System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input initial profile information; means for a user to input specific consultation contents related to household budget or insurance; means for analyzing the input consultation contents by natural language processing technology; means for searching a database to generate an appropriate answer; means for providing the generated answer to the user; means for tossing up to an expert when a face-to-face consultation is required; and means for storing all consultation contents and advice in the database.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] Currently, there is a high psychological hurdle for people to seek advice about household finances and insurance, and many people are reluctant to consult with a traditional human expert. Also, once a contract is signed, it is often left unreviewed, so advice based on the latest information is needed. For this reason, there is a need to provide an environment that reduces psychological resistance and allows people to easily seek advice about household finances and insurance. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input initial profile information, a means for a user to input specific consultation details related to household finances or insurance, a means for analyzing the input consultation details using natural language processing technology, a means for searching a database to generate an appropriate answer, a means for providing the generated answer to the user, a means for tossing up to an expert if face-to-face consultation is necessary, and a means for storing all consultation details and advice in a database. This allows users to consult about household finances or insurance anytime, anywhere without feeling any psychological resistance. Furthermore, by including a means for optimizing the system based on feedback on the advice provided, it is possible to continuously provide high-quality advice.

[0006] "User" refers to an individual who uses the system to provide advice regarding household finances or insurance.

[0007] "Initial profile information" refers to basic data such as the user's age, income, family composition, and occupation.

[0008] "Consultation content" refers to the text information entered by users with specific questions or concerns about their finances or insurance.

[0009] "Natural language processing technology" refers to technology that analyzes input text data and automatically extracts specified information.

[0010] "Database" refers to a computer-based system that stores the information needed to generate an appropriate response.

[0011] "Answer generation" refers to the process of searching a database and automatically creating an answer to provide appropriate information based on the user's inquiry.

[0012] "Toss-up to an expert" refers to the process by which the system automatically determines and connects users with the appropriate expert when detailed face-to-face consultation is required.

[0013] "Feedback" refers to the process by which users input their evaluations and thoughts on the advice provided.

[0014] "Optimization" refers to the process of improving the system's performance and the quality of its advice based on collected feedback. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] MODE FOR CARRYING OUT THE INVENTION

[0037] The present invention provides an embodiment of an AI-based financial planner system that allows users to receive advice on household finances and insurance without feeling any psychological resistance.

[0038] 1. System Overview

[0039] The system allows users to input questions about their household finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback.

[0040] 2. Program processing explanation

[0041] Enter your profile

[0042] A user launches the application and enters initial profile information (age, income, family status, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[0043] Enter consultation details

[0044] Users input specific questions about household finances and insurance into a chat interface that is designed to be intuitive and easy to use.

[0045] example:

[0046] Type, "I have a child. How should I save for his or her future education?"

[0047] Question Analysis

[0048] The server analyzes the user's input using natural language processing technology. For example, it extracts keywords from the text and identifies related categories based on them. The natural language processing technology used here is based on the latest AI technology and achieves highly accurate analysis.

[0049] example:

[0050] Keywords such as "tuition fees" and "savings" are extracted, and corresponding information is searched for in the database.

[0051] Generate answers

[0052] Based on the analysis results, the server extracts relevant information from the database and generates an appropriate answer, which is then presented to the user in a format that is easy to understand.

[0053] example:

[0054] "To save for future tuition, you have the following options:

[0055] 1. Use child allowance money and save regularly every month.

[0056] 2. Take out education insurance and use a savings-type insurance product.

[0057] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[0058] Providing answers

[0059] The device displays the generated answers in a chat interface, and, if necessary, provides visual information using charts and graphs.

[0060] Additional questions and further information provided

[0061] If the user has further questions or requires more information, they enter it into the chat interface again, and the server again analyzes and generates an answer based on this additional information.

[0062] 3. Guidance for face-to-face consultations

[0063] If a face-to-face consultation is deemed necessary or if the user requests one, the server automatically uses the reservation system to schedule an appointment with the nearest specialist, and the reservation details are sent to the user via their device.

[0064] 4. Gathering feedback and optimizing the system

[0065] After the consultation, the user inputs feedback on the advice provided. This feedback is sent to the server via the terminal. The server then optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[0066] Specific examples

[0067] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the server might generate the following answer:

[0068] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[0069] The above is an embodiment of the present invention. With this system, users can easily ask for advice on household finances and insurance without feeling any psychological resistance, and can receive the latest, personalized advice.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user launches the application and selects the financial advice service. The application displays a profile entry screen.

[0073] Step 2:

[0074] The user enters and submits initial profile information (age, income, family composition, occupation, etc.).

[0075] Step 3:

[0076] The server stores the received profile information in a database and uses it to provide personalized advice tailored to each user's individual circumstances.

[0077] Step 4:

[0078] The user inputs specific consultation content about household finances or insurance into the chat interface and sends it.

[0079] Step 5:

[0080] The server sends the received consultation content to a natural language processing engine, which analyzes the text and extracts keywords.

[0081] Step 6:

[0082] The server searches the database for relevant data based on the extracted keywords.

[0083] Step 7:

[0084] The server uses the search results to generate the best answer, which is formatted in a user-friendly format.

[0085] Step 8:

[0086] The terminal displays the generated response on the chat interface and provides it to the user.

[0087] Step 9:

[0088] If the user reviews the response and has further questions or requests for more information, they can type it again into the chat interface and submit.

[0089] Step 10:

[0090] The server then sends the newly received question back to the natural language processing engine for analysis and keyword extraction.

[0091] Step 11:

[0092] The server generates a new answer and serves it back to the user.

[0093] Step 12:

[0094] If the user desires a face-to-face consultation, or if the server determines that a face-to-face consultation is necessary, a toss-up is made to the nearest specialist through the reservation system.

[0095] Step 13:

[0096] The server checks the availability of the specialist and schedules the appointment.

[0097] Step 14:

[0098] The terminal notifies the user of the reservation details (date, time, location, etc.).

[0099] Step 15:

[0100] The user inputs and submits feedback on the advice provided.

[0101] Step 16:

[0102] The server receives the feedback and stores it in a database.

[0103] Step 17:

[0104] The server analyzes the collected feedback and uses it to optimize the system.

[0105] The above are the specific processing steps of the program, which allow the user to efficiently receive necessary advice on household finances and insurance without feeling any psychological resistance.

[0106] Example 1

[0107] 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."

[0108] Currently, many people feel psychologically reluctant to seek advice about household finances or insurance. Furthermore, receiving appropriate advice often requires a face-to-face consultation, which places a time burden on users. Furthermore, the use of feedback to improve the quality of the advice provided is insufficient. For these reasons, there is a need for a system that allows users to easily seek advice about household finances and insurance and receive high-quality advice.

[0109] 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.

[0110] In this invention, the server includes a means for a user to input initial profile information, a means for a user to input specific consultation details regarding household finances or insurance, a means for analyzing the input consultation details using natural language processing technology, a means for searching a database to generate an appropriate answer using a generative AI model, a means for providing the generated answer to the user, a means for tossing up to an expert if face-to-face consultation is necessary, a means for storing all consultation details and advice in a database, a means for a user to input feedback on the advice provided, and a means for optimizing the system and improving the accuracy of prompt sentences based on the feedback. This enables users to receive high-quality advice regarding household finances or insurance without feeling any psychological resistance.

[0111] ---

[0112] "User" refers to an individual who uses this system to seek advice on household finances or insurance.

[0113] "Initial profile information" refers to basic information about the user, such as age, income, family composition, and occupation.

[0114] "Specific consultation content regarding household finances or insurance" refers to specific questions or concerns regarding household finances or insurance that users enter through the system.

[0115] "Natural language processing technology" refers to technology used to analyze input text information and understand its meaning.

[0116] A "generative AI model" refers to an artificial intelligence model that generates optimal answers based on data analyzed using natural language processing technology.

[0117] "Database" refers to a data storage system that stores financial and insurance information.

[0118] "Face-to-face consultation" refers to a consultation where the user meets directly with a specialist.

[0119] An "expert" is someone who has knowledge and experience in household finances and insurance and is qualified to provide face-to-face consultations.

[0120] "Toss-up" refers to the process by which the system automatically schedules an appointment with a specialist when it determines that a face-to-face consultation is necessary.

[0121] "Feedback" refers to the evaluation or impressions that a user enters regarding the advice provided.

[0122] A "prompt" refers to a question or instruction input to a generative AI model.

[0123] "Optimization" refers to the process of improving the overall performance of the system and the accuracy of responses based on feedback.

[0124] ---

[0125] These are the definitions of important words that will help clarify the scope of your patent claims.

[0126] ---

[0127] MODE FOR CARRYING OUT THE INVENTION

[0128] overview

[0129] This invention is an AI-based financial planner system that allows users to receive advice on household finances and insurance without feeling any psychological resistance. Based on the initial profile information and specific consultation content entered by the user, the system uses a generative AI model to generate appropriate answers and, if necessary, guides the user to face-to-face consultations with experts.

[0130] System configuration

[0131] The system consists of a server, a terminal, and a user. The server generates answers using advanced natural language processing technology and generative AI models, and provides them to the user via the terminal.

[0132] Hardware and software used

[0133] Hardware: Servers (high-performance processors, memory, large-capacity storage), devices (smartphones, tablets, PCs)

[0134] Software: Natural language processing libraries, generative AI models, database management systems, chat interface platforms

[0135] Enter your profile

[0136] The user launches the application and enters initial profile information, such as age, income, family composition, and occupation, which is then validated in real time by the device and sent to the server.

[0137] Enter consultation details

[0138] A user types a specific financial or insurance question into the chat interface, for example, "I have a new baby. How should I save for his or her future education?" The device encodes this input and sends it to the server.

[0139] Parsing questions and generating answers

[0140] The server analyzes the input question using natural language processing technology. Specifically, it extracts keywords from the text and searches a database for related information. A generative AI model then uses that information to generate the optimal answer. For example, "To save for future tuition fees, you have the following options:

[0141] 1. Use child allowance money and save regularly every month.

[0142] 2. Take out education insurance and use a savings-type insurance product.

[0143] 3. I will open a NISA account and aim for future returns by investing in stocks and investment trusts."

[0144] Providing answers

[0145] The device displays the responses received from the server in the chat interface, providing visual information using graphs and charts as needed.

[0146] Additional questions and further information provided

[0147] If the user has further questions or requests for more information, they enter it again into the chat interface, and the server re-parses the added questions and generates appropriate answers.

[0148] Guidance for face-to-face consultation

[0149] The server analyzes the user's consultation content and profile to determine whether a face-to-face consultation is necessary. If a face-to-face consultation is determined to be necessary, the server sends reservation information to the nearest specialist through the reservation system and notifies the user of the reservation details via their terminal.

[0150] Gathering feedback and optimizing the system

[0151] The user inputs feedback on the provided advice. The device encodes the feedback and sends it to the server. The server aggregates the feedback and optimizes the entire system. Specifically, it adjusts the generative AI model to improve the accuracy of prompt sentences.

[0152] ---

[0153] The above is a specific example of how to implement the invention. This system allows users to receive high-quality advice on household finances and insurance without feeling any psychological resistance. It also allows users to smoothly connect with experts when face-to-face consultation is necessary.

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] ---

[0156] Step 1: Fill in your profile

[0157] A user launches the application and enters their initial profile information, such as age, income, family composition, and occupation. The device validates the input in real time to ensure it is in the correct format. The profile information is then sent from the device to the server and stored in a database.

[0158] Input: User's initial profile information (age, income, family composition, occupation, etc.).

[0159] Data processing / calculation: Validation is performed.

[0160] Output: Validated profile information stored in database.

[0161] Step 2: Enter your consultation details

[0162] Users enter specific financial or insurance questions into the chat interface, which the system uses to provide an intuitive and user-friendly interface design. The device encodes the questions and sends them to the server.

[0163] Input: User's inquiry (e.g., question about child's future tuition fees).

[0164] Data processing / computation: Encoding of input text.

[0165] Output: The encoded consultation content is sent to the server.

[0166] Step 3: Parsing the Question

[0167] The server analyzes the received inquiry using natural language processing technology. Specifically, it uses a generative AI model to extract keywords from the text and identify related categories. Based on the extracted keywords, it searches a database for related information.

[0168] Input: The encoded consultation content.

[0169] Data processing / calculation: Keyword extraction and category identification using natural language processing.

[0170] Output: Related categories and keywords.

[0171] Step 4: Generate an answer

[0172] The server searches the database based on the analysis results and generates an appropriate answer. A generative AI model is used to generate an answer in a format that is easy for the user to understand. Specifically, it creates an answer in the form of, "To save for future tuition fees, you have the following options: 1. Use child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts to aim for future returns."

[0173] Input: relevant categories and keywords.

[0174] Data processing / computation: Generating answers using generative AI models.

[0175] Output: The generated answer.

[0176] Step 5: Provide your answers

[0177] The device displays the generated answers in the chat interface, possibly using graphs or charts to visually supplement the answers, if necessary.

[0178] Input: The generated answer.

[0179] Data processing / calculation: Visual supplement to answers.

[0180] Output: The response displayed in the chat interface.

[0181] Step 6: Ask additional questions and provide more information

[0182] If the user has further questions or requires more information, they enter it into the chat interface again, and the device encodes the additional questions and sends them to the server, repeating the process of parsing the questions and generating answers.

[0183] Input: User's additional question.

[0184] Data processing / computation: Encoding input text, parsing questions, and generating answers.

[0185] Output: The generated answer.

[0186] Step 7: Guide to face-to-face consultation

[0187] The server analyzes the user's consultation content and profile, and if it determines that a face-to-face consultation is necessary, it sends reservation information to the nearest specialist using the reservation system. The reservation details are then notified to the user via their terminal.

[0188] Input: User's consultation content, profile.

[0189] Data processing / calculation: Analysis and generation of reservation information.

[0190] Output: Notification of reservation information.

[0191] Step 8: Gather feedback and optimize the system

[0192] The user inputs feedback on the provided advice and sends it to the server via their device. The server then uses the collected feedback to optimize the generative AI model and the entire system. Specifically, it makes adjustments to improve the accuracy of prompt sentences.

[0193] Input: User feedback.

[0194] Data processing / calculation: Feedback collection and analysis, system optimization.

[0195] Output: Optimized generative AI models and systems.

[0196] ---

[0197] The above are the specific processing steps and details of the system, which allows users to receive high-quality advice on household finances and insurance.

[0198] (Application example 1)

[0199] 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."

[0200] With conventional financial planner systems, users often felt psychological resistance when receiving face-to-face consultations, and specialized knowledge was often required to receive appropriate advice. Furthermore, the lack of visual and audio support often made the system difficult to understand.

[0201] 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.

[0202] In this invention, the server includes: means for a user to input initial profile information; means for a user to input specific consultation details regarding household finances or insurance; means for analyzing the input consultation details using natural language processing technology; means for searching a database to generate an appropriate answer; means for providing the generated answer to the user; means for tossing up to an expert if face-to-face consultation is necessary; means for saving all consultation details and advice in a database; speech synthesis means for reading out responses aloud; reservation management means for making appointments for face-to-face consultations; and means for using augmented reality technology to provide visual information. This enables users to receive easy-to-understand financial consultations supported visually and audibly without feeling any psychological resistance.

[0203] "User" means an individual user of the Financial Planner System.

[0204] "Initial profile information" refers to the user's basic attribute information, such as age, income, family composition, and occupation.

[0205] "Specific consultation content" refers to specific matters or questions that users want to ask about their household finances or insurance.

[0206] "Natural language processing technology" is a technology for analyzing and understanding input text data and generating appropriate responses.

[0207] A "database" is a collection of information that manages accumulated information and allows necessary information to be quickly searched.

[0208] "Generating a response" refers to the process of creating an appropriate answer to a user's question.

[0209] "Speech synthesis means" refers to technology or equipment that converts text information into speech.

[0210] "Appointment management tools" means the functionality and technology used to schedule and manage appointments for in-person consultations.

[0211] "Augmented reality technology" is a technology that overlays computer-generated visual information onto the real world.

[0212] This invention provides an embodiment of an AI-based financial planner system that allows users to receive consultations regarding household finances and insurance without feeling any psychological resistance. This system provides an interface through which users can input initial profile information and specific consultation details regarding household finances and insurance. The system of the present invention is implemented using the following hardware and software.

[0213] 1. System Overview

[0214] The system uses "natural language processing technology" to analyze the inputted consultation content and generates an appropriate answer from a "database." Users input questions about household finances or insurance, and the system automatically generates an appropriate answer based on the input. In addition, if a face-to-face consultation is required, the system also includes a function to toss up to an expert using "reservation management means." Furthermore, the generated answer is provided in audio format using "speech synthesis means," and visual information is provided using "augmented reality technology."

[0215] 2. Program processing explanation

[0216] Hardware and Software

[0217] Hardware:

[0218] Touchscreen

[0219] camera

[0220] microphone

[0221] speaker

[0222] Smart Glasses

[0223] software:

[0224] Python

[0225] Natural Language Processing model (Hugging Face's Transformers library)

[0226] TfidfVectorizer (skill extraction)

[0227] SQLite (database management)

[0228] pyttsx3 (speech synthesis)

[0229] Enter user profile information

[0230] First, a "user" launches the system and enters initial profile information (age, income, family composition, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[0231] Input and analysis of consultation details

[0232] When a "user" enters a specific question about household finances or insurance into the chat interface, the input is sent to the "server," which then uses natural language processing technology to analyze the question and extract keywords, thereby identifying the appropriate category.

[0233] Answer generation and speech synthesis

[0234] The "server" searches for relevant information from a database based on the extracted keywords and generates an appropriate answer. The generated answer is not only provided to the "user" via the "terminal" but also read aloud using the "speech synthesis means."

[0235] Use of Augmented Reality Technology

[0236] Furthermore, when visual information is provided, it is displayed on the smart glasses using "augmented reality technology," allowing users to intuitively understand the advice.

[0237] Book a face-to-face consultation

[0238] If a face-to-face consultation is deemed necessary or if the user requests one, the "server" will automatically use the reservation system to schedule an appointment with the nearest specialist, and the reservation details will be sent to the user via the "terminal."

[0239] Gathering feedback and optimizing the system

[0240] After the consultation is completed, the "user" inputs feedback on the advice provided. This feedback is sent to the "server" via the "terminal." The "server" optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[0241] Specific examples

[0242] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the "server" generates the following answer:

[0243] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[0244] Example prompt

[0245] "How can I save for my child's school fees?"

[0246] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0247] Program processing steps

[0248] Step 1:

[0249] The "user" starts the system and enters initial profile information (age, income, family composition, occupation, etc.) using a touchscreen or voice input. This information is sent to the "server," which then provides personalized advice based on the user's individual circumstances.

[0250] Input: Initial profile information (age, income, family composition, occupation)

[0251] Output: Profile data stored on the server

[0252] Step 2:

[0253] "Users" type specific questions about finances or insurance into a chat interface, which are sent to a "server" and prepared for analysis.

[0254] Input: Specific questions about finances or insurance

[0255] Output: Questions saved on the server

[0256] Step 3:

[0257] The "server" analyzes the input question using natural language processing techniques. It uses TensorFlow and Hugging Face's Transformers library to extract key keywords from the question. The results of this analysis are used in the next step.

[0258] Input: Question

[0259] Data processing: Extracting keywords using natural language processing technology

[0260] Output: Extracted keywords

[0261] Step 4:

[0262] The "server" searches for relevant information from the SQLite database based on the extracted keywords and generates an appropriate answer, which is then sent to the "terminal" in text format.

[0263] Input: Extracted keywords

[0264] Data Computing: Database Search and Answer Generation

[0265] Output: Textual response

[0266] Step 5:

[0267] The "terminal" displays the generated answer. At the same time, the "speech synthesis means" uses the pyttsx3 library to read the answer aloud. Visual information is also provided using "augmented reality technology" if necessary.

[0268] Input: Text response

[0269] Data processing: converting text to speech, generating visual information

[0270] Output: Audio and visual information

[0271] Step 6:

[0272] If the "user" asks a more detailed question or requests a face-to-face consultation, the "server" analyzes the question again, updates the necessary information, and provides appropriate advice. If it determines that a face-to-face consultation is necessary, the "appointment management means" is automatically used to toss up the request to an expert and make a consultation appointment. The appointment details are displayed on the "terminal" and notified to the "user."

[0273] Input: Additional questions or requests for face-to-face consultation

[0274] Data calculation: reanalysis and reservation arrangement

[0275] Output: Updated advice, booking confirmation details

[0276] Step 7:

[0277] When the "user" inputs feedback on the advice provided, this feedback is sent to the "server" via the "terminal." The "server" optimizes the system based on the collected feedback and improves the quality of advice from the next time onwards.

[0278] Input: Feedback

[0279] Data Calculations: Feedback Analysis

[0280] Output: System optimization

[0281] 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.

