System

Abstract

A system is provided.SOLUTION: A system comprising: inputting means for a user to input a question; transmitting means for transmitting the user's input to a server; analyzing means for analyzing the user's input received by the server and inputting the user's input to a generative AI model; generating means for generating an answer by the generative AI model based on the user's input; storing means for storing the generated answer in a database; returning means for returning the answer stored in the storing means to a user device; and displaying means for displaying the answer returned to the user device.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] Modern household finance management is difficult for many people to maintain, primarily due to the cumbersome task of recording daily income and expenses. Furthermore, users need specialized knowledge and information to make appropriate financial decisions, and the time and effort required to acquire this information prevents many from effectively utilizing it. This leads to inappropriate financial management, wasteful spending, and problems with future budget planning. Therefore, there is a need for a system that simplifies household finance management and provides effective, personalized financial advice quickly and efficiently. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. The system includes an input means for a user to input a question, a transmission means for transmitting the user's input to a server, an analysis means for the server to analyze the received user's input and input it into a generative AI model, a generation means for the generative AI model to generate an answer based on the user's input, a storage means for saving the generated answer in a database, a return means for returning the answer saved in the storage means to a user terminal, and a display means for displaying the answer returned to the user terminal. The server further includes a preprocessing means for preprocessing the user's input, allowing the generative AI model to provide more accurate and useful advice. Furthermore, the generative AI model generates individually customized advice based on the user's financial data, enabling the user to quickly and effectively make decisions that are optimal for their financial situation. This system simplifies household management and allows users to easily make appropriate financial decisions.

[0006] A "user" is a person who uses the system to input questions and receive advice on household management and finances.

[0007] "Input method" refers to the interface through which users enter questions or information, such as a smartphone app or web form.

[0008] The "transmission means" is a mechanism equipped with a network communication function for transmitting data entered by the user to the server.

[0009] A "server" is a computer system that receives data sent by a user, analyzes it, and generates a response.

[0010] "Analysis means" refers to the processing function that understands the user input received by the server and converts it into a format suitable for the generative AI model.

[0011] "Generative means" refers to the ability of a generative AI model to generate answers based on user input.

[0012] A "generative AI model" is an artificial intelligence algorithm that automatically generates appropriate answers based on user questions.

[0013] "Storage means" refers to the function of storing generated answers and related data in a database.

[0014] "Reply means" refers to the communication function for sending the saved answers to the user's device.

[0015] "Display means" refers to the interface that displays the response returned from the server on the user terminal.

[0016] "Preprocessing means" refers to the processing function by which the server formats user input into a format suitable for the generative AI model.

[0017] "Personalized" refers to providing optimized advice based on each user's individual financial data and situation.

[0018] "Financial Data" refers to information about your income, expenses, savings, investments, etc.

[0019] "Advice" refers to suggestions or instructions for proper financial management or improvement in response to a user's question.

[0020] "Household management" refers to the process of balancing income and expenses and managing money efficiently. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly. The system receives questions from users and performs a series of processes to provide appropriate financial advice based on a generative AI model.

[0043] System Overview

[0044] The system consists of the following main components:

[0045] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[0046] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[0047] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[0048] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[0049] 5. Generator (server): The generative AI model generates answers to the user's questions.

[0050] 6. Data storage means (server): Stores the generated answers and questions in a database.

[0051] 7. Answer return means (server): Sends the generated answer to the user terminal.

[0052] 8. Display means (terminal): Displays the generated answer on the user's terminal.

[0053] Program processing

[0054] Enter and submit your question

[0055] A user enters a question using a dedicated application or web interface, for example, "How can I save money on groceries this month?"

[0056] The device receives the user's input and sends it to the server using an HTTP POST request, which includes the question and the user ID.

[0057] Receiving and parsing questions

[0058] The server receives requests sent from the user's device, analyzes the request data, and converts the question into a format that can be processed by the generative AI model.

[0059] Generate answers

[0060] The generator (server) generates appropriate answers to the analyzed questions using a generative AI model, which understands the context of the question and provides answers by utilizing past data and a knowledge base.

[0061] For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Try taking advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home."

[0062] Data storage and return

[0063] The server stores the generated answer in a database, including the user ID, question, generated answer, and timestamp.

[0064] The generated answer is sent to the user's terminal via a return means (server). The user receives the answer on the terminal and can check it on the interface.

[0065] Specific examples

[0066] Example 1: How to save money on food

[0067] 1. The user types in a question: "How can I save on food this month?"

[0068] 2. The terminal sends the input data to the server.

[0069] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0070] 4. The generation means (server) generates a response saying, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[0071] 5. The server stores the generated response in a database and sends it back to the user device.

[0072] 6. The user terminal displays the received response.

[0073] Example 2: Investment advice

[0074] 1. The user types in a question, such as, "I would like some advice on future investments."

[0075] 2. The device sends the query data to the server.

[0076] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0077] 4. The generation means (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make plans with a medium- to long-term perspective. We also recommend consulting an expert."

[0078] 5. The server stores the generated response in a database and sends it back to the user device.

[0079] 6. The user terminal displays the received response.

[0080] In this way, the present invention realizes a system that supports users in managing their household finances and provides specific advice in real time to help them make appropriate financial decisions.

[0081] The processing flow will be explained below.

[0082] Step 1:

[0083] A user accesses a financial management application or web interface and types a question, for example, "How can I save money on food this month?"

[0084] Step 2:

[0085] The device sends the question data entered by the user to the server using an HTTP POST request, sending data including the question content and the user ID.

[0086] Step 3:

[0087] The server receives the request sent from the device. The received data includes the question and the user ID.

[0088] Step 4:

[0089] The server analyzes the received data and preprocesses the question content into a format that the generative AI model can understand, such as by checking the input for grammar and performing semantic analysis.

[0090] Step 5:

[0091] The server inputs the preprocessed data into a generative AI model, which generates an appropriate answer based on the user's question.

[0092] Step 6:

[0093] The server receives the answer from the generative AI model and stores it in a database, which contains the user ID, the question, the generated answer, and a timestamp.

[0094] Step 7:

[0095] The server sends the stored answer back to the user's device using an HTTP response.

[0096] Step 8:

[0097] The terminal receives the response returned from the server, and the generated response is included in the received data.

[0098] Step 9:

[0099] The user checks the answers on the device interface, and is shown advice such as, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home."

[0100] In this way, specific processing is carried out at each step from when the user inputs a question until the answer is returned.

[0101] Example 1

[0102] 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."

[0103] Conventional household management and financial advice systems have made it difficult for users to easily input questions and receive prompt and accurate answers. Storage and security of generated answers also present challenges. In response to these challenges, the present invention aims to provide a system that allows users to easily input household management questions, receive prompt and appropriate answers, and safely store and display those answers.

[0104] 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.

[0105] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the user's input to the server, an analysis means for the server to analyze the user's input received and input the input to a generative AI model, a generation means for the generative AI model to generate an answer based on the user's input, a storage means for saving the generated answer in a database, a return means for returning the answer saved in the storage means to a user terminal, and a display means for displaying the answer returned to the user terminal. This allows a user to input a question in natural language, and the server to analyze it, generate an appropriate answer, and safely save and return it.

[0106] "User" means any person or entity that utilizes the System to enter questions and receive financial advice.

[0107] "Input means" refers to the device or interface through which a user enters questions or data, such as a smartphone app or web form.

[0108] "Transmission means" refers to a function for transmitting data entered by a user to a server, and refers to a means for using a network communication protocol.

[0109] "Server" refers to the computing system that receives input from a user, analyzes it, stores it in a database, runs a generative AI model, and returns an answer.

[0110] "Analysis means" refers to the function by which the server analyzes user input and converts it into a format that can be processed by the generative AI model.

[0111] "Generation means" refers to the function of using a generative AI model to generate an appropriate answer based on user input.

[0112] "Storage means" refers to the function of saving the generated answers and questions in a database.

[0113] "Reply means" refers to the function of sending saved answers to the user's terminal.

[0114] "Display means" refers to the function of displaying the returned answers on the user's terminal.

[0115] A "generative AI model" refers to a model that uses artificial intelligence technology to generate appropriate answers to natural language questions.

[0116] A "prompt sentence" is text data input into a generative AI model, and refers to a sentence that explains or supplements the context of the question.

[0117] This invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly. The system receives questions from users and performs a series of processes to provide appropriate financial advice based on a generative AI model.

[0118] System Overview

[0119] The system consists of the following main components:

[0120] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[0121] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[0122] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[0123] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[0124] 5. Generator (server): The generative AI model generates answers to the user's questions.

[0125] 6. Data storage means (server): Stores the generated answers and questions in a database.

[0126] 7. Answer return means (server): Sends the generated answer to the user terminal.

[0127] 8. Display means (terminal): Displays the generated answer on the user's terminal.

[0128] Program processing

[0129] The user enters a question using a dedicated application or web interface. For example, the user might enter, "How can I save money on food this month?" The device receives the user's input and sends it to the server. Specifically, it sends the data using an HTTP POST request. The data sent includes the question and the user ID.

[0130] The server receives requests sent from the user's device, analyzes the request data, and converts the question into a format that the generative AI model can process. The analysis method uses natural language processing technology to extract keywords and context from the user's question.

[0131] The generation means (server) uses the generative AI model to generate appropriate answers to the analyzed questions. The generative AI model understands the context of the question and provides an answer by utilizing past data and knowledge bases. For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Try taking advantage of sale days and buying ingredients in bulk. It is also effective to reduce eating out and cook at home more."

[0132] The server stores the generated answer in a database. The stored information includes the user ID, question content, generated answer, and timestamp. The generated answer is sent to the user terminal via a return means (server). The user terminal displays the received answer on a user interface.

[0133] Specific examples

[0134] Example 1: How to save money on food

[0135] 1. The user types in a question: "How can I save on food this month?"

[0136] 2. The terminal sends the input data to the server.

[0137] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0138] 4. The generation means (server) generates a response saying, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[0139] 5. The server stores the generated response in a database and sends it back to the user device.

[0140] 6. The user terminal displays the received response.

[0141] Example 2: Investment advice

[0142] 1. The user types in a question, such as, "I would like some advice on future investments."

[0143] 2. The device sends the query data to the server.

[0144] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0145] 4. The generation means (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make plans with a medium- to long-term perspective. We also recommend consulting an expert."

[0146] 5. The server stores the generated response in a database and sends it back to the user device.

[0147] 6. The user terminal displays the received response.

[0148] In this way, the present invention realizes a system that supports users in managing their household finances and provides specific advice in real time to help them make quick and appropriate financial decisions.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1: User enters question

[0151] A user inputs a question using a smartphone app or a web form. For example, they input, "Please tell me how to save money on food this month." This input generates data called the question content (e.g., "Please tell me how to save money on food this month").

[0152] Step 2: The device sends the data

[0153] The device sends the question data entered by the user to the server. Specifically, the device uses an HTTP POST request to send data including the user ID and question content to the server. The specific format of the data sent at this time is JSON format, etc.

[0154] Step 3: The server receives the data

[0155] The server receives the request data sent from the device. The received data includes the user ID and question. The server first checks whether the request format and content are correct, thereby ensuring data integrity.

[0156] Step 4: The server parses the data

[0157] The server analyzes the received data. Specifically, it uses natural language processing (NLP) technology to extract keywords and context from the user's question. For example, it extracts keywords such as "food expenses" and "savings" and converts them into a format that is easy for the generative AI model to understand. The input is the user's question, and the output is a prompt to be input into the generative AI model.

[0158] Step 5: The generator (server) generates the answer

[0159] The generation means (server) runs a generative AI model based on the analyzed data and generates an appropriate answer. A prompt sentence is input into the generative AI model (e.g., GPT-3) to obtain a specific answer such as, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more." The input is the prompt sentence, and the output is the answer from the generative AI model.

[0160] Step 6: The server saves the data

[0161] The server saves the user's question along with the generated answer in a database. The saved information includes the user ID, question, generated answer, and timestamp. This saving step allows for future reference of the question and answer history. The input is the generated answer and the user's question, and the output is saving to the database.

[0162] Step 7: The server sends back a response

[0163] The server returns the answer stored in the database to the user device. The answer is sent in HTTP response format using a return method. Again, encrypted communication is used to ensure data security. The input is the data stored in the database, and the output is the data returned to the user device.

[0164] Step 8: User device displays answer

[0165] The user device receives the response data returned from the server. The received data is displayed on the user interface, where the user can check it. For example, a response such as "Try taking advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more." The input is the data returned from the server, and the output is what is displayed on the user interface.

[0166] (Application example 1)

[0167] 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."

[0168] Current electronic payment services lack the ability to help users efficiently manage their household finances and make appropriate financial decisions quickly. In particular, there is a need for a system that allows users to receive appropriate advice in real time on how to save money and make investment plans. To solve this problem, technology is needed that can quickly analyze a user's financial situation and past spending data and provide appropriate advice.

[0169] 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.

[0170] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the user's input to the server, an analysis means for analyzing the user's input received by the server and inputting the input to the generative AI model, a generation means for the generative AI model to generate an answer based on the user's input, a storage means for saving the generated answer in a database, a return means for returning the answer saved in the storage means to the user terminal, a display means for displaying the answer returned to the user terminal, and a support means for analyzing the content of the user's question and past expenditure data and providing optimal savings methods and investment plans using the generative AI model. This enables users to quickly receive individually customized financial advice through electronic payment services.

[0171] "Input means" is the interface through which the user enters questions and financial data.

[0172] "Transmission means" refers to a communication means for transmitting the user's input data to the server.

[0173] "Analysis means" refers to a device or program that has the function of analyzing user input received by the server and converting it into a format suitable for the generative AI model.

[0174] A "generator" is a device or program that has the function of using a generative AI model to generate an answer based on user input.

[0175] The "storage means" is a device or program for storing the generated answers in a database.

[0176] The "returning means" is a communication means for returning the answer stored in the storage means to the user terminal.

[0177] The "display means" is an interface for displaying the answers returned to the user terminal.

[0178] An "assistance tool" is a device or program that has the function of analyzing questions from users and past spending data, and providing optimal savings methods and investment plans using a generative AI model.

[0179] A "generative AI model" is a program that uses artificial intelligence technology to generate answers and advice to questions based on user input data.

[0180] "Server" is a computer system that analyzes data submitted by users, generates answers using generative AI models, and stores and returns them.

[0181] The present invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly, and in particular provides appropriate financial advice to users using generative AI models. The system includes the following major hardware and software components:

[0182] System configuration

[0183] 1. User Device

[0184] A user terminal is a device that allows a user to input a question and display the answer from the server. Specifically, it is a smartphone application. It includes an input means for the user to input a question and a display means for displaying the generated answer.

[0185] 2. Server

[0186] The server receives the data sent by the user, analyzes it, inputs it into the generative AI model, and generates an answer. The server includes the following means:

[0187] Analysis means: Analyzes the data received from the user and converts it into a format suitable for the generative AI model.

[0188] Generation method: Using a generative AI model, we generate appropriate answers based on the user's question.

[0189] Storage: The generated answers are stored in a database.

[0190] Return method: The saved answers are returned to the user's device.

[0191] How it helps: Analyzes users' questions and past spending data to generate optimal savings and investment plans.

[0192] Hardware and software used

[0193] Hardware:

[0194] User device: Smartphone (iOS / Android)

[0195] Server: Web server (Apache, NGINX, AWS)

[0196] software:

[0197] User Interface: Smartphone app (React Native, Flutter, Swift, Kotlin)

[0198] API Request: HTTP Request Library (requests for Python)

[0199] Database: SQL database (PostgreSQL, MySQL)

[0200] Generative AI models: Artificial intelligence models using PyTorch and TensorFlow

[0201] Process Overview

[0202] Enter and submit your question

[0203] The user inputs a question through an application installed on the user device. For example, the user inputs a question such as, "Please tell me how to save on food expenses this month." The user device then sends this input data to the server.

[0204] Receiving and parsing questions

[0205] The server receives data from the user's device and converts it into a format that can be processed by the generative AI model using analytical means, taking into account past spending data.

[0206] Generate answers

[0207] The generative AI model is used by the generation method to generate appropriate answers to user questions. For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[0208] Data storage and return

[0209] The server stores the generated answer in a database using the storage means, and then returns it to the user terminal using the return means, allowing the user to check the answer on their own terminal.

[0210] Specific examples

[0211] Example 1: How to save money on food

[0212] 1. The user types in a question: "How can I save on food this month?"

[0213] 2. The user device sends this question to the server.

[0214] 3. The server receives the question and converts it into a format suitable for the generative AI model using analytical means.

[0215] 4. The generator generates the answer using the generative AI model.

[0216] 5. The generated answers are stored in a database and sent back to the user's device.

[0217] 6. The user receives a response on their device saying, "Take advantage of sale days and consider buying ingredients in bulk. It would also be effective to eat out less and cook at home more."

[0218] Example 2: Investment advice

[0219] 1. The user types in a question such as, "I would like some advice on future investments."

[0220] 2. The user device sends this question to the server.

[0221] 3. The server receives the question and converts it into a format suitable for the generative AI model using analytical means.

[0222] 4. The generator generates the answer using the generative AI model.

[0223] 5. The generated answers are stored in a database and sent back to the user's device.

[0224] 6. The user receives the following response on their device: "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert."

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

[0226] Step 1:

[0227] A user uses an application installed on the user device to input a question. For example, a user inputs a question such as "How can I save money on food this month?" The input means captures this data and stores the input. Input: Question text. Output: User input data.

[0228] Step 2:

[0229] The user terminal sends the saved user input data to the server. The sending means formats this data as an HTTP POST request and sends it to the server. Specifically, data including the user ID, question content, and timestamp is sent. Input: User input data. Output: HTTP request to the server.

[0230] Step 3:

[0231] The server receives requests sent from the user device. The analysis means analyzes the request data and converts it into a format that the generative AI model can process. Specifically, it extracts the necessary information from the JSON data and converts it into text format. Input: HTTP request data. Output: Input data to the generative AI model.

[0232] Step 4:

[0233] The generation means uses a generative AI model to generate an appropriate answer based on the analyzed question. The generative AI model creates specific advice based on the question content and past spending data. For example, if a user asks, "How can I save on food costs this month?", the generated answer would be, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook at home more." Input: Data input to the generative AI model. Output: Generated answer.

[0234] Step 5:

[0235] The server stores the answer generated by the generation means in a database using the storage means. The stored data includes the user ID, question content, generated answer, and timestamp. Input: Generated answer. Output: Data stored in the database.

[0236] Step 6:

[0237] The server returns the saved answer to the user terminal using the return means. The return means sets the saved data as an HTTP response and sends it to the user terminal. Input: Data saved in the database. Output: HTTP response to the user terminal.

