System

Abstract

A system is provided.SOLUTION: This system is provided with a means for learning the approval / denial history of an application form by using a generation AI, a means for inspecting application contents inputted by an applicant, a means for generating and presenting a correction plan based on the inspected application contents, and a means for correcting the application contents based on the correction plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] If applicants are unfamiliar with submitting application forms, they may be confused about which options to choose or feel anxious about whether the information in the comment section is complete. If the application is rejected due to such uncertainty, they will have to go through the trouble of resubmitting the application, which is both psychologically and time-consuming. Unless this problem is resolved, it will become an obstacle to efficient business operations. [Means for solving the problem]

[0005] This invention relates to a system that uses a generation AI to learn the approval / denial history of application forms, scrutinize the application details entered by the applicant, and propose revisions. Specifically, the system includes a terminal where the applicant submits the application details, a server that receives the submitted application details and passes them to the generation AI to generate revision suggestions, and a means for returning the generated revision suggestions to the applicant. This improves the accuracy of the application details, alleviating concerns and reducing rework.

[0006] "Generative AI" is an artificial intelligence that learns from the past approval / denial history of application forms, scrutinizes the application content, and generates suggested revisions.

[0007] An "application form" is an electronic document completed and submitted by an applicant to apply for a specific matter or procedure.

[0008] "Approval / Rejection History" refers to historical data on application forms that have been submitted in the past and that have been approved or rejected.

[0009] "Applicant" means a person or entity that completes and submits an application form to request a particular subject matter or procedure.

[0010] "Scrutiny" refers to the act of carefully examining the application submitted by the applicant to confirm its accuracy and validity.

[0011] "Proposed amendments" are proposals that include improvements and corrections to the application content, proposed by the generation AI as a result of its careful review.

[0012] "Terminal" means the electronic device (e.g., PC, smartphone) on which the applicant enters and submits the application form.

[0013] The "server" is a computer system whose role is to receive the application details sent by the applicant, pass them to the generation AI to generate proposed revisions, and return them to the applicant. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0022] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] The present invention relates to a system that uses a generation AI to eliminate or reduce anxiety about submitting an unfamiliar application form. An embodiment of this system will be described in detail.

[0036] System Overview

[0037] The system consists of three main components:

[0038] 1. User terminal: The device on which the applicant fills out and submits the application form.

[0039] 2. Server: Receives the application content and passes it to the generation AI for review and generation of correction proposals.

[0040] 3. Generative AI: Learns from past approval and rejection history, evaluates the application content, and generates revision proposals.

[0041] Embodiment

[0042] Initializing the Server

[0043] When the server is initialized, a generation AI object is automatically generated. The generation AI is used to learn the approval and rejection history of past application forms. This AI carefully examines the application details entered by the applicant and generates appropriate correction suggestions, thereby reducing the user's anxiety.

[0044] Initializing the user device

[0045] The user terminal has an interface for communicating with the server. The user fills out an application form and sends it to the server through this terminal. The terminal has the function of receiving the proposed revisions sent from the server and presenting them to the user.

[0046] Submitting a form and generating suggested revisions

[0047] When a user submits an application form via their device, the server receives it and passes the content of the request to the generation AI. The generation AI evaluates the application content based on past approval / denial history and generates an appropriate revision proposal. This revision proposal is sent back to the user device via the server.

[0048] Proposal of amendments and review of application contents

[0049] By receiving and confirming the proposed revisions, users can review the content of their application. This allows users to submit applications with greater confidence. As a result, the hassle of having to reject or resubmit applications is reduced, reducing both psychological and time burdens.

[0050] Example

[0051] As an example, consider the following scenario.

[0052] Let's take the example of a user applying for "travel expenses" for business purposes. The user enters "Category: Travel Expenses," "Amount: $1,000," "Comments: Expenses for a business trip to New York," etc. into the terminal and submits it. When this application content arrives at the server, the server passes it to the generation AI and requests it to generate a revision proposal.

[0053] The generation AI evaluates this and generates suggested corrections, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested corrections to the user's device, and the user can review them, revise the application, and resubmit it.

[0054] In this way, by using the system of the present invention, applicants can increase the accuracy of their application content and submit applications with fewer deficiencies.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] Initializing the Server

[0058] When the server is initialized, it creates a generated AI object, which learns the approval / denial history of past application forms and is used to scrutinize the application content.

[0059] Step 2:

[0060] Initializing the user device

[0061] The user device is initialized and configured with a reference to the server instance (server object), which allows communication between the user and the server.

[0062] Step 3:

[0063] Fill in and submit the application form

[0064] The user enters the necessary information into the application form and sends it to the server via the terminal. For example, the input contents are "Category: Travel expenses", "Amount: 1,000 dollars", and "Comments: Expenses for business trip to New York".

[0065] Step 4:

[0066] The server receives the request

[0067] The server receives the application details sent from the user terminal. This received data is used as input for the subsequent AI generation process.

[0068] Step 5:

[0069] Request processing to generation AI

[0070] The server passes the received application content to the generation AI and requests it to generate a revision proposal. Here, the generation AI evaluates the application content based on past approval / rejection history.

[0071] Step 6:

[0072] Generate correction suggestions

[0073] The AI ​​generator then examines the application and generates suggested revisions, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs."

[0074] Step 7:

[0075] Sending proposed revisions back to the server

[0076] The AI ​​generates a proposed revision and sends it back to the server. The server receives the proposed revision and performs the following steps:

[0077] Step 8:

[0078] Sending proposed revisions to the user's device

[0079] The server then sends the received revision proposal to the user's terminal, allowing the user to confirm the revision proposal.

[0080] Step 9:

[0081] Confirmation of amendments and review of application details

[0082] The user can check the proposed revisions through their device and make any necessary changes to the application. By sending the revised application details back to the server, the accuracy of the application is improved and rework is reduced.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] When submitting conventional application forms, applicants often submit them without realizing that there are incomplete information, resulting in a high risk of the application being rejected. Furthermore, when corrections to the application content are necessary, there is a lack of guidance on how to correct inappropriate information. This often results in psychological stress for applicants and the loss of time required to resubmit. This invention solves these problems by utilizing generative AI to scrutinize application content and automatically generate suggested corrections.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes means for generating a generation AI object when initialized and training the generation AI using past application form data, means for a user terminal to communicate with the server and input and send an application form, means for passing the application data received by the server to the generation AI in prompt format and causing the generation AI to generate a revision proposal, and means for sending the generated revision proposal to the user terminal via the server and reviewing and correcting the application content. This enables the applicant to quickly check for deficiencies in the application content and review and correct it according to the appropriate revision proposal.

[0088] A "generative AI object" is an artificial intelligence program that learns from past data and generates outputs for specific tasks.

[0089] "Application form data" is a collection of information contained in past applications, including approval and denial history.

[0090] A "user terminal" is a device that allows a user to enter information into an application form and communicate with a server.

[0091] A "prompt format" is formatted text data used to give instructions to the generating AI.

[0092] "Proposed amendments" are suggestions made by the generating AI after evaluating the application and indicating any necessary amendments or additional information.

[0093] The "server" is a computer system that has the function of receiving application content from a user terminal, passing the data to the generation AI, generating a revision proposal, and returning it to the user terminal.

[0094] "Learning" is the process by which generative AI uses past data to improve its performance.

[0095] A "communication interface" is a means by which a user terminal and a server send and receive data to and from each other.

[0096] MODE FOR CARRYING OUT THE INVENTION

[0097] This invention relates to a system that uses generative AI to eliminate or reduce anxiety about submitting unfamiliar application forms. This system consists of the following three components:

[0098] User terminal

[0099] The user terminal is the device on which the applicant enters and submits the application form. The user terminal can be a PC, tablet, smartphone, or other device. This terminal has an interface for communicating with the server, and has the function of sending data to the server when the user enters and submits the application form. The user terminal also has the function of receiving proposed revisions sent from the server and presenting them to the user.