[0282] MODE FOR CARRYING OUT THE INVENTION

[0283] The present invention provides an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and by combining it with an emotion engine, provides a system that provides more personalized advice.

[0284] 1. System Overview

[0285] The system allows users to input questions about their finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[0286] 2. Program processing explanation

[0287] Enter your profile

[0288] A user launches the application and enters initial profile information (age, income, family status, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[0289] Enter consultation details

[0290] Users input specific questions about household finances and insurance into a chat interface that is designed to be intuitive and easy to use.

[0291] example:

[0292] Type, "I have a child. How should I save for his or her future education?"

[0293] Question Analysis

[0294] The server analyzes the user's input using natural language processing technology. For example, it extracts keywords from the text and identifies related categories based on them. The natural language processing technology used here is based on the latest AI technology and achieves highly accurate analysis.

[0295] example:

[0296] Keywords such as "tuition fees" and "savings" are extracted, and corresponding information is searched for in the database.

[0297] Generate answers

[0298] Based on the analysis results, the server extracts relevant information from the database and generates an appropriate answer, which is then presented to the user in a format that is easy to understand.

[0299] example:

[0300] "To save for future tuition, you have the following options:

[0301] 1. Use child allowance money and save regularly every month.

[0302] 2. Take out education insurance and use a savings-type insurance product.

[0303] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[0304] Providing answers

[0305] The device displays the generated answers in the chat interface, and, if necessary, provides visual information using charts and graphs.

[0306] Emotion Recognition and Analysis

[0307] The server analyzes the user's facial expressions and voice tone using an emotion engine, which identifies the user's emotional state (e.g., anxiety, relief, etc.) and adjusts the response accordingly.

[0308] example:

[0309] If the user appears anxious, offer follow-up questions such as, "Are you worried about saving for college?"

[0310] Additional questions and further information provided

[0311] If the user has further questions or requires more information, they enter it into the chat interface again, and the server again analyzes and generates an answer based on this additional information.

[0312] 3. Guidance for face-to-face consultations

[0313] If a face-to-face consultation is deemed necessary or if the user requests one, the server automatically uses the reservation system to schedule an appointment with the nearest specialist, and the reservation details are sent to the user via their device.

[0314] 4. Gathering feedback and optimizing the system

[0315] After the consultation, the user inputs feedback on the advice provided. This feedback is sent to the server via the terminal. The server then optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[0316] Specific examples

[0317] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the server might generate the following answer:

[0318] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[0319] Additionally, if the user shows signs of anxiety while asking a question, the emotion engine will recognize this and offer additional questions or information to provide additional reassurance.

[0320] This concludes the description of the embodiment of the present invention. This system allows users to easily seek advice about household finances and insurance without feeling any psychological resistance, and also allows users to receive personalized advice based on their emotional state.

[0321] The processing flow will be explained below.

[0322] Step 1:

[0323] The user launches the application and selects the financial advice service. The application displays a profile entry screen.

[0324] Step 2:

[0325] The user enters and submits initial profile information (age, income, family composition, occupation, etc.).

[0326] Step 3:

[0327] The server stores the received profile information in a database and uses it to provide personalized advice tailored to each user's individual circumstances.

[0328] Step 4:

[0329] The user inputs specific consultation content about household finances or insurance into the chat interface and sends it.

[0330] Step 5:

[0331] The server sends the received consultation content to a natural language processing engine, which analyzes the text and extracts keywords.

[0332] Step 6:

[0333] The server searches the database for relevant data based on the extracted keywords.

[0334] Step 7:

[0335] The server uses the search results to generate the best answer, which is formatted in a user-friendly format.

[0336] Step 8:

[0337] The terminal displays the generated response on the chat interface and provides it to the user.

[0338] Step 9:

[0339] The device uses a camera and microphone to analyze the user's facial expressions and voice in real time to obtain emotional data, which is then sent to the emotion engine.

[0340] Step 10:

[0341] The server receives the user's emotional data analyzed by the emotion engine and adjusts the response based on that data. For example, if the user is anxious, the response will be more detailed and include reassuring information.

[0342] Step 11:

[0343] The server regenerates the adjusted response and sends it to the terminal.

[0344] Step 12:

[0345] The device will redisplay the adjusted response in the chat interface.

[0346] Step 13:

[0347] If the user reviews the response and has further questions or requests for more information, they can type it again into the chat interface and submit.

[0348] Step 14:

[0349] The server then sends the newly received question to the natural language processing engine again for analysis and keyword extraction, and the process repeats from step 5 to step 12.

[0350] Step 15:

[0351] If the user desires a face-to-face consultation, or if the server determines that a face-to-face consultation is necessary, a toss-up is made to the nearest specialist through the reservation system.

[0352] Step 16:

[0353] The server checks the availability of the specialist and schedules the appointment.

[0354] Step 17:

[0355] The terminal notifies the user of the reservation details (date, time, location, etc.).

[0356] Step 18:

[0357] The user inputs and submits feedback on the advice provided.

[0358] Step 19:

[0359] The server receives the feedback and stores it in a database.

[0360] Step 20:

[0361] The server analyzes the collected feedback and uses it to optimize the system.

[0362] These are the specific processing steps of the program. These steps allow users to efficiently receive the necessary advice on household finances and insurance without feeling any psychological resistance, and by using the emotion engine, they can receive personalized advice that takes into account the user's emotions.

[0363] Example 2

[0364] 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."

[0365] Conventional financial planner systems have the problem that users find it difficult to consult with them because they do not take into account the user's psychological state, and the advice provided is often not tailored to individual circumstances. The present invention aims to solve these problems and provide a system that allows users to consult about household finances and insurance without feeling any psychological resistance. It also aims to provide personalized advice tailored to the user's individual circumstances and emotional state.

[0366] 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.

[0367] In this invention, the server includes means for a user to input initial profile information, means for a user to input specific consultation details regarding household finances or insurance, means for analyzing the input consultation details using natural language processing technology, means for searching a database to generate an appropriate answer, means for recognizing and analyzing emotions from the user's facial expressions and voice, means for adjusting the answer content based on the emotion analysis results, means for providing the generated answer to the user, means for tossing up to an expert if face-to-face consultation is necessary, and means for storing all consultation details and advice in a database. This provides an environment where users can easily seek advice and makes it possible to provide personalized advice according to individual situations and emotions.

[0368] "User" means an individual who uses the system to seek advice on household finances or insurance.

[0369] "Initial profile information" refers to the basic personal information that users enter when registering with the system, including age, income, family composition, occupation, etc.

[0370] "Consultation content" refers to the specific questions or problems that users input into the system, including matters related to household finances and insurance.

[0371] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0372] A "database" is a system or software for structuring and managing information about household finances and insurance.

[0373] "Emotion recognition" is a technology that identifies emotions from a user's facial expressions and voice, and is used to understand the user's psychological state.

[0374] The "analysis results" refer to data obtained using natural language processing technology and emotion recognition technology, and answers are generated based on this data.

[0375] The "answer content" refers to advice or suggestions to the user that the system generates based on the analysis results.

[0376] "Face-to-face consultation" refers to a consultation in which the user directly interacts with an expert.

[0377] An "expert" is someone who has knowledge and experience in household finances and insurance and provides professional advice to users.

[0378] "Toss-up" refers to the process by which the system automatically forwards a consultation request to an expert.

[0379] "Feedback" refers to the evaluations and comments that users make regarding advice and services provided by the system.

[0380] "Optimization" is the process of improving the performance of a system or the quality of advice based on collected feedback.

[0381] MODE FOR CARRYING OUT THE INVENTION

[0382] The following describes an embodiment of the present invention. This invention is an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and aims to provide more personalized advice by combining it with an emotion engine.

[0383] 1. System Overview

[0384] The system allows users to input questions about household finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[0385] 2. Enter your profile

[0386] A user launches an application and enters initial profile information (age, income, family composition, occupation, etc.), which allows the server to provide personalized advice based on the user's individual circumstances. For example, when a user enters information using a smartphone app, the data is sent from the device to the server and stored in a database.

[0387] 3. Enter your consultation details

[0388] The user types specific questions about household finances or insurance into the chat interface, which is designed to be intuitive and easy to use. For example, if the user types, "I have a child. How should I save for his or her future tuition fees?", the device sends the information to the server.

[0389] 4. Question Analysis

[0390] The server analyzes the questions it receives using natural language processing technology (such as generative AI models). Keywords are extracted from the text and related categories are identified based on these. An advanced natural language processing engine is used for the analysis, allowing for highly accurate keyword extraction and category identification.

[0391] 5. Answer Generation

[0392] The server extracts relevant information from the database based on the analysis results and generates an appropriate answer. The generated answer is provided in a format that is easy for the user to understand. For example, it could generate an answer such as, "To save for future tuition fees, you have the following options: 1. Use your child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts to aim for future returns."

[0393] 6. Providing answers

[0394] The device displays the generated answers in the chat interface, and provides visual information using charts and graphs as needed, making it easier for users to understand intuitively.

[0395] 7. Emotion Recognition and Analysis

[0396] The server analyzes the user's facial expressions and voice tone using an emotion engine. For example, the user captures their facial expressions and voice using the device's camera or microphone, and the data is sent to the server. The server analyzes the data and identifies the user's emotional state. If the user looks anxious, the server will provide follow-up questions such as, "Are you worried about saving for college?"

[0397] 8. Additional Questions and Further Information

[0398] If the user has further questions or requests for more information, they can type again into the chat interface, for example, "Can you explain in more detail?", and the device will send that information to the server, which will again analyze it and generate a response.

[0399] 9. Guidance for face-to-face consultations

[0400] If it is determined that a face-to-face consultation is necessary, or if the user requests a face-to-face consultation, the server automatically uses the reservation system to make an appointment with an expert. The reservation details are notified to the user via the terminal. For example, if the user enters "I would like to speak to an expert in person," the server will make an appointment with the nearest expert via the reservation system.

[0401] 10. Gathering feedback and optimizing the system

[0402] After the consultation, the user can enter feedback on the advice provided. For example, if the user enters "This advice was very helpful," the device will send the feedback to the server. The server will store this in a database and use the feedback to optimize the system.

[0403] Specific examples

[0404] For example, if a newly married couple who both work asks, "How should we create a savings plan?", the server will generate the following answer: "If both partners work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or a savings-type investment trust. If you are considering buying a home, it is also important to start planning early."

[0405] This concludes the description of the embodiment of the present invention. This system allows users to easily seek advice about household finances and insurance without feeling any psychological resistance, and also allows users to receive personalized advice according to their emotional state.

[0406] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0407] Step 1: Fill in your profile

[0408] The user enters initial profile information, including age, income, family composition, and occupation. This information is sent from the device to the server, which stores it in a database. Based on the information entered, the server prepares the basic data for generating personalized advice.

[0409] Specific behavior:

[0410] The user launches the smartphone app and enters the required information into the displayed form.

[0411] The terminal transmits the input information to the server.

[0412] The server stores the received information in a database and creates a user profile.

[0413] Input: Age, income, family structure, occupation

[0414] Output: Create a user profile

[0415] Step 2: Enter your consultation details

[0416] The user enters questions about household finances or insurance into the chat interface. This information is sent from the device to the server, which then passes the received question data to the next analysis step.

[0417] Specific behavior:

[0418] User: Type "I have a child, how should I save for his or her future education?"

[0419] The device sends the question to the server.

[0420] Input: Question text (e.g., "I have a child. How should I save for his or her future education?")

[0421] Output: Received query data

[0422] Step 3: Parsing the Question

[0423] The server analyzes the received question using natural language processing techniques (such as generative AI models). This analysis involves extracting keywords from the text and identifying related categories. The analysis results are used in the next step of generating an answer.

[0424] Specific behavior:

[0425] The server inputs the question text into a natural language processing engine and extracts keywords.

[0426] The NLP (natural language processing) engine extracts important keywords such as "tuition fees" and "savings" and identifies categories based on these.

[0427] Input: Question data

[0428] Output: Analysis results (extracted keywords and related categories)

[0429] Step 4: Generate an answer

[0430] The server uses the analysis results to extract relevant information from the database and generate an appropriate answer, which is then formatted in a way that is easy for the user to understand.

[0431] Specific behavior:

[0432] The server accesses the database and searches for relevant information based on the extracted keywords.

[0433] The server uses the search results and the generative AI model to generate an answer.

[0434] The answer can be formatted as follows: "To save for future school fees, you have the following options: 1. Use your child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts, aiming for future returns."

[0435] Input: Analysis results (extracted keywords and related categories)

[0436] Output: The generated answer

[0437] Step 5: Provide your answers

[0438] The device displays the generated answers in the chat interface, and, if necessary, provides visual information using charts and graphs.

[0439] Specific behavior:

[0440] The server sends the generated response to the terminal.

[0441] The device displays the received response in the chat interface.

[0442] Input: Generated Answer

[0443] Output: Displayed answer

[0444] Step 6: Emotion recognition and analysis

[0445] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to determine the user's emotional state.

[0446] Specific behavior:

[0447] The device captures the user's facial expressions and voice using a camera and microphone and sends the data to a server.

[0448] The server analyzes the data using an emotion recognition engine to identify the user's emotional state (e.g., anxiety, relief).

[0449] Input: facial expression data, voice data

[0450] Output: Emotion analysis results

[0451] Step 7: Adjust your responses based on emotion

[0452] The server adjusts the response content based on the results of emotion analysis. If the user is anxious, the response content is adjusted to provide additional information or reassurance.

[0453] Specific behavior:

[0454] The server reevaluates the response based on the results of the sentiment analysis.

[0455] Providing follow-up questions or information such as, "Do you have any concerns about saving for college?"

[0456] Input: Sentiment analysis results

[0457] Output: Adjusted answer

[0458] Step 8: Ask additional questions and provide further information

[0459] If the user has further questions or requests for more information, they enter it into the chat interface again, and this information is again sent from the device to the server for re-analysis and generation of an answer.

[0460] Specific behavior:

[0461] User: Type, "Can you explain that in more detail?"

[0462] The device sends new information to the server, which analyzes it again and generates a response.

[0463] Input: Text of follow-up question

[0464] Output: Provides detailed information

[0465] Step 9: Guide to face-to-face consultation

[0466] If the server determines that a face-to-face consultation is necessary, it will use the reservation system to make an appointment with a specialist, and notify the user of the reservation details via their device.

[0467] Specific behavior:

[0468] User: Type "I'd like to speak to an expert in real life."

[0469] The server accesses the reservation system and makes an appointment with the nearest specialist.

[0470] The device will notify you of the reservation details.

[0471] Input: Request for face-to-face consultation

[0472] Output: Reservation details notification

[0473] Step 10: Gather feedback and optimize

[0474] The user inputs feedback on the advice provided, and the device sends it to the server, which stores the feedback in a database and uses it to optimize the system.

[0475] Specific behavior:

[0476] User: Type "This advice was very helpful."

[0477] The device sends the feedback to the server.

[0478] The server stores the feedback in a database and uses it to generate advice next time.

[0479] Input: Feedback text

[0480] Output: Saved feedback

[0481] (Application example 2)

[0482] 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."

[0483] Conventional financial planner systems provide uniform answers without taking into account the user's emotional state, which can cause users to feel psychological resistance. Furthermore, ignoring the user's emotional state can lead to users not receiving satisfactory answers or advice, which can reduce the usefulness of the system.

[0484] 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 a means for inputting the user's initial profile information, a means for the user to input specific consultation details regarding household finances or insurance, a means for analyzing the input consultation details using natural language processing technology, a means for searching a database to generate an appropriate answer, a means for providing the generated answer to the user, a means for tossing up to an expert if face-to-face consultation is necessary, a means for saving all consultation details and advice in a database, a means for recognizing and analyzing the user's emotional state, and a means for adjusting the answer based on the recognized emotional state. This makes it possible to provide personalized advice according to the user's emotional state.

[0485] "User" refers to an individual who uses this system to seek advice on household finances and insurance.

[0486] "Initial profile information" refers to basic information such as the user's age, income, family composition, and occupation.

[0487] "Consultation content" refers to specific questions that users have about household finances and insurance.

[0488] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[0489] "Database" refers to a digital record device for storing financial and insurance information and advice.

[0490] "Emotional state" refers to the psychological state recognized from the user's facial expressions and voice.

[0491] "Analyze" refers to the act of analyzing input information and extracting its meaning and key points.

[0492] "Response" refers to appropriate advice or information in response to the user's inquiry.

[0493] "Tossing up" refers to the act of transferring the user's consultation to an expert as needed.

[0494] "Storing" refers to the act of recording and keeping data for later use.

[0495] "Adjust" refers to the act of changing or optimizing content according to circumstances or conditions.

[0496] MODE FOR CARRYING OUT THE INVENTION

[0497] This invention provides an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and by combining it with an emotion engine, provides a system that provides more personalized advice.

[0498] System Overview

[0499] The system is implemented as a smartphone application, allowing users to input questions about household finances and insurance, and automatically generates appropriate answers based on those questions. It also has a function to guide users to face-to-face consultations if necessary. It also has a function to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[0500] Hardware and Software Used

[0501] Smartphone: Provides user interface and some data processing.

[0502] EmotionEngine: Software for recognizing the user's emotional state.

[0503] NLPProcessor (natural language processing engine): Software for analyzing the content of users' inquiries.

[0504] FinancialAdvisor: Software for generating appropriate financial advice.

[0505] ChatBotInterface: Software that provides interaction between users and systems.

[0506] Processing flow

[0507] 1. Fill in your profile:

[0508] When a user launches the smartphone application, they enter their initial profile information (age, income, family composition, occupation, etc.) This profile information is used by the system to provide appropriate advice to the user.

[0509] 2. Enter your consultation details:

[0510] Users type specific financial and insurance questions into a chat interface that is intuitive and easy to use.

[0511] 3. Question Analysis:

[0512] The server uses an NLPProcessor to analyze the user's input and extract relevant keywords, which then identify the question category.

[0513] 4. Generate answers:

[0514] The server uses FinancialAdvisor to extract relevant information from the database and generate appropriate answers based on the analysis results.

[0515] 5. Emotional awareness and regulation:

[0516] The server uses the Emotion Engine to analyze the user's facial expressions and tone of voice to recognize their emotional state, and responds accordingly.

[0517] 6. Providing answers:

[0518] The generated answers are provided to the user through a chat interface on their smartphone, and may also include appropriate charts and graphs.

[0519] 7. Additional Questions and Face-to-Face Consultations:

[0520] If the user has further questions or requires more information, they enter it into the chat interface again. The server uses this additional information to analyze and generate a new answer. If necessary, an appointment with an expert will be scheduled.

[0521] 8. Feedback and optimization:

[0522] After the consultation, the user inputs feedback on the advice provided. The server optimizes the system based on the collected feedback and improves the quality of advice from the next time onwards.

[0523] Specific examples

[0524] For example, if a 35-year-old married user with one child and an annual income of 5 million yen asks, "I have a child. How should I save for his / her future tuition fees?", the system will generate the following answer:

[0525] "To save for future tuition, you have the following options:

[0526] 1. Use child allowance money and save regularly every month.

[0527] 2. Take out education insurance and use a savings-type insurance product.

[0528] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[0529] Additionally, if the user displays an anxious expression, the emotion engine will recognize this and provide additional questions or information to provide additional reassurance.

[0530] Example prompt sentence:

[0531] "Based on the profile of a user who is 35 years old, married (with one child) and earns ¥5 million a year, generate personalized financial advice for the question, 'How should I save for future college fees?'"

[0532] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0533] Step 1:

[0534] A user launches the smartphone application and enters initial profile information (age, income, family composition, occupation, etc.). The device sends the entered information to the server, which stores it in the user database. Future advice is personalized based on this input.

[0535] Step 2:

[0536] The user inputs specific questions about household finances or insurance into the chat interface. The device receives this input data and sends it to the server. The server uses natural language processing technology (NLPProcessor) to analyze the input consultation content and extract important keywords. The input question is imported as text data, and keywords such as "tuition fees" and "savings" are extracted.

[0537] Step 3:

[0538] The server searches a database to generate appropriate answers based on the extracted keywords. It retrieves relevant information from the database and generates personalized answers that take into account the user's profile information based on the analysis results. For example, it generates specific financial advice such as "How to save for future tuition fees."

[0539] Step 4:

[0540] The server uses the Emotion Engine to recognize the user's emotional state. It analyzes facial and voice data provided by the user through the camera and microphone to identify the user's emotional state (e.g., relief, anxiety). Based on this recognized emotional state, the generated answer is adjusted appropriately. For example, if the user has an anxious expression, the server adds supplementary information that provides additional reassurance.

[0541] Step 5:

[0542] The server then sends the tailored answers to the user's device, where they are displayed in the smartphone chat interface, allowing the user to view the information in an easy-to-understand format, with visual information such as charts and graphs provided as needed.

[0543] Step 6:

[0544] If the user has further questions or requires more information, they enter it again into the chat interface. The device sends the new question to the server, which again analyzes and generates an answer. The process repeats, and if necessary, a face-to-face consultation with an expert is scheduled.

