effective household management.

[0190] "Input means" refers to a device or interface through which a user inputs data such as income, expenses, and savings goals.

[0191] "Transmission means" refers to a device or software having a function for transmitting data acquired from an input means to a server.

[0192] "Storage means" refers to a function for safely and efficiently storing data sent to the server in a database.

[0193] "Analysis means" refers to an analysis system including machine learning algorithms and statistical models for generating optimal household management advice based on the data stored in the storage means.

[0194] "Notification means" refers to a device or software that has the function of displaying the household management advice generated by the analysis means on the user's terminal and notifying the user of recommended ways to reduce expenses and points to review in expenses.

[0195] A "generative AI model" refers to an artificial intelligence algorithm that generates household management advice based on input data.

[0196] A "prompt sentence" is a text message generated to prompt the user to take a specific action, and refers to a short sentence that clearly presents the recommended action.

[0197] A system embodying this invention has the following configuration. First, an input means is provided for a user to input data such as income, expenses, and savings goals. This input means uses a terminal such as a smartphone or tablet. This allows the user to intuitively input data through an interface.

[0198] Next, the input data is sent to the server via a transmission means. This transmission means uses internet communication technology. Specifically, the data is encrypted using HTTPS and sent securely to the server.

[0199] The server has a storage method that stores data in a temporary buffer area and transfers it from there to a database. The database is optimized to efficiently manage users' past transactions and household data. Typical database systems used are MySQL and PostgreSQL.

[0200] The data stored on the server is analyzed using an analytical methodology that incorporates generative AI models and statistical models. The generative AI models use the Python libraries TensorFlow and Scikit-learn. The server uses these models to generate optimal financial management advice based on the user's income, expenses, and savings goals.

[0201] The generated household management advice is communicated to the user through notification methods, such as smartphone apps and web apps. Notifications within the app are sent via push notifications or displayed on the app's UI.

[0202] Furthermore, a specific prompt could be, "To save 83,333 yen per month, we recommend reducing your eating out expenses by 20% and reviewing your mobile phone plan." Based on this advice, users can review their behavior and achieve optimal household management.

[0203] As a concrete example, if a user inputs their income of "500,000 yen," their expenses of "350,000 yen," and their savings goal of "200,000 yen," this data is sent from the device to the server. Once the data is saved and analyzed on the server, advice is generated and sent to the device: "To save 83,333 yen per month, we recommend reducing your dining out expenses by 20% and reviewing your mobile phone plan." By following this prompt, the user can understand specific ways to save money and improve their household finances.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] The user launches the application and enters data on income, expenses, and savings goals.

[0207] Input: A user enters revenue "500,000 yen", expenses "350,000 yen", and savings goal "200,000 yen" into an application form.

[0208] Output: The input data is temporarily stored in the device.

[0209] What it does: A user uses the touchscreen of a smartphone or tablet to enter data into an app form, which is then stored in temporary memory.

[0210] Step 2:

[0211] The terminal converts the input data into a specific format, encrypts it, and sends it to the server.

[0212] Input: The data entered by the user in step 1.

[0213] Output: The encrypted data is sent over the internet to a server.

[0214] Specific operation: The terminal uses the HTTPS protocol to convert data into a specific JSON format, encrypt it, and then send it to the server.

[0215] Step 3:

[0216] The server stores the received data in a temporary buffer area, and then stores it permanently in the database.

[0217] Input: The encrypted data sent in step 2.

[0218] Output: Raw data stored in a database.

[0219] What happens: The server receives the request, holds the data in temporary memory, and then stores the data using a database system (such as MySQL or PostgreSQL).

[0220] Step 4:

[0221] The server analyzes the data and generates optimal household management advice.

[0222] Input: Income, expenses, and savings goal data stored in a database.

[0223] Output: Financial management advice generated by the generative AI model.

[0224] Specific operation: The server uses Python libraries (TensorFlow and Scikit-learn) to analyze past data and market information. As a result of the analysis, it generates specific advice based on the data.

[0225] Step 5:

[0226] The generated household management advice is transmitted to the user's terminal.

[0227] Input: Parsed financial advice.

[0228] Output: Advice that will be displayed on the user's terminal.

[0229] Specific operation: The server formats the generated advice into JSON format as a prompt text, encrypts it again, and sends it to the terminal.

[0230] Step 6:

[0231] The device notifies the user of the advice it receives and displays specific ways to save money and recommended actions.

[0232] Input: Financial advice sent from the server.

[0233] Output: Advice displayed within the application: "To save ¥83,333 per month, we recommend reducing your dining out expenses by 20% and reviewing your mobile phone plan."

[0234] What it does: The device receives the encrypted data, decrypts it, and displays it to the user, using push notifications or the app's UI to grab the user's attention.

[0235] 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.

[0236] The present invention relates to a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. In addition, by combining it with an emotion engine that recognizes the user's emotions, the system provides household management advice that corresponds to the user's emotional state.

[0237] System Configuration

[0238] User Interface (UI)

[0239] The device provides an interface through a dedicated application or a web browser, allowing users to input data on income, expenses, and savings goals. This UI is designed to be intuitive, with graphical displays and input forms. It also has an emotion recognition function that captures the user's voice and facial expressions to collect emotional data.

[0240] data communication

[0241] The device converts the data entered by the user and emotion data into a specific format (e.g., JSON) and sends it to the server via a transmission function. The transmitted data is set to arrive at the server safely and quickly.

[0242] Data storage

[0243] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[0244] Data analysis

[0245] The server's AI analysis module generates optimal advice based on the stored data, taking into account the user's current situation, past data, and market information. In addition, the server analyzes emotional data received from the emotion engine and adjusts the advice based on the user's emotional state.

[0246] Generating and Sending Advice

[0247] The server generates specific household management advice based on the analysis results and formats it into response data to be notified to the user. The generated advice data is then sent back to the terminal via the sending function.

[0248] User Notification

[0249] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0250] Specific examples

[0251] Suppose a user enters their monthly income of "500,000 yen," their monthly expenses of "350,000 yen," and their "saving goal of 200,000 yen" into an application form. At the same time, the application captures the user's voice and facial expressions to collect emotional data. This data is sent from the device to the server when the user presses the send button.

[0252] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current income and expenditure balance and identify areas for improvement. The emotion engine also analyzes the captured emotional data and determines, for example, whether the user is feeling stressed. Based on this information, it generates advice such as, "Since this is a stressful time, you should set a reasonable savings goal."

[0253] This advice is then sent back to the device, and the application screen displays the following message: "To save 83,333 yen each month, we suggest you cut your dining out expenses by 20% and review your mobile phone plan. However, you seem to be stressed, so please be careful not to push yourself too hard." Based on this, users can review their household finances and take specific action.

[0254] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby supporting the user in effectively managing their household finances. Furthermore, by combining it with an emotion engine, it is possible to provide flexible advice according to the user's emotional state.

[0255] The processing flow will be explained below.

[0256] Step 1:

[0257] A user opens a financial management application and enters data such as income, expenses, and savings goals into an input form, while their voice and facial expressions are simultaneously captured.

[0258] Step 2:

[0259] The device generates emotion data based on the user's input data on income, expenses, and savings goals, as well as captured voice and facial expressions.

[0260] Step 3:

[0261] The user confirms that the input data and emotion data have been sent, and then presses the "Send" button.

[0262] Step 4:

[0263] The device converts the income, expenditure, savings goal, and emotional state data into a specific format (e.g., JSON format). This format includes income, expenditure, savings goal, emotional state, etc.

[0264] Step 5:

[0265] The terminal transmits the data via a network communication module to transmit the formatted data to a server.

[0266] Step 6:

[0267] The server receives the data sent from the terminal.

[0268] Step 7:

[0269] The server stores the received data in a temporary buffer area.

[0270] Step 8:

[0271] The server executes a database save process to save the data in the buffer area to the database.

[0272] Step 9:

[0273] The server's AI analysis module analyzes the user's balance based on income, expenditure, and savings goal data stored in the database.

[0274] Step 10:

[0275] The server's emotion engine analyzes the emotion data stored in the database to identify the user's emotional state. For example, it can determine whether the user is feeling stressed based on voice data and facial expressions.

[0276] Step 11:

[0277] The server's AI analysis module integrates the results of the income and expenditure balance analysis and the emotion engine to generate optimal household management advice.

[0278] Step 12:

[0279] The server formats the generated financial advice as response data, which includes specific savings suggestions and cautions based on the user's emotional state.

[0280] Step 13:

[0281] The server executes a transmission process to transmit the formatted advice data to the terminal.

[0282] Step 14:

[0283] The terminal receives the advice data transmitted from the server.

[0284] Step 15:

[0285] The terminal analyzes the received advice data and converts it into a format for display on the user interface.

[0286] Step 16:

[0287] The device displays advice on the application screen such as, "To save 83,333 yen each month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan. However, since you seem to be stressed, please be careful not to push yourself too hard."

[0288] Step 17:

[0289] The user checks the displayed advice and adjusts household management and saves money based on it.

[0290] In this way, the system of the present invention generates optimal advice on the server based on the user's input data and emotional data, and notifies the user via the terminal. By combining emotion engines, flexible advice is provided that takes into account the user's emotional state.

[0291] Example 2

[0292] 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."

[0293] Conventional household management systems only handle basic data such as a user's income, expenses, and savings goals, and are unable to provide advice that takes into account the user's emotions and psychological state. As a result, they ignore the impact of stress and emotional fluctuations on household management, resulting in insufficient effectiveness. Furthermore, they are insufficient in accurately analyzing data and personalizing advice, making it difficult to provide appropriate guidance tailored to each user's individual situation.

[0294] 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.

[0295] In this invention, the server includes input means for receiving data on income, expenses, and savings goals input by a user, conversion means for converting the data from the input means into a specific format, transmission means for transmitting the data converted by the conversion means to the server, temporary storage means for storing the data received from the transmission means in a temporary buffer area in the server, storage means for transferring the data stored in the temporary storage means to a database, analysis means for analyzing the data stored in the storage means and the user's emotional data in the server and generating optimal household management advice, adjustment means for adjusting the household management advice generated by the analysis means in accordance with the user's emotional state, and notification means for notifying the user of the household management advice adjusted by the adjustment means. This enables the user to receive more appropriate and personalized household management advice that reflects their emotional state.

[0296] "Input means" refers to a device or means for a user to input data on income, expenses, and savings goals.

[0297] "Conversion means" refers to a device or method for converting data obtained from an input means into a specific format.

[0298] The "transmission means" refers to a device or method for transmitting the data converted by the conversion means to the server.

[0299] "Temporary storage means" refers to a device or means for temporarily storing data received from a transmitting means.

[0300] "Storage means" refers to a device or means for transferring data stored in the temporary storage means to a database and storing the data therein.

[0301] The term "analysis means" refers to a device or device for analyzing the data stored in the storage means and generating household management advice.

[0302] The "adjustment means" refers to a device or means for adjusting the household management advice generated by the analysis means in accordance with the emotional state of the user.

[0303] The "notification means" refers to a device or means for notifying the user of the household management advice adjusted by the adjustment means.

[0304] A "database" refers to a structure that organizes information to efficiently store and manage user data and retrieve it when needed.

[0305] "Emotional data" refers to information about the user's psychological state and emotions analyzed from their voice, facial expressions, etc.

[0306] "Financial advice" refers to specific guidance and suggestions for improving financial management based on a user's income, expenses, savings goals, and emotional data.

[0307] The present invention relates to a system that collects data such as a user's income, expenses, and savings goals, and provides optimal household management advice based on that data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system provides household management advice according to the user's emotional state. Specific embodiments for carrying out the invention are described below.

[0308] System Configuration

[0309] User Interface (UI)

[0310] The device provides an interface for entering data on income, expenses, and savings goals through a dedicated application or a web browser. This UI features a graphical display and an input form that users can operate intuitively. In addition, the device has an emotion recognition function that captures voice and facial expressions via a camera and microphone to collect emotional data.

[0311] data communication

[0312] The terminal converts the data entered by the user about income, expenses, and savings goals into a specific format (e.g., JSON), which is then securely and quickly transmitted to the server via HTTP or HTTPS protocols.

[0313] Data storage

[0314] The server first stores the received data in a temporary buffer area. This data is then transferred to a database and saved as the user's household data. The database is managed using a general-purpose relational database management system (RDBMS), such as MySQL or PostgreSQL.

[0315] Data analysis

[0316] The server's AI analysis module analyzes the user's balance based on data stored in the database, such as income, expenses, and savings goals. The emotion engine also analyzes the emotional data and adjusts advice based on the user's emotional state. The analysis utilizes generative AI models such as TensorFlow and PyTorch.

[0317] Generating and Sending Advice

[0318] The server generates specific household management advice based on the analysis results, which is then converted back to JSON format and sent to the device as a response.

[0319] User Notification

[0320] The device displays the received advice data on the UI and notifies the user in an intuitive manner. Notifications can be pop-up or push notifications, allowing the user to effectively manage their household finances based on the suggested advice.

[0321] Specific examples

[0322] For example, if a user enters the following data into a form in your application:

[0323] Monthly income: 500,000 yen

[0324] Monthly expenses: 350,000 yen

[0325] Savings goal: "200,000 yen"

[0326] At the same time, voice and facial expression data is collected through the device's camera and microphone, and analyzed as emotional data. This data is then sent from the device to the server by pressing the send button.

[0327] The server first temporarily stores the data, then transfers it to a database for storage. The AI ​​analysis module analyzes the balance of income and expenditure, and the emotion engine determines the user's stress level, etc. Based on this information, the system generates advice such as "Set a reasonable savings goal."

[0328] The generated advice is sent to the terminal in the following format, for example:

[0329] "To save ¥83,333 each month, I suggest you cut your dining out expenses by 20% and review your cell phone plan. However, since you seem stressed, I'd like to caution you to not push yourself too hard."

[0330] This advice is displayed on the device's UI, allowing users to review their household finances and take concrete action based on it.

[0331] Prompt Sentence Examples

[0332] "Generate optimal household management advice based on data such as monthly income of 500,000 yen, expenses of 350,000 yen, and savings goal of 200,000 yen, as well as emotional information about the user's stress levels."

[0333] In this way, the system of the present invention can support more effective household management by providing personalized household management advice based on the user's input data and emotional state.

[0334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0335] Step 1:

[0336] The user enters data on income, expenses, and savings goals into a form on the application or web browser via the device. Specifically, the user enters income "500,000 yen," expenses "350,000 yen," and savings goal "200,000 yen" into each field and presses the submit button. At this time, the device's camera and microphone are used to capture the user's voice and facial expressions, and emotional data is also collected. The entered data and emotional data are obtained as input.

[0337] Step 2:

[0338] The terminal converts the data entered by the user into JSON format using a conversion algorithm within the terminal to format the data as JSON, like this:

[0339] json

[0340] {

[0341] "income": 500000,

[0342] "expenses": 350000,

[0343] "savings_goal": 200000,

[0344] "emotion_data": {"stress_level": "high"}

[0345] }

[0346] This JSON data is obtained as the output after conversion.

[0347] Step 3:

[0348] The terminal sends the converted data to the server using the HTTP or HTTPS protocol. Specifically, the terminal generates an HTTP request and attaches JSON data to the request. The sent data is received as input by the server.

[0349] Step 4:

[0350] The server first stores the received data in a temporary buffer area. Specifically, the server writes the data to a temporary storage area within the server. The temporarily stored data becomes the input for the next step.

[0351] Step 5:

[0352] The server transfers the temporarily saved data to the database and stores it. Specifically, it adds the data to the database using the INSERT command. This operation saves the user's household data to the database. The saved data becomes the input data for the next analysis.

[0353] Step 6:

[0354] The server's AI analysis module analyzes the data stored in the database. Specifically, it uses a generative AI model (e.g., TensorFlow or PyTorch) to analyze the balance, and the emotion engine analyzes the emotional data. This analysis outputs the user's possible balance, and the analysis results serve as the basis for generating the next advice.

[0355] Step 7:

[0356] The server generates household management advice based on the analysis results. Specifically, it uses an advice generation algorithm to generate specific advice such as "To save 83,333 yen per month, reduce eating out expenses by 20% and review your mobile phone plan." This generated advice is output as the next data to be sent.

[0357] Step 8:

[0358] The server formats the generated advice in JSON format and sends it to the terminal as an HTTP response. Specifically, the response is sent in the following JSON format:

[0359] json

[0360] {

[0361] "advice": "To save ¥83,333 each month, I suggest you cut your dining out expenses by 20% and review your cell phone plan. However, since you seem stressed, I'd like to caution you to not push yourself too hard."

[0362] }

[0363] This JSON data is received by the terminal and used for the next display process.