[0238] Step 7:

[0239] The user device receives the answer sent from the server and displays it to the user using display means. Specifically, the answer is displayed on the application interface and the user confirms it. Input: HTTP response from the server. Output: Display content on the user device.

[0240] 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.

[0241] The present invention is a system that helps users easily manage their household finances and make appropriate financial decisions quickly, and combines an emotion engine that provides customized advice based on the user's emotional state. The system uses a generative AI model to provide financial advice in response to user questions, and the emotion engine recognizes the user's emotional state and provides personalized advice based on that.

[0242] System Overview

[0243] The system consists of the following main components:

[0244] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[0245] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[0246] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[0247] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[0248] 5. Preprocessing means (server): Preprocesses user input and converts it into a format suitable for the generative AI model.

[0249] 6. Generator (server): The generative AI model generates an answer based on the user's question.

[0250] 7. Data storage means (server): Stores the generated answers and questions in a database.

[0251] 8. Answer return means (server): Returns the generated answer to the user terminal.

[0252] 9. Display means (terminal): Displays the generated answer on the user's terminal.

[0253] 10. Emotion Engine (Server): Recognizes user emotions and tailors responses based on that data.

[0254] Program processing

[0255] Enter and submit your question

[0256] Users use a dedicated application or web interface to enter a question, for example, "How can I save money on food this month?"

[0257] The device receives the user's input and sends it to the server using an HTTP POST request, which includes the question and the user ID.

[0258] Receiving and parsing questions

[0259] The server receives the request sent from the user's device. The received data includes the question and the user ID.

[0260] The server analyzes the incoming data and converts it into a format that the generative AI model can understand.

[0261] Question preprocessing and sentiment recognition

[0262] The preprocessing means (server) preprocesses the user's question data and inputs it into the emotion engine. The emotion engine recognizes emotions from the user's input and provides data suitable for the generative AI model based on the emotional state.

[0263] Generate and refine answers

[0264] The generator (server) uses a generative AI model to generate appropriate answers based on the preprocessed data and emotional data. The emotion engine adjusts the generated answers to best suit the user's emotional state.

[0265] For example, if a user asks, "How can I save on food costs this month?" and the emotion engine recognizes that the user is feeling stressed, the generative AI model will generate advice such as, "Try taking advantage of sale days and buying ingredients in bulk. It can also be effective to eat out less and cook more at home," and the emotion engine will include an additional reassuring message such as, "It's a good idea to start within your capabilities without overdoing it."

[0266] Data storage and return

[0267] The server stores the generated answers in a database, which includes the user ID, the question, the generated answer, and a timestamp.

[0268] The generated answer is sent to the user's terminal via the return means (server). The user receives the answer on the terminal and checks it on the interface.

[0269] Specific examples

[0270] Example 1: How to save money on food

[0271] 1. The user types in a question: "How can I save on food this month?"

[0272] 2. The terminal sends the input data to the server.

[0273] 3. The server receives the question data, analyzes and preprocesses it, and inputs it into the emotion engine.

[0274] 4. The emotion engine (server) recognizes the user's stress state and provides the data to the generative AI model.

[0275] 5. The generation means (server) generates a response saying, "Try taking advantage of sale days and buying ingredients in bulk. It's also effective to eat out less and cook at home more," and the emotion engine adds a message saying, "It's a good idea to start as much as you can without overdoing it."

[0276] 6. The server stores the generated response in a database and sends it back to the user device.

[0277] 7. The user terminal displays the received response.

[0278] Example 2: Investment advice

[0279] 1. The user types in a question, such as, "I would like some advice on future investments."

[0280] 2. The device sends the query data to the server.

[0281] 3. The server receives the question data, analyzes and preprocesses it, and inputs it into the emotion engine.

[0282] 4. The emotion engine (server) recognizes the user's anxiety and provides the data to the generative AI model.

[0283] 5. The generator (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert." The emotion engine then adds a message such as, "Start with a small amount at first and proceed with peace of mind."

[0284] 6. The server stores the generated response in a database and sends it back to the user device.

[0285] 7. The user terminal displays the received response.

[0286] In this way, the present invention not only supports users in managing their household finances and provides specific advice in real time to help them make appropriate financial decisions, but also provides individually customized advice based on the user's emotional state, thereby building a system that provides more comprehensive support.

[0287] The processing flow will be explained below.

[0288] Step 1:

[0289] A user accesses a financial management application or web interface and types a question, for example, "How can I save money on food this month?"

[0290] Step 2:

[0291] The device sends the question data entered by the user to the server using an HTTP POST request, along with the question content and user ID.

[0292] Step 3:

[0293] The server receives the request sent from the device. The received data includes the question and the user ID.

[0294] Step 4:

[0295] The server parses the received data and preprocesses it into a format that can be processed by the generative AI model, including grammar checking and semantic analysis.

[0296] Step 5:

[0297] The server inputs the preprocessed question data into an emotion engine, which recognizes emotions from the user's input and generates emotional state data.

[0298] Step 6:

[0299] The emotion engine (server) provides data based on the user's emotional state to the generative AI model. For example, if it determines that the user is feeling stressed, it includes that information.

[0300] Step 7:

[0301] The generation means (server) uses a generative AI model to generate answers based on emotion data and preprocessed question data. For example, it might generate an answer such as, "Try taking advantage of sale days and buying ingredients in bulk. It's also effective to cut down on eating out and cook at home more," and the emotion engine adds, "It's a good idea to start as much as you can without overdoing it."

[0302] Step 8:

[0303] The server stores the generated answers and additional messages generated by the emotion engine in a database. The stored data includes the user ID, question content, generated answers, and timestamps.

[0304] Step 9:

[0305] The server sends the stored answer back to the user's device using an HTTP response.

[0306] Step 10:

[0307] The terminal receives the response sent back from the server. The received data includes the generated response and an additional message.

[0308] Step 11:

[0309] The user sees the provided answers and additional messages on the device interface, such as, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home. It's a good idea to start as much as you can without overdoing it."

[0310] In this way, a series of processes are carried out, from generating an answer from a user's question using emotion recognition and generative AI models, to storing the answer in a database, and then sending it back to the user's device.

[0311] Example 2

[0312] 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."

[0313] In modern society, it is important for users to make appropriate financial decisions quickly, but in many cases, their emotional state is a barrier to this. Conventional financial advice systems only provide uniform advice without taking the user's emotional state into account, making it difficult to alleviate users' stress and anxiety. They also have limitations in providing advice tailored to individual situations in response to specific questions.

[0314] 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.

[0315] In this invention, the server includes an analysis means for analyzing user input and inputting it into the generative AI model, an emotion engine for recognizing and analyzing the user's emotional state, and a preprocessing means for preprocessing the user's input and converting it into a format suitable for the generative AI model, thereby enabling the provision of individually customized financial advice according to the user's emotional state.

[0316] "Input means" refers to the interface through which users input questions, such as a dedicated application or web interface.

[0317] "Transmission means" refers to a network communication function for transmitting user input data to a server.

[0318] "Analysis means" refers to the function of the server to process the user's input data received and convert it into a format suitable for input into the generative AI model.

[0319] An "emotion engine" refers to a function that recognizes and analyzes the emotional state of a user from their input.

[0320] "Preprocessing means" refers to functions that normalize and tokenize data to convert user input data into a format suitable for the generative AI model.

[0321] "Generative" refers to the ability to use a generative AI model to generate answers based on user input.

[0322] "Storage means" refers to a function for storing generated answers in a database.

[0323] "Reply means" refers to the function for sending saved answers to the user terminal.

[0324] "Display means" refers to the function that displays the answer returned from the server on the user terminal.

[0325] A "generative AI model" refers to an artificial intelligence model that generates appropriate answers based on user input.

[0326] This invention is a system that allows users to input financial questions and provides customized advice based on their emotional state. The system is composed of the following components: a user interface, a data transmission means, a server, an analysis means, a preprocessing means, a generation means, an emotion engine, a data storage means, an answer return means, and a display means.

[0327] First, the user enters a financial question using a dedicated application or web interface, for example, "How can I save money on food this month?" After the user enters the question, the device sends this data to the server using an HTTP POST request. The data sent includes the question and the user ID.

[0328] The server receives requests sent from user devices. The received data is temporarily stored in a database, and then an analytical tool converts it into a format that the generative AI model can understand. Specifically, preprocessing is performed, such as tokenizing the text and removing unnecessary words.

[0329] The pre-processed data is then passed through an emotion engine to recognize the user's emotional state. The emotion engine determines, for example, whether the user is feeling "stressed" or "anxious." This emotion data is an important element for the generation process.

[0330] The generator then uses a generative AI model to generate an appropriate answer based on the pre-processed data and sentiment data. For example, a prompt might be in the form of "How can I save money on food when I'm stressed?" The generative AI model generates a response like this:

[0331] "Take advantage of sale days and consider buying ingredients in bulk. It can also be effective to eat out less and cook more at home."

[0332] The emotion engine further tailors the generated answers, adding reassuring messages like "Just start with what you can do" to best suit the user's emotional state.

[0333] The generated answer is stored in a database and sent to the user terminal via the answer return means, which receives the answer and displays it on the screen of a dedicated application or on a web interface.

[0334] As a concrete example, if a question such as "I would like some advice on future investments" is asked, the emotion engine will recognize the user's anxiety, and the generative AI model will generate a response such as, "It is important to diversify your assets. Consider diversifying your investments into stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert." The emotion engine will add an additional message saying, "Start with a small amount at first and proceed with peace of mind."

[0335] In this way, the system of the present invention can provide enhanced financial support in real time by providing individually customized financial advice that is tailored to the user's emotional state.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1:

[0338] Users enter their financial questions using a dedicated application or web interface, entering a specific question such as "How can I save money on groceries this month?" and clicking submit.

[0339] Input: User's question

[0340] Output: The question is entered into the terminal.

[0341] Step 2:

[0342] The device receives the question entered by the user and sends it to the server along with the user ID as an HTTP POST request, with the data sent in JSON format.

[0343] Input: User's question, user ID

[0344] Output: Sent to the server as an HTTP POST request.

[0345] Step 3:

[0346] The server receives the request sent from the user's device. The received data includes the question and the user ID, and stores it temporarily in a database.

[0347] Input: HTTP POST request (question, user ID)

[0348] Output: Temporarily saved in a database.

[0349] Step 4:

[0350] The server (analysis means) analyzes the received data and converts it into a format that the generative AI model can understand. Specifically, it performs preprocessing such as tokenizing the question content and removing unnecessary words.

[0351] Input: Question content, user ID

[0352] Output: Preprocessed data

[0353] Step 5:

[0354] The server (pre-processing means) normalizes the parsed data and converts it into a format suitable for the emotion engine, for example, correcting grammatical errors and properly separating tokens.

[0355] Input: Preprocessed data

[0356] Output: Normalized data

[0357] Step 6:

[0358] The server (emotion engine) uses the normalized data to recognize the user's emotional state. The emotion engine performs text analysis and assigns emotion labels, such as "stress" or "anxiety."

[0359] Input: Normalized data

[0360] Output: Emotion data (emotion labels)

[0361] Step 7:

[0362] The server (generation means) uses a generative AI model to generate an appropriate answer based on the preprocessed data and emotional data. For example, the server inputs a prompt sentence such as "How to save money on food when the user is feeling stressed" into the generative AI model, and the model generates a response.

[0363] Input: Preprocessed data, emotion data

[0364] Output: The generated answer

[0365] Step 8:

[0366] The server (emotion engine) adjusts the generated answer and provides it in a way that best suits the user's emotional state. For example, it adds a reassuring message to the generated answer, such as "It's a good idea to start within your capabilities without overdoing it."

[0367] Input: Generated answers, sentiment data

[0368] Output: Adjusted answer

[0369] Step 9:

[0370] The server (storage means) stores the adjusted answers in a database. The stored data includes the user ID, question content, generated answers, and timestamps.

[0371] Input: Adjusted answer, user ID, question, timestamp

[0372] Output: Save to database

[0373] Step 10:

[0374] The server (answer return means) sends the saved answer to the user terminal, again using an HTTP POST request.

[0375] Input: adjusted answer, user ID

[0376] Output: Sent to the user's device as an HTTP POST request

[0377] Step 11:

[0378] The user terminal receives the HTTP POST request sent from the server and displays the received response on the screen of a dedicated application or on a web interface.

[0379] Input: HTTP POST request (adjusted answer)

[0380] Output: On-screen display

[0381] (Application example 2)

[0382] 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."

[0383] There is a need for a system that allows users to efficiently manage their finances and quickly make appropriate financial decisions based on their emotions. However, conventional finance management systems lack the functionality to provide customized advice that takes into account the user's emotional state, which often leaves users feeling stressed or unable to receive optimal advice. The present invention aims to provide a system that allows users to manage their finances more effectively by providing personalized advice based on the user's emotional state.

[0384] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the emotional state of the user and adjusting the response based on that data, means for preprocessing, and means for generating individually customized advice based on a generative AI model. This enables the user to quickly receive appropriate advice based on their emotions.

[0385] "User input means for entering a question" refers to the interface used by a user to enter a question or information, including a smartphone application or web form.

[0386] "Transmission means for sending user input to a server" refers to the function for sending user input data to a server via a network, using an HTTP POST request or similar.

[0387] "Analysis means for analyzing user input received by the server and inputting it into the generative AI model" refers to the function by which the server receives user input and converts that data into a format suitable for the generative AI model.

[0388] "The means by which a generative AI model generates an answer based on user input" refers to the function that enables an AI model to generate an appropriate answer based on user input.

[0389] "Storage means for storing the generated answers in a database" refers to a function for storing answers generated by a generative AI model in a database.

[0390] "Returning means for returning the answers stored in the storage means to the user terminal" refers to a function for sending the answers stored in the database to the user terminal.

[0391] "Display means for displaying the response sent back to the user terminal" refers to a function for displaying the response sent on the user terminal.

[0392] "Means for recognizing a user's emotional state using an emotion engine and adjusting responses based on that data" refers to a function that uses an emotion engine to analyze a user's emotional state and generate or adjust responses appropriate to those emotions.

[0393] "Preprocessing means by which the server preprocesses user input" refers to the function of organizing and processing user input in advance so that the generative AI model can process the data appropriately.

[0394] This invention is a system that helps users easily manage their household finances and make appropriate financial decisions quickly. In particular, it combines an emotion engine that provides customized advice based on the user's emotional state, providing advice that is more suited to the user's needs.

[0395] Key Components of the System

[0396] The system includes the following major components:

[0397] 1. User Interface (Device): The interface through which the user asks questions or enters information. This could be a smartphone app or a web form.

[0398] 2. Data transmission means (terminal): A network communication function for sending user input to the server. Data is sent using an HTTP POST request.

[0399] 3. Server: Processes the data received from the user and generates answers using generative AI models. This server achieves its functions through multiple means.

[0400] Analysis method: The server analyzes the received data and converts it into a format that the generative AI model can understand.

[0401] Preprocessing means: A function that preprocesses user input data and converts it into a format suitable for the generative AI model.

[0402] Generative means: The function by which the generative AI model generates an appropriate answer based on the user's question.

[0403] Storage: A function to store the generated answers in a database.

[0404] Return method: A function to return the generated answer to the user's terminal.

[0405] Emotion Engine: A feature that recognizes the user's emotional state and adjusts the generated answers based on that data.

[0406] 4. Display means (terminal): A function to display the generated answers on the user's terminal.

[0407] System Operation

[0408] The system operates as follows:

[0409] User input and submission of question: The user inputs a question through the user interface and submits it to the server. For example, "How can I save money on food this month?"

[0410] Receiving and parsing the question: The server receives the request sent from the user device, analyzes the received data, and converts it into a format that the generative AI model can understand.

[0411] Recognizing and adjusting emotional state: The emotion engine analyzes the user's question data and recognizes the user's emotional state. At the same time, the pre-processing means pre-processes the data.

[0412] Answer generation: Using the generation method, the generative AI model generates appropriate answers based on the data. For example, if a user asks, "How can I save on food this month?", the generative AI model might generate answers such as, "Try taking advantage of sales days and buying ingredients in bulk. It's also effective to eat out less and cook at home more."

[0413] Emotion engine adjustment: The emotion engine adjusts its answers to match the user's emotional state. For example, if it recognizes that the user is feeling stressed, it will add a reassuring message such as "It's best to start within your limits without overdoing it."

[0414] Storing and returning answers: The server stores the generated answers in a database and returns them to the user device.

[0415] Display in user interface: The user device displays the received answer.

[0416] As a specific example of use, a user can input a question such as, "How can I save on leisure expenses this month?" The system generates an answer, recognizes the user's stress level through an emotion engine, and provides customized advice such as, "Try to systematically increase free or low-cost activities. Don't push yourself too hard, and proceed at a pace that suits you."

[0417] Example prompt sentence:

[0418] "User 'user123' asked: 'How can I save on leisure expenses this month?' Analyze the emotion of the user and generate a personalized financial advice response."

[0419] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0420] Step 1:

[0421] The user enters a question. The user enters a question using a smartphone app or web interface. For example, they might enter a question like, "How can I save money on food this month?" The input data includes the user ID and the question.

[0422] Step 2:

[0423] The device sends the input data to the server using an HTTP POST request, with basic data format checks performed on the device to ensure the user input is sent in the correct format.

[0424] Step 3:

[0425] The server receives the request sent by the user. The received data includes the user ID and the question. The server temporarily stores the received data and prepares for the next analysis process.

[0426] Step 4:

[0427] The analysis means analyzes the received data. The server uses the analysis means to convert the received data into a format that the generative AI model can understand. This analysis formats the data and removes unnecessary information.

[0428] Step 5:

[0429] The preprocessing means preprocesses the user question data. The preprocessing means tokenizes the data and converts it into an input format suitable for the generative AI model. During this process, the input is passed to the emotion engine for sentiment analysis.

[0430] Step 6:

[0431] The emotion engine recognizes the user's emotional state. The emotion engine analyzes the input data and identifies the user's emotional state (e.g., stress or anxiety). This emotion data is fed into a generative AI model, which then reflects it in the answer.

[0432] Step 7:

[0433] The generator uses a generative AI model to generate answers based on the user's question and sentiment data. The generative AI model uses specific prompts to generate appropriate financial advice. For example, if the question is, "How can I save on food this month?", the generated answer might be, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to reduce eating out and cook at home more."

[0434] Step 8:

[0435] The emotion engine adjusts the generated answers. The emotion engine takes into account the user's emotional state and adds reassuring or encouraging messages to the generated answers. For example, a message like "It's a good idea to start within your capabilities without overdoing it" might be added.

[0436] Step 9:

[0437] The server stores the generated answer in a database, including the user ID, question, generated answer, and timestamp, so the data can be stored for future reference.