[0545] Step 7:

[0546] After the consultation, the user enters feedback on the advice provided into the chat interface. The device sends this feedback to the server, which then optimizes the system based on the collected feedback. This feedback improves the quality of advice provided in the future.

[0547] Specific examples

[0548] For example, if a newlywed user asks, "How should I create a savings plan?", the server generates an answer such as, "If both spouses work, we recommend that you save 20% of your income each month. In addition, it would be beneficial to consider individual pension insurance or a savings-type investment trust." If the user shows an anxious expression, the server will provide an additional question: "Is there anything you are worried about?"

[0549] Example prompt sentence:

[0550] "Based on the profile of a user who is 35 years old, married (with one child) and earns ¥5 million a year, generate personalized financial advice for the question, 'How should I save for future college fees?'"

[0551] 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.

[0552] 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.

[0553] 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.

[0554] [Second embodiment]

[0555] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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).

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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."

[0567] MODE FOR CARRYING OUT THE INVENTION

[0568] The present invention provides an embodiment of an AI-based financial planner system that allows users to receive advice on household finances and insurance without feeling any psychological resistance.

[0569] 1. System Overview

[0570] The system allows users to input questions about their household finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback.

[0571] 2. Program processing explanation

[0572] Enter your profile

[0573] A user launches the application and enters initial profile information (age, income, family status, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[0574] Enter consultation details

[0575] Users input specific questions about household finances and insurance into a chat interface that is designed to be intuitive and easy to use.

[0576] example:

[0577] Type, "I have a child. How should I save for his or her future education?"

[0578] Question Analysis

[0579] The server analyzes the user's input using natural language processing technology. For example, it extracts keywords from the text and identifies related categories based on them. The natural language processing technology used here is based on the latest AI technology and achieves highly accurate analysis.

[0580] example:

[0581] Keywords such as "tuition fees" and "savings" are extracted, and corresponding information is searched for in the database.

[0582] Generate answers

[0583] Based on the analysis results, the server extracts relevant information from the database and generates an appropriate answer, which is then presented to the user in a format that is easy to understand.

[0584] example:

[0585] "To save for future tuition, you have the following options:

[0586] 1. Use child allowance money and save regularly every month.

[0587] 2. Take out education insurance and use a savings-type insurance product.

[0588] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[0589] Providing answers

[0590] The device displays the generated answers in a chat interface, and, if necessary, provides visual information using charts and graphs.

[0591] Additional questions and further information provided

[0592] If the user has further questions or requires more information, they enter it into the chat interface again, and the server again analyzes and generates an answer based on this additional information.

[0593] 3. Guidance for face-to-face consultations

[0594] If a face-to-face consultation is deemed necessary or if the user requests one, the server automatically uses the reservation system to schedule an appointment with the nearest specialist, and the reservation details are sent to the user via their device.

[0595] 4. Gathering feedback and optimizing the system

[0596] After the consultation, the user inputs feedback on the advice provided. This feedback is sent to the server via the terminal. The server then optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[0597] Specific examples

[0598] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the server might generate the following answer:

[0599] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[0600] The above is an embodiment of the present invention. With this system, users can easily ask for advice on household finances and insurance without feeling any psychological resistance, and can receive the latest, personalized advice.

[0601] The processing flow will be explained below.

[0602] Step 1:

[0603] The user launches the application and selects the financial advice service. The application displays a profile entry screen.

[0604] Step 2:

[0605] The user enters and submits initial profile information (age, income, family composition, occupation, etc.).

[0606] Step 3:

[0607] The server stores the received profile information in a database and uses it to provide personalized advice tailored to each user's individual circumstances.

[0608] Step 4:

[0609] The user inputs specific consultation content about household finances or insurance into the chat interface and sends it.

[0610] Step 5:

[0611] The server sends the received consultation content to a natural language processing engine, which analyzes the text and extracts keywords.

[0612] Step 6:

[0613] The server searches the database for relevant data based on the extracted keywords.

[0614] Step 7:

[0615] The server uses the search results to generate the best answer, which is formatted in a user-friendly format.

[0616] Step 8:

[0617] The terminal displays the generated response on the chat interface and provides it to the user.

[0618] Step 9:

[0619] If the user reviews the response and has further questions or requests for more information, they can type it again into the chat interface and submit.

[0620] Step 10:

[0621] The server then sends the newly received question back to the natural language processing engine for analysis and keyword extraction.

[0622] Step 11:

[0623] The server generates a new answer and serves it back to the user.

[0624] Step 12:

[0625] If the user desires a face-to-face consultation, or if the server determines that a face-to-face consultation is necessary, a toss-up is made to the nearest specialist through the reservation system.

[0626] Step 13:

[0627] The server checks the availability of the specialist and schedules the appointment.

[0628] Step 14:

[0629] The terminal notifies the user of the reservation details (date, time, location, etc.).

[0630] Step 15:

[0631] The user inputs and submits feedback on the advice provided.

[0632] Step 16:

[0633] The server receives the feedback and stores it in a database.

[0634] Step 17:

[0635] The server analyzes the collected feedback and uses it to optimize the system.

[0636] The above are the specific processing steps of the program, which allow the user to efficiently receive necessary advice on household finances and insurance without feeling any psychological resistance.

[0637] Example 1

[0638] 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."

[0639] Currently, many people feel psychologically reluctant to seek advice about household finances or insurance. Furthermore, receiving appropriate advice often requires a face-to-face consultation, which places a time burden on users. Furthermore, the use of feedback to improve the quality of the advice provided is insufficient. For these reasons, there is a need for a system that allows users to easily seek advice about household finances and insurance and receive high-quality advice.

[0640] 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.

[0641] In this invention, the server includes a means for a user to input initial profile information, a means for a user to input specific consultation details regarding household finances or insurance, a means for analyzing the input consultation details using natural language processing technology, a means for searching a database to generate an appropriate answer using a generative AI model, a means for providing the generated answer to the user, a means for tossing up to an expert if face-to-face consultation is necessary, a means for storing all consultation details and advice in a database, a means for a user to input feedback on the advice provided, and a means for optimizing the system and improving the accuracy of prompt sentences based on the feedback. This enables users to receive high-quality advice regarding household finances or insurance without feeling any psychological resistance.

[0642] ---

[0643] "User" refers to an individual who uses this system to seek advice on household finances or insurance.

[0644] "Initial profile information" refers to basic information about the user, such as age, income, family composition, and occupation.

[0645] "Specific consultation content regarding household finances or insurance" refers to specific questions or concerns regarding household finances or insurance that users enter through the system.

[0646] "Natural language processing technology" refers to technology used to analyze input text information and understand its meaning.

[0647] A "generative AI model" refers to an artificial intelligence model that generates optimal answers based on data analyzed using natural language processing technology.

[0648] "Database" refers to a data storage system that stores financial and insurance information.

[0649] "Face-to-face consultation" refers to a consultation where the user meets directly with a specialist.

[0650] An "expert" is someone who has knowledge and experience in household finances and insurance and is qualified to provide face-to-face consultations.

[0651] "Toss-up" refers to the process by which the system automatically schedules an appointment with a specialist when it determines that a face-to-face consultation is necessary.

[0652] "Feedback" refers to the evaluation or impressions that a user enters regarding the advice provided.

[0653] A "prompt" refers to a question or instruction input to a generative AI model.

[0654] "Optimization" refers to the process of improving the overall performance of the system and the accuracy of responses based on feedback.

[0655] ---

[0656] These are the definitions of important words that will help clarify the scope of your patent claims.

[0657] ---

[0658] MODE FOR CARRYING OUT THE INVENTION

[0659] overview

[0660] This invention is an AI-based financial planner system that allows users to receive advice on household finances and insurance without feeling any psychological resistance. Based on the initial profile information and specific consultation content entered by the user, the system uses a generative AI model to generate appropriate answers and, if necessary, guides the user to face-to-face consultations with experts.

[0661] System configuration

[0662] The system consists of a server, a terminal, and a user. The server generates answers using advanced natural language processing technology and generative AI models, and provides them to the user via the terminal.

[0663] Hardware and software used

[0664] Hardware: Servers (high-performance processors, memory, large-capacity storage), devices (smartphones, tablets, PCs)

[0665] Software: Natural language processing libraries, generative AI models, database management systems, chat interface platforms

[0666] Enter your profile

[0667] The user launches the application and enters initial profile information, such as age, income, family composition, and occupation, which is then validated in real time by the device and sent to the server.

[0668] Enter consultation details

[0669] A user types a specific financial or insurance question into the chat interface, for example, "I have a new baby. How should I save for his or her future education?" The device encodes this input and sends it to the server.

[0670] Parsing questions and generating answers

[0671] The server analyzes the input question using natural language processing technology. Specifically, it extracts keywords from the text and searches a database for related information. A generative AI model then uses that information to generate the optimal answer. For example, "To save for future tuition fees, you have the following options:

[0672] 1. Use child allowance money and save regularly every month.

[0673] 2. Take out education insurance and use a savings-type insurance product.

[0674] 3. I will open a NISA account and aim for future returns by investing in stocks and investment trusts."

[0675] Providing answers

[0676] The device displays the responses received from the server in the chat interface, providing visual information using graphs and charts as needed.

[0677] Additional questions and further information provided

[0678] If the user has further questions or requests for more information, they enter it again into the chat interface, and the server re-parses the added questions and generates appropriate answers.

[0679] Guidance for face-to-face consultation

[0680] The server analyzes the user's consultation content and profile to determine whether a face-to-face consultation is necessary. If a face-to-face consultation is determined to be necessary, the server sends reservation information to the nearest specialist through the reservation system and notifies the user of the reservation details via their terminal.

[0681] Gathering feedback and optimizing the system

[0682] The user inputs feedback on the provided advice. The device encodes the feedback and sends it to the server. The server aggregates the feedback and optimizes the entire system. Specifically, it adjusts the generative AI model to improve the accuracy of prompt sentences.

[0683] ---

[0684] The above is a specific example of how to implement the invention. This system allows users to receive high-quality advice on household finances and insurance without feeling any psychological resistance. It also allows users to smoothly connect with experts when face-to-face consultation is necessary.

[0685] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0686] ---

[0687] Step 1: Fill in your profile

[0688] A user launches the application and enters their initial profile information, such as age, income, family composition, and occupation. The device validates the input in real time to ensure it is in the correct format. The profile information is then sent from the device to the server and stored in a database.

[0689] Input: User's initial profile information (age, income, family composition, occupation, etc.).

[0690] Data processing / calculation: Validation is performed.

[0691] Output: Validated profile information stored in database.

[0692] Step 2: Enter your consultation details

[0693] Users enter specific financial or insurance questions into the chat interface, which the system uses to provide an intuitive and user-friendly interface design. The device encodes the questions and sends them to the server.

[0694] Input: User's inquiry (e.g., question about child's future tuition fees).

[0695] Data processing / computation: Encoding of input text.

[0696] Output: The encoded consultation content is sent to the server.

[0697] Step 3: Parsing the Question

[0698] The server analyzes the received inquiry using natural language processing technology. Specifically, it uses a generative AI model to extract keywords from the text and identify related categories. Based on the extracted keywords, it searches a database for related information.

[0699] Input: The encoded consultation content.

[0700] Data processing / calculation: Keyword extraction and category identification using natural language processing.

[0701] Output: Related categories and keywords.

[0702] Step 4: Generate an answer

[0703] The server searches the database based on the analysis results and generates an appropriate answer. A generative AI model is used to generate an answer in a format that is easy for the user to understand. Specifically, it creates an answer in the form of, "To save for future tuition fees, you have the following options: 1. Use child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts to aim for future returns."

[0704] Input: relevant categories and keywords.

[0705] Data processing / computation: Generating answers using generative AI models.

[0706] Output: The generated answer.

[0707] Step 5: Provide your answers

[0708] The device displays the generated answers in the chat interface, possibly using graphs or charts to visually supplement the answers, if necessary.

[0709] Input: The generated answer.

[0710] Data processing / calculation: Visual supplement to answers.

[0711] Output: The response displayed in the chat interface.

[0712] Step 6: Ask additional questions and provide more information

[0713] If the user has further questions or requires more information, they enter it into the chat interface again, and the device encodes the additional questions and sends them to the server, repeating the process of parsing the questions and generating answers.

[0714] Input: User's additional question.

[0715] Data processing / computation: Encoding input text, parsing questions, and generating answers.

[0716] Output: The generated answer.

[0717] Step 7: Guide to face-to-face consultation

[0718] The server analyzes the user's consultation content and profile, and if it determines that a face-to-face consultation is necessary, it sends reservation information to the nearest specialist using the reservation system. The reservation details are then notified to the user via their terminal.

[0719] Input: User's consultation content, profile.

[0720] Data processing / calculation: Analysis and generation of reservation information.

[0721] Output: Notification of reservation information.

[0722] Step 8: Gather feedback and optimize the system

[0723] The user inputs feedback on the provided advice and sends it to the server via their device. The server then uses the collected feedback to optimize the generative AI model and the entire system. Specifically, it makes adjustments to improve the accuracy of prompt sentences.

[0724] Input: User feedback.

[0725] Data processing / calculation: Feedback collection and analysis, system optimization.

[0726] Output: Optimized generative AI models and systems.

[0727] ---

[0728] The above are the specific processing steps and details of the system, which allows users to receive high-quality advice on household finances and insurance.

[0729] (Application example 1)

[0730] 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."

[0731] With conventional financial planner systems, users often felt psychological resistance when receiving face-to-face consultations, and specialized knowledge was often required to receive appropriate advice. Furthermore, the lack of visual and audio support often made the system difficult to understand.

[0732] 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.

[0733] In this invention, the server includes: means for a user to input initial profile information; means for a user to input specific consultation details regarding household finances or insurance; means for analyzing the input consultation details using natural language processing technology; means for searching a database to generate an appropriate answer; means for providing the generated answer to the user; means for tossing up to an expert if face-to-face consultation is necessary; means for saving all consultation details and advice in a database; speech synthesis means for reading out responses aloud; reservation management means for making appointments for face-to-face consultations; and means for using augmented reality technology to provide visual information. This enables users to receive easy-to-understand financial consultations supported visually and audibly without feeling any psychological resistance.

[0734] "User" means an individual user of the Financial Planner System.

[0735] "Initial profile information" refers to the user's basic attribute information, such as age, income, family composition, and occupation.

[0736] "Specific consultation content" refers to specific matters or questions that users want to ask about their household finances or insurance.

[0737] "Natural language processing technology" is a technology for analyzing and understanding input text data and generating appropriate responses.

[0738] A "database" is a collection of information that manages accumulated information and allows necessary information to be quickly searched.

[0739] "Generating a response" refers to the process of creating an appropriate answer to a user's question.

[0740] "Speech synthesis means" refers to technology or equipment that converts text information into speech.

[0741] "Appointment management tools" means the functionality and technology used to schedule and manage appointments for in-person consultations.

[0742] "Augmented reality technology" is a technology that overlays computer-generated visual information onto the real world.

[0743] This invention provides an embodiment of an AI-based financial planner system that allows users to receive consultations regarding household finances and insurance without feeling any psychological resistance. This system provides an interface through which users can input initial profile information and specific consultation details regarding household finances and insurance. The system of the present invention is implemented using the following hardware and software.

[0744] 1. System Overview

[0745] The system uses "natural language processing technology" to analyze the inputted consultation content and generates an appropriate answer from a "database." Users input questions about household finances or insurance, and the system automatically generates an appropriate answer based on the input. In addition, if a face-to-face consultation is required, the system also includes a function to toss up to an expert using "reservation management means." Furthermore, the generated answer is provided in audio format using "speech synthesis means," and visual information is provided using "augmented reality technology."

[0746] 2. Program processing explanation

[0747] Hardware and Software

[0748] Hardware:

[0749] Touchscreen

[0750] camera

[0751] microphone

[0752] speaker

[0753] Smart Glasses

[0754] software:

[0755] Python

[0756] Natural Language Processing model (Hugging Face's Transformers library)

[0757] TfidfVectorizer (skill extraction)

[0758] SQLite (database management)

[0759] pyttsx3 (speech synthesis)

[0760] Enter user profile information

[0761] First, a "user" launches the system and enters initial profile information (age, income, family composition, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[0762] Input and analysis of consultation details

[0763] When a "user" enters a specific question about household finances or insurance into the chat interface, the input is sent to the "server," which then uses natural language processing technology to analyze the question and extract keywords, thereby identifying the appropriate category.

[0764] Answer generation and speech synthesis

[0765] The "server" searches for relevant information from a database based on the extracted keywords and generates an appropriate answer. The generated answer is not only provided to the "user" via the "terminal" but also read aloud using the "speech synthesis means."

[0766] Use of Augmented Reality Technology

[0767] Furthermore, when visual information is provided, it is displayed on the smart glasses using "augmented reality technology," allowing users to intuitively understand the advice.

[0768] Book a face-to-face consultation

[0769] If a face-to-face consultation is deemed necessary or if the user requests one, the "server" will automatically use the reservation system to schedule an appointment with the nearest specialist, and the reservation details will be sent to the user via the "terminal."

[0770] Gathering feedback and optimizing the system

[0771] After the consultation is completed, the "user" inputs feedback on the advice provided. This feedback is sent to the "server" via the "terminal." The "server" optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[0772] Specific examples

[0773] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the "server" generates the following answer:

[0774] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[0775] Example prompt

[0776] "How can I save for my child's school fees?"

[0777] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0778] Program processing steps

[0779] Step 1:

[0780] The "user" starts the system and enters initial profile information (age, income, family composition, occupation, etc.) using a touchscreen or voice input. This information is sent to the "server," which then provides personalized advice based on the user's individual circumstances.

[0781] Input: Initial profile information (age, income, family composition, occupation)

[0782] Output: Profile data stored on the server

[0783] Step 2:

[0784] "Users" type specific questions about finances or insurance into a chat interface, which are sent to a "server" and prepared for analysis.

[0785] Input: Specific questions about finances or insurance

[0786] Output: Questions saved on the server

[0787] Step 3:

[0788] The "server" analyzes the input question using natural language processing techniques. It uses TensorFlow and Hugging Face's Transformers library to extract key keywords from the question. The results of this analysis are used in the next step.

[0789] Input: Question

[0790] Data processing: Extracting keywords using natural language processing technology

[0791] Output: Extracted keywords

[0792] Step 4:

[0793] The "server" searches for relevant information from the SQLite database based on the extracted keywords and generates an appropriate answer, which is then sent to the "terminal" in text format.

[0794] Input: Extracted keywords

[0795] Data Computing: Database Search and Answer Generation

[0796] Output: Textual response

[0797] Step 5:

[0798] The "terminal" displays the generated answer. At the same time, the "speech synthesis means" uses the pyttsx3 library to read the answer aloud. Visual information is also provided using "augmented reality technology" if necessary.

[0799] Input: Text response

[0800] Data processing: converting text to speech, generating visual information

[0801] Output: Audio and visual information

[0802] Step 6:

[0803] If the "user" asks a more detailed question or requests a face-to-face consultation, the "server" analyzes the question again, updates the necessary information, and provides appropriate advice. If it determines that a face-to-face consultation is necessary, the "appointment management means" is automatically used to toss up the request to an expert and make a consultation appointment. The appointment details are displayed on the "terminal" and notified to the "user."

[0804] Input: Additional questions or requests for face-to-face consultation

[0805] Data calculation: reanalysis and reservation arrangement

[0806] Output: Updated advice, booking confirmation details

[0807] Step 7:

[0808] When the "user" inputs feedback on the advice provided, this feedback is sent to the "server" via the "terminal." The "server" optimizes the system based on the collected feedback and improves the quality of advice from the next time onwards.

[0809] Input: Feedback

[0810] Data Calculations: Feedback Analysis

[0811] Output: System optimization

[0812] 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.

[0813] MODE FOR CARRYING OUT THE INVENTION

[0814] The present invention provides an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and by combining it with an emotion engine, provides a system that provides more personalized advice.

[0815] 1. System Overview

[0816] The system allows users to input questions about their finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[0817] 2. Program processing explanation

[0818] Enter your profile

[0819] A user launches the application and enters initial profile information (age, income, family status, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[0820] Enter consultation details

[0821] Users input specific questions about household finances and insurance into a chat interface that is designed to be intuitive and easy to use.

[0822] example:

[0823] Type, "I have a child. How should I save for his or her future education?"

[0824] Question Analysis

[0825] The server analyzes the user's input using natural language processing technology. For example, it extracts keywords from the text and identifies related categories based on them. The natural language processing technology used here is based on the latest AI technology and achieves highly accurate analysis.

[0826] example:

[0827] Keywords such as "tuition fees" and "savings" are extracted, and corresponding information is searched for in the database.

[0828] Generate answers

[0829] Based on the analysis results, the server extracts relevant information from the database and generates an appropriate answer, which is then presented to the user in a format that is easy to understand.