[0364] Step 9:

[0365] The device displays the received advice data to the user. Specifically, the advice is displayed within the application UI. Pop-ups and push notifications are used to make the advice easily available to the user. The user can review their household management based on this advice and take specific actions.

[0366] (Application example 2)

[0367] 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."

[0368] Today, users face many challenges when managing their household finances. In particular, it is difficult to receive appropriate advice on how to achieve savings goals while balancing income and expenses. Users may also feel stressed because the advice provided is not flexible enough to reflect their emotional state. Furthermore, the lack of advice that takes savings goals into account when shopping on online shopping sites makes it difficult to plan consumption behavior. There is a need for a system that can solve these issues and enable users to manage their household finances more effectively.

[0369] 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.

[0370] In this invention, the server includes an input means for receiving data on income, expenses, and savings goals input by a user, a transmission means for transmitting the data and emotional data from the input means to the server, a storage means for storing the data received from the transmission means in a database in the server, an analysis means for analyzing the data and emotional data stored in the storage means in the server and generating optimal household management advice, a notification means for notifying the user of the household management advice generated by the analysis means, and a display means for presenting the savings goal on the user's terminal when purchasing a product. This makes it possible to comprehensively analyze the user's income, expenses, savings goals, and emotional data and provide flexible household management advice tailored to the user's emotional state. Furthermore, by providing advice that takes savings goals into consideration when shopping on an online shopping site, the user can plan their consumption behavior and achieve effective household management.

[0371] "Revenue" refers to the monetary benefit a user receives within a certain period of time.

[0372] "Expenses" refers to the total amount of money a user spends within a certain period of time.

[0373] A "savings goal" refers to the amount of spending reduction a user wants to achieve within a certain period of time.

[0374] "Input means" refers to a device or interface through which a user inputs income, expenses, savings goals, and emotional data into the system.

[0375] "Transmission means" refers to a device or software that has the function of transmitting data acquired from the input means to the server.

[0376] "Storage means" refers to a device or database for storing data received by the server for a certain period of time.

[0377] "Analysis means" refers to functions and software for generating optimal household management advice based on the data and emotional data stored in the storage means.

[0378] "Notification means" refers to a device or interface for notifying the user of the generated household management advice.

[0379] "Display means" refers to functions and software for visually presenting advice based on savings goals and emotions to the user on the user's device.

[0380] The system based on this invention collects data on a user's income, expenses, savings goals, and emotions, and provides a function to generate and display optimal household management advice based on this data. This system is composed of the following main components.

[0381] User Interface (UI)

[0382] The device provides an input form for users to enter data on income, expenses, and savings goals via a dedicated application or a web browser interface. The device also uses a camera and microphone to capture the user's voice and facial expressions to collect emotional data, allowing users to operate the device intuitively.

[0383] data communication

[0384] The device converts the data and emotion data entered by the user into a specific format (e.g., JSON) and securely transmits it to the server via the HTTPS protocol. This data includes income, expenses, savings goals, and emotion data.

[0385] Data storage

[0386] The server temporarily stores the received data in a buffer area and then transfers it safely and efficiently to a database (e.g., MongoDB) that stores the user's past transactions and household data and is optimized to provide fast access to the information needed.

[0387] Data analysis

[0388] The server is equipped with an AI analysis module (e.g., Python + TensorFlow) and an emotion engine (e.g., Python + OpenCV). This allows it to generate optimal advice based on the saved data, referring to the user's current situation, past data, and even market information. It also analyzes the user's emotional state based on emotion data and generates flexible advice that reflects their emotions.

[0389] Generating and Sending Advice

[0390] The server generates specific household management advice based on the analysis results and formats it into response data to be notified to the user. The generated advice data is then sent back to the terminal via the communication function.

[0391] User Notification

[0392] The device displays the received advice data on the UI and notifies the user in a format that is easy to understand and implement. Specifically, it presents specific action plans for achieving savings goals and advice based on the user's emotional state.

[0393] Specific examples

[0394] For example, if a user enters the following data into a form in your application:

[0395] Income: 500,000 yen

[0396] Expenses: 350,000 yen

[0397] Savings goal: ¥200,000

[0398] At the same time, the application captures the user's voice and facial expressions to collect emotional data. This data is sent from the device to the server when the user presses the send button. The server analyzes the received data and, if it determines that the user is feeling stressed, generates advice suggesting, "This is a stressful time, so set a savings goal within your limits." This advice is sent back to the device and displays, "To save 83,333 yen each month, we suggest you reduce your dining out expenses by 20% and review your mobile phone plan. However, since you seem to be stressed, please be careful not to overdo it."

[0399] Prompt Sentence Examples

[0400] An example of a prompt for a generative AI model is:

[0401] Generate appropriate advice based on user sentiment data and contract details.

[0402] Example data:

[0403] Income: 500,000 yen

[0404] Expenses: 350,000 yen

[0405] Savings goal: ¥200,000

[0406] Emotional state: Stress

[0407] Desired advice:

[0408] Emotion-based savings plan suggestions

[0409] Relaxation products for stress relief

[0410] This system allows users to manage their household finances effectively and without stress, and to plan their consumption behavior.

[0411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0412] Step 1:

[0413] The user enters data on income, expenses, and savings goals into the application form and presses the submit button. The user also uses the camera and microphone to capture facial expressions and voice to collect emotional data. The system converts this data into JSON format.

[0414] Input: Income, expenses, savings goal data, facial expressions, voice

[0415] Output: JSON formatted income, expenses, savings goal data, and sentiment data

[0416] Step 2:

[0417] The terminal sends the JSON format data generated in step 1 to the server using the HTTPS protocol.

[0418] Input: JSON format income, expenses, savings goal data, and sentiment data

[0419] Output: Confirmation of transmission to the server

[0420] Step 3:

[0421] The server temporarily stores the received data in a buffer area and then forwards it to a database (e.g., MongoDB), where the data is properly indexed and efficiently stored for future analysis.

[0422] Input: JSON format income, expenses, savings goal data, and sentiment data

[0423] Output: Data stored in the database

[0424] Step 4:

[0425] The server's AI analysis module (e.g., Python + TensorFlow) analyzes the data and emotional data stored in the database, assessing the current state of household finances based on income, expenses, and savings goals, and understanding the user's emotional state based on the emotional data.

[0426] Input: Income, expenditure, savings goal data, and emotional data stored in the database

[0427] Output: Household management advice and emotional evaluation results

[0428] Step 5:

[0429] The server uses a generative AI model to generate financial management advice tailored to the user based on the analysis results of the AI ​​analysis module. This process also includes generating flexible advice based on the user's emotional state.

[0430] Input: Household management advice and emotional evaluation results

[0431] Output: Specific household management advice

[0432] Step 6:

[0433] The server formats the generated household management advice into JSON format and prepares it as response data to be sent to the terminal.

[0434] Input: Specific household management advice

[0435] Output: Financial advice in JSON format

[0436] Step 7:

[0437] The terminal displays the household management advice received from the server on the user interface, allowing the user to take specific household management and consumption actions based on this advice.

[0438] Input: Financial advice in JSON format

[0439] Output: Financial advice displayed in the UI

[0440] 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.

[0441] 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.

[0442] 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.

[0443] [Second embodiment]

[0444] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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."

[0456] The present invention relates to a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. The system aims to be easy for users to use and to provide appropriate advice in real time.

[0457] System Configuration

[0458] User Interface (UI)

[0459] The device provides an interface through a dedicated application or a web browser, where users can input data on income, expenses, and savings goals. This UI features a graphical display and input form, and is designed to be intuitive for users to use.

[0460] data communication

[0461] The terminal converts the data entered by the user into a specific format and sends it to the server via a transmission function. The transmitted data is set to arrive at the server safely and quickly.

[0462] Data storage

[0463] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[0464] Data analysis

[0465] The server's AI analysis module uses the stored data to generate optimal advice by referencing the user's current situation, past data, and market information. This analysis is performed using sophisticated machine learning algorithms and statistical models.

[0466] Generating and Sending Advice

[0467] The server generates specific household management advice based on the analysis results and formats it as response data. The generated advice data is then sent back to the device via the sending function.

[0468] User Notification

[0469] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0470] Specific examples

[0471] Suppose a user enters their monthly income of 500,000 yen, their monthly expenses of 350,000 yen, and their savings goal of 200,000 yen into an application form. This data is sent from the device to the server by pressing the send button.

[0472] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current balance and identify areas for improvement. For example, it generates advice such as, "To save 83,333 yen per month, reduce your eating out expenses by 20% and review your mobile phone plan."

[0473] This advice is then sent back to the device, and the application screen displays, "To save 83,333 yen each month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan." Based on this, users can review their household finances and take specific actions.

[0474] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby providing support to users in effectively managing their household finances.

[0475] The processing flow will be explained below.

[0476] Step 1:

[0477] A user opens a household management application and enters household data such as income, expenses, and savings goals into an input form.

[0478] Step 2:

[0479] The user checks the entered data and presses the "Submit" button.

[0480] Step 3:

[0481] The terminal converts the input data into a specific format (e.g., JSON), which includes information such as income, expenses, and savings goals.

[0482] Step 4:

[0483] The terminal transmits the converted data via a network communication module to send the data to the server.

[0484] Step 5:

[0485] The server receives the data sent from the terminal.

[0486] Step 6:

[0487] The server stores the received data in a temporary buffer area.

[0488] Step 7:

[0489] The server executes a save process to save the data saved in the buffer area to the database.

[0490] Step 8:

[0491] The server launches an AI analysis module to analyze the data stored in the database.

[0492] Step 9:

[0493] The server's AI analysis module uses the stored data to generate optimal household management advice, referring to the user's income and expenditure balance, past data, and even market information.

[0494] Step 10:

[0495] The server acquires the generated household management advice and formats it into response data for notifying the user.

[0496] Step 11:

[0497] The server executes a transmission process to transmit the formatted advice data to the terminal.

[0498] Step 12:

[0499] The terminal receives the advice data transmitted from the server.

[0500] Step 13:

[0501] The terminal analyzes the received advice data and converts it into a format for display on the user interface.

[0502] Step 14:

[0503] The device displays advice on the application screen such as, "To save 83,333 yen each month, we suggest reducing your eating out expenses by 20% and reviewing your mobile phone plan."

[0504] Step 15:

[0505] The user checks the displayed advice and adjusts household management and saves money based on it.

[0506] In this way, at each processing step of the present system, the user, terminal, and server work together to collect data on income, expenses, and savings goals, generate optimal financial advice, and notify the user.

[0507] Example 1

[0508] 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."

[0509] The present invention relates to a system that provides optimal household management advice based on a user's income, expenses, and savings goals, and is particularly required to provide accurate advice in real time. However, conventional household management systems do not effectively utilize the user's past data or market information, and analysis results are often vague and lack specificity, making it difficult for users to intuitively understand and take concrete action.

[0510] 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.

[0511] In this invention, the server includes input means for receiving data on income, expenses, and savings goals input by a user, transmission means for transmitting the data from the input means to the server, storage means in the server for storing the data received from the transmission means in a database, analysis means in the server using a machine learning algorithm for analyzing the data stored in the storage means and generating optimal household management advice, notification means for notifying the user of the household management advice generated by the analysis means, display means by which the notification means displays the household management advice on the user's terminal, and an encryption protocol. As a result, the data input by the user is securely transmitted and stored in a database, allowing the data to be analyzed in detail and intuitive, specific household management advice to be provided to the user in real time.

[0512] The "input means" is an interface that allows a user to input data on income, expenses, and savings goals, and is constructed on a dedicated application or a web browser.

[0513] The "transmission means" refers to a function for converting data acquired from the input means into a specific format and transmitting the data to the server, and includes a data encryption protocol.

[0514] The "storage means" refers to a function that stores data sent from the transmission means to the server in a temporary buffer area, and transfers it to a database for management.

[0515] "Analysis means" refers to a function that uses a machine learning algorithm to generate optimal household management advice based on the data stored in the storage means.

[0516] A "machine learning algorithm" is an algorithm that uses a user's past data and market information to analyze data and make predictions and optimizations.

[0517] The "notification means" refers to a function for notifying the user of the household management advice generated by the analysis means.

[0518] The "display means" refers to a function for displaying the advice provided by the notification means on the screen of the user's terminal.

[0519] "Encryption protocols" are methods of encrypting communications to transmit data securely, and include SSL and TLS.

[0520] The present invention is a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. The system aims to be easy for users to use and to provide appropriate advice in real time.

[0521] System Configuration

[0522] User Interface (UI)

[0523] The device provides an interface through a dedicated application or a web browser, where users can input data on income, expenses, and savings goals. This UI features a graphical display and input form, and is designed to be intuitive for users to use.

[0524] data communication

[0525] The terminal converts the data entered by the user into a specific format and sends it to the server via the transmission function. The transmitted data is set to reach the server safely and quickly using encryption protocols such as SSL and TLS.

[0526] Data storage

[0527] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[0528] Data analysis

[0529] The server's AI analysis module uses the stored data to generate optimal advice by referencing the user's current situation, past data, and market information. This analysis is performed using Python-based machine learning libraries (e.g., scikit-learn, TensorFlow, Keras, etc.).

[0530] Generating and Sending Advice

[0531] The server generates specific household management advice based on the analysis results and formats it as response data. The generated advice data is then sent back to the device via the sending function.

[0532] User Notification

[0533] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0534] Specific examples

[0535] Suppose a user enters their monthly income of 500,000 yen, their monthly expenses of 350,000 yen, and their savings goal of 200,000 yen into an application form. This data is sent from the device to the server by pressing the send button.

[0536] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current income and expenditure balance and identify areas for improvement. For example, it generates advice such as, "To save 83,333 yen per month, reduce eating out expenses by 20% and review your mobile phone plan."

[0537] This advice is then sent back to the device, and displayed on the application screen as follows: "To save 83,333 yen per month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan." Based on this, users can review their household finances and take specific actions.

[0538] Prompt Sentence Examples

[0539] "Please tell me the specific method to provide optimal savings advice to a user who has a monthly income of 500,000 yen, monthly expenses of 350,000 yen, and a savings goal of 200,000 yen."

[0540] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby providing assistance to users in effectively managing their household finances.

[0541] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0542] Program processing flow

[0543] Step 1:

[0544] Users enter data about their income, expenses, and savings goals into a dedicated application or an input form in a web browser. The data is displayed in real time in the application to help users enter data without errors. The data is then converted into a specific format and prepared for submission.

[0545] input:

[0546] Monthly income, monthly expenses, savings goals

[0547] Specific behavior:

[0548] A user enters data into a form in an application.

[0549] The terminal displays the input data on the screen in real time.

[0550] Step 2:

[0551] The device converts the input data into a specific format (e.g., JSON format) and sends it to the server using a secure encryption protocol (SSL / TLS). Once the data is sent, a message indicating that data transmission is complete is displayed on the device.

[0552] input:

[0553] Format-converted input data

[0554] output:

[0555] Securely encrypted data packets

[0556] Specific behavior:

[0557] The device displays a send button and the user clicks it.

[0558] The device encrypts the data and sends it to the server via the HTTPS protocol.

[0559] Step 3:

[0560] The server stores the received data in a temporary buffer area, acknowledges receipt, and then transfers the data to a database for secure recording. The database also contains the user's past transactions and household data.

[0561] input:

[0562] Decrypted input data

[0563] output:

[0564] Data stored in the temporary buffer area

[0565] Data recorded in the database

[0566] Specific behavior:

[0567] The server stores the received data in a buffer.

[0568] The server transfers the data to a database and organizes it into the appropriate fields.

[0569] Step 4:

[0570] The server's AI analysis module analyzes the stored data and uses machine learning algorithms (e.g., scikit-learn, TensorFlow) to generate optimal financial management advice based on the user's income and expenditure balance and past data.

[0571] input:

[0572] User data stored in a database

[0573] output:

[0574] Analysis data for advice generation

[0575] Specific behavior:

[0576] The server loads the user's data from the database.

[0577] The server analyzes the data using machine learning algorithms to calculate the balance and areas for improvement.

[0578] Step 5:

[0579] The server generates specific financial advice based on the analysis results, including the details the user needs to take specific actions, and formats the advice in a specific way.

[0580] input:

[0581] Analysis data

[0582] output:

[0583] Formatted advice data

[0584] Specific behavior:

[0585] The server generates advice recommending, "To save 83,333 yen per month, reduce your eating out expenses by 20% and review your mobile phone plan."

[0586] Step 6:

[0587] The server then converts the generated advice data back into a specific format and sends it back to the device, again securely using an encryption protocol.

[0588] input:

[0589] Formatted advice data

[0590] output:

[0591] Encrypted Advice Data Packet

[0592] Specific behavior:

[0593] The server converts the advice data into JSON format.

[0594] The server sends it to the terminal via the HTTPS protocol.