[0438] Step 10:

[0439] The server sends the generated answer back to the user's device, and the saved answer is sent to the user's device as an HTTP response, allowing the user to receive advice in real time.

[0440] Step 11:

[0441] The user terminal displays the received answer. The generated answer is displayed to the user using a display means on the terminal. The user can check specific advice on the interface.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] [Second embodiment]

[0446] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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."

[0458] The present invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly. The system receives questions from users and performs a series of processes to provide appropriate financial advice based on a generative AI model.

[0459] System Overview

[0460] The system consists of the following main components:

[0461] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[0462] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[0463] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[0464] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[0465] 5. Generator (server): The generative AI model generates answers to the user's questions.

[0466] 6. Data storage means (server): Stores the generated answers and questions in a database.

[0467] 7. Answer return means (server): Sends the generated answer to the user terminal.

[0468] 8. Display means (terminal): Displays the generated answer on the user's terminal.

[0469] Program processing

[0470] Enter and submit your question

[0471] A user enters a question using a dedicated application or web interface, for example, "How can I save money on groceries this month?"

[0472] The device receives the user's input and sends it to the server using an HTTP POST request, which includes the question and the user ID.

[0473] Receiving and parsing questions

[0474] The server receives requests sent from the user's device, analyzes the request data, and converts the question into a format that can be processed by the generative AI model.

[0475] Generate answers

[0476] The generator (server) generates appropriate answers to the analyzed questions using a generative AI model, which understands the context of the question and provides answers by utilizing past data and a knowledge base.

[0477] For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Try taking advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home."

[0478] Data storage and return

[0479] The server stores the generated answer in a database, including the user ID, question, generated answer, and timestamp.

[0480] The generated answer is sent to the user's terminal via a return means (server). The user receives the answer on the terminal and can check it on the interface.

[0481] Specific examples

[0482] Example 1: How to save money on food

[0483] 1. The user types in a question: "How can I save on food this month?"

[0484] 2. The terminal sends the input data to the server.

[0485] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0486] 4. The generation means (server) generates a response saying, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[0487] 5. The server stores the generated response in a database and sends it back to the user device.

[0488] 6. The user terminal displays the received response.

[0489] Example 2: Investment advice

[0490] 1. The user types in a question, such as, "I would like some advice on future investments."

[0491] 2. The device sends the query data to the server.

[0492] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0493] 4. The generation means (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make plans with a medium- to long-term perspective. We also recommend consulting an expert."

[0494] 5. The server stores the generated response in a database and sends it back to the user device.

[0495] 6. The user terminal displays the received response.

[0496] In this way, the present invention realizes a system that supports users in managing their household finances and provides specific advice in real time to help them make appropriate financial decisions.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] A user accesses a financial management application or web interface and types a question, for example, "How can I save money on food this month?"

[0500] Step 2:

[0501] The device sends the question data entered by the user to the server using an HTTP POST request, sending data including the question content and the user ID.

[0502] Step 3:

[0503] The server receives the request sent from the device. The received data includes the question and the user ID.

[0504] Step 4:

[0505] The server analyzes the received data and preprocesses the question content into a format that the generative AI model can understand, such as by checking the input for grammar and performing semantic analysis.

[0506] Step 5:

[0507] The server inputs the preprocessed data into a generative AI model, which generates an appropriate answer based on the user's question.

[0508] Step 6:

[0509] The server receives the answer from the generative AI model and stores it in a database, which contains the user ID, the question, the generated answer, and a timestamp.

[0510] Step 7:

[0511] The server sends the stored answer back to the user's device using an HTTP response.

[0512] Step 8:

[0513] The terminal receives the response returned from the server, and the generated response is included in the received data.

[0514] Step 9:

[0515] The user checks the answers on the device interface, and is shown advice such as, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home."

[0516] In this way, specific processing is carried out at each step from when the user inputs a question until the answer is returned.

[0517] Example 1

[0518] 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."

[0519] Conventional household management and financial advice systems have made it difficult for users to easily input questions and receive prompt and accurate answers. Storage and security of generated answers also present challenges. In response to these challenges, the present invention aims to provide a system that allows users to easily input household management questions, receive prompt and appropriate answers, and safely store and display those answers.

[0520] 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.

[0521] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the user's input to the server, an analysis means for the server to analyze the user's input received and input the input to a generative AI model, a generation means for the generative AI model to generate an answer based on the user's input, a storage means for saving the generated answer in a database, a return means for returning the answer saved in the storage means to a user terminal, and a display means for displaying the answer returned to the user terminal. This allows a user to input a question in natural language, and the server to analyze it, generate an appropriate answer, and safely save and return it.

[0522] "User" means any person or entity that utilizes the System to enter questions and receive financial advice.

[0523] "Input means" refers to the device or interface through which a user enters questions or data, such as a smartphone app or web form.

[0524] "Transmission means" refers to a function for transmitting data entered by a user to a server, and refers to a means for using a network communication protocol.

[0525] "Server" refers to the computing system that receives input from a user, analyzes it, stores it in a database, runs a generative AI model, and returns an answer.

[0526] "Analysis means" refers to the function by which the server analyzes user input and converts it into a format that can be processed by the generative AI model.

[0527] "Generation means" refers to the function of using a generative AI model to generate an appropriate answer based on user input.

[0528] "Storage means" refers to the function of saving the generated answers and questions in a database.

[0529] "Reply means" refers to the function of sending saved answers to the user's terminal.

[0530] "Display means" refers to the function of displaying the returned answers on the user's terminal.

[0531] A "generative AI model" refers to a model that uses artificial intelligence technology to generate appropriate answers to natural language questions.

[0532] A "prompt sentence" is text data input into a generative AI model, and refers to a sentence that explains or supplements the context of the question.

[0533] This invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly. The system receives questions from users and performs a series of processes to provide appropriate financial advice based on a generative AI model.

[0534] System Overview

[0535] The system consists of the following main components:

[0536] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[0537] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[0538] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[0539] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[0540] 5. Generator (server): The generative AI model generates answers to the user's questions.

[0541] 6. Data storage means (server): Stores the generated answers and questions in a database.

[0542] 7. Answer return means (server): Sends the generated answer to the user terminal.

[0543] 8. Display means (terminal): Displays the generated answer on the user's terminal.

[0544] Program processing

[0545] The user enters a question using a dedicated application or web interface. For example, the user might enter, "How can I save money on food this month?" The device receives the user's input and sends it to the server. Specifically, it sends the data using an HTTP POST request. The data sent includes the question and the user ID.

[0546] The server receives requests sent from the user's device, analyzes the request data, and converts the question into a format that the generative AI model can process. The analysis method uses natural language processing technology to extract keywords and context from the user's question.

[0547] The generation means (server) uses the generative AI model to generate appropriate answers to the analyzed questions. The generative AI model understands the context of the question and provides an answer by utilizing past data and knowledge bases. For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Try taking advantage of sale days and buying ingredients in bulk. It is also effective to reduce eating out and cook at home more."

[0548] The server stores the generated answer in a database. The stored information includes the user ID, question content, generated answer, and timestamp. The generated answer is sent to the user terminal via a return means (server). The user terminal displays the received answer on a user interface.

[0549] Specific examples

[0550] Example 1: How to save money on food

[0551] 1. The user types in a question: "How can I save on food this month?"

[0552] 2. The terminal sends the input data to the server.

[0553] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0554] 4. The generation means (server) generates a response saying, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[0555] 5. The server stores the generated response in a database and sends it back to the user device.

[0556] 6. The user terminal displays the received response.

[0557] Example 2: Investment advice

[0558] 1. The user types in a question, such as, "I would like some advice on future investments."

[0559] 2. The device sends the query data to the server.

[0560] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0561] 4. The generation means (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make plans with a medium- to long-term perspective. We also recommend consulting an expert."

[0562] 5. The server stores the generated response in a database and sends it back to the user device.

[0563] 6. The user terminal displays the received response.

[0564] In this way, the present invention realizes a system that supports users in managing their household finances and provides specific advice in real time to help them make quick and appropriate financial decisions.

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

[0566] Step 1: User enters question

[0567] A user inputs a question using a smartphone app or a web form. For example, they input, "Please tell me how to save money on food this month." This input generates data called the question content (e.g., "Please tell me how to save money on food this month").

[0568] Step 2: The device sends the data

[0569] The device sends the question data entered by the user to the server. Specifically, the device uses an HTTP POST request to send data including the user ID and question content to the server. The specific format of the data sent at this time is JSON format, etc.

[0570] Step 3: The server receives the data

[0571] The server receives the request data sent from the device. The received data includes the user ID and question. The server first checks whether the request format and content are correct, thereby ensuring data integrity.

[0572] Step 4: The server parses the data

[0573] The server analyzes the received data. Specifically, it uses natural language processing (NLP) technology to extract keywords and context from the user's question. For example, it extracts keywords such as "food expenses" and "savings" and converts them into a format that is easy for the generative AI model to understand. The input is the user's question, and the output is a prompt to be input into the generative AI model.

[0574] Step 5: The generator (server) generates the answer

[0575] The generation means (server) runs a generative AI model based on the analyzed data and generates an appropriate answer. A prompt sentence is input into the generative AI model (e.g., GPT-3) to obtain a specific answer such as, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more." The input is the prompt sentence, and the output is the answer from the generative AI model.

[0576] Step 6: The server saves the data

[0577] The server saves the user's question along with the generated answer in a database. The saved information includes the user ID, question, generated answer, and timestamp. This saving step allows for future reference of the question and answer history. The input is the generated answer and the user's question, and the output is saving to the database.

[0578] Step 7: The server sends back a response

[0579] The server returns the answer stored in the database to the user device. The answer is sent in HTTP response format using a return method. Again, encrypted communication is used to ensure data security. The input is the data stored in the database, and the output is the data returned to the user device.

[0580] Step 8: User device displays answer

[0581] The user device receives the response data returned from the server. The received data is displayed on the user interface, where the user can check it. For example, a response such as "Try taking advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more." The input is the data returned from the server, and the output is what is displayed on the user interface.

[0582] (Application example 1)

[0583] 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."

[0584] Current electronic payment services lack the ability to help users efficiently manage their household finances and make appropriate financial decisions quickly. In particular, there is a need for a system that allows users to receive appropriate advice in real time on how to save money and make investment plans. To solve this problem, technology is needed that can quickly analyze a user's financial situation and past spending data and provide appropriate advice.

[0585] 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.

[0586] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the user's input to the server, an analysis means for analyzing the user's input received by the server and inputting the input to the generative AI model, a generation means for the generative AI model to generate an answer based on the user's input, a storage means for saving the generated answer in a database, a return means for returning the answer saved in the storage means to the user terminal, a display means for displaying the answer returned to the user terminal, and a support means for analyzing the content of the user's question and past expenditure data and providing optimal savings methods and investment plans using the generative AI model. This enables users to quickly receive individually customized financial advice through electronic payment services.

[0587] "Input means" is the interface through which the user enters questions and financial data.

[0588] "Transmission means" refers to a communication means for transmitting the user's input data to the server.

[0589] "Analysis means" refers to a device or program that has the function of analyzing user input received by the server and converting it into a format suitable for the generative AI model.

[0590] A "generator" is a device or program that has the function of using a generative AI model to generate an answer based on user input.

[0591] The "storage means" is a device or program for storing the generated answers in a database.

[0592] The "returning means" is a communication means for returning the answer stored in the storage means to the user terminal.

[0593] The "display means" is an interface for displaying the answers returned to the user terminal.

[0594] An "assistance tool" is a device or program that has the function of analyzing questions from users and past spending data, and providing optimal savings methods and investment plans using a generative AI model.

[0595] A "generative AI model" is a program that uses artificial intelligence technology to generate answers and advice to questions based on user input data.

[0596] "Server" is a computer system that analyzes data submitted by users, generates answers using generative AI models, and stores and returns them.

[0597] The present invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly, and in particular provides appropriate financial advice to users using generative AI models. The system includes the following major hardware and software components:

[0598] System configuration

[0599] 1. User Device

[0600] A user terminal is a device that allows a user to input a question and display the answer from the server. Specifically, it is a smartphone application. It includes an input means for the user to input a question and a display means for displaying the generated answer.

[0601] 2. Server

[0602] The server receives the data sent by the user, analyzes it, inputs it into the generative AI model, and generates an answer. The server includes the following means:

[0603] Analysis means: Analyzes the data received from the user and converts it into a format suitable for the generative AI model.

[0604] Generation method: Using a generative AI model, we generate appropriate answers based on the user's question.

[0605] Storage: The generated answers are stored in a database.

[0606] Return method: The saved answers are returned to the user's device.

[0607] How it helps: Analyzes users' questions and past spending data to generate optimal savings and investment plans.

[0608] Hardware and software used

[0609] Hardware:

[0610] User device: Smartphone (iOS / Android)

[0611] Server: Web server (Apache, NGINX, AWS)

[0612] software:

[0613] User Interface: Smartphone app (React Native, Flutter, Swift, Kotlin)

[0614] API Request: HTTP Request Library (requests for Python)

[0615] Database: SQL database (PostgreSQL, MySQL)

[0616] Generative AI models: Artificial intelligence models using PyTorch and TensorFlow

[0617] Process Overview

[0618] Enter and submit your question

[0619] The user inputs a question through an application installed on the user device. For example, the user inputs a question such as, "Please tell me how to save on food expenses this month." The user device then sends this input data to the server.

[0620] Receiving and parsing questions

[0621] The server receives data from the user's device and converts it into a format that can be processed by the generative AI model using analytical means, taking into account past spending data.

[0622] Generate answers

[0623] The generative AI model is used by the generation method to generate appropriate answers to user questions. For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[0624] Data storage and return

[0625] The server stores the generated answer in a database using the storage means, and then returns it to the user terminal using the return means, allowing the user to check the answer on their own terminal.

[0626] Specific examples

[0627] Example 1: How to save money on food

[0628] 1. The user types in a question: "How can I save on food this month?"

[0629] 2. The user device sends this question to the server.

[0630] 3. The server receives the question and converts it into a format suitable for the generative AI model using analytical means.

[0631] 4. The generator generates the answer using the generative AI model.

[0632] 5. The generated answers are stored in a database and sent back to the user's device.

[0633] 6. The user receives a response on their device saying, "Take advantage of sale days and consider buying ingredients in bulk. It would also be effective to eat out less and cook at home more."

[0634] Example 2: Investment advice

[0635] 1. The user types in a question such as, "I would like some advice on future investments."

[0636] 2. The user device sends this question to the server.

[0637] 3. The server receives the question and converts it into a format suitable for the generative AI model using analytical means.

[0638] 4. The generator generates the answer using the generative AI model.

[0639] 5. The generated answers are stored in a database and sent back to the user's device.

[0640] 6. The user receives the following response on their device: "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert."

[0641] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0642] Step 1:

[0643] A user uses an application installed on the user device to input a question. For example, a user inputs a question such as "How can I save money on food this month?" The input means captures this data and stores the input. Input: Question text. Output: User input data.

[0644] Step 2:

[0645] The user terminal sends the saved user input data to the server. The sending means formats this data as an HTTP POST request and sends it to the server. Specifically, data including the user ID, question content, and timestamp is sent. Input: User input data. Output: HTTP request to the server.

[0646] Step 3:

[0647] The server receives requests sent from the user device. The analysis means analyzes the request data and converts it into a format that the generative AI model can process. Specifically, it extracts the necessary information from the JSON data and converts it into text format. Input: HTTP request data. Output: Input data to the generative AI model.

[0648] Step 4:

[0649] The generation means uses a generative AI model to generate an appropriate answer based on the analyzed question. The generative AI model creates specific advice based on the question content and past spending data. For example, if a user asks, "How can I save on food costs this month?", the generated answer would be, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook at home more." Input: Data input to the generative AI model. Output: Generated answer.

[0650] Step 5:

[0651] The server stores the answer generated by the generation means in a database using the storage means. The stored data includes the user ID, question content, generated answer, and timestamp. Input: Generated answer. Output: Data stored in the database.

[0652] Step 6:

[0653] The server returns the saved answer to the user terminal using the return means. The return means sets the saved data as an HTTP response and sends it to the user terminal. Input: Data saved in the database. Output: HTTP response to the user terminal.

[0654] Step 7:

[0655] The user device receives the answer sent from the server and displays it to the user using display means. Specifically, the answer is displayed on the application interface and the user confirms it. Input: HTTP response from the server. Output: Display content on the user device.

[0656] 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.

[0657] The present invention is a system that helps users easily manage their household finances and make appropriate financial decisions quickly, and combines an emotion engine that provides customized advice based on the user's emotional state. The system uses a generative AI model to provide financial advice in response to user questions, and the emotion engine recognizes the user's emotional state and provides personalized advice based on that.

[0658] System Overview

[0659] The system consists of the following main components:

[0660] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[0661] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[0662] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[0663] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[0664] 5. Preprocessing means (server): Preprocesses user input and converts it into a format suitable for the generative AI model.

[0665] 6. Generator (server): The generative AI model generates an answer based on the user's question.

[0666] 7. Data storage means (server): Stores the generated answers and questions in a database.

[0667] 8. Answer return means (server): Returns the generated answer to the user terminal.

[0668] 9. Display means (terminal): Displays the generated answer on the user's terminal.

[0669] 10. Emotion Engine (Server): Recognizes user emotions and tailors responses based on that data.

[0670] Program processing

[0671] Enter and submit your question

[0672] Users use a dedicated application or web interface to enter a question, for example, "How can I save money on food this month?"

[0673] The device receives the user's input and sends it to the server using an HTTP POST request, which includes the question and the user ID.

[0674] Receiving and parsing questions

[0675] The server receives the request sent from the user's device. The received data includes the question and the user ID.

[0676] The server analyzes the incoming data and converts it into a format that the generative AI model can understand.

[0677] Question preprocessing and sentiment recognition

[0678] The preprocessing means (server) preprocesses the user's question data and inputs it into the emotion engine. The emotion engine recognizes emotions from the user's input and provides data suitable for the generative AI model based on the emotional state.

[0679] Generate and refine answers

[0680] The generator (server) uses a generative AI model to generate appropriate answers based on the preprocessed data and emotional data. The emotion engine adjusts the generated answers to best suit the user's emotional state.

[0681] For example, if a user asks, "How can I save on food costs this month?" and the emotion engine recognizes that the user is feeling stressed, the generative AI model will generate advice such as, "Try taking advantage of sale days and buying ingredients in bulk. It can also be effective to eat out less and cook more at home," and the emotion engine will include an additional reassuring message such as, "It's a good idea to start within your capabilities without overdoing it."

[0682] Data storage and return

[0683] The server stores the generated answers in a database, which includes the user ID, the question, the generated answer, and a timestamp.