[0100] server

[0101] The server is a device that receives the application details sent from the user terminal and passes them to the generation AI to generate revision proposals. The server uses a high-performance computer (e.g., a cloud-based service). When the server is initialized, a generation AI object is created. This generation AI learns using a dataset of past application forms (approval and denial history). Specifically, it retrieves data from a database and trains a model using a machine learning algorithm.

[0102] Generation AI

[0103] Generative AI is an artificial intelligence program that evaluates application content and generates appropriate revision suggestions. A text generation model such as GPT-4 is used as the generative AI model. This model learns from past approval and denial history and generates appropriate revision suggestions if there are any deficiencies in the application content.

[0104] Specific scenario example

[0105] Let's take the example of a user applying for "travel expenses" for business purposes. The user enters "Category: Travel Expenses," "Amount: $1,000," and "Comment: Expenses for a business trip to New York" into the terminal and submits it. When this application content arrives at the server, the server sends it to the generation AI.

[0106] The generation AI evaluates the application content and generates suggested revisions, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, and the user can review them, revise the application content, and resubmit it.

[0107] Prompt Sentence Examples

[0108] Below is an example of a prompt sentence to input to the generative AI model.

[0109] "User's request: Category: Travel expenses, Amount: $1,000, Comments: Expenses for a business trip to New York. Please generate an appropriate amendment based on this information."

[0110] This prompt allows the generation AI to generate appropriate revision suggestions based on the user's request.

[0111] In this way, the system of the present invention alleviates applicants' anxieties and improves the accuracy of application content, enabling faster and more reliable applications.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Program processing flow

[0114] Step 1:

[0115] When the server is initialized, it first creates a Generative AI object. The server retrieves past application form data (approval / denial history) from the database and provides this data to the Generative AI. The Generative AI learns from this data and acquires the knowledge necessary to evaluate the application content and generate revision proposals. The input is the past application form data, and the output is the trained Generative AI model.

[0116] Step 2:

[0117] The user enters the necessary information into the application form. The user terminal provides the interface for the application form, and the user enters information such as "Category: Travel expenses," "Amount: $1,000," and "Comments: Expenses for business trip to New York." With this information as input, the user terminal sends it to the server in the form of an HTTP request. The output is the request data sent to the server.

[0118] Step 3:

[0119] The server receives application data from the user. The server analyzes the received data and generates a prompt for the generation AI. For example, it generates a prompt that reads, "User's application details: Category: Travel expenses, Amount: 1,000 dollars, Comments: Expenses for a business trip to New York. Please generate an appropriate amendment based on this information." The input is the application data, and the output is the prompt to be passed to the generation AI.

[0120] Step 4:

[0121] The generation AI receives a prompt and evaluates the application content. It detects any deficiencies in the application content based on past data and generates suggested corrections. For example, for a travel expense application, it might create a suggested correction such as, "Please include the specific dates of the trip and a breakdown of accommodation costs." The input is the prompt, and the output is the suggested correction.

[0122] Step 5:

[0123] The server receives the proposed revision from the generation AI. The server sends this proposed revision to the user terminal in the form of an HTTP response. The input is the generated proposed revision, and the output is the response data sent to the user terminal.

[0124] Step 6:

[0125] The user terminal receives the proposed amendments from the server and displays them on the user interface. For example, the proposed amendments are displayed as a pop-up window or a notification message. The user checks the proposed amendments and amends the application content as necessary. The input is the response data from the server, and the output is the displayed proposed amendments.

[0126] Step 7:

[0127] The user reviews the application based on the proposed revisions and resubmits it. Once the revisions are complete, the user terminal again sends the application data to the server. This process is repeated until the application is finally approved. The input is the revised application data, and the output is the resent request data.

[0128] (Application example 1)

[0129] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0130] When using electronic payment services, many users are unfamiliar with filling out application forms for financial transactions and payment procedures, and are therefore prone to input errors and incomplete information. This can lead to applications being rejected or requiring reapplication, which can cause problems of psychological stress and increased time burden for users. In addition, electronic payment service providers incur time and costs in processing incomplete applications, reducing the efficiency of the overall service.

[0131] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0132] In this invention, the server includes means for correcting in advance any deficiencies in application forms for financial transactions or payment procedures using a generation AI, means for transmitting application details from a user terminal to the server and passing them from the server to the generation AI to generate a correction proposal, and means for returning the generated correction proposal to the user terminal and presenting it to the user. This allows the user to correct input deficiencies in the application form in advance, reducing the hassle of rejecting applications and having to resubmit them.

[0133] "Generative AI" is an artificial intelligence system that generates new information based on past data.

[0134] "Request Form" means a form or web form that a user fills out to request a particular service or permission.

[0135] "Approval / denial history" refers to data showing the results of past applications, including both approved and denied applications.

[0136] "User terminal" refers to the digital device used by the user to input the application form and send it to the server. Examples include smartphones and tablets.

[0137] A "server" is a computer system that provides services to other computers on a network.

[0138] "Suggested fixes" are data or information created by generative AI that suggests ways to improve input.

[0139] "Financial transactions" is a concept that refers to all transactions involving the transfer or exchange of funds.

[0140] A "payment transaction" is a formal process used to pay for goods or services.

[0141] "Defects" refer to missing information or errors in input or procedures.

[0142] This invention relates to a system that reduces psychological stress and time burdens by allowing users to correct incomplete application forms in advance when using electronic payment services, thereby improving efficiency on the part of electronic payment service providers.

[0143] System Overview

[0144] The system consists of the following main components:

[0145] 1. User device: A device such as a smartphone or tablet that the user uses to fill out and submit the application form.

[0146] 2. Server: A relay system that receives the application details, passes them to the generation AI, generates revision proposals, and returns them to the user's terminal.

[0147] 3. Generative AI: An artificial intelligence system that learns from past approval and denial history, evaluates the application content, and generates revision proposals.

[0148] Hardware and software used

[0149] User devices: smartphones (iOS, Android), tablets (iOS, Android)

[0150] Server: Remote server (Linux, Windows Server)

[0151] Generative AI: AI models that perform advanced natural language processing (e.g., GPT-3, BERT)

[0152] Specific implementation methods

[0153] 1. User device operation: The user uses a smartphone or tablet to fill out application forms for financial transactions and payment procedures. For example, they enter information such as "Category: Remittance," "Amount: 5,000 yen," and "Description: Rent payment."

[0154] 2. Data transmission: The entered application details are sent to a server via the Internet.

[0155] 3. Server processing: The server passes the received application content to the generative AI model, which analyzes the application content based on past approval / rejection history and generates suggestions for correcting deficiencies.

[0156] 4. Generate suggested amendments: The AI ​​generator suggests additional information or amendments that the user should fill in, such as, "Please specify the specific period for rent payments (e.g., October 1st to October 31st, 2023)."

[0157] 5. Return and presentation to the user's device: The proposed revisions generated by the generation AI are returned to the user's device via the server and presented to the user. The user reviews the application based on the proposed revisions and makes any necessary revisions.

[0158] Examples and prompts

[0159] Specific examples

[0160] If a user is trying to request a rent payment:

[0161] Original input:

[0162] Category:Remittance

[0163] Price: 5,000 yen

[0164] Description: Rent payment

[0165] AI's suggested correction: "Please specify the specific period for rent payment (e.g., October 1st to October 31st, 2023)."

[0166] Prompt example

[0167] text

[0168] user_form = {

[0169] "Category": "Remittance",

[0170] "Amount": "5000 yen",

[0171] "Description": "Rent payment"

[0172] }

[0173] correction_suggestions = send_application_form(user_form)

[0174] Using this prompt as an example, the user device sends the application details to the server, and the server uses a generative AI to generate an appropriate correction proposal and sends it back to the user device, completing a series of processes.

[0175] This allows users to prevent input errors in application forms and increase the accuracy of applications, while also enabling electronic payment service providers to provide services more efficiently.

[0176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0177] Step 1:

[0178] The user fills out the application form using a terminal. For example, the user enters information such as "Category: Remittance," "Amount: 5,000 yen," and "Explanation: Rent payment." This input itself becomes the application form data and is sent to the server.

