system

Abstract

We provide the system. [Solution] Means for providing an interface for inputting customer information, A means of sending the input information to the server, The server includes means for processing input information based on a generative artificial intelligence model to generate preliminary review results and a list of required documents, A means of displaying the generated preliminary screening results and required document list to the customer, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Considerable time and effort are required for housing loan procedures and information acquisition, which often results in a complex process for customers. Also, in financial institutions, it is an issue to reduce costs while improving the quality of customer service. In particular, although appropriate loan plan proposals and prompt responses according to the needs of each customer are required, it is difficult to provide these 24 hours a day. The present invention aims to solve these problems and provide a system that improves customer satisfaction.

Means for Solving the Problems

[0005] The present invention is a system that includes means for providing an interface for inputting customer information, means for transmitting the input information to a server, means for the server to process the input information based on a generative artificial intelligence model to generate a preliminary examination result and a list of required documents, and means for displaying the generated preliminary examination result and list of required documents to the customer.

[0006] Furthermore, the system includes a means for providing an interface for inputting conditions such as repayment period and interest rate, sending the input conditions to a server, and for the server to process the input conditions based on a generative artificial intelligence model to generate an optimal repayment plan and interest rate estimate.

[0007] In addition, the system includes a means for providing an interface that allows customers to request topics they wish to learn about and related news, sending the requests to a server, and for the server to process the requests based on a generative artificial intelligence model to generate educational content and market trend information.

[0008] Furthermore, it includes a means for providing an interactive interface to receive questions entered by customers, sending the entered questions to a server, and for the server to process the questions based on a generative artificial intelligence model and generate immediate answers.

[0009] The following are definitions of key terms included in the patent claims.

[0010] A "customer information input interface" refers to the screen or system that customers use to input information about their mortgage.

[0011] "Transmission method" refers to the system or function used to send information entered by the customer to the server.

[0012] A "generative artificial intelligence model" refers to artificial intelligence technology that generates various results and advice based on customer information.

[0013] "Preliminary screening results" refer to the results of a preliminary screening of a mortgage loan based on the information submitted by the customer.

[0014] The "Required Documents List" is a list of all the documents required for a loan application.

[0015] A "repayment plan" refers to a plan outlining the repayment schedule and amounts for a mortgage.

[0016] "Interest rate estimate" refers to the result of calculating the interest rate that would apply if a customer were to borrow money.

[0017] "Educational content" refers to materials and information that customers use to learn about mortgages.

[0018] "Market trend information" refers to the latest market developments and trends that affect home loans.

[0019] An "interactive interface" refers to a user interface that allows customers to interact with the system naturally.

[0020] "Instant response" refers to an answer that is generated and provided by a generative artificial intelligence model immediately after the customer enters a question. [Brief explanation of the drawing]

[0021] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0022] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

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

[0024] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0025] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0026] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0027] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0029] [First Embodiment]

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

[0031] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0034] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0038] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0041] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0042] This invention provides a system that improves efficiency and customer satisfaction in mortgage services by utilizing generative artificial intelligence models. Specific embodiments thereof are described below.

[0043] 1. Preliminary screening and customer support

[0044] System Overview:

[0045] In this system, customers who wish to undergo a preliminary mortgage application enter the necessary information through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model generates a preliminary application result based on that information. Furthermore, it guides the user through the necessary documents and procedures.

[0046] Program processing explanation:

[0047] 1. User: The customer accesses a dedicated app and enters information such as annual income, occupation, and desired loan amount.

[0048] 2. Terminal: Sends the entered information to the server.

[0049] 3. Server: Passes the transmitted information to the generative artificial intelligence model for processing.

[0050] 4. Generative AI Model: Generates preliminary review results and a list of required documents based on the received information.

[0051] 5. Server: Sends the generated results to the terminal.

[0052] 6. Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[0053] Specific example:

[0054] When a customer accesses a dedicated app and enters an annual income of 6 million yen and a desired loan amount of 30 million yen, a generative artificial intelligence model performs a preliminary screening and displays whether the application has passed the preliminary screening and a list of required documents (income verification documents, identity verification documents, etc.).

[0055] 2. Loan Planning and Advice

[0056] System Overview:

[0057] When a customer requests a repayment plan or interest rate estimate, they enter specific details (such as repayment period and desired interest rate) through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate.

[0058] Program processing explanation:

[0059] 1. User: Enter conditions such as repayment period and desired interest rate.

[0060] 2. Terminal: Sends the entered conditions to the server.

[0061] 3. Server: Passes the submitted conditions to the generative artificial intelligence model for processing.

[0062] 4. Generative artificial intelligence models: These models generate optimal repayment plans and interest rate estimates based on given conditions.

[0063] 5. Server: Sends the generated plan and estimate to the terminal.

[0064] 6. Terminal: Displays repayment plans and estimates to customers.

[0065] Specific example:

[0066] When a customer inputs a 20-year repayment period and a fixed interest rate of 1.5%, a generative artificial intelligence model displays details such as a monthly repayment amount of 180,000 yen and a total repayment amount of 32,400,000 yen.

[0067] 3. Education and Information Provision

[0068] System Overview:

[0069] If a customer wants education or information about mortgages, they can request topics and related news through a dedicated app. The request is sent to a server, where a generative artificial intelligence model generates content on foundational knowledge and market trends.

[0070] Program processing explanation:

[0071] 1. User: Requests topics they want to learn about or related news.

[0072] 2. Terminal: Sends the request to the server.

[0073] 3. Server: Passes requests to a generative artificial intelligence model for processing.

[0074] 4. Generative artificial intelligence models: These generate content related to fundamental knowledge, key terminology, and market trends.

[0075] 5. Server: Sends the generated content to the terminal.

[0076] 6. Terminal: Displays content to customers.

[0077] Specific example:

[0078] When a customer requests "basic information on home loans," a generative artificial intelligence model displays explanations of fixed and variable interest rates, the latest market trends, and more.

[0079] 4. Prompt service delivery and efficiency

[0080] System Overview:

[0081] If a customer wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer and provides the result to the customer.

[0082] Program processing explanation:

[0083] 1. User: Enter your question.

[0084] 2. Terminal: Sends the question to the server.

[0085] 3. Server: Passes the question to a generative artificial intelligence model for processing.

[0086] 4. Generative artificial intelligence models: These generate answers to questions.

[0087] 5. Server: Sends the generated response to the terminal.

[0088] 6. Terminal: Display the response to the customer.

[0089] Specific example:

[0090] When a customer enters the question, "What documents are required for a loan application?", a generative artificial intelligence model generates and displays answers such as income statements, identity verification documents, and property appraisal reports.

[0091] The above is a detailed description of embodiments for carrying out the present invention.

[0092] The following describes the processing flow.

[0093] 1. Preliminary screening and customer support

[0094] Program processing explanation:

[0095] Step 1:

[0096] User: Customers access a dedicated app and enter information such as annual income, occupation, and desired loan amount.

[0097] Step 2:

[0098] Terminal: Checks the entered information and verifies that there are no errors.

[0099] Step 3:

[0100] Terminal: After verification, the information will be sent to the server.

[0101] Step 4:

[0102] Server: Records received information in a database and passes it to a generative artificial intelligence model.

[0103] Step 5:

[0104] Generative AI model: Performs a preliminary screening based on customer information and generates the preliminary screening results and a list of required documents.

[0105] Step 6:

[0106] Server: Receives the generated results and sends them to the terminal.

[0107] Step 7:

[0108] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[0109] 2. Loan Planning and Advice

[0110] Program processing explanation:

[0111] Step 1:

[0112] User: The customer accesses a dedicated app and enters conditions such as the repayment period and desired interest rate.

[0113] Step 2:

[0114] Terminal: Checks the entered conditions and verifies that there are no errors.

[0115] Step 3:

[0116] Terminal: After confirmation, the conditions will be sent to the server.

[0117] Step 4:

[0118] Server: Records the received conditions in the database and passes them to the generative artificial intelligence model.

[0119] Step 5:

[0120] Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions.

[0121] Step 6:

[0122] Server: Receives the generated plan and estimate and sends them to the terminal.

[0123] Step 7:

[0124] Terminal: Displays repayment plans and interest rate estimates to customers.

[0125] 3. Education and Information Provision

[0126] Program processing explanation:

[0127] Step 1:

[0128] User: Customers access a dedicated app and request topics they want to learn about and related news.

[0129] Step 2:

[0130] Terminal: Check the request details and verify that there are no errors.

[0131] Step 3:

[0132] Terminal: After verification, the request will be sent to the server.

[0133] Step 4:

[0134] Server: Records received requests in the database and passes them to the generative artificial intelligence model.

[0135] Step 5:

[0136] Generative artificial intelligence models: These models generate content related to learning themes and market trends.

[0137] Step 6:

[0138] Server: Receives the generated content and sends it to the terminal.

[0139] Step 7:

[0140] Terminal: Displays educational content and related news to customers.

[0141] 4. Prompt service delivery and efficiency

[0142] Program processing explanation:

[0143] Step 1:

[0144] User: The customer accesses a dedicated app and enters their question.

[0145] Step 2:

[0146] Terminal: Check the entered questions and verify that there are no errors.

[0147] Step 3:

[0148] Terminal: After verification, the question will be sent to the server.

[0149] Step 4:

[0150] Server: Records received questions in a database and passes them to a generative artificial intelligence model.

[0151] Step 5:

[0152] Generative artificial intelligence models: Generate instant answers to questions.

[0153] Step 6:

[0154] Server: Receives the generated response and sends it to the terminal.

[0155] Step 7:

[0156] Terminal: Displays the answer to the customer immediately.

[0157] (Example 1)

[0158] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0159] In traditional mortgage services, customers had to go through complex procedures to undergo preliminary screening and loan planning consultations. This often resulted in cumbersome and time-consuming processes. Furthermore, the inability to quickly provide customers with the information and advice they needed could lead to decreased customer satisfaction. Additionally, insufficient education and information regarding mortgages could cause customer anxiety. A system is needed that effectively addresses these problems and efficiently and quickly responds to customer needs.

[0160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0161] In this invention, the server includes means for providing an interface for inputting customer information, means for transmitting the input information to a processing unit, means for the processing unit to process the input information based on a generative artificial intelligence model and generate a preliminary examination result and a list of required documents, means for displaying the generated preliminary examination result and list of required documents to the customer, means for providing conditions such as repayment period and interest rate to be input by the customer, means for transmitting the input conditions to the processing unit, means for the processing unit to process the input conditions based on a generative artificial intelligence model and generate an optimal repayment plan and interest rate estimate, means for displaying the generated repayment plan and interest rate estimate to the customer, means for providing an interface for the customer to request topics and related information they wish to learn about, means for transmitting the customer's request to the processing unit, means for the processing unit to process the customer's request based on a generative artificial intelligence model and generate educational content and market trend information, and means for displaying the generated educational content and market trend information to the customer. This simplifies the customer's procedure and enables the provision of services quickly and accurately.

[0162] A "customer" is an individual or legal entity that uses a mortgage service.

[0163] An "interface" is a user interaction mechanism that provides a means for users to input information.

[0164] A "processing device" is a computer or server used to process received information and data.

[0165] A "generative artificial intelligence model" is an algorithm or model used to generate specific results based on input data.

[0166] "Preliminary approval result" refers to information indicating the provisional approval status of a loan, generated based on the information entered by the customer.

[0167] The "list of required documents" is a list of documents that the customer must submit during the loan application process.

[0168] A "repayment plan" is a loan repayment plan based on the customer's borrowing conditions.

[0169] An "interest rate estimate" is an estimate of the interest rate that should be applied to a loan offered to a customer.

[0170] "Educational content" refers to teaching materials and documents that contain foundational knowledge and detailed information for customers to learn.

[0171] "Market trend information" refers to data that provides the latest information and trends regarding mortgage and real estate markets.

[0172] A "request" is a request that a customer sends to obtain specific information or services.

[0173] This invention provides a system that improves efficiency and customer satisfaction in mortgage services by utilizing generative artificial intelligence models. Detailed embodiments are described below.

[0174] Overview of the entire system

[0175] This system utilizes a dedicated software application (hereinafter referred to as the "dedicated app"), a server, and a generative artificial intelligence model (e.g., OpenAI® GPT-4®). The dedicated app provides an interface for the user to input information, the server transmits the input information to a processing unit, and the generative artificial intelligence model performs predetermined processing. The processing results are then displayed again on the user's terminal.

[0176] 1. Preliminary screening and customer support

[0177] System Overview:

[0178] When a user wishes to undergo a preliminary mortgage application, they enter information such as their annual income, occupation, and desired loan amount through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model uses that information to generate a preliminary application result and a list of required documents.

[0179] Specific example:

[0180] When a customer accesses a dedicated app and enters an annual income of 6 million yen and a desired loan amount of 30 million yen, a generative artificial intelligence model performs a preliminary screening and displays whether the application has passed the preliminary screening and a list of required documents (income verification documents, identity verification documents, etc.).

[0181] Example of a prompt:

[0182] "Please conduct a preliminary mortgage application with an annual income of 6 million yen and a desired loan amount of 30 million yen. Please let me know the results and required documents."

[0183] 2. Loan Planning and Advice

[0184] System Overview:

[0185] When a user requests a repayment plan or interest rate estimate, they enter conditions such as the repayment period and desired interest rate through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate.

[0186] Specific example:

[0187] When a customer inputs a 20-year repayment period and a fixed interest rate of 1.5%, a generative artificial intelligence model displays details such as a monthly repayment amount of 180,000 yen and a total repayment amount of 32,400,000 yen.

[0188] Example of a prompt:

[0189] "Please provide a mortgage repayment plan with a 20-year repayment period and a fixed interest rate of 1.5%, including the monthly payment amount and the total repayment amount."

[0190] 3. Education and Information Provision

[0191] System Overview:

[0192] If a user wishes to receive education or information about mortgages, they can request topics and related news through a dedicated app. The request is sent to a server, where a generative artificial intelligence model generates content on basic knowledge and market trends.

[0193] Specific example:

[0194] When a customer requests "basic information on home loans," a generative artificial intelligence model displays explanations of fixed and variable interest rates, the latest market trends, and more.

[0195] Example of a prompt:

[0196] "Please explain the basics of home loans, including the difference between fixed and variable interest rates."

[0197] 4. Prompt service delivery and efficiency

[0198] System Overview:

[0199] If a user wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer and provides the result to the user.

[0200] Specific example:

[0201] When a customer enters the question, "What documents are required for a loan application?", a generative artificial intelligence model generates and displays answers such as income statements, identity verification documents, and property appraisal reports.

[0202] Example of a prompt:

[0203] "What documents are required for a loan application?"

[0204] The above describes the embodiments for carrying out the present invention. This enables customers to use mortgage services simply and quickly, and allows for efficient preliminary screening, presentation of repayment plans, and provision of educational content.

[0205] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0206] 1. Preliminary screening and customer support

[0207] Processing flow

[0208] Step 1:

[0209] User: Open the dedicated app and enter information such as annual income, occupation, and desired loan amount.

[0210] Input: Information such as annual income, occupation, and desired loan amount.

[0211] Specific action: The user types text on the smartphone screen and presses the send button.

[0212] Step 2:

[0213] Terminal: Sends the entered information to the server.

[0214] Input: Information entered by the user, such as annual income, occupation, and desired loan amount.

[0215] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[0216] Step 3:

[0217] Server: Passes the received information to the generative artificial intelligence model.

[0218] Input: User information sent as an HTTP request

[0219] Data processing: Execute API calls to pass information to a generative artificial intelligence model.

[0220] Specific operation: The server passes data to the API endpoint.

[0221] Step 4:

[0222] Generative AI model: Generates preliminary review results and a list of required documents based on the input information.

[0223] Input: User information (annual income, occupation, desired loan amount, etc.)

[0224] Data processing: An algorithm analyzes user information and generates a preliminary review result and a list of required documents.

[0225] Specific operation: The model performs processing and outputs the results in a data format.

[0226] Step 5:

[0227] Server: Sends the generated preliminary review results and list of required documents to the terminal.

[0228] Input: Output data from a generative artificial intelligence model (preliminary review results, list of required documents)

[0229] Specific operation: Send output data to the terminal as an HTTP response.

[0230] Step 6:

[0231] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[0232] Input: Preliminary review results and list of required documents sent as an HTTP response.

[0233] Specific action: Display the result on the screen.

[0234] 2. Loan Planning and Advice

[0235] Processing flow

[0236] Step 1:

[0237] User: Enter conditions such as repayment period and desired interest rate using the dedicated app.

[0238] Input: Conditions such as repayment period and desired interest rate.

[0239] Specific action: The user enters text into the input field and presses the submit button.

[0240] Step 2:

[0241] Terminal: Sends the entered conditions to the server.

[0242] Input: User-entered conditions such as repayment period and desired interest rate.

[0243] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[0244] Step 3:

[0245] Server: Passes the received conditions to the generative artificial intelligence model.

[0246] Input: User conditions sent as an HTTP request

[0247] Data processing: Execute API calls to pass conditions to a generative artificial intelligence model.

[0248] Specific operation: The server passes data to the API endpoint.

[0249] Step 4:

[0250] Generative artificial intelligence models: Generate optimal repayment plans and interest rate estimates based on given conditions.

[0251] Input: User conditions (repayment period, desired interest rate, etc.)

[0252] Data processing: Algorithms analyze user conditions and create repayment plans and interest rate estimates.

[0253] Specific operation: The model performs processing and outputs the results in a data format.

[0254] Step 5:

[0255] Server: Sends the generated repayment plan and interest rate estimate to the terminal.

[0256] Input: Output data from a generative artificial intelligence model (repayment plan, interest rate estimate).

[0257] Specific operation: Send output data to the terminal as an HTTP response.

[0258] Step 6:

[0259] Terminal: Displays repayment plans and interest rate estimates to customers.

[0260] Input: Repayment plan and interest rate estimate sent as an HTTP response.

[0261] Specific action: Display the result on the screen.

[0262] 3. Education and Information Provision

[0263] Processing flow

[0264] Step 1:

[0265] User: Request topics you want to learn about or related news.

[0266] Input: Topic you want to learn about, related news

[0267] Specific steps: Select a theme using the dedicated app and submit a request.

[0268] Step 2:

[0269] Terminal: Sends a request to the server.

[0270] Input: User selected theme, related news

[0271] Specific operation: The selected data is encoded and sent to the server as an HTTP request.

[0272] Step 3:

[0273] Server: Passes the received request to the generative artificial intelligence model.

[0274] Input: User request sent as an HTTP request

[0275] Data processing: Execute API calls to pass the request content to a generative artificial intelligence model.

[0276] Specific operation: The server passes data to the API endpoint.

[0277] Step 4:

[0278] Generative artificial intelligence model: Generates educational content and market trend information based on the requested content.

[0279] Input: User request (theme, related news)

[0280] Data processing: Algorithms analyze requests and create educational content and market trend information.

[0281] Specific operation: The model performs processing and outputs the results in a data format.

[0282] Step 5:

[0283] Server: Transmits the generated educational content and market trend information to the terminal.

[0284] Input: Output data (educational content, market trend information) from the generative AI model

[0285] Specific operation: Transmits the output data to the terminal as an HTTP response

[0286] Step 6:

[0287] Terminal: Displays the educational content and market trend information to the customer.

[0288] Input: Educational content and market trend information transmitted as an HTTP response

[0289] Specific operation: Displays the result on the screen

[0290] 4. Quick service provision and efficiency improvement

[0291] Process flow

[0292] Step 1:

[0293] User: Inputs a question.

[0294] Input: Question content

[0295] Specific operation: Enters text in the question input field of the dedicated app and taps the send button.

[0296] Step 2:

[0297] Terminal: Sends the question to the server.

[0298] Input: Question content entered by the user

[0299] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[0300] Step 3:

[0301] Server: Pass the question to the generative AI model for processing.

[0302] Input: The user question sent as an HTTP request

[0303] Data processing: Execute an API call to pass the question content to the generative AI model

[0304] Specific operation: The server passes the data to the API endpoint.

[0305] Step 4:

[0306] Generative AI model: Generate an answer to the question.

[0307] Input: The user question

[0308] Data calculation: The algorithm analyzes the question and generates an optimal answer

[0309] Specific operation: The model performs the processing and outputs the result as a data format.

[0310] Step 5:

[0311] Server: Send the generated answer to the terminal.

[0312] Input: Output data (answer) from the generative AI model

[0313] Specific operation: Send the output data to the terminal as an HTTP response

[0314] Step 6:

[0315] Terminal: Display the answer to the customer.

[0316] Input: Response sent as an HTTP response

[0317] Specific action: Display the result on the screen.

[0318] (Application Example 1)

[0319] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0320] Current mortgage services often require multiple steps, making it difficult for customers to efficiently receive pre-approval, repayment plans, educational information, and answers to their questions on a single platform. Furthermore, real-time service can be time-consuming, leading to decreased customer satisfaction. Therefore, a new system that is both efficient and enhances customer satisfaction is needed.

[0321] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0322] In this invention, the server includes means for providing an interface for inputting customer information, means for transmitting the inputted information to the server, means for processing the inputted information on the server based on a generative artificial intelligence model to generate a preliminary screening result and a list of required documents, means for displaying the generated preliminary screening result and list of required documents to the customer, means for providing a mortgage service performed through a virtual reality device, and means for providing an integrated preliminary screening, repayment plan, educational information, and answers to questions within a virtual store using a head-mounted display. This enables the customer to efficiently receive mortgage services in a virtual reality environment.

[0323] "Customer information" refers to personal information necessary for mortgage services, such as annual income, occupation, desired loan amount, repayment period, and desired interest rate.

[0324] A "generative artificial intelligence model" is a type of artificial intelligence technology that generates optimal preliminary screening results, repayment plans, interest rate estimates, educational content, and answers to questions based on input conditions and information.

[0325] A "virtual reality device" is a device that allows users to experience a virtual environment, and specifically includes head-mounted displays, controllers, and the like.

[0326] A "head-mounted display" is a device that users wear on their heads to visually experience virtual reality.

[0327] A "virtual store" is a virtual store space that can be visited through a virtual reality device, providing an interface for customers to receive mortgage services.

[0328] A "preliminary assessment result" is an initial evaluation of the mortgage loan the customer is requesting, indicating the likelihood of loan approval, necessary documents, and the next steps to take.

[0329] A "repayment plan" is a detailed repayment plan for a mortgage, including the repayment period, monthly payment amount, and total repayment amount.

[0330] An "interest rate estimate" is an estimate based on the interest rate applicable to a mortgage loan, used to calculate the monthly repayment amount and the total repayment amount.

[0331] "Educational content" refers to information used to learn about mortgage-related knowledge, market trends, and important terminology.

[0332] "Answer to a question" refers to the immediate response from a generative artificial intelligence model to a question entered by the customer.

[0333] This invention is a mortgage service system using a virtual reality device. This system aims to provide customers with highly efficient and satisfying services by utilizing a generative artificial intelligence model.