[0684] The generated answer is sent to the user's terminal via the return means (server). The user receives the answer on the terminal and checks it on the interface.

[0685] Specific examples

[0686] Example 1: How to save money on food

[0687] 1. The user types in a question: "How can I save on food this month?"

[0688] 2. The terminal sends the input data to the server.

[0689] 3. The server receives the question data, analyzes and preprocesses it, and inputs it into the emotion engine.

[0690] 4. The emotion engine (server) recognizes the user's stress state and provides the data to the generative AI model.

[0691] 5. The generation means (server) generates a response saying, "Try taking advantage of sale days and buying ingredients in bulk. It's also effective to eat out less and cook at home more," and the emotion engine adds a message saying, "It's a good idea to start as much as you can without overdoing it."

[0692] 6. The server stores the generated response in a database and sends it back to the user device.

[0693] 7. The user terminal displays the received response.

[0694] Example 2: Investment advice

[0695] 1. The user types in a question, such as, "I would like some advice on future investments."

[0696] 2. The device sends the query data to the server.

[0697] 3. The server receives the question data, analyzes and preprocesses it, and inputs it into the emotion engine.

[0698] 4. The emotion engine (server) recognizes the user's anxiety and provides the data to the generative AI model.

[0699] 5. The generator (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert." The emotion engine then adds a message such as, "Start with a small amount at first and proceed with peace of mind."

[0700] 6. The server stores the generated response in a database and sends it back to the user device.

[0701] 7. The user terminal displays the received response.

[0702] In this way, the present invention not only supports users in managing their household finances and provides specific advice in real time to help them make appropriate financial decisions, but also provides individually customized advice based on the user's emotional state, thereby building a system that provides more comprehensive support.

[0703] The processing flow will be explained below.

[0704] Step 1:

[0705] A user accesses a financial management application or web interface and types a question, for example, "How can I save money on food this month?"

[0706] Step 2:

[0707] The device sends the question data entered by the user to the server using an HTTP POST request, along with the question content and user ID.

[0708] Step 3:

[0709] The server receives the request sent from the device. The received data includes the question and the user ID.

[0710] Step 4:

[0711] The server parses the received data and preprocesses it into a format that can be processed by the generative AI model, including grammar checking and semantic analysis.

[0712] Step 5:

[0713] The server inputs the preprocessed question data into an emotion engine, which recognizes emotions from the user's input and generates emotional state data.

[0714] Step 6:

[0715] The emotion engine (server) provides data based on the user's emotional state to the generative AI model. For example, if it determines that the user is feeling stressed, it includes that information.

[0716] Step 7:

[0717] The generation means (server) uses a generative AI model to generate answers based on emotion data and preprocessed question data. For example, it might generate an answer such as, "Try taking advantage of sale days and buying ingredients in bulk. It's also effective to cut down on eating out and cook at home more," and the emotion engine adds, "It's a good idea to start as much as you can without overdoing it."

[0718] Step 8:

[0719] The server stores the generated answers and additional messages generated by the emotion engine in a database. The stored data includes the user ID, question content, generated answers, and timestamps.

[0720] Step 9:

[0721] The server sends the stored answer back to the user's device using an HTTP response.

[0722] Step 10:

[0723] The terminal receives the response sent back from the server. The received data includes the generated response and an additional message.

[0724] Step 11:

[0725] The user sees the provided answers and additional messages on the device interface, such as, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home. It's a good idea to start as much as you can without overdoing it."

[0726] In this way, a series of processes are carried out, from generating an answer from a user's question using emotion recognition and generative AI models, to storing the answer in a database, and then sending it back to the user's device.

[0727] Example 2

[0728] 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."

[0729] In modern society, it is important for users to make appropriate financial decisions quickly, but in many cases, their emotional state is a barrier to this. Conventional financial advice systems only provide uniform advice without taking the user's emotional state into account, making it difficult to alleviate users' stress and anxiety. They also have limitations in providing advice tailored to individual situations in response to specific questions.

[0730] 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.

[0731] In this invention, the server includes an analysis means for analyzing user input and inputting it into the generative AI model, an emotion engine for recognizing and analyzing the user's emotional state, and a preprocessing means for preprocessing the user's input and converting it into a format suitable for the generative AI model, thereby enabling the provision of individually customized financial advice according to the user's emotional state.

[0732] "Input means" refers to the interface through which users input questions, such as a dedicated application or web interface.

[0733] "Transmission means" refers to a network communication function for transmitting user input data to a server.

[0734] "Analysis means" refers to the function of the server to process the user's input data received and convert it into a format suitable for input into the generative AI model.

[0735] An "emotion engine" refers to a function that recognizes and analyzes the emotional state of a user from their input.

[0736] "Preprocessing means" refers to functions that normalize and tokenize data to convert user input data into a format suitable for the generative AI model.

[0737] "Generative" refers to the ability to use a generative AI model to generate answers based on user input.

[0738] "Storage means" refers to a function for storing generated answers in a database.

[0739] "Reply means" refers to the function for sending saved answers to the user terminal.

[0740] "Display means" refers to the function that displays the answer returned from the server on the user terminal.

[0741] A "generative AI model" refers to an artificial intelligence model that generates appropriate answers based on user input.

[0742] This invention is a system that allows users to input financial questions and provides customized advice based on their emotional state. The system is composed of the following components: a user interface, a data transmission means, a server, an analysis means, a preprocessing means, a generation means, an emotion engine, a data storage means, an answer return means, and a display means.

[0743] First, the user enters a financial question using a dedicated application or web interface, for example, "How can I save money on food this month?" After the user enters the question, the device sends this data to the server using an HTTP POST request. The data sent includes the question and the user ID.

[0744] The server receives requests sent from user devices. The received data is temporarily stored in a database, and then an analytical tool converts it into a format that the generative AI model can understand. Specifically, preprocessing is performed, such as tokenizing the text and removing unnecessary words.

[0745] The pre-processed data is then passed through an emotion engine to recognize the user's emotional state. The emotion engine determines, for example, whether the user is feeling "stressed" or "anxious." This emotion data is an important element for the generation process.

[0746] The generator then uses a generative AI model to generate an appropriate answer based on the pre-processed data and sentiment data. For example, a prompt might be in the form of "How can I save money on food when I'm stressed?" The generative AI model generates a response like this:

[0747] "Take advantage of sale days and consider buying ingredients in bulk. It can also be effective to eat out less and cook more at home."

[0748] The emotion engine further tailors the generated answers, adding reassuring messages like "Just start with what you can do" to best suit the user's emotional state.

[0749] The generated answer is stored in a database and sent to the user terminal via the answer return means, which receives the answer and displays it on the screen of a dedicated application or on a web interface.

[0750] As a concrete example, if a question such as "I would like some advice on future investments" is asked, the emotion engine will recognize the user's anxiety, and the generative AI model will generate a response such as, "It is important to diversify your assets. Consider diversifying your investments into stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert." The emotion engine will add an additional message saying, "Start with a small amount at first and proceed with peace of mind."

[0751] In this way, the system of the present invention can provide enhanced financial support in real time by providing individually customized financial advice that is tailored to the user's emotional state.

[0752] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0753] Step 1:

[0754] Users enter their financial questions using a dedicated application or web interface, entering a specific question such as "How can I save money on groceries this month?" and clicking submit.

[0755] Input: User's question

[0756] Output: The question is entered into the terminal.

[0757] Step 2:

[0758] The device receives the question entered by the user and sends it to the server along with the user ID as an HTTP POST request, with the data sent in JSON format.

[0759] Input: User's question, user ID

[0760] Output: Sent to the server as an HTTP POST request.

[0761] Step 3:

[0762] The server receives the request sent from the user's device. The received data includes the question and the user ID, and stores it temporarily in a database.

[0763] Input: HTTP POST request (question, user ID)

[0764] Output: Temporarily saved in a database.

[0765] Step 4:

[0766] The server (analysis means) analyzes the received data and converts it into a format that the generative AI model can understand. Specifically, it performs preprocessing such as tokenizing the question content and removing unnecessary words.

[0767] Input: Question content, user ID

[0768] Output: Preprocessed data

[0769] Step 5:

[0770] The server (pre-processing means) normalizes the parsed data and converts it into a format suitable for the emotion engine, for example, correcting grammatical errors and properly separating tokens.

[0771] Input: Preprocessed data

[0772] Output: Normalized data

[0773] Step 6:

[0774] The server (emotion engine) uses the normalized data to recognize the user's emotional state. The emotion engine performs text analysis and assigns emotion labels, such as "stress" or "anxiety."

[0775] Input: Normalized data

[0776] Output: Emotion data (emotion labels)

[0777] Step 7:

[0778] The server (generation means) uses a generative AI model to generate an appropriate answer based on the preprocessed data and emotional data. For example, the server inputs a prompt sentence such as "How to save money on food when the user is feeling stressed" into the generative AI model, and the model generates a response.

[0779] Input: Preprocessed data, emotion data

[0780] Output: The generated answer

[0781] Step 8:

[0782] The server (emotion engine) adjusts the generated answer and provides it in a way that best suits the user's emotional state. For example, it adds a reassuring message to the generated answer, such as "It's a good idea to start within your capabilities without overdoing it."

[0783] Input: Generated answers, sentiment data

[0784] Output: Adjusted answer

[0785] Step 9:

[0786] The server (storage means) stores the adjusted answers in a database. The stored data includes the user ID, question content, generated answers, and timestamps.

[0787] Input: Adjusted answer, user ID, question, timestamp

[0788] Output: Save to database

[0789] Step 10:

[0790] The server (answer return means) sends the saved answer to the user terminal, again using an HTTP POST request.

[0791] Input: adjusted answer, user ID

[0792] Output: Sent to the user's device as an HTTP POST request

[0793] Step 11:

[0794] The user terminal receives the HTTP POST request sent from the server and displays the received response on the screen of a dedicated application or on a web interface.

[0795] Input: HTTP POST request (adjusted answer)

[0796] Output: On-screen display

[0797] (Application example 2)

[0798] 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."

[0799] There is a need for a system that allows users to efficiently manage their finances and quickly make appropriate financial decisions based on their emotions. However, conventional finance management systems lack the functionality to provide customized advice that takes into account the user's emotional state, which often leaves users feeling stressed or unable to receive optimal advice. The present invention aims to provide a system that allows users to manage their finances more effectively by providing personalized advice based on the user's emotional state.

[0800] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the emotional state of the user and adjusting the response based on that data, means for preprocessing, and means for generating individually customized advice based on a generative AI model. This enables the user to quickly receive appropriate advice based on their emotions.

[0801] "User input means for entering a question" refers to the interface used by a user to enter a question or information, including a smartphone application or web form.

[0802] "Transmission means for sending user input to a server" refers to the function for sending user input data to a server via a network, using an HTTP POST request or similar.

[0803] "Analysis means for analyzing user input received by the server and inputting it into the generative AI model" refers to the function by which the server receives user input and converts that data into a format suitable for the generative AI model.

[0804] "The means by which a generative AI model generates an answer based on user input" refers to the function that enables an AI model to generate an appropriate answer based on user input.

[0805] "Storage means for storing the generated answers in a database" refers to a function for storing answers generated by a generative AI model in a database.

[0806] "Returning means for returning the answers stored in the storage means to the user terminal" refers to a function for sending the answers stored in the database to the user terminal.

[0807] "Display means for displaying the response sent back to the user terminal" refers to a function for displaying the response sent on the user terminal.

[0808] "Means for recognizing a user's emotional state using an emotion engine and adjusting responses based on that data" refers to a function that uses an emotion engine to analyze a user's emotional state and generate or adjust responses appropriate to those emotions.

[0809] "Preprocessing means by which the server preprocesses user input" refers to the function of organizing and processing user input in advance so that the generative AI model can process the data appropriately.

[0810] This invention is a system that helps users easily manage their household finances and make appropriate financial decisions quickly. In particular, it combines an emotion engine that provides customized advice based on the user's emotional state, providing advice that is more suited to the user's needs.

[0811] Key Components of the System

[0812] The system includes the following major components:

[0813] 1. User Interface (Device): The interface through which the user asks questions or enters information. This could be a smartphone app or a web form.

[0814] 2. Data transmission means (terminal): A network communication function for sending user input to the server. Data is sent using an HTTP POST request.

[0815] 3. Server: Processes the data received from the user and generates answers using generative AI models. This server achieves its functions through multiple means.

[0816] Analysis method: The server analyzes the received data and converts it into a format that the generative AI model can understand.

[0817] Preprocessing means: A function that preprocesses user input data and converts it into a format suitable for the generative AI model.

[0818] Generative means: The function by which the generative AI model generates an appropriate answer based on the user's question.

[0819] Storage: A function to store the generated answers in a database.

[0820] Return method: A function to return the generated answer to the user's terminal.

[0821] Emotion Engine: A feature that recognizes the user's emotional state and adjusts the generated answers based on that data.

[0822] 4. Display means (terminal): A function to display the generated answers on the user's terminal.

[0823] System Operation

[0824] The system operates as follows:

[0825] User input and submission of question: The user inputs a question through the user interface and submits it to the server. For example, "How can I save money on food this month?"

[0826] Receiving and parsing the question: The server receives the request sent from the user device, analyzes the received data, and converts it into a format that the generative AI model can understand.

[0827] Recognizing and adjusting emotional state: The emotion engine analyzes the user's question data and recognizes the user's emotional state. At the same time, the pre-processing means pre-processes the data.

[0828] Answer generation: Using the generation method, the generative AI model generates appropriate answers based on the data. For example, if a user asks, "How can I save on food this month?", the generative AI model might generate answers such as, "Try taking advantage of sales days and buying ingredients in bulk. It's also effective to eat out less and cook at home more."

[0829] Emotion engine adjustment: The emotion engine adjusts its answers to match the user's emotional state. For example, if it recognizes that the user is feeling stressed, it will add a reassuring message such as "It's best to start within your limits without overdoing it."

[0830] Storing and returning answers: The server stores the generated answers in a database and returns them to the user device.

[0831] Display in user interface: The user device displays the received answer.

[0832] As a specific example of use, a user can input a question such as, "How can I save on leisure expenses this month?" The system generates an answer, recognizes the user's stress level through an emotion engine, and provides customized advice such as, "Try to systematically increase free or low-cost activities. Don't push yourself too hard, and proceed at a pace that suits you."

[0833] Example prompt sentence:

[0834] "User 'user123' asked: 'How can I save on leisure expenses this month?' Analyze the emotion of the user and generate a personalized financial advice response."

[0835] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0836] Step 1:

[0837] The user enters a question. The user enters a question using a smartphone app or web interface. For example, they might enter a question like, "How can I save money on food this month?" The input data includes the user ID and the question.

[0838] Step 2:

[0839] The device sends the input data to the server using an HTTP POST request, with basic data format checks performed on the device to ensure the user input is sent in the correct format.

[0840] Step 3:

[0841] The server receives the request sent by the user. The received data includes the user ID and the question. The server temporarily stores the received data and prepares for the next analysis process.

[0842] Step 4:

[0843] The analysis means analyzes the received data. The server uses the analysis means to convert the received data into a format that the generative AI model can understand. This analysis formats the data and removes unnecessary information.

[0844] Step 5:

[0845] The preprocessing means preprocesses the user question data. The preprocessing means tokenizes the data and converts it into an input format suitable for the generative AI model. During this process, the input is passed to the emotion engine for sentiment analysis.

[0846] Step 6:

[0847] The emotion engine recognizes the user's emotional state. The emotion engine analyzes the input data and identifies the user's emotional state (e.g., stress or anxiety). This emotion data is fed into a generative AI model, which then reflects it in the answer.

[0848] Step 7:

[0849] The generator uses a generative AI model to generate answers based on the user's question and sentiment data. The generative AI model uses specific prompts to generate appropriate financial advice. For example, if the question is, "How can I save on food this month?", the generated answer might be, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to reduce eating out and cook at home more."

[0850] Step 8:

[0851] The emotion engine adjusts the generated answers. The emotion engine takes into account the user's emotional state and adds reassuring or encouraging messages to the generated answers. For example, a message like "It's a good idea to start within your capabilities without overdoing it" might be added.

[0852] Step 9:

[0853] The server stores the generated answer in a database, including the user ID, question, generated answer, and timestamp, so the data can be stored for future reference.

[0854] Step 10:

[0855] The server sends the generated answer back to the user's device, and the saved answer is sent to the user's device as an HTTP response, allowing the user to receive advice in real time.

[0856] Step 11:

[0857] The user terminal displays the received answer. The generated answer is displayed to the user using a display means on the terminal. The user can check specific advice on the interface.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] [Third embodiment]

[0862] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0863] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0864] 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).

[0865] 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.

[0866] 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.

[0867] 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).

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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."

[0874] The present invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly. The system receives questions from users and performs a series of processes to provide appropriate financial advice based on a generative AI model.

[0875] System Overview

[0876] The system consists of the following main components:

[0877] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[0878] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[0879] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[0880] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[0881] 5. Generator (server): The generative AI model generates answers to the user's questions.

[0882] 6. Data storage means (server): Stores the generated answers and questions in a database.

[0883] 7. Answer return means (server): Sends the generated answer to the user terminal.

[0884] 8. Display means (terminal): Displays the generated answer on the user's terminal.

[0885] Program processing

[0886] Enter and submit your question

[0887] A user enters a question using a dedicated application or web interface, for example, "How can I save money on groceries this month?"

[0888] The device receives the user's input and sends it to the server using an HTTP POST request, which includes the question and the user ID.

[0889] Receiving and parsing questions

[0890] The server receives requests sent from the user's device, analyzes the request data, and converts the question into a format that can be processed by the generative AI model.

[0891] Generate answers

[0892] The generator (server) generates appropriate answers to the analyzed questions using a generative AI model, which understands the context of the question and provides answers by utilizing past data and a knowledge base.

[0893] For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Try taking advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home."

[0894] Data storage and return

[0895] The server stores the generated answer in a database, including the user ID, question, generated answer, and timestamp.

[0896] The generated answer is sent to the user's terminal via a return means (server). The user receives the answer on the terminal and can check it on the interface.

[0897] Specific examples

[0898] Example 1: How to save money on food

[0899] 1. The user types in a question: "How can I save on food this month?"

[0900] 2. The terminal sends the input data to the server.

[0901] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0902] 4. The generation means (server) generates a response saying, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[0903] 5. The server stores the generated response in a database and sends it back to the user device.

[0904] 6. The user terminal displays the received response.

[0905] Example 2: Investment advice

[0906] 1. The user types in a question, such as, "I would like some advice on future investments."

[0907] 2. The device sends the query data to the server.

[0908] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0909] 4. The generation means (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make plans with a medium- to long-term perspective. We also recommend consulting an expert."