[0595] Step 7:

[0596] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0597] input:

[0598] Decrypted advice data

[0599] output:

[0600] Financial advice displayed on the user interface

[0601] Specific behavior:

[0602] The device displays the advice on the application screen.

[0603] The user checks the advice displayed, which reads, "To save 83,333 yen per month, we suggest reducing your eating out expenses by 20% and reviewing your mobile phone plan," and takes specific action.

[0604] (Application example 1)

[0605] 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."

[0606] In modern household management, it is important to provide optimal advice using data such as income, expenses, and savings goals. However, conventional systems have difficulty providing specific, practical advice tailored to individual situations in real time based on the data entered by the user. In addition, they lack a mechanism to clearly present recommended actions to encourage users to manage their household finances.

[0607] 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.

[0608] In this invention, the server includes an input means for receiving data on income, expenses, and savings goals input by a user, a transmission means for transmitting the data from the input means to the server, a storage means in the server for storing the data received from the transmission means in a database, an analysis means in the server for analyzing the data stored in the storage means and generating optimal household management advice, and a notification means for displaying the household management advice generated by the analysis means on the user's terminal and notifying the user of recommended spending reduction methods and points to review in spending. This enables the user to receive specific advice in real time based on their income, expenses, and savings goals, and clearly show actionable actions, enabling effective household management.

[0609] "Input means" refers to a device or interface through which a user inputs data such as income, expenses, and savings goals.

[0610] "Transmission means" refers to a device or software having a function for transmitting data acquired from an input means to a server.

[0611] "Storage means" refers to a function for safely and efficiently storing data sent to the server in a database.

[0612] "Analysis means" refers to an analysis system including machine learning algorithms and statistical models for generating optimal household management advice based on the data stored in the storage means.

[0613] "Notification means" refers to a device or software that has the function of displaying the household management advice generated by the analysis means on the user's terminal and notifying the user of recommended ways to reduce expenses and points to review in expenses.

[0614] A "generative AI model" refers to an artificial intelligence algorithm that generates household management advice based on input data.

[0615] A "prompt sentence" is a text message generated to prompt the user to take a specific action, and refers to a short sentence that clearly presents the recommended action.

[0616] A system embodying this invention has the following configuration. First, an input means is provided for a user to input data such as income, expenses, and savings goals. This input means uses a terminal such as a smartphone or tablet. This allows the user to intuitively input data through an interface.

[0617] Next, the input data is sent to the server via a transmission means. This transmission means uses internet communication technology. Specifically, the data is encrypted using HTTPS and sent securely to the server.

[0618] The server has a storage method that stores data in a temporary buffer area and transfers it from there to a database. The database is optimized to efficiently manage users' past transactions and household data. Typical database systems used are MySQL and PostgreSQL.

[0619] The data stored on the server is analyzed using an analytical methodology that incorporates generative AI models and statistical models. The generative AI models use the Python libraries TensorFlow and Scikit-learn. The server uses these models to generate optimal financial management advice based on the user's income, expenses, and savings goals.

[0620] The generated household management advice is communicated to the user through notification methods, such as smartphone apps and web apps. Notifications within the app are sent via push notifications or displayed on the app's UI.

[0621] Furthermore, a specific prompt could be, "To save 83,333 yen per month, we recommend reducing your eating out expenses by 20% and reviewing your mobile phone plan." Based on this advice, users can review their behavior and achieve optimal household management.

[0622] As a concrete example, if a user inputs their income of "500,000 yen," their expenses of "350,000 yen," and their savings goal of "200,000 yen," this data is sent from the device to the server. Once the data is saved and analyzed on the server, advice is generated and sent to the device: "To save 83,333 yen per month, we recommend reducing your dining out expenses by 20% and reviewing your mobile phone plan." By following this prompt, the user can understand specific ways to save money and improve their household finances.

[0623] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0624] Step 1:

[0625] The user launches the application and enters data on income, expenses, and savings goals.

[0626] Input: A user enters revenue "500,000 yen", expenses "350,000 yen", and savings goal "200,000 yen" into an application form.

[0627] Output: The input data is temporarily stored in the device.

[0628] What it does: A user uses the touchscreen of a smartphone or tablet to enter data into an app form, which is then stored in temporary memory.

[0629] Step 2:

[0630] The terminal converts the input data into a specific format, encrypts it, and sends it to the server.

[0631] Input: The data entered by the user in step 1.

[0632] Output: The encrypted data is sent over the internet to a server.

[0633] Specific operation: The terminal uses the HTTPS protocol to convert data into a specific JSON format, encrypt it, and then send it to the server.

[0634] Step 3:

[0635] The server stores the received data in a temporary buffer area, and then stores it permanently in the database.

[0636] Input: The encrypted data sent in step 2.

[0637] Output: Raw data stored in a database.

[0638] What happens: The server receives the request, holds the data in temporary memory, and then stores the data using a database system (such as MySQL or PostgreSQL).

[0639] Step 4:

[0640] The server analyzes the data and generates optimal household management advice.

[0641] Input: Income, expenses, and savings goal data stored in a database.

[0642] Output: Financial management advice generated by the generative AI model.

[0643] Specific operation: The server uses Python libraries (TensorFlow and Scikit-learn) to analyze past data and market information. As a result of the analysis, it generates specific advice based on the data.

[0644] Step 5:

[0645] The generated household management advice is transmitted to the user's terminal.

[0646] Input: Parsed financial advice.

[0647] Output: Advice that will be displayed on the user's terminal.

[0648] Specific operation: The server formats the generated advice into JSON format as a prompt text, encrypts it again, and sends it to the terminal.

[0649] Step 6:

[0650] The device notifies the user of the advice it receives and displays specific ways to save money and recommended actions.

[0651] Input: Financial advice sent from the server.

[0652] Output: Advice displayed within the application: "To save ¥83,333 per month, we recommend reducing your dining out expenses by 20% and reviewing your mobile phone plan."

[0653] What it does: The device receives the encrypted data, decrypts it, and displays it to the user, using push notifications or the app's UI to grab the user's attention.

[0654] 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.

[0655] The present invention relates to a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. In addition, by combining it with an emotion engine that recognizes the user's emotions, the system provides household management advice that corresponds to the user's emotional state.

[0656] System Configuration

[0657] User Interface (UI)

[0658] The device provides an interface through a dedicated application or a web browser, allowing users to input data on income, expenses, and savings goals. This UI is designed to be intuitive, with graphical displays and input forms. It also has an emotion recognition function that captures the user's voice and facial expressions to collect emotional data.

[0659] data communication

[0660] The device converts the data entered by the user and emotion data into a specific format (e.g., JSON) and sends it to the server via a transmission function. The transmitted data is set to arrive at the server safely and quickly.

[0661] Data storage

[0662] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[0663] Data analysis

[0664] The server's AI analysis module generates optimal advice based on the stored data, taking into account the user's current situation, past data, and market information. In addition, the server analyzes emotional data received from the emotion engine and adjusts the advice based on the user's emotional state.

[0665] Generating and Sending Advice

[0666] The server generates specific household management advice based on the analysis results and formats it into response data to be notified to the user. The generated advice data is then sent back to the terminal via the sending function.

[0667] User Notification

[0668] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0669] Specific examples

[0670] Suppose a user enters their monthly income of "500,000 yen," their monthly expenses of "350,000 yen," and their "saving goal of 200,000 yen" into an application form. At the same time, the application captures the user's voice and facial expressions to collect emotional data. This data is sent from the device to the server when the user presses the send button.

[0671] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current income and expenditure balance and identify areas for improvement. The emotion engine also analyzes the captured emotional data and determines, for example, whether the user is feeling stressed. Based on this information, it generates advice such as, "Since this is a stressful time, you should set a reasonable savings goal."

[0672] This advice is then sent back to the device, and the application screen displays the following message: "To save 83,333 yen each month, we suggest you cut your dining out expenses by 20% and review your mobile phone plan. However, you seem to be stressed, so please be careful not to push yourself too hard." Based on this, users can review their household finances and take specific action.

[0673] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby supporting the user in effectively managing their household finances. Furthermore, by combining it with an emotion engine, it is possible to provide flexible advice according to the user's emotional state.

[0674] The processing flow will be explained below.

[0675] Step 1:

[0676] A user opens a financial management application and enters data such as income, expenses, and savings goals into an input form, while their voice and facial expressions are simultaneously captured.

[0677] Step 2:

[0678] The device generates emotion data based on the user's input data on income, expenses, and savings goals, as well as captured voice and facial expressions.

[0679] Step 3:

[0680] The user confirms that the input data and emotion data have been sent, and then presses the "Send" button.

[0681] Step 4:

[0682] The device converts the income, expenditure, savings goal, and emotional state data into a specific format (e.g., JSON format). This format includes income, expenditure, savings goal, emotional state, etc.

[0683] Step 5:

[0684] The terminal transmits the data via a network communication module to transmit the formatted data to a server.

[0685] Step 6:

[0686] The server receives the data sent from the terminal.

[0687] Step 7:

[0688] The server stores the received data in a temporary buffer area.

[0689] Step 8:

[0690] The server executes a database save process to save the data in the buffer area to the database.

[0691] Step 9:

[0692] The server's AI analysis module analyzes the user's balance based on income, expenditure, and savings goal data stored in the database.

[0693] Step 10:

[0694] The server's emotion engine analyzes the emotion data stored in the database to identify the user's emotional state. For example, it can determine whether the user is feeling stressed based on voice data and facial expressions.

[0695] Step 11:

[0696] The server's AI analysis module integrates the results of the income and expenditure balance analysis and the emotion engine to generate optimal household management advice.

[0697] Step 12:

[0698] The server formats the generated financial advice as response data, which includes specific savings suggestions and cautions based on the user's emotional state.

[0699] Step 13:

[0700] The server executes a transmission process to transmit the formatted advice data to the terminal.

[0701] Step 14:

[0702] The terminal receives the advice data transmitted from the server.

[0703] Step 15:

[0704] The terminal analyzes the received advice data and converts it into a format for display on the user interface.

[0705] Step 16:

[0706] The device displays advice on the application screen such as, "To save 83,333 yen each month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan. However, since you seem to be stressed, please be careful not to push yourself too hard."

[0707] Step 17:

[0708] The user checks the displayed advice and adjusts household management and saves money based on it.

[0709] In this way, the system of the present invention generates optimal advice on the server based on the user's input data and emotional data, and notifies the user via the terminal. By combining emotion engines, flexible advice is provided that takes into account the user's emotional state.

[0710] Example 2

[0711] 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."

[0712] Conventional household management systems only handle basic data such as a user's income, expenses, and savings goals, and are unable to provide advice that takes into account the user's emotions and psychological state. As a result, they ignore the impact of stress and emotional fluctuations on household management, resulting in insufficient effectiveness. Furthermore, they are insufficient in accurately analyzing data and personalizing advice, making it difficult to provide appropriate guidance tailored to each user's individual situation.

[0713] 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.

[0714] In this invention, the server includes input means for receiving data on income, expenses, and savings goals input by a user, conversion means for converting the data from the input means into a specific format, transmission means for transmitting the data converted by the conversion means to the server, temporary storage means for storing the data received from the transmission means in a temporary buffer area in the server, storage means for transferring the data stored in the temporary storage means to a database, analysis means for analyzing the data stored in the storage means and the user's emotional data in the server and generating optimal household management advice, adjustment means for adjusting the household management advice generated by the analysis means in accordance with the user's emotional state, and notification means for notifying the user of the household management advice adjusted by the adjustment means. This enables the user to receive more appropriate and personalized household management advice that reflects their emotional state.

[0715] "Input means" refers to a device or means for a user to input data on income, expenses, and savings goals.

[0716] "Conversion means" refers to a device or method for converting data obtained from an input means into a specific format.

[0717] The "transmission means" refers to a device or method for transmitting the data converted by the conversion means to the server.

[0718] "Temporary storage means" refers to a device or means for temporarily storing data received from a transmitting means.

[0719] "Storage means" refers to a device or means for transferring data stored in the temporary storage means to a database and storing the data therein.

[0720] The term "analysis means" refers to a device or device for analyzing the data stored in the storage means and generating household management advice.

[0721] The "adjustment means" refers to a device or means for adjusting the household management advice generated by the analysis means in accordance with the emotional state of the user.

[0722] The "notification means" refers to a device or means for notifying the user of the household management advice adjusted by the adjustment means.

[0723] A "database" refers to a structure that organizes information to efficiently store and manage user data and retrieve it when needed.

[0724] "Emotional data" refers to information about the user's psychological state and emotions analyzed from their voice, facial expressions, etc.

[0725] "Financial advice" refers to specific guidance and suggestions for improving financial management based on a user's income, expenses, savings goals, and emotional data.

[0726] The present invention relates to a system that collects data such as a user's income, expenses, and savings goals, and provides optimal household management advice based on that data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system provides household management advice according to the user's emotional state. Specific embodiments for carrying out the invention are described below.

[0727] System Configuration

[0728] User Interface (UI)

[0729] The device provides an interface for entering data on income, expenses, and savings goals through a dedicated application or a web browser. This UI features a graphical display and an input form that users can operate intuitively. In addition, the device has an emotion recognition function that captures voice and facial expressions via a camera and microphone to collect emotional data.

[0730] data communication

[0731] The terminal converts the data entered by the user about income, expenses, and savings goals into a specific format (e.g., JSON), which is then securely and quickly transmitted to the server via HTTP or HTTPS protocols.

[0732] Data storage

[0733] The server first stores the received data in a temporary buffer area. This data is then transferred to a database and saved as the user's household data. The database is managed using a general-purpose relational database management system (RDBMS), such as MySQL or PostgreSQL.

[0734] Data analysis

[0735] The server's AI analysis module analyzes the user's balance based on data stored in the database, such as income, expenses, and savings goals. The emotion engine also analyzes the emotional data and adjusts advice based on the user's emotional state. The analysis utilizes generative AI models such as TensorFlow and PyTorch.

[0736] Generating and Sending Advice

[0737] The server generates specific household management advice based on the analysis results, which is then converted back to JSON format and sent to the device as a response.

[0738] User Notification

[0739] The device displays the received advice data on the UI and notifies the user in an intuitive manner. Notifications can be pop-up or push notifications, allowing the user to effectively manage their household finances based on the suggested advice.

[0740] Specific examples

[0741] For example, if a user enters the following data into a form in your application:

[0742] Monthly income: 500,000 yen

[0743] Monthly expenses: 350,000 yen

[0744] Savings goal: "200,000 yen"

[0745] At the same time, voice and facial expression data is collected through the device's camera and microphone, and analyzed as emotional data. This data is then sent from the device to the server by pressing the send button.

[0746] The server first temporarily stores the data, then transfers it to a database for storage. The AI ​​analysis module analyzes the balance of income and expenditure, and the emotion engine determines the user's stress level, etc. Based on this information, the system generates advice such as "Set a reasonable savings goal."

[0747] The generated advice is sent to the terminal in the following format, for example:

[0748] "To save ¥83,333 each month, I suggest you cut your dining out expenses by 20% and review your cell phone plan. However, since you seem stressed, I'd like to caution you to not push yourself too hard."

[0749] This advice is displayed on the device's UI, allowing users to review their household finances and take concrete action based on it.

[0750] Prompt Sentence Examples

[0751] "Generate optimal household management advice based on data such as monthly income of 500,000 yen, expenses of 350,000 yen, and savings goal of 200,000 yen, as well as emotional information about the user's stress levels."

[0752] In this way, the system of the present invention can support more effective household management by providing personalized household management advice based on the user's input data and emotional state.

[0753] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0754] Step 1:

[0755] The user enters data on income, expenses, and savings goals into a form on the application or web browser via the device. Specifically, the user enters income "500,000 yen," expenses "350,000 yen," and savings goal "200,000 yen" into each field and presses the submit button. At this time, the device's camera and microphone are used to capture the user's voice and facial expressions, and emotional data is also collected. The entered data and emotional data are obtained as input.

[0756] Step 2:

[0757] The terminal converts the data entered by the user into JSON format using a conversion algorithm within the terminal to format the data as JSON, like this:

[0758] json

[0759] {

[0760] "income": 500000,

[0761] "expenses": 350000,

[0762] "savings_goal": 200000,

[0763] "emotion_data": {"stress_level": "high"}

[0764] }

[0765] This JSON data is obtained as the output after conversion.

[0766] Step 3:

[0767] The terminal sends the converted data to the server using the HTTP or HTTPS protocol. Specifically, the terminal generates an HTTP request and attaches JSON data to the request. The sent data is received as input by the server.

[0768] Step 4:

[0769] The server first stores the received data in a temporary buffer area. Specifically, the server writes the data to a temporary storage area within the server. The temporarily stored data becomes the input for the next step.

[0770] Step 5:

[0771] The server transfers the temporarily saved data to the database and stores it. Specifically, it adds the data to the database using the INSERT command. This operation saves the user's household data to the database. The saved data becomes the input data for the next analysis.