[0830] example:

[0831] "To save for future tuition, you have the following options:

[0832] 1. Use child allowance money and save regularly every month.

[0833] 2. Take out education insurance and use a savings-type insurance product.

[0834] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[0835] Providing answers

[0836] The device displays the generated answers in the chat interface, and, if necessary, provides visual information using charts and graphs.

[0837] Emotion Recognition and Analysis

[0838] The server analyzes the user's facial expressions and voice tone using an emotion engine, which identifies the user's emotional state (e.g., anxiety, relief, etc.) and adjusts the response accordingly.

[0839] example:

[0840] If the user appears anxious, offer follow-up questions such as, "Are you worried about saving for college?"

[0841] Additional questions and further information provided

[0842] If the user has further questions or requires more information, they enter it into the chat interface again, and the server again analyzes and generates an answer based on this additional information.

[0843] 3. Guidance for face-to-face consultations

[0844] If a face-to-face consultation is deemed necessary or if the user requests one, the server automatically uses the reservation system to schedule an appointment with the nearest specialist, and the reservation details are sent to the user via their device.

[0845] 4. Gathering feedback and optimizing the system

[0846] After the consultation, the user inputs feedback on the advice provided. This feedback is sent to the server via the terminal. The server then optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[0847] Specific examples

[0848] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the server might generate the following answer:

[0849] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[0850] Additionally, if the user shows signs of anxiety while asking a question, the emotion engine will recognize this and offer additional questions or information to provide additional reassurance.

[0851] This concludes the description of the embodiment of the present invention. This system allows users to easily seek advice about household finances and insurance without feeling any psychological resistance, and also allows users to receive personalized advice based on their emotional state.

[0852] The processing flow will be explained below.

[0853] Step 1:

[0854] The user launches the application and selects the financial advice service. The application displays a profile entry screen.

[0855] Step 2:

[0856] The user enters and submits initial profile information (age, income, family composition, occupation, etc.).

[0857] Step 3:

[0858] The server stores the received profile information in a database and uses it to provide personalized advice tailored to each user's individual circumstances.

[0859] Step 4:

[0860] The user inputs specific consultation content about household finances or insurance into the chat interface and sends it.

[0861] Step 5:

[0862] The server sends the received consultation content to a natural language processing engine, which analyzes the text and extracts keywords.

[0863] Step 6:

[0864] The server searches the database for relevant data based on the extracted keywords.

[0865] Step 7:

[0866] The server uses the search results to generate the best answer, which is formatted in a user-friendly format.

[0867] Step 8:

[0868] The terminal displays the generated response on the chat interface and provides it to the user.

[0869] Step 9:

[0870] The device uses a camera and microphone to analyze the user's facial expressions and voice in real time to obtain emotional data, which is then sent to the emotion engine.

[0871] Step 10:

[0872] The server receives the user's emotional data analyzed by the emotion engine and adjusts the response based on that data. For example, if the user is anxious, the response will be more detailed and include reassuring information.

[0873] Step 11:

[0874] The server regenerates the adjusted response and sends it to the terminal.

[0875] Step 12:

[0876] The device will redisplay the adjusted response in the chat interface.

[0877] Step 13:

[0878] If the user reviews the response and has further questions or requests for more information, they can type it again into the chat interface and submit.

[0879] Step 14:

[0880] The server then sends the newly received question to the natural language processing engine again for analysis and keyword extraction, and the process repeats from step 5 to step 12.

[0881] Step 15:

[0882] If the user desires a face-to-face consultation, or if the server determines that a face-to-face consultation is necessary, a toss-up is made to the nearest specialist through the reservation system.

[0883] Step 16:

[0884] The server checks the availability of the specialist and schedules the appointment.

[0885] Step 17:

[0886] The terminal notifies the user of the reservation details (date, time, location, etc.).

[0887] Step 18:

[0888] The user inputs and submits feedback on the advice provided.

[0889] Step 19:

[0890] The server receives the feedback and stores it in a database.

[0891] Step 20:

[0892] The server analyzes the collected feedback and uses it to optimize the system.

[0893] These are the specific processing steps of the program. These steps allow users to efficiently receive the necessary advice on household finances and insurance without feeling any psychological resistance, and by using the emotion engine, they can receive personalized advice that takes into account the user's emotions.

[0894] Example 2

[0895] 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."

[0896] Conventional financial planner systems have the problem that users find it difficult to consult with them because they do not take into account the user's psychological state, and the advice provided is often not tailored to individual circumstances. The present invention aims to solve these problems and provide a system that allows users to consult about household finances and insurance without feeling any psychological resistance. It also aims to provide personalized advice tailored to the user's individual circumstances and emotional state.

[0897] 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.

[0898] In this invention, the server includes means for a user to input initial profile information, means for a user to input specific consultation details regarding household finances or insurance, means for analyzing the input consultation details using natural language processing technology, means for searching a database to generate an appropriate answer, means for recognizing and analyzing emotions from the user's facial expressions and voice, means for adjusting the answer content based on the emotion analysis results, means for providing the generated answer to the user, means for tossing up to an expert if face-to-face consultation is necessary, and means for storing all consultation details and advice in a database. This provides an environment where users can easily seek advice and makes it possible to provide personalized advice according to individual situations and emotions.

[0899] "User" means an individual who uses the system to seek advice on household finances or insurance.

[0900] "Initial profile information" refers to the basic personal information that users enter when registering with the system, including age, income, family composition, occupation, etc.

[0901] "Consultation content" refers to the specific questions or problems that users input into the system, including matters related to household finances and insurance.

[0902] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0903] A "database" is a system or software for structuring and managing information about household finances and insurance.

[0904] "Emotion recognition" is a technology that identifies emotions from a user's facial expressions and voice, and is used to understand the user's psychological state.

[0905] The "analysis results" refer to data obtained using natural language processing technology and emotion recognition technology, and answers are generated based on this data.

[0906] The "answer content" refers to advice or suggestions to the user that the system generates based on the analysis results.

[0907] "Face-to-face consultation" refers to a consultation in which the user directly interacts with an expert.

[0908] An "expert" is someone who has knowledge and experience in household finances and insurance and provides professional advice to users.

[0909] "Toss-up" refers to the process by which the system automatically forwards a consultation request to an expert.

[0910] "Feedback" refers to the evaluations and comments that users make regarding advice and services provided by the system.

[0911] "Optimization" is the process of improving the performance of a system or the quality of advice based on collected feedback.

[0912] MODE FOR CARRYING OUT THE INVENTION

[0913] The following describes an embodiment of the present invention. This invention is an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and aims to provide more personalized advice by combining it with an emotion engine.

[0914] 1. System Overview

[0915] The system allows users to input questions about household finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[0916] 2. Enter your profile

[0917] A user launches an application and enters initial profile information (age, income, family composition, occupation, etc.), which allows the server to provide personalized advice based on the user's individual circumstances. For example, when a user enters information using a smartphone app, the data is sent from the device to the server and stored in a database.

[0918] 3. Enter your consultation details

[0919] The user types specific questions about household finances or insurance into the chat interface, which is designed to be intuitive and easy to use. For example, if the user types, "I have a child. How should I save for his or her future tuition fees?", the device sends the information to the server.

[0920] 4. Question Analysis

[0921] The server analyzes the questions it receives using natural language processing technology (such as generative AI models). Keywords are extracted from the text and related categories are identified based on these. An advanced natural language processing engine is used for the analysis, allowing for highly accurate keyword extraction and category identification.

[0922] 5. Answer Generation

[0923] The server extracts relevant information from the database based on the analysis results and generates an appropriate answer. The generated answer is provided in a format that is easy for the user to understand. For example, it could generate an answer such as, "To save for future tuition fees, you have the following options: 1. Use your child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts to aim for future returns."

[0924] 6. Providing answers

[0925] The device displays the generated answers in the chat interface, and provides visual information using charts and graphs as needed, making it easier for users to understand intuitively.

[0926] 7. Emotion Recognition and Analysis

[0927] The server analyzes the user's facial expressions and voice tone using an emotion engine. For example, the user captures their facial expressions and voice using the device's camera or microphone, and the data is sent to the server. The server analyzes the data and identifies the user's emotional state. If the user looks anxious, the server will provide follow-up questions such as, "Are you worried about saving for college?"

[0928] 8. Additional Questions and Further Information

[0929] If the user has further questions or requests for more information, they can type again into the chat interface, for example, "Can you explain in more detail?", and the device will send that information to the server, which will again analyze it and generate a response.

[0930] 9. Guidance for face-to-face consultations

[0931] If it is determined that a face-to-face consultation is necessary, or if the user requests a face-to-face consultation, the server automatically uses the reservation system to make an appointment with an expert. The reservation details are notified to the user via the terminal. For example, if the user enters "I would like to speak to an expert in person," the server will make an appointment with the nearest expert via the reservation system.

[0932] 10. Gathering feedback and optimizing the system

[0933] After the consultation, the user can enter feedback on the advice provided. For example, if the user enters "This advice was very helpful," the device will send the feedback to the server. The server will store this in a database and use the feedback to optimize the system.

[0934] Specific examples

[0935] For example, if a newly married couple who both work asks, "How should we create a savings plan?", the server will generate the following answer: "If both partners work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or a savings-type investment trust. If you are considering buying a home, it is also important to start planning early."

[0936] This concludes the description of the embodiment of the present invention. This system allows users to easily seek advice about household finances and insurance without feeling any psychological resistance, and also allows users to receive personalized advice according to their emotional state.

[0937] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0938] Step 1: Fill in your profile

[0939] The user enters initial profile information, including age, income, family composition, and occupation. This information is sent from the device to the server, which stores it in a database. Based on the information entered, the server prepares the basic data for generating personalized advice.

[0940] Specific behavior:

[0941] The user launches the smartphone app and enters the required information into the displayed form.

[0942] The terminal transmits the input information to the server.

[0943] The server stores the received information in a database and creates a user profile.

[0944] Input: Age, income, family structure, occupation

[0945] Output: Create a user profile

[0946] Step 2: Enter your consultation details

[0947] The user enters questions about household finances or insurance into the chat interface. This information is sent from the device to the server, which then passes the received question data to the next analysis step.

[0948] Specific behavior:

[0949] User: Type "I have a child, how should I save for his or her future education?"

[0950] The device sends the question to the server.

[0951] Input: Question text (e.g., "I have a child. How should I save for his or her future education?")

[0952] Output: Received query data

[0953] Step 3: Parsing the Question

[0954] The server analyzes the received question using natural language processing techniques (such as generative AI models). This analysis involves extracting keywords from the text and identifying related categories. The analysis results are used in the next step of generating an answer.

[0955] Specific behavior:

[0956] The server inputs the question text into a natural language processing engine and extracts keywords.

[0957] The NLP (natural language processing) engine extracts important keywords such as "tuition fees" and "savings" and identifies categories based on these.

[0958] Input: Question data

[0959] Output: Analysis results (extracted keywords and related categories)

[0960] Step 4: Generate an answer

[0961] The server uses the analysis results to extract relevant information from the database and generate an appropriate answer, which is then formatted in a way that is easy for the user to understand.

[0962] Specific behavior:

[0963] The server accesses the database and searches for relevant information based on the extracted keywords.

[0964] The server uses the search results and the generative AI model to generate an answer.

[0965] The answer can be formatted as follows: "To save for future school fees, you have the following options: 1. Use your child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts, aiming for future returns."

[0966] Input: Analysis results (extracted keywords and related categories)

[0967] Output: The generated answer

[0968] Step 5: Provide your answers

[0969] The device displays the generated answers in the chat interface, and, if necessary, provides visual information using charts and graphs.

[0970] Specific behavior:

[0971] The server sends the generated response to the terminal.

[0972] The device displays the received response in the chat interface.

[0973] Input: Generated Answer

[0974] Output: Displayed answer

[0975] Step 6: Emotion recognition and analysis

[0976] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to determine the user's emotional state.

[0977] Specific behavior:

[0978] The device captures the user's facial expressions and voice using a camera and microphone and sends the data to a server.

[0979] The server analyzes the data using an emotion recognition engine to identify the user's emotional state (e.g., anxiety, relief).

[0980] Input: facial expression data, voice data

[0981] Output: Emotion analysis results

[0982] Step 7: Adjust your responses based on emotion

[0983] The server adjusts the response content based on the results of emotion analysis. If the user is anxious, the response content is adjusted to provide additional information or reassurance.

[0984] Specific behavior:

[0985] The server reevaluates the response based on the results of the sentiment analysis.

[0986] Providing follow-up questions or information such as, "Do you have any concerns about saving for college?"

[0987] Input: Sentiment analysis results

[0988] Output: Adjusted answer

[0989] Step 8: Ask additional questions and provide further information

[0990] If the user has further questions or requests for more information, they enter it into the chat interface again, and this information is again sent from the device to the server for re-analysis and generation of an answer.

[0991] Specific behavior:

[0992] User: Type, "Can you explain that in more detail?"

[0993] The device sends new information to the server, which analyzes it again and generates a response.

[0994] Input: Text of follow-up question

[0995] Output: Provides detailed information

[0996] Step 9: Guide to face-to-face consultation

[0997] If the server determines that a face-to-face consultation is necessary, it will use the reservation system to make an appointment with a specialist, and notify the user of the reservation details via their device.

[0998] Specific behavior:

[0999] User: Type "I'd like to speak to an expert in real life."

[1000] The server accesses the reservation system and makes an appointment with the nearest specialist.

[1001] The device will notify you of the reservation details.

[1002] Input: Request for face-to-face consultation

[1003] Output: Reservation details notification

[1004] Step 10: Gather feedback and optimize

[1005] The user inputs feedback on the advice provided, and the device sends it to the server, which stores the feedback in a database and uses it to optimize the system.

[1006] Specific behavior:

[1007] User: Type "This advice was very helpful."

[1008] The device sends the feedback to the server.

[1009] The server stores the feedback in a database and uses it to generate advice next time.

[1010] Input: Feedback text

[1011] Output: Saved feedback

[1012] (Application example 2)

[1013] 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."

[1014] Conventional financial planner systems provide uniform answers without taking into account the user's emotional state, which can cause users to feel psychological resistance. Furthermore, ignoring the user's emotional state can lead to users not receiving satisfactory answers or advice, which can reduce the usefulness of the system.

[1015] 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 a means for inputting the user's initial profile information, a means for the user to input specific consultation details regarding household finances or insurance, a means for analyzing the input consultation details using natural language processing technology, a means for searching a database to generate an appropriate answer, a means for providing the generated answer to the user, a means for tossing up to an expert if face-to-face consultation is necessary, a means for saving all consultation details and advice in a database, a means for recognizing and analyzing the user's emotional state, and a means for adjusting the answer based on the recognized emotional state. This makes it possible to provide personalized advice according to the user's emotional state.

[1016] "User" refers to an individual who uses this system to seek advice on household finances and insurance.

[1017] "Initial profile information" refers to basic information such as the user's age, income, family composition, and occupation.

[1018] "Consultation content" refers to specific questions that users have about household finances and insurance.

[1019] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[1020] "Database" refers to a digital record device for storing financial and insurance information and advice.

[1021] "Emotional state" refers to the psychological state recognized from the user's facial expressions and voice.

[1022] "Analyze" refers to the act of analyzing input information and extracting its meaning and key points.

[1023] "Response" refers to appropriate advice or information in response to the user's inquiry.

[1024] "Tossing up" refers to the act of transferring the user's consultation to an expert as needed.

[1025] "Storing" refers to the act of recording and keeping data for later use.

[1026] "Adjust" refers to the act of changing or optimizing content according to circumstances or conditions.

[1027] MODE FOR CARRYING OUT THE INVENTION

[1028] This invention provides an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and by combining it with an emotion engine, provides a system that provides more personalized advice.

[1029] System Overview

[1030] The system is implemented as a smartphone application, allowing users to input questions about household finances and insurance, and automatically generates appropriate answers based on those questions. It also has a function to guide users to face-to-face consultations if necessary. It also has a function to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[1031] Hardware and Software Used

[1032] Smartphone: Provides user interface and some data processing.

[1033] EmotionEngine: Software for recognizing the user's emotional state.

[1034] NLPProcessor (natural language processing engine): Software for analyzing the content of users' inquiries.

[1035] FinancialAdvisor: Software for generating appropriate financial advice.

[1036] ChatBotInterface: Software that provides interaction between users and systems.

[1037] Processing flow

[1038] 1. Fill in your profile:

[1039] When a user launches the smartphone application, they enter their initial profile information (age, income, family composition, occupation, etc.) This profile information is used by the system to provide appropriate advice to the user.

[1040] 2. Enter your consultation details:

[1041] Users type specific financial and insurance questions into a chat interface that is intuitive and easy to use.

[1042] 3. Question Analysis:

[1043] The server uses an NLPProcessor to analyze the user's input and extract relevant keywords, which then identify the question category.

[1044] 4. Generate answers:

[1045] The server uses FinancialAdvisor to extract relevant information from the database and generate appropriate answers based on the analysis results.

[1046] 5. Emotional awareness and regulation:

[1047] The server uses the Emotion Engine to analyze the user's facial expressions and tone of voice to recognize their emotional state, and responds accordingly.

[1048] 6. Providing answers:

[1049] The generated answers are provided to the user through a chat interface on their smartphone, and may also include appropriate charts and graphs.

[1050] 7. Additional Questions and Face-to-Face Consultations:

[1051] If the user has further questions or requires more information, they enter it into the chat interface again. The server uses this additional information to analyze and generate a new answer. If necessary, an appointment with an expert will be scheduled.

[1052] 8. Feedback and optimization:

[1053] After the consultation, the user inputs feedback on the advice provided. The server optimizes the system based on the collected feedback and improves the quality of advice from the next time onwards.

[1054] Specific examples

[1055] For example, if a 35-year-old married user with one child and an annual income of 5 million yen asks, "I have a child. How should I save for his / her future tuition fees?", the system will generate the following answer:

[1056] "To save for future tuition, you have the following options:

[1057] 1. Use child allowance money and save regularly every month.

[1058] 2. Take out education insurance and use a savings-type insurance product.

[1059] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[1060] Additionally, if the user displays an anxious expression, the emotion engine will recognize this and provide additional questions or information to provide additional reassurance.

[1061] Example prompt sentence:

[1062] "Based on the profile of a user who is 35 years old, married (with one child) and earns ¥5 million a year, generate personalized financial advice for the question, 'How should I save for future college fees?'"

[1063] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1064] Step 1:

[1065] A user launches the smartphone application and enters initial profile information (age, income, family composition, occupation, etc.). The device sends the entered information to the server, which stores it in the user database. Future advice is personalized based on this input.

[1066] Step 2:

[1067] The user inputs specific questions about household finances or insurance into the chat interface. The device receives this input data and sends it to the server. The server uses natural language processing technology (NLPProcessor) to analyze the input consultation content and extract important keywords. The input question is imported as text data, and keywords such as "tuition fees" and "savings" are extracted.

[1068] Step 3:

[1069] The server searches a database to generate appropriate answers based on the extracted keywords. It retrieves relevant information from the database and generates personalized answers that take into account the user's profile information based on the analysis results. For example, it generates specific financial advice such as "How to save for future tuition fees."

[1070] Step 4:

[1071] The server uses the Emotion Engine to recognize the user's emotional state. It analyzes facial and voice data provided by the user through the camera and microphone to identify the user's emotional state (e.g., relief, anxiety). Based on this recognized emotional state, the generated answer is adjusted appropriately. For example, if the user has an anxious expression, the server adds supplementary information that provides additional reassurance.

[1072] Step 5:

[1073] The server then sends the tailored answers to the user's device, where they are displayed in the smartphone chat interface, allowing the user to view the information in an easy-to-understand format, with visual information such as charts and graphs provided as needed.

[1074] Step 6:

[1075] If the user has further questions or requires more information, they enter it again into the chat interface. The device sends the new question to the server, which again analyzes and generates an answer. The process repeats, and if necessary, a face-to-face consultation with an expert is scheduled.

[1076] Step 7:

[1077] After the consultation, the user enters feedback on the advice provided into the chat interface. The device sends this feedback to the server, which then optimizes the system based on the collected feedback. This feedback improves the quality of advice provided in the future.

[1078] Specific examples

[1079] For example, if a newlywed user asks, "How should I create a savings plan?", the server generates an answer such as, "If both spouses work, we recommend that you save 20% of your income each month. In addition, it would be beneficial to consider individual pension insurance or a savings-type investment trust." If the user shows an anxious expression, the server will provide an additional question: "Is there anything you are worried about?"

[1080] Example prompt sentence:

[1081] "Based on the profile of a user who is 35 years old, married (with one child) and earns ¥5 million a year, generate personalized financial advice for the question, 'How should I save for future college fees?'"

[1082] 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.

[1083] 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.

[1084] 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.

[1085] [Third embodiment]

[1086] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1087] 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.

