System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a financial consultation from a user; analysis means including a natural language processing engine for analyzing the consultation; means for acquiring optimal information by collating a knowledge base based on the consultation analyzed by the analysis means; means for acquiring additional information from an external data source; means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the acquired information; means for transmitting the generated answer to a user terminal; and means for displaying the generated answer to the user.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] Traditionally, when users sought financial advice, face-to-face consultations were the norm, which was problematic in terms of time and expense. Other drawbacks included the difficulty of asking basic questions, discussing personal assets in front of family members, and refusing sales pitches for financial products. In response, there was a demand for a method that allowed users to receive free, confidential advice 24 hours a day. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides a system including: means for accepting financial consultations from users; analysis means including a natural language processing engine for analyzing the consultations; means for collating the consultations with a knowledge base to obtain optimal information based on the consultations analyzed by the analysis means; means for obtaining additional information from external data sources; means for generating answers to be proposed to the user using a generative artificial intelligence engine based on the obtained information; and means for transmitting the generated answers to a user terminal and displaying the generated answers to the user. This system allows users to receive financial consultations at any time, even for basic questions, free of charge, and as many times as they like, without their family members knowing.

[0006] "User" refers to a person who uses the system to seek financial advice.

[0007] "Financial consultation" refers to the act of asking questions or seeking advice related to money, such as investment, asset management, household management, and life plan design.

[0008] A "natural language processing engine" refers to a program or system that analyzes text entered by a user and understands its meaning.

[0009] The "analysis means" refers to a process or device for analyzing the content of a user's consultation using a natural language processing engine.

[0010] A "knowledge base" refers to a database or information repository that stores information and data related to finance.

[0011] A "generative artificial intelligence engine" refers to a machine learning model or algorithm that generates new text or responses based on input data.

[0012] "External data sources" refer to sources of financial information that do not exist within the system but are accessible through APIs or other means.

[0013] "Terminal" refers to the electronic device a user uses to access the system, such as a smartphone, tablet, or PC. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The system of the present invention accepts online financial consultations from users, analyzes them, and provides appropriate advice. A specific embodiment of the system will be described below.

[0036] System Overview

[0037] The system of the present invention is mainly composed of three main components: a user, a terminal, and a server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's question and sending it to the server. The server analyzes the received question, generates an answer, and sends it back to the user via the terminal.

[0038] Specific processing of the system

[0039] 1. User Interface and Input:

[0040] Users access the system using a terminal and input financial consultations. Input can be done in the form of a chat box or email.

[0041] Example: A user types a question such as "Tell me about investing for retirement."

[0042] 2. Data transmission:

[0043] The terminal converts the input data from the user into JSON format and sends it to the server via an HTTP request.

[0044] 3. Data Receipt and Analysis:

[0045] The server passes the received data to a natural language processing engine as an analytical means, and analyzes it to understand the user's question.

[0046] The natural language processing engine performs tokenization and dependency analysis to extract key keywords and understand the context of the question.

[0047] 4. Knowledge base matching and information retrieval:

[0048] The server then checks the analyzed question against a knowledge base, which contains a variety of financial data.

[0049] When necessary, the server retrieves up-to-date financial information from external data sources, including, for example, the latest investment rates and financial news.

[0050] 5. Generate answers:

[0051] The generative AI engine generates appropriate answers for the user based on the acquired information. This engine creates responses in natural language.

[0052] Example: "You have the following options for managing your assets after retirement: 1. Use the Tsumitate NISA (Nippon Individual Savings Account) 2. Utilize the small investment tax exemption system (iDeCo) 3. Diversify your investments."

[0053] 6. Submitting and Viewing Answers:

[0054] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[0055] As described above, the system of the present invention allows users to easily consult about financial matters anytime, anywhere and receive appropriate advice on the spot. Furthermore, since no face-to-face consultation is required, privacy is maintained and there are no costs involved. This allows users to make financial decisions with peace of mind.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] A user accesses the system using a terminal.

[0059] Access it from a browser on your computer or mobile device or launch a dedicated application.

[0060] Step 2:

[0061] A user inputs a financial consultation request into a terminal.

[0062] Enter text into a chat box or email form and click the send button.

[0063] Step 3:

[0064] The terminal converts the user's input data into JSON format.

[0065] It converts the input string into an appropriate data structure and prepares the HTTP request.

[0066] Step 4:

[0067] The terminal transmits the converted data to the server.

[0068] It sends the user's question data to the server via an HTTP request.

[0069] Step 5:

[0070] The server passes the received data to an analysis means (natural language processing engine).

[0071] It parses the incoming data and passes it as input to the NLP engine.

[0072] Step 6:

[0073] The server analyzes the consultation content to extract key keywords and contextual information.

[0074] It performs tokenization and dependency structure analysis to understand the user's intent.

[0075] Step 7:

[0076] The server consults a knowledge base to retrieve relevant information.

[0077] It sends queries to the database and retrieves appropriate advice and information.

[0078] Step 8:

[0079] The server retrieves additional information from external data sources as needed.

[0080] It calls external APIs to obtain the latest financial news, rates, etc.

[0081] Step 9:

[0082] The server uses a generative artificial intelligence engine to generate suggested answers for the user.

[0083] Based on the acquired information, it generates text in natural language.

[0084] Step 10:

[0085] The server sends the generated answer to the terminal.

[0086] The generated response data is converted into JSON format and sent to the terminal as an HTTP response.

[0087] Step 11:

[0088] The terminal receives and analyzes the response data from the server.

[0089] It parses the received data and converts it into a displayable format.

[0090] Step 12:

[0091] The terminal displays the answer to the user.

[0092] The answer is displayed in the user interface so that the user can confirm it.

[0093] Examples:

[0094] Example questions:

[0095] User: "What are the best investment strategies for retirement?"

[0096] Process flow:

[0097] 1. The user enters a question into the terminal and clicks the send button.

[0098] 2. The device converts the question into JSON format and sends it to the server.

[0099] 3. The server receives the data and passes it to the natural language processing engine.

[0100] 4. The server performs the analysis and extracts keywords such as "retirement" and "investment methods."

[0101] 5. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[0102] 6. If necessary, the server retrieves the latest investment information from an external data source.

[0103] 7. The generative AI engine generates the answer, "For your retirement, we recommend diversifying your investments and using iDeCo."

[0104] 8. The server sends the generated response to the device.

[0105] 9. The device receives the answer and displays it on the user interface: "For your retirement, we recommend diversifying your investments and using iDeCo."

[0106] Through the above steps, the system of the present invention provides optimal financial advice to the user.

[0107] Example 1

[0108] 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."

[0109] Conventional financial consultation systems have difficulty generating fast and accurate answers to questions submitted by users. In particular, advanced data analysis and artificial intelligence technologies are required to correctly understand users' questions and provide appropriate advice that includes the latest financial information. Furthermore, a user-friendly interface and real-time responses are required to improve the user experience. Therefore, the challenge is to create a system that can properly analyze users' questions and provide optimal information.

[0110] 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.

[0111] In this invention, the server includes: means for accepting a financial inquiry from a user; analysis means including a natural language processing engine for analyzing the inquiry; means for comparing the inquiry analyzed by the analysis means with a knowledge base to obtain optimal information; means for obtaining additional information from an external data source; means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information; means for transmitting the generated answer to the user terminal; means for displaying the generated answer on a user interface; means for the user terminal to convert data input by the user into JSON format and transmit it to the server via an HTTP request; means for the server to analyze the received data, obtain information from the knowledge base and external data source, and generate an answer using the generative artificial intelligence engine; and means for using a prompt sentence when generating the answer. This enables the server to provide quick and accurate answers to users' financial questions.

[0112] A "User" is an individual or entity that accesses the System to provide financial advice.

[0113] A "terminal" is a computing device through which a user accesses the system and sends and receives input data.

[0114] A "server" is a computer system that receives data sent by users, analyzes it, and generates a response.

[0115] "Consultation" refers to the act of a user inputting a financial problem or question into the system, or the content of that problem or question.

[0116] A "natural language processing engine" is software or algorithms that analyze and understand users' inquiries.

[0117] The "analysis means" refers to the entire process of analyzing a user's inquiry using a natural language processing engine.

[0118] A "knowledge base" is a database that stores data and information related to finance.

[0119] "External Data Sources" are external sources for obtaining up-to-date financial information and other relevant data not present in the knowledge base.

[0120] A "generative artificial intelligence engine" is an artificial intelligence technology that generates answers to user questions based on acquired information.

[0121] A "prompt sentence" is an instruction sentence that is input to a generative artificial intelligence engine to generate a specific response.

[0122] The present invention relates to a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system is mainly composed of three main components: a user, a terminal, and a server.

[0123] User operations

[0124] Users access the system using devices such as smartphones or PCs and input financial questions. Input can be done via chat box or email. For example, a user might input a question such as, "Please tell me about asset management for retirement."

[0125] Device Features

[0126] The device is responsible for converting user input data into JSON format and sending it to the server via an HTTP request. At this time, the device's browser uses JavaScript to convert the data into JSON format and send it as an Ajax request.

[0127] Server Processing

[0128] The server analyzes the received data using a natural language processing engine. SpaCy or another natural language processing engine can be used. This engine performs tokenization and dependency structure analysis to understand the user's question and extract key keywords. The server then checks the analyzed question against a knowledge base. The knowledge base stores data and information related to finance. If necessary, the server can obtain the latest information from external data sources (for example, financial data APIs).

[0129] Answer Generation

[0130] The server uses a generative AI engine (e.g., GPT-4) to generate an appropriate answer for the user. At this time, it uses prompt sentences to instruct the AI. For example, a prompt sentence such as, "The user is asking about asset management for retirement. Please answer including the following information: 1. Tsumitate NISA 2. iDeCo 3. Diversified investment" can be used.

[0131] Providing answers

[0132] The server sends the generated answer to the terminal, which displays it on the user interface. Specifically, the server returns the generated answer in JSON format to the terminal, and JavaScript on the terminal displays it in a chat box. An example of an answer might be something like, "You have the following options for asset management in retirement: 1. Use the NISA savings plan. 2. Utilize the iDeCo small investment tax exemption system. 3. Diversify your investments."

[0133] In this way, the system of the present invention allows users to easily consult about financial matters anytime, anywhere and receive appropriate advice on the spot. Since no face-to-face consultation is required, the system has the advantage of maintaining users' privacy and being cost-effective. Furthermore, the system provides users with the latest financial information in real time, allowing them to make financial decisions with peace of mind.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] Users access the system using devices such as smartphones or PCs to input financial inquiries, which can be input via chat box or email.

[0137] Input: User types, "Tell me about investing for retirement."

[0138] Output: The entered financial consultation details.

[0139] Specific operation: The user opens a web browser, accesses the system's web page, and enters the content of their inquiry in the chat box.

[0140] Step 2:

[0141] The terminal converts the data entered by the user into JSON format and sends it to the server via an HTTP request.

[0142] Input: Financial consultation details from the user.

[0143] Output: Data in JSON format.

[0144] What happens: JavaScript is executed in the device's browser, converting the user's input into JSON format, and then sending the converted data to the server as an Ajax request.

[0145] Step 3:

[0146] The server passes the received data to a natural language processing engine to analyze the user's question.

[0147] Input: User consultation data in JSON format.

[0148] Output: Parsed question meaning and keywords.

[0149] What it does: The server uses Flask to receive HTTP requests and read the JSON data, then passes this data to a natural language processing engine (e.g., spaCy) for tokenization and dependency analysis to understand the user's question.

[0150] Step 4:

[0151] The server then searches the knowledge base for relevant information based on the parsed question, and retrieves additional information from external data sources if necessary.

[0152] Input: Parsed question meaning and keywords.

[0153] Output: Relevant information retrieved from the knowledge base and external data sources.

[0154] Specific operation: The server queries a database (e.g., MySQL) to obtain relevant financial information, and simultaneously calls an external API (e.g., a financial data API) as needed to obtain the latest financial data.

[0155] Step 5:

[0156] The server uses a generative artificial intelligence engine to generate appropriate responses for the user based on the acquired information, using prompt sentences.

[0157] Input: Information obtained from the knowledge base and external data sources, prompt statement.

[0158] Output: The answer to the user.

[0159] Specific operation: Using the acquired information, the server sends a prompt to a generative AI engine (e.g., GPT-4) to generate a natural-language answer. For example, a prompt such as "The user is asking about asset management for retirement. Please respond with the following information: 1. Tsumitate NISA 2. iDeCo 3. Diversified investment" can be used.

[0160] Step 6:

[0161] The server sends the generated answer to the terminal, which displays the answer on a user interface.

[0162] Input: Answers from a generative artificial intelligence engine.

[0163] Output: Display answer to user.

[0164] Specific operation: The server returns the generated answer in JSON format to the device, and JavaScript on the device displays it in the chat box. Specific answers such as "You have the following options for managing your assets in retirement: 1. Use the Tsumitate NISA (Nippon Individual Savings Account) 2. Utilize the small investment tax exemption system (iDeCo) 3. Diversify your investments" are displayed.

[0165] In this way, a system is realized in which each processing step works together to provide appropriate financial advice to users.

[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] Conventional financial consultation systems require users to input questions in text format and then return the analysis results in text format, which makes them difficult to understand visually. Furthermore, there are also issues with low user engagement and a lack of interactive advice, resulting in low user satisfaction.

[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 means for accepting a financial consultation from a user, analysis means including a natural language processing engine for analyzing the consultation, means for comparing the consultation with a knowledge base to obtain optimal information based on the consultation analyzed by the analysis means, means for obtaining additional information from an external data source, means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information, means for transmitting the generated answer to a user terminal, means for displaying the generated answer to the user, means for providing financial advice in a virtual space using a virtual reality device, and means for visually presenting the advice in the virtual space. This allows the user to receive financial advice visually and interactively, thereby improving user engagement and satisfaction.

[0171] The "means for accepting financial inquiries from users" is an interface that allows users to input financial questions and inquiries into the system.

[0172] The "analysis means including a natural language processing engine for analyzing a consultation" refers to a device or system that uses natural language processing technology to analyze the consultation content input by a user and understand that content.

[0173] "Means of collating a knowledge base to obtain optimal information" refers to a technology that obtains the most relevant data from pre-stored information based on the analyzed consultation content.

[0174] "Means for obtaining additional information from external data sources" refers to techniques for obtaining up-to-date financial information and related data from the Internet and other external sources.

[0175] "Means for generating answers to be suggested to users using a generative artificial intelligence engine" refers to artificial intelligence technology for generating appropriate advice and answers for users based on collected information.

[0176] The "means for transmitting the generated answer to the user terminal" is a technique for transmitting the generated answer to the terminal used by the user.

[0177] The "means for displaying to the user" refers to a technique for displaying the submitted answers so that the user can visually confirm them.

[0178] A "means for providing financial advice in a virtual space using a virtual reality device" is a device that uses virtual reality technology to provide financial advice to a user in a virtual space.

[0179] "Means for visually presenting advice in a virtual space" refers to a technology that visually displays advice in a virtual space, making it easier for the user to understand.

[0180] This invention provides a system that allows users to consult about financial matters in a virtual space and receive appropriate advice in real time. Specifically, a virtual reality device is used to enable users to receive financial advice visually and interactively. Specific embodiments of this system are described below.

[0181] The system mainly consists of a user terminal, a server, and a virtual reality device (e.g., a head-mounted display). Users use these devices to access the virtual space and receive financial consultations.

[0182] Hardware and Software

[0183] User terminal: This refers to a smartphone or PC through which the user accesses the system.

[0184] Server: A cloud server is used as the execution environment for analysis and data processing.

[0185] Virtual reality device: Using Oculus Quest 2 as an example.

[0186] Natural language processing engine: Uses Google Cloud Natural Language.

[0187] Generative artificial intelligence engine: Uses OpenAI GPT-4.

[0188] Virtual environment generation engine: Unity is used.

[0189] System action

[0190] 1. Handling User Input

[0191] The user wears a head-mounted display and receives financial consultations by voice or text input. In the case of voice input, a "voice recognition engine" converts the voice into text, which then recognizes the content of the user's question.

[0192] 2. Data transmission and analysis

[0193] The user's device converts the user's input data into JSON format and sends it to the server via an HTTP request. The server then passes the received data to Google Cloud Natural Language for analysis. This analysis method extracts key keywords through tokenization and dependency structure analysis to understand the context of the question.

[0194] 3. Knowledge base matching and information acquisition

[0195] Based on the analysis results, the server checks its knowledge base to retrieve relevant financial data, and also retrieves additional information from external data sources, such as the latest investment rates and financial news.

[0196] 4. Answer Generation

[0197] Based on the information obtained, the server generates an answer using OpenAI GPT-4, a generative artificial intelligence engine that creates responses in natural language and provides specific, context-sensitive advice.

[0198] 5. Submitting and Viewing Your Answers

[0199] The generated answers are sent from the server to the user's terminal and displayed on the user's virtual reality device. The user receives visually interactive advice while interacting with an avatar in the virtual space.

[0200] Specific examples

[0201] Below are some example prompts for users seeking financial advice:

[0202] Example: Prompt to the generative AI engine when a user types "Please tell me about asset management for retirement":

[0203] "A user is asking about investing in retirement. Please provide specific advice including the following elements: NISA, iDeCo, and diversified investments."

[0204] This allows the system to provide users with financial advice in a visual and easy-to-understand format, increasing engagement and satisfaction.

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] The user wears a head-mounted display and inputs their financial consultation request by voice or text. In the case of voice input, a "voice recognition engine" converts the voice into text, and the user's question is obtained in text format.

[0208] Step 2:

[0209] The user device converts the acquired question content into JSON format and sends it to the server via an HTTP request. The input data is speech recognition results or text data, and the output is JSON format data.

[0210] Step 3:

[0211] The server passes the received JSON data to Google Cloud Natural Language for analysis. This analysis method extracts key keywords through tokenization and dependency structure analysis to understand the context of the question. The input data is the question data in JSON format, and the output data is the analysis result.

[0212] Step 4:

[0213] Based on the analysis results, the server checks the knowledge base to obtain relevant financial data, and also collects additional information from external data sources, such as the latest investment rates and financial news. The input data is the analysis results, and the output data is the obtained financial information.

[0214] Step 5:

[0215] The server generates specific advice based on the acquired information using the generative AI engine "OpenAI GPT-4." This generative AI engine creates a response in natural language based on the prompt. The input data is the acquired financial information and the prompt, and the output data is the generated answer.

[0216] Step 6:

[0217] The server sends the generated answer to the user terminal. The input data is the generated answer, and the output data is the data sent to the user terminal.

[0218] Step 7:

[0219] The user terminal displays the received answer on a virtual reality device. The user interacts with the avatar in the virtual space and receives visual and interactive advice. Specifically, the avatar in the virtual space provides advice to the user and displays visual charts and graphs. The input data is the answer sent from the server, and the output is advice displayed to the user's eyes.

[0220] The above are the specific steps of the processing of this system.

[0221] 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.

[0222] The present invention relates to a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system is further combined with an emotion engine that recognizes the user's emotions, making it possible to generate answers that correspond to the user's emotions.

[0223] System Overview

[0224] The system of the present invention mainly consists of three main components: a user, a terminal, and a server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's questions and emotions and sending them to the server. The server analyzes the received questions and emotions, generates an answer, and sends it back to the user via the terminal.