[0910] 5. The server stores the generated response in a database and sends it back to the user device.

[0911] 6. The user terminal displays the received response.

[0912] In this way, the present invention realizes a system that supports users in managing their household finances and provides specific advice in real time to help them make appropriate financial decisions.

[0913] The processing flow will be explained below.

[0914] Step 1:

[0915] A user accesses a financial management application or web interface and types a question, for example, "How can I save money on food this month?"

[0916] Step 2:

[0917] The device sends the question data entered by the user to the server using an HTTP POST request, sending data including the question content and the user ID.

[0918] Step 3:

[0919] The server receives the request sent from the device. The received data includes the question and the user ID.

[0920] Step 4:

[0921] The server analyzes the received data and preprocesses the question content into a format that the generative AI model can understand, such as by checking the input for grammar and performing semantic analysis.

[0922] Step 5:

[0923] The server inputs the preprocessed data into a generative AI model, which generates an appropriate answer based on the user's question.

[0924] Step 6:

[0925] The server receives the answer from the generative AI model and stores it in a database, which contains the user ID, the question, the generated answer, and a timestamp.

[0926] Step 7:

[0927] The server sends the stored answer back to the user's device using an HTTP response.

[0928] Step 8:

[0929] The terminal receives the response returned from the server, and the generated response is included in the received data.

[0930] Step 9:

[0931] The user checks the answers on the device interface, and is shown advice such as, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home."

[0932] In this way, specific processing is carried out at each step from when the user inputs a question until the answer is returned.

[0933] Example 1

[0934] 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."

[0935] Conventional household management and financial advice systems have made it difficult for users to easily input questions and receive prompt and accurate answers. Storage and security of generated answers also present challenges. In response to these challenges, the present invention aims to provide a system that allows users to easily input household management questions, receive prompt and appropriate answers, and safely store and display those answers.

[0936] 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.

[0937] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the user's input to the server, an analysis means for the server to analyze the user's input received and input the input to a generative AI model, a generation means for the generative AI model to generate an answer based on the user's input, a storage means for saving the generated answer in a database, a return means for returning the answer saved in the storage means to a user terminal, and a display means for displaying the answer returned to the user terminal. This allows a user to input a question in natural language, and the server to analyze it, generate an appropriate answer, and safely save and return it.

[0938] "User" means any person or entity that utilizes the System to enter questions and receive financial advice.

[0939] "Input means" refers to the device or interface through which a user enters questions or data, such as a smartphone app or web form.

[0940] "Transmission means" refers to a function for transmitting data entered by a user to a server, and refers to a means for using a network communication protocol.

[0941] "Server" refers to the computing system that receives input from a user, analyzes it, stores it in a database, runs a generative AI model, and returns an answer.

[0942] "Analysis means" refers to the function by which the server analyzes user input and converts it into a format that can be processed by the generative AI model.

[0943] "Generation means" refers to the function of using a generative AI model to generate an appropriate answer based on user input.

[0944] "Storage means" refers to the function of saving the generated answers and questions in a database.

[0945] "Reply means" refers to the function of sending saved answers to the user's terminal.

[0946] "Display means" refers to the function of displaying the returned answers on the user's terminal.

[0947] A "generative AI model" refers to a model that uses artificial intelligence technology to generate appropriate answers to natural language questions.

[0948] A "prompt sentence" is text data input into a generative AI model, and refers to a sentence that explains or supplements the context of the question.

[0949] This invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly. The system receives questions from users and performs a series of processes to provide appropriate financial advice based on a generative AI model.

[0950] System Overview

[0951] The system consists of the following main components:

[0952] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[0953] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[0954] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[0955] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[0956] 5. Generator (server): The generative AI model generates answers to the user's questions.

[0957] 6. Data storage means (server): Stores the generated answers and questions in a database.

[0958] 7. Answer return means (server): Sends the generated answer to the user terminal.

[0959] 8. Display means (terminal): Displays the generated answer on the user's terminal.

[0960] Program processing

[0961] The user enters a question using a dedicated application or web interface. For example, the user might enter, "How can I save money on food this month?" The device receives the user's input and sends it to the server. Specifically, it sends the data using an HTTP POST request. The data sent includes the question and the user ID.

[0962] The server receives requests sent from the user's device, analyzes the request data, and converts the question into a format that the generative AI model can process. The analysis method uses natural language processing technology to extract keywords and context from the user's question.

[0963] The generation means (server) uses the generative AI model to generate appropriate answers to the analyzed questions. The generative AI model understands the context of the question and provides an answer by utilizing past data and knowledge bases. For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Try taking advantage of sale days and buying ingredients in bulk. It is also effective to reduce eating out and cook at home more."

[0964] The server stores the generated answer in a database. The stored information includes the user ID, question content, generated answer, and timestamp. The generated answer is sent to the user terminal via a return means (server). The user terminal displays the received answer on a user interface.

[0965] Specific examples

[0966] Example 1: How to save money on food

[0967] 1. The user types in a question: "How can I save on food this month?"

[0968] 2. The terminal sends the input data to the server.

[0969] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0970] 4. The generation means (server) generates a response saying, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[0971] 5. The server stores the generated response in a database and sends it back to the user device.

[0972] 6. The user terminal displays the received response.

[0973] Example 2: Investment advice

[0974] 1. The user types in a question, such as, "I would like some advice on future investments."

[0975] 2. The device sends the query data to the server.

[0976] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[0977] 4. The generation means (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make plans with a medium- to long-term perspective. We also recommend consulting an expert."

[0978] 5. The server stores the generated response in a database and sends it back to the user device.

[0979] 6. The user terminal displays the received response.

[0980] In this way, the present invention realizes a system that supports users in managing their household finances and provides specific advice in real time to help them make quick and appropriate financial decisions.

[0981] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0982] Step 1: User enters question

[0983] A user inputs a question using a smartphone app or a web form. For example, they input, "Please tell me how to save money on food this month." This input generates data called the question content (e.g., "Please tell me how to save money on food this month").

[0984] Step 2: The device sends the data

[0985] The device sends the question data entered by the user to the server. Specifically, the device uses an HTTP POST request to send data including the user ID and question content to the server. The specific format of the data sent at this time is JSON format, etc.

[0986] Step 3: The server receives the data

[0987] The server receives the request data sent from the device. The received data includes the user ID and question. The server first checks whether the request format and content are correct, thereby ensuring data integrity.

[0988] Step 4: The server parses the data

[0989] The server analyzes the received data. Specifically, it uses natural language processing (NLP) technology to extract keywords and context from the user's question. For example, it extracts keywords such as "food expenses" and "savings" and converts them into a format that is easy for the generative AI model to understand. The input is the user's question, and the output is a prompt to be input into the generative AI model.

[0990] Step 5: The generator (server) generates the answer

[0991] The generation means (server) runs a generative AI model based on the analyzed data and generates an appropriate answer. A prompt sentence is input into the generative AI model (e.g., GPT-3) to obtain a specific answer such as, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more." The input is the prompt sentence, and the output is the answer from the generative AI model.

[0992] Step 6: The server saves the data

[0993] The server saves the user's question along with the generated answer in a database. The saved information includes the user ID, question, generated answer, and timestamp. This saving step allows for future reference of the question and answer history. The input is the generated answer and the user's question, and the output is saving to the database.

[0994] Step 7: The server sends back a response

[0995] The server returns the answer stored in the database to the user device. The answer is sent in HTTP response format using a return method. Again, encrypted communication is used to ensure data security. The input is the data stored in the database, and the output is the data returned to the user device.

[0996] Step 8: User device displays answer

[0997] The user device receives the response data returned from the server. The received data is displayed on the user interface, where the user can check it. For example, a response such as "Try taking advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more." The input is the data returned from the server, and the output is what is displayed on the user interface.

[0998] (Application example 1)

[0999] 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."

[1000] Current electronic payment services lack the ability to help users efficiently manage their household finances and make appropriate financial decisions quickly. In particular, there is a need for a system that allows users to receive appropriate advice in real time on how to save money and make investment plans. To solve this problem, technology is needed that can quickly analyze a user's financial situation and past spending data and provide appropriate advice.

[1001] 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.

[1002] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the user's input to the server, an analysis means for analyzing the user's input received by the server and inputting the input to the generative AI model, a generation means for the generative AI model to generate an answer based on the user's input, a storage means for saving the generated answer in a database, a return means for returning the answer saved in the storage means to the user terminal, a display means for displaying the answer returned to the user terminal, and a support means for analyzing the content of the user's question and past expenditure data and providing optimal savings methods and investment plans using the generative AI model. This enables users to quickly receive individually customized financial advice through electronic payment services.

[1003] "Input means" is the interface through which the user enters questions and financial data.

[1004] "Transmission means" refers to a communication means for transmitting the user's input data to the server.

[1005] "Analysis means" refers to a device or program that has the function of analyzing user input received by the server and converting it into a format suitable for the generative AI model.

[1006] A "generator" is a device or program that has the function of using a generative AI model to generate an answer based on user input.

[1007] The "storage means" is a device or program for storing the generated answers in a database.

[1008] The "returning means" is a communication means for returning the answer stored in the storage means to the user terminal.

[1009] The "display means" is an interface for displaying the answers returned to the user terminal.

[1010] An "assistance tool" is a device or program that has the function of analyzing questions from users and past spending data, and providing optimal savings methods and investment plans using a generative AI model.

[1011] A "generative AI model" is a program that uses artificial intelligence technology to generate answers and advice to questions based on user input data.

[1012] "Server" is a computer system that analyzes data submitted by users, generates answers using generative AI models, and stores and returns them.

[1013] The present invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly, and in particular provides appropriate financial advice to users using generative AI models. The system includes the following major hardware and software components:

[1014] System configuration

[1015] 1. User Device

[1016] A user terminal is a device that allows a user to input a question and display the answer from the server. Specifically, it is a smartphone application. It includes an input means for the user to input a question and a display means for displaying the generated answer.

[1017] 2. Server

[1018] The server receives the data sent by the user, analyzes it, inputs it into the generative AI model, and generates an answer. The server includes the following means:

[1019] Analysis means: Analyzes the data received from the user and converts it into a format suitable for the generative AI model.

[1020] Generation method: Using a generative AI model, we generate appropriate answers based on the user's question.

[1021] Storage: The generated answers are stored in a database.

[1022] Return method: The saved answers are returned to the user's device.

[1023] How it helps: Analyzes users' questions and past spending data to generate optimal savings and investment plans.

[1024] Hardware and software used

[1025] Hardware:

[1026] User device: Smartphone (iOS / Android)

[1027] Server: Web server (Apache, NGINX, AWS)

[1028] software:

[1029] User Interface: Smartphone app (React Native, Flutter, Swift, Kotlin)

[1030] API Request: HTTP Request Library (requests for Python)

[1031] Database: SQL database (PostgreSQL, MySQL)

[1032] Generative AI models: Artificial intelligence models using PyTorch and TensorFlow

[1033] Process Overview

[1034] Enter and submit your question

[1035] The user inputs a question through an application installed on the user device. For example, the user inputs a question such as, "Please tell me how to save on food expenses this month." The user device then sends this input data to the server.

[1036] Receiving and parsing questions

[1037] The server receives data from the user's device and converts it into a format that can be processed by the generative AI model using analytical means, taking into account past spending data.

[1038] Generate answers

[1039] The generative AI model is used by the generation method to generate appropriate answers to user questions. For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[1040] Data storage and return

[1041] The server stores the generated answer in a database using the storage means, and then returns it to the user terminal using the return means, allowing the user to check the answer on their own terminal.

[1042] Specific examples

[1043] Example 1: How to save money on food

[1044] 1. The user types in a question: "How can I save on food this month?"

[1045] 2. The user device sends this question to the server.

[1046] 3. The server receives the question and converts it into a format suitable for the generative AI model using analytical means.

[1047] 4. The generator generates the answer using the generative AI model.

[1048] 5. The generated answers are stored in a database and sent back to the user's device.

[1049] 6. The user receives a response on their device saying, "Take advantage of sale days and consider buying ingredients in bulk. It would also be effective to eat out less and cook at home more."

[1050] Example 2: Investment advice

[1051] 1. The user types in a question such as, "I would like some advice on future investments."

[1052] 2. The user device sends this question to the server.

[1053] 3. The server receives the question and converts it into a format suitable for the generative AI model using analytical means.

[1054] 4. The generator generates the answer using the generative AI model.

[1055] 5. The generated answers are stored in a database and sent back to the user's device.

[1056] 6. The user receives the following response on their device: "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert."

[1057] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1058] Step 1:

[1059] A user uses an application installed on the user device to input a question. For example, a user inputs a question such as "How can I save money on food this month?" The input means captures this data and stores the input. Input: Question text. Output: User input data.

[1060] Step 2:

[1061] The user terminal sends the saved user input data to the server. The sending means formats this data as an HTTP POST request and sends it to the server. Specifically, data including the user ID, question content, and timestamp is sent. Input: User input data. Output: HTTP request to the server.

[1062] Step 3:

[1063] The server receives requests sent from the user device. The analysis means analyzes the request data and converts it into a format that the generative AI model can process. Specifically, it extracts the necessary information from the JSON data and converts it into text format. Input: HTTP request data. Output: Input data to the generative AI model.

[1064] Step 4:

[1065] The generation means uses a generative AI model to generate an appropriate answer based on the analyzed question. The generative AI model creates specific advice based on the question content and past spending data. For example, if a user asks, "How can I save on food costs this month?", the generated answer would be, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook at home more." Input: Data input to the generative AI model. Output: Generated answer.

[1066] Step 5:

[1067] The server stores the answer generated by the generation means in a database using the storage means. The stored data includes the user ID, question content, generated answer, and timestamp. Input: Generated answer. Output: Data stored in the database.

[1068] Step 6:

[1069] The server returns the saved answer to the user terminal using the return means. The return means sets the saved data as an HTTP response and sends it to the user terminal. Input: Data saved in the database. Output: HTTP response to the user terminal.

[1070] Step 7:

[1071] The user device receives the answer sent from the server and displays it to the user using display means. Specifically, the answer is displayed on the application interface and the user confirms it. Input: HTTP response from the server. Output: Display content on the user device.

[1072] 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.

[1073] The present invention is a system that helps users easily manage their household finances and make appropriate financial decisions quickly, and combines an emotion engine that provides customized advice based on the user's emotional state. The system uses a generative AI model to provide financial advice in response to user questions, and the emotion engine recognizes the user's emotional state and provides personalized advice based on that.

[1074] System Overview

[1075] The system consists of the following main components:

[1076] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[1077] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[1078] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[1079] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[1080] 5. Preprocessing means (server): Preprocesses user input and converts it into a format suitable for the generative AI model.

[1081] 6. Generator (server): The generative AI model generates an answer based on the user's question.

[1082] 7. Data storage means (server): Stores the generated answers and questions in a database.

[1083] 8. Answer return means (server): Returns the generated answer to the user terminal.

[1084] 9. Display means (terminal): Displays the generated answer on the user's terminal.

[1085] 10. Emotion Engine (Server): Recognizes user emotions and tailors responses based on that data.

[1086] Program processing

[1087] Enter and submit your question

[1088] Users use a dedicated application or web interface to enter a question, for example, "How can I save money on food this month?"

[1089] The device receives the user's input and sends it to the server using an HTTP POST request, which includes the question and the user ID.

[1090] Receiving and parsing questions

[1091] The server receives the request sent from the user's device. The received data includes the question and the user ID.

[1092] The server analyzes the incoming data and converts it into a format that the generative AI model can understand.

[1093] Question preprocessing and sentiment recognition

[1094] The preprocessing means (server) preprocesses the user's question data and inputs it into the emotion engine. The emotion engine recognizes emotions from the user's input and provides data suitable for the generative AI model based on the emotional state.

[1095] Generate and refine answers

[1096] The generator (server) uses a generative AI model to generate appropriate answers based on the preprocessed data and emotional data. The emotion engine adjusts the generated answers to best suit the user's emotional state.

[1097] For example, if a user asks, "How can I save on food costs this month?" and the emotion engine recognizes that the user is feeling stressed, the generative AI model will generate advice such as, "Try taking advantage of sale days and buying ingredients in bulk. It can also be effective to eat out less and cook more at home," and the emotion engine will include an additional reassuring message such as, "It's a good idea to start within your capabilities without overdoing it."

[1098] Data storage and return

[1099] The server stores the generated answers in a database, which includes the user ID, the question, the generated answer, and a timestamp.

[1100] The generated answer is sent to the user's terminal via the return means (server). The user receives the answer on the terminal and checks it on the interface.

[1101] Specific examples

[1102] Example 1: How to save money on food

[1103] 1. The user types in a question: "How can I save on food this month?"

[1104] 2. The terminal sends the input data to the server.

[1105] 3. The server receives the question data, analyzes and preprocesses it, and inputs it into the emotion engine.

[1106] 4. The emotion engine (server) recognizes the user's stress state and provides the data to the generative AI model.

[1107] 5. The generation means (server) generates a response saying, "Try taking advantage of sale days and buying ingredients in bulk. It's also effective to eat out less and cook at home more," and the emotion engine adds a message saying, "It's a good idea to start as much as you can without overdoing it."

[1108] 6. The server stores the generated response in a database and sends it back to the user device.

[1109] 7. The user terminal displays the received response.

[1110] Example 2: Investment advice

[1111] 1. The user types in a question, such as, "I would like some advice on future investments."

[1112] 2. The device sends the query data to the server.

[1113] 3. The server receives the question data, analyzes and preprocesses it, and inputs it into the emotion engine.

[1114] 4. The emotion engine (server) recognizes the user's anxiety and provides the data to the generative AI model.

[1115] 5. The generator (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert." The emotion engine then adds a message such as, "Start with a small amount at first and proceed with peace of mind."

[1116] 6. The server stores the generated response in a database and sends it back to the user device.

[1117] 7. The user terminal displays the received response.

[1118] In this way, the present invention not only supports users in managing their household finances and provides specific advice in real time to help them make appropriate financial decisions, but also provides individually customized advice based on the user's emotional state, thereby building a system that provides more comprehensive support.

[1119] The processing flow will be explained below.

[1120] Step 1:

[1121] A user accesses a financial management application or web interface and types a question, for example, "How can I save money on food this month?"

[1122] Step 2:

[1123] The device sends the question data entered by the user to the server using an HTTP POST request, along with the question content and user ID.

[1124] Step 3:

[1125] The server receives the request sent from the device. The received data includes the question and the user ID.

[1126] Step 4:

[1127] The server parses the received data and preprocesses it into a format that can be processed by the generative AI model, including grammar checking and semantic analysis.

[1128] Step 5:

[1129] The server inputs the preprocessed question data into an emotion engine, which recognizes emotions from the user's input and generates emotional state data.