[0772] Step 6:

[0773] The server's AI analysis module analyzes the data stored in the database. Specifically, it uses a generative AI model (e.g., TensorFlow or PyTorch) to analyze the balance, and the emotion engine analyzes the emotional data. This analysis outputs the user's possible balance, and the analysis results serve as the basis for generating the next advice.

[0774] Step 7:

[0775] The server generates household management advice based on the analysis results. Specifically, it uses an advice generation algorithm to generate specific advice such as "To save 83,333 yen per month, reduce eating out expenses by 20% and review your mobile phone plan." This generated advice is output as the next data to be sent.

[0776] Step 8:

[0777] The server formats the generated advice in JSON format and sends it to the terminal as an HTTP response. Specifically, the response is sent in the following JSON format:

[0778] json

[0779] {

[0780] "advice": "To save ¥83,333 each month, I suggest you cut your dining out expenses by 20% and review your cell phone plan. However, since you seem stressed, I'd like to caution you to not push yourself too hard."

[0781] }

[0782] This JSON data is received by the terminal and used for the next display process.

[0783] Step 9:

[0784] The device displays the received advice data to the user. Specifically, the advice is displayed within the application UI. Pop-ups and push notifications are used to make the advice easily available to the user. The user can review their household management based on this advice and take specific actions.

[0785] (Application example 2)

[0786] 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."

[0787] Today, users face many challenges when managing their household finances. In particular, it is difficult to receive appropriate advice on how to achieve savings goals while balancing income and expenses. Users may also feel stressed because the advice provided is not flexible enough to reflect their emotional state. Furthermore, the lack of advice that takes savings goals into account when shopping on online shopping sites makes it difficult to plan consumption behavior. There is a need for a system that can solve these issues and enable users to manage their household finances more effectively.

[0788] 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.

[0789] In this invention, the server includes an input means for receiving data on income, expenses, and savings goals input by a user, a transmission means for transmitting the data and emotional data from the input means to the server, a storage means for storing the data received from the transmission means in a database in the server, an analysis means for analyzing the data and emotional data stored in the storage means in the server and generating optimal household management advice, a notification means for notifying the user of the household management advice generated by the analysis means, and a display means for presenting the savings goal on the user's terminal when purchasing a product. This makes it possible to comprehensively analyze the user's income, expenses, savings goals, and emotional data and provide flexible household management advice tailored to the user's emotional state. Furthermore, by providing advice that takes savings goals into consideration when shopping on an online shopping site, the user can plan their consumption behavior and achieve effective household management.

[0790] "Revenue" refers to the monetary benefit a user receives within a certain period of time.

[0791] "Expenses" refers to the total amount of money a user spends within a certain period of time.

[0792] A "savings goal" refers to the amount of spending reduction a user wants to achieve within a certain period of time.

[0793] "Input means" refers to a device or interface through which a user inputs income, expenses, savings goals, and emotional data into the system.

[0794] "Transmission means" refers to a device or software that has the function of transmitting data acquired from the input means to the server.

[0795] "Storage means" refers to a device or database for storing data received by the server for a certain period of time.

[0796] "Analysis means" refers to functions and software for generating optimal household management advice based on the data and emotional data stored in the storage means.

[0797] "Notification means" refers to a device or interface for notifying the user of the generated household management advice.

[0798] "Display means" refers to functions and software for visually presenting advice based on savings goals and emotions to the user on the user's device.

[0799] The system based on this invention collects data on a user's income, expenses, savings goals, and emotions, and provides a function to generate and display optimal household management advice based on this data. This system is composed of the following main components.

[0800] User Interface (UI)

[0801] The device provides an input form for users to enter data on income, expenses, and savings goals via a dedicated application or a web browser interface. The device also uses a camera and microphone to capture the user's voice and facial expressions to collect emotional data, allowing users to operate the device intuitively.

[0802] data communication

[0803] The device converts the data and emotion data entered by the user into a specific format (e.g., JSON) and securely transmits it to the server via the HTTPS protocol. This data includes income, expenses, savings goals, and emotion data.

[0804] Data storage

[0805] The server temporarily stores the received data in a buffer area and then transfers it safely and efficiently to a database (e.g., MongoDB) that stores the user's past transactions and household data and is optimized to provide fast access to the information needed.

[0806] Data analysis

[0807] The server is equipped with an AI analysis module (e.g., Python + TensorFlow) and an emotion engine (e.g., Python + OpenCV). This allows it to generate optimal advice based on the saved data, referring to the user's current situation, past data, and even market information. It also analyzes the user's emotional state based on emotion data and generates flexible advice that reflects their emotions.

[0808] Generating and Sending Advice

[0809] The server generates specific household management advice based on the analysis results and formats it into response data to be notified to the user. The generated advice data is then sent back to the terminal via the communication function.

[0810] User Notification

[0811] The device displays the received advice data on the UI and notifies the user in a format that is easy to understand and implement. Specifically, it presents specific action plans for achieving savings goals and advice based on the user's emotional state.

[0812] Specific examples

[0813] For example, if a user enters the following data into a form in your application:

[0814] Income: 500,000 yen

[0815] Expenses: 350,000 yen

[0816] Savings goal: ¥200,000

[0817] At the same time, the application captures the user's voice and facial expressions to collect emotional data. This data is sent from the device to the server when the user presses the send button. The server analyzes the received data and, if it determines that the user is feeling stressed, generates advice suggesting, "This is a stressful time, so set a savings goal within your limits." This advice is sent back to the device and displays, "To save 83,333 yen each month, we suggest you reduce your dining out expenses by 20% and review your mobile phone plan. However, since you seem to be stressed, please be careful not to overdo it."

[0818] Prompt Sentence Examples

[0819] An example of a prompt for a generative AI model is:

[0820] Generate appropriate advice based on user sentiment data and contract details.

[0821] Example data:

[0822] Income: 500,000 yen

[0823] Expenses: 350,000 yen

[0824] Savings goal: ¥200,000

[0825] Emotional state: Stress

[0826] Desired advice:

[0827] Emotion-based savings plan suggestions

[0828] Relaxation products for stress relief

[0829] This system allows users to manage their household finances effectively and without stress, and to plan their consumption behavior.

[0830] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0831] Step 1:

[0832] The user enters data on income, expenses, and savings goals into the application form and presses the submit button. The user also uses the camera and microphone to capture facial expressions and voice to collect emotional data. The system converts this data into JSON format.

[0833] Input: Income, expenses, savings goal data, facial expressions, voice

[0834] Output: JSON formatted income, expenses, savings goal data, and sentiment data

[0835] Step 2:

[0836] The terminal sends the JSON format data generated in step 1 to the server using the HTTPS protocol.

[0837] Input: JSON format income, expenses, savings goal data, and sentiment data

[0838] Output: Confirmation of transmission to the server

[0839] Step 3:

[0840] The server temporarily stores the received data in a buffer area and then forwards it to a database (e.g., MongoDB), where the data is properly indexed and efficiently stored for future analysis.

[0841] Input: JSON format income, expenses, savings goal data, and sentiment data

[0842] Output: Data stored in the database

[0843] Step 4:

[0844] The server's AI analysis module (e.g., Python + TensorFlow) analyzes the data and emotional data stored in the database, assessing the current state of household finances based on income, expenses, and savings goals, and understanding the user's emotional state based on the emotional data.

[0845] Input: Income, expenditure, savings goal data, and emotional data stored in the database

[0846] Output: Household management advice and emotional evaluation results

[0847] Step 5:

[0848] The server uses a generative AI model to generate financial management advice tailored to the user based on the analysis results of the AI ​​analysis module. This process also includes generating flexible advice based on the user's emotional state.

[0849] Input: Household management advice and emotional evaluation results

[0850] Output: Specific household management advice

[0851] Step 6:

[0852] The server formats the generated household management advice into JSON format and prepares it as response data to be sent to the terminal.

[0853] Input: Specific household management advice

[0854] Output: Financial advice in JSON format

[0855] Step 7:

[0856] The terminal displays the household management advice received from the server on the user interface, allowing the user to take specific household management and consumption actions based on this advice.

[0857] Input: Financial advice in JSON format

[0858] Output: Financial advice displayed in the UI

[0859] 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.

[0860] 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.

[0861] 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.

[0862] [Third embodiment]

[0863] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0864] 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.

[0865] 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).

[0866] 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.

[0867] 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.

[0868] 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).

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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."

[0875] The present invention relates to a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. The system aims to be easy for users to use and to provide appropriate advice in real time.

[0876] System Configuration

[0877] User Interface (UI)

[0878] The device provides an interface through a dedicated application or a web browser, where users can input data on income, expenses, and savings goals. This UI features a graphical display and input form, and is designed to be intuitive for users to use.

[0879] data communication

[0880] The terminal converts the data entered by the user into a specific format and sends it to the server via a transmission function. The transmitted data is set to arrive at the server safely and quickly.

[0881] Data storage

[0882] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[0883] Data analysis

[0884] The server's AI analysis module uses the stored data to generate optimal advice by referencing the user's current situation, past data, and market information. This analysis is performed using sophisticated machine learning algorithms and statistical models.

[0885] Generating and Sending Advice

[0886] The server generates specific household management advice based on the analysis results and formats it as response data. The generated advice data is then sent back to the device via the sending function.

[0887] User Notification

[0888] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0889] Specific examples

[0890] Suppose a user enters their monthly income of 500,000 yen, their monthly expenses of 350,000 yen, and their savings goal of 200,000 yen into an application form. This data is sent from the device to the server by pressing the send button.

[0891] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current balance and identify areas for improvement. For example, it generates advice such as, "To save 83,333 yen per month, reduce your eating out expenses by 20% and review your mobile phone plan."

[0892] This advice is then sent back to the device, and the application screen displays, "To save 83,333 yen each month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan." Based on this, users can review their household finances and take specific actions.

[0893] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby providing support to users in effectively managing their household finances.

[0894] The processing flow will be explained below.

[0895] Step 1:

[0896] A user opens a household management application and enters household data such as income, expenses, and savings goals into an input form.

[0897] Step 2:

[0898] The user checks the entered data and presses the "Submit" button.

[0899] Step 3:

[0900] The terminal converts the input data into a specific format (e.g., JSON), which includes information such as income, expenses, and savings goals.

[0901] Step 4:

[0902] The terminal transmits the converted data via a network communication module to send the data to the server.

[0903] Step 5:

[0904] The server receives the data sent from the terminal.

[0905] Step 6:

[0906] The server stores the received data in a temporary buffer area.

[0907] Step 7:

[0908] The server executes a save process to save the data saved in the buffer area to the database.

[0909] Step 8:

[0910] The server launches an AI analysis module to analyze the data stored in the database.

[0911] Step 9:

[0912] The server's AI analysis module uses the stored data to generate optimal household management advice, referring to the user's income and expenditure balance, past data, and even market information.

[0913] Step 10:

[0914] The server acquires the generated household management advice and formats it into response data for notifying the user.

[0915] Step 11:

[0916] The server executes a transmission process to transmit the formatted advice data to the terminal.

[0917] Step 12:

[0918] The terminal receives the advice data transmitted from the server.

[0919] Step 13:

[0920] The terminal analyzes the received advice data and converts it into a format for display on the user interface.

[0921] Step 14:

[0922] The device displays advice on the application screen such as, "To save 83,333 yen each month, we suggest reducing your eating out expenses by 20% and reviewing your mobile phone plan."

[0923] Step 15:

[0924] The user checks the displayed advice and adjusts household management and saves money based on it.

[0925] In this way, at each processing step of the present system, the user, terminal, and server work together to collect data on income, expenses, and savings goals, generate optimal financial advice, and notify the user.

[0926] Example 1

[0927] 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."

[0928] The present invention relates to a system that provides optimal household management advice based on a user's income, expenses, and savings goals, and is particularly required to provide accurate advice in real time. However, conventional household management systems do not effectively utilize the user's past data or market information, and analysis results are often vague and lack specificity, making it difficult for users to intuitively understand and take concrete action.

[0929] 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.

[0930] In this invention, the server includes input means for receiving data on income, expenses, and savings goals input by a user, transmission means for transmitting the data from the input means to the server, storage means in the server for storing the data received from the transmission means in a database, analysis means in the server using a machine learning algorithm for analyzing the data stored in the storage means and generating optimal household management advice, notification means for notifying the user of the household management advice generated by the analysis means, display means by which the notification means displays the household management advice on the user's terminal, and an encryption protocol. As a result, the data input by the user is securely transmitted and stored in a database, allowing the data to be analyzed in detail and intuitive, specific household management advice to be provided to the user in real time.

[0931] The "input means" is an interface that allows a user to input data on income, expenses, and savings goals, and is constructed on a dedicated application or a web browser.

[0932] The "transmission means" refers to a function for converting data acquired from the input means into a specific format and transmitting the data to the server, and includes a data encryption protocol.

[0933] The "storage means" refers to a function that stores data sent from the transmission means to the server in a temporary buffer area, and transfers it to a database for management.

[0934] "Analysis means" refers to a function that uses a machine learning algorithm to generate optimal household management advice based on the data stored in the storage means.

[0935] A "machine learning algorithm" is an algorithm that uses a user's past data and market information to analyze data and make predictions and optimizations.

[0936] The "notification means" refers to a function for notifying the user of the household management advice generated by the analysis means.

[0937] The "display means" refers to a function for displaying the advice provided by the notification means on the screen of the user's terminal.

[0938] "Encryption protocols" are methods of encrypting communications to transmit data securely, and include SSL and TLS.

[0939] The present invention is a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. The system aims to be easy for users to use and to provide appropriate advice in real time.

[0940] System Configuration

[0941] User Interface (UI)

[0942] The device provides an interface through a dedicated application or a web browser, where users can input data on income, expenses, and savings goals. This UI features a graphical display and input form, and is designed to be intuitive for users to use.

[0943] data communication

[0944] The terminal converts the data entered by the user into a specific format and sends it to the server via the transmission function. The transmitted data is set to reach the server safely and quickly using encryption protocols such as SSL and TLS.

[0945] Data storage

[0946] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[0947] Data analysis

[0948] The server's AI analysis module uses the stored data to generate optimal advice by referencing the user's current situation, past data, and market information. This analysis is performed using Python-based machine learning libraries (e.g., scikit-learn, TensorFlow, Keras, etc.).

[0949] Generating and Sending Advice

[0950] The server generates specific household management advice based on the analysis results and formats it as response data. The generated advice data is then sent back to the device via the sending function.

[0951] User Notification

[0952] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[0953] Specific examples

[0954] Suppose a user enters their monthly income of 500,000 yen, their monthly expenses of 350,000 yen, and their savings goal of 200,000 yen into an application form. This data is sent from the device to the server by pressing the send button.

[0955] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current income and expenditure balance and identify areas for improvement. For example, it generates advice such as, "To save 83,333 yen per month, reduce eating out expenses by 20% and review your mobile phone plan."

[0956] This advice is then sent back to the device, and displayed on the application screen as follows: "To save 83,333 yen per month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan." Based on this, users can review their household finances and take specific actions.

[0957] Prompt Sentence Examples

[0958] "Please tell me the specific method to provide optimal savings advice to a user who has a monthly income of 500,000 yen, monthly expenses of 350,000 yen, and a savings goal of 200,000 yen."

[0959] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby providing assistance to users in effectively managing their household finances.

[0960] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0961] Program processing flow

[0962] Step 1:

[0963] Users enter data about their income, expenses, and savings goals into a dedicated application or an input form in a web browser. The data is displayed in real time in the application to help users enter data without errors. The data is then converted into a specific format and prepared for submission.

[0964] input:

[0965] Monthly income, monthly expenses, savings goals

[0966] Specific behavior:

[0967] A user enters data into a form in an application.

[0968] The terminal displays the input data on the screen in real time.

[0969] Step 2:

[0970] The device converts the input data into a specific format (e.g., JSON format) and sends it to the server using a secure encryption protocol (SSL / TLS). Once the data is sent, a message indicating that data transmission is complete is displayed on the device.

[0971] input:

[0972] Format-converted input data

[0973] output:

[0974] Securely encrypted data packets

[0975] Specific behavior:

[0976] The device displays a send button and the user clicks it.

[0977] The device encrypts the data and sends it to the server via the HTTPS protocol.

[0978] Step 3:

[0979] The server stores the received data in a temporary buffer area, acknowledges receipt, and then transfers the data to a database for secure recording. The database also contains the user's past transactions and household data.

[0980] input:

[0981] Decrypted input data

[0982] output:

[0983] Data stored in the temporary buffer area

[0984] Data recorded in the database

[0985] Specific behavior:

[0986] The server stores the received data in a buffer.

[0987] The server transfers the data to a database and organizes it into the appropriate fields.