[0179] Step 2:

[0180] The terminal sends the entered application form data to the server. Specifically, it sends the application details to the server via the Internet using an HTTP request. The input data is encoded in JSON format and passed to the server.

[0181] Step 3:

[0182] The server receives the submitted application, which is first checked for format and required fields, eliminating basic input errors.

[0183] Step 4:

[0184] The server passes the received request to the generation AI, which evaluates the request using a model trained on past approval and denial history. The input data is sent to the generation AI model as a prompt.

[0185] Step 5:

[0186] The generative AI analyzes the application content and detects deficiencies and areas for improvement. Specifically, it uses natural language processing technology to analyze text data and generate revision proposals by referencing past approval and rejection patterns. The data operations performed during this process include text pattern matching, probability evaluation, and calculation of a generation score.

[0187] Step 6:

[0188] The proposed revisions generated by the generation AI are sent back to the server. The server receives the proposed revisions and prepares them to send back to the user device. The proposed revisions are also encoded in JSON format and sent to the user device as an HTTP response.

[0189] Step 7:

[0190] The terminal displays the proposed amendment received from the server to the user. The user confirms the proposed amendment and amends the application. Specifically, the proposed amendment includes specific instructions such as "Please specify the specific period for rent payment (e.g., October 1 to October 31, 2023)."

[0191] Step 8:

[0192] The user then modifies the application form based on the proposed modifications. The modified data is then sent to the server as in the first step. This process is repeated until the user is satisfied with the modified application.

[0193] This allows users to accurately correct their input data and increase the accuracy of their applications, while electronic payment service providers can reduce the number of incomplete applications from users and improve processing efficiency.

[0194] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0195] The present invention relates to a system that uses a generative AI and an emotion engine in combination to eliminate or reduce anxiety about submitting an application form. An embodiment of this system will be specifically described.

[0196] System Overview

[0197] The system consists of four main components:

[0198] 1. User terminal: The device on which the applicant fills out and submits the application form.

[0199] 2. Server: Receives the application content and passes it to the generation AI for review and generation of correction proposals.

[0200] 3. Generative AI: Learns from past approval and rejection history, evaluates the application content, and generates revision proposals.

[0201] 4. Emotion engine: Recognizes user emotions in real time and provides that information to the generative AI.

[0202] Embodiment

[0203] Initializing the Server

[0204] When the server is initialized, it creates a generation AI object and an emotion engine object. The generation AI is used to learn the approval / denial history of past application forms, and the emotion engine recognizes the user's emotions in real time and provides that information to the generation AI.

[0205] Initializing the user device

[0206] The user terminal has an interface for communicating with the server. The user fills out the application form and sends it to the server through this terminal. The terminal has the function of receiving the proposed revisions sent from the server and presenting them to the user.

[0207] Submitting a form and generating suggested revisions

[0208] When a user submits an application form via their device, the server receives it and passes the application details to the generation AI and emotion engine. The emotion engine recognizes the user's emotions in real time and provides this information to the generation AI. The generation AI evaluates the application details based on past approval / denial history and information obtained from the emotion engine, and generates appropriate revision suggestions. These revision suggestions are sent back to the user's device via the server.

[0209] Proposal of amendments and review of application contents

[0210] By receiving and confirming the proposed revisions, users can review the content of their application. By utilizing information from the emotion engine, revisions are generated that reflect the user's emotions, improving the user experience. This allows users to submit applications with greater confidence, reducing the hassle of having to reject or resubmit applications, and also reducing the psychological and time burden.

[0211] Example

[0212] As an example, consider the following scenario.

[0213] When a user applies for "travel expenses," they input "Category: Travel Expenses," "Amount: $1,000," "Comments: Expenses for a business trip to New York," etc. into their device and submit it. When this application content arrives at the server, the server passes it to the generation AI and emotion engine and requests them to generate a revision proposal.

[0214] The emotion engine recognizes the user's emotions in real time and provides the generation AI with emotions such as "I'm worried about the application content." The generation AI evaluates the application content based on past approval / denial history and information from the emotion engine, and generates suggested revisions such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, allowing the user to review and resubmit the application.

[0215] By using the emotion engine, the system can provide revision suggestions that take the user's emotions into consideration, allowing for more effective revision of application content. This allows applicants to submit applications with confidence and confidence, resulting in improved application accuracy and reduced resubmission efforts.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] Initializing the Server

[0219] When the server is initialized, it creates a generation AI object and an emotion engine object. The generation AI object learns the approval / denial history of past application forms and is used to scrutinize the application content. The emotion engine is used to recognize the user's emotions in real time.

[0220] Step 2:

[0221] Initializing the user device

[0222] The user device is initialized and configured with a reference to the server instance (server object), which allows communication between the user and the server.

[0223] Step 3:

[0224] Fill in and submit the application form

[0225] The user enters the necessary information into the application form and sends it to the server via the terminal. For example, the input contents are "Category: Travel Expenses", "Amount: $1,000", and "Comment: Expenses for a business trip to New York". The terminal also sends the user's emotions to the emotion engine.

[0226] Step 4:

[0227] The server receives the request

[0228] The server receives the application details and emotion information sent from the user terminal, and this received data is used as input for subsequent processing of the generation AI and emotion engine.

[0229] Step 5:

[0230] Submitting to generative AI and emotion engines

[0231] The server passes the received application details and emotion engine information to the generation AI and requests it to generate a revision proposal. The emotion engine analyzes the user's emotions in real time and provides that information to the generation AI.

[0232] Step 6:

[0233] Generate correction suggestions

[0234] The generative AI scrutinizes the application content based on past approval and rejection history, and generates appropriate revision suggestions based on the user's emotional information provided by the emotion engine. Suggested revisions include, for example, "Please include the specific dates of the business trip and a breakdown of accommodation costs."

[0235] Step 7:

[0236] Sending proposed revisions back to the server

[0237] The AI ​​generates a proposed revision and sends it back to the server. The server receives the proposed revision and performs the following steps:

[0238] Step 8:

[0239] Sending proposed revisions to the user's device

[0240] The server then sends the received revision proposal to the user's terminal, allowing the user to confirm the revision proposal.

[0241] Step 9:

[0242] Confirmation of amendments and review of application details

[0243] The user checks the proposed revisions through their device and makes any necessary changes to the application. By sending the revised application details back to the server, the accuracy of the application is improved and rework is reduced. Specifically, the user can add details about the business trip schedule and accommodation costs based on the proposed revisions. This makes the application more specific and accurate, increasing the likelihood of approval.

[0244] Example 2

[0245] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0246] The present invention aims to reduce the anxiety and psychological burden felt by applicants when submitting application forms. Conventional systems examine application content and provide suggested revisions, but do not consider the applicant's feelings, often resulting in significant stress for the applicant. Furthermore, rejection of applications due to inappropriate content and the hassle of resubmissions have also become an issue. There is a need for a system that solves these issues, reduces applicants' psychological burden, and improves application accuracy.

[0247] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0248] In this invention, the server includes means for using a generation AI to learn the approval / denial history of the application form, means for scrutinizing the application content entered by the applicant, means for generating and presenting proposed revisions based on the scrutinized application content, means for using an emotion engine to recognize the applicant's emotions in real time and providing that information to the generation AI, and means for reducing psychological burden by generating proposed revisions that reflect the applicant's emotional information. This makes it possible to create more accurate application content while taking the applicant's emotions into consideration, thereby reducing psychological burden and increasing the success rate of applications.

[0249] "Generative AI" is artificial intelligence that learns from past data and generates new information and suggestions based on that data.

[0250] An "application form" is a format in which a user applies for some service or approval by inputting and submitting specific information.

[0251] "Approval / denial history" refers to the record of approvals or denials of applications submitted in the past.

[0252] An "emotion engine" is a technology that recognizes a user's emotions in real time and provides that information to other systems.

[0253] "Proposed amendments" are suggested changes or improvements to the current application.