[1130] Step 6:

[1131] The emotion engine (server) provides data based on the user's emotional state to the generative AI model. For example, if it determines that the user is feeling stressed, it includes that information.

[1132] Step 7:

[1133] The generation means (server) uses a generative AI model to generate answers based on emotion data and preprocessed question data. For example, it might generate an answer such as, "Try taking advantage of sale days and buying ingredients in bulk. It's also effective to cut down on eating out and cook at home more," and the emotion engine adds, "It's a good idea to start as much as you can without overdoing it."

[1134] Step 8:

[1135] The server stores the generated answers and additional messages generated by the emotion engine in a database. The stored data includes the user ID, question content, generated answers, and timestamps.

[1136] Step 9:

[1137] The server sends the stored answer back to the user's device using an HTTP response.

[1138] Step 10:

[1139] The terminal receives the response sent back from the server. The received data includes the generated response and an additional message.

[1140] Step 11:

[1141] The user sees the provided answers and additional messages on the device interface, such as, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home. It's a good idea to start as much as you can without overdoing it."

[1142] In this way, a series of processes are carried out, from generating an answer from a user's question using emotion recognition and generative AI models, to storing the answer in a database, and then sending it back to the user's device.

[1143] Example 2

[1144] 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."

[1145] In modern society, it is important for users to make appropriate financial decisions quickly, but in many cases, their emotional state is a barrier to this. Conventional financial advice systems only provide uniform advice without taking the user's emotional state into account, making it difficult to alleviate users' stress and anxiety. They also have limitations in providing advice tailored to individual situations in response to specific questions.

[1146] 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.

[1147] In this invention, the server includes an analysis means for analyzing user input and inputting it into the generative AI model, an emotion engine for recognizing and analyzing the user's emotional state, and a preprocessing means for preprocessing the user's input and converting it into a format suitable for the generative AI model, thereby enabling the provision of individually customized financial advice according to the user's emotional state.

[1148] "Input means" refers to the interface through which users input questions, such as a dedicated application or web interface.

[1149] "Transmission means" refers to a network communication function for transmitting user input data to a server.

[1150] "Analysis means" refers to the function of the server to process the user's input data received and convert it into a format suitable for input into the generative AI model.

[1151] An "emotion engine" refers to a function that recognizes and analyzes the emotional state of a user from their input.

[1152] "Preprocessing means" refers to functions that normalize and tokenize data to convert user input data into a format suitable for the generative AI model.

[1153] "Generative" refers to the ability to use a generative AI model to generate answers based on user input.

[1154] "Storage means" refers to a function for storing generated answers in a database.

[1155] "Reply means" refers to the function for sending saved answers to the user terminal.

[1156] "Display means" refers to the function that displays the answer returned from the server on the user terminal.

[1157] A "generative AI model" refers to an artificial intelligence model that generates appropriate answers based on user input.

[1158] This invention is a system that allows users to input financial questions and provides customized advice based on their emotional state. The system is composed of the following components: a user interface, a data transmission means, a server, an analysis means, a preprocessing means, a generation means, an emotion engine, a data storage means, an answer return means, and a display means.

[1159] First, the user enters a financial question using a dedicated application or web interface, for example, "How can I save money on food this month?" After the user enters the question, the device sends this data to the server using an HTTP POST request. The data sent includes the question and the user ID.

[1160] The server receives requests sent from user devices. The received data is temporarily stored in a database, and then an analytical tool converts it into a format that the generative AI model can understand. Specifically, preprocessing is performed, such as tokenizing the text and removing unnecessary words.

[1161] The pre-processed data is then passed through an emotion engine to recognize the user's emotional state. The emotion engine determines, for example, whether the user is feeling "stressed" or "anxious." This emotion data is an important element for the generation process.

[1162] The generator then uses a generative AI model to generate an appropriate answer based on the pre-processed data and sentiment data. For example, a prompt might be in the form of "How can I save money on food when I'm stressed?" The generative AI model generates a response like this:

[1163] "Take advantage of sale days and consider buying ingredients in bulk. It can also be effective to eat out less and cook more at home."

[1164] The emotion engine further tailors the generated answers, adding reassuring messages like "Just start with what you can do" to best suit the user's emotional state.

[1165] The generated answer is stored in a database and sent to the user terminal via the answer return means, which receives the answer and displays it on the screen of a dedicated application or on a web interface.

[1166] As a concrete example, if a question such as "I would like some advice on future investments" is asked, the emotion engine will recognize the user's anxiety, and the generative AI model will generate a response such as, "It is important to diversify your assets. Consider diversifying your investments into stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert." The emotion engine will add an additional message saying, "Start with a small amount at first and proceed with peace of mind."

[1167] In this way, the system of the present invention can provide enhanced financial support in real time by providing individually customized financial advice that is tailored to the user's emotional state.

[1168] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1169] Step 1:

[1170] Users enter their financial questions using a dedicated application or web interface, entering a specific question such as "How can I save money on groceries this month?" and clicking submit.

[1171] Input: User's question

[1172] Output: The question is entered into the terminal.

[1173] Step 2:

[1174] The device receives the question entered by the user and sends it to the server along with the user ID as an HTTP POST request, with the data sent in JSON format.

[1175] Input: User's question, user ID

[1176] Output: Sent to the server as an HTTP POST request.

[1177] Step 3:

[1178] The server receives the request sent from the user's device. The received data includes the question and the user ID, and stores it temporarily in a database.

[1179] Input: HTTP POST request (question, user ID)

[1180] Output: Temporarily saved in a database.

[1181] Step 4:

[1182] The server (analysis means) analyzes the received data and converts it into a format that the generative AI model can understand. Specifically, it performs preprocessing such as tokenizing the question content and removing unnecessary words.

[1183] Input: Question content, user ID

[1184] Output: Preprocessed data

[1185] Step 5:

[1186] The server (pre-processing means) normalizes the parsed data and converts it into a format suitable for the emotion engine, for example, correcting grammatical errors and properly separating tokens.

[1187] Input: Preprocessed data

[1188] Output: Normalized data

[1189] Step 6:

[1190] The server (emotion engine) uses the normalized data to recognize the user's emotional state. The emotion engine performs text analysis and assigns emotion labels, such as "stress" or "anxiety."

[1191] Input: Normalized data

[1192] Output: Emotion data (emotion labels)

[1193] Step 7:

[1194] The server (generation means) uses a generative AI model to generate an appropriate answer based on the preprocessed data and emotional data. For example, the server inputs a prompt sentence such as "How to save money on food when the user is feeling stressed" into the generative AI model, and the model generates a response.

[1195] Input: Preprocessed data, emotion data

[1196] Output: The generated answer

[1197] Step 8:

[1198] The server (emotion engine) adjusts the generated answer and provides it in a way that best suits the user's emotional state. For example, it adds a reassuring message to the generated answer, such as "It's a good idea to start within your capabilities without overdoing it."

[1199] Input: Generated answers, sentiment data

[1200] Output: Adjusted answer

[1201] Step 9:

[1202] The server (storage means) stores the adjusted answers in a database. The stored data includes the user ID, question content, generated answers, and timestamps.

[1203] Input: Adjusted answer, user ID, question, timestamp

[1204] Output: Save to database

[1205] Step 10:

[1206] The server (answer return means) sends the saved answer to the user terminal, again using an HTTP POST request.

[1207] Input: adjusted answer, user ID

[1208] Output: Sent to the user's device as an HTTP POST request

[1209] Step 11:

[1210] The user terminal receives the HTTP POST request sent from the server and displays the received response on the screen of a dedicated application or on a web interface.

[1211] Input: HTTP POST request (adjusted answer)

[1212] Output: On-screen display

[1213] (Application example 2)

[1214] 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."

[1215] There is a need for a system that allows users to efficiently manage their finances and quickly make appropriate financial decisions based on their emotions. However, conventional finance management systems lack the functionality to provide customized advice that takes into account the user's emotional state, which often leaves users feeling stressed or unable to receive optimal advice. The present invention aims to provide a system that allows users to manage their finances more effectively by providing personalized advice based on the user's emotional state.

[1216] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the emotional state of the user and adjusting the response based on that data, means for preprocessing, and means for generating individually customized advice based on a generative AI model. This enables the user to quickly receive appropriate advice based on their emotions.

[1217] "User input means for entering a question" refers to the interface used by a user to enter a question or information, including a smartphone application or web form.

[1218] "Transmission means for sending user input to a server" refers to the function for sending user input data to a server via a network, using an HTTP POST request or similar.

[1219] "Analysis means for analyzing user input received by the server and inputting it into the generative AI model" refers to the function by which the server receives user input and converts that data into a format suitable for the generative AI model.

[1220] "The means by which a generative AI model generates an answer based on user input" refers to the function that enables an AI model to generate an appropriate answer based on user input.

[1221] "Storage means for storing the generated answers in a database" refers to a function for storing answers generated by a generative AI model in a database.

[1222] "Returning means for returning the answers stored in the storage means to the user terminal" refers to a function for sending the answers stored in the database to the user terminal.

[1223] "Display means for displaying the response sent back to the user terminal" refers to a function for displaying the response sent on the user terminal.

[1224] "Means for recognizing a user's emotional state using an emotion engine and adjusting responses based on that data" refers to a function that uses an emotion engine to analyze a user's emotional state and generate or adjust responses appropriate to those emotions.

[1225] "Preprocessing means by which the server preprocesses user input" refers to the function of organizing and processing user input in advance so that the generative AI model can process the data appropriately.

[1226] This invention is a system that helps users easily manage their household finances and make appropriate financial decisions quickly. In particular, it combines an emotion engine that provides customized advice based on the user's emotional state, providing advice that is more suited to the user's needs.

[1227] Key Components of the System

[1228] The system includes the following major components:

[1229] 1. User Interface (Device): The interface through which the user asks questions or enters information. This could be a smartphone app or a web form.

[1230] 2. Data transmission means (terminal): A network communication function for sending user input to the server. Data is sent using an HTTP POST request.

[1231] 3. Server: Processes the data received from the user and generates answers using generative AI models. This server achieves its functions through multiple means.

[1232] Analysis method: The server analyzes the received data and converts it into a format that the generative AI model can understand.

[1233] Preprocessing means: A function that preprocesses user input data and converts it into a format suitable for the generative AI model.

[1234] Generative means: The function by which the generative AI model generates an appropriate answer based on the user's question.

[1235] Storage: A function to store the generated answers in a database.

[1236] Return method: A function to return the generated answer to the user's terminal.

[1237] Emotion Engine: A feature that recognizes the user's emotional state and adjusts the generated answers based on that data.

[1238] 4. Display means (terminal): A function to display the generated answers on the user's terminal.

[1239] System Operation

[1240] The system operates as follows:

[1241] User input and submission of question: The user inputs a question through the user interface and submits it to the server. For example, "How can I save money on food this month?"

[1242] Receiving and parsing the question: The server receives the request sent from the user device, analyzes the received data, and converts it into a format that the generative AI model can understand.

[1243] Recognizing and adjusting emotional state: The emotion engine analyzes the user's question data and recognizes the user's emotional state. At the same time, the pre-processing means pre-processes the data.

[1244] Answer generation: Using the generation method, the generative AI model generates appropriate answers based on the data. For example, if a user asks, "How can I save on food this month?", the generative AI model might generate answers such as, "Try taking advantage of sales days and buying ingredients in bulk. It's also effective to eat out less and cook at home more."

[1245] Emotion engine adjustment: The emotion engine adjusts its answers to match the user's emotional state. For example, if it recognizes that the user is feeling stressed, it will add a reassuring message such as "It's best to start within your limits without overdoing it."

[1246] Storing and returning answers: The server stores the generated answers in a database and returns them to the user device.

[1247] Display in user interface: The user device displays the received answer.

[1248] As a specific example of use, a user can input a question such as, "How can I save on leisure expenses this month?" The system generates an answer, recognizes the user's stress level through an emotion engine, and provides customized advice such as, "Try to systematically increase free or low-cost activities. Don't push yourself too hard, and proceed at a pace that suits you."

[1249] Example prompt sentence:

[1250] "User 'user123' asked: 'How can I save on leisure expenses this month?' Analyze the emotion of the user and generate a personalized financial advice response."

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

[1252] Step 1:

[1253] The user enters a question. The user enters a question using a smartphone app or web interface. For example, they might enter a question like, "How can I save money on food this month?" The input data includes the user ID and the question.

[1254] Step 2:

[1255] The device sends the input data to the server using an HTTP POST request, with basic data format checks performed on the device to ensure the user input is sent in the correct format.

[1256] Step 3:

[1257] The server receives the request sent by the user. The received data includes the user ID and the question. The server temporarily stores the received data and prepares for the next analysis process.

[1258] Step 4:

[1259] The analysis means analyzes the received data. The server uses the analysis means to convert the received data into a format that the generative AI model can understand. This analysis formats the data and removes unnecessary information.

[1260] Step 5:

[1261] The preprocessing means preprocesses the user question data. The preprocessing means tokenizes the data and converts it into an input format suitable for the generative AI model. During this process, the input is passed to the emotion engine for sentiment analysis.

[1262] Step 6:

[1263] The emotion engine recognizes the user's emotional state. The emotion engine analyzes the input data and identifies the user's emotional state (e.g., stress or anxiety). This emotion data is fed into a generative AI model, which then reflects it in the answer.

[1264] Step 7:

[1265] The generator uses a generative AI model to generate answers based on the user's question and sentiment data. The generative AI model uses specific prompts to generate appropriate financial advice. For example, if the question is, "How can I save on food this month?", the generated answer might be, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to reduce eating out and cook at home more."

[1266] Step 8:

[1267] The emotion engine adjusts the generated answers. The emotion engine takes into account the user's emotional state and adds reassuring or encouraging messages to the generated answers. For example, a message like "It's a good idea to start within your capabilities without overdoing it" might be added.

[1268] Step 9:

[1269] The server stores the generated answer in a database, including the user ID, question, generated answer, and timestamp, so the data can be stored for future reference.

[1270] Step 10:

[1271] The server sends the generated answer back to the user's device, and the saved answer is sent to the user's device as an HTTP response, allowing the user to receive advice in real time.

[1272] Step 11:

[1273] The user terminal displays the received answer. The generated answer is displayed to the user using a display means on the terminal. The user can check specific advice on the interface.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] [Fourth embodiment]

[1278] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1279] 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.

[1280] 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).

[1281] 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.

[1282] 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.

[1283] 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).

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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.

[1290] 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."

[1291] The present invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly. The system receives questions from users and performs a series of processes to provide appropriate financial advice based on a generative AI model.

[1292] System Overview

[1293] The system consists of the following main components:

[1294] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[1295] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[1296] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[1297] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[1298] 5. Generator (server): The generative AI model generates answers to the user's questions.

[1299] 6. Data storage means (server): Stores the generated answers and questions in a database.

[1300] 7. Answer return means (server): Sends the generated answer to the user terminal.

[1301] 8. Display means (terminal): Displays the generated answer on the user's terminal.

[1302] Program processing

[1303] Enter and submit your question

[1304] A user enters a question using a dedicated application or web interface, for example, "How can I save money on groceries this month?"

[1305] The device receives the user's input and sends it to the server using an HTTP POST request, which includes the question and the user ID.

[1306] Receiving and parsing questions

[1307] The server receives requests sent from the user's device, analyzes the request data, and converts the question into a format that can be processed by the generative AI model.

[1308] Generate answers

[1309] The generator (server) generates appropriate answers to the analyzed questions using a generative AI model, which understands the context of the question and provides answers by utilizing past data and a knowledge base.

[1310] For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Try taking advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home."

[1311] Data storage and return

[1312] The server stores the generated answer in a database, including the user ID, question, generated answer, and timestamp.

[1313] The generated answer is sent to the user's terminal via a return means (server). The user receives the answer on the terminal and can check it on the interface.

[1314] Specific examples

[1315] Example 1: How to save money on food

[1316] 1. The user types in a question: "How can I save on food this month?"

[1317] 2. The terminal sends the input data to the server.

[1318] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[1319] 4. The generation means (server) generates a response saying, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[1320] 5. The server stores the generated response in a database and sends it back to the user device.

[1321] 6. The user terminal displays the received response.

[1322] Example 2: Investment advice

[1323] 1. The user types in a question, such as, "I would like some advice on future investments."

[1324] 2. The device sends the query data to the server.

[1325] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[1326] 4. The generation means (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make plans with a medium- to long-term perspective. We also recommend consulting an expert."

[1327] 5. The server stores the generated response in a database and sends it back to the user device.

[1328] 6. The user terminal displays the received response.

[1329] In this way, the present invention realizes a system that supports users in managing their household finances and provides specific advice in real time to help them make appropriate financial decisions.

[1330] The processing flow will be explained below.

[1331] Step 1:

[1332] A user accesses a financial management application or web interface and types a question, for example, "How can I save money on food this month?"

[1333] Step 2:

[1334] The device sends the question data entered by the user to the server using an HTTP POST request, sending data including the question content and the user ID.

[1335] Step 3:

[1336] The server receives the request sent from the device. The received data includes the question and the user ID.

[1337] Step 4:

[1338] The server analyzes the received data and preprocesses the question content into a format that the generative AI model can understand, such as by checking the input for grammar and performing semantic analysis.

[1339] Step 5:

[1340] The server inputs the preprocessed data into a generative AI model, which generates an appropriate answer based on the user's question.

[1341] Step 6:

[1342] The server receives the answer from the generative AI model and stores it in a database, which contains the user ID, the question, the generated answer, and a timestamp.

[1343] Step 7:

[1344] The server sends the stored answer back to the user's device using an HTTP response.

[1345] Step 8:

[1346] The terminal receives the response returned from the server, and the generated response is included in the received data.

[1347] Step 9:

[1348] The user checks the answers on the device interface, and is shown advice such as, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home."

[1349] In this way, specific processing is carried out at each step from when the user inputs a question until the answer is returned.

[1350] Example 1

[1351] 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."

[1352] Conventional household management and financial advice systems have made it difficult for users to easily input questions and receive prompt and accurate answers. Storage and security of generated answers also present challenges. In response to these challenges, the present invention aims to provide a system that allows users to easily input household management questions, receive prompt and appropriate answers, and safely store and display those answers.

[1353] 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.

[1354] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the user's input to the server, an analysis means for the server to analyze the user's input received and input the input to a generative AI model, a generation means for the generative AI model to generate an answer based on the user's input, a storage means for saving the generated answer in a database, a return means for returning the answer saved in the storage means to a user terminal, and a display means for displaying the answer returned to the user terminal. This allows a user to input a question in natural language, and the server to analyze it, generate an appropriate answer, and safely save and return it.

[1355] "User" means any person or entity that utilizes the System to enter questions and receive financial advice.