[0334] 1. Required hardware and software

[0335] Hardware:

[0336] Virtual reality devices (e.g., regular head-mounted displays)

[0337] Communication server

[0338] User device (smartphone or computer)

[0339] software:

[0340] OpenAI API (an API for using generative artificial intelligence models)

[0341] Virtual reality interface software

[0342] Server communication software

[0343] 2. Data processing and data calculation

[0344] server:

[0345] The system receives user information (annual income, occupation, desired loan amount, etc.) and passes it to a generative artificial intelligence model.

[0346] Based on the output from the model, preliminary screening results, repayment plans, educational content, and answers to questions are generated.

[0347] The generated results are sent back to the user.

[0348] User terminal:

[0349] It provides an interface for users to input information.

[0350] Send the entered information to the server.

[0351] The results returned from the server are displayed within the virtual reality environment.

[0352] Generative artificial intelligence models:

[0353] Based on the entered user information and conditions, the system generates the necessary results.

[0354] Specifically, it outputs preliminary screening results, repayment plans, interest rate estimates, educational content, and answers to frequently asked questions.

[0355] 3. Specific Examples

[0356] Preliminary screening and customer support:

[0357] As a concrete example, a user wears a head-mounted display and accesses a dedicated terminal in a virtual store, entering an annual income of 6 million yen, occupation as a company employee, and desired loan amount of 30 million yen. This information is sent to a server, where a generative artificial intelligence model performs a preliminary assessment, and the preliminary assessment results, including a list of required documents, are immediately displayed within the virtual reality environment.

[0358] Example of a prompt:

[0359] Please use the following information to pre-approve your mortgage:

[0360] Annual income: 6 million yen

[0361] Occupation: Company employee

[0362] Desired loan amount: 30 million yen

[0363] In this way, customers can efficiently utilize mortgage services in real time within a virtual reality environment.

[0364] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0365] Step 1:

[0366] The user wears a head-mounted display and accesses a virtual store. The user operates the interface to enter personal information such as annual income, occupation, and desired loan amount. The data entered includes an annual income of 6 million yen, occupation of company employee, and desired loan amount of 30 million yen.

[0367] Step 2:

[0368] The terminal sends the entered information to the server. This input data includes the user's annual income, occupation, and desired loan amount. The terminal then sends this data to the server in an appropriate format.

[0369] Step 3:

[0370] The server passes the received information to a generative artificial intelligence model. Specifically, it uses the OpenAI API to pass data such as the user's annual income, occupation, and desired loan amount as prompts to the model. Based on the input data, the generative AI model generates a preliminary screening result and a list of required documents. As a result of this process, the preliminary screening result and the list of required documents are generated.

[0371] Step 4:

[0372] The server receives the preliminary review results and required document list returned from the generative artificial intelligence model. The server verifies that this data is accurate and then prepares it for transmission.

[0373] Step 5:

[0374] The server sends the generated preliminary review results and a list of required documents to the terminal. The server formats the received data through the appropriate interface and sends it to the user's terminal.

[0375] Step 6:

[0376] The terminal displays the preliminary screening results and a list of required documents to the user within a virtual reality environment. Specifically, this includes displaying the preliminary screening results and required documents on a head-mounted display. The user can then confirm the next steps based on the list within the virtual store.

[0377] This processing flow allows users to efficiently access mortgage services within a virtual reality environment.

[0378] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0379] This invention provides a system that combines a generative artificial intelligence model and an emotion engine in mortgage services to provide personalized support that takes into account the emotional state of the customer. Specific embodiments thereof are described below.

[0380] 1. Preliminary screening and customer support

[0381] System Overview:

[0382] In this system, customers who wish to undergo a preliminary mortgage application enter the necessary information through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model generates a preliminary application result based on that information. Furthermore, an emotion engine analyzes the customer's emotional state and provides a preliminary application result and guidance on necessary documents that reflects the results.

[0383] Program processing explanation:

[0384] 1. User: The customer accesses a dedicated app and enters information such as annual income, occupation, and desired loan amount.

[0385] 2. Terminal: Collects emotional data from sensors such as cameras and microphones, along with customer input information.

[0386] 3. Terminal: Sends input information and emotion data to the server.

[0387] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[0388] 5. Generative AI Model: Generates preliminary screening results and a list of required documents based on customer information and sentiment analysis results.

[0389] 6. Server: Sends the generated results to the terminal.

[0390] 7. Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[0391] Specific example:

[0392] If a customer enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and simultaneously displays an anxious expression, the emotion engine will detect the anxiety, and the generative artificial intelligence model will display a preliminary approval status and a list of required documents along with a more detailed explanation.

[0393] 2. Loan Planning and Advice

[0394] System Overview:

[0395] When a customer requests a repayment plan or interest rate estimate, they enter specific details (such as repayment period and desired interest rate) through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate. Furthermore, an emotion engine analyzes the customer's emotional state and provides advice reflecting the results.

[0396] Program processing explanation:

[0397] 1. User: Enter conditions such as repayment period and desired interest rate.

[0398] 2. Terminal: Collects customer sentiment data while they are entering conditions.

[0399] 3. Terminal: Sends input conditions and emotion data to the server.

[0400] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[0401] 5. Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions and sentiment analysis results.

[0402] 6. Server: Sends the generated plan and estimate to the terminal.

[0403] 7. Terminal: Displays repayment plans and interest rate estimates to customers.

[0404] Specific example:

[0405] If a customer inputs a 20-year repayment period and a desired interest rate of 1.5%, and simultaneously displays a reassuring expression, the emotion engine detects this reassurance, and the generative artificial intelligence model provides a repayment plan along with standard explanations.

[0406] 3. Education and Information Provision

[0407] System Overview:

[0408] When customers wish to receive education and information about mortgages, they can request topics and related news through a dedicated app. The requests are sent to a server, where a generative artificial intelligence model generates content on fundamental knowledge and market trends. An emotion engine analyzes the customer's emotional state and provides educational content that reflects the results.

[0409] Program processing explanation:

[0410] 1. User: Requests topics they want to learn about or related news.

[0411] 2. Terminal: Collects customer sentiment data during the request.

[0412] 3. Terminal: Sends requests and sentiment data to the server.

[0413] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[0414] 5. Generative AI Models: These models generate content based on the request content and sentiment analysis results.

[0415] 6. Server: Sends the generated content to the terminal.

[0416] 7. Terminal: Displays content to customers.

[0417] Specific example:

[0418] If a customer requests "basic knowledge about home loans" and simultaneously shows an interested expression, the emotion engine detects their interest, and the generative artificial intelligence model provides more in-depth basic knowledge.

[0419] 4. Prompt service delivery and efficiency

[0420] System Overview:

[0421] If a customer wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer. The emotion engine then analyzes the customer's emotional state and customizes the answer before providing it.

[0422] Program processing explanation:

[0423] 1. User: Enter your question.

[0424] 2. Terminal: Collects emotional data from customers as they are entering information.

[0425] 3. Terminal: Sends question and sentiment data to the server.

[0426] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[0427] 5. Generative AI Model: Generates immediate responses based on the question content and sentiment analysis results.

[0428] 6. Server: Sends the generated response to the terminal.

[0429] 7. Terminal: Displays the answer to the customer immediately.

[0430] Specific example:

[0431] If a customer enters a question like, "What documents are needed for a loan application?" and is feeling anxious, the emotion engine detects the anxiety, and the generative artificial intelligence model provides a polite and easy-to-understand explanation.

[0432] The following describes the processing flow.

[0433] 1. Preliminary screening and customer support

[0434] Program processing explanation:

[0435] Step 1:

[0436] User: Customers access a dedicated app and enter information such as annual income, occupation, and desired loan amount.

[0437] Step 2:

[0438] Terminal: Uses the camera and microphone on the input screen to collect data on the customer's facial expressions and voice.

[0439] Step 3:

[0440] Terminal: Sends input information and collected sentiment data to the server.

[0441] Step 4:

[0442] Server: Records received information in a database and passes it to a generative artificial intelligence model.

[0443] Step 5:

[0444] Server: The emotion engine analyzes emotional data and determines the customer's emotional state (e.g., feeling safe, anxious, etc.).

[0445] Step 6:

[0446] Generative AI model: Generates preliminary screening results and a list of required documents based on the determined emotional state and customer information.

[0447] Step 7:

[0448] Server: Sends the generated preliminary review results and list of required documents to the terminal.

[0449] Step 8:

[0450] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[0451] 2. Loan Planning and Advice

[0452] Program processing explanation:

[0453] Step 1:

[0454] User: The customer enters conditions such as the repayment period and desired interest rate.

[0455] Step 2:

[0456] Terminal: Collects data on the customer's facial expressions and voice while they are entering repayment terms.

[0457] Step 3:

[0458] Terminal: Sends input conditions and collected sentiment data to the server.

[0459] Step 4:

[0460] Server: Records the received conditions in the database and passes them to the generative artificial intelligence model.

[0461] Step 5:

[0462] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[0463] Step 6:

[0464] Generative artificial intelligence model: Generates optimal repayment plans and interest rate estimates based on customer conditions and sentiment analysis results.

[0465] Step 7:

[0466] Server: Sends the generated repayment plan and interest rate estimate to the terminal.

[0467] Step 8:

[0468] Terminal: Displays repayment plans and interest rate estimates to customers.

[0469] 3. Education and Information Provision

[0470] Program processing explanation:

[0471] Step 1:

[0472] User: Requests topics and related news that customers want to learn about.

[0473] Step 2:

[0474] Terminal: Collects data on the customer's facial expressions and voice while they are making a request.

[0475] Step 3:

[0476] Terminal: Sends request information and collected sentiment data to the server.

[0477] Step 4:

[0478] Server: Records received requests in the database and passes them to the generative artificial intelligence model.

[0479] Step 5:

[0480] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[0481] Step 6:

[0482] Generative AI model: Generates educational content and market trend information based on the request content and sentiment analysis results.

[0483] Step 7:

[0484] Server: Sends the generated content to the terminal.

[0485] Step 8:

[0486] Terminal: Displays educational content and related news to customers.

[0487] 4. Prompt service delivery and efficiency

[0488] Program processing explanation:

[0489] Step 1:

[0490] User: The customer enters their question.

[0491] Step 2:

[0492] Terminal: Collects data on the customer's facial expressions and voice while they are entering questions.

[0493] Step 3:

[0494] Terminal: Sends question information and collected sentiment data to the server.

[0495] Step 4:

[0496] Server: Records received questions in a database and passes them to a generative artificial intelligence model.

[0497] Step 5:

[0498] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[0499] Step 6:

[0500] Generative AI model: Generates an immediate response based on the question content and sentiment analysis results.

[0501] Step 7:

[0502] Server: Sends the generated response to the terminal.

[0503] Step 8:

[0504] Terminal: Displays the answer to the customer immediately.

[0505] (Example 2)

[0506] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0507] Traditional mortgage services have a standardized approach to pre-approval, repayment plan proposals, education, and information provision, making it difficult to address the individual emotional states and circumstances of customers. Furthermore, the lack of consideration of emotional data made it challenging to provide timely and appropriate answers to customer questions and requests. This resulted in lower customer satisfaction and presented challenges in improving service quality.

[0508] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0509] In this invention, the server includes means for providing an input device for inputting customer information, means for transmitting the input information, means for processing the input information and sentiment data collected from a terminal using a sentiment analysis device on the server to generate preliminary examination results and a list of required documents based on a generative artificial intelligence model, and means for displaying the generated preliminary examination results and list of required documents.

[0510] This will enable the provision of personalized mortgage services that take into account the emotional state of the customer.

[0511] 1. "Input device" refers to a device used by a customer to input information, and includes devices such as keyboards, touchscreens, and mice.

[0512] 2. "Transmission means" refers to a system equipped with functions and protocols for transmitting input information or data to a server.

[0513] 3. An "emotion analysis device" is a combination of software and hardware used to analyze collected emotional data and determine the emotional state of a customer.

[0514] 4. A "generative artificial intelligence model" is an artificial intelligence algorithm and system that generates preliminary screening results, lists of required documents, repayment plans, educational content, etc., based on input information and emotional data.

[0515] 5. The "preliminary screening result" is a prediction of the mortgage loan approval based on the information entered by the customer.

[0516] 6. The "Required Documents List" is a list of documents required for a mortgage application, based on the preliminary screening results.

[0517] 7. A "repayment plan" is a system that shows the optimal loan repayment schedule and plan based on the customer's input conditions and emotional data.

[0518] 8. "Interest rate estimate" refers to information that shows the predicted loan interest rate based on the customer's input conditions.

[0519] 9. "Educational content" refers to materials and documents that include information and market trends related to mortgages, generated based on topics that customers wish to learn about.

[0520] 10. "Market Trend Information" refers to data and analysis results that show the latest information and trends in the mortgage market.

[0521] 11. "Interface" refers to all elements, including input screens and control panels, that customers interact with in order to use the system.

[0522] This invention provides a system that combines a generative artificial intelligence model and an emotion analysis device in mortgage services to provide personalized support that takes into account the emotional state of the customer. Specific embodiments of this system will be described in detail below.

[0523] Preliminary screening and customer support

[0524] The system begins with the user (customer) entering basic information such as annual income, occupation, and desired loan amount through a dedicated application. This information is entered using the terminal's input device (keyboard, touchscreen, etc.). Simultaneously, sensors such as the terminal's camera and microphone capture the customer's facial expressions and voice tone, collecting emotional data. The terminal encrypts this information and transmits it to the server. On the server, an emotion analysis device analyzes the emotional data to determine the customer's emotional state. Next, a generative artificial intelligence model generates a preliminary screening result and a list of required documents based on the customer's basic information and the emotional analysis results. The generated results are transmitted from the server to the terminal and displayed to the customer.

[0525] Specific example

[0526] If a customer enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and shows an anxious expression, the emotion analysis device will detect "anxiety," and the generative artificial intelligence model will display a preliminary approval status and a list of required documents along with a more detailed explanation.

[0527] Loan planning and advice

[0528] Next, the user (customer) inputs conditions such as repayment period and desired interest rate through a dedicated application. The terminal's camera and microphone capture the customer's facial expressions and voice in real time during this input process, collecting emotional data. The terminal sends the data to a server, where an emotion analysis device analyzes the emotional data and determines the customer's emotional state. A generative artificial intelligence model generates an optimal repayment plan and interest rate estimate based on the customer's conditions and emotional state. The generated results are sent from the server to the terminal and displayed to the customer.

[0529] Specific example

[0530] If a customer enters a 20-year repayment period and a desired interest rate of 1.5%, and displays a reassuring expression, the emotion analysis device detects "reassurance," and the generative artificial intelligence model provides a repayment plan along with a standard explanation.

[0531] Education and information provision

[0532] Furthermore, if a customer wishes to receive education or information about mortgages, they can request topics and related news through a dedicated application. During this request, the device's camera and microphone collect customer emotional data, which the device then sends to a server. The server's emotion analysis device analyzes the emotional data and determines the customer's emotional state. A generative artificial intelligence model generates educational content and market trend information based on the request and emotional state, sends it from the server to the device, and displays it to the customer.

[0533] Specific example

[0534] If a customer requests "basic knowledge about home loans" and shows an interested expression, the emotion analysis device detects "interest," and the generative artificial intelligence model provides more detailed basic knowledge.

[0535] Rapid service delivery and efficiency

[0536] When a customer requires an immediate answer, they enter their question through a dedicated application. While the customer is entering the question, the device's camera and microphone collect emotional data, which the device then sends to a server. The server's emotion analysis system analyzes this data to determine the customer's emotional state. A generative artificial intelligence model generates an immediate answer based on the question and the customer's emotional state, which is then sent from the server to the device and displayed to the customer.

[0537] Specific example

[0538] If a customer enters a question like, "What documents are needed for a loan application?" and is feeling anxious, the emotion analysis system will detect this anxiety, and the generative artificial intelligence model will provide a polite and easy-to-understand explanation.

[0539] Example of a prompt

[0540] 1. "Please explain the system's workflow: When a customer enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and shows an anxious expression, the emotional engine detects that anxiety."

[0541] 2. "Please describe the advice system that provides guidance when a customer enters a 20-year repayment period and a desired interest rate of 1.5%, and then appears relieved."

[0542] 3. "Please describe an example of educational content you would offer to a customer who requests and expresses interest in 'Basic Knowledge of Home Loans'."

[0543] 4. "Please describe the flow of a system that allows customers to instantly input questions and receive quick responses if they are feeling anxious."

[0544] The above details relate to embodiments of the present invention.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Preliminary screening and customer support

[0547] Program processing steps:

[0548] Step 1:

[0549] User: Open the dedicated application and enter information such as annual income, occupation, and desired loan amount.

[0550] Input information: Annual income (6 million yen), occupation (company employee), desired loan amount (30 million yen), etc.

[0551] Output: The input data is saved to the terminal.

[0552] Specific operation: The user enters the necessary information using a touchscreen or keyboard on a device such as a smartphone or PC, and then presses the "Send" button.

[0553] Step 2:

[0554] Device: Collects emotional data from sensors such as cameras and microphones, along with input information.

[0555] Input: User's facial expression data, voice tone.

[0556] Output: The collected emotional data is saved to the device.

[0557] Specific operation: The device's front camera automatically activates and captures the user's facial expressions. The microphone also activates and analyzes the voice tone to obtain emotion data.

[0558] Step 3:

[0559] Terminal: Sends input information and emotion data to the server.

[0560] Input: User information (annual income, occupation, desired loan amount), emotional data (facial expression, tone of voice).

[0561] Output: Data is sent to the server.

[0562] Specific operation: The terminal encrypts the data into packets and sends them to the server over the network.

[0563] Step 4:

[0564] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[0565] Input: Emotional data (facial expression, tone of voice).

[0566] Output: Analysis results (e.g., anxiety, reassurance, etc.).

[0567] Specific operation: Using an emotion analysis algorithm, identify the customer's emotional state (e.g., anxiety) from changes in facial expression and tone of voice.

[0568] Step 5:

[0569] Generative AI model: Generates preliminary screening results and a list of required documents based on customer information and sentiment analysis results.

[0570] Input: User information, sentiment analysis results.

[0571] Output: Preliminary review results, list of required documents.

[0572] Specific operation: A generative artificial intelligence model uses historical data and statistical models to generate a preliminary screening result (e.g., A-rank screening passed) and a list of required documents (e.g., identification card, residence certificate, income certificate).

[0573] Step 6:

[0574] Server: Sends the generated results to the terminal.

[0575] Input: Preliminary screening results, list of required documents.

[0576] Output: Data is sent to the terminal.

[0577] Specific operation: The server generates data, packages it into packets, encrypts them, and sends them to the terminal.

[0578] Step 7:

[0579] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[0580] Input: Preliminary screening results, list of required documents.

[0581] Output: Data displayed to the customer.

[0582] Specific operation: The terminal's display shows the result of passing the preliminary screening and a list of required documents. For example, it might display "Preliminary screening result: A rank approved, Required documents: Identification document, resident registration certificate, income certificate."

[0583] Loan planning and advice

[0584] Program processing steps:

[0585] Step 1:

[0586] User: Enter conditions such as repayment period and desired interest rate.

[0587] Input: Conditions such as repayment period (20 years) and desired interest rate (1.5%).

[0588] Output: The input data is saved to the terminal.

[0589] Specific operation: The user enters the repayment period and interest rate into the input form of the dedicated application and presses the "Submit" button.

[0590] Step 2:

[0591] Terminal: Collects customer sentiment data while they are entering conditions.

[0592] Input: User's facial expression data, voice tone.

[0593] Output: The collected emotional data is saved to the device.

[0594] Specific operation: While inputting conditions, the terminal's front camera activates to monitor the customer's facial expressions. The microphone also activates to analyze the tone of voice.

[0595] Step 3:

[0596] Terminal: Sends input conditions and emotion data to the server.

[0597] Input: Conditional information (repayment period, desired interest rate), emotional data (facial expression, tone of voice).

[0598] Output: The transmitted data reaches the server.

[0599] Specific operation: The terminal encrypts the data, bundles it into packets, and sends them to the server.

[0600] Step 4:

[0601] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[0602] Input: Emotional data (facial expression, tone of voice).

[0603] Output: Analysis results (e.g., feelings of security, interest, etc.).

[0604] Specific operation: Emotion analysis software analyzes the user's facial expressions and tone of voice to identify their current emotional state.

[0605] Step 5:

[0606] Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions and sentiment analysis results.

[0607] Input: Conditional information, sentiment analysis results.

[0608] Output: Repayment plan (e.g., 20-year repayment period, monthly payment of 150,000 yen), interest rate estimate (1.5%).

[0609] Specific operation: A generative artificial intelligence model calculates and generates the optimal repayment plan and interest rate estimate based on historical data and statistical models.

[0610] Step 6:

[0611] Server: Sends the generated plan and estimate to the terminal.

[0612] Input: Repayment plan, interest rate estimate.

[0613] Output: The transmitted data reaches the terminal.

[0614] Specific operation: The server encrypts the generated data, packages it into packets, and sends them to the terminal.

[0615] Step 7:

[0616] Terminal: Displays repayment plans and interest rate estimates to customers.

[0617] Input: Repayment plan, interest rate estimate.

[0618] Output: Data displayed to the customer.

[0619] Specific operation: The device's display shows the repayment plan and interest rate estimate. For example, information such as "Repayment period: 20 years, monthly repayment amount: 150,000 yen (interest rate: 1.5%)" will be displayed.

[0620] Education and information provision

[0621] Program processing steps:

[0622] Step 1:

[0623] User: Request topics you want to learn about or related news.

[0624] Input: Request details such as "Basic knowledge about home loans."

[0625] Output: The entered request is saved to the terminal.

[0626] Specific operation: The user enters the topic they want to learn about into the application's request form and presses the "Submit" button.

[0627] Step 2:

[0628] Terminal: Collects customer sentiment data during the request.

[0629] Input: User's facial expression data, voice tone.

[0630] Output: The collected emotional data is saved to the device.

[0631] Specific operation: While a request is being entered, the device's front camera activates to capture the user's facial expression. The microphone also activates to collect voice tone.

[0632] Step 3:

[0633] Terminal: Sends requests and sentiment data to the server.

[0634] Input: Request details, emotional data (facial expressions, tone of voice).

[0635] Output: The transmitted data reaches the server.

[0636] Specific operation: The terminal encrypts the data, packages it into packets, and sends them to the server.

[0637] Step 4:

[0638] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[0639] Input: Emotional data (facial expression, tone of voice).

[0640] Output: Analysis results (e.g., interest, reassurance, etc.).

[0641] Specific operation: The emotion analysis software analyzes facial expressions and voice tone collected during the request to identify the current emotional state.

[0642] Step 5:

[0643] Generative AI model: Generates educational content and market trend information based on the request content and sentiment analysis results.

[0644] Input: Request details, sentiment analysis results.

[0645] Output: Educational content (e.g., detailed information on the basics of home loans), market trend information.

[0646] Specific operation: A generative artificial intelligence model generates educational content and market trend information that reflects the request and emotional state.