[1088] 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).

[1089] 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.

[1090] 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.

[1091] 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).

[1092] 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.

[1093] 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.

[1094] 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.

[1095] 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.

[1096] 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.

[1097] 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."

[1098] MODE FOR CARRYING OUT THE INVENTION

[1099] The present invention provides an embodiment of an AI-based financial planner system that allows users to receive advice on household finances and insurance without feeling any psychological resistance.

[1100] 1. System Overview

[1101] The system allows users to input questions about their household finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback.

[1102] 2. Program processing explanation

[1103] Enter your profile

[1104] A user launches the application and enters initial profile information (age, income, family status, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[1105] Enter consultation details

[1106] Users input specific questions about household finances and insurance into a chat interface that is designed to be intuitive and easy to use.

[1107] example:

[1108] Type, "I have a child. How should I save for his or her future education?"

[1109] Question Analysis

[1110] The server analyzes the user's input using natural language processing technology. For example, it extracts keywords from the text and identifies related categories based on them. The natural language processing technology used here is based on the latest AI technology and achieves highly accurate analysis.

[1111] example:

[1112] Keywords such as "tuition fees" and "savings" are extracted, and corresponding information is searched for in the database.

[1113] Generate answers

[1114] Based on the analysis results, the server extracts relevant information from the database and generates an appropriate answer, which is then presented to the user in a format that is easy to understand.

[1115] example:

[1116] "To save for future tuition, you have the following options:

[1117] 1. Use child allowance money and save regularly every month.

[1118] 2. Take out education insurance and use a savings-type insurance product.

[1119] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[1120] Providing answers

[1121] The device displays the generated answers in a chat interface, and, if necessary, provides visual information using charts and graphs.

[1122] Additional questions and further information provided

[1123] If the user has further questions or requires more information, they enter it into the chat interface again, and the server again analyzes and generates an answer based on this additional information.

[1124] 3. Guidance for face-to-face consultations

[1125] If a face-to-face consultation is deemed necessary or if the user requests one, the server automatically uses the reservation system to schedule an appointment with the nearest specialist, and the reservation details are sent to the user via their device.

[1126] 4. Gathering feedback and optimizing the system

[1127] After the consultation, the user inputs feedback on the advice provided. This feedback is sent to the server via the terminal. The server then optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[1128] Specific examples

[1129] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the server might generate the following answer:

[1130] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[1131] The above is an embodiment of the present invention. With this system, users can easily ask for advice on household finances and insurance without feeling any psychological resistance, and can receive the latest, personalized advice.

[1132] The processing flow will be explained below.

[1133] Step 1:

[1134] The user launches the application and selects the financial advice service. The application displays a profile entry screen.

[1135] Step 2:

[1136] The user enters and submits initial profile information (age, income, family composition, occupation, etc.).

[1137] Step 3:

[1138] The server stores the received profile information in a database and uses it to provide personalized advice tailored to each user's individual circumstances.

[1139] Step 4:

[1140] The user inputs specific consultation content about household finances or insurance into the chat interface and sends it.

[1141] Step 5:

[1142] The server sends the received consultation content to a natural language processing engine, which analyzes the text and extracts keywords.

[1143] Step 6:

[1144] The server searches the database for relevant data based on the extracted keywords.

[1145] Step 7:

[1146] The server uses the search results to generate the best answer, which is formatted in a user-friendly format.

[1147] Step 8:

[1148] The terminal displays the generated response on the chat interface and provides it to the user.

[1149] Step 9:

[1150] If the user reviews the response and has further questions or requests for more information, they can type it again into the chat interface and submit.

[1151] Step 10:

[1152] The server then sends the newly received question back to the natural language processing engine for analysis and keyword extraction.

[1153] Step 11:

[1154] The server generates a new answer and serves it back to the user.

[1155] Step 12:

[1156] If the user desires a face-to-face consultation, or if the server determines that a face-to-face consultation is necessary, a toss-up is made to the nearest specialist through the reservation system.

[1157] Step 13:

[1158] The server checks the availability of the specialist and schedules the appointment.

[1159] Step 14:

[1160] The terminal notifies the user of the reservation details (date, time, location, etc.).

[1161] Step 15:

[1162] The user inputs and submits feedback on the advice provided.

[1163] Step 16:

[1164] The server receives the feedback and stores it in a database.

[1165] Step 17:

[1166] The server analyzes the collected feedback and uses it to optimize the system.

[1167] The above are the specific processing steps of the program, which allow the user to efficiently receive necessary advice on household finances and insurance without feeling any psychological resistance.

[1168] Example 1

[1169] 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."

[1170] Currently, many people feel psychologically reluctant to seek advice about household finances or insurance. Furthermore, receiving appropriate advice often requires a face-to-face consultation, which places a time burden on users. Furthermore, the use of feedback to improve the quality of the advice provided is insufficient. For these reasons, there is a need for a system that allows users to easily seek advice about household finances and insurance and receive high-quality advice.

[1171] 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.

[1172] In this invention, the server includes a means for a user to input initial profile information, a means for a user to input specific consultation details regarding household finances or insurance, a means for analyzing the input consultation details using natural language processing technology, a means for searching a database to generate an appropriate answer using a generative AI model, a means for providing the generated answer to the user, a means for tossing up to an expert if face-to-face consultation is necessary, a means for storing all consultation details and advice in a database, a means for a user to input feedback on the advice provided, and a means for optimizing the system and improving the accuracy of prompt sentences based on the feedback. This enables users to receive high-quality advice regarding household finances or insurance without feeling any psychological resistance.

[1173] ---

[1174] "User" refers to an individual who uses this system to seek advice on household finances or insurance.

[1175] "Initial profile information" refers to basic information about the user, such as age, income, family composition, and occupation.

[1176] "Specific consultation content regarding household finances or insurance" refers to specific questions or concerns regarding household finances or insurance that users enter through the system.

[1177] "Natural language processing technology" refers to technology used to analyze input text information and understand its meaning.

[1178] A "generative AI model" refers to an artificial intelligence model that generates optimal answers based on data analyzed using natural language processing technology.

[1179] "Database" refers to a data storage system that stores financial and insurance information.

[1180] "Face-to-face consultation" refers to a consultation where the user meets directly with a specialist.

[1181] An "expert" is someone who has knowledge and experience in household finances and insurance and is qualified to provide face-to-face consultations.

[1182] "Toss-up" refers to the process by which the system automatically schedules an appointment with a specialist when it determines that a face-to-face consultation is necessary.

[1183] "Feedback" refers to the evaluation or impressions that a user enters regarding the advice provided.

[1184] A "prompt" refers to a question or instruction input to a generative AI model.

[1185] "Optimization" refers to the process of improving the overall performance of the system and the accuracy of responses based on feedback.

[1186] ---

[1187] These are the definitions of important words that will help clarify the scope of your patent claims.

[1188] ---

[1189] MODE FOR CARRYING OUT THE INVENTION

[1190] overview

[1191] This invention is an AI-based financial planner system that allows users to receive advice on household finances and insurance without feeling any psychological resistance. Based on the initial profile information and specific consultation content entered by the user, the system uses a generative AI model to generate appropriate answers and, if necessary, guides the user to face-to-face consultations with experts.

[1192] System configuration

[1193] The system consists of a server, a terminal, and a user. The server generates answers using advanced natural language processing technology and generative AI models, and provides them to the user via the terminal.

[1194] Hardware and software used

[1195] Hardware: Servers (high-performance processors, memory, large-capacity storage), devices (smartphones, tablets, PCs)

[1196] Software: Natural language processing libraries, generative AI models, database management systems, chat interface platforms

[1197] Enter your profile

[1198] The user launches the application and enters initial profile information, such as age, income, family composition, and occupation, which is then validated in real time by the device and sent to the server.

[1199] Enter consultation details

[1200] A user types a specific financial or insurance question into the chat interface, for example, "I have a new baby. How should I save for his or her future education?" The device encodes this input and sends it to the server.

[1201] Parsing questions and generating answers

[1202] The server analyzes the input question using natural language processing technology. Specifically, it extracts keywords from the text and searches a database for related information. A generative AI model then uses that information to generate the optimal answer. For example, "To save for future tuition fees, you have the following options:

[1203] 1. Use child allowance money and save regularly every month.

[1204] 2. Take out education insurance and use a savings-type insurance product.

[1205] 3. I will open a NISA account and aim for future returns by investing in stocks and investment trusts."

[1206] Providing answers

[1207] The device displays the responses received from the server in the chat interface, providing visual information using graphs and charts as needed.

[1208] Additional questions and further information provided

[1209] If the user has further questions or requests for more information, they enter it again into the chat interface, and the server re-parses the added questions and generates appropriate answers.

[1210] Guidance for face-to-face consultation

[1211] The server analyzes the user's consultation content and profile to determine whether a face-to-face consultation is necessary. If a face-to-face consultation is determined to be necessary, the server sends reservation information to the nearest specialist through the reservation system and notifies the user of the reservation details via their terminal.

[1212] Gathering feedback and optimizing the system

[1213] The user inputs feedback on the provided advice. The device encodes the feedback and sends it to the server. The server aggregates the feedback and optimizes the entire system. Specifically, it adjusts the generative AI model to improve the accuracy of prompt sentences.

[1214] ---

[1215] The above is a specific example of how to implement the invention. This system allows users to receive high-quality advice on household finances and insurance without feeling any psychological resistance. It also allows users to smoothly connect with experts when face-to-face consultation is necessary.

[1216] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1217] ---

[1218] Step 1: Fill in your profile

[1219] A user launches the application and enters their initial profile information, such as age, income, family composition, and occupation. The device validates the input in real time to ensure it is in the correct format. The profile information is then sent from the device to the server and stored in a database.

[1220] Input: User's initial profile information (age, income, family composition, occupation, etc.).

[1221] Data processing / calculation: Validation is performed.

[1222] Output: Validated profile information stored in database.

[1223] Step 2: Enter your consultation details

[1224] Users enter specific financial or insurance questions into the chat interface, which the system uses to provide an intuitive and user-friendly interface design. The device encodes the questions and sends them to the server.

[1225] Input: User's inquiry (e.g., question about child's future tuition fees).

[1226] Data processing / computation: Encoding of input text.

[1227] Output: The encoded consultation content is sent to the server.

[1228] Step 3: Parsing the Question

[1229] The server analyzes the received inquiry using natural language processing technology. Specifically, it uses a generative AI model to extract keywords from the text and identify related categories. Based on the extracted keywords, it searches a database for related information.

[1230] Input: The encoded consultation content.

[1231] Data processing / calculation: Keyword extraction and category identification using natural language processing.

[1232] Output: Related categories and keywords.

[1233] Step 4: Generate an answer

[1234] The server searches the database based on the analysis results and generates an appropriate answer. A generative AI model is used to generate an answer in a format that is easy for the user to understand. Specifically, it creates an answer in the form of, "To save for future tuition fees, you have the following options: 1. Use child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts to aim for future returns."

[1235] Input: relevant categories and keywords.

[1236] Data processing / computation: Generating answers using generative AI models.

[1237] Output: The generated answer.

[1238] Step 5: Provide your answers

[1239] The device displays the generated answers in the chat interface, possibly using graphs or charts to visually supplement the answers, if necessary.

[1240] Input: The generated answer.

[1241] Data processing / calculation: Visual supplement to answers.

[1242] Output: The response displayed in the chat interface.

[1243] Step 6: Ask additional questions and provide more information

[1244] If the user has further questions or requires more information, they enter it into the chat interface again, and the device encodes the additional questions and sends them to the server, repeating the process of parsing the questions and generating answers.

[1245] Input: User's additional question.

[1246] Data processing / computation: Encoding input text, parsing questions, and generating answers.

[1247] Output: The generated answer.

[1248] Step 7: Guide to face-to-face consultation

[1249] The server analyzes the user's consultation content and profile, and if it determines that a face-to-face consultation is necessary, it sends reservation information to the nearest specialist using the reservation system. The reservation details are then notified to the user via their terminal.

[1250] Input: User's consultation content, profile.

[1251] Data processing / calculation: Analysis and generation of reservation information.

[1252] Output: Notification of reservation information.

[1253] Step 8: Gather feedback and optimize the system

[1254] The user inputs feedback on the provided advice and sends it to the server via their device. The server then uses the collected feedback to optimize the generative AI model and the entire system. Specifically, it makes adjustments to improve the accuracy of prompt sentences.

[1255] Input: User feedback.

[1256] Data processing / calculation: Feedback collection and analysis, system optimization.

[1257] Output: Optimized generative AI models and systems.

[1258] ---

[1259] The above are the specific processing steps and details of the system, which allows users to receive high-quality advice on household finances and insurance.

[1260] (Application example 1)

[1261] 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."

[1262] With conventional financial planner systems, users often felt psychological resistance when receiving face-to-face consultations, and specialized knowledge was often required to receive appropriate advice. Furthermore, the lack of visual and audio support often made the system difficult to understand.

[1263] 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.

[1264] In this invention, the server includes: means for a user to input initial profile information; means for a user to input specific consultation details regarding household finances or insurance; means for analyzing the input consultation details using natural language processing technology; means for searching a database to generate an appropriate answer; means for providing the generated answer to the user; means for tossing up to an expert if face-to-face consultation is necessary; means for saving all consultation details and advice in a database; speech synthesis means for reading out responses aloud; reservation management means for making appointments for face-to-face consultations; and means for using augmented reality technology to provide visual information. This enables users to receive easy-to-understand financial consultations supported visually and audibly without feeling any psychological resistance.

[1265] "User" means an individual user of the Financial Planner System.

[1266] "Initial profile information" refers to the user's basic attribute information, such as age, income, family composition, and occupation.

[1267] "Specific consultation content" refers to specific matters or questions that users want to ask about their household finances or insurance.

[1268] "Natural language processing technology" is a technology for analyzing and understanding input text data and generating appropriate responses.

[1269] A "database" is a collection of information that manages accumulated information and allows necessary information to be quickly searched.

[1270] "Generating a response" refers to the process of creating an appropriate answer to a user's question.

[1271] "Speech synthesis means" refers to technology or equipment that converts text information into speech.

[1272] "Appointment management tools" means the functionality and technology used to schedule and manage appointments for in-person consultations.

[1273] "Augmented reality technology" is a technology that overlays computer-generated visual information onto the real world.

[1274] This invention provides an embodiment of an AI-based financial planner system that allows users to receive consultations regarding household finances and insurance without feeling any psychological resistance. This system provides an interface through which users can input initial profile information and specific consultation details regarding household finances and insurance. The system of the present invention is implemented using the following hardware and software.

[1275] 1. System Overview

[1276] The system uses "natural language processing technology" to analyze the inputted consultation content and generates an appropriate answer from a "database." Users input questions about household finances or insurance, and the system automatically generates an appropriate answer based on the input. In addition, if a face-to-face consultation is required, the system also includes a function to toss up to an expert using "reservation management means." Furthermore, the generated answer is provided in audio format using "speech synthesis means," and visual information is provided using "augmented reality technology."

[1277] 2. Program processing explanation

[1278] Hardware and Software

[1279] Hardware:

[1280] Touchscreen

[1281] camera

[1282] microphone

[1283] speaker

[1284] Smart Glasses

[1285] software:

[1286] Python

[1287] Natural Language Processing model (Hugging Face's Transformers library)

[1288] TfidfVectorizer (skill extraction)

[1289] SQLite (database management)

[1290] pyttsx3 (speech synthesis)

[1291] Enter user profile information

[1292] First, a "user" launches the system and enters initial profile information (age, income, family composition, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[1293] Input and analysis of consultation details

[1294] When a "user" enters a specific question about household finances or insurance into the chat interface, the input is sent to the "server," which then uses natural language processing technology to analyze the question and extract keywords, thereby identifying the appropriate category.

[1295] Answer generation and speech synthesis

[1296] The "server" searches for relevant information from a database based on the extracted keywords and generates an appropriate answer. The generated answer is not only provided to the "user" via the "terminal" but also read aloud using the "speech synthesis means."

[1297] Use of Augmented Reality Technology

[1298] Furthermore, when visual information is provided, it is displayed on the smart glasses using "augmented reality technology," allowing users to intuitively understand the advice.

[1299] Book a face-to-face consultation

[1300] If a face-to-face consultation is deemed necessary or if the user requests one, the "server" will automatically use the reservation system to schedule an appointment with the nearest specialist, and the reservation details will be sent to the user via the "terminal."

[1301] Gathering feedback and optimizing the system

[1302] After the consultation is completed, the "user" inputs feedback on the advice provided. This feedback is sent to the "server" via the "terminal." The "server" optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[1303] Specific examples

[1304] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the "server" generates the following answer:

[1305] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[1306] Example prompt

[1307] "How can I save for my child's school fees?"

[1308] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1309] Program processing steps

[1310] Step 1:

[1311] The "user" starts the system and enters initial profile information (age, income, family composition, occupation, etc.) using a touchscreen or voice input. This information is sent to the "server," which then provides personalized advice based on the user's individual circumstances.

[1312] Input: Initial profile information (age, income, family composition, occupation)

[1313] Output: Profile data stored on the server

[1314] Step 2:

[1315] "Users" type specific questions about finances or insurance into a chat interface, which are sent to a "server" and prepared for analysis.

[1316] Input: Specific questions about finances or insurance

[1317] Output: Questions saved on the server

[1318] Step 3:

[1319] The "server" analyzes the input question using natural language processing techniques. It uses TensorFlow and Hugging Face's Transformers library to extract key keywords from the question. The results of this analysis are used in the next step.

[1320] Input: Question

[1321] Data processing: Extracting keywords using natural language processing technology

[1322] Output: Extracted keywords

[1323] Step 4:

[1324] The "server" searches for relevant information from the SQLite database based on the extracted keywords and generates an appropriate answer, which is then sent to the "terminal" in text format.

[1325] Input: Extracted keywords

[1326] Data Computing: Database Search and Answer Generation

[1327] Output: Textual response

[1328] Step 5:

[1329] The "terminal" displays the generated answer. At the same time, the "speech synthesis means" uses the pyttsx3 library to read the answer aloud. Visual information is also provided using "augmented reality technology" if necessary.

[1330] Input: Text response

[1331] Data processing: converting text to speech, generating visual information

[1332] Output: Audio and visual information

[1333] Step 6:

[1334] If the "user" asks a more detailed question or requests a face-to-face consultation, the "server" analyzes the question again, updates the necessary information, and provides appropriate advice. If it determines that a face-to-face consultation is necessary, the "appointment management means" is automatically used to toss up the request to an expert and make a consultation appointment. The appointment details are displayed on the "terminal" and notified to the "user."

[1335] Input: Additional questions or requests for face-to-face consultation

[1336] Data calculation: reanalysis and reservation arrangement

[1337] Output: Updated advice, booking confirmation details

[1338] Step 7:

[1339] When the "user" inputs feedback on the advice provided, this feedback is sent to the "server" via the "terminal." The "server" optimizes the system based on the collected feedback and improves the quality of advice from the next time onwards.

[1340] Input: Feedback

[1341] Data Calculations: Feedback Analysis

[1342] Output: System optimization

[1343] 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.

[1344] MODE FOR CARRYING OUT THE INVENTION

[1345] The present invention provides an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and by combining it with an emotion engine, provides a system that provides more personalized advice.

[1346] 1. System Overview

[1347] The system allows users to input questions about their finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[1348] 2. Program processing explanation

[1349] Enter your profile

[1350] A user launches the application and enters initial profile information (age, income, family status, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[1351] Enter consultation details

[1352] Users input specific questions about household finances and insurance into a chat interface that is designed to be intuitive and easy to use.

[1353] example:

[1354] Type, "I have a child. How should I save for his or her future education?"

[1355] Question Analysis

[1356] The server analyzes the user's input using natural language processing technology. For example, it extracts keywords from the text and identifies related categories based on them. The natural language processing technology used here is based on the latest AI technology and achieves highly accurate analysis.

[1357] example:

[1358] Keywords such as "tuition fees" and "savings" are extracted, and corresponding information is searched for in the database.

[1359] Generate answers

[1360] Based on the analysis results, the server extracts relevant information from the database and generates an appropriate answer, which is then presented to the user in a format that is easy to understand.

[1361] example:

[1362] "To save for future tuition, you have the following options:

[1363] 1. Use child allowance money and save regularly every month.

[1364] 2. Take out education insurance and use a savings-type insurance product.

[1365] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[1366] Providing answers

[1367] The device displays the generated answers in the chat interface, and, if necessary, provides visual information using charts and graphs.

[1368] Emotion Recognition and Analysis

[1369] The server analyzes the user's facial expressions and voice tone using an emotion engine, which identifies the user's emotional state (e.g., anxiety, relief, etc.) and adjusts the response accordingly.

[1370] example:

[1371] If the user appears anxious, offer follow-up questions such as, "Are you worried about saving for college?"