[0254] "Server" means a computer system that receives, processes, and transmits data over a network to other devices.

[0255] A "terminal" is a device that allows a user to input data and exchanges information with a server through communication.

[0256] "Verification" is the process of examining data or information in detail based on specific criteria.

[0257] "Training" is the process by which generative AI learns from past data and improves its accuracy.

[0258] "Psychological burden" refers to the mental stress and anxiety experienced by the user.

[0259] This invention is a system that combines generative AI and an emotion engine to reduce the anxiety and psychological burden felt by users when submitting application forms. This system is mainly composed of four components: a user terminal, a server, generative AI, and an emotion engine.

[0260] User terminal

[0261] A user terminal is a device on which a user enters and submits an application form. The terminal is equipped with an interface for communicating with the server and has the function of sending the application details entered by the user to the server. The terminal also has a screen that receives proposed revisions returned from the server and presents them to the user. Specific devices include PCs, tablets, and smartphones.

[0262] server

[0263] The role of the server is to receive the application content sent from the user terminal and pass it to the generation AI and emotion engine to generate a revision proposal. The server instantiates a generation AI object and an emotion engine object during initialization. This generation AI is used to learn from past application data and evaluate the application content. Meanwhile, the emotion engine recognizes the user's emotions in real time and provides that information to the generation AI.

[0264] Generation AI

[0265] Generative AI is an AI model that learns from the approval and rejection history of past application forms, examines new application content, and generates suggested revisions. Generative AI runs on the server and compares the received application content with past data to identify areas for improvement and potential problems in the application content. This allows for the generation of more appropriate application content.

[0266] Emotion Engine

[0267] The emotion engine is a technology that recognizes the user's emotions in real time while they are filling out an application form. The emotion engine analyzes the user's facial expressions, voice, input speed, etc. to infer the user's psychological state. This information is provided to the generative AI and used to evaluate the application content and generate revision suggestions.

[0268] Example

[0269] When a user applies for "travel expenses," they enter information such as "Category: Travel Expenses," "Amount: $1,000," and "Comments: Expenses for a business trip to New York" into their device and press the send button. When this application information arrives at the server, the server passes it to the generation AI and emotion engine and requests them to generate a revision proposal.

[0270] The emotion engine recognizes emotions such as "the user is anxious about the application content" and provides this information to the generation AI. The generation AI evaluates the application content based on past approval / denial history and emotional data, and generates suggested revisions such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, where the user can confirm them. Through this process, the user can review the application content based on accurate information, eliminating any anxiety and enabling them to submit a reliable application.

[0271] An example of a specific prompt for a generative AI model is, "Travel expense request subject: Business trip to New York, October 1, 2023 - October 5, 2023. Add details: Accommodation $800, Transportation $200."

[0272] In this way, by combining a generative AI and an emotion engine, the system of the present invention has the effect of reducing the psychological burden on users while improving the accuracy of application content.

[0273] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0274] Step 1:

[0275] Initializing the Server

[0276] When the server is initialized, it instantiates a generation AI object and an emotion engine object. The generation AI object learns from past application data and is used to evaluate the application content. The emotion engine object recognizes the user's emotions in real time and provides that information to the generation AI.

[0277] Input: Server startup sequence

[0278] Output: Generate AI objects and emotion engine objects

[0279] Specific operation: The server program executes the startup sequence, and each object of the generation AI and emotion engine is loaded into memory.

[0280] Step 2:

[0281] Initializing the user device

[0282] The user terminal has an interface for communicating with the server. When the terminal is initialized, a form is displayed in which the user can enter the application details. The terminal also has the function of receiving and displaying proposed revisions sent from the server.

[0283] Input: Start user terminal

[0284] Output: Display of application form, initialization of communication interface

[0285] Specific operation: The user terminal starts up, the application form is initialized and displayed, and at the same time, a network connection is established to communicate with the server.

[0286] Step 3:

[0287] Fill in and submit the application form

[0288] The user enters the necessary information into the application form on the terminal. For example, the category, amount, comments, etc. Once the input is complete, the user clicks the "Submit" button. The terminal then sends the entered data to the server.

[0289] Input: User inputs application details (category, amount, comments)

[0290] Output: Application data sent to the server

[0291] Specific operation: The user enters "Category: Travel expenses", "Amount: $1,000", and "Comment: Expenses for business trip to New York" into the form and presses the submit button. The terminal sends this data to the server.

[0292] Step 4:

[0293] Receipt of application and start of processing

[0294] The server receives the application content sent from the device and passes the received data to the generation AI and emotion engine, which then starts the application content review process.

[0295] Input: Application data sent from the terminal

[0296] Output: Providing data to generative AI and emotion engines

[0297] Specific operation: The server receives the application data and converts it into the appropriate data format to be passed to the generation AI and emotion engine.

[0298] Step 5:

[0299] Emotion recognition with emotion engine

[0300] The emotion engine collects and analyzes emotional data in real time while the user is filling out the application form. If the user shows any anxiety or tension, that information is provided to the generation AI.

[0301] Input: Application data and emotion data provided by the server

[0302] Output: User emotion data provided to the generative AI

[0303] Specific operation: The emotion engine collects data on the speed at which the user fills out the application form and analysis of the user's facial expressions, and sends information such as "I am worried about the content of the application" to the generation AI.

[0304] Step 6:

[0305] Generative AI generates revision suggestions

[0306] The generative AI evaluates the current application form based on past application data and emotional data sent from the emotion engine. The generative AI identifies avoidable issues and generates appropriate corrections.

[0307] Input: Emotion data from the emotion engine and past approval / disapproval data

[0308] Output: Revision proposal

[0309] Specific operation: The generative AI analyzes the input data and generates a correction suggestion such as, "Please provide the specific dates of your business trip and a breakdown of accommodation costs."

[0310] Step 7:

[0311] Return and view proposed revisions

[0312] The server receives the proposed amendments generated by the AI ​​and returns them to the user's device. The user's device then displays the proposed amendments, allowing the user to review the application contents.

[0313] Input: Correction suggestions from the generative AI

[0314] Output: Send and display suggested revisions to the user's device

[0315] Specific operation: The server receives the proposed revision and sends it to the user's terminal, which then displays the proposed revision to the user.

[0316] Step 8:

[0317] Review of application contents based on proposed amendments

[0318] The user reviews the proposed revisions, returns to the application form, makes any necessary revisions, and finally clicks the submit button again to submit the application.

[0319] Input: User makes corrections to the application based on the proposed corrections

[0320] Output: Resubmit corrected application data

[0321] Specific actions: The user adds specific information such as "Specific dates of business trip to New York: October 1st - October 5th, 2023" and "Breakdown of accommodation expenses: $800, transportation expenses: $200" and resubmits the application.

[0322] (Application example 2)

[0323] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0324] The present invention relates to a system that reduces the anxiety users feel when submitting application forms. Currently, when filling out and submitting application forms, users often feel anxious about whether the content is appropriate. In particular, in electronic payment services, where money is involved, there is a risk that the application will be rejected if the applicant enters incorrect or incomplete information. There is a need to reduce such anxiety and stress and provide an environment in which users can apply with peace of mind.

[0325] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for using a generation AI to learn the approval / denial history of the application form, means for scrutinizing the application content entered by the applicant, means for generating and presenting amendment proposals based on the scrutinized application content, means for using an emotion engine to recognize the applicant's emotions in real time and provide that information to the generation AI, and means for providing feedback that takes into consideration the applicant's emotional state. As a result, the applicant's emotional state is recognized in real time by the emotion engine, and the generation AI provides appropriate amendment proposals based on that information, so the applicant can be assured of the accuracy of the application content and can apply with confidence.

[0326] "Generative AI" is artificial intelligence that learns from past data and is used to evaluate and make suggestions for new data.

[0327] An "emotion engine" is a system that analyzes a user's facial expressions, tone of voice, and other physical signals to recognize their emotional state in real time.

[0328] "Applicant" means a person who completes and submits an application form for a particular purpose.