[0647] Step 6:

[0648] Server: Sends the generated content to the terminal.

[0649] Input: Educational content, market trend information.

[0650] Output: The transmitted data reaches the terminal.

[0651] Specific operation: The server encrypts the generated data, packages it into packets, and sends them to the terminal.

[0652] Step 7:

[0653] Terminal: Displays generated educational content and market trend information to customers.

[0654] Input: Educational content, market trend information.

[0655] Output: Data displayed to the customer.

[0656] Specific operation: The device's display will show educational content and market trend information. For example, a title such as "Basic Knowledge of Home Loans: Basic Mechanisms and How to Choose" will be displayed, along with a detailed explanation and a video link.

[0657] Rapid service delivery and efficiency

[0658] Program processing steps:

[0659] Step 1:

[0660] User: Enter your question.

[0661] Input: Questions such as "What documents are required for a loan application?"

[0662] Output: The entered question is saved to the device.

[0663] Specific action: The user enters a question into the application's question form and presses the "Submit" button.

[0664] Step 2:

[0665] Terminal: Collects emotional data from customers as they input information.

[0666] Input: User's facial expression data, voice tone.

[0667] Output: The collected emotional data is saved to the device.

[0668] Specific operation: While a question is being entered, the device's front camera activates to capture the user's facial expression. The microphone also activates to collect voice tone.

[0669] Step 3:

[0670] Terminal: Sends questions and sentiment data to the server.

[0671] Input: Question content, emotional data (facial expression, tone of voice).

[0672] Output: The transmitted data reaches the server.

[0673] Specific operation: The terminal encrypts the data, packages it into packets, and sends them to the server.

[0674] Step 4:

[0675] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[0676] Input: Emotional data (facial expression, tone of voice).

[0677] Output: Analysis results (e.g., anxiety, confusion, etc.).

[0678] Specific operation: The emotion analysis software analyzes facial expressions and voice tone collected during question input to identify the current emotional state.

[0679] Step 5:

[0680] Generative AI model: Generates an immediate response based on the question content and sentiment analysis results.

[0681] Input: Question content, sentiment analysis results.

[0682] Output: Generated response (e.g., a list of documents required for the application).

[0683] Specific operation: A generative artificial intelligence model generates an answer that reflects the question and the emotional state.

[0684] Step 6:

[0685] Server: Sends the generated response to the terminal.

[0686] Input: Answer content.

[0687] Output: The transmitted data reaches the terminal.

[0688] Specific operation: The server encrypts the generated response, packages it into packets, and sends them to the terminal.

[0689] Step 7:

[0690] Terminal: Displays the generated response to the customer.

[0691] Input: Answer content.

[0692] Output: Data displayed to the customer.

[0693] Specific actions: The answer will be displayed on the device's screen. For example, it will show "Required documents for application: ID card, residence certificate, income certificate," etc.

[0694] (Application Example 2)

[0695] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0696] Traditional mortgage services often provide uniform preliminary assessment results and advice without considering the customer's emotional state, resulting in insufficient customer support. Furthermore, when providing repayment plans and market information, individual customer psychological backgrounds are often not taken into account, leading to situations where the most suitable information is not provided. It is necessary to address these issues and ensure that customers can use mortgage services with peace of mind.

[0697] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing customer information and emotional data using an emotion analysis engine and inputting the analysis results into a generative artificial intelligence model, means for generating preliminary examination results and a list of necessary documents based on the input information and emotional data, and means for displaying the generated results to the customer. This enables personalized support that takes into account the customer's emotional state.

[0698] "Emotional data" refers to information about a customer's psychological state, obtained from their facial expressions, tone of voice, and other similar data.

[0699] An "emotion analysis engine" is software or a system that analyzes acquired emotional data to identify a customer's emotional state.

[0700] A "generative artificial intelligence model" is an algorithm or model used to generate preliminary loan approval results, repayment plans, educational content, etc., based on input information and sentiment analysis results.

[0701] A "preliminary approval result" is the initial assessment result when a customer applies for a mortgage, indicating whether or not the loan will be approved.

[0702] A "list of required documents" is a compilation of all the documents needed to apply for a mortgage.

[0703] A "repayment plan" is a document outlining how a customer plans to repay their mortgage, and includes details such as the repayment period and interest rate.

[0704] "Educational content" refers to information and teaching materials used to provide customers with knowledge about home loans.

[0705] "Market trend information" refers to data and trend information regarding the current mortgage market.

[0706] An "interface" refers to the screens and functions that customers use to input information or make requests.

[0707] A "server" is a computer system that processes information sent by customers and generates and manages results using generative artificial intelligence models and sentiment analysis engines.

[0708] This invention is a system that analyzes the emotional state of customers in mortgage services and provides personalized support that reflects the results. The following describes in detail how this invention is specifically implemented.

[0709] Overall system configuration

[0710] This system includes the following main components:

[0711] 1. Interface: Provide an interface for customers to enter information and requests regarding their mortgage. This includes smartphone apps, web applications, etc.

[0712] 2. Terminal: A device such as a smartphone or PC that collects emotional data (facial expressions, tone of voice, etc.) along with customer input information.

[0713] 3. Server: A computer system that analyzes received customer information and sentiment data and generates results using a generative artificial intelligence model.

[0714] 4. Emotion analysis engine (e.g., EmotionAPI): Software used to analyze emotional data and identify a customer's emotional state.

[0715] 5. Generative artificial intelligence models (such as OpenAI GPT-3®): Algorithms for generating preliminary loan approval results, repayment plans, educational content, etc., based on customer information and sentiment analysis results.

[0716] Program Processing Description

[0717] Data collection and transmission

[0718] Users access a smartphone app and enter information such as their annual income, occupation, and desired loan amount. Simultaneously, emotional data is collected from the camera and microphone.

[0719] The device sends this input information and emotional data to the server.

[0720] Data Analysis

[0721] The server first sends the received information to an emotion analysis engine (such as EmotionAPI) to analyze the emotion data.

[0722] The emotion analysis engine determines the customer's emotional state (e.g., anxiety, reassurance, interest, etc.).

[0723] result generation

[0724] Generative artificial intelligence models (such as OpenAI GPT-3) generate preliminary screening results and lists of required documents based on customer input information and sentiment analysis results.

[0725] Furthermore, repayment plans, interest rate estimates, educational content, and market trend information are also generated.

[0726] Results display

[0727] The server sends the generated results to the terminal, and the terminal displays them to the user.

[0728] Specific example

[0729] If a user enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and displays an anxious expression, the emotion analysis engine will detect the anxiety. The generative artificial intelligence model will then provide a preliminary assessment result along with a more detailed explanation and offer advice to reassure the user.

[0730] Example of a prompt:

[0731] If a user enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and shows an anxious expression, but requests a preliminary assessment for home purchase: Question: "Is this preliminary assessment result appropriate?" Advice: "Please use the loan service with confidence based on this result. Please see below for a list of required documents and details."

[0732] This will enable the creation of a system that provides mortgage services that take into account the emotional state of the user, thereby improving customer satisfaction.

[0733] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0734] Step 1:

[0735] The user opens a smartphone app and enters information for a preliminary mortgage application (annual income, occupation, desired loan amount, etc.). The app also collects the user's emotional data (facial expressions and tone of voice) through the camera and microphone. This input information and emotional data are then transmitted to the device.

[0736] Input: Annual income, occupation, desired loan amount, facial expression, tone of voice

[0737] Output: Collected input information and sentiment data

[0738] Step 2:

[0739] The device sends the collected input information and sentiment data to the server. A protocol for securely transmitting data over the network (e.g., HTTPS) is used here.

[0740] Input: Collected input information and sentiment data

[0741] Output: Data sent to the server

[0742] Step 3:

[0743] The server sends the received data to the emotion analysis engine. The emotion analysis engine analyzes emotional data such as facial expressions and tone of voice to determine the user's emotional state (e.g., anxiety, reassurance, interest, etc.).

[0744] Input: Collected input information and sentiment data

[0745] Output: Emotional analysis results (anxiety, reassurance, interest, etc.)

[0746] Step 4:

[0747] The server inputs the sentiment analysis results and user input information into a generative artificial intelligence model. Based on this data, the generative AI model generates a preliminary assessment result and a list of required documents. Here, the algorithm evaluates the customer's credit information and creates optimal advice and a list of required documents.

[0748] Input: User input information, sentiment analysis results

[0749] Output: Preliminary review results and list of required documents

[0750] Step 5:

[0751] The server sends the generated preliminary review results and a list of required documents to the terminal. A security protocol (e.g., HTTPS) is used during data transfer.

[0752] Input: Preliminary screening results and list of required documents

[0753] Output: Results sent to the terminal

[0754] Step 6:

[0755] The device displays the received preliminary review results and a list of required documents to the user. Here, the smartphone app presents the results clearly through a user-friendly interface, for example, using notifications or dialog boxes.

[0756] Input: Preliminary screening results and list of required documents

[0757] Output: Results displayed to the user

[0758] Step 7:

[0759] The user reviews the provided results and decides on the next steps. For example, they may prepare necessary documents or answer additional questions.

[0760] Input: Provided result

[0761] Output: User's next action

[0762] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0763] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0764] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0765] [Second Embodiment]

[0766] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0767] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0768] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0769] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0770] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0771] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0772] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0773] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0774] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0775] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0776] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0777] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0778] This invention provides a system that improves efficiency and customer satisfaction in mortgage services by utilizing generative artificial intelligence models. Specific embodiments thereof are described below.

[0779] 1. Preliminary screening and customer support

[0780] System Overview:

[0781] In this system, customers who wish to undergo a preliminary mortgage application enter the necessary information through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model generates a preliminary application result based on that information. Furthermore, it guides the user through the necessary documents and procedures.

[0782] Program processing explanation:

[0783] 1. User: The customer accesses a dedicated app and enters information such as annual income, occupation, and desired loan amount.

[0784] 2. Terminal: Sends the entered information to the server.

[0785] 3. Server: Passes the transmitted information to the generative artificial intelligence model for processing.

[0786] 4. Generative AI Model: Generates preliminary review results and a list of required documents based on the received information.

[0787] 5. Server: Sends the generated results to the terminal.

[0788] 6. Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[0789] Specific example:

[0790] When a customer accesses a dedicated app and enters an annual income of 6 million yen and a desired loan amount of 30 million yen, a generative artificial intelligence model performs a preliminary screening and displays whether the application has passed the preliminary screening and a list of required documents (income verification documents, identity verification documents, etc.).

[0791] 2. Loan Planning and Advice

[0792] System Overview:

[0793] When a customer requests a repayment plan or interest rate estimate, they enter specific details (such as repayment period and desired interest rate) through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate.

[0794] Program processing explanation:

[0795] 1. User: Enter conditions such as repayment period and desired interest rate.

[0796] 2. Terminal: Sends the entered conditions to the server.

[0797] 3. Server: Passes the submitted conditions to the generative artificial intelligence model for processing.

[0798] 4. Generative artificial intelligence models: These models generate optimal repayment plans and interest rate estimates based on given conditions.

[0799] 5. Server: Sends the generated plan and estimate to the terminal.

[0800] 6. Terminal: Displays repayment plans and estimates to customers.

[0801] Specific example:

[0802] When a customer inputs a 20-year repayment period and a fixed interest rate of 1.5%, a generative artificial intelligence model displays details such as a monthly repayment amount of 180,000 yen and a total repayment amount of 32,400,000 yen.

[0803] 3. Education and Information Provision

[0804] System Overview:

[0805] If a customer wants education or information about mortgages, they can request topics and related news through a dedicated app. The request is sent to a server, where a generative artificial intelligence model generates content on foundational knowledge and market trends.

[0806] Program processing explanation:

[0807] 1. User: Requests topics they want to learn about or related news.

[0808] 2. Terminal: Sends the request to the server.

[0809] 3. Server: Passes requests to a generative artificial intelligence model for processing.

[0810] 4. Generative artificial intelligence models: These generate content related to fundamental knowledge, key terminology, and market trends.

[0811] 5. Server: Sends the generated content to the terminal.

[0812] 6. Terminal: Displays content to customers.

[0813] Specific example:

[0814] When a customer requests "basic information on home loans," a generative artificial intelligence model displays explanations of fixed and variable interest rates, the latest market trends, and more.

[0815] 4. Prompt service delivery and efficiency

[0816] System Overview:

[0817] If a customer wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer and provides the result to the customer.

[0818] Program processing explanation:

[0819] 1. User: Enter your question.

[0820] 2. Terminal: Sends the question to the server.

[0821] 3. Server: Passes the question to a generative artificial intelligence model for processing.

[0822] 4. Generative artificial intelligence models: These generate answers to questions.

[0823] 5. Server: Sends the generated response to the terminal.

[0824] 6. Terminal: Display the response to the customer.

[0825] Specific example:

[0826] When a customer enters the question, "What documents are required for a loan application?", a generative artificial intelligence model generates and displays answers such as income statements, identity verification documents, and property appraisal reports.

[0827] The above is a detailed description of embodiments for carrying out the present invention.

[0828] The following describes the processing flow.

[0829] 1. Preliminary screening and customer support

[0830] Program processing explanation:

[0831] Step 1:

[0832] User: Customers access a dedicated app and enter information such as annual income, occupation, and desired loan amount.

[0833] Step 2:

[0834] Terminal: Checks the entered information and verifies that there are no errors.

[0835] Step 3:

[0836] Terminal: After verification, the information will be sent to the server.

[0837] Step 4:

[0838] Server: Records received information in a database and passes it to a generative artificial intelligence model.

[0839] Step 5:

[0840] Generative AI model: Performs a preliminary screening based on customer information and generates the preliminary screening results and a list of required documents.

[0841] Step 6:

[0842] Server: Receives the generated results and sends them to the terminal.

[0843] Step 7:

[0844] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[0845] 2. Loan Planning and Advice

[0846] Program processing explanation:

[0847] Step 1:

[0848] User: The customer accesses a dedicated app and enters conditions such as the repayment period and desired interest rate.

[0849] Step 2:

[0850] Terminal: Checks the entered conditions and verifies that there are no errors.

[0851] Step 3:

[0852] Terminal: After confirmation, the conditions will be sent to the server.

[0853] Step 4:

[0854] Server: Records the received conditions in the database and passes them to the generative artificial intelligence model.

[0855] Step 5:

[0856] Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions.

[0857] Step 6:

[0858] Server: Receives the generated plan and estimate and sends them to the terminal.

[0859] Step 7:

[0860] Terminal: Displays repayment plans and interest rate estimates to customers.

[0861] 3. Education and Information Provision

[0862] Program processing explanation:

[0863] Step 1:

[0864] User: Customers access a dedicated app and request topics they want to learn about and related news.

[0865] Step 2:

[0866] Terminal: Check the request details and verify that there are no errors.

[0867] Step 3:

[0868] Terminal: After verification, the request will be sent to the server.

[0869] Step 4:

[0870] Server: Records received requests in the database and passes them to the generative artificial intelligence model.

[0871] Step 5:

[0872] Generative artificial intelligence models: These models generate content related to learning themes and market trends.

[0873] Step 6:

[0874] Server: Receives the generated content and sends it to the terminal.

[0875] Step 7:

[0876] Terminal: Displays educational content and related news to customers.

[0877] 4. Prompt service delivery and efficiency

[0878] Program processing explanation:

[0879] Step 1:

[0880] User: The customer accesses a dedicated app and enters their question.

[0881] Step 2:

[0882] Terminal: Check the entered questions and verify that there are no errors.

[0883] Step 3:

[0884] Terminal: After verification, the question will be sent to the server.

[0885] Step 4:

[0886] Server: Records received questions in a database and passes them to a generative artificial intelligence model.

[0887] Step 5:

[0888] Generative artificial intelligence models: Generate instant answers to questions.

[0889] Step 6:

[0890] Server: Receives the generated response and sends it to the terminal.

[0891] Step 7:

[0892] Terminal: Displays the answer to the customer immediately.

[0893] (Example 1)

[0894] Next, we will describe Example 1. 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".

[0895] In traditional mortgage services, customers had to go through complex procedures to undergo preliminary screening and loan planning consultations. This often resulted in cumbersome and time-consuming processes. Furthermore, the inability to quickly provide customers with the information and advice they needed could lead to decreased customer satisfaction. Additionally, insufficient education and information regarding mortgages could cause customer anxiety. A system is needed that effectively addresses these problems and efficiently and quickly responds to customer needs.

[0896] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0897] In this invention, the server includes means for providing an interface for inputting customer information, means for transmitting the input information to a processing unit, means for the processing unit to process the input information based on a generative artificial intelligence model and generate a preliminary examination result and a list of required documents, means for displaying the generated preliminary examination result and list of required documents to the customer, means for providing conditions such as repayment period and interest rate to be input by the customer, means for transmitting the input conditions to the processing unit, means for the processing unit to process the input conditions based on a generative artificial intelligence model and generate an optimal repayment plan and interest rate estimate, means for displaying the generated repayment plan and interest rate estimate to the customer, means for providing an interface for the customer to request topics and related information they wish to learn about, means for transmitting the customer's request to the processing unit, means for the processing unit to process the customer's request based on a generative artificial intelligence model and generate educational content and market trend information, and means for displaying the generated educational content and market trend information to the customer. This simplifies the customer's procedure and enables the provision of services quickly and accurately.

[0898] A "customer" is an individual or legal entity that uses a mortgage service.

[0899] An "interface" is a user interaction mechanism that provides a means for users to input information.

[0900] A "processing device" is a computer or server used to process received information and data.

[0901] A "generative artificial intelligence model" is an algorithm or model used to generate specific results based on input data.

[0902] "Preliminary approval result" refers to information indicating the provisional approval status of a loan, generated based on the information entered by the customer.

[0903] The "list of required documents" is a list of documents that the customer must submit during the loan application process.

[0904] A "repayment plan" is a loan repayment plan based on the customer's borrowing conditions.

[0905] An "interest rate estimate" is an estimate of the interest rate that should be applied to a loan offered to a customer.

[0906] "Educational content" refers to teaching materials and documents that contain foundational knowledge and detailed information for customers to learn.

[0907] "Market trend information" refers to data that provides the latest information and trends regarding mortgage and real estate markets.

[0908] A "request" is a request that a customer sends to obtain specific information or services.

[0909] This invention provides a system that improves efficiency and customer satisfaction in mortgage services by utilizing generative artificial intelligence models. Detailed embodiments are described below.

[0910] Overview of the entire system

[0911] This system utilizes a dedicated software application (hereinafter referred to as the dedicated app), a server, and a generative artificial intelligence model (e.g., OpenAI GPT-4). The dedicated app provides an interface for the user to input information, the server transmits the input information to a processing unit, and the generative artificial intelligence model performs predetermined processing. The processing results are then displayed again on the user's terminal.

[0912] 1. Preliminary screening and customer support

[0913] System Overview:

[0914] When a user wishes to undergo a preliminary mortgage application, they enter information such as their annual income, occupation, and desired loan amount through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model uses that information to generate a preliminary application result and a list of required documents.

[0915] Specific example:

[0916] When a customer accesses a dedicated app and enters an annual income of 6 million yen and a desired loan amount of 30 million yen, a generative artificial intelligence model performs a preliminary screening and displays whether the application has passed the preliminary screening and a list of required documents (income verification documents, identity verification documents, etc.).

[0917] Example of a prompt:

[0918] "Please conduct a preliminary mortgage application with an annual income of 6 million yen and a desired loan amount of 30 million yen. Please let me know the results and required documents."

[0919] 2. Loan Planning and Advice

[0920] System Overview:

[0921] When a user requests a repayment plan or interest rate estimate, they enter conditions such as the repayment period and desired interest rate through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate.

[0922] Specific example:

[0923] When a customer inputs a 20-year repayment period and a fixed interest rate of 1.5%, a generative artificial intelligence model displays details such as a monthly repayment amount of 180,000 yen and a total repayment amount of 32,400,000 yen.

[0924] Example of a prompt:

[0925] "Please provide a mortgage repayment plan with a 20-year repayment period and a fixed interest rate of 1.5%, including the monthly payment amount and the total repayment amount."

[0926] 3. Education and Information Provision

[0927] System Overview:

[0928] If a user wishes to receive education or information about mortgages, they can request topics and related news through a dedicated app. The request is sent to a server, where a generative artificial intelligence model generates content on basic knowledge and market trends.

[0929] Specific example:

[0930] When a customer requests "basic information on home loans," a generative artificial intelligence model displays explanations of fixed and variable interest rates, the latest market trends, and more.

[0931] Example of a prompt:

[0932] "Please explain the basics of home loans, including the difference between fixed and variable interest rates."

[0933] 4. Prompt service delivery and efficiency

[0934] System Overview:

[0935] If a user wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer and provides the result to the user.

[0936] Specific example:

[0937] When a customer enters the question, "What documents are required for a loan application?", a generative artificial intelligence model generates and displays answers such as income statements, identity verification documents, and property appraisal reports.

[0938] Example of a prompt:

[0939] "What documents are required for a loan application?"

[0940] The above describes the embodiments for carrying out the present invention. This enables customers to use mortgage services simply and quickly, and allows for efficient preliminary screening, presentation of repayment plans, and provision of educational content.

[0941] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0942] 1. Preliminary screening and customer support

[0943] Processing flow

[0944] Step 1:

[0945] User: Open the dedicated app and enter information such as annual income, occupation, and desired loan amount.

[0946] Input: Information such as annual income, occupation, and desired loan amount.

[0947] Specific action: The user types text on the smartphone screen and presses the send button.

[0948] Step 2:

[0949] Terminal: Sends the entered information to the server.

[0950] Input: Information entered by the user, such as annual income, occupation, and desired loan amount.

[0951] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[0952] Step 3:

[0953] Server: Passes the received information to the generative artificial intelligence model.

[0954] Input: User information sent as an HTTP request

[0955] Data processing: Execute API calls to pass information to a generative artificial intelligence model.

[0956] Specific operation: The server passes data to the API endpoint.

[0957] Step 4:

[0958] Generative AI model: Generates preliminary review results and a list of required documents based on the input information.

[0959] Input: User information (annual income, occupation, desired loan amount, etc.)

[0960] Data processing: An algorithm analyzes user information and generates a preliminary review result and a list of required documents.

[0961] Specific operation: The model performs processing and outputs the results in a data format.

[0962] Step 5:

[0963] Server: Sends the generated preliminary review results and list of required documents to the terminal.

[0964] Input: Output data from a generative artificial intelligence model (preliminary review results, list of required documents)

[0965] Specific operation: Send output data to the terminal as an HTTP response.

[0966] Step 6:

[0967] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[0968] Input: Preliminary review results and list of required documents sent as an HTTP response.

[0969] Specific action: Display the result on the screen.

[0970] 2. Loan Planning and Advice

[0971] Processing flow

[0972] Step 1:

[0973] User: Enter conditions such as repayment period and desired interest rate using the dedicated app.

[0974] Input: Conditions such as repayment period and desired interest rate.

[0975] Specific action: The user enters text into the input field and presses the submit button.