[0225] Specific processing of the system

[0226] 1. User Interface and Input:

[0227] Users access the system using a terminal and input financial consultations. Input can be made in the form of a chat box or email, and the emotion engine analyzes the emotions from the user's text.

[0228] Example: A user types a question such as, "Tell me about how to manage my finances for retirement. I'm very worried."

[0229] 2. Data transmission:

[0230] The terminal converts the input data from the user into JSON format and sends it to the server via an HTTP request.

[0231] 3. Data Receipt and Analysis:

[0232] The server passes the received data to a natural language processing engine as an analytical tool, and analyzes it to understand the user's questions and emotions.

[0233] The natural language processing engine performs tokenization and dependency analysis to extract key keywords and understand the context of the question.

[0234] The emotion engine analyzes emotions (e.g., anxiety, joy, anger, etc.) from the user's text input and provides them to the server.

[0235] 4. Knowledge base matching and information retrieval:

[0236] The server then checks the analyzed question against a knowledge base, which contains a variety of financial data.

[0237] When necessary, the server retrieves up-to-date financial information from external data sources, including, for example, the latest investment rates and financial news.

[0238] 5. Generate answers:

[0239] The generative AI engine generates responses appropriate to the user based on the acquired information and the analysis results of the emotion engine. This engine creates responses in natural language.

[0240] Example: "It's natural to be worried about managing your assets in retirement. We'll introduce you to ways to put your mind at ease, such as diversifying your investments and taking advantage of iDeCo."

[0241] 6. Submitting and Viewing Answers:

[0242] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[0243] Examples:

[0244] Example questions:

[0245] User: "What are some recommendations for investing in retirement? I'm very worried."

[0246] Process flow:

[0247] 1. The user enters a question into the terminal and clicks the send button.

[0248] 2. The device converts the question and emotion into JSON format and sends it to the server.

[0249] 3. The server receives the data and passes it to the natural language processing engine and emotion engine.

[0250] 4. The server performs the analysis and extracts keywords such as "retirement" and "investment methods" as well as the user's emotion of "worry."

[0251] 5. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[0252] 6. If necessary, the server retrieves the latest investment information from an external data source.

[0253] 7. The generative AI engine generates an emotionally sensitive response: "It's natural to be worried about asset management in retirement. Please consider diversifying your investments or using iDeCo."

[0254] 8. The server sends the generated response to the device.

[0255] 9. The device receives the answer and displays it on the user interface: "It's natural to be worried about asset management in your retirement years. Please consider diversifying your investments or using iDeCo."

[0256] The system allows users to receive specific, emotionally sensitive financial advice.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] A user accesses the system using a terminal.

[0260] Access it from a browser on your computer or mobile device or launch a dedicated application.

[0261] Step 2:

[0262] A user inputs a financial consultation request into a terminal.

[0263] Enter text into a chat box or email form and click the send button.

[0264] Step 3:

[0265] The terminal converts the user-entered data into JSON format.

[0266] It converts the input string into an appropriate data structure and prepares the HTTP request.

[0267] Step 4:

[0268] The terminal transmits the converted data to the server.

[0269] It sends the user's question data to the server via an HTTP request.

[0270] Step 5:

[0271] The server passes the received data to the analysis means (natural language processing engine and emotion engine).

[0272] It parses the received data and passes it as input to the natural language processing engine and emotion engine.

[0273] Step 6:

[0274] The server analyzes the consultation content using a natural language processing engine.

[0275] It performs tokenization and dependency structure analysis to extract key keywords and contextual information from the user's question.

[0276] Step 7:

[0277] The server analyzes the user's emotions using an emotion engine.

[0278] It extracts emotional patterns from input text and identifies major emotional categories such as "anxiety," "joy," and "anger."

[0279] Step 8:

[0280] The server consults a knowledge base to retrieve relevant information.

[0281] It works by sending queries to a database to retrieve relevant financial information and advice.

[0282] Step 9:

[0283] The server retrieves additional information from external data sources as needed.

[0284] It calls external APIs to retrieve the latest financial news, market rates, etc.

[0285] Step 10:

[0286] The server uses a generative artificial intelligence engine to generate answers to suggest to the user based on the acquired information and the results of emotion analysis.

[0287] The system creates responses in natural language and incorporates content that takes the user's feelings into consideration.

[0288] Step 11:

[0289] The server sends the generated answer to the terminal.

[0290] The generated response data is converted into JSON format and sent to the terminal as an HTTP response.

[0291] Step 12:

[0292] The terminal receives and analyzes the response data from the server.

[0293] It parses the received data and converts it into strings and other UI elements.

[0294] Step 13:

[0295] The terminal displays the answer to the user.

[0296] The answer is displayed in the user interface so that the user can confirm it.

[0297] Examples:

[0298] Example questions:

[0299] User: "What are some recommendations for investing in retirement? I'm very worried."

[0300] Process flow:

[0301] 1. The user enters a question into the terminal and clicks the send button.

[0302] 2. The device converts the question and emotion data into JSON format and sends it to the server.

[0303] 3. The server receives the data and passes it to the natural language processing engine and emotion engine.

[0304] 4. The server performs natural language processing to extract keywords such as "retirement" and "investment methods."

[0305] 5. The server runs the emotion engine and identifies the user's emotion of "worry."

[0306] 6. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[0307] 7. If necessary, the server retrieves the latest investment information from an external data source.

[0308] 8. The generative AI engine generates an emotionally sensitive response: "It's natural to be worried about asset management in retirement. Please consider diversifying your investments or using iDeCo."

[0309] 9. The server sends the generated response to the device.

[0310] 10. The device receives the answer and displays it on the user interface: "It's natural to be worried about asset management in your retirement years. Please consider diversifying your investments or using iDeCo."

[0311] The system allows users to receive specific, emotionally sensitive financial advice.

[0312] Example 2

[0313] 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."

[0314] Conventional financial consultation systems provide only simple information without considering the user's feelings, which means that users are unable to receive satisfactory advice. This can result in users taking a long time to resolve their financial concerns and questions, which can lead to a decrease in satisfaction.

[0315] 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.

[0316] In this invention, the server includes means for accepting financial consultations from users, analysis means including a natural language processing engine and an emotion engine for analyzing the consultations and the user's emotions, means for comparing the consultations and emotions analyzed by the analysis means with a knowledge base to obtain optimal information, means for obtaining additional information from an external data source, means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information and the analyzed emotions, means for transmitting the generated answer to a user terminal, and means for displaying the generated answer to the user. This makes it possible to provide appropriate advice that takes the user's emotions into consideration, thereby improving user satisfaction.

[0317] "User" refers to any individual or entity that accesses the System and provides financial advice.

[0318] "Financial consultation" refers to the act of a user asking questions or concerns about financial matters such as asset management, investment, savings, and insurance.

[0319] A "terminal" is a device used by a user to access the system, and includes a smartphone, PC, tablet, etc.

[0320] "Server" refers to a computer system that receives requests from users, analyzes data, retrieves information, and generates and sends responses.

[0321] A "natural language processing engine" refers to a software system that analyzes user input and understands key keywords and context.

[0322] "Emotion engine" refers to a software system for analyzing emotions from a user's text and identifying their emotional state.

[0323] "Analysis means" refers to the process of analyzing the user's consultation content and emotions using a natural language processing engine and an emotion engine.

[0324] A "knowledge base" refers to a database that stores financial data and is used to provide information appropriate to a user's inquiry.

[0325] "External Data Source" refers to an external data provider service or API that the Server accesses to obtain additional information as needed.

[0326] A "generative artificial intelligence engine" refers to an artificial intelligence system that generates responses to users in natural language based on acquired information and the results of sentiment analysis.

[0327] "Means for generating an answer" refers to a process of using a generative artificial intelligence engine to generate an answer appropriate for the user.

[0328] "Means for sending to the user terminal" refers to the process by which the server sends the generated response to the terminal used by the user.

[0329] The term "means for displaying to the user" refers to a process in which the terminal displays the answer received from the server on the user interface and presents it to the user.

[0330] The present invention is a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. By combining an emotion engine that recognizes the user's emotions, the system generates answers that correspond to the user's emotions. Detailed embodiments of the present invention will be described below.

[0331] System Overview

[0332] This system is primarily composed of three main components: a server, a device, and a user. Users access the system using a device such as a smartphone or PC and input a question. The device acquires the user's question along with its associated emotions and sends them to the server. The server analyzes this information, generates an appropriate answer, and sends it back to the device. The device then displays the received answer on a user interface and presents it to the user.

[0333] Hardware and software used

[0334] Hardware

[0335] The devices used by users are electronic devices such as general smartphones and personal computers, while the servers use a cloud-based server system and are equipped with high-performance computing power and storage.

[0336] software

[0337] The following software is used:

[0338] Natural language processing engines (e.g., SpaCy, NLTK, etc.): Used to analyze user text input and understand key keywords and context.

[0339] Sentiment engine (e.g., Hugging Face sentiment analysis model): Used to parse emotions from user text.

[0340] Knowledge base: A financial database, including SQL databases and Elasticsearch.

[0341] Generative AI engine (e.g., OpenAI GPT-3): Generates answers based on acquired information and sentiment analysis results.

[0342] Overview of the processing flow

[0343] The server receives the user's input and analyzes it using a natural language processing engine and an emotion engine. It then retrieves the necessary information from a knowledge base and external data sources and generates an appropriate answer using a generative AI engine. The generated answer is then presented to the user via the terminal from the server.

[0344] Specific examples

[0345] User input:

[0346] "What investment strategies would you recommend for my retirement? I'm very worried."

[0347] Example prompt sentence:

[0348] "It's natural to be worried about asset management in retirement. We will introduce ways to give you peace of mind, such as diversifying your investments and utilizing iDeCo."

[0349] In this way, the user can receive specific and emotionally sensitive financial advice from the system. The present invention allows the user to resolve financial issues in a more satisfactory manner.

[0350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0351] Step 1: User Input

[0352] Users access the system using devices such as smartphones or PCs and input financial questions. Input can be done via chat box or email. The emotion engine analyzes the emotions from the user's text.

[0353] Specific behavior:

[0354] The user launches a browser and accesses the system's website.

[0355] In the chat box, type, "Please tell me about asset management for retirement. I'm very worried." and click the send button.

[0356] Input: User's question text (e.g., "Please tell me about asset management for retirement. I'm very worried.")

[0357] Output: User question text and sentiment estimation results

[0358] Step 2: Sending data

[0359] The device converts the input data from the user (question text and emotion estimation results) into JSON format and sends it to the server using an HTTP request.

[0360] Specific behavior:

[0361] The terminal parses the input data into JSON format.

[0362] The front-end JavaScript sends the JSON formatted data to the server as an HTTP POST request.

[0363] Input: User question text and sentiment estimation results

[0364] Output: JSON format data sent to the server

[0365] Step 3: Receiving and analyzing data

[0366] The server receives the data sent from the device and passes it to a natural language processing engine and emotion engine. The natural language processing engine analyzes the question and extracts key keywords, and the emotion engine analyzes emotions.

[0367] Specific behavior:

[0368] The server receives the HTTP request and extracts the data from the body.

[0369] The data is passed to a natural language processing engine (e.g., SpaCy) to perform tokenization and dependency analysis.

[0370] A natural language processing engine extracts keywords from the question (e.g., "retirement" and "asset management").

[0371] The emotion engine analyzes the text and identifies emotions (e.g., "worry").

[0372] Input: JSON format data sent to the server

[0373] Output: Parsed keywords and sentiment data

[0374] Step 4: Knowledge base matching and information acquisition

[0375] The server then compares the analyzed question content and sentiment against a knowledge base to retrieve relevant information, including the latest financial information from external data sources if necessary.

[0376] Specific behavior:

[0377] The server uses SQL or Elasticsearch queries to retrieve relevant information from a built-in knowledge base.

[0378] If necessary, API requests are used to obtain new information from external financial data providers.

[0379] Input: Parsed keywords and sentiment data

[0380] Output: Information related to the user's question

[0381] Step 5: Generate an answer

[0382] The server uses a generative AI engine to generate responses appropriate for the user based on the acquired information and the results of sentiment analysis. This engine creates responses in natural language.

[0383] Specific behavior:

[0384] The server creates a prompt for the generative AI engine (e.g., GPT-3) and sends the question data and related information.

[0385] The AI ​​engine generates answers based on the prompts.

[0386] The generated answer is returned to the server.

[0387] Input: Acquired information and sentiment analysis results

[0388] Output: Generated answer text

[0389] Step 6: Submit and view your responses

[0390] The server sends the generated answer to the user terminal, which displays the received answer on a user interface and presents it to the user.

[0391] Specific behavior:

[0392] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[0393] The device receives the HTTP response, and the front-end JavaScript displays the response in the chat box.

[0394] Input: Generated answer text

[0395] Output: The answer text that is displayed in the user interface

[0396] (Application example 2)

[0397] 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."

[0398] With the recent diversification of financial services, users have more opportunities to seek a wide range of financial advice. In particular, online financial advice often provides uniform answers that ignore the user's feelings, which can lead to user dissatisfaction. Therefore, there is a need for a system that takes the user's feelings into consideration and provides more appropriate and personalized financial advice.

[0399] The identification process by the identification 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 analysis means including a natural language processing engine that analyzes the consultation and an emotion engine that analyzes the user's emotions, means for collating the consultation and emotions analyzed by the analysis means with a knowledge base to obtain optimal information, and means for obtaining additional information from an external data source. This makes it possible to provide appropriate financial advice that takes into consideration the user's emotions.

[0400] A "user" is an entity that uses the system to provide financial advice.

[0401] "Financial consultation" refers to questions or doubts that a user has about financial information or advice.

[0402] A "natural language processing engine" is software that analyzes text entered by a user and understands its intent and content.

[0403] An "emotion engine" is software that analyzes and recognizes emotions from user input text.

[0404] The "analysis means" includes a natural language processing engine and an emotion engine, and has the function of analyzing the user's consultation and emotions.

[0405] A "knowledge base" is a database containing data related to finance.

[0406] An "external data source" is a data source other than the knowledge base for obtaining up-to-date financial information.

[0407] The "generative artificial intelligence engine" is an engine that generates answers based on the acquired information and emotion analysis results.

[0408] A "user terminal" is a device through which a user accesses the system, inputs financial inquiries, and receives responses.

[0409] This invention is a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system has the function of generating answers that correspond to the user's emotions by combining it with an emotion engine that recognizes the user's emotions.

[0410] System Overview

[0411] This system is primarily composed of three main components: the user, the terminal, and the server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's questions and emotions and sending them to the server. The server analyzes the received questions and emotions, generates an answer, and sends it back to the user via the terminal.

[0412] Hardware or software used

[0413] Hardware: smartphones, PCs, servers

[0414] software:

[0415] Natural Language Processing Engine: Natural Language Processing model using Hugging Face's transformers library

[0416] Emotion Engine: An emotion analysis model using the Hugging Face transformers library

[0417] Generative AI engine: Generative AI model using the same library

[0418] Data processing and calculation

[0419] The server performs the following processing on the data received from the user.

[0420] 1. Data reception and analysis:

[0421] Consultation data of the user transmitted from the terminal is received.

[0422] A natural language processing engine is used to analyze the user's question through tokenization and dependency structure analysis.

[0423] An emotion engine is used to analyze emotions (e.g., anxiety, joy, anger, etc.) from user text.

[0424] 2. Knowledge base matching and external data retrieval:

[0425] Based on the analyzed question content and sentiment, the knowledge base is collated to obtain the most appropriate information.

[0426] Obtain up-to-date financial information from external data sources.

[0427] 3. Generate answers:

[0428] A generative artificial intelligence engine is used to generate answers appropriate for the user based on the acquired information and sentiment analysis results.

[0429] 4. Submit and view your answers:

[0430] The generated answer is sent to the device.

[0431] The terminal displays the received answer on the user interface and presents it to the user.

[0432] Specific examples

[0433] For example, if a user inputs a question into the system such as "I'm not good at saving money and I don't know what to do," the following processing will be carried out.

[0434] 1. The natural language processing engine extracts the keywords "savings" and "not good at," and the emotion engine analyzes the emotion "anxiety."

[0435] 2. The server checks the knowledge base to get the best information on how to save money.

[0436] 3. Also obtain information on the latest savings methods from external data sources.

[0437] 4. In response to a user's question, "I'm not good at saving money and I don't know what to do," the generative artificial intelligence engine generates the answer, "It's natural to find saving money difficult. The important thing is to save even a little bit each month. Don't rush, just keep at it."

[0438] 5. This generated answer is sent to the user's device.

[0439] Prompt Sentence Examples

[0440] text

[0441] User input: "I'm not good at saving money and I don't know what to do."

[0442] Question: "How to save money", Emotions from Japanese analysis: "Anxiety"

[0443] Example advice it might generate: "It's natural to feel like saving money is difficult. The key is to save a little each month. Don't rush it, just keep at it."

[0444] In this way, the present invention can provide specific and appropriate financial advice while taking into consideration the user's feelings.

[0445] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0446] Step 1:

[0447] The user inputs a financial inquiry into the terminal and clicks the send button. The input inquiry is in text format, such as "I'm not good at saving money and I don't know what to do."

[0448] Step 2:

[0449] The device converts the input data from the user into JSON format and sends it to the server via an HTTP request. The input data includes the user's inquiry.

[0450] Input: User-entered consultation text

[0451] Output: Data converted to JSON format

[0452] Step 3:

[0453] The server passes the data received from the terminal to a natural language processing engine as an analytical means, and analyzes the user's consultation content. It performs tokenization and dependency structure analysis to extract the user's intention and topic.

[0454] Input: JSON format consultation data

[0455] Output: Extracted keywords and context information

[0456] Step 4:

[0457] The server uses an emotion engine to analyze the user's emotions from the consultation content, and identifies emotions such as "anxiety" or "joy" as examples.

[0458] Input: Text of consultation

[0459] Output: User's emotional information (e.g., anxiety)

[0460] Step 5:

[0461] Based on the analyzed question content and sentiment, the server compares it with a knowledge base that stores financial data and retrieves the most appropriate information.

[0462] Input: Extracted keywords and sentiment information

[0463] Output: The best information retrieved from the knowledge base

[0464] Step 6:

[0465] If necessary, the server also retrieves up-to-date financial information from external data sources, such as the latest savings and investment information.

[0466] Input: Query required financial information

[0467] Output: Latest information retrieved from an external data source

[0468] Step 7:

[0469] The server uses a generative artificial intelligence engine to generate appropriate answers for the user based on the analyzed data and acquired information, and the generated answers are expressed in natural language and take emotional aspects into consideration.

[0470] Input: Information from knowledge base and external data sources, sentiment analysis results

[0471] Output: Generated answer text (e.g., "It's natural to feel that saving money is difficult. The important thing is to save even a little each month. Don't rush, just keep at it.")

[0472] Step 8:

[0473] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[0474] Input: Answer text sent from the server

[0475] Output: The answer displayed in the user interface

[0476] The above is a series of processing steps for the system that realizes the application example.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] [Second embodiment]

[0481] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0482] 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.

[0483] 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).

[0484] 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.

[0485] 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.

[0486] 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).

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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."

[0493] The system of the present invention accepts online financial consultations from users, analyzes them, and provides appropriate advice. A specific embodiment of the system will be described below.

[0494] System Overview

[0495] The system of the present invention is mainly composed of three main components: a user, a terminal, and a server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's question and sending it to the server. The server analyzes the received question, generates an answer, and sends it back to the user via the terminal.