[1356] "Input means" refers to the device or interface through which a user enters questions or data, such as a smartphone app or web form.

[1357] "Transmission means" refers to a function for transmitting data entered by a user to a server, and refers to a means for using a network communication protocol.

[1358] "Server" refers to the computing system that receives input from a user, analyzes it, stores it in a database, runs a generative AI model, and returns an answer.

[1359] "Analysis means" refers to the function by which the server analyzes user input and converts it into a format that can be processed by the generative AI model.

[1360] "Generation means" refers to the function of using a generative AI model to generate an appropriate answer based on user input.

[1361] "Storage means" refers to the function of saving the generated answers and questions in a database.

[1362] "Reply means" refers to the function of sending saved answers to the user's terminal.

[1363] "Display means" refers to the function of displaying the returned answers on the user's terminal.

[1364] A "generative AI model" refers to a model that uses artificial intelligence technology to generate appropriate answers to natural language questions.

[1365] A "prompt sentence" is text data input into a generative AI model, and refers to a sentence that explains or supplements the context of the question.

[1366] This invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly. The system receives questions from users and performs a series of processes to provide appropriate financial advice based on a generative AI model.

[1367] System Overview

[1368] The system consists of the following main components:

[1369] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[1370] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[1371] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[1372] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[1373] 5. Generator (server): The generative AI model generates answers to the user's questions.

[1374] 6. Data storage means (server): Stores the generated answers and questions in a database.

[1375] 7. Answer return means (server): Sends the generated answer to the user terminal.

[1376] 8. Display means (terminal): Displays the generated answer on the user's terminal.

[1377] Program processing

[1378] The user enters a question using a dedicated application or web interface. For example, the user might enter, "How can I save money on food this month?" The device receives the user's input and sends it to the server. Specifically, it sends the data using an HTTP POST request. The data sent includes the question and the user ID.

[1379] The server receives requests sent from the user's device, analyzes the request data, and converts the question into a format that the generative AI model can process. The analysis method uses natural language processing technology to extract keywords and context from the user's question.

[1380] The generation means (server) uses the generative AI model to generate appropriate answers to the analyzed questions. The generative AI model understands the context of the question and provides an answer by utilizing past data and knowledge bases. For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Try taking advantage of sale days and buying ingredients in bulk. It is also effective to reduce eating out and cook at home more."

[1381] The server stores the generated answer in a database. The stored information includes the user ID, question content, generated answer, and timestamp. The generated answer is sent to the user terminal via a return means (server). The user terminal displays the received answer on a user interface.

[1382] Specific examples

[1383] Example 1: How to save money on food

[1384] 1. The user types in a question: "How can I save on food this month?"

[1385] 2. The terminal sends the input data to the server.

[1386] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[1387] 4. The generation means (server) generates a response saying, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[1388] 5. The server stores the generated response in a database and sends it back to the user device.

[1389] 6. The user terminal displays the received response.

[1390] Example 2: Investment advice

[1391] 1. The user types in a question, such as, "I would like some advice on future investments."

[1392] 2. The device sends the query data to the server.

[1393] 3. The server receives the question data, analyzes it, and inputs it into the generative AI model.

[1394] 4. The generation means (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make plans with a medium- to long-term perspective. We also recommend consulting an expert."

[1395] 5. The server stores the generated response in a database and sends it back to the user device.

[1396] 6. The user terminal displays the received response.

[1397] In this way, the present invention realizes a system that supports users in managing their household finances and provides specific advice in real time to help them make quick and appropriate financial decisions.

[1398] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1399] Step 1: User enters question

[1400] A user inputs a question using a smartphone app or a web form. For example, they input, "Please tell me how to save money on food this month." This input generates data called the question content (e.g., "Please tell me how to save money on food this month").

[1401] Step 2: The device sends the data

[1402] The device sends the question data entered by the user to the server. Specifically, the device uses an HTTP POST request to send data including the user ID and question content to the server. The specific format of the data sent at this time is JSON format, etc.

[1403] Step 3: The server receives the data

[1404] The server receives the request data sent from the device. The received data includes the user ID and question. The server first checks whether the request format and content are correct, thereby ensuring data integrity.

[1405] Step 4: The server parses the data

[1406] The server analyzes the received data. Specifically, it uses natural language processing (NLP) technology to extract keywords and context from the user's question. For example, it extracts keywords such as "food expenses" and "savings" and converts them into a format that is easy for the generative AI model to understand. The input is the user's question, and the output is a prompt to be input into the generative AI model.

[1407] Step 5: The generator (server) generates the answer

[1408] The generation means (server) runs a generative AI model based on the analyzed data and generates an appropriate answer. A prompt sentence is input into the generative AI model (e.g., GPT-3) to obtain a specific answer such as, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more." The input is the prompt sentence, and the output is the answer from the generative AI model.

[1409] Step 6: The server saves the data

[1410] The server saves the user's question along with the generated answer in a database. The saved information includes the user ID, question, generated answer, and timestamp. This saving step allows for future reference of the question and answer history. The input is the generated answer and the user's question, and the output is saving to the database.

[1411] Step 7: The server sends back a response

[1412] The server returns the answer stored in the database to the user device. The answer is sent in HTTP response format using a return method. Again, encrypted communication is used to ensure data security. The input is the data stored in the database, and the output is the data returned to the user device.

[1413] Step 8: User device displays answer

[1414] The user device receives the response data returned from the server. The received data is displayed on the user interface, where the user can check it. For example, a response such as "Try taking advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more." The input is the data returned from the server, and the output is what is displayed on the user interface.

[1415] (Application example 1)

[1416] 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."

[1417] Current electronic payment services lack the ability to help users efficiently manage their household finances and make appropriate financial decisions quickly. In particular, there is a need for a system that allows users to receive appropriate advice in real time on how to save money and make investment plans. To solve this problem, technology is needed that can quickly analyze a user's financial situation and past spending data and provide appropriate advice.

[1418] 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.

[1419] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the user's input to the server, an analysis means for analyzing the user's input received by the server and inputting the input to the generative AI model, a generation means for the generative AI model to generate an answer based on the user's input, a storage means for saving the generated answer in a database, a return means for returning the answer saved in the storage means to the user terminal, a display means for displaying the answer returned to the user terminal, and a support means for analyzing the content of the user's question and past expenditure data and providing optimal savings methods and investment plans using the generative AI model. This enables users to quickly receive individually customized financial advice through electronic payment services.

[1420] "Input means" is the interface through which the user enters questions and financial data.

[1421] "Transmission means" refers to a communication means for transmitting the user's input data to the server.

[1422] "Analysis means" refers to a device or program that has the function of analyzing user input received by the server and converting it into a format suitable for the generative AI model.

[1423] A "generator" is a device or program that has the function of using a generative AI model to generate an answer based on user input.

[1424] The "storage means" is a device or program for storing the generated answers in a database.

[1425] The "returning means" is a communication means for returning the answer stored in the storage means to the user terminal.

[1426] The "display means" is an interface for displaying the answers returned to the user terminal.

[1427] An "assistance tool" is a device or program that has the function of analyzing questions from users and past spending data, and providing optimal savings methods and investment plans using a generative AI model.

[1428] A "generative AI model" is a program that uses artificial intelligence technology to generate answers and advice to questions based on user input data.

[1429] "Server" is a computer system that analyzes data submitted by users, generates answers using generative AI models, and stores and returns them.

[1430] The present invention is a system that allows users to easily manage their household finances and make appropriate financial decisions quickly, and in particular provides appropriate financial advice to users using generative AI models. The system includes the following major hardware and software components:

[1431] System configuration

[1432] 1. User Device

[1433] A user terminal is a device that allows a user to input a question and display the answer from the server. Specifically, it is a smartphone application. It includes an input means for the user to input a question and a display means for displaying the generated answer.

[1434] 2. Server

[1435] The server receives the data sent by the user, analyzes it, inputs it into the generative AI model, and generates an answer. The server includes the following means:

[1436] Analysis means: Analyzes the data received from the user and converts it into a format suitable for the generative AI model.

[1437] Generation method: Using a generative AI model, we generate appropriate answers based on the user's question.

[1438] Storage: The generated answers are stored in a database.

[1439] Return method: The saved answers are returned to the user's device.

[1440] How it helps: Analyzes users' questions and past spending data to generate optimal savings and investment plans.

[1441] Hardware and software used

[1442] Hardware:

[1443] User device: Smartphone (iOS / Android)

[1444] Server: Web server (Apache, NGINX, AWS)

[1445] software:

[1446] User Interface: Smartphone app (React Native, Flutter, Swift, Kotlin)

[1447] API Request: HTTP Request Library (requests for Python)

[1448] Database: SQL database (PostgreSQL, MySQL)

[1449] Generative AI models: Artificial intelligence models using PyTorch and TensorFlow

[1450] Process Overview

[1451] Enter and submit your question

[1452] The user inputs a question through an application installed on the user device. For example, the user inputs a question such as, "Please tell me how to save on food expenses this month." The user device then sends this input data to the server.

[1453] Receiving and parsing questions

[1454] The server receives data from the user's device and converts it into a format that can be processed by the generative AI model using analytical means, taking into account past spending data.

[1455] Generate answers

[1456] The generative AI model is used by the generation method to generate appropriate answers to user questions. For example, if a user asks, "Please tell me how I can save on food costs this month," the generative AI model will generate advice such as, "Take advantage of sale days and consider buying ingredients in bulk. It is also effective to eat out less and cook at home more."

[1457] Data storage and return

[1458] The server stores the generated answer in a database using the storage means, and then returns it to the user terminal using the return means, allowing the user to check the answer on their own terminal.

[1459] Specific examples

[1460] Example 1: How to save money on food

[1461] 1. The user types in a question: "How can I save on food this month?"

[1462] 2. The user device sends this question to the server.

[1463] 3. The server receives the question and converts it into a format suitable for the generative AI model using analytical means.

[1464] 4. The generator generates the answer using the generative AI model.

[1465] 5. The generated answers are stored in a database and sent back to the user's device.

[1466] 6. The user receives a response on their device saying, "Take advantage of sale days and consider buying ingredients in bulk. It would also be effective to eat out less and cook at home more."

[1467] Example 2: Investment advice

[1468] 1. The user types in a question such as, "I would like some advice on future investments."

[1469] 2. The user device sends this question to the server.

[1470] 3. The server receives the question and converts it into a format suitable for the generative AI model using analytical means.

[1471] 4. The generator generates the answer using the generative AI model.

[1472] 5. The generated answers are stored in a database and sent back to the user's device.

[1473] 6. The user receives the following response on their device: "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert."

[1474] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1475] Step 1:

[1476] A user uses an application installed on the user device to input a question. For example, a user inputs a question such as "How can I save money on food this month?" The input means captures this data and stores the input. Input: Question text. Output: User input data.

[1477] Step 2:

[1478] The user terminal sends the saved user input data to the server. The sending means formats this data as an HTTP POST request and sends it to the server. Specifically, data including the user ID, question content, and timestamp is sent. Input: User input data. Output: HTTP request to the server.

[1479] Step 3:

[1480] The server receives requests sent from the user device. The analysis means analyzes the request data and converts it into a format that the generative AI model can process. Specifically, it extracts the necessary information from the JSON data and converts it into text format. Input: HTTP request data. Output: Input data to the generative AI model.

[1481] Step 4:

[1482] The generation means uses a generative AI model to generate an appropriate answer based on the analyzed question. The generative AI model creates specific advice based on the question content and past spending data. For example, if a user asks, "How can I save on food costs this month?", the generated answer would be, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook at home more." Input: Data input to the generative AI model. Output: Generated answer.

[1483] Step 5:

[1484] The server stores the answer generated by the generation means in a database using the storage means. The stored data includes the user ID, question content, generated answer, and timestamp. Input: Generated answer. Output: Data stored in the database.

[1485] Step 6:

[1486] The server returns the saved answer to the user terminal using the return means. The return means sets the saved data as an HTTP response and sends it to the user terminal. Input: Data saved in the database. Output: HTTP response to the user terminal.

[1487] Step 7:

[1488] The user device receives the answer sent from the server and displays it to the user using display means. Specifically, the answer is displayed on the application interface and the user confirms it. Input: HTTP response from the server. Output: Display content on the user device.

[1489] 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.

[1490] The present invention is a system that helps users easily manage their household finances and make appropriate financial decisions quickly, and combines an emotion engine that provides customized advice based on the user's emotional state. The system uses a generative AI model to provide financial advice in response to user questions, and the emotion engine recognizes the user's emotional state and provides personalized advice based on that.

[1491] System Overview

[1492] The system consists of the following main components:

[1493] 1. User interface (terminal): The interface through which the user enters questions, such as a smartphone app or a web form.

[1494] 2. Data transmission means (terminal): A network communication function that transmits user input to the server.

[1495] 3. Server: Processes the data received from the user and generates an answer using a generative AI model.

[1496] 4. Parsing means (server): Parses the user's input and converts it into an appropriate format.

[1497] 5. Preprocessing means (server): Preprocesses user input and converts it into a format suitable for the generative AI model.

[1498] 6. Generator (server): The generative AI model generates an answer based on the user's question.

[1499] 7. Data storage means (server): Stores the generated answers and questions in a database.

[1500] 8. Answer return means (server): Returns the generated answer to the user terminal.

[1501] 9. Display means (terminal): Displays the generated answer on the user's terminal.

[1502] 10. Emotion Engine (Server): Recognizes user emotions and tailors responses based on that data.

[1503] Program processing

[1504] Enter and submit your question

[1505] Users use a dedicated application or web interface to enter a question, for example, "How can I save money on food this month?"

[1506] The device receives the user's input and sends it to the server using an HTTP POST request, which includes the question and the user ID.

[1507] Receiving and parsing questions

[1508] The server receives the request sent from the user's device. The received data includes the question and the user ID.

[1509] The server analyzes the incoming data and converts it into a format that the generative AI model can understand.

[1510] Question preprocessing and sentiment recognition

[1511] The preprocessing means (server) preprocesses the user's question data and inputs it into the emotion engine. The emotion engine recognizes emotions from the user's input and provides data suitable for the generative AI model based on the emotional state.

[1512] Generate and refine answers

[1513] The generator (server) uses a generative AI model to generate appropriate answers based on the preprocessed data and emotional data. The emotion engine adjusts the generated answers to best suit the user's emotional state.

[1514] For example, if a user asks, "How can I save on food costs this month?" and the emotion engine recognizes that the user is feeling stressed, the generative AI model will generate advice such as, "Try taking advantage of sale days and buying ingredients in bulk. It can also be effective to eat out less and cook more at home," and the emotion engine will include an additional reassuring message such as, "It's a good idea to start within your capabilities without overdoing it."

[1515] Data storage and return

[1516] The server stores the generated answers in a database, which includes the user ID, the question, the generated answer, and a timestamp.

[1517] The generated answer is sent to the user's terminal via the return means (server). The user receives the answer on the terminal and checks it on the interface.

[1518] Specific examples

[1519] Example 1: How to save money on food

[1520] 1. The user types in a question: "How can I save on food this month?"

[1521] 2. The terminal sends the input data to the server.

[1522] 3. The server receives the question data, analyzes and preprocesses it, and inputs it into the emotion engine.

[1523] 4. The emotion engine (server) recognizes the user's stress state and provides the data to the generative AI model.

[1524] 5. The generation means (server) generates a response saying, "Try taking advantage of sale days and buying ingredients in bulk. It's also effective to eat out less and cook at home more," and the emotion engine adds a message saying, "It's a good idea to start as much as you can without overdoing it."

[1525] 6. The server stores the generated response in a database and sends it back to the user device.

[1526] 7. The user terminal displays the received response.

[1527] Example 2: Investment advice

[1528] 1. The user types in a question, such as, "I would like some advice on future investments."

[1529] 2. The device sends the query data to the server.

[1530] 3. The server receives the question data, analyzes and preprocesses it, and inputs it into the emotion engine.

[1531] 4. The emotion engine (server) recognizes the user's anxiety and provides the data to the generative AI model.

[1532] 5. The generator (server) generates a response saying, "It is important to diversify your assets. Consider diversifying your investments across stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert." The emotion engine then adds a message such as, "Start with a small amount at first and proceed with peace of mind."

[1533] 6. The server stores the generated response in a database and sends it back to the user device.

[1534] 7. The user terminal displays the received response.

[1535] In this way, the present invention not only supports users in managing their household finances and provides specific advice in real time to help them make appropriate financial decisions, but also provides individually customized advice based on the user's emotional state, thereby building a system that provides more comprehensive support.

[1536] The processing flow will be explained below.

[1537] Step 1:

[1538] A user accesses a financial management application or web interface and types a question, for example, "How can I save money on food this month?"

[1539] Step 2:

[1540] The device sends the question data entered by the user to the server using an HTTP POST request, along with the question content and user ID.

[1541] Step 3:

[1542] The server receives the request sent from the device. The received data includes the question and the user ID.

[1543] Step 4:

[1544] The server parses the received data and preprocesses it into a format that can be processed by the generative AI model, including grammar checking and semantic analysis.

[1545] Step 5:

[1546] The server inputs the preprocessed question data into an emotion engine, which recognizes emotions from the user's input and generates emotional state data.

[1547] Step 6:

[1548] The emotion engine (server) provides data based on the user's emotional state to the generative AI model. For example, if it determines that the user is feeling stressed, it includes that information.

[1549] Step 7:

[1550] The generation means (server) uses a generative AI model to generate answers based on emotion data and preprocessed question data. For example, it might generate an answer such as, "Try taking advantage of sale days and buying ingredients in bulk. It's also effective to cut down on eating out and cook at home more," and the emotion engine adds, "It's a good idea to start as much as you can without overdoing it."

[1551] Step 8:

[1552] The server stores the generated answers and additional messages generated by the emotion engine in a database. The stored data includes the user ID, question content, generated answers, and timestamps.

[1553] Step 9:

[1554] The server sends the stored answer back to the user's device using an HTTP response.

[1555] Step 10:

[1556] The terminal receives the response sent back from the server. The received data includes the generated response and an additional message.

[1557] Step 11:

[1558] The user sees the provided answers and additional messages on the device interface, such as, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to eat out less and cook more at home. It's a good idea to start as much as you can without overdoing it."

[1559] In this way, a series of processes are carried out, from generating an answer from a user's question using emotion recognition and generative AI models, to storing the answer in a database, and then sending it back to the user's device.

[1560] Example 2

[1561] 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."

[1562] In modern society, it is important for users to make appropriate financial decisions quickly, but in many cases, their emotional state is a barrier to this. Conventional financial advice systems only provide uniform advice without taking the user's emotional state into account, making it difficult to alleviate users' stress and anxiety. They also have limitations in providing advice tailored to individual situations in response to specific questions.

[1563] 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.