[0976] Step 2:

[0977] Terminal: Sends the entered conditions to the server.

[0978] Input: User-entered conditions such as repayment period and desired interest rate.

[0979] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[0980] Step 3:

[0981] Server: Passes the received conditions to the generative artificial intelligence model.

[0982] Input: User conditions sent as an HTTP request

[0983] Data processing: Execute API calls to pass conditions to a generative artificial intelligence model.

[0984] Specific operation: The server passes data to the API endpoint.

[0985] Step 4:

[0986] Generative artificial intelligence models: Generate optimal repayment plans and interest rate estimates based on given conditions.

[0987] Input: User conditions (repayment period, desired interest rate, etc.)

[0988] Data processing: Algorithms analyze user conditions and create repayment plans and interest rate estimates.

[0989] Specific operation: The model performs processing and outputs the results in a data format.

[0990] Step 5:

[0991] Server: Sends the generated repayment plan and interest rate estimate to the terminal.

[0992] Input: Output data from a generative artificial intelligence model (repayment plan, interest rate estimate).

[0993] Specific operation: Send output data to the terminal as an HTTP response.

[0994] Step 6:

[0995] Terminal: Displays repayment plans and interest rate estimates to customers.

[0996] Input: Repayment plan and interest rate estimate sent as an HTTP response.

[0997] Specific action: Display the result on the screen.

[0998] 3. Education and Information Provision

[0999] Processing flow

[1000] Step 1:

[1001] User: Request topics you want to learn about or related news.

[1002] Input: Topic you want to learn about, related news

[1003] Specific steps: Select a theme using the dedicated app and submit a request.

[1004] Step 2:

[1005] Terminal: Sends a request to the server.

[1006] Input: User selected theme, related news

[1007] Specific operation: The selected data is encoded and sent to the server as an HTTP request.

[1008] Step 3:

[1009] Server: Passes the received request to the generative artificial intelligence model.

[1010] Input: User request sent as an HTTP request

[1011] Data processing: Execute API calls to pass the request content to a generative artificial intelligence model.

[1012] Specific operation: The server passes data to the API endpoint.

[1013] Step 4:

[1014] Generative artificial intelligence model: Generates educational content and market trend information based on the requested content.

[1015] Input: User request (theme, related news)

[1016] Data processing: Algorithms analyze requests and create educational content and market trend information.

[1017] Specific operation: The model performs processing and outputs the results in a data format.

[1018] Step 5:

[1019] Server: Sends generated educational content and market trend information to terminals.

[1020] Input: Output data from generative artificial intelligence models (educational content, market trend information).

[1021] Specific operation: Send output data to the terminal as an HTTP response.

[1022] Step 6:

[1023] Terminal: Displays educational content and market trend information to customers.

[1024] Input: Educational content and market trend information sent as an HTTP response.

[1025] Specific action: Display the result on the screen.

[1026] 4. Prompt service delivery and efficiency

[1027] Processing flow

[1028] Step 1:

[1029] User: Enter your question.

[1030] Input: Question content

[1031] Specific actions: Enter text into the question input field in the dedicated app and tap the submit button.

[1032] Step 2:

[1033] Terminal: Sends the question to the server.

[1034] Input: Question entered by the user

[1035] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[1036] Step 3:

[1037] Server: Passes the question to a generative artificial intelligence model for processing.

[1038] Input: User question sent as an HTTP request

[1039] Data processing: Execute API calls to pass the question content to a generative artificial intelligence model.

[1040] Specific operation: The server passes data to the API endpoint.

[1041] Step 4:

[1042] Generative artificial intelligence models: Generate answers to questions.

[1043] Input: User Question

[1044] Data processing: Algorithms analyze the question and generate the optimal answer.

[1045] Specific operation: The model performs processing and outputs the results in a data format.

[1046] Step 5:

[1047] Server: Sends the generated response to the terminal.

[1048] Input: Output data (answer) from a generative artificial intelligence model.

[1049] Specific operation: Send output data to the terminal as an HTTP response.

[1050] Step 6:

[1051] Terminal: Display the answer to the customer.

[1052] Input: Response sent as an HTTP response

[1053] Specific action: Display the result on the screen.

[1054] (Application Example 1)

[1055] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1056] Current mortgage services often require multiple steps, making it difficult for customers to efficiently receive pre-approval, repayment plans, educational information, and answers to their questions on a single platform. Furthermore, real-time service can be time-consuming, leading to decreased customer satisfaction. Therefore, a new system that is both efficient and enhances customer satisfaction is needed.

[1057] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1058] In this invention, the server includes means for providing an interface for inputting customer information, means for transmitting the inputted information to the server, means for processing the inputted information on the server based on a generative artificial intelligence model to generate a preliminary screening result and a list of required documents, means for displaying the generated preliminary screening result and list of required documents to the customer, means for providing a mortgage service performed through a virtual reality device, and means for providing an integrated preliminary screening, repayment plan, educational information, and answers to questions within a virtual store using a head-mounted display. This enables the customer to efficiently receive mortgage services in a virtual reality environment.

[1059] "Customer information" refers to personal information necessary for mortgage services, such as annual income, occupation, desired loan amount, repayment period, and desired interest rate.

[1060] A "generative artificial intelligence model" is a type of artificial intelligence technology that generates optimal preliminary screening results, repayment plans, interest rate estimates, educational content, and answers to questions based on input conditions and information.

[1061] A "virtual reality device" is a device that allows users to experience a virtual environment, and specifically includes head-mounted displays, controllers, and the like.

[1062] A "head-mounted display" is a device that users wear on their heads to visually experience virtual reality.

[1063] A "virtual store" is a virtual store space that can be visited through a virtual reality device, providing an interface for customers to receive mortgage services.

[1064] A "preliminary assessment result" is an initial evaluation of the mortgage loan the customer is requesting, indicating the likelihood of loan approval, necessary documents, and the next steps to take.

[1065] A "repayment plan" is a detailed repayment plan for a mortgage, including the repayment period, monthly payment amount, and total repayment amount.

[1066] An "interest rate estimate" is an estimate based on the interest rate applicable to a mortgage loan, used to calculate the monthly repayment amount and the total repayment amount.

[1067] "Educational content" refers to information used to learn about mortgage-related knowledge, market trends, and important terminology.

[1068] "Answer to a question" refers to the immediate response from a generative artificial intelligence model to a question entered by the customer.

[1069] This invention is a mortgage service system using a virtual reality device. This system aims to provide customers with highly efficient and satisfying services by utilizing a generative artificial intelligence model.

[1070] 1. Required hardware and software

[1071] Hardware:

[1072] Virtual reality devices (e.g., regular head-mounted displays)

[1073] Communication server

[1074] User device (smartphone or computer)

[1075] software:

[1076] OpenAI API (an API for using generative artificial intelligence models)

[1077] Virtual reality interface software

[1078] Server communication software

[1079] 2. Data processing and data calculation

[1080] server:

[1081] The system receives user information (annual income, occupation, desired loan amount, etc.) and passes it to a generative artificial intelligence model.

[1082] Based on the output from the model, preliminary screening results, repayment plans, educational content, and answers to questions are generated.

[1083] The generated results are sent back to the user.

[1084] User terminal:

[1085] It provides an interface for users to input information.

[1086] Send the entered information to the server.

[1087] The results returned from the server are displayed within the virtual reality environment.

[1088] Generative artificial intelligence models:

[1089] Based on the entered user information and conditions, the system generates the necessary results.

[1090] Specifically, it outputs preliminary screening results, repayment plans, interest rate estimates, educational content, and answers to frequently asked questions.

[1091] 3. Specific Examples

[1092] Preliminary screening and customer support:

[1093] As a concrete example, a user wears a head-mounted display and accesses a dedicated terminal in a virtual store, entering an annual income of 6 million yen, occupation as a company employee, and desired loan amount of 30 million yen. This information is sent to a server, where a generative artificial intelligence model performs a preliminary assessment, and the preliminary assessment results, including a list of required documents, are immediately displayed within the virtual reality environment.

[1094] Example of a prompt:

[1095] Please use the following information to pre-approve your mortgage:

[1096] Annual income: 6 million yen

[1097] Occupation: Company employee

[1098] Desired loan amount: 30 million yen

[1099] In this way, customers can efficiently utilize mortgage services in real time within a virtual reality environment.

[1100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1101] Step 1:

[1102] The user wears a head-mounted display and accesses a virtual store. The user operates the interface to enter personal information such as annual income, occupation, and desired loan amount. The data entered includes an annual income of 6 million yen, occupation of company employee, and desired loan amount of 30 million yen.

[1103] Step 2:

[1104] The terminal sends the entered information to the server. This input data includes the user's annual income, occupation, and desired loan amount. The terminal then sends this data to the server in an appropriate format.

[1105] Step 3:

[1106] The server passes the received information to a generative artificial intelligence model. Specifically, it uses the OpenAI API to pass data such as the user's annual income, occupation, and desired loan amount as prompts to the model. Based on the input data, the generative AI model generates a preliminary screening result and a list of required documents. As a result of this process, the preliminary screening result and the list of required documents are generated.

[1107] Step 4:

[1108] The server receives the preliminary review results and required document list returned from the generative artificial intelligence model. The server verifies that this data is accurate and then prepares it for transmission.

[1109] Step 5:

[1110] The server sends the generated preliminary review results and a list of required documents to the terminal. The server formats the received data through the appropriate interface and sends it to the user's terminal.

[1111] Step 6:

[1112] The terminal displays the preliminary screening results and a list of required documents to the user within a virtual reality environment. Specifically, this includes displaying the preliminary screening results and required documents on a head-mounted display. The user can then confirm the next steps based on the list within the virtual store.

[1113] This processing flow allows users to efficiently access mortgage services within a virtual reality environment.

[1114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1115] This invention provides a system that combines a generative artificial intelligence model and an emotion engine in mortgage services to provide personalized support that takes into account the emotional state of the customer. Specific embodiments thereof are described below.

[1116] 1. Preliminary screening and customer support

[1117] System Overview:

[1118] In this system, customers who wish to undergo a preliminary mortgage application enter the necessary information through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model generates a preliminary application result based on that information. Furthermore, an emotion engine analyzes the customer's emotional state and provides a preliminary application result and guidance on necessary documents that reflects the results.

[1119] Program processing explanation:

[1120] 1. User: The customer accesses a dedicated app and enters information such as annual income, occupation, and desired loan amount.

[1121] 2. Terminal: Collects emotional data from sensors such as cameras and microphones, along with customer input information.

[1122] 3. Terminal: Sends input information and emotion data to the server.

[1123] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[1124] 5. Generative AI Model: Generates preliminary screening results and a list of required documents based on customer information and sentiment analysis results.

[1125] 6. Server: Sends the generated results to the terminal.

[1126] 7. Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[1127] Specific example:

[1128] If a customer enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and simultaneously displays an anxious expression, the emotion engine will detect the anxiety, and the generative artificial intelligence model will display a preliminary approval status and a list of required documents along with a more detailed explanation.

[1129] 2. Loan Planning and Advice

[1130] System Overview:

[1131] When a customer requests a repayment plan or interest rate estimate, they enter specific details (such as repayment period and desired interest rate) through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate. Furthermore, an emotion engine analyzes the customer's emotional state and provides advice reflecting the results.

[1132] Program processing explanation:

[1133] 1. User: Enter conditions such as repayment period and desired interest rate.

[1134] 2. Terminal: Collects customer sentiment data while they are entering conditions.

[1135] 3. Terminal: Sends input conditions and emotion data to the server.

[1136] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[1137] 5. Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions and sentiment analysis results.

[1138] 6. Server: Sends the generated plan and estimate to the terminal.

[1139] 7. Terminal: Displays repayment plans and interest rate estimates to customers.

[1140] Specific example:

[1141] If a customer inputs a 20-year repayment period and a desired interest rate of 1.5%, and simultaneously displays a reassuring expression, the emotion engine detects this reassurance, and the generative artificial intelligence model provides a repayment plan along with standard explanations.

[1142] 3. Education and Information Provision

[1143] System Overview:

[1144] When customers wish to receive education and information about mortgages, they can request topics and related news through a dedicated app. The requests are sent to a server, where a generative artificial intelligence model generates content on fundamental knowledge and market trends. An emotion engine analyzes the customer's emotional state and provides educational content that reflects the results.

[1145] Program processing explanation:

[1146] 1. User: Requests topics they want to learn about or related news.

[1147] 2. Terminal: Collects customer sentiment data during the request.

[1148] 3. Terminal: Sends requests and sentiment data to the server.

[1149] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[1150] 5. Generative AI Models: These models generate content based on the request content and sentiment analysis results.

[1151] 6. Server: Sends the generated content to the terminal.

[1152] 7. Terminal: Displays content to customers.

[1153] Specific example:

[1154] If a customer requests "basic knowledge about home loans" and simultaneously shows an interested expression, the emotion engine detects their interest, and the generative artificial intelligence model provides more in-depth basic knowledge.

[1155] 4. Prompt service delivery and efficiency

[1156] System Overview:

[1157] If a customer wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer. The emotion engine then analyzes the customer's emotional state and customizes the answer before providing it.

[1158] Program processing explanation:

[1159] 1. User: Enter your question.

[1160] 2. Terminal: Collects emotional data from customers as they are entering information.

[1161] 3. Terminal: Sends question and sentiment data to the server.

[1162] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[1163] 5. Generative AI Model: Generates immediate responses based on the question content and sentiment analysis results.

[1164] 6. Server: Sends the generated response to the terminal.

[1165] 7. Terminal: Displays the answer to the customer immediately.

[1166] Specific example:

[1167] If a customer enters a question like, "What documents are needed for a loan application?" and is feeling anxious, the emotion engine detects the anxiety, and the generative artificial intelligence model provides a polite and easy-to-understand explanation.

[1168] The following describes the processing flow.

[1169] 1. Preliminary screening and customer support

[1170] Program processing explanation:

[1171] Step 1:

[1172] User: Customers access a dedicated app and enter information such as annual income, occupation, and desired loan amount.

[1173] Step 2:

[1174] Terminal: Uses the camera and microphone on the input screen to collect data on the customer's facial expressions and voice.

[1175] Step 3:

[1176] Terminal: Sends input information and collected sentiment data to the server.

[1177] Step 4:

[1178] Server: Records received information in a database and passes it to a generative artificial intelligence model.

[1179] Step 5:

[1180] Server: The emotion engine analyzes emotional data and determines the customer's emotional state (e.g., feeling safe, anxious, etc.).

[1181] Step 6:

[1182] Generative AI model: Generates preliminary screening results and a list of required documents based on the determined emotional state and customer information.

[1183] Step 7:

[1184] Server: Sends the generated preliminary review results and list of required documents to the terminal.

[1185] Step 8:

[1186] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[1187] 2. Loan Planning and Advice

[1188] Program processing explanation:

[1189] Step 1:

[1190] User: The customer enters conditions such as the repayment period and desired interest rate.

[1191] Step 2:

[1192] Terminal: Collects data on the customer's facial expressions and voice while they are entering repayment terms.

[1193] Step 3:

[1194] Terminal: Sends input conditions and collected sentiment data to the server.

[1195] Step 4:

[1196] Server: Records the received conditions in the database and passes them to the generative artificial intelligence model.

[1197] Step 5:

[1198] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1199] Step 6:

[1200] Generative artificial intelligence model: Generates optimal repayment plans and interest rate estimates based on customer conditions and sentiment analysis results.

[1201] Step 7:

[1202] Server: Sends the generated repayment plan and interest rate estimate to the terminal.

[1203] Step 8:

[1204] Terminal: Displays repayment plans and interest rate estimates to customers.

[1205] 3. Education and Information Provision

[1206] Program processing explanation:

[1207] Step 1:

[1208] User: Requests topics and related news that customers want to learn about.

[1209] Step 2:

[1210] Terminal: Collects data on the customer's facial expressions and voice while they are making a request.

[1211] Step 3:

[1212] Terminal: Sends request information and collected sentiment data to the server.

[1213] Step 4:

[1214] Server: Records received requests in the database and passes them to the generative artificial intelligence model.

[1215] Step 5:

[1216] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1217] Step 6:

[1218] Generative AI model: Generates educational content and market trend information based on the request content and sentiment analysis results.

[1219] Step 7:

[1220] Server: Sends the generated content to the terminal.

[1221] Step 8:

[1222] Terminal: Displays educational content and related news to customers.

[1223] 4. Prompt service delivery and efficiency

[1224] Program processing explanation:

[1225] Step 1:

[1226] User: The customer enters their question.

[1227] Step 2:

[1228] Terminal: Collects data on the customer's facial expressions and voice while they are entering questions.

[1229] Step 3:

[1230] Terminal: Sends question information and collected sentiment data to the server.

[1231] Step 4:

[1232] Server: Records received questions in a database and passes them to a generative artificial intelligence model.

[1233] Step 5:

[1234] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1235] Step 6:

[1236] Generative AI model: Generates an immediate response based on the question content and sentiment analysis results.

[1237] Step 7:

[1238] Server: Sends the generated response to the terminal.

[1239] Step 8:

[1240] Terminal: Displays the answer to the customer immediately.

[1241] (Example 2)

[1242] Next, we will describe Example 2. 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".

[1243] Traditional mortgage services have a standardized approach to pre-approval, repayment plan proposals, education, and information provision, making it difficult to address the individual emotional states and circumstances of customers. Furthermore, the lack of consideration of emotional data made it challenging to provide timely and appropriate answers to customer questions and requests. This resulted in lower customer satisfaction and presented challenges in improving service quality.

[1244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1245] In this invention, the server includes means for providing an input device for inputting customer information, means for transmitting the input information, means for processing the input information and sentiment data collected from a terminal using a sentiment analysis device on the server to generate preliminary examination results and a list of required documents based on a generative artificial intelligence model, and means for displaying the generated preliminary examination results and list of required documents.

[1246] This will enable the provision of personalized mortgage services that take into account the emotional state of the customer.

[1247] 1. "Input device" refers to a device used by a customer to input information, and includes devices such as keyboards, touchscreens, and mice.

[1248] 2. "Transmission means" refers to a system equipped with functions and protocols for transmitting input information or data to a server.

[1249] 3. An "emotion analysis device" is a combination of software and hardware used to analyze collected emotional data and determine the emotional state of a customer.

[1250] 4. A "generative artificial intelligence model" is an artificial intelligence algorithm and system that generates preliminary screening results, lists of required documents, repayment plans, educational content, etc., based on input information and emotional data.

[1251] 5. The "preliminary screening result" is a prediction of the mortgage loan approval based on the information entered by the customer.

[1252] 6. The "Required Documents List" is a list of documents required for a mortgage application, based on the preliminary screening results.

[1253] 7. A "repayment plan" is a system that shows the optimal loan repayment schedule and plan based on the customer's input conditions and emotional data.

[1254] 8. "Interest rate estimate" refers to information that shows the predicted loan interest rate based on the customer's input conditions.

[1255] 9. "Educational content" refers to materials and documents that include information and market trends related to mortgages, generated based on topics that customers wish to learn about.

[1256] 10. "Market Trend Information" refers to data and analysis results that show the latest information and trends in the mortgage market.

[1257] 11. "Interface" refers to all elements, including input screens and control panels, that customers interact with in order to use the system.

[1258] This invention provides a system that combines a generative artificial intelligence model and an emotion analysis device in mortgage services to provide personalized support that takes into account the emotional state of the customer. Specific embodiments of this system will be described in detail below.

[1259] Preliminary screening and customer support

[1260] The system begins with the user (customer) entering basic information such as annual income, occupation, and desired loan amount through a dedicated application. This information is entered using the terminal's input device (keyboard, touchscreen, etc.). Simultaneously, sensors such as the terminal's camera and microphone capture the customer's facial expressions and voice tone, collecting emotional data. The terminal encrypts this information and transmits it to the server. On the server, an emotion analysis device analyzes the emotional data to determine the customer's emotional state. Next, a generative artificial intelligence model generates a preliminary screening result and a list of required documents based on the customer's basic information and the emotional analysis results. The generated results are transmitted from the server to the terminal and displayed to the customer.

[1261] Specific example

[1262] If a customer enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and shows an anxious expression, the emotion analysis device will detect "anxiety," and the generative artificial intelligence model will display a preliminary approval status and a list of required documents along with a more detailed explanation.

[1263] Loan planning and advice

[1264] Next, the user (customer) inputs conditions such as repayment period and desired interest rate through a dedicated application. The terminal's camera and microphone capture the customer's facial expressions and voice in real time during this input process, collecting emotional data. The terminal sends the data to a server, where an emotion analysis device analyzes the emotional data and determines the customer's emotional state. A generative artificial intelligence model generates an optimal repayment plan and interest rate estimate based on the customer's conditions and emotional state. The generated results are sent from the server to the terminal and displayed to the customer.

[1265] Specific example

[1266] If a customer enters a 20-year repayment period and a desired interest rate of 1.5%, and displays a reassuring expression, the emotion analysis device detects "reassurance," and the generative artificial intelligence model provides a repayment plan along with a standard explanation.

[1267] Education and information provision

[1268] Furthermore, if a customer wishes to receive education or information about mortgages, they can request topics and related news through a dedicated application. During this request, the device's camera and microphone collect customer emotional data, which the device then sends to a server. The server's emotion analysis device analyzes the emotional data and determines the customer's emotional state. A generative artificial intelligence model generates educational content and market trend information based on the request and emotional state, sends it from the server to the device, and displays it to the customer.

[1269] Specific example

[1270] If a customer requests "basic knowledge about home loans" and shows an interested expression, the emotion analysis device detects "interest," and the generative artificial intelligence model provides more detailed basic knowledge.

[1271] Rapid service delivery and efficiency

[1272] When a customer requires an immediate answer, they enter their question through a dedicated application. While the customer is entering the question, the device's camera and microphone collect emotional data, which the device then sends to a server. The server's emotion analysis system analyzes this data to determine the customer's emotional state. A generative artificial intelligence model generates an immediate answer based on the question and the customer's emotional state, which is then sent from the server to the device and displayed to the customer.

[1273] Specific example

[1274] If a customer enters a question like, "What documents are needed for a loan application?" and is feeling anxious, the emotion analysis system will detect this anxiety, and the generative artificial intelligence model will provide a polite and easy-to-understand explanation.

[1275] Example of a prompt

[1276] 1. "Please explain the system's workflow: When a customer enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and shows an anxious expression, the emotional engine detects that anxiety."

[1277] 2. "Please describe the advice system that provides guidance when a customer enters a 20-year repayment period and a desired interest rate of 1.5%, and then appears relieved."

[1278] 3. "Please describe an example of educational content you would offer to a customer who requests and expresses interest in 'Basic Knowledge of Home Loans'."

[1279] 4. "Please describe the flow of a system that allows customers to instantly input questions and receive quick responses if they are feeling anxious."

[1280] The above details relate to embodiments of the present invention.

[1281] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1282] Preliminary screening and customer support

[1283] Program processing steps:

[1284] Step 1:

[1285] User: Open the dedicated application and enter information such as annual income, occupation, and desired loan amount.