[0496] Specific processing of the system

[0497] 1. User Interface and Input:

[0498] Users access the system using a terminal and input financial consultations. Input can be done in the form of a chat box or email.

[0499] Example: A user types a question such as "Tell me about investing for retirement."

[0500] 2. Data transmission:

[0501] The terminal converts the input data from the user into JSON format and sends it to the server via an HTTP request.

[0502] 3. Data Receipt and Analysis:

[0503] The server passes the received data to a natural language processing engine as an analytical means, and analyzes it to understand the user's question.

[0504] The natural language processing engine performs tokenization and dependency analysis to extract key keywords and understand the context of the question.

[0505] 4. Knowledge base matching and information retrieval:

[0506] The server then checks the analyzed question against a knowledge base, which contains a variety of financial data.

[0507] When necessary, the server retrieves up-to-date financial information from external data sources, including, for example, the latest investment rates and financial news.

[0508] 5. Generate answers:

[0509] The generative AI engine generates appropriate answers for the user based on the acquired information. This engine creates responses in natural language.

[0510] Example: "You have the following options for managing your assets after retirement: 1. Use the Tsumitate NISA (Nippon Individual Savings Account) 2. Utilize the small investment tax exemption system (iDeCo) 3. Diversify your investments."

[0511] 6. Submitting and Viewing Answers:

[0512] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[0513] As described above, the system of the present invention allows users to easily consult about financial matters anytime, anywhere and receive appropriate advice on the spot. Furthermore, since no face-to-face consultation is required, privacy is maintained and there are no costs involved. This allows users to make financial decisions with peace of mind.

[0514] The processing flow will be explained below.

[0515] Step 1:

[0516] A user accesses the system using a terminal.

[0517] Access it from a browser on your computer or mobile device or launch a dedicated application.

[0518] Step 2:

[0519] A user inputs a financial consultation request into a terminal.

[0520] Enter text into a chat box or email form and click the send button.

[0521] Step 3:

[0522] The terminal converts the user's input data into JSON format.

[0523] It converts the input string into an appropriate data structure and prepares the HTTP request.

[0524] Step 4:

[0525] The terminal transmits the converted data to the server.

[0526] It sends the user's question data to the server via an HTTP request.

[0527] Step 5:

[0528] The server passes the received data to an analysis means (natural language processing engine).

[0529] It parses the incoming data and passes it as input to the NLP engine.

[0530] Step 6:

[0531] The server analyzes the consultation content to extract key keywords and contextual information.

[0532] It performs tokenization and dependency structure analysis to understand the user's intent.

[0533] Step 7:

[0534] The server consults a knowledge base to retrieve relevant information.

[0535] It sends queries to the database and retrieves appropriate advice and information.

[0536] Step 8:

[0537] The server retrieves additional information from external data sources as needed.

[0538] It calls external APIs to obtain the latest financial news, rates, etc.

[0539] Step 9:

[0540] The server uses a generative artificial intelligence engine to generate suggested answers for the user.

[0541] Based on the acquired information, it generates text in natural language.

[0542] Step 10:

[0543] The server sends the generated answer to the terminal.

[0544] The generated response data is converted into JSON format and sent to the terminal as an HTTP response.

[0545] Step 11:

[0546] The terminal receives and analyzes the response data from the server.

[0547] It parses the received data and converts it into a displayable format.

[0548] Step 12:

[0549] The terminal displays the answer to the user.

[0550] The answer is displayed in the user interface so that the user can confirm it.

[0551] Examples:

[0552] Example questions:

[0553] User: "What are the best investment strategies for retirement?"

[0554] Process flow:

[0555] 1. The user enters a question into the terminal and clicks the send button.

[0556] 2. The device converts the question into JSON format and sends it to the server.

[0557] 3. The server receives the data and passes it to the natural language processing engine.

[0558] 4. The server performs the analysis and extracts keywords such as "retirement" and "investment methods."

[0559] 5. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[0560] 6. If necessary, the server retrieves the latest investment information from an external data source.

[0561] 7. The generative AI engine generates the answer, "For your retirement, we recommend diversifying your investments and using iDeCo."

[0562] 8. The server sends the generated response to the device.

[0563] 9. The device receives the answer and displays it on the user interface: "For your retirement, we recommend diversifying your investments and using iDeCo."

[0564] Through the above steps, the system of the present invention provides optimal financial advice to the user.

[0565] Example 1

[0566] 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."

[0567] Conventional financial consultation systems have difficulty generating fast and accurate answers to questions submitted by users. In particular, advanced data analysis and artificial intelligence technologies are required to correctly understand users' questions and provide appropriate advice that includes the latest financial information. Furthermore, a user-friendly interface and real-time responses are required to improve the user experience. Therefore, the challenge is to create a system that can properly analyze users' questions and provide optimal information.

[0568] 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.

[0569] In this invention, the server includes: means for accepting a financial inquiry from a user; analysis means including a natural language processing engine for analyzing the inquiry; means for comparing the inquiry analyzed by the analysis means with a knowledge base to obtain optimal information; means for obtaining additional information from an external data source; means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information; means for transmitting the generated answer to the user terminal; means for displaying the generated answer on a user interface; means for the user terminal to convert data input by the user into JSON format and transmit it to the server via an HTTP request; means for the server to analyze the received data, obtain information from the knowledge base and external data source, and generate an answer using the generative artificial intelligence engine; and means for using a prompt sentence when generating the answer. This enables the server to provide quick and accurate answers to users' financial questions.

[0570] A "User" is an individual or entity that accesses the System to provide financial advice.

[0571] A "terminal" is a computing device through which a user accesses the system and sends and receives input data.

[0572] A "server" is a computer system that receives data sent by users, analyzes it, and generates a response.

[0573] "Consultation" refers to the act of a user inputting a financial problem or question into the system, or the content of that problem or question.

[0574] A "natural language processing engine" is software or algorithms that analyze and understand users' inquiries.

[0575] The "analysis means" refers to the entire process of analyzing a user's inquiry using a natural language processing engine.

[0576] A "knowledge base" is a database that stores data and information related to finance.

[0577] "External Data Sources" are external sources for obtaining up-to-date financial information and other relevant data not present in the knowledge base.

[0578] A "generative artificial intelligence engine" is an artificial intelligence technology that generates answers to user questions based on acquired information.

[0579] A "prompt sentence" is an instruction sentence that is input to a generative artificial intelligence engine to generate a specific response.

[0580] The present invention relates to a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system is mainly composed of three main components: a user, a terminal, and a server.

[0581] User operations

[0582] Users access the system using devices such as smartphones or PCs and input financial questions. Input can be done via chat box or email. For example, a user might input a question such as, "Please tell me about asset management for retirement."

[0583] Device Features

[0584] The device is responsible for converting user input data into JSON format and sending it to the server via an HTTP request. At this time, the device's browser uses JavaScript to convert the data into JSON format and send it as an Ajax request.

[0585] Server Processing

[0586] The server analyzes the received data using a natural language processing engine. SpaCy or another natural language processing engine can be used. This engine performs tokenization and dependency structure analysis to understand the user's question and extract key keywords. The server then checks the analyzed question against a knowledge base. The knowledge base stores data and information related to finance. If necessary, the server can obtain the latest information from external data sources (for example, financial data APIs).

[0587] Answer Generation

[0588] The server uses a generative AI engine (e.g., GPT-4) to generate an appropriate answer for the user. At this time, it uses prompt sentences to instruct the AI. For example, a prompt sentence such as, "The user is asking about asset management for retirement. Please answer including the following information: 1. Tsumitate NISA 2. iDeCo 3. Diversified investment" can be used.

[0589] Providing answers

[0590] The server sends the generated answer to the terminal, which displays it on the user interface. Specifically, the server returns the generated answer in JSON format to the terminal, and JavaScript on the terminal displays it in a chat box. An example of an answer might be something like, "You have the following options for asset management in retirement: 1. Use the NISA savings plan. 2. Utilize the iDeCo small investment tax exemption system. 3. Diversify your investments."

[0591] In this way, the system of the present invention allows users to easily consult about financial matters anytime, anywhere and receive appropriate advice on the spot. Since no face-to-face consultation is required, the system has the advantage of maintaining users' privacy and being cost-effective. Furthermore, the system provides users with the latest financial information in real time, allowing them to make financial decisions with peace of mind.

[0592] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0593] Step 1:

[0594] Users access the system using devices such as smartphones or PCs to input financial inquiries, which can be input via chat box or email.

[0595] Input: User types, "Tell me about investing for retirement."

[0596] Output: The entered financial consultation details.

[0597] Specific operation: The user opens a web browser, accesses the system's web page, and enters the content of their inquiry in the chat box.

[0598] Step 2:

[0599] The terminal converts the data entered by the user into JSON format and sends it to the server via an HTTP request.

[0600] Input: Financial consultation details from the user.

[0601] Output: Data in JSON format.

[0602] What happens: JavaScript is executed in the device's browser, converting the user's input into JSON format, and then sending the converted data to the server as an Ajax request.

[0603] Step 3:

[0604] The server passes the received data to a natural language processing engine to analyze the user's question.

[0605] Input: User consultation data in JSON format.

[0606] Output: Parsed question meaning and keywords.

[0607] What it does: The server uses Flask to receive HTTP requests and read the JSON data, then passes this data to a natural language processing engine (e.g., spaCy) for tokenization and dependency analysis to understand the user's question.

[0608] Step 4:

[0609] The server then searches the knowledge base for relevant information based on the parsed question, and retrieves additional information from external data sources if necessary.

[0610] Input: Parsed question meaning and keywords.

[0611] Output: Relevant information retrieved from the knowledge base and external data sources.

[0612] Specific operation: The server queries a database (e.g., MySQL) to obtain relevant financial information, and simultaneously calls an external API (e.g., a financial data API) as needed to obtain the latest financial data.

[0613] Step 5:

[0614] The server uses a generative artificial intelligence engine to generate appropriate responses for the user based on the acquired information, using prompt sentences.

[0615] Input: Information obtained from the knowledge base and external data sources, prompt statement.

[0616] Output: The answer to the user.

[0617] Specific operation: Using the acquired information, the server sends a prompt to a generative AI engine (e.g., GPT-4) to generate a natural-language answer. For example, a prompt such as "The user is asking about asset management for retirement. Please respond with the following information: 1. Tsumitate NISA 2. iDeCo 3. Diversified investment" can be used.

[0618] Step 6:

[0619] The server sends the generated answer to the terminal, which displays the answer on a user interface.

[0620] Input: Answers from a generative artificial intelligence engine.

[0621] Output: Display answer to user.

[0622] Specific operation: The server returns the generated answer in JSON format to the device, and JavaScript on the device displays it in the chat box. Specific answers such as "You have the following options for managing your assets in retirement: 1. Use the Tsumitate NISA (Nippon Individual Savings Account) 2. Utilize the small investment tax exemption system (iDeCo) 3. Diversify your investments" are displayed.

[0623] In this way, a system is realized in which each processing step works together to provide appropriate financial advice to users.

[0624] (Application example 1)

[0625] 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."

[0626] Conventional financial consultation systems require users to input questions in text format and then return the analysis results in text format, which makes them difficult to understand visually. Furthermore, there are also issues with low user engagement and a lack of interactive advice, resulting in low user satisfaction.

[0627] 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.

[0628] In this invention, the server includes means for accepting a financial consultation from a user, analysis means including a natural language processing engine for analyzing the consultation, means for comparing the consultation with a knowledge base to obtain optimal information based on the consultation analyzed by the analysis means, means for obtaining additional information from an external data source, means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information, means for transmitting the generated answer to a user terminal, means for displaying the generated answer to the user, means for providing financial advice in a virtual space using a virtual reality device, and means for visually presenting the advice in the virtual space. This allows the user to receive financial advice visually and interactively, thereby improving user engagement and satisfaction.

[0629] The "means for accepting financial inquiries from users" is an interface that allows users to input financial questions and inquiries into the system.

[0630] The "analysis means including a natural language processing engine for analyzing a consultation" refers to a device or system that uses natural language processing technology to analyze the consultation content input by a user and understand that content.

[0631] "Means of collating a knowledge base to obtain optimal information" refers to a technology that obtains the most relevant data from pre-stored information based on the analyzed consultation content.

[0632] "Means for obtaining additional information from external data sources" refers to techniques for obtaining up-to-date financial information and related data from the Internet and other external sources.

[0633] "Means for generating answers to be suggested to users using a generative artificial intelligence engine" refers to artificial intelligence technology for generating appropriate advice and answers for users based on collected information.

[0634] The "means for transmitting the generated answer to the user terminal" is a technique for transmitting the generated answer to the terminal used by the user.

[0635] The "means for displaying to the user" refers to a technique for displaying the submitted answers so that the user can visually confirm them.

[0636] A "means for providing financial advice in a virtual space using a virtual reality device" is a device that uses virtual reality technology to provide financial advice to a user in a virtual space.

[0637] "Means for visually presenting advice in a virtual space" refers to a technology that visually displays advice in a virtual space, making it easier for the user to understand.

[0638] This invention provides a system that allows users to consult about financial matters in a virtual space and receive appropriate advice in real time. Specifically, a virtual reality device is used to enable users to receive financial advice visually and interactively. Specific embodiments of this system are described below.

[0639] The system mainly consists of a user terminal, a server, and a virtual reality device (e.g., a head-mounted display). Users use these devices to access the virtual space and receive financial consultations.

[0640] Hardware and Software

[0641] User terminal: This refers to a smartphone or PC through which the user accesses the system.

[0642] Server: A cloud server is used as the execution environment for analysis and data processing.

[0643] Virtual reality device: Using Oculus Quest 2 as an example.

[0644] Natural language processing engine: Uses Google Cloud Natural Language.

[0645] Generative artificial intelligence engine: Uses OpenAI GPT-4.

[0646] Virtual environment generation engine: Unity is used.

[0647] System action

[0648] 1. Handling User Input

[0649] The user wears a head-mounted display and receives financial consultations by voice or text input. In the case of voice input, a "voice recognition engine" converts the voice into text, which then recognizes the content of the user's question.

[0650] 2. Data transmission and analysis

[0651] The user's device converts the user's input data into JSON format and sends it to the server via an HTTP request. The server then passes the received data to Google Cloud Natural Language for analysis. This analysis method extracts key keywords through tokenization and dependency structure analysis to understand the context of the question.

[0652] 3. Knowledge base matching and information acquisition

[0653] Based on the analysis results, the server checks its knowledge base to retrieve relevant financial data, and also retrieves additional information from external data sources, such as the latest investment rates and financial news.

[0654] 4. Answer Generation

[0655] Based on the information obtained, the server generates an answer using OpenAI GPT-4, a generative artificial intelligence engine that creates responses in natural language and provides specific, context-sensitive advice.

[0656] 5. Submitting and Viewing Your Answers

[0657] The generated answers are sent from the server to the user's terminal and displayed on the user's virtual reality device. The user receives visually interactive advice while interacting with an avatar in the virtual space.

[0658] Specific examples

[0659] Below are some example prompts for users seeking financial advice:

[0660] Example: Prompt to the generative AI engine when a user types "Please tell me about asset management for retirement":

[0661] "A user is asking about investing in retirement. Please provide specific advice including the following elements: NISA, iDeCo, and diversified investments."

[0662] This allows the system to provide users with financial advice in a visual and easy-to-understand format, increasing engagement and satisfaction.

[0663] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0664] Step 1:

[0665] The user wears a head-mounted display and inputs their financial consultation request by voice or text. In the case of voice input, a "voice recognition engine" converts the voice into text, and the user's question is obtained in text format.

[0666] Step 2:

[0667] The user device converts the acquired question content into JSON format and sends it to the server via an HTTP request. The input data is speech recognition results or text data, and the output is JSON format data.

[0668] Step 3:

[0669] The server passes the received JSON data to Google Cloud Natural Language for analysis. This analysis method extracts key keywords through tokenization and dependency structure analysis to understand the context of the question. The input data is the question data in JSON format, and the output data is the analysis result.

[0670] Step 4:

[0671] Based on the analysis results, the server checks the knowledge base to obtain relevant financial data, and also collects additional information from external data sources, such as the latest investment rates and financial news. The input data is the analysis results, and the output data is the obtained financial information.

[0672] Step 5:

[0673] The server generates specific advice based on the acquired information using the generative AI engine "OpenAI GPT-4." This generative AI engine creates a response in natural language based on the prompt. The input data is the acquired financial information and the prompt, and the output data is the generated answer.

[0674] Step 6:

[0675] The server sends the generated answer to the user terminal. The input data is the generated answer, and the output data is the data sent to the user terminal.

[0676] Step 7:

[0677] The user terminal displays the received answer on a virtual reality device. The user interacts with the avatar in the virtual space and receives visual and interactive advice. Specifically, the avatar in the virtual space provides advice to the user and displays visual charts and graphs. The input data is the answer sent from the server, and the output is advice displayed to the user's eyes.

[0678] The above are the specific steps of the processing of this system.

[0679] 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.

[0680] The present invention relates to a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system is further combined with an emotion engine that recognizes the user's emotions, making it possible to generate answers that correspond to the user's emotions.

[0681] System Overview

[0682] The system of the present invention mainly consists of three main components: a user, a terminal, and a server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's questions and emotions and sending them to the server. The server analyzes the received questions and emotions, generates an answer, and sends it back to the user via the terminal.

[0683] Specific processing of the system

[0684] 1. User Interface and Input:

[0685] Users access the system using a terminal and input financial consultations. Input can be made in the form of a chat box or email, and the emotion engine analyzes the emotions from the user's text.

[0686] Example: A user types a question such as, "Tell me about how to manage my finances for retirement. I'm very worried."

[0687] 2. Data transmission:

[0688] The terminal converts the input data from the user into JSON format and sends it to the server via an HTTP request.

[0689] 3. Data Receipt and Analysis:

[0690] The server passes the received data to a natural language processing engine as an analytical tool, and analyzes it to understand the user's questions and emotions.

[0691] The natural language processing engine performs tokenization and dependency analysis to extract key keywords and understand the context of the question.

[0692] The emotion engine analyzes emotions (e.g., anxiety, joy, anger, etc.) from the user's text input and provides them to the server.

[0693] 4. Knowledge base matching and information retrieval:

[0694] The server then checks the analyzed question against a knowledge base, which contains a variety of financial data.

[0695] When necessary, the server retrieves up-to-date financial information from external data sources, including, for example, the latest investment rates and financial news.

[0696] 5. Generate answers:

[0697] The generative AI engine generates responses appropriate to the user based on the acquired information and the analysis results of the emotion engine. This engine creates responses in natural language.

[0698] Example: "It's natural to be worried about managing your assets in retirement. We'll introduce you to ways to put your mind at ease, such as diversifying your investments and taking advantage of iDeCo."

[0699] 6. Submitting and Viewing Answers:

[0700] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[0701] Examples:

[0702] Example questions:

[0703] User: "What are some recommendations for investing in retirement? I'm very worried."

[0704] Process flow:

[0705] 1. The user enters a question into the terminal and clicks the send button.

[0706] 2. The device converts the question and emotion into JSON format and sends it to the server.

[0707] 3. The server receives the data and passes it to the natural language processing engine and emotion engine.

[0708] 4. The server performs the analysis and extracts keywords such as "retirement" and "investment methods" as well as the user's emotion of "worry."