[0329] "Application Content" refers to all the information entered by the applicant in the application form.

[0330] "Proposed amendments" are changes proposed by the generation AI in response to points in the application that it determines require improvement after evaluating the application.

[0331] The "server" is a computing system that receives data sent from the user's device, processes and analyzes the data using the generative AI and emotion engine, and returns the results to the user.

[0332] "User terminal" refers to the device (e.g., a smartphone or computer) on which the applicant enters and submits the application form.

[0333] "Feedback" refers to advice and information provided to the Applicant based on the evaluation of the Generative AI and Emotion Engine.

[0334] "Approval / denial history" refers to data on whether applications have been approved or denied in the past.

[0335] "Scrutiny" refers to the act of examining the application in detail and determining its applicability and accuracy.

[0336] "Real-time" refers to processing or reaction occurring immediately, without delay.

[0337] The present invention is a system for reducing the anxiety felt by applicants when submitting application forms, and combines generative AI and an emotion engine to provide a user-friendly application experience. Detailed embodiments are described below.

[0338] System configuration

[0339] The system mainly consists of the following components:

[0340] 1. User device: The device on which the applicant fills out and submits the application form, such as a smartphone or computer.

[0341] 2. Server: Receives the application and analyzes it using generative AI and emotion engines.

[0342] 3. Generative AI: Learns from past approval and rejection history and generates evaluations and revision proposals for new applications.

[0343] 4. Emotion engine: Recognizes user emotions in real time and provides that information to the generative AI.

[0344] Hardware and software used

[0345] Hardware:

[0346] Smartphones and computers: Used as user terminals to fill out and submit application forms.

[0347] Camera: Captures the user's face and is used for emotion recognition by the emotion engine.

[0348] software:

[0349] DeepFace library: Used by the emotion engine to recognize user emotions in real time.

[0350] The transformers library: Used as a generative AI, specifically using the GPT-2 model to evaluate submissions and generate revisions.

[0351] Flask: A framework for building APIs on the server side and communicating with user devices.

[0352] Data processing and calculation

[0353] emotion recognition

[0354] The server analyzes the facial image sent from the user's device using the DeepFace library to identify the user's emotional state. The result of emotion recognition is returned as a major emotion such as "sad," "anxiety," or "anger."

[0355] Generate correction suggestions

[0356] The server examines the application content using the emotion information provided by the emotion engine and the generation AI. The generation AI generates correction suggestions and additional information for the application content based on past approval / denial history. In this case, the generation AI uses the Transformers library, specifically the GPT-2 model.

[0357] User Feedback

[0358] The server provides feedback to the applicant based on the generated revision proposal and sentiment information. This feedback is sent to the user's terminal, where the applicant can review, revise, and resubmit the application.

[0359] Examples of concrete examples and prompts

[0360] Specific examples

[0361] 1. Situation: A user is filling out an application form to make a large payment.

[0362] 2. User emotions: Anxiety.

[0363] 3. Correction suggestion: Please provide more details about the payment and the reason for it. If you have any questions, please contact our support center.

[0364] Prompt Sentence Examples

[0365] plaintext

[0366] The user is concerned. Please provide additional suggestions for the following request: Payment amount 50,000 yen, Purpose Travel

[0367] In this way, applicants can receive appropriate revision suggestions based on their feelings in real time, allowing them to submit applications with confidence. This is expected to improve the accuracy of applications and reduce the hassle of resubmissions.

[0368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0369] Step 1:

[0370] The user enters the necessary information into the application form, attaches a photo of their face, and submits it via their terminal. The input data includes the specific application details (e.g., payment amount and reason for application) and a photo of their face. This sends the application information to the server.

[0371] Step 2:

[0372] The server receives the request and facial image sent by the user. The received facial image is passed to the emotion engine and used to analyze the user's emotional state. The emotion engine uses the DeepFace library to analyze the user's facial expressions and identify the dominant emotion. For example, the user's emotion may be identified as "anxiety."

[0373] Step 3:

[0374] The server creates a prompt for the generation AI based on the user's emotional information obtained from the emotion engine. Here, the emotional information and the application details are combined to generate a prompt. For example, a prompt such as "The user is feeling anxious. Please make additional suggestions for the following application details: Payment amount: 50,000 yen, Purpose: Travel" can be generated.

[0375] Step 4:

[0376] The generative AI receives a prompt as input and generates appropriate revision suggestions based on its content. The generative AI model used here is the GPT-2 model from the Transformers library. The model receives the prompt and generates specific revision suggestions for the application based on past approval and rejection history. For example, a revision suggestion might be generated such as, "Please provide a detailed explanation of the specific reason for the payment."

[0377] Step 5:

[0378] The generated revision proposal is sent to the user's terminal via the server. The revision proposal is displayed to the user in real time, allowing the user to review the application contents based on the revision proposal and make any necessary revisions.

[0379] Step 6:

[0380] The user then sends the revised application details back to the server via their device. The server receives the resent application details and performs a final check using the generation AI again. This allows the user to submit their application with peace of mind.

[0381] Step 7:

[0382] After the final confirmation, the server receives the complete application and generates appropriate feedback for the user. The feedback is provided by the AI ​​based on the user's emotional state and the application content. This completes the application process and allows the user to use the service with peace of mind.

[0383] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0384] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0385] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0386] [Second embodiment]

[0387] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0388] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0389] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0390] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0391] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0393] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0394] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0395] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0396] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0397] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0398] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0399] The present invention relates to a system that uses a generation AI to eliminate or reduce anxiety about submitting an unfamiliar application form. An embodiment of this system will be described in detail.

[0400] System Overview

[0401] The system consists of three main components:

[0402] 1. User terminal: The device on which the applicant fills out and submits the application form.

[0403] 2. Server: Receives the application content and passes it to the generation AI for review and generation of correction proposals.

[0404] 3. Generative AI: Learns from past approval and rejection history, evaluates the application content, and generates revision proposals.

[0405] Embodiment

[0406] Initializing the Server

[0407] When the server is initialized, a generation AI object is automatically generated. The generation AI is used to learn the approval and rejection history of past application forms. This AI carefully examines the application details entered by the applicant and generates appropriate correction suggestions, thereby reducing the user's anxiety.

[0408] Initializing the user device

[0409] The user terminal has an interface for communicating with the server. The user fills out an application form and sends it to the server through this terminal. The terminal has the function of receiving the proposed revisions sent from the server and presenting them to the user.

[0410] Submitting a form and generating suggested revisions

[0411] When a user submits an application form via their device, the server receives it and passes the content of the request to the generation AI. The generation AI evaluates the application content based on past approval / denial history and generates an appropriate revision proposal. This revision proposal is sent back to the user device via the server.

[0412] Proposal of amendments and review of application contents

[0413] By receiving and confirming the proposed revisions, users can review the content of their application. This allows users to submit applications with greater confidence. As a result, the hassle of having to reject or resubmit applications is reduced, reducing both psychological and time burdens.

[0414] Example

[0415] As an example, consider the following scenario.

[0416] Let's take the example of a user applying for "travel expenses" for business purposes. The user enters "Category: Travel Expenses," "Amount: $1,000," "Comments: Expenses for a business trip to New York," etc. into the terminal and submits it. When this application content arrives at the server, the server passes it to the generation AI and requests it to generate a revision proposal.

[0417] The generation AI evaluates this and generates suggested corrections, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested corrections to the user's device, and the user can review them, revise the application, and resubmit it.

[0418] In this way, by using the system of the present invention, applicants can increase the accuracy of their application content and submit applications with fewer deficiencies.

[0419] The processing flow will be explained below.

[0420] Step 1:

[0421] Initializing the Server

[0422] When the server is initialized, it creates a generated AI object, which learns the approval / denial history of past application forms and is used to scrutinize the application content.

[0423] Step 2:

[0424] Initializing the user device

[0425] The user device is initialized and configured with a reference to the server instance (server object), which allows communication between the user and the server.