[1286] Input information: Annual income (6 million yen), occupation (company employee), desired loan amount (30 million yen), etc.

[1287] Output: The input data is saved to the terminal.

[1288] Specific operation: The user enters the necessary information using a touchscreen or keyboard on a device such as a smartphone or PC, and then presses the "Send" button.

[1289] Step 2:

[1290] Device: Collects emotional data from sensors such as cameras and microphones, along with input information.

[1291] Input: User's facial expression data, voice tone.

[1292] Output: The collected emotional data is saved to the device.

[1293] Specific operation: The device's front camera automatically activates and captures the user's facial expressions. The microphone also activates and analyzes the voice tone to obtain emotion data.

[1294] Step 3:

[1295] Terminal: Sends input information and emotion data to the server.

[1296] Input: User information (annual income, occupation, desired loan amount), emotional data (facial expression, tone of voice).

[1297] Output: Data is sent to the server.

[1298] Specific operation: The terminal encrypts the data into packets and sends them to the server over the network.

[1299] Step 4:

[1300] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1301] Input: Emotional data (facial expression, tone of voice).

[1302] Output: Analysis results (e.g., anxiety, reassurance, etc.).

[1303] Specific operation: Using an emotion analysis algorithm, identify the customer's emotional state (e.g., anxiety) from changes in facial expression and tone of voice.

[1304] Step 5:

[1305] Generative AI model: Generates preliminary screening results and a list of required documents based on customer information and sentiment analysis results.

[1306] Input: User information, sentiment analysis results.

[1307] Output: Preliminary review results, list of required documents.

[1308] Specific operation: A generative artificial intelligence model uses historical data and statistical models to generate a preliminary screening result (e.g., A-rank screening passed) and a list of required documents (e.g., identification card, residence certificate, income certificate).

[1309] Step 6:

[1310] Server: Sends the generated results to the terminal.

[1311] Input: Preliminary screening results, list of required documents.

[1312] Output: Data is sent to the terminal.

[1313] Specific operation: The server generates data, packages it into packets, encrypts them, and sends them to the terminal.

[1314] Step 7:

[1315] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[1316] Input: Preliminary screening results, list of required documents.

[1317] Output: Data displayed to the customer.

[1318] Specific operation: The terminal's display shows the result of passing the preliminary screening and a list of required documents. For example, it might display "Preliminary screening result: A rank approved, Required documents: Identification document, resident registration certificate, income certificate."

[1319] Loan planning and advice

[1320] Program processing steps:

[1321] Step 1:

[1322] User: Enter conditions such as repayment period and desired interest rate.

[1323] Input: Conditions such as repayment period (20 years) and desired interest rate (1.5%).

[1324] Output: The input data is saved to the terminal.

[1325] Specific operation: The user enters the repayment period and interest rate into the input form of the dedicated application and presses the "Submit" button.

[1326] Step 2:

[1327] Terminal: Collects customer sentiment data while they are entering conditions.

[1328] Input: User's facial expression data, voice tone.

[1329] Output: The collected emotional data is saved to the device.

[1330] Specific operation: While inputting conditions, the terminal's front camera activates to monitor the customer's facial expressions. The microphone also activates to analyze the tone of voice.

[1331] Step 3:

[1332] Terminal: Sends input conditions and emotion data to the server.

[1333] Input: Conditional information (repayment period, desired interest rate), emotional data (facial expression, tone of voice).

[1334] Output: The transmitted data reaches the server.

[1335] Specific operation: The terminal encrypts the data, bundles it into packets, and sends them to the server.

[1336] Step 4:

[1337] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1338] Input: Emotional data (facial expression, tone of voice).

[1339] Output: Analysis results (e.g., feelings of security, interest, etc.).

[1340] Specific operation: Emotion analysis software analyzes the user's facial expressions and tone of voice to identify their current emotional state.

[1341] Step 5:

[1342] Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions and sentiment analysis results.

[1343] Input: Conditional information, sentiment analysis results.

[1344] Output: Repayment plan (e.g., 20-year repayment period, monthly payment of 150,000 yen), interest rate estimate (1.5%).

[1345] Specific operation: A generative artificial intelligence model calculates and generates the optimal repayment plan and interest rate estimate based on historical data and statistical models.

[1346] Step 6:

[1347] Server: Sends the generated plan and estimate to the terminal.

[1348] Input: Repayment plan, interest rate estimate.

[1349] Output: The transmitted data reaches the terminal.

[1350] Specific operation: The server encrypts the generated data, packages it into packets, and sends them to the terminal.

[1351] Step 7:

[1352] Terminal: Displays repayment plans and interest rate estimates to customers.

[1353] Input: Repayment plan, interest rate estimate.

[1354] Output: Data displayed to the customer.

[1355] Specific operation: The device's display shows the repayment plan and interest rate estimate. For example, information such as "Repayment period: 20 years, monthly repayment amount: 150,000 yen (interest rate: 1.5%)" will be displayed.

[1356] Education and information provision

[1357] Program processing steps:

[1358] Step 1:

[1359] User: Request topics you want to learn about or related news.

[1360] Input: Request details such as "Basic knowledge about home loans."

[1361] Output: The entered request is saved to the terminal.

[1362] Specific operation: The user enters the topic they want to learn about into the application's request form and presses the "Submit" button.

[1363] Step 2:

[1364] Terminal: Collects customer sentiment data during the request.

[1365] Input: User's facial expression data, voice tone.

[1366] Output: The collected emotional data is saved to the device.

[1367] Specific operation: While a request is being entered, the device's front camera activates to capture the user's facial expression. The microphone also activates to collect voice tone.

[1368] Step 3:

[1369] Terminal: Sends requests and sentiment data to the server.

[1370] Input: Request details, emotional data (facial expressions, tone of voice).

[1371] Output: The transmitted data reaches the server.

[1372] Specific operation: The terminal encrypts the data, packages it into packets, and sends them to the server.

[1373] Step 4:

[1374] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1375] Input: Emotional data (facial expression, tone of voice).

[1376] Output: Analysis results (e.g., interest, reassurance, etc.).

[1377] Specific operation: The emotion analysis software analyzes facial expressions and voice tone collected during the request to identify the current emotional state.

[1378] Step 5:

[1379] Generative AI model: Generates educational content and market trend information based on the request content and sentiment analysis results.

[1380] Input: Request details, sentiment analysis results.

[1381] Output: Educational content (e.g., detailed information on the basics of home loans), market trend information.

[1382] Specific operation: A generative artificial intelligence model generates educational content and market trend information that reflects the request and emotional state.

[1383] Step 6:

[1384] Server: Sends the generated content to the terminal.

[1385] Input: Educational content, market trend information.

[1386] Output: The transmitted data reaches the terminal.

[1387] Specific operation: The server encrypts the generated data, packages it into packets, and sends them to the terminal.

[1388] Step 7:

[1389] Terminal: Displays generated educational content and market trend information to customers.

[1390] Input: Educational content, market trend information.

[1391] Output: Data displayed to the customer.

[1392] Specific operation: The device's display will show educational content and market trend information. For example, a title such as "Basic Knowledge of Home Loans: Basic Mechanisms and How to Choose" will be displayed, along with a detailed explanation and a video link.

[1393] Rapid service delivery and efficiency

[1394] Program processing steps:

[1395] Step 1:

[1396] User: Enter your question.

[1397] Input: Questions such as "What documents are required for a loan application?"

[1398] Output: The entered question is saved to the device.

[1399] Specific action: The user enters a question into the application's question form and presses the "Submit" button.

[1400] Step 2:

[1401] Terminal: Collects emotional data from customers as they input information.

[1402] Input: User's facial expression data, voice tone.

[1403] Output: The collected emotional data is saved to the device.

[1404] Specific operation: While a question is being entered, the device's front camera activates to capture the user's facial expression. The microphone also activates to collect voice tone.

[1405] Step 3:

[1406] Terminal: Sends questions and sentiment data to the server.

[1407] Input: Question content, emotional data (facial expression, tone of voice).

[1408] Output: The transmitted data reaches the server.

[1409] Specific operation: The terminal encrypts the data, packages it into packets, and sends them to the server.

[1410] Step 4:

[1411] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1412] Input: Emotional data (facial expression, tone of voice).

[1413] Output: Analysis results (e.g., anxiety, confusion, etc.).

[1414] Specific operation: The emotion analysis software analyzes facial expressions and voice tone collected during question input to identify the current emotional state.

[1415] Step 5:

[1416] Generative AI model: Generates an immediate response based on the question content and sentiment analysis results.

[1417] Input: Question content, sentiment analysis results.

[1418] Output: Generated response (e.g., a list of documents required for the application).

[1419] Specific operation: A generative artificial intelligence model generates an answer that reflects the question and the emotional state.

[1420] Step 6:

[1421] Server: Sends the generated response to the terminal.

[1422] Input: Answer content.

[1423] Output: The transmitted data reaches the terminal.

[1424] Specific operation: The server encrypts the generated response, packages it into packets, and sends them to the terminal.

[1425] Step 7:

[1426] Terminal: Displays the generated response to the customer.

[1427] Input: Answer content.

[1428] Output: Data displayed to the customer.

[1429] Specific actions: The answer will be displayed on the device's screen. For example, it will show "Required documents for application: ID card, residence certificate, income certificate," etc.

[1430] (Application Example 2)

[1431] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1432] Traditional mortgage services often provide uniform preliminary assessment results and advice without considering the customer's emotional state, resulting in insufficient customer support. Furthermore, when providing repayment plans and market information, individual customer psychological backgrounds are often not taken into account, leading to situations where the most suitable information is not provided. It is necessary to address these issues and ensure that customers can use mortgage services with peace of mind.

[1433] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing customer information and emotional data using an emotion analysis engine and inputting the analysis results into a generative artificial intelligence model, means for generating preliminary examination results and a list of necessary documents based on the input information and emotional data, and means for displaying the generated results to the customer. This enables personalized support that takes into account the customer's emotional state.

[1434] "Emotional data" refers to information about a customer's psychological state, obtained from their facial expressions, tone of voice, and other similar data.

[1435] An "emotion analysis engine" is software or a system that analyzes acquired emotional data to identify a customer's emotional state.

[1436] A "generative artificial intelligence model" is an algorithm or model used to generate preliminary loan approval results, repayment plans, educational content, etc., based on input information and sentiment analysis results.

[1437] A "preliminary approval result" is the initial assessment result when a customer applies for a mortgage, indicating whether or not the loan will be approved.

[1438] A "list of required documents" is a compilation of all the documents needed to apply for a mortgage.

[1439] A "repayment plan" is a document outlining how a customer plans to repay their mortgage, and includes details such as the repayment period and interest rate.

[1440] "Educational content" refers to information and teaching materials used to provide customers with knowledge about home loans.

[1441] "Market trend information" refers to data and trend information regarding the current mortgage market.

[1442] An "interface" refers to the screens and functions that customers use to input information or make requests.

[1443] A "server" is a computer system that processes information sent by customers and generates and manages results using generative artificial intelligence models and sentiment analysis engines.

[1444] This invention is a system that analyzes the emotional state of customers in mortgage services and provides personalized support that reflects the results. The following describes in detail how this invention is specifically implemented.

[1445] Overall system configuration

[1446] This system includes the following main components:

[1447] 1. Interface: Provide an interface for customers to enter information and requests regarding their mortgage. This includes smartphone apps, web applications, etc.

[1448] 2. Terminal: A device such as a smartphone or PC that collects emotional data (facial expressions, tone of voice, etc.) along with customer input information.

[1449] 3. Server: A computer system that analyzes received customer information and sentiment data and generates results using a generative artificial intelligence model.

[1450] 4. Emotion analysis engine (e.g., EmotionAPI): Software used to analyze emotional data and identify a customer's emotional state.

[1451] 5. Generative artificial intelligence models (such as OpenAI GPT-3): Algorithms used to generate preliminary loan approval results, repayment plans, educational content, etc., based on customer information and sentiment analysis results.

[1452] Program Processing Description

[1453] Data collection and transmission

[1454] Users access a smartphone app and enter information such as their annual income, occupation, and desired loan amount. Simultaneously, emotional data is collected from the camera and microphone.

[1455] The device sends this input information and emotional data to the server.

[1456] Data Analysis

[1457] The server first sends the received information to an emotion analysis engine (such as EmotionAPI) to analyze the emotion data.

[1458] The emotion analysis engine determines the customer's emotional state (e.g., anxiety, reassurance, interest, etc.).

[1459] result generation

[1460] Generative artificial intelligence models (such as OpenAI GPT-3) generate preliminary screening results and lists of required documents based on customer input information and sentiment analysis results.

[1461] Furthermore, repayment plans, interest rate estimates, educational content, and market trend information are also generated.

[1462] Results display

[1463] The server sends the generated results to the terminal, and the terminal displays them to the user.

[1464] Specific example

[1465] If a user enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and displays an anxious expression, the emotion analysis engine will detect the anxiety. The generative artificial intelligence model will then provide a preliminary assessment result along with a more detailed explanation and offer advice to reassure the user.

[1466] Example of a prompt:

[1467] If a user enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and shows an anxious expression, but requests a preliminary assessment for home purchase: Question: "Is this preliminary assessment result appropriate?" Advice: "Please use the loan service with confidence based on this result. Please see below for a list of required documents and details."

[1468] This will enable the creation of a system that provides mortgage services that take into account the emotional state of the user, thereby improving customer satisfaction.

[1469] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1470] Step 1:

[1471] The user opens a smartphone app and enters information for a preliminary mortgage application (annual income, occupation, desired loan amount, etc.). The app also collects the user's emotional data (facial expressions and tone of voice) through the camera and microphone. This input information and emotional data are then transmitted to the device.

[1472] Input: Annual income, occupation, desired loan amount, facial expression, tone of voice

[1473] Output: Collected input information and sentiment data

[1474] Step 2:

[1475] The device sends the collected input information and sentiment data to the server. A protocol for securely transmitting data over the network (e.g., HTTPS) is used here.

[1476] Input: Collected input information and sentiment data

[1477] Output: Data sent to the server

[1478] Step 3:

[1479] The server sends the received data to the emotion analysis engine. The emotion analysis engine analyzes emotional data such as facial expressions and tone of voice to determine the user's emotional state (e.g., anxiety, reassurance, interest, etc.).

[1480] Input: Collected input information and sentiment data

[1481] Output: Emotional analysis results (anxiety, reassurance, interest, etc.)

[1482] Step 4:

[1483] The server inputs the sentiment analysis results and user input information into a generative artificial intelligence model. Based on this data, the generative AI model generates a preliminary assessment result and a list of required documents. Here, the algorithm evaluates the customer's credit information and creates optimal advice and a list of required documents.

[1484] Input: User input information, sentiment analysis results

[1485] Output: Preliminary review results and list of required documents

[1486] Step 5:

[1487] The server sends the generated preliminary review results and a list of required documents to the terminal. A security protocol (e.g., HTTPS) is used during data transfer.

[1488] Input: Preliminary screening results and list of required documents

[1489] Output: Results sent to the terminal

[1490] Step 6:

[1491] The device displays the received preliminary review results and a list of required documents to the user. Here, the smartphone app presents the results clearly through a user-friendly interface, for example, using notifications or dialog boxes.

[1492] Input: Preliminary screening results and list of required documents

[1493] Output: Results displayed to the user

[1494] Step 7:

[1495] The user reviews the provided results and decides on the next steps. For example, they may prepare necessary documents or answer additional questions.

[1496] Input: Provided result

[1497] Output: User's next action

[1498] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1499] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1500] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1501] [Third Embodiment]

[1502] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1503] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1504] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1505] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1506] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1507] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1508] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1509] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1510] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1511] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1512] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1513] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1514] This invention provides a system that improves efficiency and customer satisfaction in mortgage services by utilizing generative artificial intelligence models. Specific embodiments thereof are described below.

[1515] 1. Preliminary screening and customer support

[1516] System Overview:

[1517] In this system, customers who wish to undergo a preliminary mortgage application enter the necessary information through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model generates a preliminary application result based on that information. Furthermore, it guides the user through the necessary documents and procedures.

[1518] Program processing explanation:

[1519] 1. User: The customer accesses a dedicated app and enters information such as annual income, occupation, and desired loan amount.

[1520] 2. Terminal: Sends the entered information to the server.

[1521] 3. Server: Passes the transmitted information to the generative artificial intelligence model for processing.

[1522] 4. Generative AI Model: Generates preliminary review results and a list of required documents based on the received information.

[1523] 5. Server: Sends the generated results to the terminal.

[1524] 6. Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[1525] Specific example:

[1526] When a customer accesses a dedicated app and enters an annual income of 6 million yen and a desired loan amount of 30 million yen, a generative artificial intelligence model performs a preliminary screening and displays whether the application has passed the preliminary screening and a list of required documents (income verification documents, identity verification documents, etc.).

[1527] 2. Loan Planning and Advice

[1528] System Overview:

[1529] When a customer requests a repayment plan or interest rate estimate, they enter specific details (such as repayment period and desired interest rate) through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate.

[1530] Program processing explanation:

[1531] 1. User: Enter conditions such as repayment period and desired interest rate.

[1532] 2. Terminal: Sends the entered conditions to the server.

[1533] 3. Server: Passes the submitted conditions to the generative artificial intelligence model for processing.

[1534] 4. Generative artificial intelligence models: These models generate optimal repayment plans and interest rate estimates based on given conditions.

[1535] 5. Server: Sends the generated plan and estimate to the terminal.

[1536] 6. Terminal: Displays repayment plans and estimates to customers.

[1537] Specific example:

[1538] When a customer inputs a 20-year repayment period and a fixed interest rate of 1.5%, a generative artificial intelligence model displays details such as a monthly repayment amount of 180,000 yen and a total repayment amount of 32,400,000 yen.

[1539] 3. Education and Information Provision

[1540] System Overview:

[1541] If a customer wants education or information about mortgages, they can request topics and related news through a dedicated app. The request is sent to a server, where a generative artificial intelligence model generates content on foundational knowledge and market trends.

[1542] Program processing explanation:

[1543] 1. User: Requests topics they want to learn about or related news.

[1544] 2. Terminal: Sends the request to the server.

[1545] 3. Server: Passes requests to a generative artificial intelligence model for processing.

[1546] 4. Generative artificial intelligence models: These generate content related to fundamental knowledge, key terminology, and market trends.

[1547] 5. Server: Sends the generated content to the terminal.

[1548] 6. Terminal: Displays content to customers.

[1549] Specific example:

[1550] When a customer requests "basic information on home loans," a generative artificial intelligence model displays explanations of fixed and variable interest rates, the latest market trends, and more.

[1551] 4. Prompt service delivery and efficiency

[1552] System Overview:

[1553] If a customer wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer and provides the result to the customer.

[1554] Program processing explanation:

[1555] 1. User: Enter your question.

[1556] 2. Terminal: Sends the question to the server.

[1557] 3. Server: Passes the question to a generative artificial intelligence model for processing.

[1558] 4. Generative artificial intelligence models: These generate answers to questions.

[1559] 5. Server: Sends the generated response to the terminal.

[1560] 6. Terminal: Display the response to the customer.

[1561] Specific example:

[1562] When a customer enters the question, "What documents are required for a loan application?", a generative artificial intelligence model generates and displays answers such as income statements, identity verification documents, and property appraisal reports.

[1563] The above is a detailed description of embodiments for carrying out the present invention.

[1564] The following describes the processing flow.

[1565] 1. Preliminary screening and customer support

[1566] Program processing explanation:

[1567] Step 1:

[1568] User: Customers access a dedicated app and enter information such as annual income, occupation, and desired loan amount.

[1569] Step 2:

[1570] Terminal: Checks the entered information and verifies that there are no errors.

[1571] Step 3:

[1572] Terminal: After verification, the information will be sent to the server.

[1573] Step 4:

[1574] Server: Records received information in a database and passes it to a generative artificial intelligence model.

[1575] Step 5:

[1576] Generative AI model: Performs a preliminary screening based on customer information and generates the preliminary screening results and a list of required documents.

[1577] Step 6:

[1578] Server: Receives the generated results and sends them to the terminal.

[1579] Step 7:

[1580] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[1581] 2. Loan Planning and Advice

[1582] Program processing explanation:

[1583] Step 1:

[1584] User: The customer accesses a dedicated app and enters conditions such as the repayment period and desired interest rate.

[1585] Step 2:

[1586] Terminal: Checks the entered conditions and verifies that there are no errors.

[1587] Step 3:

[1588] Terminal: After confirmation, the conditions will be sent to the server.

[1589] Step 4:

[1590] Server: Records the received conditions in the database and passes them to the generative artificial intelligence model.

[1591] Step 5:

[1592] Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions.

[1593] Step 6:

[1594] Server: Receives the generated plan and estimate and sends them to the terminal.

[1595] Step 7:

[1596] Terminal: Displays repayment plans and interest rate estimates to customers.

[1597] 3. Education and Information Provision

[1598] Program processing explanation:

[1599] Step 1:

[1600] User: Customers access a dedicated app and request topics they want to learn about and related news.

[1601] Step 2:

[1602] Terminal: Check the request details and verify that there are no errors.

[1603] Step 3:

[1604] Terminal: After verification, the request will be sent to the server.

[1605] Step 4:

[1606] Server: Records received requests in the database and passes them to the generative artificial intelligence model.

[1607] Step 5:

[1608] Generative artificial intelligence models: These models generate content related to learning themes and market trends.

[1609] Step 6:

[1610] Server: Receives the generated content and sends it to the terminal.

[1611] Step 7:

[1612] Terminal: Displays educational content and related news to customers.

[1613] 4. Prompt service delivery and efficiency

[1614] Program processing explanation:

[1615] Step 1:

[1616] User: The customer accesses a dedicated app and enters their question.

[1617] Step 2:

[1618] Terminal: Check the entered questions and verify that there are no errors.

[1619] Step 3:

[1620] Terminal: After verification, the question will be sent to the server.

[1621] Step 4:

[1622] Server: Records received questions in a database and passes them to a generative artificial intelligence model.

[1623] Step 5:

[1624] Generative artificial intelligence models: Generate instant answers to questions.

[1625] Step 6:

[1626] Server: Receives the generated response and sends it to the terminal.

[1627] Step 7:

[1628] Terminal: Displays the answer to the customer immediately.

[1629] (Example 1)

[1630] Next, we will describe Example 1. 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."

[1631] In traditional mortgage services, customers had to go through complex procedures to undergo preliminary screening and loan planning consultations. This often resulted in cumbersome and time-consuming processes. Furthermore, the inability to quickly provide customers with the information and advice they needed could lead to decreased customer satisfaction. Additionally, insufficient education and information regarding mortgages could cause customer anxiety. A system is needed that effectively addresses these problems and efficiently and quickly responds to customer needs.