[0709] 5. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[0710] 6. If necessary, the server retrieves the latest investment information from an external data source.

[0711] 7. The generative AI engine generates an emotionally sensitive response: "It's natural to be worried about asset management in retirement. Please consider diversifying your investments or using iDeCo."

[0712] 8. The server sends the generated response to the device.

[0713] 9. The device receives the answer and displays it on the user interface: "It's natural to be worried about asset management in your retirement years. Please consider diversifying your investments or using iDeCo."

[0714] The system allows users to receive specific, emotionally sensitive financial advice.

[0715] The processing flow will be explained below.

[0716] Step 1:

[0717] A user accesses the system using a terminal.

[0718] Access it from a browser on your computer or mobile device or launch a dedicated application.

[0719] Step 2:

[0720] A user inputs a financial consultation request into a terminal.

[0721] Enter text into a chat box or email form and click the send button.

[0722] Step 3:

[0723] The terminal converts the user-entered data into JSON format.

[0724] It converts the input string into an appropriate data structure and prepares the HTTP request.

[0725] Step 4:

[0726] The terminal transmits the converted data to the server.

[0727] It sends the user's question data to the server via an HTTP request.

[0728] Step 5:

[0729] The server passes the received data to the analysis means (natural language processing engine and emotion engine).

[0730] It parses the received data and passes it as input to the natural language processing engine and emotion engine.

[0731] Step 6:

[0732] The server analyzes the consultation content using a natural language processing engine.

[0733] It performs tokenization and dependency structure analysis to extract key keywords and contextual information from the user's question.

[0734] Step 7:

[0735] The server analyzes the user's emotions using an emotion engine.

[0736] It extracts emotional patterns from input text and identifies major emotional categories such as "anxiety," "joy," and "anger."

[0737] Step 8:

[0738] The server consults a knowledge base to retrieve relevant information.

[0739] It works by sending queries to a database to retrieve relevant financial information and advice.

[0740] Step 9:

[0741] The server retrieves additional information from external data sources as needed.

[0742] It calls external APIs to retrieve the latest financial news, market rates, etc.

[0743] Step 10:

[0744] The server uses a generative artificial intelligence engine to generate answers to suggest to the user based on the acquired information and the results of emotion analysis.

[0745] The system creates responses in natural language and incorporates content that takes the user's feelings into consideration.

[0746] Step 11:

[0747] The server sends the generated answer to the terminal.

[0748] The generated response data is converted into JSON format and sent to the terminal as an HTTP response.

[0749] Step 12:

[0750] The terminal receives and analyzes the response data from the server.

[0751] It parses the received data and converts it into strings and other UI elements.

[0752] Step 13:

[0753] The terminal displays the answer to the user.

[0754] The answer is displayed in the user interface so that the user can confirm it.

[0755] Examples:

[0756] Example questions:

[0757] User: "What are some recommendations for investing in retirement? I'm very worried."

[0758] Process flow:

[0759] 1. The user enters a question into the terminal and clicks the send button.

[0760] 2. The device converts the question and emotion data into JSON format and sends it to the server.

[0761] 3. The server receives the data and passes it to the natural language processing engine and emotion engine.

[0762] 4. The server performs natural language processing to extract keywords such as "retirement" and "investment methods."

[0763] 5. The server runs the emotion engine and identifies the user's emotion of "worry."

[0764] 6. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[0765] 7. If necessary, the server retrieves the latest investment information from an external data source.

[0766] 8. The generative AI engine generates an emotionally sensitive response: "It's natural to be worried about asset management in retirement. Please consider diversifying your investments or using iDeCo."

[0767] 9. The server sends the generated response to the device.

[0768] 10. The device receives the answer and displays it on the user interface: "It's natural to be worried about asset management in your retirement years. Please consider diversifying your investments or using iDeCo."

[0769] The system allows users to receive specific, emotionally sensitive financial advice.

[0770] Example 2

[0771] 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."

[0772] Conventional financial consultation systems provide only simple information without considering the user's feelings, which means that users are unable to receive satisfactory advice. This can result in users taking a long time to resolve their financial concerns and questions, which can lead to a decrease in satisfaction.

[0773] 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.

[0774] In this invention, the server includes means for accepting financial consultations from users, analysis means including a natural language processing engine and an emotion engine for analyzing the consultations and the user's emotions, means for comparing the consultations and emotions analyzed by the analysis means with a knowledge base to obtain optimal information, means for obtaining additional information from an external data source, means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information and the analyzed emotions, means for transmitting the generated answer to a user terminal, and means for displaying the generated answer to the user. This makes it possible to provide appropriate advice that takes the user's emotions into consideration, thereby improving user satisfaction.

[0775] "User" refers to any individual or entity that accesses the System and provides financial advice.

[0776] "Financial consultation" refers to the act of a user asking questions or concerns about financial matters such as asset management, investment, savings, and insurance.

[0777] A "terminal" is a device used by a user to access the system, and includes a smartphone, PC, tablet, etc.

[0778] "Server" refers to a computer system that receives requests from users, analyzes data, retrieves information, and generates and sends responses.

[0779] A "natural language processing engine" refers to a software system that analyzes user input and understands key keywords and context.

[0780] "Emotion engine" refers to a software system for analyzing emotions from a user's text and identifying their emotional state.

[0781] "Analysis means" refers to the process of analyzing the user's consultation content and emotions using a natural language processing engine and an emotion engine.

[0782] A "knowledge base" refers to a database that stores financial data and is used to provide information appropriate to a user's inquiry.

[0783] "External Data Source" refers to an external data provider service or API that the Server accesses to obtain additional information as needed.

[0784] A "generative artificial intelligence engine" refers to an artificial intelligence system that generates responses to users in natural language based on acquired information and the results of sentiment analysis.

[0785] "Means for generating an answer" refers to a process of using a generative artificial intelligence engine to generate an answer appropriate for the user.

[0786] "Means for sending to the user terminal" refers to the process by which the server sends the generated response to the terminal used by the user.

[0787] The term "means for displaying to the user" refers to a process in which the terminal displays the answer received from the server on the user interface and presents it to the user.

[0788] The present invention is a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. By combining an emotion engine that recognizes the user's emotions, the system generates answers that correspond to the user's emotions. Detailed embodiments of the present invention will be described below.

[0789] System Overview

[0790] This system is primarily composed of three main components: a server, a device, and a user. Users access the system using a device such as a smartphone or PC and input a question. The device acquires the user's question along with its associated emotions and sends them to the server. The server analyzes this information, generates an appropriate answer, and sends it back to the device. The device then displays the received answer on a user interface and presents it to the user.

[0791] Hardware and software used

[0792] Hardware

[0793] The devices used by users are electronic devices such as general smartphones and personal computers, while the servers use a cloud-based server system and are equipped with high-performance computing power and storage.

[0794] software

[0795] The following software is used:

[0796] Natural language processing engines (e.g., SpaCy, NLTK, etc.): Used to analyze user text input and understand key keywords and context.

[0797] Sentiment engine (e.g., Hugging Face sentiment analysis model): Used to parse emotions from user text.

[0798] Knowledge base: A financial database, including SQL databases and Elasticsearch.

[0799] Generative AI engine (e.g., OpenAI GPT-3): Generates answers based on acquired information and sentiment analysis results.

[0800] Overview of the processing flow

[0801] The server receives the user's input and analyzes it using a natural language processing engine and an emotion engine. It then retrieves the necessary information from a knowledge base and external data sources and generates an appropriate answer using a generative AI engine. The generated answer is then presented to the user via the terminal from the server.

[0802] Specific examples

[0803] User input:

[0804] "What investment strategies would you recommend for my retirement? I'm very worried."

[0805] Example prompt sentence:

[0806] "It's natural to be worried about asset management in retirement. We will introduce ways to give you peace of mind, such as diversifying your investments and utilizing iDeCo."

[0807] In this way, the user can receive specific and emotionally sensitive financial advice from the system. The present invention allows the user to resolve financial issues in a more satisfactory manner.

[0808] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0809] Step 1: User Input

[0810] Users access the system using devices such as smartphones or PCs and input financial questions. Input can be done via chat box or email. The emotion engine analyzes the emotions from the user's text.

[0811] Specific behavior:

[0812] The user launches a browser and accesses the system's website.

[0813] In the chat box, type, "Please tell me about asset management for retirement. I'm very worried." and click the send button.

[0814] Input: User's question text (e.g., "Please tell me about asset management for retirement. I'm very worried.")

[0815] Output: User question text and sentiment estimation results

[0816] Step 2: Sending data

[0817] The device converts the input data from the user (question text and emotion estimation results) into JSON format and sends it to the server using an HTTP request.

[0818] Specific behavior:

[0819] The terminal parses the input data into JSON format.

[0820] The front-end JavaScript sends the JSON formatted data to the server as an HTTP POST request.

[0821] Input: User question text and sentiment estimation results

[0822] Output: JSON format data sent to the server

[0823] Step 3: Receiving and analyzing data

[0824] The server receives the data sent from the device and passes it to a natural language processing engine and emotion engine. The natural language processing engine analyzes the question and extracts key keywords, and the emotion engine analyzes emotions.

[0825] Specific behavior:

[0826] The server receives the HTTP request and extracts the data from the body.

[0827] The data is passed to a natural language processing engine (e.g., SpaCy) to perform tokenization and dependency analysis.

[0828] A natural language processing engine extracts keywords from the question (e.g., "retirement" and "asset management").

[0829] The emotion engine analyzes the text and identifies emotions (e.g., "worry").

[0830] Input: JSON format data sent to the server

[0831] Output: Parsed keywords and sentiment data

[0832] Step 4: Knowledge base matching and information acquisition

[0833] The server then compares the analyzed question content and sentiment against a knowledge base to retrieve relevant information, including the latest financial information from external data sources if necessary.

[0834] Specific behavior:

[0835] The server uses SQL or Elasticsearch queries to retrieve relevant information from a built-in knowledge base.

[0836] If necessary, API requests are used to obtain new information from external financial data providers.

[0837] Input: Parsed keywords and sentiment data

[0838] Output: Information related to the user's question

[0839] Step 5: Generate an answer

[0840] The server uses a generative AI engine to generate responses appropriate for the user based on the acquired information and the results of sentiment analysis. This engine creates responses in natural language.

[0841] Specific behavior:

[0842] The server creates a prompt for the generative AI engine (e.g., GPT-3) and sends the question data and related information.

[0843] The AI ​​engine generates answers based on the prompts.

[0844] The generated answer is returned to the server.

[0845] Input: Acquired information and sentiment analysis results

[0846] Output: Generated answer text

[0847] Step 6: Submit and view your responses

[0848] The server sends the generated answer to the user terminal, which displays the received answer on a user interface and presents it to the user.

[0849] Specific behavior:

[0850] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[0851] The device receives the HTTP response, and the front-end JavaScript displays the response in the chat box.

[0852] Input: Generated answer text

[0853] Output: The answer text that is displayed in the user interface

[0854] (Application example 2)

[0855] 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."

[0856] With the recent diversification of financial services, users have more opportunities to seek a wide range of financial advice. In particular, online financial advice often provides uniform answers that ignore the user's feelings, which can lead to user dissatisfaction. Therefore, there is a need for a system that takes the user's feelings into consideration and provides more appropriate and personalized financial advice.

[0857] The identification process by the identification 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 analysis means including a natural language processing engine that analyzes the consultation and an emotion engine that analyzes the user's emotions, means for collating the consultation and emotions analyzed by the analysis means with a knowledge base to obtain optimal information, and means for obtaining additional information from an external data source. This makes it possible to provide appropriate financial advice that takes into consideration the user's emotions.

[0858] A "user" is an entity that uses the system to provide financial advice.

[0859] "Financial consultation" refers to questions or doubts that a user has about financial information or advice.

[0860] A "natural language processing engine" is software that analyzes text entered by a user and understands its intent and content.

[0861] An "emotion engine" is software that analyzes and recognizes emotions from user input text.

[0862] The "analysis means" includes a natural language processing engine and an emotion engine, and has the function of analyzing the user's consultation and emotions.

[0863] A "knowledge base" is a database containing data related to finance.

[0864] An "external data source" is a data source other than the knowledge base for obtaining up-to-date financial information.

[0865] The "generative artificial intelligence engine" is an engine that generates answers based on the acquired information and emotion analysis results.

[0866] A "user terminal" is a device through which a user accesses the system, inputs financial inquiries, and receives responses.

[0867] This invention is a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system has the function of generating answers that correspond to the user's emotions by combining it with an emotion engine that recognizes the user's emotions.

[0868] System Overview

[0869] This system is primarily composed of three main components: the user, the terminal, and the server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's questions and emotions and sending them to the server. The server analyzes the received questions and emotions, generates an answer, and sends it back to the user via the terminal.

[0870] Hardware or software used

[0871] Hardware: smartphones, PCs, servers

[0872] software:

[0873] Natural Language Processing Engine: Natural Language Processing model using Hugging Face's transformers library

[0874] Emotion Engine: An emotion analysis model using the Hugging Face transformers library

[0875] Generative AI engine: Generative AI model using the same library

[0876] Data processing and calculation

[0877] The server performs the following processing on the data received from the user.

[0878] 1. Data reception and analysis:

[0879] Consultation data of the user transmitted from the terminal is received.

[0880] A natural language processing engine is used to analyze the user's question through tokenization and dependency structure analysis.

[0881] An emotion engine is used to analyze emotions (e.g., anxiety, joy, anger, etc.) from user text.

[0882] 2. Knowledge base matching and external data retrieval:

[0883] Based on the analyzed question content and sentiment, the knowledge base is collated to obtain the most appropriate information.

[0884] Obtain up-to-date financial information from external data sources.

[0885] 3. Generate answers:

[0886] A generative artificial intelligence engine is used to generate answers appropriate for the user based on the acquired information and sentiment analysis results.

[0887] 4. Submit and view your answers:

[0888] The generated answer is sent to the device.

[0889] The terminal displays the received answer on the user interface and presents it to the user.

[0890] Specific examples

[0891] For example, if a user inputs a question into the system such as "I'm not good at saving money and I don't know what to do," the following processing will be carried out.

[0892] 1. The natural language processing engine extracts the keywords "savings" and "not good at," and the emotion engine analyzes the emotion "anxiety."

[0893] 2. The server checks the knowledge base to get the best information on how to save money.

[0894] 3. Also obtain information on the latest savings methods from external data sources.

[0895] 4. In response to a user's question, "I'm not good at saving money and I don't know what to do," the generative artificial intelligence engine generates the answer, "It's natural to find saving money difficult. The important thing is to save even a little bit each month. Don't rush, just keep at it."

[0896] 5. This generated answer is sent to the user's device.

[0897] Prompt Sentence Examples

[0898] text

[0899] User input: "I'm not good at saving money and I don't know what to do."

[0900] Question: "How to save money", Emotions from Japanese analysis: "Anxiety"

[0901] Example advice it might generate: "It's natural to feel like saving money is difficult. The key is to save a little each month. Don't rush it, just keep at it."

[0902] In this way, the present invention can provide specific and appropriate financial advice while taking into consideration the user's feelings.

[0903] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0904] Step 1:

[0905] The user inputs a financial inquiry into the terminal and clicks the send button. The input inquiry is in text format, such as "I'm not good at saving money and I don't know what to do."

[0906] Step 2:

[0907] The device converts the input data from the user into JSON format and sends it to the server via an HTTP request. The input data includes the user's inquiry.

[0908] Input: User-entered consultation text

[0909] Output: Data converted to JSON format

[0910] Step 3:

[0911] The server passes the data received from the terminal to a natural language processing engine as an analytical means, and analyzes the user's consultation content. It performs tokenization and dependency structure analysis to extract the user's intention and topic.

[0912] Input: JSON format consultation data

[0913] Output: Extracted keywords and context information

[0914] Step 4:

[0915] The server uses an emotion engine to analyze the user's emotions from the consultation content, and identifies emotions such as "anxiety" or "joy" as examples.

[0916] Input: Text of consultation

[0917] Output: User's emotional information (e.g., anxiety)

[0918] Step 5:

[0919] Based on the analyzed question content and sentiment, the server compares it with a knowledge base that stores financial data and retrieves the most appropriate information.

[0920] Input: Extracted keywords and sentiment information

[0921] Output: The best information retrieved from the knowledge base

[0922] Step 6:

[0923] If necessary, the server also retrieves up-to-date financial information from external data sources, such as the latest savings and investment information.

[0924] Input: Query required financial information

[0925] Output: Latest information retrieved from an external data source

[0926] Step 7:

[0927] The server uses a generative artificial intelligence engine to generate appropriate answers for the user based on the analyzed data and acquired information, and the generated answers are expressed in natural language and take emotional aspects into consideration.

[0928] Input: Information from knowledge base and external data sources, sentiment analysis results

[0929] Output: Generated answer text (e.g., "It's natural to feel that saving money is difficult. The important thing is to save even a little each month. Don't rush, just keep at it.")

[0930] Step 8:

[0931] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[0932] Input: Answer text sent from the server

[0933] Output: The answer displayed in the user interface

[0934] The above is a series of processing steps for the system that realizes the application example.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] [Third embodiment]

[0939] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0940] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0941] 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).

[0942] 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.

[0943] 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.

[0944] 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).

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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."

[0951] The system of the present invention accepts online financial consultations from users, analyzes them, and provides appropriate advice. A specific embodiment of the system will be described below.

[0952] System Overview

[0953] The system of the present invention is mainly composed of three main components: a user, a terminal, and a server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's question and sending it to the server. The server analyzes the received question, generates an answer, and sends it back to the user via the terminal.

[0954] Specific processing of the system

[0955] 1. User Interface and Input:

[0956] Users access the system using a terminal and input financial consultations. Input can be done in the form of a chat box or email.

[0957] Example: A user types a question such as "Tell me about investing for retirement."

[0958] 2. Data transmission:

[0959] The terminal converts the input data from the user into JSON format and sends it to the server via an HTTP request.

[0960] 3. Data Receipt and Analysis:

[0961] The server passes the received data to a natural language processing engine as an analytical means, and analyzes it to understand the user's question.

[0962] The natural language processing engine performs tokenization and dependency analysis to extract key keywords and understand the context of the question.

[0963] 4. Knowledge base matching and information retrieval:

[0964] The server then checks the analyzed question against a knowledge base, which contains a variety of financial data.

[0965] When necessary, the server retrieves up-to-date financial information from external data sources, including, for example, the latest investment rates and financial news.

[0966] 5. Generate answers:

[0967] The generative AI engine generates appropriate answers for the user based on the acquired information. This engine creates responses in natural language.

[0968] Example: "You have the following options for managing your assets after retirement: 1. Use the Tsumitate NISA (Nippon Individual Savings Account) 2. Utilize the small investment tax exemption system (iDeCo) 3. Diversify your investments."

[0969] 6. Submitting and Viewing Answers:

[0970] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[0971] As described above, the system of the present invention allows users to easily consult about financial matters anytime, anywhere and receive appropriate advice on the spot. Furthermore, since no face-to-face consultation is required, privacy is maintained and there are no costs involved. This allows users to make financial decisions with peace of mind.

[0972] The processing flow will be explained below.

[0973] Step 1:

[0974] A user accesses the system using a terminal.

[0975] Access it from a browser on your computer or mobile device or launch a dedicated application.

[0976] Step 2:

[0977] A user inputs a financial consultation request into a terminal.