[0988] Step 4:

[0989] The server's AI analysis module analyzes the stored data and uses machine learning algorithms (e.g., scikit-learn, TensorFlow) to generate optimal financial management advice based on the user's income and expenditure balance and past data.

[0990] input:

[0991] User data stored in a database

[0992] output:

[0993] Analysis data for advice generation

[0994] Specific behavior:

[0995] The server loads the user's data from the database.

[0996] The server analyzes the data using machine learning algorithms to calculate the balance and areas for improvement.

[0997] Step 5:

[0998] The server generates specific financial advice based on the analysis results, including the details the user needs to take specific actions, and formats the advice in a specific way.

[0999] input:

[1000] Analysis data

[1001] output:

[1002] Formatted advice data

[1003] Specific behavior:

[1004] The server generates advice recommending, "To save 83,333 yen per month, reduce your eating out expenses by 20% and review your mobile phone plan."

[1005] Step 6:

[1006] The server then converts the generated advice data back into a specific format and sends it back to the device, again securely using an encryption protocol.

[1007] input:

[1008] Formatted advice data

[1009] output:

[1010] Encrypted Advice Data Packet

[1011] Specific behavior:

[1012] The server converts the advice data into JSON format.

[1013] The server sends it to the terminal via the HTTPS protocol.

[1014] Step 7:

[1015] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[1016] input:

[1017] Decrypted advice data

[1018] output:

[1019] Financial advice displayed on the user interface

[1020] Specific behavior:

[1021] The device displays the advice on the application screen.

[1022] The user checks the advice displayed, which reads, "To save 83,333 yen per month, we suggest reducing your eating out expenses by 20% and reviewing your mobile phone plan," and takes specific action.

[1023] (Application example 1)

[1024] 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."

[1025] In modern household management, it is important to provide optimal advice using data such as income, expenses, and savings goals. However, conventional systems have difficulty providing specific, practical advice tailored to individual situations in real time based on the data entered by the user. In addition, they lack a mechanism to clearly present recommended actions to encourage users to manage their household finances.

[1026] 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.

[1027] In this invention, the server includes an input means for receiving data on income, expenses, and savings goals input by a user, a transmission means for transmitting the data from the input means to the server, a storage means in the server for storing the data received from the transmission means in a database, an analysis means in the server for analyzing the data stored in the storage means and generating optimal household management advice, and a notification means for displaying the household management advice generated by the analysis means on the user's terminal and notifying the user of recommended spending reduction methods and points to review in spending. This enables the user to receive specific advice in real time based on their income, expenses, and savings goals, and clearly show actionable actions, enabling effective household management.

[1028] "Input means" refers to a device or interface through which a user inputs data such as income, expenses, and savings goals.

[1029] "Transmission means" refers to a device or software having a function for transmitting data acquired from an input means to a server.

[1030] "Storage means" refers to a function for safely and efficiently storing data sent to the server in a database.

[1031] "Analysis means" refers to an analysis system including machine learning algorithms and statistical models for generating optimal household management advice based on the data stored in the storage means.

[1032] "Notification means" refers to a device or software that has the function of displaying the household management advice generated by the analysis means on the user's terminal and notifying the user of recommended ways to reduce expenses and points to review in expenses.

[1033] A "generative AI model" refers to an artificial intelligence algorithm that generates household management advice based on input data.

[1034] A "prompt sentence" is a text message generated to prompt the user to take a specific action, and refers to a short sentence that clearly presents the recommended action.

[1035] A system embodying this invention has the following configuration. First, an input means is provided for a user to input data such as income, expenses, and savings goals. This input means uses a terminal such as a smartphone or tablet. This allows the user to intuitively input data through an interface.

[1036] Next, the input data is sent to the server via a transmission means. This transmission means uses internet communication technology. Specifically, the data is encrypted using HTTPS and sent securely to the server.

[1037] The server has a storage method that stores data in a temporary buffer area and transfers it from there to a database. The database is optimized to efficiently manage users' past transactions and household data. Typical database systems used are MySQL and PostgreSQL.

[1038] The data stored on the server is analyzed using an analytical methodology that incorporates generative AI models and statistical models. The generative AI models use the Python libraries TensorFlow and Scikit-learn. The server uses these models to generate optimal financial management advice based on the user's income, expenses, and savings goals.

[1039] The generated household management advice is communicated to the user through notification methods, such as smartphone apps and web apps. Notifications within the app are sent via push notifications or displayed on the app's UI.

[1040] Furthermore, a specific prompt could be, "To save 83,333 yen per month, we recommend reducing your eating out expenses by 20% and reviewing your mobile phone plan." Based on this advice, users can review their behavior and achieve optimal household management.

[1041] As a concrete example, if a user inputs their income of "500,000 yen," their expenses of "350,000 yen," and their savings goal of "200,000 yen," this data is sent from the device to the server. Once the data is saved and analyzed on the server, advice is generated and sent to the device: "To save 83,333 yen per month, we recommend reducing your dining out expenses by 20% and reviewing your mobile phone plan." By following this prompt, the user can understand specific ways to save money and improve their household finances.

[1042] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1043] Step 1:

[1044] The user launches the application and enters data on income, expenses, and savings goals.

[1045] Input: A user enters revenue "500,000 yen", expenses "350,000 yen", and savings goal "200,000 yen" into an application form.

[1046] Output: The input data is temporarily stored in the device.

[1047] What it does: A user uses the touchscreen of a smartphone or tablet to enter data into an app form, which is then stored in temporary memory.

[1048] Step 2:

[1049] The terminal converts the input data into a specific format, encrypts it, and sends it to the server.

[1050] Input: The data entered by the user in step 1.

[1051] Output: The encrypted data is sent over the internet to a server.

[1052] Specific operation: The terminal uses the HTTPS protocol to convert data into a specific JSON format, encrypt it, and then send it to the server.

[1053] Step 3:

[1054] The server stores the received data in a temporary buffer area, and then stores it permanently in the database.

[1055] Input: The encrypted data sent in step 2.

[1056] Output: Raw data stored in a database.

[1057] What happens: The server receives the request, holds the data in temporary memory, and then stores the data using a database system (such as MySQL or PostgreSQL).

[1058] Step 4:

[1059] The server analyzes the data and generates optimal household management advice.

[1060] Input: Income, expenses, and savings goal data stored in a database.

[1061] Output: Financial management advice generated by the generative AI model.

[1062] Specific operation: The server uses Python libraries (TensorFlow and Scikit-learn) to analyze past data and market information. As a result of the analysis, it generates specific advice based on the data.

[1063] Step 5:

[1064] The generated household management advice is transmitted to the user's terminal.

[1065] Input: Parsed financial advice.

[1066] Output: Advice that will be displayed on the user's terminal.

[1067] Specific operation: The server formats the generated advice into JSON format as a prompt text, encrypts it again, and sends it to the terminal.

[1068] Step 6:

[1069] The device notifies the user of the advice it receives and displays specific ways to save money and recommended actions.

[1070] Input: Financial advice sent from the server.

[1071] Output: Advice displayed within the application: "To save ¥83,333 per month, we recommend reducing your dining out expenses by 20% and reviewing your mobile phone plan."

[1072] What it does: The device receives the encrypted data, decrypts it, and displays it to the user, using push notifications or the app's UI to grab the user's attention.

[1073] 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.

[1074] The present invention relates to a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. In addition, by combining it with an emotion engine that recognizes the user's emotions, the system provides household management advice that corresponds to the user's emotional state.

[1075] System Configuration

[1076] User Interface (UI)

[1077] The device provides an interface through a dedicated application or a web browser, allowing users to input data on income, expenses, and savings goals. This UI is designed to be intuitive, with graphical displays and input forms. It also has an emotion recognition function that captures the user's voice and facial expressions to collect emotional data.

[1078] data communication

[1079] The device converts the data entered by the user and emotion data into a specific format (e.g., JSON) and sends it to the server via a transmission function. The transmitted data is set to arrive at the server safely and quickly.

[1080] Data storage

[1081] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[1082] Data analysis

[1083] The server's AI analysis module generates optimal advice based on the stored data, taking into account the user's current situation, past data, and market information. In addition, the server analyzes emotional data received from the emotion engine and adjusts the advice based on the user's emotional state.

[1084] Generating and Sending Advice

[1085] The server generates specific household management advice based on the analysis results and formats it into response data to be notified to the user. The generated advice data is then sent back to the terminal via the sending function.

[1086] User Notification

[1087] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[1088] Specific examples

[1089] Suppose a user enters their monthly income of "500,000 yen," their monthly expenses of "350,000 yen," and their "saving goal of 200,000 yen" into an application form. At the same time, the application captures the user's voice and facial expressions to collect emotional data. This data is sent from the device to the server when the user presses the send button.

[1090] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current income and expenditure balance and identify areas for improvement. The emotion engine also analyzes the captured emotional data and determines, for example, whether the user is feeling stressed. Based on this information, it generates advice such as, "Since this is a stressful time, you should set a reasonable savings goal."

[1091] This advice is then sent back to the device, and the application screen displays the following message: "To save 83,333 yen each month, we suggest you cut your dining out expenses by 20% and review your mobile phone plan. However, you seem to be stressed, so please be careful not to push yourself too hard." Based on this, users can review their household finances and take specific action.

[1092] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby supporting the user in effectively managing their household finances. Furthermore, by combining it with an emotion engine, it is possible to provide flexible advice according to the user's emotional state.

[1093] The processing flow will be explained below.

[1094] Step 1:

[1095] A user opens a financial management application and enters data such as income, expenses, and savings goals into an input form, while their voice and facial expressions are simultaneously captured.

[1096] Step 2:

[1097] The device generates emotion data based on the user's input data on income, expenses, and savings goals, as well as captured voice and facial expressions.

[1098] Step 3:

[1099] The user confirms that the input data and emotion data have been sent, and then presses the "Send" button.

[1100] Step 4:

[1101] The device converts the income, expenditure, savings goal, and emotional state data into a specific format (e.g., JSON format). This format includes income, expenditure, savings goal, emotional state, etc.

[1102] Step 5:

[1103] The terminal transmits the data via a network communication module to transmit the formatted data to a server.

[1104] Step 6:

[1105] The server receives the data sent from the terminal.

[1106] Step 7:

[1107] The server stores the received data in a temporary buffer area.

[1108] Step 8:

[1109] The server executes a database save process to save the data in the buffer area to the database.

[1110] Step 9:

[1111] The server's AI analysis module analyzes the user's balance based on income, expenditure, and savings goal data stored in the database.

[1112] Step 10:

[1113] The server's emotion engine analyzes the emotion data stored in the database to identify the user's emotional state. For example, it can determine whether the user is feeling stressed based on voice data and facial expressions.

[1114] Step 11:

[1115] The server's AI analysis module integrates the results of the income and expenditure balance analysis and the emotion engine to generate optimal household management advice.

[1116] Step 12:

[1117] The server formats the generated financial advice as response data, which includes specific savings suggestions and cautions based on the user's emotional state.

[1118] Step 13:

[1119] The server executes a transmission process to transmit the formatted advice data to the terminal.

[1120] Step 14:

[1121] The terminal receives the advice data transmitted from the server.

[1122] Step 15:

[1123] The terminal analyzes the received advice data and converts it into a format for display on the user interface.

[1124] Step 16:

[1125] The device displays advice on the application screen such as, "To save 83,333 yen each month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan. However, since you seem to be stressed, please be careful not to push yourself too hard."

[1126] Step 17:

[1127] The user checks the displayed advice and adjusts household management and saves money based on it.

[1128] In this way, the system of the present invention generates optimal advice on the server based on the user's input data and emotional data, and notifies the user via the terminal. By combining emotion engines, flexible advice is provided that takes into account the user's emotional state.

[1129] Example 2

[1130] 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."

[1131] Conventional household management systems only handle basic data such as a user's income, expenses, and savings goals, and are unable to provide advice that takes into account the user's emotions and psychological state. As a result, they ignore the impact of stress and emotional fluctuations on household management, resulting in insufficient effectiveness. Furthermore, they are insufficient in accurately analyzing data and personalizing advice, making it difficult to provide appropriate guidance tailored to each user's individual situation.

[1132] 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.

[1133] In this invention, the server includes input means for receiving data on income, expenses, and savings goals input by a user, conversion means for converting the data from the input means into a specific format, transmission means for transmitting the data converted by the conversion means to the server, temporary storage means for storing the data received from the transmission means in a temporary buffer area in the server, storage means for transferring the data stored in the temporary storage means to a database, analysis means for analyzing the data stored in the storage means and the user's emotional data in the server and generating optimal household management advice, adjustment means for adjusting the household management advice generated by the analysis means in accordance with the user's emotional state, and notification means for notifying the user of the household management advice adjusted by the adjustment means. This enables the user to receive more appropriate and personalized household management advice that reflects their emotional state.

[1134] "Input means" refers to a device or means for a user to input data on income, expenses, and savings goals.

[1135] "Conversion means" refers to a device or method for converting data obtained from an input means into a specific format.

[1136] The "transmission means" refers to a device or method for transmitting the data converted by the conversion means to the server.

[1137] "Temporary storage means" refers to a device or means for temporarily storing data received from a transmitting means.

[1138] "Storage means" refers to a device or means for transferring data stored in the temporary storage means to a database and storing the data therein.

[1139] The term "analysis means" refers to a device or device for analyzing the data stored in the storage means and generating household management advice.

[1140] The "adjustment means" refers to a device or means for adjusting the household management advice generated by the analysis means in accordance with the emotional state of the user.

[1141] The "notification means" refers to a device or means for notifying the user of the household management advice adjusted by the adjustment means.

[1142] A "database" refers to a structure that organizes information to efficiently store and manage user data and retrieve it when needed.

[1143] "Emotional data" refers to information about the user's psychological state and emotions analyzed from their voice, facial expressions, etc.

[1144] "Financial advice" refers to specific guidance and suggestions for improving financial management based on a user's income, expenses, savings goals, and emotional data.

[1145] The present invention relates to a system that collects data such as a user's income, expenses, and savings goals, and provides optimal household management advice based on that data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system provides household management advice according to the user's emotional state. Specific embodiments for carrying out the invention are described below.

[1146] System Configuration

[1147] User Interface (UI)

[1148] The device provides an interface for entering data on income, expenses, and savings goals through a dedicated application or a web browser. This UI features a graphical display and an input form that users can operate intuitively. In addition, the device has an emotion recognition function that captures voice and facial expressions via a camera and microphone to collect emotional data.

[1149] data communication

[1150] The terminal converts the data entered by the user about income, expenses, and savings goals into a specific format (e.g., JSON), which is then securely and quickly transmitted to the server via HTTP or HTTPS protocols.

[1151] Data storage

[1152] The server first stores the received data in a temporary buffer area. This data is then transferred to a database and saved as the user's household data. The database is managed using a general-purpose relational database management system (RDBMS), such as MySQL or PostgreSQL.

[1153] Data analysis

[1154] The server's AI analysis module analyzes the user's balance based on data stored in the database, such as income, expenses, and savings goals. The emotion engine also analyzes the emotional data and adjusts advice based on the user's emotional state. The analysis utilizes generative AI models such as TensorFlow and PyTorch.

[1155] Generating and Sending Advice

[1156] The server generates specific household management advice based on the analysis results, which is then converted back to JSON format and sent to the device as a response.

[1157] User Notification

[1158] The device displays the received advice data on the UI and notifies the user in an intuitive manner. Notifications can be pop-up or push notifications, allowing the user to effectively manage their household finances based on the suggested advice.

[1159] Specific examples

[1160] For example, if a user enters the following data into a form in your application:

[1161] Monthly income: 500,000 yen

[1162] Monthly expenses: 350,000 yen

[1163] Savings goal: "200,000 yen"

[1164] At the same time, voice and facial expression data is collected through the device's camera and microphone, and analyzed as emotional data. This data is then sent from the device to the server by pressing the send button.

[1165] The server first temporarily stores the data, then transfers it to a database for storage. The AI ​​analysis module analyzes the balance of income and expenditure, and the emotion engine determines the user's stress level, etc. Based on this information, the system generates advice such as "Set a reasonable savings goal."

[1166] The generated advice is sent to the terminal in the following format, for example:

[1167] "To save ¥83,333 each month, I suggest you cut your dining out expenses by 20% and review your cell phone plan. However, since you seem stressed, I'd like to caution you to not push yourself too hard."

[1168] This advice is displayed on the device's UI, allowing users to review their household finances and take concrete action based on it.

[1169] Prompt Sentence Examples

[1170] "Generate optimal household management advice based on data such as monthly income of 500,000 yen, expenses of 350,000 yen, and savings goal of 200,000 yen, as well as emotional information about the user's stress levels."

[1171] In this way, the system of the present invention can support more effective household management by providing personalized household management advice based on the user's input data and emotional state.