[0426] Step 3:

[0427] Fill in and submit the application form

[0428] The user enters the necessary information into the application form and sends it to the server via the terminal. For example, the input contents are "Category: Travel expenses", "Amount: 1,000 dollars", and "Comments: Expenses for business trip to New York".

[0429] Step 4:

[0430] The server receives the request

[0431] The server receives the application details sent from the user terminal. This received data is used as input for the subsequent AI generation process.

[0432] Step 5:

[0433] Request processing to generation AI

[0434] The server passes the received application content to the generation AI and requests it to generate a revision proposal. Here, the generation AI evaluates the application content based on past approval / rejection history.

[0435] Step 6:

[0436] Generate correction suggestions

[0437] The AI ​​generator then examines the application and generates suggested revisions, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs."

[0438] Step 7:

[0439] Sending proposed revisions back to the server

[0440] The AI ​​generates a proposed revision and sends it back to the server. The server receives the proposed revision and performs the following steps:

[0441] Step 8:

[0442] Sending proposed revisions to the user's device

[0443] The server then sends the received revision proposal to the user's terminal, allowing the user to confirm the revision proposal.

[0444] Step 9:

[0445] Confirmation of amendments and review of application details

[0446] The user can check the proposed revisions through their device and make any necessary changes to the application. By sending the revised application details back to the server, the accuracy of the application is improved and rework is reduced.

[0447] Example 1

[0448] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0449] When submitting conventional application forms, applicants often submit them without realizing that there are incomplete information, resulting in a high risk of the application being rejected. Furthermore, when corrections to the application content are necessary, there is a lack of guidance on how to correct inappropriate information. This often results in psychological stress for applicants and the loss of time required to resubmit. This invention solves these problems by utilizing generative AI to scrutinize application content and automatically generate suggested corrections.

[0450] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0451] In this invention, the server includes means for generating a generation AI object when initialized and training the generation AI using past application form data, means for a user terminal to communicate with the server and input and send an application form, means for passing the application data received by the server to the generation AI in prompt format and causing the generation AI to generate a revision proposal, and means for sending the generated revision proposal to the user terminal via the server and reviewing and correcting the application content. This enables the applicant to quickly check for deficiencies in the application content and review and correct it according to the appropriate revision proposal.

[0452] A "generative AI object" is an artificial intelligence program that learns from past data and generates outputs for specific tasks.

[0453] "Application form data" is a collection of information contained in past applications, including approval and denial history.

[0454] A "user terminal" is a device that allows a user to enter information into an application form and communicate with a server.

[0455] A "prompt format" is formatted text data used to give instructions to the generating AI.

[0456] "Proposed amendments" are suggestions made by the generating AI after evaluating the application and indicating any necessary amendments or additional information.

[0457] The "server" is a computer system that has the function of receiving application content from a user terminal, passing the data to the generation AI, generating a revision proposal, and returning it to the user terminal.

[0458] "Learning" is the process by which generative AI uses past data to improve its performance.

[0459] A "communication interface" is a means by which a user terminal and a server send and receive data to and from each other.

[0460] MODE FOR CARRYING OUT THE INVENTION

[0461] This invention relates to a system that uses generative AI to eliminate or reduce anxiety about submitting unfamiliar application forms. This system consists of the following three components:

[0462] User terminal

[0463] The user terminal is the device on which the applicant enters and submits the application form. The user terminal can be a PC, tablet, smartphone, or other device. This terminal has an interface for communicating with the server, and has the function of sending data to the server when the user enters and submits the application form. The user terminal also has the function of receiving proposed revisions sent from the server and presenting them to the user.

[0464] server

[0465] The server is a device that receives the application details sent from the user terminal and passes them to the generation AI to generate revision proposals. The server uses a high-performance computer (e.g., a cloud-based service). When the server is initialized, a generation AI object is created. This generation AI learns using a dataset of past application forms (approval and denial history). Specifically, it retrieves data from a database and trains a model using a machine learning algorithm.

[0466] Generation AI

[0467] Generative AI is an artificial intelligence program that evaluates application content and generates appropriate revision suggestions. A text generation model such as GPT-4 is used as the generative AI model. This model learns from past approval and denial history and generates appropriate revision suggestions if there are any deficiencies in the application content.

[0468] Specific scenario example

[0469] Let's take the example of a user applying for "travel expenses" for business purposes. The user enters "Category: Travel Expenses," "Amount: $1,000," and "Comment: Expenses for a business trip to New York" into the terminal and submits it. When this application content arrives at the server, the server sends it to the generation AI.

[0470] The generation AI evaluates the application content and generates suggested revisions, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, and the user can review them, revise the application content, and resubmit it.

[0471] Prompt Sentence Examples

[0472] Below is an example of a prompt sentence to input to the generative AI model.

[0473] "User's request: Category: Travel expenses, Amount: $1,000, Comments: Expenses for a business trip to New York. Please generate an appropriate amendment based on this information."

[0474] This prompt allows the generation AI to generate appropriate revision suggestions based on the user's request.

[0475] In this way, the system of the present invention alleviates applicants' anxieties and improves the accuracy of application content, enabling faster and more reliable applications.

[0476] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0477] Program processing flow

[0478] Step 1:

[0479] When the server is initialized, it first creates a Generative AI object. The server retrieves past application form data (approval / denial history) from the database and provides this data to the Generative AI. The Generative AI learns from this data and acquires the knowledge necessary to evaluate the application content and generate revision proposals. The input is the past application form data, and the output is the trained Generative AI model.

[0480] Step 2:

[0481] The user enters the necessary information into the application form. The user terminal provides the interface for the application form, and the user enters information such as "Category: Travel expenses," "Amount: $1,000," and "Comments: Expenses for business trip to New York." With this information as input, the user terminal sends it to the server in the form of an HTTP request. The output is the request data sent to the server.

[0482] Step 3:

[0483] The server receives application data from the user. The server analyzes the received data and generates a prompt for the generation AI. For example, it generates a prompt that reads, "User's application details: Category: Travel expenses, Amount: 1,000 dollars, Comments: Expenses for a business trip to New York. Please generate an appropriate amendment based on this information." The input is the application data, and the output is the prompt to be passed to the generation AI.

[0484] Step 4:

[0485] The generation AI receives a prompt and evaluates the application content. It detects any deficiencies in the application content based on past data and generates suggested corrections. For example, for a travel expense application, it might create a suggested correction such as, "Please include the specific dates of the trip and a breakdown of accommodation costs." The input is the prompt, and the output is the suggested correction.

[0486] Step 5:

[0487] The server receives the proposed revision from the generation AI. The server sends this proposed revision to the user terminal in the form of an HTTP response. The input is the generated proposed revision, and the output is the response data sent to the user terminal.

[0488] Step 6:

[0489] The user terminal receives the proposed amendments from the server and displays them on the user interface. For example, the proposed amendments are displayed as a pop-up window or a notification message. The user checks the proposed amendments and amends the application content as necessary. The input is the response data from the server, and the output is the displayed proposed amendments.

[0490] Step 7:

[0491] The user reviews the application based on the proposed revisions and resubmits it. Once the revisions are complete, the user terminal again sends the application data to the server. This process is repeated until the application is finally approved. The input is the revised application data, and the output is the resent request data.

[0492] (Application example 1)

[0493] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0494] When using electronic payment services, many users are unfamiliar with filling out application forms for financial transactions and payment procedures, and are therefore prone to input errors and incomplete information. This can lead to applications being rejected or requiring reapplication, which can cause problems of psychological stress and increased time burden for users. In addition, electronic payment service providers incur time and costs in processing incomplete applications, reducing the efficiency of the overall service.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0496] In this invention, the server includes means for correcting in advance any deficiencies in application forms for financial transactions or payment procedures using a generation AI, means for transmitting application details from a user terminal to the server and passing them from the server to the generation AI to generate a correction proposal, and means for returning the generated correction proposal to the user terminal and presenting it to the user. This allows the user to correct input deficiencies in the application form in advance, reducing the hassle of rejecting applications and having to resubmit them.