[0978] Enter text into a chat box or email form and click the send button.

[0979] Step 3:

[0980] The terminal converts the user's input data into JSON format.

[0981] It converts the input string into an appropriate data structure and prepares the HTTP request.

[0982] Step 4:

[0983] The terminal transmits the converted data to the server.

[0984] It sends the user's question data to the server via an HTTP request.

[0985] Step 5:

[0986] The server passes the received data to an analysis means (natural language processing engine).

[0987] It parses the incoming data and passes it as input to the NLP engine.

[0988] Step 6:

[0989] The server analyzes the consultation content to extract key keywords and contextual information.

[0990] It performs tokenization and dependency structure analysis to understand the user's intent.

[0991] Step 7:

[0992] The server consults a knowledge base to retrieve relevant information.

[0993] It sends queries to the database and retrieves appropriate advice and information.

[0994] Step 8:

[0995] The server retrieves additional information from external data sources as needed.

[0996] It calls external APIs to obtain the latest financial news, rates, etc.

[0997] Step 9:

[0998] The server uses a generative artificial intelligence engine to generate suggested answers for the user.

[0999] Based on the acquired information, it generates text in natural language.

[1000] Step 10:

[1001] The server sends the generated answer to the terminal.

[1002] The generated response data is converted into JSON format and sent to the terminal as an HTTP response.

[1003] Step 11:

[1004] The terminal receives and analyzes the response data from the server.

[1005] It parses the received data and converts it into a displayable format.

[1006] Step 12:

[1007] The terminal displays the answer to the user.

[1008] The answer is displayed in the user interface so that the user can confirm it.

[1009] Examples:

[1010] Example questions:

[1011] User: "What are the best investment strategies for retirement?"

[1012] Process flow:

[1013] 1. The user enters a question into the terminal and clicks the send button.

[1014] 2. The device converts the question into JSON format and sends it to the server.

[1015] 3. The server receives the data and passes it to the natural language processing engine.

[1016] 4. The server performs the analysis and extracts keywords such as "retirement" and "investment methods."

[1017] 5. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[1018] 6. If necessary, the server retrieves the latest investment information from an external data source.

[1019] 7. The generative AI engine generates the answer, "For your retirement, we recommend diversifying your investments and using iDeCo."

[1020] 8. The server sends the generated response to the device.

[1021] 9. The device receives the answer and displays it on the user interface: "For your retirement, we recommend diversifying your investments and using iDeCo."

[1022] Through the above steps, the system of the present invention provides optimal financial advice to the user.

[1023] Example 1

[1024] 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."

[1025] Conventional financial consultation systems have difficulty generating fast and accurate answers to questions submitted by users. In particular, advanced data analysis and artificial intelligence technologies are required to correctly understand users' questions and provide appropriate advice that includes the latest financial information. Furthermore, a user-friendly interface and real-time responses are required to improve the user experience. Therefore, the challenge is to create a system that can properly analyze users' questions and provide optimal information.

[1026] 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.

[1027] In this invention, the server includes: means for accepting a financial inquiry from a user; analysis means including a natural language processing engine for analyzing the inquiry; means for comparing the inquiry analyzed by the analysis means with a knowledge base to obtain optimal information; means for obtaining additional information from an external data source; means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information; means for transmitting the generated answer to the user terminal; means for displaying the generated answer on a user interface; means for the user terminal to convert data input by the user into JSON format and transmit it to the server via an HTTP request; means for the server to analyze the received data, obtain information from the knowledge base and external data source, and generate an answer using the generative artificial intelligence engine; and means for using a prompt sentence when generating the answer. This enables the server to provide quick and accurate answers to users' financial questions.

[1028] A "User" is an individual or entity that accesses the System to provide financial advice.

[1029] A "terminal" is a computing device through which a user accesses the system and sends and receives input data.

[1030] A "server" is a computer system that receives data sent by users, analyzes it, and generates a response.

[1031] "Consultation" refers to the act of a user inputting a financial problem or question into the system, or the content of that problem or question.

[1032] A "natural language processing engine" is software or algorithms that analyze and understand users' inquiries.

[1033] The "analysis means" refers to the entire process of analyzing a user's inquiry using a natural language processing engine.

[1034] A "knowledge base" is a database that stores data and information related to finance.

[1035] "External Data Sources" are external sources for obtaining up-to-date financial information and other relevant data not present in the knowledge base.

[1036] A "generative artificial intelligence engine" is an artificial intelligence technology that generates answers to user questions based on acquired information.

[1037] A "prompt sentence" is an instruction sentence that is input to a generative artificial intelligence engine to generate a specific response.

[1038] The present invention relates to a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system is mainly composed of three main components: a user, a terminal, and a server.

[1039] User operations

[1040] Users access the system using devices such as smartphones or PCs and input financial questions. Input can be done via chat box or email. For example, a user might input a question such as, "Please tell me about asset management for retirement."

[1041] Device Features

[1042] The device is responsible for converting user input data into JSON format and sending it to the server via an HTTP request. At this time, the device's browser uses JavaScript to convert the data into JSON format and send it as an Ajax request.

[1043] Server Processing

[1044] The server analyzes the received data using a natural language processing engine. SpaCy or another natural language processing engine can be used. This engine performs tokenization and dependency structure analysis to understand the user's question and extract key keywords. The server then checks the analyzed question against a knowledge base. The knowledge base stores data and information related to finance. If necessary, the server can obtain the latest information from external data sources (for example, financial data APIs).

[1045] Answer Generation

[1046] The server uses a generative AI engine (e.g., GPT-4) to generate an appropriate answer for the user. At this time, it uses prompt sentences to instruct the AI. For example, a prompt sentence such as, "The user is asking about asset management for retirement. Please answer including the following information: 1. Tsumitate NISA 2. iDeCo 3. Diversified investment" can be used.

[1047] Providing answers

[1048] The server sends the generated answer to the terminal, which displays it on the user interface. Specifically, the server returns the generated answer in JSON format to the terminal, and JavaScript on the terminal displays it in a chat box. An example of an answer might be something like, "You have the following options for asset management in retirement: 1. Use the NISA savings plan. 2. Utilize the iDeCo small investment tax exemption system. 3. Diversify your investments."

[1049] In this way, the system of the present invention allows users to easily consult about financial matters anytime, anywhere and receive appropriate advice on the spot. Since no face-to-face consultation is required, the system has the advantage of maintaining users' privacy and being cost-effective. Furthermore, the system provides users with the latest financial information in real time, allowing them to make financial decisions with peace of mind.

[1050] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1051] Step 1:

[1052] Users access the system using devices such as smartphones or PCs to input financial inquiries, which can be input via chat box or email.

[1053] Input: User types, "Tell me about investing for retirement."

[1054] Output: The entered financial consultation details.

[1055] Specific operation: The user opens a web browser, accesses the system's web page, and enters the content of their inquiry in the chat box.

[1056] Step 2:

[1057] The terminal converts the data entered by the user into JSON format and sends it to the server via an HTTP request.

[1058] Input: Financial consultation details from the user.

[1059] Output: Data in JSON format.

[1060] What happens: JavaScript is executed in the device's browser, converting the user's input into JSON format, and then sending the converted data to the server as an Ajax request.

[1061] Step 3:

[1062] The server passes the received data to a natural language processing engine to analyze the user's question.

[1063] Input: User consultation data in JSON format.

[1064] Output: Parsed question meaning and keywords.

[1065] What it does: The server uses Flask to receive HTTP requests and read the JSON data, then passes this data to a natural language processing engine (e.g., spaCy) for tokenization and dependency analysis to understand the user's question.

[1066] Step 4:

[1067] The server then searches the knowledge base for relevant information based on the parsed question, and retrieves additional information from external data sources if necessary.

[1068] Input: Parsed question meaning and keywords.

[1069] Output: Relevant information retrieved from the knowledge base and external data sources.

[1070] Specific operation: The server queries a database (e.g., MySQL) to obtain relevant financial information, and simultaneously calls an external API (e.g., a financial data API) as needed to obtain the latest financial data.

[1071] Step 5:

[1072] The server uses a generative artificial intelligence engine to generate appropriate responses for the user based on the acquired information, using prompt sentences.

[1073] Input: Information obtained from the knowledge base and external data sources, prompt statement.

[1074] Output: The answer to the user.

[1075] Specific operation: Using the acquired information, the server sends a prompt to a generative AI engine (e.g., GPT-4) to generate a natural-language answer. For example, a prompt such as "The user is asking about asset management for retirement. Please respond with the following information: 1. Tsumitate NISA 2. iDeCo 3. Diversified investment" can be used.

[1076] Step 6:

[1077] The server sends the generated answer to the terminal, which displays the answer on a user interface.

[1078] Input: Answers from a generative artificial intelligence engine.

[1079] Output: Display answer to user.

[1080] Specific operation: The server returns the generated answer in JSON format to the device, and JavaScript on the device displays it in the chat box. Specific answers such as "You have the following options for managing your assets in retirement: 1. Use the Tsumitate NISA (Nippon Individual Savings Account) 2. Utilize the small investment tax exemption system (iDeCo) 3. Diversify your investments" are displayed.

[1081] In this way, a system is realized in which each processing step works together to provide appropriate financial advice to users.

[1082] (Application example 1)

[1083] 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."

[1084] Conventional financial consultation systems require users to input questions in text format and then return the analysis results in text format, which makes them difficult to understand visually. Furthermore, there are also issues with low user engagement and a lack of interactive advice, resulting in low user satisfaction.

[1085] 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.

[1086] In this invention, the server includes means for accepting a financial consultation from a user, analysis means including a natural language processing engine for analyzing the consultation, means for comparing the consultation with a knowledge base to obtain optimal information based on the consultation analyzed by the analysis means, means for obtaining additional information from an external data source, means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information, means for transmitting the generated answer to a user terminal, means for displaying the generated answer to the user, means for providing financial advice in a virtual space using a virtual reality device, and means for visually presenting the advice in the virtual space. This allows the user to receive financial advice visually and interactively, thereby improving user engagement and satisfaction.

[1087] The "means for accepting financial inquiries from users" is an interface that allows users to input financial questions and inquiries into the system.

[1088] The "analysis means including a natural language processing engine for analyzing a consultation" refers to a device or system that uses natural language processing technology to analyze the consultation content input by a user and understand that content.

[1089] "Means of collating a knowledge base to obtain optimal information" refers to a technology that obtains the most relevant data from pre-stored information based on the analyzed consultation content.

[1090] "Means for obtaining additional information from external data sources" refers to techniques for obtaining up-to-date financial information and related data from the Internet and other external sources.

[1091] "Means for generating answers to be suggested to users using a generative artificial intelligence engine" refers to artificial intelligence technology for generating appropriate advice and answers for users based on collected information.

[1092] The "means for transmitting the generated answer to the user terminal" is a technique for transmitting the generated answer to the terminal used by the user.

[1093] The "means for displaying to the user" refers to a technique for displaying the submitted answers so that the user can visually confirm them.

[1094] A "means for providing financial advice in a virtual space using a virtual reality device" is a device that uses virtual reality technology to provide financial advice to a user in a virtual space.

[1095] "Means for visually presenting advice in a virtual space" refers to a technology that visually displays advice in a virtual space, making it easier for the user to understand.

[1096] This invention provides a system that allows users to consult about financial matters in a virtual space and receive appropriate advice in real time. Specifically, a virtual reality device is used to enable users to receive financial advice visually and interactively. Specific embodiments of this system are described below.

[1097] The system mainly consists of a user terminal, a server, and a virtual reality device (e.g., a head-mounted display). Users use these devices to access the virtual space and receive financial consultations.

[1098] Hardware and Software

[1099] User terminal: This refers to a smartphone or PC through which the user accesses the system.

[1100] Server: A cloud server is used as the execution environment for analysis and data processing.

[1101] Virtual reality device: Using Oculus Quest 2 as an example.

[1102] Natural language processing engine: Uses Google Cloud Natural Language.

[1103] Generative artificial intelligence engine: Uses OpenAI GPT-4.

[1104] Virtual environment generation engine: Unity is used.

[1105] System action

[1106] 1. Handling User Input

[1107] The user wears a head-mounted display and receives financial consultations by voice or text input. In the case of voice input, a "voice recognition engine" converts the voice into text, which then recognizes the content of the user's question.

[1108] 2. Data transmission and analysis

[1109] The user's device converts the user's input data into JSON format and sends it to the server via an HTTP request. The server then passes the received data to Google Cloud Natural Language for analysis. This analysis method extracts key keywords through tokenization and dependency structure analysis to understand the context of the question.

[1110] 3. Knowledge base matching and information acquisition

[1111] Based on the analysis results, the server checks its knowledge base to retrieve relevant financial data, and also retrieves additional information from external data sources, such as the latest investment rates and financial news.

[1112] 4. Answer Generation

[1113] Based on the information obtained, the server generates an answer using OpenAI GPT-4, a generative artificial intelligence engine that creates responses in natural language and provides specific, context-sensitive advice.

[1114] 5. Submitting and Viewing Your Answers

[1115] The generated answers are sent from the server to the user's terminal and displayed on the user's virtual reality device. The user receives visually interactive advice while interacting with an avatar in the virtual space.

[1116] Specific examples

[1117] Below are some example prompts for users seeking financial advice:

[1118] Example: Prompt to the generative AI engine when a user types "Please tell me about asset management for retirement":

[1119] "A user is asking about investing in retirement. Please provide specific advice including the following elements: NISA, iDeCo, and diversified investments."

[1120] This allows the system to provide users with financial advice in a visual and easy-to-understand format, increasing engagement and satisfaction.

[1121] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1122] Step 1:

[1123] The user wears a head-mounted display and inputs their financial consultation request by voice or text. In the case of voice input, a "voice recognition engine" converts the voice into text, and the user's question is obtained in text format.

[1124] Step 2:

[1125] The user device converts the acquired question content into JSON format and sends it to the server via an HTTP request. The input data is speech recognition results or text data, and the output is JSON format data.

[1126] Step 3:

[1127] The server passes the received JSON data to Google Cloud Natural Language for analysis. This analysis method extracts key keywords through tokenization and dependency structure analysis to understand the context of the question. The input data is the question data in JSON format, and the output data is the analysis result.

[1128] Step 4:

[1129] Based on the analysis results, the server checks the knowledge base to obtain relevant financial data, and also collects additional information from external data sources, such as the latest investment rates and financial news. The input data is the analysis results, and the output data is the obtained financial information.

[1130] Step 5:

[1131] The server generates specific advice based on the acquired information using the generative AI engine "OpenAI GPT-4." This generative AI engine creates a response in natural language based on the prompt. The input data is the acquired financial information and the prompt, and the output data is the generated answer.

[1132] Step 6:

[1133] The server sends the generated answer to the user terminal. The input data is the generated answer, and the output data is the data sent to the user terminal.

[1134] Step 7:

[1135] The user terminal displays the received answer on a virtual reality device. The user interacts with the avatar in the virtual space and receives visual and interactive advice. Specifically, the avatar in the virtual space provides advice to the user and displays visual charts and graphs. The input data is the answer sent from the server, and the output is advice displayed to the user's eyes.

[1136] The above are the specific steps of the processing of this system.

[1137] 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.

[1138] The present invention relates to a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system is further combined with an emotion engine that recognizes the user's emotions, making it possible to generate answers that correspond to the user's emotions.

[1139] System Overview

[1140] The system of the present invention mainly consists of three main components: a user, a terminal, and a server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's questions and emotions and sending them to the server. The server analyzes the received questions and emotions, generates an answer, and sends it back to the user via the terminal.

[1141] Specific processing of the system

[1142] 1. User Interface and Input:

[1143] Users access the system using a terminal and input financial consultations. Input can be made in the form of a chat box or email, and the emotion engine analyzes the emotions from the user's text.

[1144] Example: A user types a question such as, "Tell me about how to manage my finances for retirement. I'm very worried."

[1145] 2. Data transmission:

[1146] The terminal converts the input data from the user into JSON format and sends it to the server via an HTTP request.

[1147] 3. Data Receipt and Analysis:

[1148] The server passes the received data to a natural language processing engine as an analytical tool, and analyzes it to understand the user's questions and emotions.

[1149] The natural language processing engine performs tokenization and dependency analysis to extract key keywords and understand the context of the question.

[1150] The emotion engine analyzes emotions (e.g., anxiety, joy, anger, etc.) from the user's text input and provides them to the server.

[1151] 4. Knowledge base matching and information retrieval:

[1152] The server then checks the analyzed question against a knowledge base, which contains a variety of financial data.

[1153] When necessary, the server retrieves up-to-date financial information from external data sources, including, for example, the latest investment rates and financial news.

[1154] 5. Generate answers:

[1155] The generative AI engine generates responses appropriate to the user based on the acquired information and the analysis results of the emotion engine. This engine creates responses in natural language.

[1156] Example: "It's natural to be worried about managing your assets in retirement. We'll introduce you to ways to put your mind at ease, such as diversifying your investments and taking advantage of iDeCo."

[1157] 6. Submitting and Viewing Answers:

[1158] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[1159] Examples:

[1160] Example questions:

[1161] User: "What are some recommendations for investing in retirement? I'm very worried."

[1162] Process flow:

[1163] 1. The user enters a question into the terminal and clicks the send button.

[1164] 2. The device converts the question and emotion into JSON format and sends it to the server.

[1165] 3. The server receives the data and passes it to the natural language processing engine and emotion engine.

[1166] 4. The server performs the analysis and extracts keywords such as "retirement" and "investment methods" as well as the user's emotion of "worry."

[1167] 5. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[1168] 6. If necessary, the server retrieves the latest investment information from an external data source.

[1169] 7. The generative AI engine generates an emotionally sensitive response: "It's natural to be worried about asset management in retirement. Please consider diversifying your investments or using iDeCo."

[1170] 8. The server sends the generated response to the device.

[1171] 9. The device receives the answer and displays it on the user interface: "It's natural to be worried about asset management in your retirement years. Please consider diversifying your investments or using iDeCo."

[1172] The system allows users to receive specific, emotionally sensitive financial advice.

[1173] The processing flow will be explained below.

[1174] Step 1:

[1175] A user accesses the system using a terminal.

[1176] Access it from a browser on your computer or mobile device or launch a dedicated application.

[1177] Step 2:

[1178] A user inputs a financial consultation request into a terminal.

[1179] Enter text into a chat box or email form and click the send button.

[1180] Step 3:

[1181] The terminal converts the user-entered data into JSON format.

[1182] It converts the input string into an appropriate data structure and prepares the HTTP request.

[1183] Step 4:

[1184] The terminal transmits the converted data to the server.

[1185] It sends the user's question data to the server via an HTTP request.

[1186] Step 5:

[1187] The server passes the received data to the analysis means (natural language processing engine and emotion engine).

[1188] It parses the received data and passes it as input to the natural language processing engine and emotion engine.

[1189] Step 6:

[1190] The server analyzes the consultation content using a natural language processing engine.

[1191] It performs tokenization and dependency structure analysis to extract key keywords and contextual information from the user's question.

[1192] Step 7:

[1193] The server analyzes the user's emotions using an emotion engine.

[1194] It extracts emotional patterns from input text and identifies major emotional categories such as "anxiety," "joy," and "anger."

[1195] Step 8:

[1196] The server consults a knowledge base to retrieve relevant information.

[1197] It works by sending queries to a database to retrieve relevant financial information and advice.