[1632] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1633] In this invention, the server includes means for providing an interface for inputting customer information, means for transmitting the input information to a processing unit, means for the processing unit to process the input information based on a generative artificial intelligence model and generate a preliminary examination result and a list of required documents, means for displaying the generated preliminary examination result and list of required documents to the customer, means for providing conditions such as repayment period and interest rate to be input by the customer, means for transmitting the input conditions to the processing unit, means for the processing unit to process the input conditions based on a generative artificial intelligence model and generate an optimal repayment plan and interest rate estimate, means for displaying the generated repayment plan and interest rate estimate to the customer, means for providing an interface for the customer to request topics and related information they wish to learn about, means for transmitting the customer's request to the processing unit, means for the processing unit to process the customer's request based on a generative artificial intelligence model and generate educational content and market trend information, and means for displaying the generated educational content and market trend information to the customer. This simplifies the customer's procedure and enables the provision of services quickly and accurately.

[1634] A "customer" is an individual or legal entity that uses a mortgage service.

[1635] An "interface" is a user interaction mechanism that provides a means for users to input information.

[1636] A "processing device" is a computer or server used to process received information and data.

[1637] A "generative artificial intelligence model" is an algorithm or model used to generate specific results based on input data.

[1638] "Preliminary approval result" refers to information indicating the provisional approval status of a loan, generated based on the information entered by the customer.

[1639] The "list of required documents" is a list of documents that the customer must submit during the loan application process.

[1640] A "repayment plan" is a loan repayment plan based on the customer's borrowing conditions.

[1641] An "interest rate estimate" is an estimate of the interest rate that should be applied to a loan offered to a customer.

[1642] "Educational content" refers to teaching materials and documents that contain foundational knowledge and detailed information for customers to learn.

[1643] "Market trend information" refers to data that provides the latest information and trends regarding mortgage and real estate markets.

[1644] A "request" is a request that a customer sends to obtain specific information or services.

[1645] This invention provides a system that improves efficiency and customer satisfaction in mortgage services by utilizing generative artificial intelligence models. Detailed embodiments are described below.

[1646] Overview of the entire system

[1647] This system utilizes a dedicated software application (hereinafter referred to as the dedicated app), a server, and a generative artificial intelligence model (e.g., OpenAI GPT-4). The dedicated app provides an interface for the user to input information, the server transmits the input information to a processing unit, and the generative artificial intelligence model performs predetermined processing. The processing results are then displayed again on the user's terminal.

[1648] 1. Preliminary screening and customer support

[1649] System Overview:

[1650] When a user wishes to undergo a preliminary mortgage application, they enter information such as their annual income, occupation, and desired loan amount through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model uses that information to generate a preliminary application result and a list of required documents.

[1651] Specific example:

[1652] When a customer accesses a dedicated app and enters an annual income of 6 million yen and a desired loan amount of 30 million yen, a generative artificial intelligence model performs a preliminary screening and displays whether the application has passed the preliminary screening and a list of required documents (income verification documents, identity verification documents, etc.).

[1653] Example of a prompt:

[1654] "Please conduct a preliminary mortgage application with an annual income of 6 million yen and a desired loan amount of 30 million yen. Please let me know the results and required documents."

[1655] 2. Loan Planning and Advice

[1656] System Overview:

[1657] When a user requests a repayment plan or interest rate estimate, they enter conditions such as the repayment period and desired interest rate through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate.

[1658] Specific example:

[1659] When a customer inputs a 20-year repayment period and a fixed interest rate of 1.5%, a generative artificial intelligence model displays details such as a monthly repayment amount of 180,000 yen and a total repayment amount of 32,400,000 yen.

[1660] Example of a prompt:

[1661] "Please provide a mortgage repayment plan with a 20-year repayment period and a fixed interest rate of 1.5%, including the monthly payment amount and the total repayment amount."

[1662] 3. Education and Information Provision

[1663] System Overview:

[1664] If a user wishes to receive education or information about mortgages, they can request topics and related news through a dedicated app. The request is sent to a server, where a generative artificial intelligence model generates content on basic knowledge and market trends.

[1665] Specific example:

[1666] When a customer requests "basic information on home loans," a generative artificial intelligence model displays explanations of fixed and variable interest rates, the latest market trends, and more.

[1667] Example of a prompt:

[1668] "Please explain the basics of home loans, including the difference between fixed and variable interest rates."

[1669] 4. Prompt service delivery and efficiency

[1670] System Overview:

[1671] If a user wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer and provides the result to the user.

[1672] Specific example:

[1673] When a customer enters the question, "What documents are required for a loan application?", a generative artificial intelligence model generates and displays answers such as income statements, identity verification documents, and property appraisal reports.

[1674] Example of a prompt:

[1675] "What documents are required for a loan application?"

[1676] The above describes the embodiments for carrying out the present invention. This enables customers to use mortgage services simply and quickly, and allows for efficient preliminary screening, presentation of repayment plans, and provision of educational content.

[1677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1678] 1. Preliminary screening and customer support

[1679] Processing flow

[1680] Step 1:

[1681] User: Open the dedicated app and enter information such as annual income, occupation, and desired loan amount.

[1682] Input: Information such as annual income, occupation, and desired loan amount.

[1683] Specific action: The user types text on the smartphone screen and presses the send button.

[1684] Step 2:

[1685] Terminal: Sends the entered information to the server.

[1686] Input: Information entered by the user, such as annual income, occupation, and desired loan amount.

[1687] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[1688] Step 3:

[1689] Server: Passes the received information to the generative artificial intelligence model.

[1690] Input: User information sent as an HTTP request

[1691] Data processing: Execute API calls to pass information to a generative artificial intelligence model.

[1692] Specific operation: The server passes data to the API endpoint.

[1693] Step 4:

[1694] Generative AI model: Generates preliminary review results and a list of required documents based on the input information.

[1695] Input: User information (annual income, occupation, desired loan amount, etc.)

[1696] Data processing: An algorithm analyzes user information and generates a preliminary review result and a list of required documents.

[1697] Specific operation: The model performs processing and outputs the results in a data format.

[1698] Step 5:

[1699] Server: Sends the generated preliminary review results and list of required documents to the terminal.

[1700] Input: Output data from a generative artificial intelligence model (preliminary review results, list of required documents)

[1701] Specific operation: Send output data to the terminal as an HTTP response.

[1702] Step 6:

[1703] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[1704] Input: Preliminary review results and list of required documents sent as an HTTP response.

[1705] Specific action: Display the result on the screen.

[1706] 2. Loan Planning and Advice

[1707] Processing flow

[1708] Step 1:

[1709] User: Enter conditions such as repayment period and desired interest rate using the dedicated app.

[1710] Input: Conditions such as repayment period and desired interest rate.

[1711] Specific action: The user enters text into the input field and presses the submit button.

[1712] Step 2:

[1713] Terminal: Sends the entered conditions to the server.

[1714] Input: User-entered conditions such as repayment period and desired interest rate.

[1715] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[1716] Step 3:

[1717] Server: Passes the received conditions to the generative artificial intelligence model.

[1718] Input: User conditions sent as an HTTP request

[1719] Data processing: Execute API calls to pass conditions to a generative artificial intelligence model.

[1720] Specific operation: The server passes data to the API endpoint.

[1721] Step 4:

[1722] Generative artificial intelligence models: Generate optimal repayment plans and interest rate estimates based on given conditions.

[1723] Input: User conditions (repayment period, desired interest rate, etc.)

[1724] Data processing: Algorithms analyze user conditions and create repayment plans and interest rate estimates.

[1725] Specific operation: The model performs processing and outputs the results in a data format.

[1726] Step 5:

[1727] Server: Sends the generated repayment plan and interest rate estimate to the terminal.

[1728] Input: Output data from a generative artificial intelligence model (repayment plan, interest rate estimate).

[1729] Specific operation: Send output data to the terminal as an HTTP response.

[1730] Step 6:

[1731] Terminal: Displays repayment plans and interest rate estimates to customers.

[1732] Input: Repayment plan and interest rate estimate sent as an HTTP response.

[1733] Specific action: Display the result on the screen.

[1734] 3. Education and Information Provision

[1735] Processing flow

[1736] Step 1:

[1737] User: Request topics you want to learn about or related news.

[1738] Input: Topic you want to learn about, related news

[1739] Specific steps: Select a theme using the dedicated app and submit a request.

[1740] Step 2:

[1741] Terminal: Sends a request to the server.

[1742] Input: User selected theme, related news

[1743] Specific operation: The selected data is encoded and sent to the server as an HTTP request.

[1744] Step 3:

[1745] Server: Passes the received request to the generative artificial intelligence model.

[1746] Input: User request sent as an HTTP request

[1747] Data processing: Execute API calls to pass the request content to a generative artificial intelligence model.

[1748] Specific operation: The server passes data to the API endpoint.

[1749] Step 4:

[1750] Generative artificial intelligence model: Generates educational content and market trend information based on the requested content.

[1751] Input: User request (theme, related news)

[1752] Data processing: Algorithms analyze requests and create educational content and market trend information.

[1753] Specific operation: The model performs processing and outputs the results in a data format.

[1754] Step 5:

[1755] Server: Sends generated educational content and market trend information to terminals.

[1756] Input: Output data from generative artificial intelligence models (educational content, market trend information).

[1757] Specific operation: Send output data to the terminal as an HTTP response.

[1758] Step 6:

[1759] Terminal: Displays educational content and market trend information to customers.

[1760] Input: Educational content and market trend information sent as an HTTP response.

[1761] Specific action: Display the result on the screen.

[1762] 4. Prompt service delivery and efficiency

[1763] Processing flow

[1764] Step 1:

[1765] User: Enter your question.

[1766] Input: Question content

[1767] Specific actions: Enter text into the question input field in the dedicated app and tap the submit button.

[1768] Step 2:

[1769] Terminal: Sends the question to the server.

[1770] Input: Question entered by the user

[1771] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[1772] Step 3:

[1773] Server: Passes the question to a generative artificial intelligence model for processing.

[1774] Input: User question sent as an HTTP request

[1775] Data processing: Execute API calls to pass the question content to a generative artificial intelligence model.

[1776] Specific operation: The server passes data to the API endpoint.

[1777] Step 4:

[1778] Generative artificial intelligence models: Generate answers to questions.

[1779] Input: User Question

[1780] Data processing: Algorithms analyze the question and generate the optimal answer.

[1781] Specific operation: The model performs processing and outputs the results in a data format.

[1782] Step 5:

[1783] Server: Sends the generated response to the terminal.

[1784] Input: Output data (answer) from a generative artificial intelligence model.

[1785] Specific operation: Send output data to the terminal as an HTTP response.

[1786] Step 6:

[1787] Terminal: Display the answer to the customer.

[1788] Input: Response sent as an HTTP response

[1789] Specific action: Display the result on the screen.

[1790] (Application Example 1)

[1791] Next, we will explain Application Example 1. In the following explanation, 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."

[1792] Current mortgage services often require multiple steps, making it difficult for customers to efficiently receive pre-approval, repayment plans, educational information, and answers to their questions on a single platform. Furthermore, real-time service can be time-consuming, leading to decreased customer satisfaction. Therefore, a new system that is both efficient and enhances customer satisfaction is needed.

[1793] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1794] In this invention, the server includes means for providing an interface for inputting customer information, means for transmitting the inputted information to the server, means for processing the inputted information on the server based on a generative artificial intelligence model to generate a preliminary screening result and a list of required documents, means for displaying the generated preliminary screening result and list of required documents to the customer, means for providing a mortgage service performed through a virtual reality device, and means for providing an integrated preliminary screening, repayment plan, educational information, and answers to questions within a virtual store using a head-mounted display. This enables the customer to efficiently receive mortgage services in a virtual reality environment.

[1795] "Customer information" refers to personal information necessary for mortgage services, such as annual income, occupation, desired loan amount, repayment period, and desired interest rate.

[1796] A "generative artificial intelligence model" is a type of artificial intelligence technology that generates optimal preliminary screening results, repayment plans, interest rate estimates, educational content, and answers to questions based on input conditions and information.

[1797] A "virtual reality device" is a device that allows users to experience a virtual environment, and specifically includes head-mounted displays, controllers, and the like.

[1798] A "head-mounted display" is a device that users wear on their heads to visually experience virtual reality.

[1799] A "virtual store" is a virtual store space that can be visited through a virtual reality device, providing an interface for customers to receive mortgage services.

[1800] A "preliminary assessment result" is an initial evaluation of the mortgage loan the customer is requesting, indicating the likelihood of loan approval, necessary documents, and the next steps to take.

[1801] A "repayment plan" is a detailed repayment plan for a mortgage, including the repayment period, monthly payment amount, and total repayment amount.

[1802] An "interest rate estimate" is an estimate based on the interest rate applicable to a mortgage loan, used to calculate the monthly repayment amount and the total repayment amount.

[1803] "Educational content" refers to information used to learn about mortgage-related knowledge, market trends, and important terminology.

[1804] "Answer to a question" refers to the immediate response from a generative artificial intelligence model to a question entered by the customer.

[1805] This invention is a mortgage service system using a virtual reality device. This system aims to provide customers with highly efficient and satisfying services by utilizing a generative artificial intelligence model.

[1806] 1. Required hardware and software

[1807] Hardware:

[1808] Virtual reality devices (e.g., regular head-mounted displays)

[1809] Communication server

[1810] User device (smartphone or computer)

[1811] software:

[1812] OpenAI API (an API for using generative artificial intelligence models)

[1813] Virtual reality interface software

[1814] Server communication software

[1815] 2. Data processing and data calculation

[1816] server:

[1817] The system receives user information (annual income, occupation, desired loan amount, etc.) and passes it to a generative artificial intelligence model.

[1818] Based on the output from the model, preliminary screening results, repayment plans, educational content, and answers to questions are generated.

[1819] The generated results are sent back to the user.

[1820] User terminal:

[1821] It provides an interface for users to input information.

[1822] Send the entered information to the server.

[1823] The results returned from the server are displayed within the virtual reality environment.

[1824] Generative artificial intelligence models:

[1825] Based on the entered user information and conditions, the system generates the necessary results.

[1826] Specifically, it outputs preliminary screening results, repayment plans, interest rate estimates, educational content, and answers to frequently asked questions.

[1827] 3. Specific Examples

[1828] Preliminary screening and customer support:

[1829] As a concrete example, a user wears a head-mounted display and accesses a dedicated terminal in a virtual store, entering an annual income of 6 million yen, occupation as a company employee, and desired loan amount of 30 million yen. This information is sent to a server, where a generative artificial intelligence model performs a preliminary assessment, and the preliminary assessment results, including a list of required documents, are immediately displayed within the virtual reality environment.

[1830] Example of a prompt:

[1831] Please use the following information to pre-approve your mortgage:

[1832] Annual income: 6 million yen

[1833] Occupation: Company employee

[1834] Desired loan amount: 30 million yen

[1835] In this way, customers can efficiently utilize mortgage services in real time within a virtual reality environment.

[1836] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1837] Step 1:

[1838] The user wears a head-mounted display and accesses a virtual store. The user operates the interface to enter personal information such as annual income, occupation, and desired loan amount. The data entered includes an annual income of 6 million yen, occupation of company employee, and desired loan amount of 30 million yen.

[1839] Step 2:

[1840] The terminal sends the entered information to the server. This input data includes the user's annual income, occupation, and desired loan amount. The terminal then sends this data to the server in an appropriate format.

[1841] Step 3:

[1842] The server passes the received information to a generative artificial intelligence model. Specifically, it uses the OpenAI API to pass data such as the user's annual income, occupation, and desired loan amount as prompts to the model. Based on the input data, the generative AI model generates a preliminary screening result and a list of required documents. As a result of this process, the preliminary screening result and the list of required documents are generated.

[1843] Step 4:

[1844] The server receives the preliminary review results and required document list returned from the generative artificial intelligence model. The server verifies that this data is accurate and then prepares it for transmission.

[1845] Step 5:

[1846] The server sends the generated preliminary review results and a list of required documents to the terminal. The server formats the received data through the appropriate interface and sends it to the user's terminal.

[1847] Step 6:

[1848] The terminal displays the preliminary screening results and a list of required documents to the user within a virtual reality environment. Specifically, this includes displaying the preliminary screening results and required documents on a head-mounted display. The user can then confirm the next steps based on the list within the virtual store.

[1849] This processing flow allows users to efficiently access mortgage services within a virtual reality environment.

[1850] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1851] This invention provides a system that combines a generative artificial intelligence model and an emotion engine in mortgage services to provide personalized support that takes into account the emotional state of the customer. Specific embodiments thereof are described below.

[1852] 1. Preliminary screening and customer support

[1853] System Overview:

[1854] In this system, customers who wish to undergo a preliminary mortgage application enter the necessary information through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model generates a preliminary application result based on that information. Furthermore, an emotion engine analyzes the customer's emotional state and provides a preliminary application result and guidance on necessary documents that reflects the results.

[1855] Program processing explanation:

[1856] 1. User: The customer accesses a dedicated app and enters information such as annual income, occupation, and desired loan amount.

[1857] 2. Terminal: Collects emotional data from sensors such as cameras and microphones, along with customer input information.

[1858] 3. Terminal: Sends input information and emotion data to the server.

[1859] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[1860] 5. Generative AI Model: Generates preliminary screening results and a list of required documents based on customer information and sentiment analysis results.

[1861] 6. Server: Sends the generated results to the terminal.

[1862] 7. Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[1863] Specific example:

[1864] If a customer enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and simultaneously displays an anxious expression, the emotion engine will detect the anxiety, and the generative artificial intelligence model will display a preliminary approval status and a list of required documents along with a more detailed explanation.

[1865] 2. Loan Planning and Advice

[1866] System Overview:

[1867] When a customer requests a repayment plan or interest rate estimate, they enter specific details (such as repayment period and desired interest rate) through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate. Furthermore, an emotion engine analyzes the customer's emotional state and provides advice reflecting the results.

[1868] Program processing explanation:

[1869] 1. User: Enter conditions such as repayment period and desired interest rate.

[1870] 2. Terminal: Collects customer sentiment data while they are entering conditions.

[1871] 3. Terminal: Sends input conditions and emotion data to the server.

[1872] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[1873] 5. Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions and sentiment analysis results.

[1874] 6. Server: Sends the generated plan and estimate to the terminal.

[1875] 7. Terminal: Displays repayment plans and interest rate estimates to customers.

[1876] Specific example:

[1877] If a customer inputs a 20-year repayment period and a desired interest rate of 1.5%, and simultaneously displays a reassuring expression, the emotion engine detects this reassurance, and the generative artificial intelligence model provides a repayment plan along with standard explanations.

[1878] 3. Education and Information Provision

[1879] System Overview:

[1880] When customers wish to receive education and information about mortgages, they can request topics and related news through a dedicated app. The requests are sent to a server, where a generative artificial intelligence model generates content on fundamental knowledge and market trends. An emotion engine analyzes the customer's emotional state and provides educational content that reflects the results.

[1881] Program processing explanation:

[1882] 1. User: Requests topics they want to learn about or related news.

[1883] 2. Terminal: Collects customer sentiment data during the request.

[1884] 3. Terminal: Sends requests and sentiment data to the server.

[1885] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[1886] 5. Generative AI Models: These models generate content based on the request content and sentiment analysis results.

[1887] 6. Server: Sends the generated content to the terminal.

[1888] 7. Terminal: Displays content to customers.

[1889] Specific example:

[1890] If a customer requests "basic knowledge about home loans" and simultaneously shows an interested expression, the emotion engine detects their interest, and the generative artificial intelligence model provides more in-depth basic knowledge.

[1891] 4. Prompt service delivery and efficiency

[1892] System Overview:

[1893] If a customer wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer. The emotion engine then analyzes the customer's emotional state and customizes the answer before providing it.

[1894] Program processing explanation:

[1895] 1. User: Enter your question.

[1896] 2. Terminal: Collects emotional data from customers as they are entering information.

[1897] 3. Terminal: Sends question and sentiment data to the server.

[1898] 4. Server: Analyzes emotional data using the emotion engine to determine the customer's emotional state.

[1899] 5. Generative AI Model: Generates immediate responses based on the question content and sentiment analysis results.

[1900] 6. Server: Sends the generated response to the terminal.

[1901] 7. Terminal: Displays the answer to the customer immediately.

[1902] Specific example:

[1903] If a customer enters a question like, "What documents are needed for a loan application?" and is feeling anxious, the emotion engine detects the anxiety, and the generative artificial intelligence model provides a polite and easy-to-understand explanation.

[1904] The following describes the processing flow.

[1905] 1. Preliminary screening and customer support

[1906] Program processing explanation:

[1907] Step 1:

[1908] User: Customers access a dedicated app and enter information such as annual income, occupation, and desired loan amount.

[1909] Step 2:

[1910] Terminal: Uses the camera and microphone on the input screen to collect data on the customer's facial expressions and voice.

[1911] Step 3:

[1912] Terminal: Sends input information and collected sentiment data to the server.

[1913] Step 4:

[1914] Server: Records received information in a database and passes it to a generative artificial intelligence model.

[1915] Step 5:

[1916] Server: The emotion engine analyzes emotional data and determines the customer's emotional state (e.g., feeling safe, anxious, etc.).

[1917] Step 6:

[1918] Generative AI model: Generates preliminary screening results and a list of required documents based on the determined emotional state and customer information.

[1919] Step 7:

[1920] Server: Sends the generated preliminary review results and list of required documents to the terminal.

[1921] Step 8:

[1922] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[1923] 2. Loan Planning and Advice

[1924] Program processing explanation:

[1925] Step 1:

[1926] User: The customer enters conditions such as the repayment period and desired interest rate.

[1927] Step 2:

[1928] Terminal: Collects data on the customer's facial expressions and voice while they are entering repayment terms.

[1929] Step 3:

[1930] Terminal: Sends input conditions and collected sentiment data to the server.

[1931] Step 4:

[1932] Server: Records the received conditions in the database and passes them to the generative artificial intelligence model.

[1933] Step 5:

[1934] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1935] Step 6:

[1936] Generative artificial intelligence model: Generates optimal repayment plans and interest rate estimates based on customer conditions and sentiment analysis results.

[1937] Step 7:

[1938] Server: Sends the generated repayment plan and interest rate estimate to the terminal.

[1939] Step 8:

[1940] Terminal: Displays repayment plans and interest rate estimates to customers.

[1941] 3. Education and Information Provision

[1942] Program processing explanation:

[1943] Step 1:

[1944] User: Requests topics and related news that customers want to learn about.

[1945] Step 2:

[1946] Terminal: Collects data on the customer's facial expressions and voice while they are making a request.

[1947] Step 3:

[1948] Terminal: Sends request information and collected sentiment data to the server.

[1949] Step 4:

[1950] Server: Records received requests in the database and passes them to the generative artificial intelligence model.

[1951] Step 5:

[1952] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1953] Step 6:

[1954] Generative AI model: Generates educational content and market trend information based on the request content and sentiment analysis results.

[1955] Step 7:

[1956] Server: Sends the generated content to the terminal.

[1957] Step 8:

[1958] Terminal: Displays educational content and related news to customers.