[1172] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1173] Step 1:

[1174] The user enters data on income, expenses, and savings goals into a form on the application or web browser via the device. Specifically, the user enters income "500,000 yen," expenses "350,000 yen," and savings goal "200,000 yen" into each field and presses the submit button. At this time, the device's camera and microphone are used to capture the user's voice and facial expressions, and emotional data is also collected. The entered data and emotional data are obtained as input.

[1175] Step 2:

[1176] The terminal converts the data entered by the user into JSON format using a conversion algorithm within the terminal to format the data as JSON, like this:

[1177] json

[1178] {

[1179] "income": 500000,

[1180] "expenses": 350000,

[1181] "savings_goal": 200000,

[1182] "emotion_data": {"stress_level": "high"}

[1183] }

[1184] This JSON data is obtained as the output after conversion.

[1185] Step 3:

[1186] The terminal sends the converted data to the server using the HTTP or HTTPS protocol. Specifically, the terminal generates an HTTP request and attaches JSON data to the request. The sent data is received as input by the server.

[1187] Step 4:

[1188] The server first stores the received data in a temporary buffer area. Specifically, the server writes the data to a temporary storage area within the server. The temporarily stored data becomes the input for the next step.

[1189] Step 5:

[1190] The server transfers the temporarily saved data to the database and stores it. Specifically, it adds the data to the database using the INSERT command. This operation saves the user's household data to the database. The saved data becomes the input data for the next analysis.

[1191] Step 6:

[1192] The server's AI analysis module analyzes the data stored in the database. Specifically, it uses a generative AI model (e.g., TensorFlow or PyTorch) to analyze the balance, and the emotion engine analyzes the emotional data. This analysis outputs the user's possible balance, and the analysis results serve as the basis for generating the next advice.

[1193] Step 7:

[1194] The server generates household management advice based on the analysis results. Specifically, it uses an advice generation algorithm to generate specific advice such as "To save 83,333 yen per month, reduce eating out expenses by 20% and review your mobile phone plan." This generated advice is output as the next data to be sent.

[1195] Step 8:

[1196] The server formats the generated advice in JSON format and sends it to the terminal as an HTTP response. Specifically, the response is sent in the following JSON format:

[1197] json

[1198] {

[1199] "advice": "To save ¥83,333 each month, I suggest you cut your dining out expenses by 20% and review your cell phone plan. However, since you seem stressed, I'd like to caution you to not push yourself too hard."

[1200] }

[1201] This JSON data is received by the terminal and used for the next display process.

[1202] Step 9:

[1203] The device displays the received advice data to the user. Specifically, the advice is displayed within the application UI. Pop-ups and push notifications are used to make the advice easily available to the user. The user can review their household management based on this advice and take specific actions.

[1204] (Application example 2)

[1205] 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."

[1206] Today, users face many challenges when managing their household finances. In particular, it is difficult to receive appropriate advice on how to achieve savings goals while balancing income and expenses. Users may also feel stressed because the advice provided is not flexible enough to reflect their emotional state. Furthermore, the lack of advice that takes savings goals into account when shopping on online shopping sites makes it difficult to plan consumption behavior. There is a need for a system that can solve these issues and enable users to manage their household finances more effectively.

[1207] 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.

[1208] In this invention, the server includes an input means for receiving data on income, expenses, and savings goals input by a user, a transmission means for transmitting the data and emotional data from the input means to the server, a storage means for storing the data received from the transmission means in a database in the server, an analysis means for analyzing the data and emotional data stored in the storage means in the server and generating optimal household management advice, a notification means for notifying the user of the household management advice generated by the analysis means, and a display means for presenting the savings goal on the user's terminal when purchasing a product. This makes it possible to comprehensively analyze the user's income, expenses, savings goals, and emotional data and provide flexible household management advice tailored to the user's emotional state. Furthermore, by providing advice that takes savings goals into consideration when shopping on an online shopping site, the user can plan their consumption behavior and achieve effective household management.

[1209] "Revenue" refers to the monetary benefit a user receives within a certain period of time.

[1210] "Expenses" refers to the total amount of money a user spends within a certain period of time.

[1211] A "savings goal" refers to the amount of spending reduction a user wants to achieve within a certain period of time.

[1212] "Input means" refers to a device or interface through which a user inputs income, expenses, savings goals, and emotional data into the system.

[1213] "Transmission means" refers to a device or software that has the function of transmitting data acquired from the input means to the server.

[1214] "Storage means" refers to a device or database for storing data received by the server for a certain period of time.

[1215] "Analysis means" refers to functions and software for generating optimal household management advice based on the data and emotional data stored in the storage means.

[1216] "Notification means" refers to a device or interface for notifying the user of the generated household management advice.

[1217] "Display means" refers to functions and software for visually presenting advice based on savings goals and emotions to the user on the user's device.

[1218] The system based on this invention collects data on a user's income, expenses, savings goals, and emotions, and provides a function to generate and display optimal household management advice based on this data. This system is composed of the following main components.

[1219] User Interface (UI)

[1220] The device provides an input form for users to enter data on income, expenses, and savings goals via a dedicated application or a web browser interface. The device also uses a camera and microphone to capture the user's voice and facial expressions to collect emotional data, allowing users to operate the device intuitively.

[1221] data communication

[1222] The device converts the data and emotion data entered by the user into a specific format (e.g., JSON) and securely transmits it to the server via the HTTPS protocol. This data includes income, expenses, savings goals, and emotion data.

[1223] Data storage

[1224] The server temporarily stores the received data in a buffer area and then transfers it safely and efficiently to a database (e.g., MongoDB) that stores the user's past transactions and household data and is optimized to provide fast access to the information needed.

[1225] Data analysis

[1226] The server is equipped with an AI analysis module (e.g., Python + TensorFlow) and an emotion engine (e.g., Python + OpenCV). This allows it to generate optimal advice based on the saved data, referring to the user's current situation, past data, and even market information. It also analyzes the user's emotional state based on emotion data and generates flexible advice that reflects their emotions.

[1227] Generating and Sending Advice

[1228] The server generates specific household management advice based on the analysis results and formats it into response data to be notified to the user. The generated advice data is then sent back to the terminal via the communication function.

[1229] User Notification

[1230] The device displays the received advice data on the UI and notifies the user in a format that is easy to understand and implement. Specifically, it presents specific action plans for achieving savings goals and advice based on the user's emotional state.

[1231] Specific examples

[1232] For example, if a user enters the following data into a form in your application:

[1233] Income: 500,000 yen

[1234] Expenses: 350,000 yen

[1235] Savings goal: ¥200,000

[1236] At the same time, the application captures the user's voice and facial expressions to collect emotional data. This data is sent from the device to the server when the user presses the send button. The server analyzes the received data and, if it determines that the user is feeling stressed, generates advice suggesting, "This is a stressful time, so set a savings goal within your limits." This advice is sent back to the device and displays, "To save 83,333 yen each month, we suggest you reduce your dining out expenses by 20% and review your mobile phone plan. However, since you seem to be stressed, please be careful not to overdo it."

[1237] Prompt Sentence Examples

[1238] An example of a prompt for a generative AI model is:

[1239] Generate appropriate advice based on user sentiment data and contract details.

[1240] Example data:

[1241] Income: 500,000 yen

[1242] Expenses: 350,000 yen

[1243] Savings goal: ¥200,000

[1244] Emotional state: Stress

[1245] Desired advice:

[1246] Emotion-based savings plan suggestions

[1247] Relaxation products for stress relief

[1248] This system allows users to manage their household finances effectively and without stress, and to plan their consumption behavior.

[1249] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1250] Step 1:

[1251] The user enters data on income, expenses, and savings goals into the application form and presses the submit button. The user also uses the camera and microphone to capture facial expressions and voice to collect emotional data. The system converts this data into JSON format.

[1252] Input: Income, expenses, savings goal data, facial expressions, voice

[1253] Output: JSON formatted income, expenses, savings goal data, and sentiment data

[1254] Step 2:

[1255] The terminal sends the JSON format data generated in step 1 to the server using the HTTPS protocol.

[1256] Input: JSON format income, expenses, savings goal data, and sentiment data

[1257] Output: Confirmation of transmission to the server

[1258] Step 3:

[1259] The server temporarily stores the received data in a buffer area and then forwards it to a database (e.g., MongoDB), where the data is properly indexed and efficiently stored for future analysis.

[1260] Input: JSON format income, expenses, savings goal data, and sentiment data

[1261] Output: Data stored in the database

[1262] Step 4:

[1263] The server's AI analysis module (e.g., Python + TensorFlow) analyzes the data and emotional data stored in the database, assessing the current state of household finances based on income, expenses, and savings goals, and understanding the user's emotional state based on the emotional data.

[1264] Input: Income, expenditure, savings goal data, and emotional data stored in the database

[1265] Output: Household management advice and emotional evaluation results

[1266] Step 5:

[1267] The server uses a generative AI model to generate financial management advice tailored to the user based on the analysis results of the AI ​​analysis module. This process also includes generating flexible advice based on the user's emotional state.

[1268] Input: Household management advice and emotional evaluation results

[1269] Output: Specific household management advice

[1270] Step 6:

[1271] The server formats the generated household management advice into JSON format and prepares it as response data to be sent to the terminal.

[1272] Input: Specific household management advice

[1273] Output: Financial advice in JSON format

[1274] Step 7:

[1275] The terminal displays the household management advice received from the server on the user interface, allowing the user to take specific household management and consumption actions based on this advice.

[1276] Input: Financial advice in JSON format

[1277] Output: Financial advice displayed in the UI

[1278] 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.

[1279] 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.

[1280] 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.

[1281] [Fourth embodiment]

[1282] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1283] 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.

[1284] 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).

[1285] 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.

[1286] 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.

[1287] 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).

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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.

[1292] 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.

[1293] 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.

[1294] 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."

[1295] The present invention relates to a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. The system aims to be easy for users to use and to provide appropriate advice in real time.

[1296] System Configuration

[1297] User Interface (UI)

[1298] The device provides an interface through a dedicated application or a web browser, where users can input data on income, expenses, and savings goals. This UI features a graphical display and input form, and is designed to be intuitive for users to use.

[1299] data communication

[1300] The terminal converts the data entered by the user into a specific format and sends it to the server via a transmission function. The transmitted data is set to arrive at the server safely and quickly.

[1301] Data storage

[1302] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[1303] Data analysis

[1304] The server's AI analysis module uses the stored data to generate optimal advice by referencing the user's current situation, past data, and market information. This analysis is performed using sophisticated machine learning algorithms and statistical models.

[1305] Generating and Sending Advice

[1306] The server generates specific household management advice based on the analysis results and formats it as response data. The generated advice data is then sent back to the device via the sending function.

[1307] User Notification

[1308] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[1309] Specific examples

[1310] Suppose a user enters their monthly income of 500,000 yen, their monthly expenses of 350,000 yen, and their savings goal of 200,000 yen into an application form. This data is sent from the device to the server by pressing the send button.

[1311] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current balance and identify areas for improvement. For example, it generates advice such as, "To save 83,333 yen per month, reduce your eating out expenses by 20% and review your mobile phone plan."

[1312] This advice is then sent back to the device, and the application screen displays, "To save 83,333 yen each month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan." Based on this, users can review their household finances and take specific actions.

[1313] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby providing support to users in effectively managing their household finances.

[1314] The processing flow will be explained below.

[1315] Step 1:

[1316] A user opens a household management application and enters household data such as income, expenses, and savings goals into an input form.

[1317] Step 2:

[1318] The user checks the entered data and presses the "Submit" button.

[1319] Step 3:

[1320] The terminal converts the input data into a specific format (e.g., JSON), which includes information such as income, expenses, and savings goals.

[1321] Step 4:

[1322] The terminal transmits the converted data via a network communication module to send the data to the server.

[1323] Step 5:

[1324] The server receives the data sent from the terminal.

[1325] Step 6:

[1326] The server stores the received data in a temporary buffer area.

[1327] Step 7:

[1328] The server executes a save process to save the data saved in the buffer area to the database.

[1329] Step 8:

[1330] The server launches an AI analysis module to analyze the data stored in the database.

[1331] Step 9:

[1332] The server's AI analysis module uses the stored data to generate optimal household management advice, referring to the user's income and expenditure balance, past data, and even market information.

[1333] Step 10:

[1334] The server acquires the generated household management advice and formats it into response data for notifying the user.

[1335] Step 11:

[1336] The server executes a transmission process to transmit the formatted advice data to the terminal.

[1337] Step 12:

[1338] The terminal receives the advice data transmitted from the server.

[1339] Step 13:

[1340] The terminal analyzes the received advice data and converts it into a format for display on the user interface.

[1341] Step 14:

[1342] The device displays advice on the application screen such as, "To save 83,333 yen each month, we suggest reducing your eating out expenses by 20% and reviewing your mobile phone plan."

[1343] Step 15:

[1344] The user checks the displayed advice and adjusts household management and saves money based on it.

[1345] In this way, at each processing step of the present system, the user, terminal, and server work together to collect data on income, expenses, and savings goals, generate optimal financial advice, and notify the user.

[1346] Example 1

[1347] 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."

[1348] The present invention relates to a system that provides optimal household management advice based on a user's income, expenses, and savings goals, and is particularly required to provide accurate advice in real time. However, conventional household management systems do not effectively utilize the user's past data or market information, and analysis results are often vague and lack specificity, making it difficult for users to intuitively understand and take concrete action.

[1349] 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.

[1350] In this invention, the server includes input means for receiving data on income, expenses, and savings goals input by a user, transmission means for transmitting the data from the input means to the server, storage means in the server for storing the data received from the transmission means in a database, analysis means in the server using a machine learning algorithm for analyzing the data stored in the storage means and generating optimal household management advice, notification means for notifying the user of the household management advice generated by the analysis means, display means by which the notification means displays the household management advice on the user's terminal, and an encryption protocol. As a result, the data input by the user is securely transmitted and stored in a database, allowing the data to be analyzed in detail and intuitive, specific household management advice to be provided to the user in real time.

[1351] The "input means" is an interface that allows a user to input data on income, expenses, and savings goals, and is constructed on a dedicated application or a web browser.

[1352] The "transmission means" refers to a function for converting data acquired from the input means into a specific format and transmitting the data to the server, and includes a data encryption protocol.

[1353] The "storage means" refers to a function that stores data sent from the transmission means to the server in a temporary buffer area, and transfers it to a database for management.

[1354] "Analysis means" refers to a function that uses a machine learning algorithm to generate optimal household management advice based on the data stored in the storage means.

[1355] A "machine learning algorithm" is an algorithm that uses a user's past data and market information to analyze data and make predictions and optimizations.

[1356] The "notification means" refers to a function for notifying the user of the household management advice generated by the analysis means.

[1357] The "display means" refers to a function for displaying the advice provided by the notification means on the screen of the user's terminal.

[1358] "Encryption protocols" are methods of encrypting communications to transmit data securely, and include SSL and TLS.

[1359] The present invention is a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. The system aims to be easy for users to use and to provide appropriate advice in real time.

[1360] System Configuration

[1361] User Interface (UI)

[1362] The device provides an interface through a dedicated application or a web browser, where users can input data on income, expenses, and savings goals. This UI features a graphical display and input form, and is designed to be intuitive for users to use.

[1363] data communication

[1364] The terminal converts the data entered by the user into a specific format and sends it to the server via the transmission function. The transmitted data is set to reach the server safely and quickly using encryption protocols such as SSL and TLS.

[1365] Data storage

[1366] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[1367] Data analysis

[1368] The server's AI analysis module uses the stored data to generate optimal advice by referencing the user's current situation, past data, and market information. This analysis is performed using Python-based machine learning libraries (e.g., scikit-learn, TensorFlow, Keras, etc.).

[1369] Generating and Sending Advice

[1370] The server generates specific household management advice based on the analysis results and formats it as response data. The generated advice data is then sent back to the device via the sending function.

[1371] User Notification

[1372] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[1373] Specific examples

[1374] Suppose a user enters their monthly income of 500,000 yen, their monthly expenses of 350,000 yen, and their savings goal of 200,000 yen into an application form. This data is sent from the device to the server by pressing the send button.

[1375] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current income and expenditure balance and identify areas for improvement. For example, it generates advice such as, "To save 83,333 yen per month, reduce eating out expenses by 20% and review your mobile phone plan."

[1376] This advice is then sent back to the device, and displayed on the application screen as follows: "To save 83,333 yen per month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan." Based on this, users can review their household finances and take specific actions.

[1377] Prompt Sentence Examples

[1378] "Please tell me the specific method to provide optimal savings advice to a user who has a monthly income of 500,000 yen, monthly expenses of 350,000 yen, and a savings goal of 200,000 yen."

[1379] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby providing assistance to users in effectively managing their household finances.