[1198] Step 9:

[1199] The server retrieves additional information from external data sources as needed.

[1200] It calls external APIs to retrieve the latest financial news, market rates, etc.

[1201] Step 10:

[1202] The server uses a generative artificial intelligence engine to generate answers to suggest to the user based on the acquired information and the results of emotion analysis.

[1203] The system creates responses in natural language and incorporates content that takes the user's feelings into consideration.

[1204] Step 11:

[1205] The server sends the generated answer to the terminal.

[1206] The generated response data is converted into JSON format and sent to the terminal as an HTTP response.

[1207] Step 12:

[1208] The terminal receives and analyzes the response data from the server.

[1209] It parses the received data and converts it into strings and other UI elements.

[1210] Step 13:

[1211] The terminal displays the answer to the user.

[1212] The answer is displayed in the user interface so that the user can confirm it.

[1213] Examples:

[1214] Example questions:

[1215] User: "What are some recommendations for investing in retirement? I'm very worried."

[1216] Process flow:

[1217] 1. The user enters a question into the terminal and clicks the send button.

[1218] 2. The device converts the question and emotion data into JSON format and sends it to the server.

[1219] 3. The server receives the data and passes it to the natural language processing engine and emotion engine.

[1220] 4. The server performs natural language processing to extract keywords such as "retirement" and "investment methods."

[1221] 5. The server runs the emotion engine and identifies the user's emotion of "worry."

[1222] 6. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[1223] 7. If necessary, the server retrieves the latest investment information from an external data source.

[1224] 8. The generative AI engine generates an emotionally sensitive response: "It's natural to be worried about asset management in retirement. Please consider diversifying your investments or using iDeCo."

[1225] 9. The server sends the generated response to the device.

[1226] 10. The device receives the answer and displays it on the user interface: "It's natural to be worried about asset management in your retirement years. Please consider diversifying your investments or using iDeCo."

[1227] The system allows users to receive specific, emotionally sensitive financial advice.

[1228] Example 2

[1229] 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."

[1230] Conventional financial consultation systems provide only simple information without considering the user's feelings, which means that users are unable to receive satisfactory advice. This can result in users taking a long time to resolve their financial concerns and questions, which can lead to a decrease in satisfaction.

[1231] 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.

[1232] In this invention, the server includes means for accepting financial consultations from users, analysis means including a natural language processing engine and an emotion engine for analyzing the consultations and the user's emotions, means for comparing the consultations and emotions analyzed by the analysis means with a knowledge base to obtain optimal information, means for obtaining additional information from an external data source, means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information and the analyzed emotions, means for transmitting the generated answer to a user terminal, and means for displaying the generated answer to the user. This makes it possible to provide appropriate advice that takes the user's emotions into consideration, thereby improving user satisfaction.

[1233] "User" refers to any individual or entity that accesses the System and provides financial advice.

[1234] "Financial consultation" refers to the act of a user asking questions or concerns about financial matters such as asset management, investment, savings, and insurance.

[1235] A "terminal" is a device used by a user to access the system, and includes a smartphone, PC, tablet, etc.

[1236] "Server" refers to a computer system that receives requests from users, analyzes data, retrieves information, and generates and sends responses.

[1237] A "natural language processing engine" refers to a software system that analyzes user input and understands key keywords and context.

[1238] "Emotion engine" refers to a software system for analyzing emotions from a user's text and identifying their emotional state.

[1239] "Analysis means" refers to the process of analyzing the user's consultation content and emotions using a natural language processing engine and an emotion engine.

[1240] A "knowledge base" refers to a database that stores financial data and is used to provide information appropriate to a user's inquiry.

[1241] "External Data Source" refers to an external data provider service or API that the Server accesses to obtain additional information as needed.

[1242] A "generative artificial intelligence engine" refers to an artificial intelligence system that generates responses to users in natural language based on acquired information and the results of sentiment analysis.

[1243] "Means for generating an answer" refers to a process of using a generative artificial intelligence engine to generate an answer appropriate for the user.

[1244] "Means for sending to the user terminal" refers to the process by which the server sends the generated response to the terminal used by the user.

[1245] The term "means for displaying to the user" refers to a process in which the terminal displays the answer received from the server on the user interface and presents it to the user.

[1246] The present invention is a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. By combining an emotion engine that recognizes the user's emotions, the system generates answers that correspond to the user's emotions. Detailed embodiments of the present invention will be described below.

[1247] System Overview

[1248] This system is primarily composed of three main components: a server, a device, and a user. Users access the system using a device such as a smartphone or PC and input a question. The device acquires the user's question along with its associated emotions and sends them to the server. The server analyzes this information, generates an appropriate answer, and sends it back to the device. The device then displays the received answer on a user interface and presents it to the user.

[1249] Hardware and software used

[1250] Hardware

[1251] The devices used by users are electronic devices such as general smartphones and personal computers, while the servers use a cloud-based server system and are equipped with high-performance computing power and storage.

[1252] software

[1253] The following software is used:

[1254] Natural language processing engines (e.g., SpaCy, NLTK, etc.): Used to analyze user text input and understand key keywords and context.

[1255] Sentiment engine (e.g., Hugging Face sentiment analysis model): Used to parse emotions from user text.

[1256] Knowledge base: A financial database, including SQL databases and Elasticsearch.

[1257] Generative AI engine (e.g., OpenAI GPT-3): Generates answers based on acquired information and sentiment analysis results.

[1258] Overview of the processing flow

[1259] The server receives the user's input and analyzes it using a natural language processing engine and an emotion engine. It then retrieves the necessary information from a knowledge base and external data sources and generates an appropriate answer using a generative AI engine. The generated answer is then presented to the user via the terminal from the server.

[1260] Specific examples

[1261] User input:

[1262] "What investment strategies would you recommend for my retirement? I'm very worried."

[1263] Example prompt sentence:

[1264] "It's natural to be worried about asset management in retirement. We will introduce ways to give you peace of mind, such as diversifying your investments and utilizing iDeCo."

[1265] In this way, the user can receive specific and emotionally sensitive financial advice from the system. The present invention allows the user to resolve financial issues in a more satisfactory manner.

[1266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1267] Step 1: User Input

[1268] Users access the system using devices such as smartphones or PCs and input financial questions. Input can be done via chat box or email. The emotion engine analyzes the emotions from the user's text.

[1269] Specific behavior:

[1270] The user launches a browser and accesses the system's website.

[1271] In the chat box, type, "Please tell me about asset management for retirement. I'm very worried." and click the send button.

[1272] Input: User's question text (e.g., "Please tell me about asset management for retirement. I'm very worried.")

[1273] Output: User question text and sentiment estimation results

[1274] Step 2: Sending data

[1275] The device converts the input data from the user (question text and emotion estimation results) into JSON format and sends it to the server using an HTTP request.

[1276] Specific behavior:

[1277] The terminal parses the input data into JSON format.

[1278] The front-end JavaScript sends the JSON formatted data to the server as an HTTP POST request.

[1279] Input: User question text and sentiment estimation results

[1280] Output: JSON format data sent to the server

[1281] Step 3: Receiving and analyzing data

[1282] The server receives the data sent from the device and passes it to a natural language processing engine and emotion engine. The natural language processing engine analyzes the question and extracts key keywords, and the emotion engine analyzes emotions.

[1283] Specific behavior:

[1284] The server receives the HTTP request and extracts the data from the body.

[1285] The data is passed to a natural language processing engine (e.g., SpaCy) to perform tokenization and dependency analysis.

[1286] A natural language processing engine extracts keywords from the question (e.g., "retirement" and "asset management").

[1287] The emotion engine analyzes the text and identifies emotions (e.g., "worry").

[1288] Input: JSON format data sent to the server

[1289] Output: Parsed keywords and sentiment data

[1290] Step 4: Knowledge base matching and information acquisition

[1291] The server then compares the analyzed question content and sentiment against a knowledge base to retrieve relevant information, including the latest financial information from external data sources if necessary.

[1292] Specific behavior:

[1293] The server uses SQL or Elasticsearch queries to retrieve relevant information from a built-in knowledge base.

[1294] If necessary, API requests are used to obtain new information from external financial data providers.

[1295] Input: Parsed keywords and sentiment data

[1296] Output: Information related to the user's question

[1297] Step 5: Generate an answer

[1298] The server uses a generative AI engine to generate responses appropriate for the user based on the acquired information and the results of sentiment analysis. This engine creates responses in natural language.

[1299] Specific behavior:

[1300] The server creates a prompt for the generative AI engine (e.g., GPT-3) and sends the question data and related information.

[1301] The AI ​​engine generates answers based on the prompts.

[1302] The generated answer is returned to the server.

[1303] Input: Acquired information and sentiment analysis results

[1304] Output: Generated answer text

[1305] Step 6: Submit and view your responses

[1306] The server sends the generated answer to the user terminal, which displays the received answer on a user interface and presents it to the user.

[1307] Specific behavior:

[1308] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[1309] The device receives the HTTP response, and the front-end JavaScript displays the response in the chat box.

[1310] Input: Generated answer text

[1311] Output: The answer text that is displayed in the user interface

[1312] (Application example 2)

[1313] 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."

[1314] With the recent diversification of financial services, users have more opportunities to seek a wide range of financial advice. In particular, online financial advice often provides uniform answers that ignore the user's feelings, which can lead to user dissatisfaction. Therefore, there is a need for a system that takes the user's feelings into consideration and provides more appropriate and personalized financial advice.

[1315] The identification process by the identification 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 analysis means including a natural language processing engine that analyzes the consultation and an emotion engine that analyzes the user's emotions, means for collating the consultation and emotions analyzed by the analysis means with a knowledge base to obtain optimal information, and means for obtaining additional information from an external data source. This makes it possible to provide appropriate financial advice that takes into consideration the user's emotions.

[1316] A "user" is an entity that uses the system to provide financial advice.

[1317] "Financial consultation" refers to questions or doubts that a user has about financial information or advice.

[1318] A "natural language processing engine" is software that analyzes text entered by a user and understands its intent and content.

[1319] An "emotion engine" is software that analyzes and recognizes emotions from user input text.

[1320] The "analysis means" includes a natural language processing engine and an emotion engine, and has the function of analyzing the user's consultation and emotions.

[1321] A "knowledge base" is a database containing data related to finance.

[1322] An "external data source" is a data source other than the knowledge base for obtaining up-to-date financial information.

[1323] The "generative artificial intelligence engine" is an engine that generates answers based on the acquired information and emotion analysis results.

[1324] A "user terminal" is a device through which a user accesses the system, inputs financial inquiries, and receives responses.

[1325] This invention is a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system has the function of generating answers that correspond to the user's emotions by combining it with an emotion engine that recognizes the user's emotions.

[1326] System Overview

[1327] This system is primarily composed of three main components: the user, the terminal, and the server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's questions and emotions and sending them to the server. The server analyzes the received questions and emotions, generates an answer, and sends it back to the user via the terminal.

[1328] Hardware or software used

[1329] Hardware: smartphones, PCs, servers

[1330] software:

[1331] Natural Language Processing Engine: Natural Language Processing model using Hugging Face's transformers library

[1332] Emotion Engine: An emotion analysis model using the Hugging Face transformers library

[1333] Generative AI engine: Generative AI model using the same library

[1334] Data processing and calculation

[1335] The server performs the following processing on the data received from the user.

[1336] 1. Data reception and analysis:

[1337] Consultation data of the user transmitted from the terminal is received.

[1338] A natural language processing engine is used to analyze the user's question through tokenization and dependency structure analysis.

[1339] An emotion engine is used to analyze emotions (e.g., anxiety, joy, anger, etc.) from user text.

[1340] 2. Knowledge base matching and external data retrieval:

[1341] Based on the analyzed question content and sentiment, the knowledge base is collated to obtain the most appropriate information.

[1342] Obtain up-to-date financial information from external data sources.

[1343] 3. Generate answers:

[1344] A generative artificial intelligence engine is used to generate answers appropriate for the user based on the acquired information and sentiment analysis results.

[1345] 4. Submit and view your answers:

[1346] The generated answer is sent to the device.

[1347] The terminal displays the received answer on the user interface and presents it to the user.

[1348] Specific examples

[1349] For example, if a user inputs a question into the system such as "I'm not good at saving money and I don't know what to do," the following processing will be carried out.

[1350] 1. The natural language processing engine extracts the keywords "savings" and "not good at," and the emotion engine analyzes the emotion "anxiety."

[1351] 2. The server checks the knowledge base to get the best information on how to save money.

[1352] 3. Also obtain information on the latest savings methods from external data sources.

[1353] 4. In response to a user's question, "I'm not good at saving money and I don't know what to do," the generative artificial intelligence engine generates the answer, "It's natural to find saving money difficult. The important thing is to save even a little bit each month. Don't rush, just keep at it."

[1354] 5. This generated answer is sent to the user's device.

[1355] Prompt Sentence Examples

[1356] text

[1357] User input: "I'm not good at saving money and I don't know what to do."

[1358] Question: "How to save money", Emotions from Japanese analysis: "Anxiety"

[1359] Example advice it might generate: "It's natural to feel like saving money is difficult. The key is to save a little each month. Don't rush it, just keep at it."

[1360] In this way, the present invention can provide specific and appropriate financial advice while taking into consideration the user's feelings.

[1361] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1362] Step 1:

[1363] The user inputs a financial inquiry into the terminal and clicks the send button. The input inquiry is in text format, such as "I'm not good at saving money and I don't know what to do."

[1364] Step 2:

[1365] The device converts the input data from the user into JSON format and sends it to the server via an HTTP request. The input data includes the user's inquiry.

[1366] Input: User-entered consultation text

[1367] Output: Data converted to JSON format

[1368] Step 3:

[1369] The server passes the data received from the terminal to a natural language processing engine as an analytical means, and analyzes the user's consultation content. It performs tokenization and dependency structure analysis to extract the user's intention and topic.

[1370] Input: JSON format consultation data

[1371] Output: Extracted keywords and context information

[1372] Step 4:

[1373] The server uses an emotion engine to analyze the user's emotions from the consultation content, and identifies emotions such as "anxiety" or "joy" as examples.

[1374] Input: Text of consultation

[1375] Output: User's emotional information (e.g., anxiety)

[1376] Step 5:

[1377] Based on the analyzed question content and sentiment, the server compares it with a knowledge base that stores financial data and retrieves the most appropriate information.

[1378] Input: Extracted keywords and sentiment information

[1379] Output: The best information retrieved from the knowledge base

[1380] Step 6:

[1381] If necessary, the server also retrieves up-to-date financial information from external data sources, such as the latest savings and investment information.

[1382] Input: Query required financial information

[1383] Output: Latest information retrieved from an external data source

[1384] Step 7:

[1385] The server uses a generative artificial intelligence engine to generate appropriate answers for the user based on the analyzed data and acquired information, and the generated answers are expressed in natural language and take emotional aspects into consideration.

[1386] Input: Information from knowledge base and external data sources, sentiment analysis results

[1387] Output: Generated answer text (e.g., "It's natural to feel that saving money is difficult. The important thing is to save even a little each month. Don't rush, just keep at it.")

[1388] Step 8:

[1389] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[1390] Input: Answer text sent from the server

[1391] Output: The answer displayed in the user interface

[1392] The above is a series of processing steps for the system that realizes the application example.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] [Fourth embodiment]

[1397] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1398] 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.

[1399] 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).

[1400] 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.

[1401] 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.

[1402] 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).

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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."

[1410] The system of the present invention accepts online financial consultations from users, analyzes them, and provides appropriate advice. A specific embodiment of the system will be described below.

[1411] System Overview

[1412] The system of the present invention is mainly composed of three main components: a user, a terminal, and a server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's question and sending it to the server. The server analyzes the received question, generates an answer, and sends it back to the user via the terminal.

[1413] Specific processing of the system

[1414] 1. User Interface and Input:

[1415] Users access the system using a terminal and input financial consultations. Input can be done in the form of a chat box or email.

[1416] Example: A user types a question such as "Tell me about investing for retirement."

[1417] 2. Data transmission:

[1418] The terminal converts the input data from the user into JSON format and sends it to the server via an HTTP request.

[1419] 3. Data Receipt and Analysis:

[1420] The server passes the received data to a natural language processing engine as an analytical means, and analyzes it to understand the user's question.

[1421] The natural language processing engine performs tokenization and dependency analysis to extract key keywords and understand the context of the question.

[1422] 4. Knowledge base matching and information retrieval:

[1423] The server then checks the analyzed question against a knowledge base, which contains a variety of financial data.

[1424] When necessary, the server retrieves up-to-date financial information from external data sources, including, for example, the latest investment rates and financial news.

[1425] 5. Generate answers:

[1426] The generative AI engine generates appropriate answers for the user based on the acquired information. This engine creates responses in natural language.

[1427] Example: "You have the following options for managing your assets after retirement: 1. Use the Tsumitate NISA (Nippon Individual Savings Account) 2. Utilize the small investment tax exemption system (iDeCo) 3. Diversify your investments."

[1428] 6. Submitting and Viewing Answers:

[1429] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[1430] As described above, the system of the present invention allows users to easily consult about financial matters anytime, anywhere and receive appropriate advice on the spot. Furthermore, since no face-to-face consultation is required, privacy is maintained and there are no costs involved. This allows users to make financial decisions with peace of mind.

[1431] The processing flow will be explained below.

[1432] Step 1:

[1433] A user accesses the system using a terminal.

[1434] Access it from a browser on your computer or mobile device or launch a dedicated application.

[1435] Step 2:

[1436] A user inputs a financial consultation request into a terminal.

[1437] Enter text into a chat box or email form and click the send button.

[1438] Step 3:

[1439] The terminal converts the user's input data into JSON format.

[1440] It converts the input string into an appropriate data structure and prepares the HTTP request.

[1441] Step 4:

[1442] The terminal transmits the converted data to the server.

[1443] It sends the user's question data to the server via an HTTP request.

[1444] Step 5:

[1445] The server passes the received data to an analysis means (natural language processing engine).

[1446] It parses the incoming data and passes it as input to the NLP engine.

[1447] Step 6:

[1448] The server analyzes the consultation content to extract key keywords and contextual information.

[1449] It performs tokenization and dependency structure analysis to understand the user's intent.

[1450] Step 7:

[1451] The server consults a knowledge base to retrieve relevant information.

[1452] It sends queries to the database and retrieves appropriate advice and information.

[1453] Step 8:

[1454] The server retrieves additional information from external data sources as needed.

[1455] It calls external APIs to obtain the latest financial news, rates, etc.

[1456] Step 9:

[1457] The server uses a generative artificial intelligence engine to generate suggested answers for the user.

[1458] Based on the acquired information, it generates text in natural language.

[1459] Step 10:

[1460] The server sends the generated answer to the terminal.

[1461] The generated response data is converted into JSON format and sent to the terminal as an HTTP response.

[1462] Step 11:

[1463] The terminal receives and analyzes the response data from the server.

[1464] It parses the received data and converts it into a displayable format.

[1465] Step 12:

[1466] The terminal displays the answer to the user.

[1467] The answer is displayed in the user interface so that the user can confirm it.

[1468] Examples:

[1469] Example questions:

[1470] User: "What are the best investment strategies for retirement?"

[1471] Process flow:

[1472] 1. The user enters a question into the terminal and clicks the send button.

[1473] 2. The device converts the question into JSON format and sends it to the server.