[1959] 4. Prompt service delivery and efficiency

[1960] Program processing explanation:

[1961] Step 1:

[1962] User: The customer enters their question.

[1963] Step 2:

[1964] Terminal: Collects data on the customer's facial expressions and voice while they are entering questions.

[1965] Step 3:

[1966] Terminal: Sends question information and collected sentiment data to the server.

[1967] Step 4:

[1968] Server: Records received questions in a database and passes them to a generative artificial intelligence model.

[1969] Step 5:

[1970] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[1971] Step 6:

[1972] Generative AI model: Generates an immediate response based on the question content and sentiment analysis results.

[1973] Step 7:

[1974] Server: Sends the generated response to the terminal.

[1975] Step 8:

[1976] Terminal: Displays the answer to the customer immediately.

[1977] (Example 2)

[1978] Next, we will describe Example 2. 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."

[1979] Traditional mortgage services have a standardized approach to pre-approval, repayment plan proposals, education, and information provision, making it difficult to address the individual emotional states and circumstances of customers. Furthermore, the lack of consideration of emotional data made it challenging to provide timely and appropriate answers to customer questions and requests. This resulted in lower customer satisfaction and presented challenges in improving service quality.

[1980] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1981] In this invention, the server includes means for providing an input device for inputting customer information, means for transmitting the input information, means for processing the input information and sentiment data collected from a terminal using a sentiment analysis device on the server to generate preliminary examination results and a list of required documents based on a generative artificial intelligence model, and means for displaying the generated preliminary examination results and list of required documents.

[1982] This will enable the provision of personalized mortgage services that take into account the emotional state of the customer.

[1983] 1. "Input device" refers to a device used by a customer to input information, and includes devices such as keyboards, touchscreens, and mice.

[1984] 2. "Transmission means" refers to a system equipped with functions and protocols for transmitting input information or data to a server.

[1985] 3. An "emotion analysis device" is a combination of software and hardware used to analyze collected emotional data and determine the emotional state of a customer.

[1986] 4. A "generative artificial intelligence model" is an artificial intelligence algorithm and system that generates preliminary screening results, lists of required documents, repayment plans, educational content, etc., based on input information and emotional data.

[1987] 5. The "preliminary screening result" is a prediction of the mortgage loan approval based on the information entered by the customer.

[1988] 6. The "Required Documents List" is a list of documents required for a mortgage application, based on the preliminary screening results.

[1989] 7. A "repayment plan" is a system that shows the optimal loan repayment schedule and plan based on the customer's input conditions and emotional data.

[1990] 8. "Interest rate estimate" refers to information that shows the predicted loan interest rate based on the customer's input conditions.

[1991] 9. "Educational content" refers to materials and documents that include information and market trends related to mortgages, generated based on topics that customers wish to learn about.

[1992] 10. "Market Trend Information" refers to data and analysis results that show the latest information and trends in the mortgage market.

[1993] 11. "Interface" refers to all elements, including input screens and control panels, that customers interact with in order to use the system.

[1994] This invention provides a system that combines a generative artificial intelligence model and an emotion analysis device in mortgage services to provide personalized support that takes into account the emotional state of the customer. Specific embodiments of this system will be described in detail below.

[1995] Preliminary screening and customer support

[1996] The system begins with the user (customer) entering basic information such as annual income, occupation, and desired loan amount through a dedicated application. This information is entered using the terminal's input device (keyboard, touchscreen, etc.). Simultaneously, sensors such as the terminal's camera and microphone capture the customer's facial expressions and voice tone, collecting emotional data. The terminal encrypts this information and transmits it to the server. On the server, an emotion analysis device analyzes the emotional data to determine the customer's emotional state. Next, a generative artificial intelligence model generates a preliminary screening result and a list of required documents based on the customer's basic information and the emotional analysis results. The generated results are transmitted from the server to the terminal and displayed to the customer.

[1997] Specific example

[1998] If a customer enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and shows an anxious expression, the emotion analysis device will detect "anxiety," and the generative artificial intelligence model will display a preliminary approval status and a list of required documents along with a more detailed explanation.

[1999] Loan planning and advice

[2000] Next, the user (customer) inputs conditions such as repayment period and desired interest rate through a dedicated application. The terminal's camera and microphone capture the customer's facial expressions and voice in real time during this input process, collecting emotional data. The terminal sends the data to a server, where an emotion analysis device analyzes the emotional data and determines the customer's emotional state. A generative artificial intelligence model generates an optimal repayment plan and interest rate estimate based on the customer's conditions and emotional state. The generated results are sent from the server to the terminal and displayed to the customer.

[2001] Specific example

[2002] If a customer enters a 20-year repayment period and a desired interest rate of 1.5%, and displays a reassuring expression, the emotion analysis device detects "reassurance," and the generative artificial intelligence model provides a repayment plan along with a standard explanation.

[2003] Education and information provision

[2004] Furthermore, if a customer wishes to receive education or information about mortgages, they can request topics and related news through a dedicated application. During this request, the device's camera and microphone collect customer emotional data, which the device then sends to a server. The server's emotion analysis device analyzes the emotional data and determines the customer's emotional state. A generative artificial intelligence model generates educational content and market trend information based on the request and emotional state, sends it from the server to the device, and displays it to the customer.

[2005] Specific example

[2006] If a customer requests "basic knowledge about home loans" and shows an interested expression, the emotion analysis device detects "interest," and the generative artificial intelligence model provides more detailed basic knowledge.

[2007] Rapid service delivery and efficiency

[2008] When a customer requires an immediate answer, they enter their question through a dedicated application. While the customer is entering the question, the device's camera and microphone collect emotional data, which the device then sends to a server. The server's emotion analysis system analyzes this data to determine the customer's emotional state. A generative artificial intelligence model generates an immediate answer based on the question and the customer's emotional state, which is then sent from the server to the device and displayed to the customer.

[2009] Specific example

[2010] If a customer enters a question like, "What documents are needed for a loan application?" and is feeling anxious, the emotion analysis system will detect this anxiety, and the generative artificial intelligence model will provide a polite and easy-to-understand explanation.

[2011] Example of a prompt

[2012] 1. "Please explain the system's workflow: When a customer enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and shows an anxious expression, the emotional engine detects that anxiety."

[2013] 2. "Please describe the advice system that provides guidance when a customer enters a 20-year repayment period and a desired interest rate of 1.5%, and then appears relieved."

[2014] 3. "Please describe an example of educational content you would offer to a customer who requests and expresses interest in 'Basic Knowledge of Home Loans'."

[2015] 4. "Please describe the flow of a system that allows customers to instantly input questions and receive quick responses if they are feeling anxious."

[2016] The above details relate to embodiments of the present invention.

[2017] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2018] Preliminary screening and customer support

[2019] Program processing steps:

[2020] Step 1:

[2021] User: Open the dedicated application and enter information such as annual income, occupation, and desired loan amount.

[2022] Input information: Annual income (6 million yen), occupation (company employee), desired loan amount (30 million yen), etc.

[2023] Output: The input data is saved to the terminal.

[2024] Specific operation: The user enters the necessary information using a touchscreen or keyboard on a device such as a smartphone or PC, and then presses the "Send" button.

[2025] Step 2:

[2026] Device: Collects emotional data from sensors such as cameras and microphones, along with input information.

[2027] Input: User's facial expression data, voice tone.

[2028] Output: The collected emotional data is saved to the device.

[2029] Specific operation: The device's front camera automatically activates and captures the user's facial expressions. The microphone also activates and analyzes the voice tone to obtain emotion data.

[2030] Step 3:

[2031] Terminal: Sends input information and emotion data to the server.

[2032] Input: User information (annual income, occupation, desired loan amount), emotional data (facial expression, tone of voice).

[2033] Output: Data is sent to the server.

[2034] Specific operation: The terminal encrypts the data into packets and sends them to the server over the network.

[2035] Step 4:

[2036] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[2037] Input: Emotional data (facial expression, tone of voice).

[2038] Output: Analysis results (e.g., anxiety, reassurance, etc.).

[2039] Specific operation: Using an emotion analysis algorithm, identify the customer's emotional state (e.g., anxiety) from changes in facial expression and tone of voice.

[2040] Step 5:

[2041] Generative AI model: Generates preliminary screening results and a list of required documents based on customer information and sentiment analysis results.

[2042] Input: User information, sentiment analysis results.

[2043] Output: Preliminary review results, list of required documents.

[2044] Specific operation: A generative artificial intelligence model uses historical data and statistical models to generate a preliminary screening result (e.g., A-rank screening passed) and a list of required documents (e.g., identification card, residence certificate, income certificate).

[2045] Step 6:

[2046] Server: Sends the generated results to the terminal.

[2047] Input: Preliminary screening results, list of required documents.

[2048] Output: Data is sent to the terminal.

[2049] Specific operation: The server generates data, packages it into packets, encrypts them, and sends them to the terminal.

[2050] Step 7:

[2051] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[2052] Input: Preliminary screening results, list of required documents.

[2053] Output: Data displayed to the customer.

[2054] Specific operation: The terminal's display shows the result of passing the preliminary screening and a list of required documents. For example, it might display "Preliminary screening result: A rank approved, Required documents: Identification document, resident registration certificate, income certificate."

[2055] Loan planning and advice

[2056] Program processing steps:

[2057] Step 1:

[2058] User: Enter conditions such as repayment period and desired interest rate.

[2059] Input: Conditions such as repayment period (20 years) and desired interest rate (1.5%).

[2060] Output: The input data is saved to the terminal.

[2061] Specific operation: The user enters the repayment period and interest rate into the input form of the dedicated application and presses the "Submit" button.

[2062] Step 2:

[2063] Terminal: Collects customer sentiment data while they are entering conditions.

[2064] Input: User's facial expression data, voice tone.

[2065] Output: The collected emotional data is saved to the device.

[2066] Specific operation: While inputting conditions, the terminal's front camera activates to monitor the customer's facial expressions. The microphone also activates to analyze the tone of voice.

[2067] Step 3:

[2068] Terminal: Sends input conditions and emotion data to the server.

[2069] Input: Conditional information (repayment period, desired interest rate), emotional data (facial expression, tone of voice).

[2070] Output: The transmitted data reaches the server.

[2071] Specific operation: The terminal encrypts the data, bundles it into packets, and sends them to the server.

[2072] Step 4:

[2073] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[2074] Input: Emotional data (facial expression, tone of voice).

[2075] Output: Analysis results (e.g., feelings of security, interest, etc.).

[2076] Specific operation: Emotion analysis software analyzes the user's facial expressions and tone of voice to identify their current emotional state.

[2077] Step 5:

[2078] Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions and sentiment analysis results.

[2079] Input: Conditional information, sentiment analysis results.

[2080] Output: Repayment plan (e.g., 20-year repayment period, monthly payment of 150,000 yen), interest rate estimate (1.5%).

[2081] Specific operation: A generative artificial intelligence model calculates and generates the optimal repayment plan and interest rate estimate based on historical data and statistical models.

[2082] Step 6:

[2083] Server: Sends the generated plan and estimate to the terminal.

[2084] Input: Repayment plan, interest rate estimate.

[2085] Output: The transmitted data reaches the terminal.

[2086] Specific operation: The server encrypts the generated data, packages it into packets, and sends them to the terminal.

[2087] Step 7:

[2088] Terminal: Displays repayment plans and interest rate estimates to customers.

[2089] Input: Repayment plan, interest rate estimate.

[2090] Output: Data displayed to the customer.

[2091] Specific operation: The device's display shows the repayment plan and interest rate estimate. For example, information such as "Repayment period: 20 years, monthly repayment amount: 150,000 yen (interest rate: 1.5%)" will be displayed.

[2092] Education and information provision

[2093] Program processing steps:

[2094] Step 1:

[2095] User: Request topics you want to learn about or related news.

[2096] Input: Request details such as "Basic knowledge about home loans."

[2097] Output: The entered request is saved to the terminal.

[2098] Specific operation: The user enters the topic they want to learn about into the application's request form and presses the "Submit" button.

[2099] Step 2:

[2100] Terminal: Collects customer sentiment data during the request.

[2101] Input: User's facial expression data, voice tone.

[2102] Output: The collected emotional data is saved to the device.

[2103] Specific operation: While a request is being entered, the device's front camera activates to capture the user's facial expression. The microphone also activates to collect voice tone.

[2104] Step 3:

[2105] Terminal: Sends requests and sentiment data to the server.

[2106] Input: Request details, emotional data (facial expressions, tone of voice).

[2107] Output: The transmitted data reaches the server.

[2108] Specific operation: The terminal encrypts the data, packages it into packets, and sends them to the server.

[2109] Step 4:

[2110] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[2111] Input: Emotional data (facial expression, tone of voice).

[2112] Output: Analysis results (e.g., interest, reassurance, etc.).

[2113] Specific operation: The emotion analysis software analyzes facial expressions and voice tone collected during the request to identify the current emotional state.

[2114] Step 5:

[2115] Generative AI model: Generates educational content and market trend information based on the request content and sentiment analysis results.

[2116] Input: Request details, sentiment analysis results.

[2117] Output: Educational content (e.g., detailed information on the basics of home loans), market trend information.

[2118] Specific operation: A generative artificial intelligence model generates educational content and market trend information that reflects the request and emotional state.

[2119] Step 6:

[2120] Server: Sends the generated content to the terminal.

[2121] Input: Educational content, market trend information.

[2122] Output: The transmitted data reaches the terminal.

[2123] Specific operation: The server encrypts the generated data, packages it into packets, and sends them to the terminal.

[2124] Step 7:

[2125] Terminal: Displays generated educational content and market trend information to customers.

[2126] Input: Educational content, market trend information.

[2127] Output: Data displayed to the customer.

[2128] Specific operation: The device's display will show educational content and market trend information. For example, a title such as "Basic Knowledge of Home Loans: Basic Mechanisms and How to Choose" will be displayed, along with a detailed explanation and a video link.

[2129] Rapid service delivery and efficiency

[2130] Program processing steps:

[2131] Step 1:

[2132] User: Enter your question.

[2133] Input: Questions such as "What documents are required for a loan application?"

[2134] Output: The entered question is saved to the device.

[2135] Specific action: The user enters a question into the application's question form and presses the "Submit" button.

[2136] Step 2:

[2137] Terminal: Collects emotional data from customers as they input information.

[2138] Input: User's facial expression data, voice tone.

[2139] Output: The collected emotional data is saved to the device.

[2140] Specific operation: While a question is being entered, the device's front camera activates to capture the user's facial expression. The microphone also activates to collect voice tone.

[2141] Step 3:

[2142] Terminal: Sends questions and sentiment data to the server.

[2143] Input: Question content, emotional data (facial expression, tone of voice).

[2144] Output: The transmitted data reaches the server.

[2145] Specific operation: The terminal encrypts the data, packages it into packets, and sends them to the server.

[2146] Step 4:

[2147] Server: Analyzes emotional data using an emotion engine to determine the customer's emotional state.

[2148] Input: Emotional data (facial expression, tone of voice).

[2149] Output: Analysis results (e.g., anxiety, confusion, etc.).

[2150] Specific operation: The emotion analysis software analyzes facial expressions and voice tone collected during question input to identify the current emotional state.

[2151] Step 5:

[2152] Generative AI model: Generates an immediate response based on the question content and sentiment analysis results.

[2153] Input: Question content, sentiment analysis results.

[2154] Output: Generated response (e.g., a list of documents required for the application).

[2155] Specific operation: A generative artificial intelligence model generates an answer that reflects the question and the emotional state.

[2156] Step 6:

[2157] Server: Sends the generated response to the terminal.

[2158] Input: Answer content.

[2159] Output: The transmitted data reaches the terminal.

[2160] Specific operation: The server encrypts the generated response, packages it into packets, and sends them to the terminal.

[2161] Step 7:

[2162] Terminal: Displays the generated response to the customer.

[2163] Input: Answer content.

[2164] Output: Data displayed to the customer.

[2165] Specific actions: The answer will be displayed on the device's screen. For example, it will show "Required documents for application: ID card, residence certificate, income certificate," etc.

[2166] (Application Example 2)

[2167] Next, we will explain application example 2. In the following explanation, 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."

[2168] Traditional mortgage services often provide uniform preliminary assessment results and advice without considering the customer's emotional state, resulting in insufficient customer support. Furthermore, when providing repayment plans and market information, individual customer psychological backgrounds are often not taken into account, leading to situations where the most suitable information is not provided. It is necessary to address these issues and ensure that customers can use mortgage services with peace of mind.

[2169] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing customer information and emotional data using an emotion analysis engine and inputting the analysis results into a generative artificial intelligence model, means for generating preliminary examination results and a list of necessary documents based on the input information and emotional data, and means for displaying the generated results to the customer. This enables personalized support that takes into account the customer's emotional state.

[2170] "Emotional data" refers to information about a customer's psychological state, obtained from their facial expressions, tone of voice, and other similar data.

[2171] An "emotion analysis engine" is software or a system that analyzes acquired emotional data to identify a customer's emotional state.

[2172] A "generative artificial intelligence model" is an algorithm or model used to generate preliminary loan approval results, repayment plans, educational content, etc., based on input information and sentiment analysis results.

[2173] A "preliminary approval result" is the initial assessment result when a customer applies for a mortgage, indicating whether or not the loan will be approved.

[2174] A "list of required documents" is a compilation of all the documents needed to apply for a mortgage.

[2175] A "repayment plan" is a document outlining how a customer plans to repay their mortgage, and includes details such as the repayment period and interest rate.

[2176] "Educational content" refers to information and teaching materials used to provide customers with knowledge about home loans.

[2177] "Market trend information" refers to data and trend information regarding the current mortgage market.

[2178] An "interface" refers to the screens and functions that customers use to input information or make requests.

[2179] A "server" is a computer system that processes information sent by customers and generates and manages results using generative artificial intelligence models and sentiment analysis engines.

[2180] This invention is a system that analyzes the emotional state of customers in mortgage services and provides personalized support that reflects the results. The following describes in detail how this invention is specifically implemented.

[2181] Overall system configuration

[2182] This system includes the following main components:

[2183] 1. Interface: Provide an interface for customers to enter information and requests regarding their mortgage. This includes smartphone apps, web applications, etc.

[2184] 2. Terminal: A device such as a smartphone or PC that collects emotional data (facial expressions, tone of voice, etc.) along with customer input information.

[2185] 3. Server: A computer system that analyzes received customer information and sentiment data and generates results using a generative artificial intelligence model.

[2186] 4. Emotion analysis engine (e.g., EmotionAPI): Software used to analyze emotional data and identify a customer's emotional state.

[2187] 5. Generative artificial intelligence models (such as OpenAI GPT-3): Algorithms used to generate preliminary loan approval results, repayment plans, educational content, etc., based on customer information and sentiment analysis results.

[2188] Program Processing Description

[2189] Data collection and transmission

[2190] Users access a smartphone app and enter information such as their annual income, occupation, and desired loan amount. Simultaneously, emotional data is collected from the camera and microphone.

[2191] The device sends this input information and emotional data to the server.

[2192] Data Analysis

[2193] The server first sends the received information to an emotion analysis engine (such as EmotionAPI) to analyze the emotion data.

[2194] The emotion analysis engine determines the customer's emotional state (e.g., anxiety, reassurance, interest, etc.).

[2195] result generation

[2196] Generative artificial intelligence models (such as OpenAI GPT-3) generate preliminary screening results and lists of required documents based on customer input information and sentiment analysis results.

[2197] Furthermore, repayment plans, interest rate estimates, educational content, and market trend information are also generated.

[2198] Results display

[2199] The server sends the generated results to the terminal, and the terminal displays them to the user.

[2200] Specific example

[2201] If a user enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and displays an anxious expression, the emotion analysis engine will detect the anxiety. The generative artificial intelligence model will then provide a preliminary assessment result along with a more detailed explanation and offer advice to reassure the user.

[2202] Example of a prompt:

[2203] If a user enters an annual income of 6 million yen and a desired loan amount of 30 million yen, and shows an anxious expression, but requests a preliminary assessment for home purchase: Question: "Is this preliminary assessment result appropriate?" Advice: "Please use the loan service with confidence based on this result. Please see below for a list of required documents and details."

[2204] This will enable the creation of a system that provides mortgage services that take into account the emotional state of the user, thereby improving customer satisfaction.

[2205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2206] Step 1:

[2207] The user opens a smartphone app and enters information for a preliminary mortgage application (annual income, occupation, desired loan amount, etc.). The app also collects the user's emotional data (facial expressions and tone of voice) through the camera and microphone. This input information and emotional data are then transmitted to the device.

[2208] Input: Annual income, occupation, desired loan amount, facial expression, tone of voice

[2209] Output: Collected input information and sentiment data

[2210] Step 2:

[2211] The device sends the collected input information and sentiment data to the server. A protocol for securely transmitting data over the network (e.g., HTTPS) is used here.

[2212] Input: Collected input information and sentiment data

[2213] Output: Data sent to the server

[2214] Step 3:

[2215] The server sends the received data to the emotion analysis engine. The emotion analysis engine analyzes emotional data such as facial expressions and tone of voice to determine the user's emotional state (e.g., anxiety, reassurance, interest, etc.).

[2216] Input: Collected input information and sentiment data

[2217] Output: Emotional analysis results (anxiety, reassurance, interest, etc.)

[2218] Step 4:

[2219] The server inputs the sentiment analysis results and user input information into a generative artificial intelligence model. Based on this data, the generative AI model generates a preliminary assessment result and a list of required documents. Here, the algorithm evaluates the customer's credit information and creates optimal advice and a list of required documents.

[2220] Input: User input information, sentiment analysis results

[2221] Output: Preliminary review results and list of required documents

[2222] Step 5:

[2223] The server sends the generated preliminary review results and a list of required documents to the terminal. A security protocol (e.g., HTTPS) is used during data transfer.

[2224] Input: Preliminary screening results and list of required documents

[2225] Output: Results sent to the terminal

[2226] Step 6:

[2227] The device displays the received preliminary review results and a list of required documents to the user. Here, the smartphone app presents the results clearly through a user-friendly interface, for example, using notifications or dialog boxes.

[2228] Input: Preliminary screening results and list of required documents

[2229] Output: Results displayed to the user

[2230] Step 7:

[2231] The user reviews the provided results and decides on the next steps. For example, they may prepare necessary documents or answer additional questions.

[2232] Input: Provided result

[2233] Output: User's next action

[2234] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2235] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2236] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[2237] [Fourth Embodiment]

[2238] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[2239] As shown in Figure 7, the 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.

[2240] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2241] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[2242] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[2243] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[2244] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[2245] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[2246] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[2247] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[2248] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2249] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[2250] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2251] This invention provides a system that improves efficiency and customer satisfaction in mortgage services by utilizing generative artificial intelligence models. Specific embodiments thereof are described below.

[2252] 1. Preliminary screening and customer support

[2253] System Overview:

[2254] In this system, customers who wish to undergo a preliminary mortgage application enter the necessary information through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model generates a preliminary application result based on that information. Furthermore, it guides the user through the necessary documents and procedures.