[1380] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1381] Program processing flow

[1382] Step 1:

[1383] Users enter data about their income, expenses, and savings goals into a dedicated application or an input form in a web browser. The data is displayed in real time in the application to help users enter data without errors. The data is then converted into a specific format and prepared for submission.

[1384] input:

[1385] Monthly income, monthly expenses, savings goals

[1386] Specific behavior:

[1387] A user enters data into a form in an application.

[1388] The terminal displays the input data on the screen in real time.

[1389] Step 2:

[1390] The device converts the input data into a specific format (e.g., JSON format) and sends it to the server using a secure encryption protocol (SSL / TLS). Once the data is sent, a message indicating that data transmission is complete is displayed on the device.

[1391] input:

[1392] Format-converted input data

[1393] output:

[1394] Securely encrypted data packets

[1395] Specific behavior:

[1396] The device displays a send button and the user clicks it.

[1397] The device encrypts the data and sends it to the server via the HTTPS protocol.

[1398] Step 3:

[1399] The server stores the received data in a temporary buffer area, acknowledges receipt, and then transfers the data to a database for secure recording. The database also contains the user's past transactions and household data.

[1400] input:

[1401] Decrypted input data

[1402] output:

[1403] Data stored in the temporary buffer area

[1404] Data recorded in the database

[1405] Specific behavior:

[1406] The server stores the received data in a buffer.

[1407] The server transfers the data to a database and organizes it into the appropriate fields.

[1408] Step 4:

[1409] The server's AI analysis module analyzes the stored data and uses machine learning algorithms (e.g., scikit-learn, TensorFlow) to generate optimal financial management advice based on the user's income and expenditure balance and past data.

[1410] input:

[1411] User data stored in a database

[1412] output:

[1413] Analysis data for advice generation

[1414] Specific behavior:

[1415] The server loads the user's data from the database.

[1416] The server analyzes the data using machine learning algorithms to calculate the balance and areas for improvement.

[1417] Step 5:

[1418] The server generates specific financial advice based on the analysis results, including the details the user needs to take specific actions, and formats the advice in a specific way.

[1419] input:

[1420] Analysis data

[1421] output:

[1422] Formatted advice data

[1423] Specific behavior:

[1424] The server generates advice recommending, "To save 83,333 yen per month, reduce your eating out expenses by 20% and review your mobile phone plan."

[1425] Step 6:

[1426] The server then converts the generated advice data back into a specific format and sends it back to the device, again securely using an encryption protocol.

[1427] input:

[1428] Formatted advice data

[1429] output:

[1430] Encrypted Advice Data Packet

[1431] Specific behavior:

[1432] The server converts the advice data into JSON format.

[1433] The server sends it to the terminal via the HTTPS protocol.

[1434] Step 7:

[1435] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[1436] input:

[1437] Decrypted advice data

[1438] output:

[1439] Financial advice displayed on the user interface

[1440] Specific behavior:

[1441] The device displays the advice on the application screen.

[1442] The user checks the advice displayed, which reads, "To save 83,333 yen per month, we suggest reducing your eating out expenses by 20% and reviewing your mobile phone plan," and takes specific action.

[1443] (Application example 1)

[1444] 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."

[1445] In modern household management, it is important to provide optimal advice using data such as income, expenses, and savings goals. However, conventional systems have difficulty providing specific, practical advice tailored to individual situations in real time based on the data entered by the user. In addition, they lack a mechanism to clearly present recommended actions to encourage users to manage their household finances.

[1446] 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.

[1447] In this invention, the server includes an input means for receiving data on income, expenses, and savings goals input by a user, a transmission means for transmitting the data from the input means to the server, a storage means in the server for storing the data received from the transmission means in a database, an analysis means in the server for analyzing the data stored in the storage means and generating optimal household management advice, and a notification means for displaying the household management advice generated by the analysis means on the user's terminal and notifying the user of recommended spending reduction methods and points to review in spending. This enables the user to receive specific advice in real time based on their income, expenses, and savings goals, and clearly show actionable actions, enabling effective household management.

[1448] "Input means" refers to a device or interface through which a user inputs data such as income, expenses, and savings goals.

[1449] "Transmission means" refers to a device or software having a function for transmitting data acquired from an input means to a server.

[1450] "Storage means" refers to a function for safely and efficiently storing data sent to the server in a database.

[1451] "Analysis means" refers to an analysis system including machine learning algorithms and statistical models for generating optimal household management advice based on the data stored in the storage means.

[1452] "Notification means" refers to a device or software that has the function of displaying the household management advice generated by the analysis means on the user's terminal and notifying the user of recommended ways to reduce expenses and points to review in expenses.

[1453] A "generative AI model" refers to an artificial intelligence algorithm that generates household management advice based on input data.

[1454] A "prompt sentence" is a text message generated to prompt the user to take a specific action, and refers to a short sentence that clearly presents the recommended action.

[1455] A system embodying this invention has the following configuration. First, an input means is provided for a user to input data such as income, expenses, and savings goals. This input means uses a terminal such as a smartphone or tablet. This allows the user to intuitively input data through an interface.

[1456] Next, the input data is sent to the server via a transmission means. This transmission means uses internet communication technology. Specifically, the data is encrypted using HTTPS and sent securely to the server.

[1457] The server has a storage method that stores data in a temporary buffer area and transfers it from there to a database. The database is optimized to efficiently manage users' past transactions and household data. Typical database systems used are MySQL and PostgreSQL.

[1458] The data stored on the server is analyzed using an analytical methodology that incorporates generative AI models and statistical models. The generative AI models use the Python libraries TensorFlow and Scikit-learn. The server uses these models to generate optimal financial management advice based on the user's income, expenses, and savings goals.

[1459] The generated household management advice is communicated to the user through notification methods, such as smartphone apps and web apps. Notifications within the app are sent via push notifications or displayed on the app's UI.

[1460] Furthermore, a specific prompt could be, "To save 83,333 yen per month, we recommend reducing your eating out expenses by 20% and reviewing your mobile phone plan." Based on this advice, users can review their behavior and achieve optimal household management.

[1461] As a concrete example, if a user inputs their income of "500,000 yen," their expenses of "350,000 yen," and their savings goal of "200,000 yen," this data is sent from the device to the server. Once the data is saved and analyzed on the server, advice is generated and sent to the device: "To save 83,333 yen per month, we recommend reducing your dining out expenses by 20% and reviewing your mobile phone plan." By following this prompt, the user can understand specific ways to save money and improve their household finances.

[1462] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1463] Step 1:

[1464] The user launches the application and enters data on income, expenses, and savings goals.

[1465] Input: A user enters revenue "500,000 yen", expenses "350,000 yen", and savings goal "200,000 yen" into an application form.

[1466] Output: The input data is temporarily stored in the device.

[1467] What it does: A user uses the touchscreen of a smartphone or tablet to enter data into an app form, which is then stored in temporary memory.

[1468] Step 2:

[1469] The terminal converts the input data into a specific format, encrypts it, and sends it to the server.

[1470] Input: The data entered by the user in step 1.

[1471] Output: The encrypted data is sent over the internet to a server.

[1472] Specific operation: The terminal uses the HTTPS protocol to convert data into a specific JSON format, encrypt it, and then send it to the server.

[1473] Step 3:

[1474] The server stores the received data in a temporary buffer area, and then stores it permanently in the database.

[1475] Input: The encrypted data sent in step 2.

[1476] Output: Raw data stored in a database.

[1477] What happens: The server receives the request, holds the data in temporary memory, and then stores the data using a database system (such as MySQL or PostgreSQL).

[1478] Step 4:

[1479] The server analyzes the data and generates optimal household management advice.

[1480] Input: Income, expenses, and savings goal data stored in a database.

[1481] Output: Financial management advice generated by the generative AI model.

[1482] Specific operation: The server uses Python libraries (TensorFlow and Scikit-learn) to analyze past data and market information. As a result of the analysis, it generates specific advice based on the data.

[1483] Step 5:

[1484] The generated household management advice is transmitted to the user's terminal.

[1485] Input: Parsed financial advice.

[1486] Output: Advice that will be displayed on the user's terminal.

[1487] Specific operation: The server formats the generated advice into JSON format as a prompt text, encrypts it again, and sends it to the terminal.

[1488] Step 6:

[1489] The device notifies the user of the advice it receives and displays specific ways to save money and recommended actions.

[1490] Input: Financial advice sent from the server.

[1491] Output: Advice displayed within the application: "To save ¥83,333 per month, we recommend reducing your dining out expenses by 20% and reviewing your mobile phone plan."

[1492] What it does: The device receives the encrypted data, decrypts it, and displays it to the user, using push notifications or the app's UI to grab the user's attention.

[1493] 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.

[1494] The present invention relates to a system that collects data on a user's income, expenses, savings goals, etc., and provides optimal household management advice based on that data. In addition, by combining it with an emotion engine that recognizes the user's emotions, the system provides household management advice that corresponds to the user's emotional state.

[1495] System Configuration

[1496] User Interface (UI)

[1497] The device provides an interface through a dedicated application or a web browser, allowing users to input data on income, expenses, and savings goals. This UI is designed to be intuitive, with graphical displays and input forms. It also has an emotion recognition function that captures the user's voice and facial expressions to collect emotional data.

[1498] data communication

[1499] The device converts the data entered by the user and emotion data into a specific format (e.g., JSON) and sends it to the server via a transmission function. The transmitted data is set to arrive at the server safely and quickly.

[1500] Data storage

[1501] The server stores the received data in a temporary buffer area and then transfers it to a database optimized for securely storing users' past transactions and household data.

[1502] Data analysis

[1503] The server's AI analysis module generates optimal advice based on the stored data, taking into account the user's current situation, past data, and market information. In addition, the server analyzes emotional data received from the emotion engine and adjusts the advice based on the user's emotional state.

[1504] Generating and Sending Advice

[1505] The server generates specific household management advice based on the analysis results and formats it into response data to be notified to the user. The generated advice data is then sent back to the terminal via the sending function.

[1506] User Notification

[1507] The device displays the received advice data on the UI and notifies the user in an intuitive manner, allowing the user to manage their household finances based on the suggested advice.

[1508] Specific examples

[1509] Suppose a user enters their monthly income of "500,000 yen," their monthly expenses of "350,000 yen," and their "saving goal of 200,000 yen" into an application form. At the same time, the application captures the user's voice and facial expressions to collect emotional data. This data is sent from the device to the server when the user presses the send button.

[1510] The server first stores the received data in a database. Next, the AI ​​analysis module uses this data to analyze the user's current income and expenditure balance and identify areas for improvement. The emotion engine also analyzes the captured emotional data and determines, for example, whether the user is feeling stressed. Based on this information, it generates advice such as, "Since this is a stressful time, you should set a reasonable savings goal."

[1511] This advice is then sent back to the device, and the application screen displays the following message: "To save 83,333 yen each month, we suggest you cut your dining out expenses by 20% and review your mobile phone plan. However, you seem to be stressed, so please be careful not to push yourself too hard." Based on this, users can review their household finances and take specific action.

[1512] In this way, the system of the present invention collects data based on user input, analyzes it on the server, and notifies the terminal of optimal advice, thereby supporting the user in effectively managing their household finances. Furthermore, by combining it with an emotion engine, it is possible to provide flexible advice according to the user's emotional state.

[1513] The processing flow will be explained below.

[1514] Step 1:

[1515] A user opens a financial management application and enters data such as income, expenses, and savings goals into an input form, while their voice and facial expressions are simultaneously captured.

[1516] Step 2:

[1517] The device generates emotion data based on the user's input data on income, expenses, and savings goals, as well as captured voice and facial expressions.

[1518] Step 3:

[1519] The user confirms that the input data and emotion data have been sent, and then presses the "Send" button.

[1520] Step 4:

[1521] The device converts the income, expenditure, savings goal, and emotional state data into a specific format (e.g., JSON format). This format includes income, expenditure, savings goal, emotional state, etc.

[1522] Step 5:

[1523] The terminal transmits the data via a network communication module to transmit the formatted data to a server.

[1524] Step 6:

[1525] The server receives the data sent from the terminal.

[1526] Step 7:

[1527] The server stores the received data in a temporary buffer area.

[1528] Step 8:

[1529] The server executes a database save process to save the data in the buffer area to the database.

[1530] Step 9:

[1531] The server's AI analysis module analyzes the user's balance based on income, expenditure, and savings goal data stored in the database.

[1532] Step 10:

[1533] The server's emotion engine analyzes the emotion data stored in the database to identify the user's emotional state. For example, it can determine whether the user is feeling stressed based on voice data and facial expressions.

[1534] Step 11:

[1535] The server's AI analysis module integrates the results of the income and expenditure balance analysis and the emotion engine to generate optimal household management advice.

[1536] Step 12:

[1537] The server formats the generated financial advice as response data, which includes specific savings suggestions and cautions based on the user's emotional state.

[1538] Step 13:

[1539] The server executes a transmission process to transmit the formatted advice data to the terminal.

[1540] Step 14:

[1541] The terminal receives the advice data transmitted from the server.

[1542] Step 15:

[1543] The terminal analyzes the received advice data and converts it into a format for display on the user interface.

[1544] Step 16:

[1545] The device displays advice on the application screen such as, "To save 83,333 yen each month, we suggest you reduce your eating out expenses by 20% and review your mobile phone plan. However, since you seem to be stressed, please be careful not to push yourself too hard."

[1546] Step 17:

[1547] The user checks the displayed advice and adjusts household management and saves money based on it.

[1548] In this way, the system of the present invention generates optimal advice on the server based on the user's input data and emotional data, and notifies the user via the terminal. By combining emotion engines, flexible advice is provided that takes into account the user's emotional state.

[1549] Example 2

[1550] 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."

[1551] Conventional household management systems only handle basic data such as a user's income, expenses, and savings goals, and are unable to provide advice that takes into account the user's emotions and psychological state. As a result, they ignore the impact of stress and emotional fluctuations on household management, resulting in insufficient effectiveness. Furthermore, they are insufficient in accurately analyzing data and personalizing advice, making it difficult to provide appropriate guidance tailored to each user's individual situation.

[1552] 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.

[1553] In this invention, the server includes input means for receiving data on income, expenses, and savings goals input by a user, conversion means for converting the data from the input means into a specific format, transmission means for transmitting the data converted by the conversion means to the server, temporary storage means for storing the data received from the transmission means in a temporary buffer area in the server, storage means for transferring the data stored in the temporary storage means to a database, analysis means for analyzing the data stored in the storage means and the user's emotional data in the server and generating optimal household management advice, adjustment means for adjusting the household management advice generated by the analysis means in accordance with the user's emotional state, and notification means for notifying the user of the household management advice adjusted by the adjustment means. This enables the user to receive more appropriate and personalized household management advice that reflects their emotional state.

[1554] "Input means" refers to a device or means for a user to input data on income, expenses, and savings goals.

[1555] "Conversion means" refers to a device or method for converting data obtained from an input means into a specific format.

[1556] The "transmission means" refers to a device or method for transmitting the data converted by the conversion means to the server.

[1557] "Temporary storage means" refers to a device or means for temporarily storing data received from a transmitting means.

[1558] "Storage means" refers to a device or means for transferring data stored in the temporary storage means to a database and storing the data therein.

[1559] The term "analysis means" refers to a device or device for analyzing the data stored in the storage means and generating household management advice.

[1560] The "adjustment means" refers to a device or means for adjusting the household management advice generated by the analysis means in accordance with the emotional state of the user.

[1561] The "notification means" refers to a device or means for notifying the user of the household management advice adjusted by the adjustment means.

[1562] A "database" refers to a structure that organizes information to efficiently store and manage user data and retrieve it when needed.

[1563] "Emotional data" refers to information about the user's psychological state and emotions analyzed from their voice, facial expressions, etc.

[1564] "Financial advice" refers to specific guidance and suggestions for improving financial management based on a user's income, expenses, savings goals, and emotional data.

[1565] The present invention relates to a system that collects data such as a user's income, expenses, and savings goals, and provides optimal household management advice based on that data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system provides household management advice according to the user's emotional state. Specific embodiments for carrying out the invention are described below.

[1566] System Configuration

[1567] User Interface (UI)

[1568] The device provides an interface for entering data on income, expenses, and savings goals through a dedicated application or a web browser. This UI features a graphical display and an input form that users can operate intuitively. In addition, the device has an emotion recognition function that captures voice and facial expressions via a camera and microphone to collect emotional data.

[1569] data communication

[1570] The terminal converts the data entered by the user about income, expenses, and savings goals into a specific format (e.g., JSON), which is then securely and quickly transmitted to the server via HTTP or HTTPS protocols.

[1571] Data storage

[1572] The server first stores the received data in a temporary buffer area. This data is then transferred to a database and saved as the user's household data. The database is managed using a general-purpose relational database management system (RDBMS), such as MySQL or PostgreSQL.