[1372] Additional questions and further information provided

[1373] If the user has further questions or requires more information, they enter it into the chat interface again, and the server again analyzes and generates an answer based on this additional information.

[1374] 3. Guidance for face-to-face consultations

[1375] If a face-to-face consultation is deemed necessary or if the user requests one, the server automatically uses the reservation system to schedule an appointment with the nearest specialist, and the reservation details are sent to the user via their device.

[1376] 4. Gathering feedback and optimizing the system

[1377] After the consultation, the user inputs feedback on the advice provided. This feedback is sent to the server via the terminal. The server then optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[1378] Specific examples

[1379] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the server might generate the following answer:

[1380] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[1381] Additionally, if the user shows signs of anxiety while asking a question, the emotion engine will recognize this and offer additional questions or information to provide additional reassurance.

[1382] This concludes the description of the embodiment of the present invention. This system allows users to easily seek advice about household finances and insurance without feeling any psychological resistance, and also allows users to receive personalized advice based on their emotional state.

[1383] The processing flow will be explained below.

[1384] Step 1:

[1385] The user launches the application and selects the financial advice service. The application displays a profile entry screen.

[1386] Step 2:

[1387] The user enters and submits initial profile information (age, income, family composition, occupation, etc.).

[1388] Step 3:

[1389] The server stores the received profile information in a database and uses it to provide personalized advice tailored to each user's individual circumstances.

[1390] Step 4:

[1391] The user inputs specific consultation content about household finances or insurance into the chat interface and sends it.

[1392] Step 5:

[1393] The server sends the received consultation content to a natural language processing engine, which analyzes the text and extracts keywords.

[1394] Step 6:

[1395] The server searches the database for relevant data based on the extracted keywords.

[1396] Step 7:

[1397] The server uses the search results to generate the best answer, which is formatted in a user-friendly format.

[1398] Step 8:

[1399] The terminal displays the generated response on the chat interface and provides it to the user.

[1400] Step 9:

[1401] The device uses a camera and microphone to analyze the user's facial expressions and voice in real time to obtain emotional data, which is then sent to the emotion engine.

[1402] Step 10:

[1403] The server receives the user's emotional data analyzed by the emotion engine and adjusts the response based on that data. For example, if the user is anxious, the response will be more detailed and include reassuring information.

[1404] Step 11:

[1405] The server regenerates the adjusted response and sends it to the terminal.

[1406] Step 12:

[1407] The device will redisplay the adjusted response in the chat interface.

[1408] Step 13:

[1409] If the user reviews the response and has further questions or requests for more information, they can type it again into the chat interface and submit.

[1410] Step 14:

[1411] The server then sends the newly received question to the natural language processing engine again for analysis and keyword extraction, and the process repeats from step 5 to step 12.

[1412] Step 15:

[1413] If the user desires a face-to-face consultation, or if the server determines that a face-to-face consultation is necessary, a toss-up is made to the nearest specialist through the reservation system.

[1414] Step 16:

[1415] The server checks the availability of the specialist and schedules the appointment.

[1416] Step 17:

[1417] The terminal notifies the user of the reservation details (date, time, location, etc.).

[1418] Step 18:

[1419] The user inputs and submits feedback on the advice provided.

[1420] Step 19:

[1421] The server receives the feedback and stores it in a database.

[1422] Step 20:

[1423] The server analyzes the collected feedback and uses it to optimize the system.

[1424] These are the specific processing steps of the program. These steps allow users to efficiently receive the necessary advice on household finances and insurance without feeling any psychological resistance, and by using the emotion engine, they can receive personalized advice that takes into account the user's emotions.

[1425] Example 2

[1426] 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."

[1427] Conventional financial planner systems have the problem that users find it difficult to consult with them because they do not take into account the user's psychological state, and the advice provided is often not tailored to individual circumstances. The present invention aims to solve these problems and provide a system that allows users to consult about household finances and insurance without feeling any psychological resistance. It also aims to provide personalized advice tailored to the user's individual circumstances and emotional state.

[1428] 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.

[1429] In this invention, the server includes means for a user to input initial profile information, means for a user to input specific consultation details regarding household finances or insurance, means for analyzing the input consultation details using natural language processing technology, means for searching a database to generate an appropriate answer, means for recognizing and analyzing emotions from the user's facial expressions and voice, means for adjusting the answer content based on the emotion analysis results, means for providing the generated answer to the user, means for tossing up to an expert if face-to-face consultation is necessary, and means for storing all consultation details and advice in a database. This provides an environment where users can easily seek advice and makes it possible to provide personalized advice according to individual situations and emotions.

[1430] "User" means an individual who uses the system to seek advice on household finances or insurance.

[1431] "Initial profile information" refers to the basic personal information that users enter when registering with the system, including age, income, family composition, occupation, etc.

[1432] "Consultation content" refers to the specific questions or problems that users input into the system, including matters related to household finances and insurance.

[1433] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[1434] A "database" is a system or software for structuring and managing information about household finances and insurance.

[1435] "Emotion recognition" is a technology that identifies emotions from a user's facial expressions and voice, and is used to understand the user's psychological state.

[1436] The "analysis results" refer to data obtained using natural language processing technology and emotion recognition technology, and answers are generated based on this data.

[1437] The "answer content" refers to advice or suggestions to the user that the system generates based on the analysis results.

[1438] "Face-to-face consultation" refers to a consultation in which the user directly interacts with an expert.

[1439] An "expert" is someone who has knowledge and experience in household finances and insurance and provides professional advice to users.

[1440] "Toss-up" refers to the process by which the system automatically forwards a consultation request to an expert.

[1441] "Feedback" refers to the evaluations and comments that users make regarding advice and services provided by the system.

[1442] "Optimization" is the process of improving the performance of a system or the quality of advice based on collected feedback.

[1443] MODE FOR CARRYING OUT THE INVENTION

[1444] The following describes an embodiment of the present invention. This invention is an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and aims to provide more personalized advice by combining it with an emotion engine.

[1445] 1. System Overview

[1446] The system allows users to input questions about household finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[1447] 2. Enter your profile

[1448] A user launches an application and enters initial profile information (age, income, family composition, occupation, etc.), which allows the server to provide personalized advice based on the user's individual circumstances. For example, when a user enters information using a smartphone app, the data is sent from the device to the server and stored in a database.

[1449] 3. Enter your consultation details

[1450] The user types specific questions about household finances or insurance into the chat interface, which is designed to be intuitive and easy to use. For example, if the user types, "I have a child. How should I save for his or her future tuition fees?", the device sends the information to the server.

[1451] 4. Question Analysis

[1452] The server analyzes the questions it receives using natural language processing technology (such as generative AI models). Keywords are extracted from the text and related categories are identified based on these. An advanced natural language processing engine is used for the analysis, allowing for highly accurate keyword extraction and category identification.

[1453] 5. Answer Generation

[1454] The server extracts relevant information from the database based on the analysis results and generates an appropriate answer. The generated answer is provided in a format that is easy for the user to understand. For example, it could generate an answer such as, "To save for future tuition fees, you have the following options: 1. Use your child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts to aim for future returns."

[1455] 6. Providing answers

[1456] The device displays the generated answers in the chat interface, and provides visual information using charts and graphs as needed, making it easier for users to understand intuitively.

[1457] 7. Emotion Recognition and Analysis

[1458] The server analyzes the user's facial expressions and voice tone using an emotion engine. For example, the user captures their facial expressions and voice using the device's camera or microphone, and the data is sent to the server. The server analyzes the data and identifies the user's emotional state. If the user looks anxious, the server will provide follow-up questions such as, "Are you worried about saving for college?"

[1459] 8. Additional Questions and Further Information

[1460] If the user has further questions or requests for more information, they can type again into the chat interface, for example, "Can you explain in more detail?", and the device will send that information to the server, which will again analyze it and generate a response.

[1461] 9. Guidance for face-to-face consultations

[1462] If it is determined that a face-to-face consultation is necessary, or if the user requests a face-to-face consultation, the server automatically uses the reservation system to make an appointment with an expert. The reservation details are notified to the user via the terminal. For example, if the user enters "I would like to speak to an expert in person," the server will make an appointment with the nearest expert via the reservation system.

[1463] 10. Gathering feedback and optimizing the system

[1464] After the consultation, the user can enter feedback on the advice provided. For example, if the user enters "This advice was very helpful," the device will send the feedback to the server. The server will store this in a database and use the feedback to optimize the system.

[1465] Specific examples

[1466] For example, if a newly married couple who both work asks, "How should we create a savings plan?", the server will generate the following answer: "If both partners work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or a savings-type investment trust. If you are considering buying a home, it is also important to start planning early."

[1467] This concludes the description of the embodiment of the present invention. This system allows users to easily seek advice about household finances and insurance without feeling any psychological resistance, and also allows users to receive personalized advice according to their emotional state.

[1468] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1469] Step 1: Fill in your profile

[1470] The user enters initial profile information, including age, income, family composition, and occupation. This information is sent from the device to the server, which stores it in a database. Based on the information entered, the server prepares the basic data for generating personalized advice.

[1471] Specific behavior:

[1472] The user launches the smartphone app and enters the required information into the displayed form.

[1473] The terminal transmits the input information to the server.

[1474] The server stores the received information in a database and creates a user profile.

[1475] Input: Age, income, family structure, occupation

[1476] Output: Create a user profile

[1477] Step 2: Enter your consultation details

[1478] The user enters questions about household finances or insurance into the chat interface. This information is sent from the device to the server, which then passes the received question data to the next analysis step.

[1479] Specific behavior:

[1480] User: Type "I have a child, how should I save for his or her future education?"

[1481] The device sends the question to the server.

[1482] Input: Question text (e.g., "I have a child. How should I save for his or her future education?")

[1483] Output: Received query data

[1484] Step 3: Parsing the Question

[1485] The server analyzes the received question using natural language processing techniques (such as generative AI models). This analysis involves extracting keywords from the text and identifying related categories. The analysis results are used in the next step of generating an answer.

[1486] Specific behavior:

[1487] The server inputs the question text into a natural language processing engine and extracts keywords.

[1488] The NLP (natural language processing) engine extracts important keywords such as "tuition fees" and "savings" and identifies categories based on these.

[1489] Input: Question data

[1490] Output: Analysis results (extracted keywords and related categories)

[1491] Step 4: Generate an answer

[1492] The server uses the analysis results to extract relevant information from the database and generate an appropriate answer, which is then formatted in a way that is easy for the user to understand.

[1493] Specific behavior:

[1494] The server accesses the database and searches for relevant information based on the extracted keywords.

[1495] The server uses the search results and the generative AI model to generate an answer.

[1496] The answer can be formatted as follows: "To save for future school fees, you have the following options: 1. Use your child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts, aiming for future returns."

[1497] Input: Analysis results (extracted keywords and related categories)

[1498] Output: The generated answer

[1499] Step 5: Provide your answers

[1500] The device displays the generated answers in the chat interface, and, if necessary, provides visual information using charts and graphs.

[1501] Specific behavior:

[1502] The server sends the generated response to the terminal.

[1503] The device displays the received response in the chat interface.

[1504] Input: Generated Answer

[1505] Output: Displayed answer

[1506] Step 6: Emotion recognition and analysis

[1507] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to determine the user's emotional state.

[1508] Specific behavior:

[1509] The device captures the user's facial expressions and voice using a camera and microphone and sends the data to a server.

[1510] The server analyzes the data using an emotion recognition engine to identify the user's emotional state (e.g., anxiety, relief).

[1511] Input: facial expression data, voice data

[1512] Output: Emotion analysis results

[1513] Step 7: Adjust your responses based on emotion

[1514] The server adjusts the response content based on the results of emotion analysis. If the user is anxious, the response content is adjusted to provide additional information or reassurance.

[1515] Specific behavior:

[1516] The server reevaluates the response based on the results of the sentiment analysis.

[1517] Providing follow-up questions or information such as, "Do you have any concerns about saving for college?"

[1518] Input: Sentiment analysis results

[1519] Output: Adjusted answer

[1520] Step 8: Ask additional questions and provide further information

[1521] If the user has further questions or requests for more information, they enter it into the chat interface again, and this information is again sent from the device to the server for re-analysis and generation of an answer.

[1522] Specific behavior:

[1523] User: Type, "Can you explain that in more detail?"

[1524] The device sends new information to the server, which analyzes it again and generates a response.

[1525] Input: Text of follow-up question

[1526] Output: Provides detailed information

[1527] Step 9: Guide to face-to-face consultation

[1528] If the server determines that a face-to-face consultation is necessary, it will use the reservation system to make an appointment with a specialist, and notify the user of the reservation details via their device.

[1529] Specific behavior:

[1530] User: Type "I'd like to speak to an expert in real life."

[1531] The server accesses the reservation system and makes an appointment with the nearest specialist.

[1532] The device will notify you of the reservation details.

[1533] Input: Request for face-to-face consultation

[1534] Output: Reservation details notification

[1535] Step 10: Gather feedback and optimize

[1536] The user inputs feedback on the advice provided, and the device sends it to the server, which stores the feedback in a database and uses it to optimize the system.

[1537] Specific behavior:

[1538] User: Type "This advice was very helpful."

[1539] The device sends the feedback to the server.

[1540] The server stores the feedback in a database and uses it to generate advice next time.

[1541] Input: Feedback text

[1542] Output: Saved feedback

[1543] (Application example 2)

[1544] 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."

[1545] Conventional financial planner systems provide uniform answers without taking into account the user's emotional state, which can cause users to feel psychological resistance. Furthermore, ignoring the user's emotional state can lead to users not receiving satisfactory answers or advice, which can reduce the usefulness of the system.

[1546] 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 a means for inputting the user's initial profile information, a means for the user to input specific consultation details regarding household finances or insurance, a means for analyzing the input consultation details using natural language processing technology, a means for searching a database to generate an appropriate answer, a means for providing the generated answer to the user, a means for tossing up to an expert if face-to-face consultation is necessary, a means for saving all consultation details and advice in a database, a means for recognizing and analyzing the user's emotional state, and a means for adjusting the answer based on the recognized emotional state. This makes it possible to provide personalized advice according to the user's emotional state.

[1547] "User" refers to an individual who uses this system to seek advice on household finances and insurance.

[1548] "Initial profile information" refers to basic information such as the user's age, income, family composition, and occupation.

[1549] "Consultation content" refers to specific questions that users have about household finances and insurance.

[1550] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[1551] "Database" refers to a digital record device for storing financial and insurance information and advice.

[1552] "Emotional state" refers to the psychological state recognized from the user's facial expressions and voice.

[1553] "Analyze" refers to the act of analyzing input information and extracting its meaning and key points.

[1554] "Response" refers to appropriate advice or information in response to the user's inquiry.

[1555] "Tossing up" refers to the act of transferring the user's consultation to an expert as needed.

[1556] "Storing" refers to the act of recording and keeping data for later use.

[1557] "Adjust" refers to the act of changing or optimizing content according to circumstances or conditions.

[1558] MODE FOR CARRYING OUT THE INVENTION

[1559] This invention provides an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and by combining it with an emotion engine, provides a system that provides more personalized advice.

[1560] System Overview

[1561] The system is implemented as a smartphone application, allowing users to input questions about household finances and insurance, and automatically generates appropriate answers based on those questions. It also has a function to guide users to face-to-face consultations if necessary. It also has a function to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[1562] Hardware and Software Used

[1563] Smartphone: Provides user interface and some data processing.

[1564] EmotionEngine: Software for recognizing the user's emotional state.

[1565] NLPProcessor (natural language processing engine): Software for analyzing the content of users' inquiries.

[1566] FinancialAdvisor: Software for generating appropriate financial advice.

[1567] ChatBotInterface: Software that provides interaction between users and systems.

[1568] Processing flow

[1569] 1. Fill in your profile:

[1570] When a user launches the smartphone application, they enter their initial profile information (age, income, family composition, occupation, etc.) This profile information is used by the system to provide appropriate advice to the user.

[1571] 2. Enter your consultation details:

[1572] Users type specific financial and insurance questions into a chat interface that is intuitive and easy to use.

[1573] 3. Question Analysis:

[1574] The server uses an NLPProcessor to analyze the user's input and extract relevant keywords, which then identify the question category.

[1575] 4. Generate answers:

[1576] The server uses FinancialAdvisor to extract relevant information from the database and generate appropriate answers based on the analysis results.

[1577] 5. Emotional awareness and regulation:

[1578] The server uses the Emotion Engine to analyze the user's facial expressions and tone of voice to recognize their emotional state, and responds accordingly.

[1579] 6. Providing answers:

[1580] The generated answers are provided to the user through a chat interface on their smartphone, and may also include appropriate charts and graphs.

[1581] 7. Additional Questions and Face-to-Face Consultations:

[1582] If the user has further questions or requires more information, they enter it into the chat interface again. The server uses this additional information to analyze and generate a new answer. If necessary, an appointment with an expert will be scheduled.

[1583] 8. Feedback and optimization:

[1584] After the consultation, the user inputs feedback on the advice provided. The server optimizes the system based on the collected feedback and improves the quality of advice from the next time onwards.

[1585] Specific examples

[1586] For example, if a 35-year-old married user with one child and an annual income of 5 million yen asks, "I have a child. How should I save for his / her future tuition fees?", the system will generate the following answer:

[1587] "To save for future tuition, you have the following options:

[1588] 1. Use child allowance money and save regularly every month.

[1589] 2. Take out education insurance and use a savings-type insurance product.

[1590] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[1591] Additionally, if the user displays an anxious expression, the emotion engine will recognize this and provide additional questions or information to provide additional reassurance.

[1592] Example prompt sentence:

[1593] "Based on the profile of a user who is 35 years old, married (with one child) and earns ¥5 million a year, generate personalized financial advice for the question, 'How should I save for future college fees?'"

[1594] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1595] Step 1:

[1596] A user launches the smartphone application and enters initial profile information (age, income, family composition, occupation, etc.). The device sends the entered information to the server, which stores it in the user database. Future advice is personalized based on this input.

[1597] Step 2:

[1598] The user inputs specific questions about household finances or insurance into the chat interface. The device receives this input data and sends it to the server. The server uses natural language processing technology (NLPProcessor) to analyze the input consultation content and extract important keywords. The input question is imported as text data, and keywords such as "tuition fees" and "savings" are extracted.

[1599] Step 3:

[1600] The server searches a database to generate appropriate answers based on the extracted keywords. It retrieves relevant information from the database and generates personalized answers that take into account the user's profile information based on the analysis results. For example, it generates specific financial advice such as "How to save for future tuition fees."

[1601] Step 4:

[1602] The server uses the Emotion Engine to recognize the user's emotional state. It analyzes facial and voice data provided by the user through the camera and microphone to identify the user's emotional state (e.g., relief, anxiety). Based on this recognized emotional state, the generated answer is adjusted appropriately. For example, if the user has an anxious expression, the server adds supplementary information that provides additional reassurance.

[1603] Step 5:

[1604] The server then sends the tailored answers to the user's device, where they are displayed in the smartphone chat interface, allowing the user to view the information in an easy-to-understand format, with visual information such as charts and graphs provided as needed.

[1605] Step 6:

[1606] If the user has further questions or requires more information, they enter it again into the chat interface. The device sends the new question to the server, which again analyzes and generates an answer. The process repeats, and if necessary, a face-to-face consultation with an expert is scheduled.

[1607] Step 7:

[1608] After the consultation, the user enters feedback on the advice provided into the chat interface. The device sends this feedback to the server, which then optimizes the system based on the collected feedback. This feedback improves the quality of advice provided in the future.

[1609] Specific examples

[1610] For example, if a newlywed user asks, "How should I create a savings plan?", the server generates an answer such as, "If both spouses work, we recommend that you save 20% of your income each month. In addition, it would be beneficial to consider individual pension insurance or a savings-type investment trust." If the user shows an anxious expression, the server will provide an additional question: "Is there anything you are worried about?"

[1611] Example prompt sentence:

[1612] "Based on the profile of a user who is 35 years old, married (with one child) and earns ¥5 million a year, generate personalized financial advice for the question, 'How should I save for future college fees?'"

[1613] 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.

[1614] 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.

[1615] 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.

[1616] [Fourth embodiment]

[1617] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1618] 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.

[1619] 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).

[1620] 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.

[1621] 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.

[1622] 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).

[1623] 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.

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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."

[1630] MODE FOR CARRYING OUT THE INVENTION

[1631] The present invention provides an embodiment of an AI-based financial planner system that allows users to receive advice on household finances and insurance without feeling any psychological resistance.

[1632] 1. System Overview

[1633] The system allows users to input questions about their household finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback.

[1634] 2. Program processing explanation

[1635] Enter your profile

[1636] A user launches the application and enters initial profile information (age, income, family status, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[1637] Enter consultation details

[1638] Users input specific questions about household finances and insurance into a chat interface that is designed to be intuitive and easy to use.

[1639] example:

[1640] Type, "I have a child. How should I save for his or her future education?"