[0497] "Generative AI" is an artificial intelligence system that generates new information based on past data.

[0498] "Request Form" means a form or web form that a user fills out to request a particular service or permission.

[0499] "Approval / denial history" refers to data showing the results of past applications, including both approved and denied applications.

[0500] "User terminal" refers to the digital device used by the user to input the application form and send it to the server. Examples include smartphones and tablets.

[0501] A "server" is a computer system that provides services to other computers on a network.

[0502] "Suggested fixes" are data or information created by generative AI that suggests ways to improve input.

[0503] "Financial transactions" is a concept that refers to all transactions involving the transfer or exchange of funds.

[0504] A "payment transaction" is a formal process used to pay for goods or services.

[0505] "Defects" refer to missing information or errors in input or procedures.

[0506] This invention relates to a system that reduces psychological stress and time burdens by allowing users to correct incomplete application forms in advance when using electronic payment services, thereby improving efficiency on the part of electronic payment service providers.

[0507] System Overview

[0508] The system consists of the following main components:

[0509] 1. User device: A device such as a smartphone or tablet that the user uses to fill out and submit the application form.

[0510] 2. Server: A relay system that receives the application details, passes them to the generation AI, generates revision proposals, and returns them to the user's terminal.

[0511] 3. Generative AI: An artificial intelligence system that learns from past approval and denial history, evaluates the application content, and generates revision proposals.

[0512] Hardware and software used

[0513] User devices: smartphones (iOS, Android), tablets (iOS, Android)

[0514] Server: Remote server (Linux, Windows Server)

[0515] Generative AI: AI models that perform advanced natural language processing (e.g., GPT-3, BERT)

[0516] Specific implementation methods

[0517] 1. User device operation: The user uses a smartphone or tablet to fill out application forms for financial transactions and payment procedures. For example, they enter information such as "Category: Remittance," "Amount: 5,000 yen," and "Description: Rent payment."

[0518] 2. Data transmission: The entered application details are sent to a server via the Internet.

[0519] 3. Server processing: The server passes the received application content to the generative AI model, which analyzes the application content based on past approval / rejection history and generates suggestions for correcting deficiencies.

[0520] 4. Generate suggested amendments: The AI ​​generator suggests additional information or amendments that the user should fill in, such as, "Please specify the specific period for rent payments (e.g., October 1st to October 31st, 2023)."

[0521] 5. Return and presentation to the user's device: The proposed revisions generated by the generation AI are returned to the user's device via the server and presented to the user. The user reviews the application based on the proposed revisions and makes any necessary revisions.

[0522] Examples and prompts

[0523] Specific examples

[0524] If a user is trying to request a rent payment:

[0525] Original input:

[0526] Category:Remittance

[0527] Price: 5,000 yen

[0528] Description: Rent payment

[0529] AI's suggested correction: "Please specify the specific period for rent payment (e.g., October 1st to October 31st, 2023)."

[0530] Prompt example

[0531] text

[0532] user_form = {

[0533] "Category": "Remittance",

[0534] "Amount": "5000 yen",

[0535] "Description": "Rent payment"

[0536] }

[0537] correction_suggestions = send_application_form(user_form)

[0538] Using this prompt as an example, the user device sends the application details to the server, and the server uses a generative AI to generate an appropriate correction proposal and sends it back to the user device, completing a series of processes.

[0539] This allows users to prevent input errors in application forms and increase the accuracy of applications, while also enabling electronic payment service providers to provide services more efficiently.

[0540] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0541] Step 1:

[0542] The user fills out the application form using a terminal. For example, the user enters information such as "Category: Remittance," "Amount: 5,000 yen," and "Explanation: Rent payment." This input itself becomes the application form data and is sent to the server.

[0543] Step 2:

[0544] The terminal sends the entered application form data to the server. Specifically, it sends the application details to the server via the Internet using an HTTP request. The input data is encoded in JSON format and passed to the server.

[0545] Step 3:

[0546] The server receives the submitted application, which is first checked for format and required fields, eliminating basic input errors.

[0547] Step 4:

[0548] The server passes the received request to the generation AI, which evaluates the request using a model trained on past approval and denial history. The input data is sent to the generation AI model as a prompt.

[0549] Step 5:

[0550] The generative AI analyzes the application content and detects deficiencies and areas for improvement. Specifically, it uses natural language processing technology to analyze text data and generate revision proposals by referencing past approval and rejection patterns. The data operations performed during this process include text pattern matching, probability evaluation, and calculation of a generation score.

[0551] Step 6:

[0552] The proposed revisions generated by the generation AI are sent back to the server. The server receives the proposed revisions and prepares them to send back to the user device. The proposed revisions are also encoded in JSON format and sent to the user device as an HTTP response.

[0553] Step 7:

[0554] The terminal displays the proposed amendment received from the server to the user. The user confirms the proposed amendment and amends the application. Specifically, the proposed amendment includes specific instructions such as "Please specify the specific period for rent payment (e.g., October 1 to October 31, 2023)."

[0555] Step 8:

[0556] The user then modifies the application form based on the proposed modifications. The modified data is then sent to the server as in the first step. This process is repeated until the user is satisfied with the modified application.

[0557] This allows users to accurately correct their input data and increase the accuracy of their applications, while electronic payment service providers can reduce the number of incomplete applications from users and improve processing efficiency.

[0558] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0559] The present invention relates to a system that uses a generative AI and an emotion engine in combination to eliminate or reduce anxiety about submitting an application form. An embodiment of this system will be specifically described.

[0560] System Overview

[0561] The system consists of four main components:

[0562] 1. User terminal: The device on which the applicant fills out and submits the application form.

[0563] 2. Server: Receives the application content and passes it to the generation AI for review and generation of correction proposals.

[0564] 3. Generative AI: Learns from past approval and rejection history, evaluates the application content, and generates revision proposals.

[0565] 4. Emotion engine: Recognizes user emotions in real time and provides that information to the generative AI.

[0566] Embodiment

[0567] Initializing the Server

[0568] When the server is initialized, it creates a generation AI object and an emotion engine object. The generation AI is used to learn the approval / denial history of past application forms, and the emotion engine recognizes the user's emotions in real time and provides that information to the generation AI.

[0569] Initializing the user device

[0570] The user terminal has an interface for communicating with the server. The user fills out the application form and sends it to the server through this terminal. The terminal has the function of receiving the proposed revisions sent from the server and presenting them to the user.

[0571] Submitting a form and generating suggested revisions

[0572] When a user submits an application form via their device, the server receives it and passes the application details to the generation AI and emotion engine. The emotion engine recognizes the user's emotions in real time and provides this information to the generation AI. The generation AI evaluates the application details based on past approval / denial history and information obtained from the emotion engine, and generates appropriate revision suggestions. These revision suggestions are sent back to the user's device via the server.

[0573] Proposal of amendments and review of application contents

[0574] By receiving and confirming the proposed revisions, users can review the content of their application. By utilizing information from the emotion engine, revisions are generated that reflect the user's emotions, improving the user experience. This allows users to submit applications with greater confidence, reducing the hassle of having to reject or resubmit applications, and also reducing the psychological and time burden.

[0575] Example

[0576] As an example, consider the following scenario.

[0577] When a user applies for "travel expenses," they input "Category: Travel Expenses," "Amount: $1,000," "Comments: Expenses for a business trip to New York," etc. into their device and submit it. When this application content arrives at the server, the server passes it to the generation AI and emotion engine and requests them to generate a revision proposal.

[0578] The emotion engine recognizes the user's emotions in real time and provides the generation AI with emotions such as "I'm worried about the application content." The generation AI evaluates the application content based on past approval / denial history and information from the emotion engine, and generates suggested revisions such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, allowing the user to review and resubmit the application.

[0579] By using the emotion engine, the system can provide revision suggestions that take the user's emotions into consideration, allowing for more effective revision of application content. This allows applicants to submit applications with confidence and confidence, resulting in improved application accuracy and reduced resubmission efforts.