[1573] Data analysis

[1574] The server's AI analysis module analyzes the user's balance based on data stored in the database, such as income, expenses, and savings goals. The emotion engine also analyzes the emotional data and adjusts advice based on the user's emotional state. The analysis utilizes generative AI models such as TensorFlow and PyTorch.

[1575] Generating and Sending Advice

[1576] The server generates specific household management advice based on the analysis results, which is then converted back to JSON format and sent to the device as a response.

[1577] User Notification

[1578] The device displays the received advice data on the UI and notifies the user in an intuitive manner. Notifications can be pop-up or push notifications, allowing the user to effectively manage their household finances based on the suggested advice.

[1579] Specific examples

[1580] For example, if a user enters the following data into a form in your application:

[1581] Monthly income: 500,000 yen

[1582] Monthly expenses: 350,000 yen

[1583] Savings goal: "200,000 yen"

[1584] At the same time, voice and facial expression data is collected through the device's camera and microphone, and analyzed as emotional data. This data is then sent from the device to the server by pressing the send button.

[1585] The server first temporarily stores the data, then transfers it to a database for storage. The AI ​​analysis module analyzes the balance of income and expenditure, and the emotion engine determines the user's stress level, etc. Based on this information, the system generates advice such as "Set a reasonable savings goal."

[1586] The generated advice is sent to the terminal in the following format, for example:

[1587] "To save ¥83,333 each month, I suggest you cut your dining out expenses by 20% and review your cell phone plan. However, since you seem stressed, I'd like to caution you to not push yourself too hard."

[1588] This advice is displayed on the device's UI, allowing users to review their household finances and take concrete action based on it.

[1589] Prompt Sentence Examples

[1590] "Generate optimal household management advice based on data such as monthly income of 500,000 yen, expenses of 350,000 yen, and savings goal of 200,000 yen, as well as emotional information about the user's stress levels."

[1591] In this way, the system of the present invention can support more effective household management by providing personalized household management advice based on the user's input data and emotional state.

[1592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1593] Step 1:

[1594] The user enters data on income, expenses, and savings goals into a form on the application or web browser via the device. Specifically, the user enters income "500,000 yen," expenses "350,000 yen," and savings goal "200,000 yen" into each field and presses the submit button. At this time, the device's camera and microphone are used to capture the user's voice and facial expressions, and emotional data is also collected. The entered data and emotional data are obtained as input.

[1595] Step 2:

[1596] The terminal converts the data entered by the user into JSON format using a conversion algorithm within the terminal to format the data as JSON, like this:

[1597] json

[1598] {

[1599] "income": 500000,

[1600] "expenses": 350000,

[1601] "savings_goal": 200000,

[1602] "emotion_data": {"stress_level": "high"}

[1603] }

[1604] This JSON data is obtained as the output after conversion.

[1605] Step 3:

[1606] The terminal sends the converted data to the server using the HTTP or HTTPS protocol. Specifically, the terminal generates an HTTP request and attaches JSON data to the request. The sent data is received as input by the server.

[1607] Step 4:

[1608] The server first stores the received data in a temporary buffer area. Specifically, the server writes the data to a temporary storage area within the server. The temporarily stored data becomes the input for the next step.

[1609] Step 5:

[1610] The server transfers the temporarily saved data to the database and stores it. Specifically, it adds the data to the database using the INSERT command. This operation saves the user's household data to the database. The saved data becomes the input data for the next analysis.

[1611] Step 6:

[1612] The server's AI analysis module analyzes the data stored in the database. Specifically, it uses a generative AI model (e.g., TensorFlow or PyTorch) to analyze the balance, and the emotion engine analyzes the emotional data. This analysis outputs the user's possible balance, and the analysis results serve as the basis for generating the next advice.

[1613] Step 7:

[1614] The server generates household management advice based on the analysis results. Specifically, it uses an advice generation algorithm to generate specific advice such as "To save 83,333 yen per month, reduce eating out expenses by 20% and review your mobile phone plan." This generated advice is output as the next data to be sent.

[1615] Step 8:

[1616] The server formats the generated advice in JSON format and sends it to the terminal as an HTTP response. Specifically, the response is sent in the following JSON format:

[1617] json

[1618] {

[1619] "advice": "To save ¥83,333 each month, I suggest you cut your dining out expenses by 20% and review your cell phone plan. However, since you seem stressed, I'd like to caution you to not push yourself too hard."

[1620] }

[1621] This JSON data is received by the terminal and used for the next display process.

[1622] Step 9:

[1623] The device displays the received advice data to the user. Specifically, the advice is displayed within the application UI. Pop-ups and push notifications are used to make the advice easily available to the user. The user can review their household management based on this advice and take specific actions.

[1624] (Application example 2)

[1625] 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."

[1626] Today, users face many challenges when managing their household finances. In particular, it is difficult to receive appropriate advice on how to achieve savings goals while balancing income and expenses. Users may also feel stressed because the advice provided is not flexible enough to reflect their emotional state. Furthermore, the lack of advice that takes savings goals into account when shopping on online shopping sites makes it difficult to plan consumption behavior. There is a need for a system that can solve these issues and enable users to manage their household finances more effectively.

[1627] 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.

[1628] In this invention, the server includes an input means for receiving data on income, expenses, and savings goals input by a user, a transmission means for transmitting the data and emotional data from the input means to the server, a storage means for storing the data received from the transmission means in a database in the server, an analysis means for analyzing the data and emotional data stored in the storage means in the server and generating optimal household management advice, a notification means for notifying the user of the household management advice generated by the analysis means, and a display means for presenting the savings goal on the user's terminal when purchasing a product. This makes it possible to comprehensively analyze the user's income, expenses, savings goals, and emotional data and provide flexible household management advice tailored to the user's emotional state. Furthermore, by providing advice that takes savings goals into consideration when shopping on an online shopping site, the user can plan their consumption behavior and achieve effective household management.

[1629] "Revenue" refers to the monetary benefit a user receives within a certain period of time.

[1630] "Expenses" refers to the total amount of money a user spends within a certain period of time.

[1631] A "savings goal" refers to the amount of spending reduction a user wants to achieve within a certain period of time.

[1632] "Input means" refers to a device or interface through which a user inputs income, expenses, savings goals, and emotional data into the system.

[1633] "Transmission means" refers to a device or software that has the function of transmitting data acquired from the input means to the server.

[1634] "Storage means" refers to a device or database for storing data received by the server for a certain period of time.

[1635] "Analysis means" refers to functions and software for generating optimal household management advice based on the data and emotional data stored in the storage means.

[1636] "Notification means" refers to a device or interface for notifying the user of the generated household management advice.

[1637] "Display means" refers to functions and software for visually presenting advice based on savings goals and emotions to the user on the user's device.

[1638] The system based on this invention collects data on a user's income, expenses, savings goals, and emotions, and provides a function to generate and display optimal household management advice based on this data. This system is composed of the following main components.

[1639] User Interface (UI)

[1640] The device provides an input form for users to enter data on income, expenses, and savings goals via a dedicated application or a web browser interface. The device also uses a camera and microphone to capture the user's voice and facial expressions to collect emotional data, allowing users to operate the device intuitively.

[1641] data communication

[1642] The device converts the data and emotion data entered by the user into a specific format (e.g., JSON) and securely transmits it to the server via the HTTPS protocol. This data includes income, expenses, savings goals, and emotion data.

[1643] Data storage

[1644] The server temporarily stores the received data in a buffer area and then transfers it safely and efficiently to a database (e.g., MongoDB) that stores the user's past transactions and household data and is optimized to provide fast access to the information needed.

[1645] Data analysis

[1646] The server is equipped with an AI analysis module (e.g., Python + TensorFlow) and an emotion engine (e.g., Python + OpenCV). This allows it to generate optimal advice based on the saved data, referring to the user's current situation, past data, and even market information. It also analyzes the user's emotional state based on emotion data and generates flexible advice that reflects their emotions.

[1647] Generating and Sending Advice

[1648] The server generates specific household management advice based on the analysis results and formats it into response data to be notified to the user. The generated advice data is then sent back to the terminal via the communication function.

[1649] User Notification

[1650] The device displays the received advice data on the UI and notifies the user in a format that is easy to understand and implement. Specifically, it presents specific action plans for achieving savings goals and advice based on the user's emotional state.

[1651] Specific examples

[1652] For example, if a user enters the following data into a form in your application:

[1653] Income: 500,000 yen

[1654] Expenses: 350,000 yen

[1655] Savings goal: ¥200,000

[1656] At the same time, the application captures the user's voice and facial expressions to collect emotional data. This data is sent from the device to the server when the user presses the send button. The server analyzes the received data and, if it determines that the user is feeling stressed, generates advice suggesting, "This is a stressful time, so set a savings goal within your limits." This advice is sent back to the device and displays, "To save 83,333 yen each month, we suggest you reduce your dining out expenses by 20% and review your mobile phone plan. However, since you seem to be stressed, please be careful not to overdo it."

[1657] Prompt Sentence Examples

[1658] An example of a prompt for a generative AI model is:

[1659] Generate appropriate advice based on user sentiment data and contract details.

[1660] Example data:

[1661] Income: 500,000 yen

[1662] Expenses: 350,000 yen

[1663] Savings goal: ¥200,000

[1664] Emotional state: Stress

[1665] Desired advice:

[1666] Emotion-based savings plan suggestions

[1667] Relaxation products for stress relief

[1668] This system allows users to manage their household finances effectively and without stress, and to plan their consumption behavior.

[1669] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1670] Step 1:

[1671] The user enters data on income, expenses, and savings goals into the application form and presses the submit button. The user also uses the camera and microphone to capture facial expressions and voice to collect emotional data. The system converts this data into JSON format.

[1672] Input: Income, expenses, savings goal data, facial expressions, voice

[1673] Output: JSON formatted income, expenses, savings goal data, and sentiment data

[1674] Step 2:

[1675] The terminal sends the JSON format data generated in step 1 to the server using the HTTPS protocol.

[1676] Input: JSON format income, expenses, savings goal data, and sentiment data

[1677] Output: Confirmation of transmission to the server

[1678] Step 3:

[1679] The server temporarily stores the received data in a buffer area and then forwards it to a database (e.g., MongoDB), where the data is properly indexed and efficiently stored for future analysis.

[1680] Input: JSON format income, expenses, savings goal data, and sentiment data

[1681] Output: Data stored in the database

[1682] Step 4:

[1683] The server's AI analysis module (e.g., Python + TensorFlow) analyzes the data and emotional data stored in the database, assessing the current state of household finances based on income, expenses, and savings goals, and understanding the user's emotional state based on the emotional data.

[1684] Input: Income, expenditure, savings goal data, and emotional data stored in the database

[1685] Output: Household management advice and emotional evaluation results

[1686] Step 5:

[1687] The server uses a generative AI model to generate financial management advice tailored to the user based on the analysis results of the AI ​​analysis module. This process also includes generating flexible advice based on the user's emotional state.

[1688] Input: Household management advice and emotional evaluation results

[1689] Output: Specific household management advice

[1690] Step 6:

[1691] The server formats the generated household management advice into JSON format and prepares it as response data to be sent to the terminal.

[1692] Input: Specific household management advice

[1693] Output: Financial advice in JSON format

[1694] Step 7:

[1695] The terminal displays the household management advice received from the server on the user interface, allowing the user to take specific household management and consumption actions based on this advice.

[1696] Input: Financial advice in JSON format

[1697] Output: Financial advice displayed in the UI

[1698] 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.

[1699] 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.

[1700] 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.

[1701] 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.

[1702] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1703] 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.

[1704] 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).

[1705] 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.

[1706] 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."

[1707] 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.

[1708] 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).

[1709] 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.

[1710] 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.

[1711] 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.

[1712] 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.

[1713] 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.

[1714] 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.

[1715] 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.

[1716] 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.

[1717] 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.

[1718] 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.

[1719] The following is further disclosed regarding the above embodiment.

[1720] (Claim 1)

[1721] input means for receiving income, expenditure and savings goal data input by a user;

[1722] a transmitting means for transmitting data from the input means to a server;

[1723] a storage means in the server for storing the data received from the transmission means in a database;

[1724] an analysis means in the server for analyzing the data stored in the storage means and generating optimal household management advice;

[1725] notification means for notifying a user of the household management advice generated by the analysis means;

[1726] A system including:

[1727] (Claim 2)

[1728] 2. The system according to claim 1, wherein said analysis means generates household management advice by utilizing the user's past data and market information.

[1729] (Claim 3)

[1730] 2. The system according to claim 1, wherein said notification means displays said household management advice on a terminal of the user.

[1731] "Example 1"

[1732] (Claim 1)

[1733] input means for receiving income, expenditure and savings goal data input by a user;

[1734] a transmitting means for transmitting data from the input means to a server;

[1735] a storage means in the server for storing the data received from the transmission means in a database;

[1736] an analysis means using a machine learning algorithm to analyze the data stored in the storage means in the server and generate optimal household management advice;

[1737] notification means for notifying a user of the household management advice generated by the analysis means;

[1738] a display means for displaying the household management advice on a terminal of the user by the notification means;

[1739] A system including:

[1740] (Claim 2)

[1741] 2. The system according to claim 1, wherein said analysis means generates household management advice by utilizing the user's past data and market information.

[1742] (Claim 3)

[1743] 10. The system of claim 1, including an encryption protocol that converts user input data into a specific format and transmits it securely to the server.

[1744] "Application Example 1"

[1745] (Claim 1)

[1746] input means for receiving income, expenditure and savings goal data input by a user;

[1747] a transmitting means for transmitting data from the input means to a server;

[1748] a storage means in the server for storing the data received from the transmission means in a database;

[1749] an analysis means in the server for analyzing the data stored in the storage means and generating optimal household management advice;

[1750] a notification means for displaying the household management advice generated by the analysis means on a user's terminal and notifying the user of recommended ways to reduce expenses and points to review in expenses;

[1751] A system including:

[1752] (Claim 2)

[1753] The system according to claim 1, characterized in that the analysis means generates household management advice based on the user's past data and market information using a generative AI model.

[1754] (Claim 3)

[1755] 2. The system according to claim 1, wherein the notification means provides the generated advice to the user's terminal as a prompt sentence and notifies the user of a specific action.

[1756] "Example 2: Combining Emotion Engines"

[1757] (Claim 1)

[1758] input means for receiving income, expenditure and savings goal data input by a user;

[1759] a conversion means for converting data from the input means into a specific format;

[1760] a transmitting means for transmitting the data converted by the converting means to a server;

[1761] a temporary storage means for storing the data received from the transmission means in a temporary buffer area in the server;

[1762] a storage means for transferring the data stored in the temporary storage means to a database;

[1763] an analysis means in the server for analyzing the data stored in the storage means and the user's emotion data to generate optimal household management advice;

[1764] an adjusting means for adjusting the household management advice generated by the analyzing means in accordance with the emotional state of the user;

[1765] a notification means for notifying a user of the household management advice adjusted by the adjustment means;

[1766] A system including:

[1767] (Claim 2)

[1768] 2. The system according to claim 1, wherein said analysis means generates household management advice by utilizing the user's past data and market information.

[1769] (Claim 3)

[1770] 2. The system according to claim 1, wherein said notification means displays said household management advice on a terminal of the user.

[1771] "Application example 2 when combining emotion engines"

[1772] New Claims

[1773] (Claim 1)

[1774] input means for receiving income, expenditure and savings goal data input by a user;

[1775] a transmitting means for transmitting the data and emotion data from the input means to a server;

[1776] a storage means in the server for storing the data received from the transmission means in a database;

[1777] an analysis means for analyzing the data and emotion data stored in the storage means in the server and generating optimal household management advice;

[1778] notification means for notifying a user of the household management advice generated by the analysis means;

[1779] a display means for displaying a saving goal when purchasing a product on a user's terminal;

[1780] A system including:

[1781] (Claim 2)

[1782] 2. The system of claim 1, wherein the analysis means generates financial management advice using the user's past data, market information, and sentiment data.

[1783] (Claim 3)

[1784] 2. The system according to claim 1, wherein said notification means displays said household management advice and shopping advice according to said emotional state on a terminal of the user. [Explanation of symbols]

[1785] 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. input means for receiving income, expenditure and savings goal data input by a user; a transmitting means for transmitting data from the input means to a server; a storage means in the server for storing the data received from the transmission means in a database; an analysis means in the server for analyzing the data stored in the storage means and generating optimal household management advice; notification means for notifying a user of the household management advice generated by the analysis means; A system including:

2. 2. The system according to claim 1, wherein said analysis means generates household management advice by utilizing the user's past data and market information.

3. 2. The system according to claim 1, wherein said notification means displays said household management advice on a terminal of the user.

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