[1641] Question Analysis

[1642] The server analyzes the user's input using natural language processing technology. For example, it extracts keywords from the text and identifies related categories based on them. The natural language processing technology used here is based on the latest AI technology and achieves highly accurate analysis.

[1643] example:

[1644] Keywords such as "tuition fees" and "savings" are extracted, and corresponding information is searched for in the database.

[1645] Generate answers

[1646] Based on the analysis results, the server extracts relevant information from the database and generates an appropriate answer, which is then presented to the user in a format that is easy to understand.

[1647] example:

[1648] "To save for future tuition, you have the following options:

[1649] 1. Use child allowance money and save regularly every month.

[1650] 2. Take out education insurance and use a savings-type insurance product.

[1651] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[1652] Providing answers

[1653] The device displays the generated answers in a chat interface, and, if necessary, provides visual information using charts and graphs.

[1654] Additional questions and further information provided

[1655] If the user has further questions or requires more information, they enter it into the chat interface again, and the server again analyzes and generates an answer based on this additional information.

[1656] 3. Guidance for face-to-face consultations

[1657] If a face-to-face consultation is deemed necessary or if the user requests one, the server automatically uses the reservation system to schedule an appointment with the nearest specialist, and the reservation details are sent to the user via their device.

[1658] 4. Gathering feedback and optimizing the system

[1659] After the consultation, the user inputs feedback on the advice provided. This feedback is sent to the server via the terminal. The server then optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[1660] Specific examples

[1661] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the server might generate the following answer:

[1662] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[1663] The above is an embodiment of the present invention. With this system, users can easily ask for advice on household finances and insurance without feeling any psychological resistance, and can receive the latest, personalized advice.

[1664] The processing flow will be explained below.

[1665] Step 1:

[1666] The user launches the application and selects the financial advice service. The application displays a profile entry screen.

[1667] Step 2:

[1668] The user enters and submits initial profile information (age, income, family composition, occupation, etc.).

[1669] Step 3:

[1670] The server stores the received profile information in a database and uses it to provide personalized advice tailored to each user's individual circumstances.

[1671] Step 4:

[1672] The user inputs specific consultation content about household finances or insurance into the chat interface and sends it.

[1673] Step 5:

[1674] The server sends the received consultation content to a natural language processing engine, which analyzes the text and extracts keywords.

[1675] Step 6:

[1676] The server searches the database for relevant data based on the extracted keywords.

[1677] Step 7:

[1678] The server uses the search results to generate the best answer, which is formatted in a user-friendly format.

[1679] Step 8:

[1680] The terminal displays the generated response on the chat interface and provides it to the user.

[1681] Step 9:

[1682] If the user reviews the response and has further questions or requests for more information, they can type it again into the chat interface and submit.

[1683] Step 10:

[1684] The server then sends the newly received question back to the natural language processing engine for analysis and keyword extraction.

[1685] Step 11:

[1686] The server generates a new answer and serves it back to the user.

[1687] Step 12:

[1688] If the user desires a face-to-face consultation, or if the server determines that a face-to-face consultation is necessary, a toss-up is made to the nearest specialist through the reservation system.

[1689] Step 13:

[1690] The server checks the availability of the specialist and schedules the appointment.

[1691] Step 14:

[1692] The terminal notifies the user of the reservation details (date, time, location, etc.).

[1693] Step 15:

[1694] The user inputs and submits feedback on the advice provided.

[1695] Step 16:

[1696] The server receives the feedback and stores it in a database.

[1697] Step 17:

[1698] The server analyzes the collected feedback and uses it to optimize the system.

[1699] The above are the specific processing steps of the program, which allow the user to efficiently receive necessary advice on household finances and insurance without feeling any psychological resistance.

[1700] Example 1

[1701] 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."

[1702] Currently, many people feel psychologically reluctant to seek advice about household finances or insurance. Furthermore, receiving appropriate advice often requires a face-to-face consultation, which places a time burden on users. Furthermore, the use of feedback to improve the quality of the advice provided is insufficient. For these reasons, there is a need for a system that allows users to easily seek advice about household finances and insurance and receive high-quality advice.

[1703] 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.

[1704] In this invention, the server includes a means for a user to input initial profile information, a means for a user to input specific consultation details regarding household finances or insurance, a means for analyzing the input consultation details using natural language processing technology, a means for searching a database to generate an appropriate answer using a generative AI model, a means for providing the generated answer to the user, a means for tossing up to an expert if face-to-face consultation is necessary, a means for storing all consultation details and advice in a database, a means for a user to input feedback on the advice provided, and a means for optimizing the system and improving the accuracy of prompt sentences based on the feedback. This enables users to receive high-quality advice regarding household finances or insurance without feeling any psychological resistance.

[1705] ---

[1706] "User" refers to an individual who uses this system to seek advice on household finances or insurance.

[1707] "Initial profile information" refers to basic information about the user, such as age, income, family composition, and occupation.

[1708] "Specific consultation content regarding household finances or insurance" refers to specific questions or concerns regarding household finances or insurance that users enter through the system.

[1709] "Natural language processing technology" refers to technology used to analyze input text information and understand its meaning.

[1710] A "generative AI model" refers to an artificial intelligence model that generates optimal answers based on data analyzed using natural language processing technology.

[1711] "Database" refers to a data storage system that stores financial and insurance information.

[1712] "Face-to-face consultation" refers to a consultation where the user meets directly with a specialist.

[1713] An "expert" is someone who has knowledge and experience in household finances and insurance and is qualified to provide face-to-face consultations.

[1714] "Toss-up" refers to the process by which the system automatically schedules an appointment with a specialist when it determines that a face-to-face consultation is necessary.

[1715] "Feedback" refers to the evaluation or impressions that a user enters regarding the advice provided.

[1716] A "prompt" refers to a question or instruction input to a generative AI model.

[1717] "Optimization" refers to the process of improving the overall performance of the system and the accuracy of responses based on feedback.

[1718] ---

[1719] These are the definitions of important words that will help clarify the scope of your patent claims.

[1720] ---

[1721] MODE FOR CARRYING OUT THE INVENTION

[1722] overview

[1723] This invention is an AI-based financial planner system that allows users to receive advice on household finances and insurance without feeling any psychological resistance. Based on the initial profile information and specific consultation content entered by the user, the system uses a generative AI model to generate appropriate answers and, if necessary, guides the user to face-to-face consultations with experts.

[1724] System configuration

[1725] The system consists of a server, a terminal, and a user. The server generates answers using advanced natural language processing technology and generative AI models, and provides them to the user via the terminal.

[1726] Hardware and software used

[1727] Hardware: Servers (high-performance processors, memory, large-capacity storage), devices (smartphones, tablets, PCs)

[1728] Software: Natural language processing libraries, generative AI models, database management systems, chat interface platforms

[1729] Enter your profile

[1730] The user launches the application and enters initial profile information, such as age, income, family composition, and occupation, which is then validated in real time by the device and sent to the server.

[1731] Enter consultation details

[1732] A user types a specific financial or insurance question into the chat interface, for example, "I have a new baby. How should I save for his or her future education?" The device encodes this input and sends it to the server.

[1733] Parsing questions and generating answers

[1734] The server analyzes the input question using natural language processing technology. Specifically, it extracts keywords from the text and searches a database for related information. A generative AI model then uses that information to generate the optimal answer. For example, "To save for future tuition fees, you have the following options:

[1735] 1. Use child allowance money and save regularly every month.

[1736] 2. Take out education insurance and use a savings-type insurance product.

[1737] 3. I will open a NISA account and aim for future returns by investing in stocks and investment trusts."

[1738] Providing answers

[1739] The device displays the responses received from the server in the chat interface, providing visual information using graphs and charts as needed.

[1740] Additional questions and further information provided

[1741] If the user has further questions or requests for more information, they enter it again into the chat interface, and the server re-parses the added questions and generates appropriate answers.

[1742] Guidance for face-to-face consultation

[1743] The server analyzes the user's consultation content and profile to determine whether a face-to-face consultation is necessary. If a face-to-face consultation is determined to be necessary, the server sends reservation information to the nearest specialist through the reservation system and notifies the user of the reservation details via their terminal.

[1744] Gathering feedback and optimizing the system

[1745] The user inputs feedback on the provided advice. The device encodes the feedback and sends it to the server. The server aggregates the feedback and optimizes the entire system. Specifically, it adjusts the generative AI model to improve the accuracy of prompt sentences.

[1746] ---

[1747] The above is a specific example of how to implement the invention. This system allows users to receive high-quality advice on household finances and insurance without feeling any psychological resistance. It also allows users to smoothly connect with experts when face-to-face consultation is necessary.

[1748] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1749] ---

[1750] Step 1: Fill in your profile

[1751] A user launches the application and enters their initial profile information, such as age, income, family composition, and occupation. The device validates the input in real time to ensure it is in the correct format. The profile information is then sent from the device to the server and stored in a database.

[1752] Input: User's initial profile information (age, income, family composition, occupation, etc.).

[1753] Data processing / calculation: Validation is performed.

[1754] Output: Validated profile information stored in database.

[1755] Step 2: Enter your consultation details

[1756] Users enter specific financial or insurance questions into the chat interface, which the system uses to provide an intuitive and user-friendly interface design. The device encodes the questions and sends them to the server.

[1757] Input: User's inquiry (e.g., question about child's future tuition fees).

[1758] Data processing / computation: Encoding of input text.

[1759] Output: The encoded consultation content is sent to the server.

[1760] Step 3: Parsing the Question

[1761] The server analyzes the received inquiry using natural language processing technology. Specifically, it uses a generative AI model to extract keywords from the text and identify related categories. Based on the extracted keywords, it searches a database for related information.

[1762] Input: The encoded consultation content.

[1763] Data processing / calculation: Keyword extraction and category identification using natural language processing.

[1764] Output: Related categories and keywords.

[1765] Step 4: Generate an answer

[1766] The server searches the database based on the analysis results and generates an appropriate answer. A generative AI model is used to generate an answer in a format that is easy for the user to understand. Specifically, it creates an answer in the form of, "To save for future tuition fees, you have the following options: 1. Use child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts to aim for future returns."

[1767] Input: relevant categories and keywords.

[1768] Data processing / computation: Generating answers using generative AI models.

[1769] Output: The generated answer.

[1770] Step 5: Provide your answers

[1771] The device displays the generated answers in the chat interface, possibly using graphs or charts to visually supplement the answers, if necessary.

[1772] Input: The generated answer.

[1773] Data processing / calculation: Visual supplement to answers.

[1774] Output: The response displayed in the chat interface.

[1775] Step 6: Ask additional questions and provide more information

[1776] If the user has further questions or requires more information, they enter it into the chat interface again, and the device encodes the additional questions and sends them to the server, repeating the process of parsing the questions and generating answers.

[1777] Input: User's additional question.

[1778] Data processing / computation: Encoding input text, parsing questions, and generating answers.

[1779] Output: The generated answer.

[1780] Step 7: Guide to face-to-face consultation

[1781] The server analyzes the user's consultation content and profile, and if it determines that a face-to-face consultation is necessary, it sends reservation information to the nearest specialist using the reservation system. The reservation details are then notified to the user via their terminal.

[1782] Input: User's consultation content, profile.

[1783] Data processing / calculation: Analysis and generation of reservation information.

[1784] Output: Notification of reservation information.

[1785] Step 8: Gather feedback and optimize the system

[1786] The user inputs feedback on the provided advice and sends it to the server via their device. The server then uses the collected feedback to optimize the generative AI model and the entire system. Specifically, it makes adjustments to improve the accuracy of prompt sentences.

[1787] Input: User feedback.

[1788] Data processing / calculation: Feedback collection and analysis, system optimization.

[1789] Output: Optimized generative AI models and systems.

[1790] ---

[1791] The above are the specific processing steps and details of the system, which allows users to receive high-quality advice on household finances and insurance.

[1792] (Application example 1)

[1793] 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."

[1794] With conventional financial planner systems, users often felt psychological resistance when receiving face-to-face consultations, and specialized knowledge was often required to receive appropriate advice. Furthermore, the lack of visual and audio support often made the system difficult to understand.

[1795] 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.

[1796] In this invention, the server includes: means for a user to input initial profile information; means for a user to input specific consultation details regarding household finances or insurance; means for analyzing the input consultation details using natural language processing technology; means for searching a database to generate an appropriate answer; means for providing the generated answer to the user; means for tossing up to an expert if face-to-face consultation is necessary; means for saving all consultation details and advice in a database; speech synthesis means for reading out responses aloud; reservation management means for making appointments for face-to-face consultations; and means for using augmented reality technology to provide visual information. This enables users to receive easy-to-understand financial consultations supported visually and audibly without feeling any psychological resistance.

[1797] "User" means an individual user of the Financial Planner System.

[1798] "Initial profile information" refers to the user's basic attribute information, such as age, income, family composition, and occupation.

[1799] "Specific consultation content" refers to specific matters or questions that users want to ask about their household finances or insurance.

[1800] "Natural language processing technology" is a technology for analyzing and understanding input text data and generating appropriate responses.

[1801] A "database" is a collection of information that manages accumulated information and allows necessary information to be quickly searched.

[1802] "Generating a response" refers to the process of creating an appropriate answer to a user's question.

[1803] "Speech synthesis means" refers to technology or equipment that converts text information into speech.

[1804] "Appointment management tools" means the functionality and technology used to schedule and manage appointments for in-person consultations.

[1805] "Augmented reality technology" is a technology that overlays computer-generated visual information onto the real world.

[1806] This invention provides an embodiment of an AI-based financial planner system that allows users to receive consultations regarding household finances and insurance without feeling any psychological resistance. This system provides an interface through which users can input initial profile information and specific consultation details regarding household finances and insurance. The system of the present invention is implemented using the following hardware and software.

[1807] 1. System Overview

[1808] The system uses "natural language processing technology" to analyze the inputted consultation content and generates an appropriate answer from a "database." Users input questions about household finances or insurance, and the system automatically generates an appropriate answer based on the input. In addition, if a face-to-face consultation is required, the system also includes a function to toss up to an expert using "reservation management means." Furthermore, the generated answer is provided in audio format using "speech synthesis means," and visual information is provided using "augmented reality technology."

[1809] 2. Program processing explanation

[1810] Hardware and Software

[1811] Hardware:

[1812] Touchscreen

[1813] camera

[1814] microphone

[1815] speaker

[1816] Smart Glasses

[1817] software:

[1818] Python

[1819] Natural Language Processing model (Hugging Face's Transformers library)

[1820] TfidfVectorizer (skill extraction)

[1821] SQLite (database management)

[1822] pyttsx3 (speech synthesis)

[1823] Enter user profile information

[1824] First, a "user" launches the system and enters initial profile information (age, income, family composition, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[1825] Input and analysis of consultation details

[1826] When a "user" enters a specific question about household finances or insurance into the chat interface, the input is sent to the "server," which then uses natural language processing technology to analyze the question and extract keywords, thereby identifying the appropriate category.

[1827] Answer generation and speech synthesis

[1828] The "server" searches for relevant information from a database based on the extracted keywords and generates an appropriate answer. The generated answer is not only provided to the "user" via the "terminal" but also read aloud using the "speech synthesis means."

[1829] Use of Augmented Reality Technology

[1830] Furthermore, when visual information is provided, it is displayed on the smart glasses using "augmented reality technology," allowing users to intuitively understand the advice.

[1831] Book a face-to-face consultation

[1832] If a face-to-face consultation is deemed necessary or if the user requests one, the "server" will automatically use the reservation system to schedule an appointment with the nearest specialist, and the reservation details will be sent to the user via the "terminal."

[1833] Gathering feedback and optimizing the system

[1834] After the consultation is completed, the "user" inputs feedback on the advice provided. This feedback is sent to the "server" via the "terminal." The "server" optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[1835] Specific examples

[1836] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the "server" generates the following answer:

[1837] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[1838] Example prompt

[1839] "How can I save for my child's school fees?"

[1840] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1841] Program processing steps

[1842] Step 1:

[1843] The "user" starts the system and enters initial profile information (age, income, family composition, occupation, etc.) using a touchscreen or voice input. This information is sent to the "server," which then provides personalized advice based on the user's individual circumstances.

[1844] Input: Initial profile information (age, income, family composition, occupation)

[1845] Output: Profile data stored on the server

[1846] Step 2:

[1847] "Users" type specific questions about finances or insurance into a chat interface, which are sent to a "server" and prepared for analysis.

[1848] Input: Specific questions about finances or insurance

[1849] Output: Questions saved on the server

[1850] Step 3:

[1851] The "server" analyzes the input question using natural language processing techniques. It uses TensorFlow and Hugging Face's Transformers library to extract key keywords from the question. The results of this analysis are used in the next step.

[1852] Input: Question

[1853] Data processing: Extracting keywords using natural language processing technology

[1854] Output: Extracted keywords

[1855] Step 4:

[1856] The "server" searches for relevant information from the SQLite database based on the extracted keywords and generates an appropriate answer, which is then sent to the "terminal" in text format.

[1857] Input: Extracted keywords

[1858] Data Computing: Database Search and Answer Generation

[1859] Output: Textual response

[1860] Step 5:

[1861] The "terminal" displays the generated answer. At the same time, the "speech synthesis means" uses the pyttsx3 library to read the answer aloud. Visual information is also provided using "augmented reality technology" if necessary.

[1862] Input: Text response

[1863] Data processing: converting text to speech, generating visual information

[1864] Output: Audio and visual information

[1865] Step 6:

[1866] If the "user" asks a more detailed question or requests a face-to-face consultation, the "server" analyzes the question again, updates the necessary information, and provides appropriate advice. If it determines that a face-to-face consultation is necessary, the "appointment management means" is automatically used to toss up the request to an expert and make a consultation appointment. The appointment details are displayed on the "terminal" and notified to the "user."

[1867] Input: Additional questions or requests for face-to-face consultation

[1868] Data calculation: reanalysis and reservation arrangement

[1869] Output: Updated advice, booking confirmation details

[1870] Step 7:

[1871] When the "user" inputs feedback on the advice provided, this feedback is sent to the "server" via the "terminal." The "server" optimizes the system based on the collected feedback and improves the quality of advice from the next time onwards.

[1872] Input: Feedback

[1873] Data Calculations: Feedback Analysis

[1874] Output: System optimization

[1875] 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.

[1876] MODE FOR CARRYING OUT THE INVENTION

[1877] The present invention provides an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and by combining it with an emotion engine, provides a system that provides more personalized advice.

[1878] 1. System Overview

[1879] The system allows users to input questions about their finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[1880] 2. Program processing explanation

[1881] Enter your profile

[1882] A user launches the application and enters initial profile information (age, income, family status, occupation, etc.), which allows the system to provide personalized advice based on the user's individual situation.

[1883] Enter consultation details

[1884] Users input specific questions about household finances and insurance into a chat interface that is designed to be intuitive and easy to use.

[1885] example:

[1886] Type, "I have a child. How should I save for his or her future education?"

[1887] Question Analysis

[1888] The server analyzes the user's input using natural language processing technology. For example, it extracts keywords from the text and identifies related categories based on them. The natural language processing technology used here is based on the latest AI technology and achieves highly accurate analysis.

[1889] example:

[1890] Keywords such as "tuition fees" and "savings" are extracted, and corresponding information is searched for in the database.

[1891] Generate answers

[1892] Based on the analysis results, the server extracts relevant information from the database and generates an appropriate answer, which is then presented to the user in a format that is easy to understand.

[1893] example:

[1894] "To save for future tuition, you have the following options:

[1895] 1. Use child allowance money and save regularly every month.

[1896] 2. Take out education insurance and use a savings-type insurance product.

[1897] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[1898] Providing answers

[1899] The device displays the generated answers in the chat interface, and, if necessary, provides visual information using charts and graphs.

[1900] Emotion Recognition and Analysis

[1901] The server analyzes the user's facial expressions and voice tone using an emotion engine, which identifies the user's emotional state (e.g., anxiety, relief, etc.) and adjusts the response accordingly.

[1902] example:

[1903] If the user appears anxious, offer follow-up questions such as, "Are you worried about saving for college?"

[1904] Additional questions and further information provided

[1905] If the user has further questions or requires more information, they enter it into the chat interface again, and the server again analyzes and generates an answer based on this additional information.

[1906] 3. Guidance for face-to-face consultations

[1907] If a face-to-face consultation is deemed necessary or if the user requests one, the server automatically uses the reservation system to schedule an appointment with the nearest specialist, and the reservation details are sent to the user via their device.

[1908] 4. Gathering feedback and optimizing the system

[1909] After the consultation, the user inputs feedback on the advice provided. This feedback is sent to the server via the terminal. The server then optimizes the system based on the collected feedback, improving the quality of advice provided in the future.

[1910] Specific examples

[1911] For example, if a newly married, dual-income user asks, "How should we create a savings plan?" the server might generate the following answer:

[1912] "If both spouses work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or savings-type investment trusts. If you are considering buying a home, it is also important to start planning early."