[1474] 3. The server receives the data and passes it to the natural language processing engine.

[1475] 4. The server performs the analysis and extracts keywords such as "retirement" and "investment methods."

[1476] 5. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[1477] 6. If necessary, the server retrieves the latest investment information from an external data source.

[1478] 7. The generative AI engine generates the answer, "For your retirement, we recommend diversifying your investments and using iDeCo."

[1479] 8. The server sends the generated response to the device.

[1480] 9. The device receives the answer and displays it on the user interface: "For your retirement, we recommend diversifying your investments and using iDeCo."

[1481] Through the above steps, the system of the present invention provides optimal financial advice to the user.

[1482] Example 1

[1483] 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."

[1484] Conventional financial consultation systems have difficulty generating fast and accurate answers to questions submitted by users. In particular, advanced data analysis and artificial intelligence technologies are required to correctly understand users' questions and provide appropriate advice that includes the latest financial information. Furthermore, a user-friendly interface and real-time responses are required to improve the user experience. Therefore, the challenge is to create a system that can properly analyze users' questions and provide optimal information.

[1485] 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.

[1486] In this invention, the server includes: means for accepting a financial inquiry from a user; analysis means including a natural language processing engine for analyzing the inquiry; means for comparing the inquiry analyzed by the analysis means with a knowledge base to obtain optimal information; means for obtaining additional information from an external data source; means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information; means for transmitting the generated answer to the user terminal; means for displaying the generated answer on a user interface; means for the user terminal to convert data input by the user into JSON format and transmit it to the server via an HTTP request; means for the server to analyze the received data, obtain information from the knowledge base and external data source, and generate an answer using the generative artificial intelligence engine; and means for using a prompt sentence when generating the answer. This enables the server to provide quick and accurate answers to users' financial questions.

[1487] A "User" is an individual or entity that accesses the System to provide financial advice.

[1488] A "terminal" is a computing device through which a user accesses the system and sends and receives input data.

[1489] A "server" is a computer system that receives data sent by users, analyzes it, and generates a response.

[1490] "Consultation" refers to the act of a user inputting a financial problem or question into the system, or the content of that problem or question.

[1491] A "natural language processing engine" is software or algorithms that analyze and understand users' inquiries.

[1492] The "analysis means" refers to the entire process of analyzing a user's inquiry using a natural language processing engine.

[1493] A "knowledge base" is a database that stores data and information related to finance.

[1494] "External Data Sources" are external sources for obtaining up-to-date financial information and other relevant data not present in the knowledge base.

[1495] A "generative artificial intelligence engine" is an artificial intelligence technology that generates answers to user questions based on acquired information.

[1496] A "prompt sentence" is an instruction sentence that is input to a generative artificial intelligence engine to generate a specific response.

[1497] The present invention relates to a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system is mainly composed of three main components: a user, a terminal, and a server.

[1498] User operations

[1499] Users access the system using devices such as smartphones or PCs and input financial questions. Input can be done via chat box or email. For example, a user might input a question such as, "Please tell me about asset management for retirement."

[1500] Device Features

[1501] The device is responsible for converting user input data into JSON format and sending it to the server via an HTTP request. At this time, the device's browser uses JavaScript to convert the data into JSON format and send it as an Ajax request.

[1502] Server Processing

[1503] The server analyzes the received data using a natural language processing engine. SpaCy or another natural language processing engine can be used. This engine performs tokenization and dependency structure analysis to understand the user's question and extract key keywords. The server then checks the analyzed question against a knowledge base. The knowledge base stores data and information related to finance. If necessary, the server can obtain the latest information from external data sources (for example, financial data APIs).

[1504] Answer Generation

[1505] The server uses a generative AI engine (e.g., GPT-4) to generate an appropriate answer for the user. At this time, it uses prompt sentences to instruct the AI. For example, a prompt sentence such as, "The user is asking about asset management for retirement. Please answer including the following information: 1. Tsumitate NISA 2. iDeCo 3. Diversified investment" can be used.

[1506] Providing answers

[1507] The server sends the generated answer to the terminal, which displays it on the user interface. Specifically, the server returns the generated answer in JSON format to the terminal, and JavaScript on the terminal displays it in a chat box. An example of an answer might be something like, "You have the following options for asset management in retirement: 1. Use the NISA savings plan. 2. Utilize the iDeCo small investment tax exemption system. 3. Diversify your investments."

[1508] In this way, the system of the present invention allows users to easily consult about financial matters anytime, anywhere and receive appropriate advice on the spot. Since no face-to-face consultation is required, the system has the advantage of maintaining users' privacy and being cost-effective. Furthermore, the system provides users with the latest financial information in real time, allowing them to make financial decisions with peace of mind.

[1509] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1510] Step 1:

[1511] Users access the system using devices such as smartphones or PCs to input financial inquiries, which can be input via chat box or email.

[1512] Input: User types, "Tell me about investing for retirement."

[1513] Output: The entered financial consultation details.

[1514] Specific operation: The user opens a web browser, accesses the system's web page, and enters the content of their inquiry in the chat box.

[1515] Step 2:

[1516] The terminal converts the data entered by the user into JSON format and sends it to the server via an HTTP request.

[1517] Input: Financial consultation details from the user.

[1518] Output: Data in JSON format.

[1519] What happens: JavaScript is executed in the device's browser, converting the user's input into JSON format, and then sending the converted data to the server as an Ajax request.

[1520] Step 3:

[1521] The server passes the received data to a natural language processing engine to analyze the user's question.

[1522] Input: User consultation data in JSON format.

[1523] Output: Parsed question meaning and keywords.

[1524] What it does: The server uses Flask to receive HTTP requests and read the JSON data, then passes this data to a natural language processing engine (e.g., spaCy) for tokenization and dependency analysis to understand the user's question.

[1525] Step 4:

[1526] The server then searches the knowledge base for relevant information based on the parsed question, and retrieves additional information from external data sources if necessary.

[1527] Input: Parsed question meaning and keywords.

[1528] Output: Relevant information retrieved from the knowledge base and external data sources.

[1529] Specific operation: The server queries a database (e.g., MySQL) to obtain relevant financial information, and simultaneously calls an external API (e.g., a financial data API) as needed to obtain the latest financial data.

[1530] Step 5:

[1531] The server uses a generative artificial intelligence engine to generate appropriate responses for the user based on the acquired information, using prompt sentences.

[1532] Input: Information obtained from the knowledge base and external data sources, prompt statement.

[1533] Output: The answer to the user.

[1534] Specific operation: Using the acquired information, the server sends a prompt to a generative AI engine (e.g., GPT-4) to generate a natural-language answer. For example, a prompt such as "The user is asking about asset management for retirement. Please respond with the following information: 1. Tsumitate NISA 2. iDeCo 3. Diversified investment" can be used.

[1535] Step 6:

[1536] The server sends the generated answer to the terminal, which displays the answer on a user interface.

[1537] Input: Answers from a generative artificial intelligence engine.

[1538] Output: Display answer to user.

[1539] Specific operation: The server returns the generated answer in JSON format to the device, and JavaScript on the device displays it in the chat box. Specific answers such as "You have the following options for managing your assets in retirement: 1. Use the Tsumitate NISA (Nippon Individual Savings Account) 2. Utilize the small investment tax exemption system (iDeCo) 3. Diversify your investments" are displayed.

[1540] In this way, a system is realized in which each processing step works together to provide appropriate financial advice to users.

[1541] (Application example 1)

[1542] 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."

[1543] Conventional financial consultation systems require users to input questions in text format and then return the analysis results in text format, which makes them difficult to understand visually. Furthermore, there are also issues with low user engagement and a lack of interactive advice, resulting in low user satisfaction.

[1544] 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.

[1545] In this invention, the server includes means for accepting a financial consultation from a user, analysis means including a natural language processing engine for analyzing the consultation, means for comparing the consultation with a knowledge base to obtain optimal information based on the consultation analyzed by the analysis means, means for obtaining additional information from an external data source, means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information, means for transmitting the generated answer to a user terminal, means for displaying the generated answer to the user, means for providing financial advice in a virtual space using a virtual reality device, and means for visually presenting the advice in the virtual space. This allows the user to receive financial advice visually and interactively, thereby improving user engagement and satisfaction.

[1546] The "means for accepting financial inquiries from users" is an interface that allows users to input financial questions and inquiries into the system.

[1547] The "analysis means including a natural language processing engine for analyzing a consultation" refers to a device or system that uses natural language processing technology to analyze the consultation content input by a user and understand that content.

[1548] "Means of collating a knowledge base to obtain optimal information" refers to a technology that obtains the most relevant data from pre-stored information based on the analyzed consultation content.

[1549] "Means for obtaining additional information from external data sources" refers to techniques for obtaining up-to-date financial information and related data from the Internet and other external sources.

[1550] "Means for generating answers to be suggested to users using a generative artificial intelligence engine" refers to artificial intelligence technology for generating appropriate advice and answers for users based on collected information.

[1551] The "means for transmitting the generated answer to the user terminal" is a technique for transmitting the generated answer to the terminal used by the user.

[1552] The "means for displaying to the user" refers to a technique for displaying the submitted answers so that the user can visually confirm them.

[1553] A "means for providing financial advice in a virtual space using a virtual reality device" is a device that uses virtual reality technology to provide financial advice to a user in a virtual space.

[1554] "Means for visually presenting advice in a virtual space" refers to a technology that visually displays advice in a virtual space, making it easier for the user to understand.

[1555] This invention provides a system that allows users to consult about financial matters in a virtual space and receive appropriate advice in real time. Specifically, a virtual reality device is used to enable users to receive financial advice visually and interactively. Specific embodiments of this system are described below.

[1556] The system mainly consists of a user terminal, a server, and a virtual reality device (e.g., a head-mounted display). Users use these devices to access the virtual space and receive financial consultations.

[1557] Hardware and Software

[1558] User terminal: This refers to a smartphone or PC through which the user accesses the system.

[1559] Server: A cloud server is used as the execution environment for analysis and data processing.

[1560] Virtual reality device: Using Oculus Quest 2 as an example.

[1561] Natural language processing engine: Uses Google Cloud Natural Language.

[1562] Generative artificial intelligence engine: Uses OpenAI GPT-4.

[1563] Virtual environment generation engine: Unity is used.

[1564] System action

[1565] 1. Handling User Input

[1566] The user wears a head-mounted display and receives financial consultations by voice or text input. In the case of voice input, a "voice recognition engine" converts the voice into text, which then recognizes the content of the user's question.

[1567] 2. Data transmission and analysis

[1568] The user's device converts the user's input data into JSON format and sends it to the server via an HTTP request. The server then passes the received data to Google Cloud Natural Language for analysis. This analysis method extracts key keywords through tokenization and dependency structure analysis to understand the context of the question.

[1569] 3. Knowledge base matching and information acquisition

[1570] Based on the analysis results, the server checks its knowledge base to retrieve relevant financial data, and also retrieves additional information from external data sources, such as the latest investment rates and financial news.

[1571] 4. Answer Generation

[1572] Based on the information obtained, the server generates an answer using OpenAI GPT-4, a generative artificial intelligence engine that creates responses in natural language and provides specific, context-sensitive advice.

[1573] 5. Submitting and Viewing Your Answers

[1574] The generated answers are sent from the server to the user's terminal and displayed on the user's virtual reality device. The user receives visually interactive advice while interacting with an avatar in the virtual space.

[1575] Specific examples

[1576] Below are some example prompts for users seeking financial advice:

[1577] Example: Prompt to the generative AI engine when a user types "Please tell me about asset management for retirement":

[1578] "A user is asking about investing in retirement. Please provide specific advice including the following elements: NISA, iDeCo, and diversified investments."

[1579] This allows the system to provide users with financial advice in a visual and easy-to-understand format, increasing engagement and satisfaction.

[1580] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1581] Step 1:

[1582] The user wears a head-mounted display and inputs their financial consultation request by voice or text. In the case of voice input, a "voice recognition engine" converts the voice into text, and the user's question is obtained in text format.

[1583] Step 2:

[1584] The user device converts the acquired question content into JSON format and sends it to the server via an HTTP request. The input data is speech recognition results or text data, and the output is JSON format data.

[1585] Step 3:

[1586] The server passes the received JSON data to Google Cloud Natural Language for analysis. This analysis method extracts key keywords through tokenization and dependency structure analysis to understand the context of the question. The input data is the question data in JSON format, and the output data is the analysis result.

[1587] Step 4:

[1588] Based on the analysis results, the server checks the knowledge base to obtain relevant financial data, and also collects additional information from external data sources, such as the latest investment rates and financial news. The input data is the analysis results, and the output data is the obtained financial information.

[1589] Step 5:

[1590] The server generates specific advice based on the acquired information using the generative AI engine "OpenAI GPT-4." This generative AI engine creates a response in natural language based on the prompt. The input data is the acquired financial information and the prompt, and the output data is the generated answer.

[1591] Step 6:

[1592] The server sends the generated answer to the user terminal. The input data is the generated answer, and the output data is the data sent to the user terminal.

[1593] Step 7:

[1594] The user terminal displays the received answer on a virtual reality device. The user interacts with the avatar in the virtual space and receives visual and interactive advice. Specifically, the avatar in the virtual space provides advice to the user and displays visual charts and graphs. The input data is the answer sent from the server, and the output is advice displayed to the user's eyes.

[1595] The above are the specific steps of the processing of this system.

[1596] 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.

[1597] The present invention relates to a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system is further combined with an emotion engine that recognizes the user's emotions, making it possible to generate answers that correspond to the user's emotions.

[1598] System Overview

[1599] The system of the present invention mainly consists of three main components: a user, a terminal, and a server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's questions and emotions and sending them to the server. The server analyzes the received questions and emotions, generates an answer, and sends it back to the user via the terminal.

[1600] Specific processing of the system

[1601] 1. User Interface and Input:

[1602] Users access the system using a terminal and input financial consultations. Input can be made in the form of a chat box or email, and the emotion engine analyzes the emotions from the user's text.

[1603] Example: A user types a question such as, "Tell me about how to manage my finances for retirement. I'm very worried."

[1604] 2. Data transmission:

[1605] The terminal converts the input data from the user into JSON format and sends it to the server via an HTTP request.

[1606] 3. Data Receipt and Analysis:

[1607] The server passes the received data to a natural language processing engine as an analytical tool, and analyzes it to understand the user's questions and emotions.

[1608] The natural language processing engine performs tokenization and dependency analysis to extract key keywords and understand the context of the question.

[1609] The emotion engine analyzes emotions (e.g., anxiety, joy, anger, etc.) from the user's text input and provides them to the server.

[1610] 4. Knowledge base matching and information retrieval:

[1611] The server then checks the analyzed question against a knowledge base, which contains a variety of financial data.

[1612] When necessary, the server retrieves up-to-date financial information from external data sources, including, for example, the latest investment rates and financial news.

[1613] 5. Generate answers:

[1614] The generative AI engine generates responses appropriate to the user based on the acquired information and the analysis results of the emotion engine. This engine creates responses in natural language.

[1615] Example: "It's natural to be worried about managing your assets in retirement. We'll introduce you to ways to put your mind at ease, such as diversifying your investments and taking advantage of iDeCo."

[1616] 6. Submitting and Viewing Answers:

[1617] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[1618] Examples:

[1619] Example questions:

[1620] User: "What are some recommendations for investing in retirement? I'm very worried."

[1621] Process flow:

[1622] 1. The user enters a question into the terminal and clicks the send button.

[1623] 2. The device converts the question and emotion into JSON format and sends it to the server.

[1624] 3. The server receives the data and passes it to the natural language processing engine and emotion engine.

[1625] 4. The server performs the analysis and extracts keywords such as "retirement" and "investment methods" as well as the user's emotion of "worry."

[1626] 5. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[1627] 6. If necessary, the server retrieves the latest investment information from an external data source.

[1628] 7. The generative AI engine generates an emotionally sensitive response: "It's natural to be worried about asset management in retirement. Please consider diversifying your investments or using iDeCo."

[1629] 8. The server sends the generated response to the device.

[1630] 9. The device receives the answer and displays it on the user interface: "It's natural to be worried about asset management in your retirement years. Please consider diversifying your investments or using iDeCo."

[1631] The system allows users to receive specific, emotionally sensitive financial advice.

[1632] The processing flow will be explained below.

[1633] Step 1:

[1634] A user accesses the system using a terminal.

[1635] Access it from a browser on your computer or mobile device or launch a dedicated application.

[1636] Step 2:

[1637] A user inputs a financial consultation request into a terminal.

[1638] Enter text into a chat box or email form and click the send button.

[1639] Step 3:

[1640] The terminal converts the user-entered data into JSON format.

[1641] It converts the input string into an appropriate data structure and prepares the HTTP request.

[1642] Step 4:

[1643] The terminal transmits the converted data to the server.

[1644] It sends the user's question data to the server via an HTTP request.

[1645] Step 5:

[1646] The server passes the received data to the analysis means (natural language processing engine and emotion engine).

[1647] It parses the received data and passes it as input to the natural language processing engine and emotion engine.

[1648] Step 6:

[1649] The server analyzes the consultation content using a natural language processing engine.

[1650] It performs tokenization and dependency structure analysis to extract key keywords and contextual information from the user's question.

[1651] Step 7:

[1652] The server analyzes the user's emotions using an emotion engine.

[1653] It extracts emotional patterns from input text and identifies major emotional categories such as "anxiety," "joy," and "anger."

[1654] Step 8:

[1655] The server consults a knowledge base to retrieve relevant information.

[1656] It works by sending queries to a database to retrieve relevant financial information and advice.

[1657] Step 9:

[1658] The server retrieves additional information from external data sources as needed.

[1659] It calls external APIs to retrieve the latest financial news, market rates, etc.

[1660] Step 10:

[1661] The server uses a generative artificial intelligence engine to generate answers to suggest to the user based on the acquired information and the results of emotion analysis.

[1662] The system creates responses in natural language and incorporates content that takes the user's feelings into consideration.

[1663] Step 11:

[1664] The server sends the generated answer to the terminal.

[1665] The generated response data is converted into JSON format and sent to the terminal as an HTTP response.

[1666] Step 12:

[1667] The terminal receives and analyzes the response data from the server.

[1668] It parses the received data and converts it into strings and other UI elements.

[1669] Step 13:

[1670] The terminal displays the answer to the user.

[1671] The answer is displayed in the user interface so that the user can confirm it.

[1672] Examples:

[1673] Example questions:

[1674] User: "What are some recommendations for investing in retirement? I'm very worried."

[1675] Process flow:

[1676] 1. The user enters a question into the terminal and clicks the send button.

[1677] 2. The device converts the question and emotion data into JSON format and sends it to the server.

[1678] 3. The server receives the data and passes it to the natural language processing engine and emotion engine.

[1679] 4. The server performs natural language processing to extract keywords such as "retirement" and "investment methods."

[1680] 5. The server runs the emotion engine and identifies the user's emotion of "worry."

[1681] 6. The server checks the knowledge base and obtains relevant information (e.g., NISA, iDeCo).

[1682] 7. If necessary, the server retrieves the latest investment information from an external data source.

[1683] 8. The generative AI engine generates an emotionally sensitive response: "It's natural to be worried about asset management in retirement. Please consider diversifying your investments or using iDeCo."

[1684] 9. The server sends the generated response to the device.