[1564] In this invention, the server includes an analysis means for analyzing user input and inputting it into the generative AI model, an emotion engine for recognizing and analyzing the user's emotional state, and a preprocessing means for preprocessing the user's input and converting it into a format suitable for the generative AI model, thereby enabling the provision of individually customized financial advice according to the user's emotional state.

[1565] "Input means" refers to the interface through which users input questions, such as a dedicated application or web interface.

[1566] "Transmission means" refers to a network communication function for transmitting user input data to a server.

[1567] "Analysis means" refers to the function of the server to process the user's input data received and convert it into a format suitable for input into the generative AI model.

[1568] An "emotion engine" refers to a function that recognizes and analyzes the emotional state of a user from their input.

[1569] "Preprocessing means" refers to functions that normalize and tokenize data to convert user input data into a format suitable for the generative AI model.

[1570] "Generative" refers to the ability to use a generative AI model to generate answers based on user input.

[1571] "Storage means" refers to a function for storing generated answers in a database.

[1572] "Reply means" refers to the function for sending saved answers to the user terminal.

[1573] "Display means" refers to the function that displays the answer returned from the server on the user terminal.

[1574] A "generative AI model" refers to an artificial intelligence model that generates appropriate answers based on user input.

[1575] This invention is a system that allows users to input financial questions and provides customized advice based on their emotional state. The system is composed of the following components: a user interface, a data transmission means, a server, an analysis means, a preprocessing means, a generation means, an emotion engine, a data storage means, an answer return means, and a display means.

[1576] First, the user enters a financial question using a dedicated application or web interface, for example, "How can I save money on food this month?" After the user enters the question, the device sends this data to the server using an HTTP POST request. The data sent includes the question and the user ID.

[1577] The server receives requests sent from user devices. The received data is temporarily stored in a database, and then an analytical tool converts it into a format that the generative AI model can understand. Specifically, preprocessing is performed, such as tokenizing the text and removing unnecessary words.

[1578] The pre-processed data is then passed through an emotion engine to recognize the user's emotional state. The emotion engine determines, for example, whether the user is feeling "stressed" or "anxious." This emotion data is an important element for the generation process.

[1579] The generator then uses a generative AI model to generate an appropriate answer based on the pre-processed data and sentiment data. For example, a prompt might be in the form of "How can I save money on food when I'm stressed?" The generative AI model generates a response like this:

[1580] "Take advantage of sale days and consider buying ingredients in bulk. It can also be effective to eat out less and cook more at home."

[1581] The emotion engine further tailors the generated answers, adding reassuring messages like "Just start with what you can do" to best suit the user's emotional state.

[1582] The generated answer is stored in a database and sent to the user terminal via the answer return means, which receives the answer and displays it on the screen of a dedicated application or on a web interface.

[1583] As a concrete example, if a question such as "I would like some advice on future investments" is asked, the emotion engine will recognize the user's anxiety, and the generative AI model will generate a response such as, "It is important to diversify your assets. Consider diversifying your investments into stocks, bonds, and real estate, and make a plan with a medium- to long-term perspective. We also recommend consulting an expert." The emotion engine will add an additional message saying, "Start with a small amount at first and proceed with peace of mind."

[1584] In this way, the system of the present invention can provide enhanced financial support in real time by providing individually customized financial advice that is tailored to the user's emotional state.

[1585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1586] Step 1:

[1587] Users enter their financial questions using a dedicated application or web interface, entering a specific question such as "How can I save money on groceries this month?" and clicking submit.

[1588] Input: User's question

[1589] Output: The question is entered into the terminal.

[1590] Step 2:

[1591] The device receives the question entered by the user and sends it to the server along with the user ID as an HTTP POST request, with the data sent in JSON format.

[1592] Input: User's question, user ID

[1593] Output: Sent to the server as an HTTP POST request.

[1594] Step 3:

[1595] The server receives the request sent from the user's device. The received data includes the question and the user ID, and stores it temporarily in a database.

[1596] Input: HTTP POST request (question, user ID)

[1597] Output: Temporarily saved in a database.

[1598] Step 4:

[1599] The server (analysis means) analyzes the received data and converts it into a format that the generative AI model can understand. Specifically, it performs preprocessing such as tokenizing the question content and removing unnecessary words.

[1600] Input: Question content, user ID

[1601] Output: Preprocessed data

[1602] Step 5:

[1603] The server (pre-processing means) normalizes the parsed data and converts it into a format suitable for the emotion engine, for example, correcting grammatical errors and properly separating tokens.

[1604] Input: Preprocessed data

[1605] Output: Normalized data

[1606] Step 6:

[1607] The server (emotion engine) uses the normalized data to recognize the user's emotional state. The emotion engine performs text analysis and assigns emotion labels, such as "stress" or "anxiety."

[1608] Input: Normalized data

[1609] Output: Emotion data (emotion labels)

[1610] Step 7:

[1611] The server (generation means) uses a generative AI model to generate an appropriate answer based on the preprocessed data and emotional data. For example, the server inputs a prompt sentence such as "How to save money on food when the user is feeling stressed" into the generative AI model, and the model generates a response.

[1612] Input: Preprocessed data, emotion data

[1613] Output: The generated answer

[1614] Step 8:

[1615] The server (emotion engine) adjusts the generated answer and provides it in a way that best suits the user's emotional state. For example, it adds a reassuring message to the generated answer, such as "It's a good idea to start within your capabilities without overdoing it."

[1616] Input: Generated answers, sentiment data

[1617] Output: Adjusted answer

[1618] Step 9:

[1619] The server (storage means) stores the adjusted answers in a database. The stored data includes the user ID, question content, generated answers, and timestamps.

[1620] Input: Adjusted answer, user ID, question, timestamp

[1621] Output: Save to database

[1622] Step 10:

[1623] The server (answer return means) sends the saved answer to the user terminal, again using an HTTP POST request.

[1624] Input: adjusted answer, user ID

[1625] Output: Sent to the user's device as an HTTP POST request

[1626] Step 11:

[1627] The user terminal receives the HTTP POST request sent from the server and displays the received response on the screen of a dedicated application or on a web interface.

[1628] Input: HTTP POST request (adjusted answer)

[1629] Output: On-screen display

[1630] (Application example 2)

[1631] 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."

[1632] There is a need for a system that allows users to efficiently manage their finances and quickly make appropriate financial decisions based on their emotions. However, conventional finance management systems lack the functionality to provide customized advice that takes into account the user's emotional state, which often leaves users feeling stressed or unable to receive optimal advice. The present invention aims to provide a system that allows users to manage their finances more effectively by providing personalized advice based on the user's emotional state.

[1633] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the emotional state of the user and adjusting the response based on that data, means for preprocessing, and means for generating individually customized advice based on a generative AI model. This enables the user to quickly receive appropriate advice based on their emotions.

[1634] "User input means for entering a question" refers to the interface used by a user to enter a question or information, including a smartphone application or web form.

[1635] "Transmission means for sending user input to a server" refers to the function for sending user input data to a server via a network, using an HTTP POST request or similar.

[1636] "Analysis means for analyzing user input received by the server and inputting it into the generative AI model" refers to the function by which the server receives user input and converts that data into a format suitable for the generative AI model.

[1637] "The means by which a generative AI model generates an answer based on user input" refers to the function that enables an AI model to generate an appropriate answer based on user input.

[1638] "Storage means for storing the generated answers in a database" refers to a function for storing answers generated by a generative AI model in a database.

[1639] "Returning means for returning the answers stored in the storage means to the user terminal" refers to a function for sending the answers stored in the database to the user terminal.

[1640] "Display means for displaying the response sent back to the user terminal" refers to a function for displaying the response sent on the user terminal.

[1641] "Means for recognizing a user's emotional state using an emotion engine and adjusting responses based on that data" refers to a function that uses an emotion engine to analyze a user's emotional state and generate or adjust responses appropriate to those emotions.

[1642] "Preprocessing means by which the server preprocesses user input" refers to the function of organizing and processing user input in advance so that the generative AI model can process the data appropriately.

[1643] This invention is a system that helps users easily manage their household finances and make appropriate financial decisions quickly. In particular, it combines an emotion engine that provides customized advice based on the user's emotional state, providing advice that is more suited to the user's needs.

[1644] Key Components of the System

[1645] The system includes the following major components:

[1646] 1. User Interface (Device): The interface through which the user asks questions or enters information. This could be a smartphone app or a web form.

[1647] 2. Data transmission means (terminal): A network communication function for sending user input to the server. Data is sent using an HTTP POST request.

[1648] 3. Server: Processes the data received from the user and generates answers using generative AI models. This server achieves its functions through multiple means.

[1649] Analysis method: The server analyzes the received data and converts it into a format that the generative AI model can understand.

[1650] Preprocessing means: A function that preprocesses user input data and converts it into a format suitable for the generative AI model.

[1651] Generative means: The function by which the generative AI model generates an appropriate answer based on the user's question.

[1652] Storage: A function to store the generated answers in a database.

[1653] Return method: A function to return the generated answer to the user's terminal.

[1654] Emotion Engine: A feature that recognizes the user's emotional state and adjusts the generated answers based on that data.

[1655] 4. Display means (terminal): A function to display the generated answers on the user's terminal.

[1656] System Operation

[1657] The system operates as follows:

[1658] User input and submission of question: The user inputs a question through the user interface and submits it to the server. For example, "How can I save money on food this month?"

[1659] Receiving and parsing the question: The server receives the request sent from the user device, analyzes the received data, and converts it into a format that the generative AI model can understand.

[1660] Recognizing and adjusting emotional state: The emotion engine analyzes the user's question data and recognizes the user's emotional state. At the same time, the pre-processing means pre-processes the data.

[1661] Answer generation: Using the generation method, the generative AI model generates appropriate answers based on the data. For example, if a user asks, "How can I save on food this month?", the generative AI model might generate answers such as, "Try taking advantage of sales days and buying ingredients in bulk. It's also effective to eat out less and cook at home more."

[1662] Emotion engine adjustment: The emotion engine adjusts its answers to match the user's emotional state. For example, if it recognizes that the user is feeling stressed, it will add a reassuring message such as "It's best to start within your limits without overdoing it."

[1663] Storing and returning answers: The server stores the generated answers in a database and returns them to the user device.

[1664] Display in user interface: The user device displays the received answer.

[1665] As a specific example of use, a user can input a question such as, "How can I save on leisure expenses this month?" The system generates an answer, recognizes the user's stress level through an emotion engine, and provides customized advice such as, "Try to systematically increase free or low-cost activities. Don't push yourself too hard, and proceed at a pace that suits you."

[1666] Example prompt sentence:

[1667] "User 'user123' asked: 'How can I save on leisure expenses this month?' Analyze the emotion of the user and generate a personalized financial advice response."

[1668] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1669] Step 1:

[1670] The user enters a question. The user enters a question using a smartphone app or web interface. For example, they might enter a question like, "How can I save money on food this month?" The input data includes the user ID and the question.

[1671] Step 2:

[1672] The device sends the input data to the server using an HTTP POST request, with basic data format checks performed on the device to ensure the user input is sent in the correct format.

[1673] Step 3:

[1674] The server receives the request sent by the user. The received data includes the user ID and the question. The server temporarily stores the received data and prepares for the next analysis process.

[1675] Step 4:

[1676] The analysis means analyzes the received data. The server uses the analysis means to convert the received data into a format that the generative AI model can understand. This analysis formats the data and removes unnecessary information.

[1677] Step 5:

[1678] The preprocessing means preprocesses the user question data. The preprocessing means tokenizes the data and converts it into an input format suitable for the generative AI model. During this process, the input is passed to the emotion engine for sentiment analysis.

[1679] Step 6:

[1680] The emotion engine recognizes the user's emotional state. The emotion engine analyzes the input data and identifies the user's emotional state (e.g., stress or anxiety). This emotion data is fed into a generative AI model, which then reflects it in the answer.

[1681] Step 7:

[1682] The generator uses a generative AI model to generate answers based on the user's question and sentiment data. The generative AI model uses specific prompts to generate appropriate financial advice. For example, if the question is, "How can I save on food this month?", the generated answer might be, "Take advantage of sale days and consider buying ingredients in bulk. It's also effective to reduce eating out and cook at home more."

[1683] Step 8:

[1684] The emotion engine adjusts the generated answers. The emotion engine takes into account the user's emotional state and adds reassuring or encouraging messages to the generated answers. For example, a message like "It's a good idea to start within your capabilities without overdoing it" might be added.

[1685] Step 9:

[1686] The server stores the generated answer in a database, including the user ID, question, generated answer, and timestamp, so the data can be stored for future reference.

[1687] Step 10:

[1688] The server sends the generated answer back to the user's device, and the saved answer is sent to the user's device as an HTTP response, allowing the user to receive advice in real time.

[1689] Step 11:

[1690] The user terminal displays the received answer. The generated answer is displayed to the user using a display means on the terminal. The user can check specific advice on the interface.

[1691] 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.

[1692] 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.

[1693] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] 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).

[1698] 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.

[1699] 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."

[1700] 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.

[1701] 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).

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] 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.

[1708] 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.

[1709] 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.

[1710] 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.

[1711] 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.

[1712] The following is further disclosed regarding the above embodiment.

[1713] (Claim 1)

[1714] an input means for a user to input a question;

[1715] transmitting means for transmitting the user's input to a server;

[1716] an analysis means for analyzing the user input received by the server and inputting the input into the generative AI model;

[1717] A generation means for generating an answer based on the user's input by the generative AI model;

[1718] a storage means for storing the generated answers in a database;

[1719] a return means for returning the answer stored in the storage means to the user terminal;

[1720] a display means for displaying the returned answer on the user terminal;

[1721] A system including:

[1722] (Claim 2)

[1723] 10. The system of claim 1, wherein the server further comprises preprocessing means for preprocessing the user's input.

[1724] (Claim 3)

[1725] 10. The system of claim 1, wherein the generative AI model generates personalized advice based on the user's financial data.

[1726] "Example 1"

[1727] (Claim 1)

[1728] an input means for a user to input a question;

[1729] transmitting means for transmitting the user's input to a server;

[1730] an analysis means for analyzing the user input received by the server and inputting the input into the generative AI model;

[1731] A generation means for generating an answer based on the user's input by the generative AI model;

[1732] a storage means for storing the generated answers in a database;

[1733] a return means for returning the answer stored in the storage means to the user terminal;

[1734] a display means for displaying the returned answer on the user terminal;

[1735] A system including:

[1736] (Claim 2)

[1737] 10. The system of claim 1, wherein the server further comprises preprocessing means for preprocessing the user's input.

[1738] (Claim 3)

[1739] 10. The system of claim 1, wherein the generative AI model generates personalized advice based on the user's financial data.

[1740] (Claim 4)

[1741] 10. The system of claim 1, wherein the terminal includes means for transmitting user input to the server using an HTTP POST request.

[1742] (Claim 5)

[1743] 2. The system according to claim 1, further comprising means for extracting keywords from the content of the question by natural language processing using an analysis means for the request data received by the server.

[1744] (Claim 6)

[1745] 2. The system according to claim 1, wherein the server includes means for storing the generated answers and question contents in a database together with the user ID and a timestamp.

[1746] (Claim 7)

[1747] 2. The system according to claim 1, further comprising means for the terminal to receive the response sent back from the server by encrypted communication and to display it securely.

[1748] "Application Example 1"

[1749] (Claim 1)

[1750] an input means for a user to input a question;

[1751] transmitting means for transmitting the user's input to a server;

[1752] an analysis means for analyzing the user input received by the server and inputting the input into the generative AI model;

[1753] A generation means for generating an answer based on the user's input by the generative AI model;

[1754] a storage means for storing the generated answers in a database;

[1755] a return means for returning the answer stored in the storage means to the user terminal;

[1756] a display means for displaying the returned answer on the user terminal;

[1757] A support tool that analyzes user questions and past spending data to generate optimal savings methods and investment plans using AI models.

[1758] A system including:

[1759] (Claim 2)

[1760] 10. The system of claim 1, wherein the server further comprises preprocessing means for preprocessing the user's input.

[1761] (Claim 3)

[1762] The system of claim 1, wherein the generative AI model generates personalized advice based on the user's financial data and provides it to the user through an electronic payment service.

[1763] "Example 2: Combining Emotion Engines"

[1764] (Claim 1)

[1765] an input means for a user to input a question;

[1766] transmitting means for transmitting the user's input to a server;

[1767] an analysis means for analyzing the user input received by the server and inputting the input into the generative AI model;

[1768] An emotion engine that recognizes and analyzes the user's emotional state,

[1769] A preprocessing means for preprocessing user input and converting it into a format suitable for the generative AI model;

[1770] A generation means for generating an answer based on the user's input by the generative AI model;

[1771] means for the emotion engine to adjust the generated answers based on the emotional state of the user;

[1772] a storage means for storing the generated answers in a database;

[1773] a return means for returning the answer stored in the storage means to the user terminal;

[1774] a display means for displaying the returned answer on the user terminal;

[1775] A system including:

[1776] (Claim 2)

[1777] 10. The system of claim 1, wherein the server further comprises preprocessing means for preprocessing the user's input.

[1778] (Claim 3)

[1779] 10. The system of claim 1, wherein the generative AI model generates personalized advice based on the user's financial data and emotional state.

[1780] "Application example 2 when combining emotion engines"

[1781] (Claim 1)

[1782] an input means for a user to input a question;

[1783] transmitting means for transmitting the user's input to a server;

[1784] an analysis means for analyzing the user input received by the server and inputting the input into the generative AI model;

[1785] A generation means for generating an answer based on the user's input by the generative AI model;

[1786] a storage means for storing the generated answers in a database;

[1787] a return means for returning the answer stored in the storage means to the user terminal;

[1788] a display means for displaying the returned answer on the user terminal;

[1789] a means for recognizing a user's emotional state using an emotion engine and tailoring responses based on that data;

[1790] A system including:

[1791] (Claim 2)

[1792] 10. The system of claim 1, wherein the server further comprises preprocessing means for preprocessing the user's input.

[1793] (Claim 3)

[1794] 10. The system of claim 1, wherein the generative AI model generates personalized advice based on the user's financial data and emotional state. [Explanation of symbols]

[1795] 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. an input means for a user to input a question; transmitting means for transmitting the user's input to a server; an analysis means for analyzing the user input received by the server and inputting the input into the generative AI model; A generation means for generating an answer based on the user's input by the generative AI model; a storage means for storing the generated answers in a database; a return means for returning the answer stored in the storage means to the user terminal; a display means for displaying the returned answer on the user terminal; A system including:

2. 2. The system of claim 1, wherein the server further comprises preprocessing means for preprocessing the user's input.

3. 10. The system of claim 1, wherein the generative AI model generates personalized advice based on the user's financial data.

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