[1913] Additionally, if the user shows signs of anxiety while asking a question, the emotion engine will recognize this and offer additional questions or information to provide additional reassurance.

[1914] This concludes the description of the embodiment of the present invention. This system allows users to easily seek advice about household finances and insurance without feeling any psychological resistance, and also allows users to receive personalized advice based on their emotional state.

[1915] The processing flow will be explained below.

[1916] Step 1:

[1917] The user launches the application and selects the financial advice service. The application displays a profile entry screen.

[1918] Step 2:

[1919] The user enters and submits initial profile information (age, income, family composition, occupation, etc.).

[1920] Step 3:

[1921] The server stores the received profile information in a database and uses it to provide personalized advice tailored to each user's individual circumstances.

[1922] Step 4:

[1923] The user inputs specific consultation content about household finances or insurance into the chat interface and sends it.

[1924] Step 5:

[1925] The server sends the received consultation content to a natural language processing engine, which analyzes the text and extracts keywords.

[1926] Step 6:

[1927] The server searches the database for relevant data based on the extracted keywords.

[1928] Step 7:

[1929] The server uses the search results to generate the best answer, which is formatted in a user-friendly format.

[1930] Step 8:

[1931] The terminal displays the generated response on the chat interface and provides it to the user.

[1932] Step 9:

[1933] The device uses a camera and microphone to analyze the user's facial expressions and voice in real time to obtain emotional data, which is then sent to the emotion engine.

[1934] Step 10:

[1935] The server receives the user's emotional data analyzed by the emotion engine and adjusts the response based on that data. For example, if the user is anxious, the response will be more detailed and include reassuring information.

[1936] Step 11:

[1937] The server regenerates the adjusted response and sends it to the terminal.

[1938] Step 12:

[1939] The device will redisplay the adjusted response in the chat interface.

[1940] Step 13:

[1941] If the user reviews the response and has further questions or requests for more information, they can type it again into the chat interface and submit.

[1942] Step 14:

[1943] The server then sends the newly received question to the natural language processing engine again for analysis and keyword extraction, and the process repeats from step 5 to step 12.

[1944] Step 15:

[1945] If the user desires a face-to-face consultation, or if the server determines that a face-to-face consultation is necessary, a toss-up is made to the nearest specialist through the reservation system.

[1946] Step 16:

[1947] The server checks the availability of the specialist and schedules the appointment.

[1948] Step 17:

[1949] The terminal notifies the user of the reservation details (date, time, location, etc.).

[1950] Step 18:

[1951] The user inputs and submits feedback on the advice provided.

[1952] Step 19:

[1953] The server receives the feedback and stores it in a database.

[1954] Step 20:

[1955] The server analyzes the collected feedback and uses it to optimize the system.

[1956] These are the specific processing steps of the program. These steps allow users to efficiently receive the necessary advice on household finances and insurance without feeling any psychological resistance, and by using the emotion engine, they can receive personalized advice that takes into account the user's emotions.

[1957] Example 2

[1958] 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."

[1959] Conventional financial planner systems have the problem that users find it difficult to consult with them because they do not take into account the user's psychological state, and the advice provided is often not tailored to individual circumstances. The present invention aims to solve these problems and provide a system that allows users to consult about household finances and insurance without feeling any psychological resistance. It also aims to provide personalized advice tailored to the user's individual circumstances and emotional state.

[1960] 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.

[1961] In this invention, the server includes means for a user to input initial profile information, means for a user to input specific consultation details regarding household finances or insurance, means for analyzing the input consultation details using natural language processing technology, means for searching a database to generate an appropriate answer, means for recognizing and analyzing emotions from the user's facial expressions and voice, means for adjusting the answer content based on the emotion analysis results, means for providing the generated answer to the user, means for tossing up to an expert if face-to-face consultation is necessary, and means for storing all consultation details and advice in a database. This provides an environment where users can easily seek advice and makes it possible to provide personalized advice according to individual situations and emotions.

[1962] "User" means an individual who uses the system to seek advice on household finances or insurance.

[1963] "Initial profile information" refers to the basic personal information that users enter when registering with the system, including age, income, family composition, occupation, etc.

[1964] "Consultation content" refers to the specific questions or problems that users input into the system, including matters related to household finances and insurance.

[1965] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[1966] A "database" is a system or software for structuring and managing information about household finances and insurance.

[1967] "Emotion recognition" is a technology that identifies emotions from a user's facial expressions and voice, and is used to understand the user's psychological state.

[1968] The "analysis results" refer to data obtained using natural language processing technology and emotion recognition technology, and answers are generated based on this data.

[1969] The "answer content" refers to advice or suggestions to the user that the system generates based on the analysis results.

[1970] "Face-to-face consultation" refers to a consultation in which the user directly interacts with an expert.

[1971] An "expert" is someone who has knowledge and experience in household finances and insurance and provides professional advice to users.

[1972] "Toss-up" refers to the process by which the system automatically forwards a consultation request to an expert.

[1973] "Feedback" refers to the evaluations and comments that users make regarding advice and services provided by the system.

[1974] "Optimization" is the process of improving the performance of a system or the quality of advice based on collected feedback.

[1975] MODE FOR CARRYING OUT THE INVENTION

[1976] The following describes an embodiment of the present invention. This invention is an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and aims to provide more personalized advice by combining it with an emotion engine.

[1977] 1. System Overview

[1978] The system allows users to input questions about household finances and insurance, and automatically generates appropriate answers based on those questions. It also includes a function to guide users to face-to-face consultations if necessary. It also has the ability to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[1979] 2. Enter your profile

[1980] A user launches an application and enters initial profile information (age, income, family composition, occupation, etc.), which allows the server to provide personalized advice based on the user's individual circumstances. For example, when a user enters information using a smartphone app, the data is sent from the device to the server and stored in a database.

[1981] 3. Enter your consultation details

[1982] The user types specific questions about household finances or insurance into the chat interface, which is designed to be intuitive and easy to use. For example, if the user types, "I have a child. How should I save for his or her future tuition fees?", the device sends the information to the server.

[1983] 4. Question Analysis

[1984] The server analyzes the questions it receives using natural language processing technology (such as generative AI models). Keywords are extracted from the text and related categories are identified based on these. An advanced natural language processing engine is used for the analysis, allowing for highly accurate keyword extraction and category identification.

[1985] 5. Answer Generation

[1986] The server extracts relevant information from the database based on the analysis results and generates an appropriate answer. The generated answer is provided in a format that is easy for the user to understand. For example, it could generate an answer such as, "To save for future tuition fees, you have the following options: 1. Use your child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts to aim for future returns."

[1987] 6. Providing answers

[1988] The device displays the generated answers in the chat interface, and provides visual information using charts and graphs as needed, making it easier for users to understand intuitively.

[1989] 7. Emotion Recognition and Analysis

[1990] The server analyzes the user's facial expressions and voice tone using an emotion engine. For example, the user captures their facial expressions and voice using the device's camera or microphone, and the data is sent to the server. The server analyzes the data and identifies the user's emotional state. If the user looks anxious, the server will provide follow-up questions such as, "Are you worried about saving for college?"

[1991] 8. Additional Questions and Further Information

[1992] If the user has further questions or requests for more information, they can type again into the chat interface, for example, "Can you explain in more detail?", and the device will send that information to the server, which will again analyze it and generate a response.

[1993] 9. Guidance for face-to-face consultations

[1994] If it is determined that a face-to-face consultation is necessary, or if the user requests a face-to-face consultation, the server automatically uses the reservation system to make an appointment with an expert. The reservation details are notified to the user via the terminal. For example, if the user enters "I would like to speak to an expert in person," the server will make an appointment with the nearest expert via the reservation system.

[1995] 10. Gathering feedback and optimizing the system

[1996] After the consultation, the user can enter feedback on the advice provided. For example, if the user enters "This advice was very helpful," the device will send the feedback to the server. The server will store this in a database and use the feedback to optimize the system.

[1997] Specific examples

[1998] For example, if a newly married couple who both work asks, "How should we create a savings plan?", the server will generate the following answer: "If both partners work, we recommend saving 20% ​​of your income each month. It would also be beneficial to consider individual pension insurance or a savings-type investment trust. If you are considering buying a home, it is also important to start planning early."

[1999] This concludes the description of the embodiment of the present invention. This system allows users to easily seek advice about household finances and insurance without feeling any psychological resistance, and also allows users to receive personalized advice according to their emotional state.

[2000] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2001] Step 1: Fill in your profile

[2002] The user enters initial profile information, including age, income, family composition, and occupation. This information is sent from the device to the server, which stores it in a database. Based on the information entered, the server prepares the basic data for generating personalized advice.

[2003] Specific behavior:

[2004] The user launches the smartphone app and enters the required information into the displayed form.

[2005] The terminal transmits the input information to the server.

[2006] The server stores the received information in a database and creates a user profile.

[2007] Input: Age, income, family structure, occupation

[2008] Output: Create a user profile

[2009] Step 2: Enter your consultation details

[2010] The user enters questions about household finances or insurance into the chat interface. This information is sent from the device to the server, which then passes the received question data to the next analysis step.

[2011] Specific behavior:

[2012] User: Type "I have a child, how should I save for his or her future education?"

[2013] The device sends the question to the server.

[2014] Input: Question text (e.g., "I have a child. How should I save for his or her future education?")

[2015] Output: Received query data

[2016] Step 3: Parsing the Question

[2017] The server analyzes the received question using natural language processing techniques (such as generative AI models). This analysis involves extracting keywords from the text and identifying related categories. The analysis results are used in the next step of generating an answer.

[2018] Specific behavior:

[2019] The server inputs the question text into a natural language processing engine and extracts keywords.

[2020] The NLP (natural language processing) engine extracts important keywords such as "tuition fees" and "savings" and identifies categories based on these.

[2021] Input: Question data

[2022] Output: Analysis results (extracted keywords and related categories)

[2023] Step 4: Generate an answer

[2024] The server uses the analysis results to extract relevant information from the database and generate an appropriate answer, which is then formatted in a way that is easy for the user to understand.

[2025] Specific behavior:

[2026] The server accesses the database and searches for relevant information based on the extracted keywords.

[2027] The server uses the search results and the generative AI model to generate an answer.

[2028] The answer can be formatted as follows: "To save for future school fees, you have the following options: 1. Use your child allowance and save regularly every month. 2. Take out education insurance and use a savings-type insurance product. 3. Open a NISA account and invest in stocks and investment trusts, aiming for future returns."

[2029] Input: Analysis results (extracted keywords and related categories)

[2030] Output: The generated answer

[2031] Step 5: Provide your answers

[2032] The device displays the generated answers in the chat interface, and, if necessary, provides visual information using charts and graphs.

[2033] Specific behavior:

[2034] The server sends the generated response to the terminal.

[2035] The device displays the received response in the chat interface.

[2036] Input: Generated Answer

[2037] Output: Displayed answer

[2038] Step 6: Emotion recognition and analysis

[2039] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to determine the user's emotional state.

[2040] Specific behavior:

[2041] The device captures the user's facial expressions and voice using a camera and microphone and sends the data to a server.

[2042] The server analyzes the data using an emotion recognition engine to identify the user's emotional state (e.g., anxiety, relief).

[2043] Input: facial expression data, voice data

[2044] Output: Emotion analysis results

[2045] Step 7: Adjust your responses based on emotion

[2046] The server adjusts the response content based on the results of emotion analysis. If the user is anxious, the response content is adjusted to provide additional information or reassurance.

[2047] Specific behavior:

[2048] The server reevaluates the response based on the results of the sentiment analysis.

[2049] Providing follow-up questions or information such as, "Do you have any concerns about saving for college?"

[2050] Input: Sentiment analysis results

[2051] Output: Adjusted answer

[2052] Step 8: Ask additional questions and provide further information

[2053] If the user has further questions or requests for more information, they enter it into the chat interface again, and this information is again sent from the device to the server for re-analysis and generation of an answer.

[2054] Specific behavior:

[2055] User: Type, "Can you explain that in more detail?"

[2056] The device sends new information to the server, which analyzes it again and generates a response.

[2057] Input: Text of follow-up question

[2058] Output: Provides detailed information

[2059] Step 9: Guide to face-to-face consultation

[2060] If the server determines that a face-to-face consultation is necessary, it will use the reservation system to make an appointment with a specialist, and notify the user of the reservation details via their device.

[2061] Specific behavior:

[2062] User: Type "I'd like to speak to an expert in real life."

[2063] The server accesses the reservation system and makes an appointment with the nearest specialist.

[2064] The device will notify you of the reservation details.

[2065] Input: Request for face-to-face consultation

[2066] Output: Reservation details notification

[2067] Step 10: Gather feedback and optimize

[2068] The user inputs feedback on the advice provided, and the device sends it to the server, which stores the feedback in a database and uses it to optimize the system.

[2069] Specific behavior:

[2070] User: Type "This advice was very helpful."

[2071] The device sends the feedback to the server.

[2072] The server stores the feedback in a database and uses it to generate advice next time.

[2073] Input: Feedback text

[2074] Output: Saved feedback

[2075] (Application example 2)

[2076] 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."

[2077] Conventional financial planner systems provide uniform answers without taking into account the user's emotional state, which can cause users to feel psychological resistance. Furthermore, ignoring the user's emotional state can lead to users not receiving satisfactory answers or advice, which can reduce the usefulness of the system.

[2078] 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 a means for inputting the user's initial profile information, a means for the user to input specific consultation details regarding household finances or insurance, a means for analyzing the input consultation details using natural language processing technology, a means for searching a database to generate an appropriate answer, a means for providing the generated answer to the user, a means for tossing up to an expert if face-to-face consultation is necessary, a means for saving all consultation details and advice in a database, a means for recognizing and analyzing the user's emotional state, and a means for adjusting the answer based on the recognized emotional state. This makes it possible to provide personalized advice according to the user's emotional state.

[2079] "User" refers to an individual who uses this system to seek advice on household finances and insurance.

[2080] "Initial profile information" refers to basic information such as the user's age, income, family composition, and occupation.

[2081] "Consultation content" refers to specific questions that users have about household finances and insurance.

[2082] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[2083] "Database" refers to a digital record device for storing financial and insurance information and advice.

[2084] "Emotional state" refers to the psychological state recognized from the user's facial expressions and voice.

[2085] "Analyze" refers to the act of analyzing input information and extracting its meaning and key points.

[2086] "Response" refers to appropriate advice or information in response to the user's inquiry.

[2087] "Tossing up" refers to the act of transferring the user's consultation to an expert as needed.

[2088] "Storing" refers to the act of recording and keeping data for later use.

[2089] "Adjust" refers to the act of changing or optimizing content according to circumstances or conditions.

[2090] MODE FOR CARRYING OUT THE INVENTION

[2091] This invention provides an AI-based financial planner system that allows users to consult about household finances and insurance without feeling any psychological resistance, and by combining it with an emotion engine, provides a system that provides more personalized advice.

[2092] System Overview

[2093] The system is implemented as a smartphone application, allowing users to input questions about household finances and insurance, and automatically generates appropriate answers based on those questions. It also has a function to guide users to face-to-face consultations if necessary. It also has a function to optimize the system based on user feedback, recognize user emotions, and adjust responses based on the analysis results.

[2094] Hardware and Software Used

[2095] Smartphone: Provides user interface and some data processing.

[2096] EmotionEngine: Software for recognizing the user's emotional state.

[2097] NLPProcessor (natural language processing engine): Software for analyzing the content of users' inquiries.

[2098] FinancialAdvisor: Software for generating appropriate financial advice.

[2099] ChatBotInterface: Software that provides interaction between users and systems.

[2100] Processing flow

[2101] 1. Fill in your profile:

[2102] When a user launches the smartphone application, they enter their initial profile information (age, income, family composition, occupation, etc.) This profile information is used by the system to provide appropriate advice to the user.

[2103] 2. Enter your consultation details:

[2104] Users type specific financial and insurance questions into a chat interface that is intuitive and easy to use.

[2105] 3. Question Analysis:

[2106] The server uses an NLPProcessor to analyze the user's input and extract relevant keywords, which then identify the question category.

[2107] 4. Generate answers:

[2108] The server uses FinancialAdvisor to extract relevant information from the database and generate appropriate answers based on the analysis results.

[2109] 5. Emotional awareness and regulation:

[2110] The server uses the Emotion Engine to analyze the user's facial expressions and tone of voice to recognize their emotional state, and responds accordingly.

[2111] 6. Providing answers:

[2112] The generated answers are provided to the user through a chat interface on their smartphone, and may also include appropriate charts and graphs.

[2113] 7. Additional Questions and Face-to-Face Consultations:

[2114] If the user has further questions or requires more information, they enter it into the chat interface again. The server uses this additional information to analyze and generate a new answer. If necessary, an appointment with an expert will be scheduled.

[2115] 8. Feedback and optimization:

[2116] After the consultation, the user inputs feedback on the advice provided. The server optimizes the system based on the collected feedback and improves the quality of advice from the next time onwards.

[2117] Specific examples

[2118] For example, if a 35-year-old married user with one child and an annual income of 5 million yen asks, "I have a child. How should I save for his / her future tuition fees?", the system will generate the following answer:

[2119] "To save for future tuition, you have the following options:

[2120] 1. Use child allowance money and save regularly every month.

[2121] 2. Take out education insurance and use a savings-type insurance product.

[2122] 3. Open a NISA account and aim for future returns by investing in stocks and investment trusts.

[2123] Additionally, if the user displays an anxious expression, the emotion engine will recognize this and provide additional questions or information to provide additional reassurance.

[2124] Example prompt sentence:

[2125] "Based on the profile of a user who is 35 years old, married (with one child) and earns ¥5 million a year, generate personalized financial advice for the question, 'How should I save for future college fees?'"

[2126] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2127] Step 1:

[2128] A user launches the smartphone application and enters initial profile information (age, income, family composition, occupation, etc.). The device sends the entered information to the server, which stores it in the user database. Future advice is personalized based on this input.

[2129] Step 2:

[2130] The user inputs specific questions about household finances or insurance into the chat interface. The device receives this input data and sends it to the server. The server uses natural language processing technology (NLPProcessor) to analyze the input consultation content and extract important keywords. The input question is imported as text data, and keywords such as "tuition fees" and "savings" are extracted.

[2131] Step 3:

[2132] The server searches a database to generate appropriate answers based on the extracted keywords. It retrieves relevant information from the database and generates personalized answers that take into account the user's profile information based on the analysis results. For example, it generates specific financial advice such as "How to save for future tuition fees."

[2133] Step 4:

[2134] The server uses the Emotion Engine to recognize the user's emotional state. It analyzes facial and voice data provided by the user through the camera and microphone to identify the user's emotional state (e.g., relief, anxiety). Based on this recognized emotional state, the generated answer is adjusted appropriately. For example, if the user has an anxious expression, the server adds supplementary information that provides additional reassurance.

[2135] Step 5:

[2136] The server then sends the tailored answers to the user's device, where they are displayed in the smartphone chat interface, allowing the user to view the information in an easy-to-understand format, with visual information such as charts and graphs provided as needed.

[2137] Step 6:

[2138] If the user has further questions or requires more information, they enter it again into the chat interface. The device sends the new question to the server, which again analyzes and generates an answer. The process repeats, and if necessary, a face-to-face consultation with an expert is scheduled.

[2139] Step 7:

[2140] After the consultation, the user enters feedback on the advice provided into the chat interface. The device sends this feedback to the server, which then optimizes the system based on the collected feedback. This feedback improves the quality of advice provided in the future.

[2141] Specific examples

[2142] For example, if a newlywed user asks, "How should I create a savings plan?", the server generates an answer such as, "If both spouses work, we recommend that you save 20% of your income each month. In addition, it would be beneficial to consider individual pension insurance or a savings-type investment trust." If the user shows an anxious expression, the server will provide an additional question: "Is there anything you are worried about?"

[2143] Example prompt sentence:

[2144] "Based on the profile of a user who is 35 years old, married (with one child) and earns ¥5 million a year, generate personalized financial advice for the question, 'How should I save for future college fees?'"

[2145] 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.

[2146] 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.

[2147] 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.

[2148] 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.

[2149] 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.

[2150] 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.

[2151] 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).

[2152] 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.

[2153] 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."

[2154] 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.

[2155] 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).

[2156] 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.

[2157] In the above embodiment, an example in which the s...

Claims

1. a means for a user to input initial profile information; A means for users to input specific consultation details regarding household finances or insurance; A means for analyzing the input consultation content using natural language processing technology; a means of searching a database to generate an appropriate response; a means for providing the generated answer to a user; A way to toss up to an expert if you need a face-to-face consultation, The system includes a means of storing all consultations and advice in a database.

2. Based on the results of the analysis using the natural language processing technology, The system of claim 1 further comprising means for generating an answer.

3. a means for the user to input feedback on the provided advice; The system of claim 1 further comprising means for optimizing the system based on said feedback.

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