[2255] Program processing explanation:

[2256] 1. User: The customer accesses a dedicated app and enters information such as annual income, occupation, and desired loan amount.

[2257] 2. Terminal: Sends the entered information to the server.

[2258] 3. Server: Passes the transmitted information to the generative artificial intelligence model for processing.

[2259] 4. Generative AI Model: Generates preliminary review results and a list of required documents based on the received information.

[2260] 5. Server: Sends the generated results to the terminal.

[2261] 6. Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[2262] Specific example:

[2263] When a customer accesses a dedicated app and enters an annual income of 6 million yen and a desired loan amount of 30 million yen, a generative artificial intelligence model performs a preliminary screening and displays whether the application has passed the preliminary screening and a list of required documents (income verification documents, identity verification documents, etc.).

[2264] 2. Loan Planning and Advice

[2265] System Overview:

[2266] When a customer requests a repayment plan or interest rate estimate, they enter specific details (such as repayment period and desired interest rate) through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate.

[2267] Program processing explanation:

[2268] 1. User: Enter conditions such as repayment period and desired interest rate.

[2269] 2. Terminal: Sends the entered conditions to the server.

[2270] 3. Server: Passes the submitted conditions to the generative artificial intelligence model for processing.

[2271] 4. Generative artificial intelligence models: These models generate optimal repayment plans and interest rate estimates based on given conditions.

[2272] 5. Server: Sends the generated plan and estimate to the terminal.

[2273] 6. Terminal: Displays repayment plans and estimates to customers.

[2274] Specific example:

[2275] When a customer inputs a 20-year repayment period and a fixed interest rate of 1.5%, a generative artificial intelligence model displays details such as a monthly repayment amount of 180,000 yen and a total repayment amount of 32,400,000 yen.

[2276] 3. Education and Information Provision

[2277] System Overview:

[2278] If a customer wants education or information about mortgages, they can request topics and related news through a dedicated app. The request is sent to a server, where a generative artificial intelligence model generates content on foundational knowledge and market trends.

[2279] Program processing explanation:

[2280] 1. User: Requests topics they want to learn about or related news.

[2281] 2. Terminal: Sends the request to the server.

[2282] 3. Server: Passes requests to a generative artificial intelligence model for processing.

[2283] 4. Generative artificial intelligence models: These generate content related to fundamental knowledge, key terminology, and market trends.

[2284] 5. Server: Sends the generated content to the terminal.

[2285] 6. Terminal: Displays content to customers.

[2286] Specific example:

[2287] When a customer requests "basic information on home loans," a generative artificial intelligence model displays explanations of fixed and variable interest rates, the latest market trends, and more.

[2288] 4. Prompt service delivery and efficiency

[2289] System Overview:

[2290] If a customer wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer and provides the result to the customer.

[2291] Program processing explanation:

[2292] 1. User: Enter your question.

[2293] 2. Terminal: Sends the question to the server.

[2294] 3. Server: Passes the question to a generative artificial intelligence model for processing.

[2295] 4. Generative artificial intelligence models: These generate answers to questions.

[2296] 5. Server: Sends the generated response to the terminal.

[2297] 6. Terminal: Display the response to the customer.

[2298] Specific example:

[2299] When a customer enters the question, "What documents are required for a loan application?", a generative artificial intelligence model generates and displays answers such as income statements, identity verification documents, and property appraisal reports.

[2300] The above is a detailed description of embodiments for carrying out the present invention.

[2301] The following describes the processing flow.

[2302] 1. Preliminary screening and customer support

[2303] Program processing explanation:

[2304] Step 1:

[2305] User: Customers access a dedicated app and enter information such as annual income, occupation, and desired loan amount.

[2306] Step 2:

[2307] Terminal: Checks the entered information and verifies that there are no errors.

[2308] Step 3:

[2309] Terminal: After verification, the information will be sent to the server.

[2310] Step 4:

[2311] Server: Records received information in a database and passes it to a generative artificial intelligence model.

[2312] Step 5:

[2313] Generative AI model: Performs a preliminary screening based on customer information and generates the preliminary screening results and a list of required documents.

[2314] Step 6:

[2315] Server: Receives the generated results and sends them to the terminal.

[2316] Step 7:

[2317] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[2318] 2. Loan Planning and Advice

[2319] Program processing explanation:

[2320] Step 1:

[2321] User: The customer accesses a dedicated app and enters conditions such as the repayment period and desired interest rate.

[2322] Step 2:

[2323] Terminal: Checks the entered conditions and verifies that there are no errors.

[2324] Step 3:

[2325] Terminal: After confirmation, the conditions will be sent to the server.

[2326] Step 4:

[2327] Server: Records the received conditions in the database and passes them to the generative artificial intelligence model.

[2328] Step 5:

[2329] Generative artificial intelligence model: Generates the optimal repayment plan and interest rate estimate based on customer conditions.

[2330] Step 6:

[2331] Server: Receives the generated plan and estimate and sends them to the terminal.

[2332] Step 7:

[2333] Terminal: Displays repayment plans and interest rate estimates to customers.

[2334] 3. Education and Information Provision

[2335] Program processing explanation:

[2336] Step 1:

[2337] User: Customers access a dedicated app and request topics they want to learn about and related news.

[2338] Step 2:

[2339] Terminal: Check the request details and verify that there are no errors.

[2340] Step 3:

[2341] Terminal: After verification, the request will be sent to the server.

[2342] Step 4:

[2343] Server: Records received requests in the database and passes them to the generative artificial intelligence model.

[2344] Step 5:

[2345] Generative artificial intelligence models: These models generate content related to learning themes and market trends.

[2346] Step 6:

[2347] Server: Receives the generated content and sends it to the terminal.

[2348] Step 7:

[2349] Terminal: Displays educational content and related news to customers.

[2350] 4. Prompt service delivery and efficiency

[2351] Program processing explanation:

[2352] Step 1:

[2353] User: The customer accesses a dedicated app and enters their question.

[2354] Step 2:

[2355] Terminal: Check the entered questions and verify that there are no errors.

[2356] Step 3:

[2357] Terminal: After verification, the question will be sent to the server.

[2358] Step 4:

[2359] Server: Records received questions in a database and passes them to a generative artificial intelligence model.

[2360] Step 5:

[2361] Generative artificial intelligence models: Generate instant answers to questions.

[2362] Step 6:

[2363] Server: Receives the generated response and sends it to the terminal.

[2364] Step 7:

[2365] Terminal: Displays the answer to the customer immediately.

[2366] (Example 1)

[2367] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2368] In traditional mortgage services, customers had to go through complex procedures to undergo preliminary screening and loan planning consultations. This often resulted in cumbersome and time-consuming processes. Furthermore, the inability to quickly provide customers with the information and advice they needed could lead to decreased customer satisfaction. Additionally, insufficient education and information regarding mortgages could cause customer anxiety. A system is needed that effectively addresses these problems and efficiently and quickly responds to customer needs.

[2369] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[2370] In this invention, the server includes means for providing an interface for inputting customer information, means for transmitting the input information to a processing unit, means for the processing unit to process the input information based on a generative artificial intelligence model and generate a preliminary examination result and a list of required documents, means for displaying the generated preliminary examination result and list of required documents to the customer, means for providing conditions such as repayment period and interest rate to be input by the customer, means for transmitting the input conditions to the processing unit, means for the processing unit to process the input conditions based on a generative artificial intelligence model and generate an optimal repayment plan and interest rate estimate, means for displaying the generated repayment plan and interest rate estimate to the customer, means for providing an interface for the customer to request topics and related information they wish to learn about, means for transmitting the customer's request to the processing unit, means for the processing unit to process the customer's request based on a generative artificial intelligence model and generate educational content and market trend information, and means for displaying the generated educational content and market trend information to the customer. This simplifies the customer's procedure and enables the provision of services quickly and accurately.

[2371] A "customer" is an individual or legal entity that uses a mortgage service.

[2372] An "interface" is a user interaction mechanism that provides a means for users to input information.

[2373] A "processing device" is a computer or server used to process received information and data.

[2374] A "generative artificial intelligence model" is an algorithm or model used to generate specific results based on input data.

[2375] "Preliminary approval result" refers to information indicating the provisional approval status of a loan, generated based on the information entered by the customer.

[2376] The "list of required documents" is a list of documents that the customer must submit during the loan application process.

[2377] A "repayment plan" is a loan repayment plan based on the customer's borrowing conditions.

[2378] An "interest rate estimate" is an estimate of the interest rate that should be applied to a loan offered to a customer.

[2379] "Educational content" refers to teaching materials and documents that contain foundational knowledge and detailed information for customers to learn.

[2380] "Market trend information" refers to data that provides the latest information and trends regarding mortgage and real estate markets.

[2381] A "request" is a request that a customer sends to obtain specific information or services.

[2382] This invention provides a system that improves efficiency and customer satisfaction in mortgage services by utilizing generative artificial intelligence models. Detailed embodiments are described below.

[2383] Overview of the entire system

[2384] This system utilizes a dedicated software application (hereinafter referred to as the dedicated app), a server, and a generative artificial intelligence model (e.g., OpenAI GPT-4). The dedicated app provides an interface for the user to input information, the server transmits the input information to a processing unit, and the generative artificial intelligence model performs predetermined processing. The processing results are then displayed again on the user's terminal.

[2385] 1. Preliminary screening and customer support

[2386] System Overview:

[2387] When a user wishes to undergo a preliminary mortgage application, they enter information such as their annual income, occupation, and desired loan amount through a dedicated app. The entered information is sent to a server, and a generative artificial intelligence model uses that information to generate a preliminary application result and a list of required documents.

[2388] Specific example:

[2389] When a customer accesses a dedicated app and enters an annual income of 6 million yen and a desired loan amount of 30 million yen, a generative artificial intelligence model performs a preliminary screening and displays whether the application has passed the preliminary screening and a list of required documents (income verification documents, identity verification documents, etc.).

[2390] Example of a prompt:

[2391] "Please conduct a preliminary mortgage application with an annual income of 6 million yen and a desired loan amount of 30 million yen. Please let me know the results and required documents."

[2392] 2. Loan Planning and Advice

[2393] System Overview:

[2394] When a user requests a repayment plan or interest rate estimate, they enter conditions such as the repayment period and desired interest rate through a dedicated app. This information is sent to a server, where a generative artificial intelligence model generates the optimal repayment plan and interest rate estimate.

[2395] Specific example:

[2396] When a customer inputs a 20-year repayment period and a fixed interest rate of 1.5%, a generative artificial intelligence model displays details such as a monthly repayment amount of 180,000 yen and a total repayment amount of 32,400,000 yen.

[2397] Example of a prompt:

[2398] "Please provide a mortgage repayment plan with a 20-year repayment period and a fixed interest rate of 1.5%, including the monthly payment amount and the total repayment amount."

[2399] 3. Education and Information Provision

[2400] System Overview:

[2401] If a user wishes to receive education or information about mortgages, they can request topics and related news through a dedicated app. The request is sent to a server, where a generative artificial intelligence model generates content on basic knowledge and market trends.

[2402] Specific example:

[2403] When a customer requests "basic information on home loans," a generative artificial intelligence model displays explanations of fixed and variable interest rates, the latest market trends, and more.

[2404] Example of a prompt:

[2405] "Please explain the basics of home loans, including the difference between fixed and variable interest rates."

[2406] 4. Prompt service delivery and efficiency

[2407] System Overview:

[2408] If a user wants an immediate answer, they enter their question through a dedicated app. The question is sent to a server, where a generative artificial intelligence model instantly generates an answer and provides the result to the user.

[2409] Specific example:

[2410] When a customer enters the question, "What documents are required for a loan application?", a generative artificial intelligence model generates and displays answers such as income statements, identity verification documents, and property appraisal reports.

[2411] Example of a prompt:

[2412] "What documents are required for a loan application?"

[2413] The above describes the embodiments for carrying out the present invention. This enables customers to use mortgage services simply and quickly, and allows for efficient preliminary screening, presentation of repayment plans, and provision of educational content.

[2414] The flow of the specific processing in Example 1 will be explained using Figure 11.

[2415] 1. Preliminary screening and customer support

[2416] Processing flow

[2417] Step 1:

[2418] User: Open the dedicated app and enter information such as annual income, occupation, and desired loan amount.

[2419] Input: Information such as annual income, occupation, and desired loan amount.

[2420] Specific action: The user types text on the smartphone screen and presses the send button.

[2421] Step 2:

[2422] Terminal: Sends the entered information to the server.

[2423] Input: Information entered by the user, such as annual income, occupation, and desired loan amount.

[2424] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[2425] Step 3:

[2426] Server: Passes the received information to the generative artificial intelligence model.

[2427] Input: User information sent as an HTTP request

[2428] Data processing: Execute API calls to pass information to a generative artificial intelligence model.

[2429] Specific operation: The server passes data to the API endpoint.

[2430] Step 4:

[2431] Generative AI model: Generates preliminary review results and a list of required documents based on the input information.

[2432] Input: User information (annual income, occupation, desired loan amount, etc.)

[2433] Data processing: An algorithm analyzes user information and generates a preliminary review result and a list of required documents.

[2434] Specific operation: The model performs processing and outputs the results in a data format.

[2435] Step 5:

[2436] Server: Sends the generated preliminary review results and list of required documents to the terminal.

[2437] Input: Output data from a generative artificial intelligence model (preliminary review results, list of required documents)

[2438] Specific operation: Send output data to the terminal as an HTTP response.

[2439] Step 6:

[2440] Terminal: Displays the preliminary screening results and a list of required documents to the customer.

[2441] Input: Preliminary review results and list of required documents sent as an HTTP response.

[2442] Specific action: Display the result on the screen.

[2443] 2. Loan Planning and Advice

[2444] Processing flow

[2445] Step 1:

[2446] User: Enter conditions such as repayment period and desired interest rate using the dedicated app.

[2447] Input: Conditions such as repayment period and desired interest rate.

[2448] Specific action: The user enters text into the input field and presses the submit button.

[2449] Step 2:

[2450] Terminal: Sends the entered conditions to the server.

[2451] Input: User-entered conditions such as repayment period and desired interest rate.

[2452] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[2453] Step 3:

[2454] Server: Passes the received conditions to the generative artificial intelligence model.

[2455] Input: User conditions sent as an HTTP request

[2456] Data processing: Execute API calls to pass conditions to a generative artificial intelligence model.

[2457] Specific operation: The server passes data to the API endpoint.

[2458] Step 4:

[2459] Generative artificial intelligence models: Generate optimal repayment plans and interest rate estimates based on given conditions.

[2460] Input: User conditions (repayment period, desired interest rate, etc.)

[2461] Data processing: Algorithms analyze user conditions and create repayment plans and interest rate estimates.

[2462] Specific operation: The model performs processing and outputs the results in a data format.

[2463] Step 5:

[2464] Server: Sends the generated repayment plan and interest rate estimate to the terminal.

[2465] Input: Output data from a generative artificial intelligence model (repayment plan, interest rate estimate).

[2466] Specific operation: Send output data to the terminal as an HTTP response.

[2467] Step 6:

[2468] Terminal: Displays repayment plans and interest rate estimates to customers.

[2469] Input: Repayment plan and interest rate estimate sent as an HTTP response.

[2470] Specific action: Display the result on the screen.

[2471] 3. Education and Information Provision

[2472] Processing flow

[2473] Step 1:

[2474] User: Request topics you want to learn about or related news.

[2475] Input: Topic you want to learn about, related news

[2476] Specific steps: Select a theme using the dedicated app and submit a request.

[2477] Step 2:

[2478] Terminal: Sends a request to the server.

[2479] Input: User selected theme, related news

[2480] Specific operation: The selected data is encoded and sent to the server as an HTTP request.

[2481] Step 3:

[2482] Server: Passes the received request to the generative artificial intelligence model.

[2483] Input: User request sent as an HTTP request

[2484] Data processing: Execute API calls to pass the request content to a generative artificial intelligence model.

[2485] Specific operation: The server passes data to the API endpoint.

[2486] Step 4:

[2487] Generative artificial intelligence model: Generates educational content and market trend information based on the requested content.

[2488] Input: User request (theme, related news)

[2489] Data processing: Algorithms analyze requests and create educational content and market trend information.

[2490] Specific operation: The model performs processing and outputs the results in a data format.

[2491] Step 5:

[2492] Server: Sends generated educational content and market trend information to terminals.

[2493] Input: Output data from generative artificial intelligence models (educational content, market trend information).

[2494] Specific operation: Send output data to the terminal as an HTTP response.

[2495] Step 6:

[2496] Terminal: Displays educational content and market trend information to customers.

[2497] Input: Educational content and market trend information sent as an HTTP response.

[2498] Specific action: Display the result on the screen.

[2499] 4. Prompt service delivery and efficiency

[2500] Processing flow

[2501] Step 1:

[2502] User: Enter your question.

[2503] Input: Question content

[2504] Specific actions: Enter text into the question input field in the dedicated app and tap the submit button.

[2505] Step 2:

[2506] Terminal: Sends the question to the server.

[2507] Input: Question entered by the user

[2508] Specific operation: The input data is encoded and sent to the server as an HTTP request.

[2509] Step 3:

[2510] Server: Passes the question to a generative artificial intelligence model for processing.

[2511] Input: User question sent as an HTTP request

[2512] Data processing: Execute API calls to pass the question content to a generative artificial intelligence model.

[2513] Specific operation: The server passes data to the API endpoint.

[2514] Step 4:

[2515] Generative artificial intelligence models: Generate answers to questions.

[2516] Input: User Question

[2517] Data processing: Algorithms analyze the question and generate the optimal answer.

[2518] Specific operation: The model performs processing and outputs the results in a data format.

[2519] Step 5:

[2520] Server: Sends the generated response to the terminal.

[2521] Input: Output data (answer) from a generative artificial intelligence model.

[2522] Specific operation: Send output data to the terminal as an HTTP response.

[2523] Step 6:

[2524] Terminal: Display the answer to the customer.

[2525] Input: Response sent as an HTTP response

[2526] Specific action: Display the result on the screen.

[2527] (Application Example 1)

[2528] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2529] Current mortgage services often require multiple steps, making it difficult for customers to efficiently receive pre-approval, repayment plans, educational information, and answers to their questions on a single platform. Furthermore, real-time service can be time-consuming, leading to decreased customer satisfaction. Therefore, a new system that is both efficient and enhances customer satisfaction is needed.

[2530] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[2531] In this invention, the server includes means for providing an interface for inputting customer information, means for transmitting the inputted information to the server, means for processing the inputted information on the server based on a generative artificial intelligence model to generate a preliminary screening result and a list of required documents, means for displaying the generated preliminary screening result and list of required documents to the customer, means for providing a mortgage service performed through a virtual reality device, and means for providing an integrated preliminary screening, repayment plan, educational information, and answers to questions within a virtual store using a head-mounted display. This enables the customer to efficiently receive mortgage services in a virtual reality environment.

[2532] "Customer information" refers to personal information necessary for mortgage services, such as annual income, occupation, desired loan amount, repayment period, and desired interest rate.

[2533] A "generative artificial intelligence model" is a type of artificial intelligence technology that generates optimal preliminary screening results, repayment plans, interest rate estimates, educational content, and answers to questions based on input conditions and information.

[2534] A "virtual reality device" is a device that allows users to experience a virtual environment, and specifically includes head-mounted displays, controllers, and the like.

[2535] A "head-mounted display" is a device that users wear on their heads to visually experience virtual reality.

[2536] A "virtual store" is a virtual store space that can be visited through a virtual reality device, providing an interface for customers to receive mortgage services.

[2537] A "preliminary assessment result" is an initial evaluation of the mortgage loan the customer is requesting, indicating the likelihood of loan approval, necessary documents, and the next steps to take.

[2538] A "repayment plan" is a detailed repayment plan for a mortgage, including the repayment period, monthly payment amount, and total repayment amount.

[2539] An "interest rate estimate" is an estimate based on the interest rate applicable to a mortgage loan, used to calculate the monthly repayment amount and the total repayment amount.

[2540] "Educational content" refers to information used to learn about mortgage-related knowledge, market trends, and important terminology.

[2541] "Answer to a question" refers to the immediate response from a generative artificial intelligence model to a question entered by the customer.

[2542] This invention is a mortgage service system using a virtual reality device. This system aims to provide customers with highly efficient and satisfying services by utilizing a generative artificial intelligence model.

[2543] 1. Required hardware and software

[2544] Hardware:

[2545] Virtual reality devices (e.g., regular head-mounted displays)

[2546] Communication server

[2547] User device (smartphone or computer)

[2548] software:

[2549] OpenAI API (an API for using generative artificial intelligence models)

[2550] Virtual reality interface software

[2551] Server communication software

[2552] 2. Data processing and data calculation

[2553] server:

[2554] The system receives user information (annual income, occupation, desired loan amount, etc.) and passes it to a generative artificial intelligence model.

[2555] Based on the output from the model, preliminary screening results, repayment plans, educational content, and answers to questions are generated.

[2556] The generated results are sent back to the user.

[2557] User terminal:

[2558] It provides an interface for users to input information.

[2559] Send the entered information to the server.

[2560] The results returned from the server are displayed within the virtual reality environment.

[2561] Generative artificial intelligence models:

[2562] Based on the entered user information and conditions, the system generates the necessary results.

[2563] Specifically, it outputs preliminary screening results, repayment plans, interest rate estimates, educational content, and answers to frequently asked questions.

[2564] 3. Specific Examples

[2565] Preliminary screening and customer support:

[2566] As a concrete example, a user wears a head-mounted display and accesses a dedicated terminal in a virtual store, entering an annual income of 6 million yen, occupation as a company employee, and desired loan amount of 30 million yen. This information is sent to a server, where a generative artificial intelligence model performs a preliminary assessment, and the preliminary assessment results, including a list of required documents, are immediately displayed within the virtual reality environment.

[2567] Example of a prompt:

[2568] Please use the following information to pre-approve your mortgage:

[2569] Annual income: 6 million yen ...

Claims

1. A means of providing an interface for inputting customer information, A means of sending the input information to the server, The server includes means for processing input information based on a generative artificial intelligence model to generate preliminary review results and a list of required documents, A means of displaying the generated preliminary screening results and required document list to the customer, A system that includes this.

2. A means of providing customers with terms such as repayment period and interest rate, A means for sending the entered conditions to the server, On the server, the input conditions are processed based on a generative artificial intelligence model to generate an optimal repayment plan and interest rate estimate. A means of displaying the generated repayment plan and interest rate estimate to the customer, The system according to claim 1, including the following:

3. A means of providing an interface for customers to request topics they want to learn about and related news, A means of sending customer requests to the server, A server that processes customer requests based on a generative artificial intelligence model and generates educational content and market trend information, A means of displaying generated educational content and market trend information to customers, The system according to claim 1, including the following:

4. A means of providing an interactive interface for receiving questions entered by customers, A means of sending the entered question to the server, The server has means for processing input questions based on a generative artificial intelligence model and generating immediate answers. A means of displaying the generated response to the customer, The system according to claim 1, including the following:

Images