[1685] 10. The device receives the answer and displays it on the user interface: "It's natural to be worried about asset management in your retirement years. Please consider diversifying your investments or using iDeCo."

[1686] The system allows users to receive specific, emotionally sensitive financial advice.

[1687] Example 2

[1688] 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."

[1689] Conventional financial consultation systems provide only simple information without considering the user's feelings, which means that users are unable to receive satisfactory advice. This can result in users taking a long time to resolve their financial concerns and questions, which can lead to a decrease in satisfaction.

[1690] 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.

[1691] In this invention, the server includes means for accepting financial consultations from users, analysis means including a natural language processing engine and an emotion engine for analyzing the consultations and the user's emotions, means for comparing the consultations and emotions analyzed by the analysis means with a knowledge base to obtain optimal information, means for obtaining additional information from an external data source, means for generating an answer to be proposed to the user using a generative artificial intelligence engine based on the obtained information and the analyzed emotions, means for transmitting the generated answer to a user terminal, and means for displaying the generated answer to the user. This makes it possible to provide appropriate advice that takes the user's emotions into consideration, thereby improving user satisfaction.

[1692] "User" refers to any individual or entity that accesses the System and provides financial advice.

[1693] "Financial consultation" refers to the act of a user asking questions or concerns about financial matters such as asset management, investment, savings, and insurance.

[1694] A "terminal" is a device used by a user to access the system, and includes a smartphone, PC, tablet, etc.

[1695] "Server" refers to a computer system that receives requests from users, analyzes data, retrieves information, and generates and sends responses.

[1696] A "natural language processing engine" refers to a software system that analyzes user input and understands key keywords and context.

[1697] "Emotion engine" refers to a software system for analyzing emotions from a user's text and identifying their emotional state.

[1698] "Analysis means" refers to the process of analyzing the user's consultation content and emotions using a natural language processing engine and an emotion engine.

[1699] A "knowledge base" refers to a database that stores financial data and is used to provide information appropriate to a user's inquiry.

[1700] "External Data Source" refers to an external data provider service or API that the Server accesses to obtain additional information as needed.

[1701] A "generative artificial intelligence engine" refers to an artificial intelligence system that generates responses to users in natural language based on acquired information and the results of sentiment analysis.

[1702] "Means for generating an answer" refers to a process of using a generative artificial intelligence engine to generate an answer appropriate for the user.

[1703] "Means for sending to the user terminal" refers to the process by which the server sends the generated response to the terminal used by the user.

[1704] The term "means for displaying to the user" refers to a process in which the terminal displays the answer received from the server on the user interface and presents it to the user.

[1705] The present invention is a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. By combining an emotion engine that recognizes the user's emotions, the system generates answers that correspond to the user's emotions. Detailed embodiments of the present invention will be described below.

[1706] System Overview

[1707] This system is primarily composed of three main components: a server, a device, and a user. Users access the system using a device such as a smartphone or PC and input a question. The device acquires the user's question along with its associated emotions and sends them to the server. The server analyzes this information, generates an appropriate answer, and sends it back to the device. The device then displays the received answer on a user interface and presents it to the user.

[1708] Hardware and software used

[1709] Hardware

[1710] The devices used by users are electronic devices such as general smartphones and personal computers, while the servers use a cloud-based server system and are equipped with high-performance computing power and storage.

[1711] software

[1712] The following software is used:

[1713] Natural language processing engines (e.g., SpaCy, NLTK, etc.): Used to analyze user text input and understand key keywords and context.

[1714] Sentiment engine (e.g., Hugging Face sentiment analysis model): Used to parse emotions from user text.

[1715] Knowledge base: A financial database, including SQL databases and Elasticsearch.

[1716] Generative AI engine (e.g., OpenAI GPT-3): Generates answers based on acquired information and sentiment analysis results.

[1717] Overview of the processing flow

[1718] The server receives the user's input and analyzes it using a natural language processing engine and an emotion engine. It then retrieves the necessary information from a knowledge base and external data sources and generates an appropriate answer using a generative AI engine. The generated answer is then presented to the user via the terminal from the server.

[1719] Specific examples

[1720] User input:

[1721] "What investment strategies would you recommend for my retirement? I'm very worried."

[1722] Example prompt sentence:

[1723] "It's natural to be worried about asset management in retirement. We will introduce ways to give you peace of mind, such as diversifying your investments and utilizing iDeCo."

[1724] In this way, the user can receive specific and emotionally sensitive financial advice from the system. The present invention allows the user to resolve financial issues in a more satisfactory manner.

[1725] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1726] Step 1: User Input

[1727] Users access the system using devices such as smartphones or PCs and input financial questions. Input can be done via chat box or email. The emotion engine analyzes the emotions from the user's text.

[1728] Specific behavior:

[1729] The user launches a browser and accesses the system's website.

[1730] In the chat box, type, "Please tell me about asset management for retirement. I'm very worried." and click the send button.

[1731] Input: User's question text (e.g., "Please tell me about asset management for retirement. I'm very worried.")

[1732] Output: User question text and sentiment estimation results

[1733] Step 2: Sending data

[1734] The device converts the input data from the user (question text and emotion estimation results) into JSON format and sends it to the server using an HTTP request.

[1735] Specific behavior:

[1736] The terminal parses the input data into JSON format.

[1737] The front-end JavaScript sends the JSON formatted data to the server as an HTTP POST request.

[1738] Input: User question text and sentiment estimation results

[1739] Output: JSON format data sent to the server

[1740] Step 3: Receiving and analyzing data

[1741] The server receives the data sent from the device and passes it to a natural language processing engine and emotion engine. The natural language processing engine analyzes the question and extracts key keywords, and the emotion engine analyzes emotions.

[1742] Specific behavior:

[1743] The server receives the HTTP request and extracts the data from the body.

[1744] The data is passed to a natural language processing engine (e.g., SpaCy) to perform tokenization and dependency analysis.

[1745] A natural language processing engine extracts keywords from the question (e.g., "retirement" and "asset management").

[1746] The emotion engine analyzes the text and identifies emotions (e.g., "worry").

[1747] Input: JSON format data sent to the server

[1748] Output: Parsed keywords and sentiment data

[1749] Step 4: Knowledge base matching and information acquisition

[1750] The server then compares the analyzed question content and sentiment against a knowledge base to retrieve relevant information, including the latest financial information from external data sources if necessary.

[1751] Specific behavior:

[1752] The server uses SQL or Elasticsearch queries to retrieve relevant information from a built-in knowledge base.

[1753] If necessary, API requests are used to obtain new information from external financial data providers.

[1754] Input: Parsed keywords and sentiment data

[1755] Output: Information related to the user's question

[1756] Step 5: Generate an answer

[1757] The server uses a generative AI engine to generate responses appropriate for the user based on the acquired information and the results of sentiment analysis. This engine creates responses in natural language.

[1758] Specific behavior:

[1759] The server creates a prompt for the generative AI engine (e.g., GPT-3) and sends the question data and related information.

[1760] The AI ​​engine generates answers based on the prompts.

[1761] The generated answer is returned to the server.

[1762] Input: Acquired information and sentiment analysis results

[1763] Output: Generated answer text

[1764] Step 6: Submit and view your responses

[1765] The server sends the generated answer to the user terminal, which displays the received answer on a user interface and presents it to the user.

[1766] Specific behavior:

[1767] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[1768] The device receives the HTTP response, and the front-end JavaScript displays the response in the chat box.

[1769] Input: Generated answer text

[1770] Output: The answer text that is displayed in the user interface

[1771] (Application example 2)

[1772] 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."

[1773] With the recent diversification of financial services, users have more opportunities to seek a wide range of financial advice. In particular, online financial advice often provides uniform answers that ignore the user's feelings, which can lead to user dissatisfaction. Therefore, there is a need for a system that takes the user's feelings into consideration and provides more appropriate and personalized financial advice.

[1774] The identification process by the identification 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 analysis means including a natural language processing engine that analyzes the consultation and an emotion engine that analyzes the user's emotions, means for collating the consultation and emotions analyzed by the analysis means with a knowledge base to obtain optimal information, and means for obtaining additional information from an external data source. This makes it possible to provide appropriate financial advice that takes into consideration the user's emotions.

[1775] A "user" is an entity that uses the system to provide financial advice.

[1776] "Financial consultation" refers to questions or doubts that a user has about financial information or advice.

[1777] A "natural language processing engine" is software that analyzes text entered by a user and understands its intent and content.

[1778] An "emotion engine" is software that analyzes and recognizes emotions from user input text.

[1779] The "analysis means" includes a natural language processing engine and an emotion engine, and has the function of analyzing the user's consultation and emotions.

[1780] A "knowledge base" is a database containing data related to finance.

[1781] An "external data source" is a data source other than the knowledge base for obtaining up-to-date financial information.

[1782] The "generative artificial intelligence engine" is an engine that generates answers based on the acquired information and emotion analysis results.

[1783] A "user terminal" is a device through which a user accesses the system, inputs financial inquiries, and receives responses.

[1784] This invention is a system that accepts online financial consultations from users, analyzes them, and provides appropriate advice. This system has the function of generating answers that correspond to the user's emotions by combining it with an emotion engine that recognizes the user's emotions.

[1785] System Overview

[1786] This system is primarily composed of three main components: the user, the terminal, and the server. Users access the system using a terminal such as a smartphone or PC. The terminal is responsible for acquiring the user's questions and emotions and sending them to the server. The server analyzes the received questions and emotions, generates an answer, and sends it back to the user via the terminal.

[1787] Hardware or software used

[1788] Hardware: smartphones, PCs, servers

[1789] software:

[1790] Natural Language Processing Engine: Natural Language Processing model using Hugging Face's transformers library

[1791] Emotion Engine: An emotion analysis model using the Hugging Face transformers library

[1792] Generative AI engine: Generative AI model using the same library

[1793] Data processing and calculation

[1794] The server performs the following processing on the data received from the user.

[1795] 1. Data reception and analysis:

[1796] Consultation data of the user transmitted from the terminal is received.

[1797] A natural language processing engine is used to analyze the user's question through tokenization and dependency structure analysis.

[1798] An emotion engine is used to analyze emotions (e.g., anxiety, joy, anger, etc.) from user text.

[1799] 2. Knowledge base matching and external data retrieval:

[1800] Based on the analyzed question content and sentiment, the knowledge base is collated to obtain the most appropriate information.

[1801] Obtain up-to-date financial information from external data sources.

[1802] 3. Generate answers:

[1803] A generative artificial intelligence engine is used to generate answers appropriate for the user based on the acquired information and sentiment analysis results.

[1804] 4. Submit and view your answers:

[1805] The generated answer is sent to the device.

[1806] The terminal displays the received answer on the user interface and presents it to the user.

[1807] Specific examples

[1808] For example, if a user inputs a question into the system such as "I'm not good at saving money and I don't know what to do," the following processing will be carried out.

[1809] 1. The natural language processing engine extracts the keywords "savings" and "not good at," and the emotion engine analyzes the emotion "anxiety."

[1810] 2. The server checks the knowledge base to get the best information on how to save money.

[1811] 3. Also obtain information on the latest savings methods from external data sources.

[1812] 4. In response to a user's question, "I'm not good at saving money and I don't know what to do," the generative artificial intelligence engine generates the answer, "It's natural to find saving money difficult. The important thing is to save even a little bit each month. Don't rush, just keep at it."

[1813] 5. This generated answer is sent to the user's device.

[1814] Prompt Sentence Examples

[1815] text

[1816] User input: "I'm not good at saving money and I don't know what to do."

[1817] Question: "How to save money", Emotions from Japanese analysis: "Anxiety"

[1818] Example advice it might generate: "It's natural to feel like saving money is difficult. The key is to save a little each month. Don't rush it, just keep at it."

[1819] In this way, the present invention can provide specific and appropriate financial advice while taking into consideration the user's feelings.

[1820] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1821] Step 1:

[1822] The user inputs a financial inquiry into the terminal and clicks the send button. The input inquiry is in text format, such as "I'm not good at saving money and I don't know what to do."

[1823] Step 2:

[1824] The device converts the input data from the user into JSON format and sends it to the server via an HTTP request. The input data includes the user's inquiry.

[1825] Input: User-entered consultation text

[1826] Output: Data converted to JSON format

[1827] Step 3:

[1828] The server passes the data received from the terminal to a natural language processing engine as an analytical means, and analyzes the user's consultation content. It performs tokenization and dependency structure analysis to extract the user's intention and topic.

[1829] Input: JSON format consultation data

[1830] Output: Extracted keywords and context information

[1831] Step 4:

[1832] The server uses an emotion engine to analyze the user's emotions from the consultation content, and identifies emotions such as "anxiety" or "joy" as examples.

[1833] Input: Text of consultation

[1834] Output: User's emotional information (e.g., anxiety)

[1835] Step 5:

[1836] Based on the analyzed question content and sentiment, the server compares it with a knowledge base that stores financial data and retrieves the most appropriate information.

[1837] Input: Extracted keywords and sentiment information

[1838] Output: The best information retrieved from the knowledge base

[1839] Step 6:

[1840] If necessary, the server also retrieves up-to-date financial information from external data sources, such as the latest savings and investment information.

[1841] Input: Query required financial information

[1842] Output: Latest information retrieved from an external data source

[1843] Step 7:

[1844] The server uses a generative artificial intelligence engine to generate appropriate answers for the user based on the analyzed data and acquired information, and the generated answers are expressed in natural language and take emotional aspects into consideration.

[1845] Input: Information from knowledge base and external data sources, sentiment analysis results

[1846] Output: Generated answer text (e.g., "It's natural to feel that saving money is difficult. The important thing is to save even a little each month. Don't rush, just keep at it.")

[1847] Step 8:

[1848] The server sends the generated answer to the terminal, which displays the received answer on a user interface and presents it to the user.

[1849] Input: Answer text sent from the server

[1850] Output: The answer displayed in the user interface

[1851] The above is a series of processing steps for the system that realizes the application example.

[1852] 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.

[1853] 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.

[1854] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1855] 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.

[1856] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1857] 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.

[1858] 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).

[1859] 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.

[1860] 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."

[1861] 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.

[1862] 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).

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] 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.

[1873] The following is further disclosed regarding the above embodiment.

[1874] (Claim 1)

[1875] A means for accepting financial consultations from users;

[1876] analysis means including a natural language processing engine for analyzing the consultation;

[1877] a means for collating a knowledge base based on the consultation analyzed by the analysis means to obtain optimal information;

[1878] a means of obtaining additional information from external data sources; and

[1879] means for generating an answer to be suggested to a user using a generative artificial intelligence engine based on the acquired information;

[1880] means for transmitting the generated answer to a user terminal;

[1881] The system includes means for displaying the generated answer to the user.

[1882] (Claim 2)

[1883] 2. The system of claim 1, wherein the parsing means is a natural language processing engine that performs tokenization and dependency structure analysis.

[1884] (Claim 3)

[1885] 2. The system of claim 1, wherein the knowledge base is a database containing data related to finance.

[1886] "Example 1"

[1887] (Claim 1)

[1888] A means for accepting financial consultations from users;

[1889] analysis means including a natural language processing engine for analyzing the consultation;

[1890] a means for collating a knowledge base based on the consultation analyzed by the analysis means to obtain optimal information;

[1891] a means of obtaining additional information from external data sources; and

[1892] means for generating an answer to be suggested to a user using a generative artificial intelligence engine based on the acquired information;

[1893] means for transmitting the generated answer to a user terminal;

[1894] means for displaying the generated answers in a user interface;

[1895] a means for converting data input by the user into JSON format and transmitting the JSON format data to a server via an HTTP request;

[1896] means for analyzing the data received by the server, obtaining information from a knowledge base and external data sources, and generating answers using a generative artificial intelligence engine;

[1897] a means for using a prompt sentence when generating the answer;

[1898] Including system.

[1899] (Claim 2)

[1900] 2. The system of claim 1, wherein the parsing means is a natural language processing engine that performs tokenization and dependency structure analysis.

[1901] (Claim 3)

[1902] 2. The system of claim 1, wherein the knowledge base is a database containing data related to finance.

[1903] "Application Example 1"

[1904] (Claim 1)

[1905] A means for accepting financial consultations from users;

[1906] analysis means including a natural language processing engine for analyzing the consultation;

[1907] a means for collating a knowledge base based on the consultation analyzed by the analysis means to obtain optimal information;

[1908] a means of obtaining additional information from external data sources; and

[1909] means for generating an answer to be suggested to a user using a generative artificial intelligence engine based on the acquired information;

[1910] means for transmitting the generated answer to a user terminal;

[1911] means for displaying the generated answer to the user;

[1912] A means for providing financial advice in a virtual space using a virtual reality device;

[1913] The system includes means for visually presenting advice within the virtual space.

[1914] (Claim 2)

[1915] 2. The system of claim 1, wherein the parsing means is a natural language processing engine that performs tokenization and dependency structure analysis.

[1916] (Claim 3)

[1917] 2. The system of claim 1, wherein the knowledge base is a database containing data related to finance.

[1918] "Example 2: Combining Emotion Engines"

[1919] (Claim 1)

[1920] A means for accepting financial consultations from users;

[1921] analysis means including a natural language processing engine and an emotion engine for analyzing the consultation and the user's emotion;

[1922] a means for collating a knowledge base based on the consultation and emotion analyzed by the analyzing means to obtain optimal information;

[1923] a means of obtaining additional information from external data sources; and

[1924] means for generating an answer to be suggested to a user using a generative artificial intelligence engine based on the acquired information and the analyzed emotions;

[1925] means for transmitting the generated answer to a user terminal;

[1926] The system includes means for displaying the generated answer to the user.

[1927] (Claim 2)

[1928] 2. The system of claim 1, wherein the parsing means is a natural language processing engine that performs tokenization and dependency structure analysis.

[1929] (Claim 3)

[1930] 2. The system of claim 1, wherein the knowledge base is a database containing data related to finance.

[1931] "Application example 2 when combining emotion engines"

[1932] (Claim 1)

[1933] A means for accepting financial consultations from users;

[1934] analysis means including a natural language processing engine for analyzing the consultation and an emotion engine for analyzing the user's emotion;

[1935] a means for collating a knowledge base based on the consultation and emotion analyzed by the analyzing means to obtain optimal information;

[1936] a means of obtaining additional information from external data sources; and

[1937] means for generating an answer to be suggested to a user using a generative artificial intelligence engine based on the acquired information and emotions;

[1938] means for transmitting the generated answer to a user terminal;

[1939] The system includes means for displaying the generated answer to the user.

[1940] (Claim 2)

[1941] 2. The system of claim 1, wherein the parsing means is a natural language processing engine and an emotion engine that performs tokenization and dependency structure analysis.

[1942] (Claim 3)

[1943] 2. The system of claim 1, wherein the knowledge base is a database containing data related to finance. [Explanation of symbols]

[1944] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for accepting financial consultations from users; analysis means including a natural language processing engine for analyzing the consultation; a means for collating a knowledge base based on the consultation analyzed by the analysis means to obtain optimal information; a means of obtaining additional information from external data sources; and means for generating an answer to be suggested to a user using a generative artificial intelligence engine based on the acquired information; means for transmitting the generated answer to a user terminal; The system includes means for displaying the generated answer to the user.

2. 2. The system of claim 1, wherein the parsing means is a natural language processing engine that performs tokenization and dependency structure analysis.

3. 2. The system of claim 1, wherein the knowledge base is a database containing data relating to finance.

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