[0580] The processing flow will be explained below.

[0581] Step 1:

[0582] Initializing the Server

[0583] When the server is initialized, it creates a generation AI object and an emotion engine object. The generation AI object learns the approval / denial history of past application forms and is used to scrutinize the application content. The emotion engine is used to recognize the user's emotions in real time.

[0584] Step 2:

[0585] Initializing the user device

[0586] The user device is initialized and configured with a reference to the server instance (server object), which allows communication between the user and the server.

[0587] Step 3:

[0588] Fill in and submit the application form

[0589] The user enters the necessary information into the application form and sends it to the server via the terminal. For example, the input contents are "Category: Travel Expenses", "Amount: $1,000", and "Comment: Expenses for a business trip to New York". The terminal also sends the user's emotions to the emotion engine.

[0590] Step 4:

[0591] The server receives the request

[0592] The server receives the application details and emotion information sent from the user terminal, and this received data is used as input for subsequent processing of the generation AI and emotion engine.

[0593] Step 5:

[0594] Submitting to generative AI and emotion engines

[0595] The server passes the received application details and emotion engine information to the generation AI and requests it to generate a revision proposal. The emotion engine analyzes the user's emotions in real time and provides that information to the generation AI.

[0596] Step 6:

[0597] Generate correction suggestions

[0598] The generative AI scrutinizes the application content based on past approval and rejection history, and generates appropriate revision suggestions based on the user's emotional information provided by the emotion engine. Suggested revisions include, for example, "Please include the specific dates of the business trip and a breakdown of accommodation costs."

[0599] Step 7:

[0600] Sending proposed revisions back to the server

[0601] The AI ​​generates a proposed revision and sends it back to the server. The server receives the proposed revision and performs the following steps:

[0602] Step 8:

[0603] Sending proposed revisions to the user's device

[0604] The server then sends the received revision proposal to the user's terminal, allowing the user to confirm the revision proposal.

[0605] Step 9:

[0606] Confirmation of amendments and review of application details

[0607] The user checks the proposed revisions through their device and makes any necessary changes to the application. By sending the revised application details back to the server, the accuracy of the application is improved and rework is reduced. Specifically, the user can add details about the business trip schedule and accommodation costs based on the proposed revisions. This makes the application more specific and accurate, increasing the likelihood of approval.

[0608] Example 2

[0609] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0610] The present invention aims to reduce the anxiety and psychological burden felt by applicants when submitting application forms. Conventional systems examine application content and provide suggested revisions, but do not consider the applicant's feelings, often resulting in significant stress for the applicant. Furthermore, rejection of applications due to inappropriate content and the hassle of resubmissions have also become an issue. There is a need for a system that solves these issues, reduces applicants' psychological burden, and improves application accuracy.

[0611] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0612] In this invention, the server includes means for using a generation AI to learn the approval / denial history of the application form, means for scrutinizing the application content entered by the applicant, means for generating and presenting proposed revisions based on the scrutinized application content, means for using an emotion engine to recognize the applicant's emotions in real time and providing that information to the generation AI, and means for reducing psychological burden by generating proposed revisions that reflect the applicant's emotional information. This makes it possible to create more accurate application content while taking the applicant's emotions into consideration, thereby reducing psychological burden and increasing the success rate of applications.

[0613] "Generative AI" is artificial intelligence that learns from past data and generates new information and suggestions based on that data.

[0614] An "application form" is a format in which a user applies for some service or approval by inputting and submitting specific information.

[0615] "Approval / denial history" refers to the record of approvals or denials of applications submitted in the past.

[0616] An "emotion engine" is a technology that recognizes a user's emotions in real time and provides that information to other systems.

[0617] "Proposed amendments" are suggested changes or improvements to the current application.

[0618] "Server" means a computer system that receives, processes, and transmits data over a network to other devices.

[0619] A "terminal" is a device that allows a user to input data and exchanges information with a server through communication.

[0620] "Verification" is the process of examining data or information in detail based on specific criteria.

[0621] "Training" is the process by which generative AI learns from past data and improves its accuracy.

[0622] "Psychological burden" refers to the mental stress and anxiety experienced by the user.

[0623] This invention is a system that combines generative AI and an emotion engine to reduce the anxiety and psychological burden felt by users when submitting application forms. This system is mainly composed of four components: a user terminal, a server, generative AI, and an emotion engine.

[0624] User terminal

[0625] A user terminal is a device on which a user enters and submits an application form. The terminal is equipped with an interface for communicating with the server and has the function of sending the application details entered by the user to the server. The terminal also has a screen that receives proposed revisions returned from the server and presents them to the user. Specific devices include PCs, tablets, and smartphones.

[0626] server

[0627] The role of the server is to receive the application content sent from the user terminal and pass it to the generation AI and emotion engine to generate a revision proposal. The server instantiates a generation AI object and an emotion engine object during initialization. This generation AI is used to learn from past application data and evaluate the application content. Meanwhile, the emotion engine recognizes the user's emotions in real time and provides that information to the generation AI.

[0628] Generation AI

[0629] Generative AI is an AI model that learns from the approval and rejection history of past application forms, examines new application content, and generates suggested revisions. Generative AI runs on the server and compares the received application content with past data to identify areas for improvement and potential problems in the application content. This allows for the generation of more appropriate application content.

[0630] Emotion Engine

[0631] The emotion engine is a technology that recognizes the user's emotions in real time while they are filling out an application form. The emotion engine analyzes the user's facial expressions, voice, input speed, etc. to infer the user's psychological state. This information is provided to the generative AI and used to evaluate the application content and generate revision suggestions.

[0632] Example

[0633] When a user applies for "travel expenses," they enter information such as "Category: Travel Expenses," "Amount: $1,000," and "Comments: Expenses for a business trip to New York" into their device and press the send button. When this application information arrives at the server, the server passes it to the generation AI and emotion engine and requests them to generate a revision proposal.

[0634] The emotion engine recognizes emotions such as "the user is anxious about the application content" and provides this information to the generation AI. The generation AI evaluates the application content based on past approval / denial history and emotional data, and generates suggested revisions such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, where the user can confirm them. Through this process, the user can review the application content based on accurate information, eliminating any anxiety and enabling them to submit a reliable application.

[0635] An example of a specific prompt for a generative AI model is, "Travel expense request subject: Business trip to New York, October 1, 2023 - October 5, 2023. Add details: Accommodation $800, Transportation $200."

[0636] In this way, by combining a generative AI and an emotion engine, the system of the present invention has the effect of reducing the psychological burden on users while improving the accuracy of application content.

[0637] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0638] Step 1:

[0639] Initializing the Server

[0640] When the server is initialized, it instantiates a generation AI object and an emotion engine object. The generation AI object learns from past application data and is used to evaluate the application content. The emotion engine object recognizes the user's emotions in real time and provides that information to the generation AI.

[0641] Input: Server startup sequence

[0642] Output: Generate AI objects and emotion engine objects

[0643] Specific operation: The server program executes the startup sequence, and each object of the generation AI and emotion engine is loaded into memory.

[0644] Step 2:

[0645] Initializing the user device

[0646] The user terminal has an interface for communicating with the server. When the terminal is initialized, a form is displayed in which the user can enter the application details. The terminal also has the function of receiving and displaying proposed revisions sent from the server.

[0647] Input: Start user terminal

[0648] Output: Display of application form, initialization of communication interface

[0649] Specific operation: The user terminal starts up, the application form is initialized and displayed, and at the same time, a network connection is established to communicate with the server.

[0650] Step 3:

[0651] Fill in and submit the application form

[0652] The user enters the necessary information into the application form on the terminal. For example, the category, amount, comments, etc. Once the input is complete, the user clicks the "Submit" button. The terminal then sends the entered data to the server.

[0653] Input: User inputs application details (category, amount, comments)

[0654] Output: Application data sent to the server

[0655] Specific operation: The user enters "Category: Travel expenses", "Amount: $1,000", and "Comment: Expenses for business trip to New York" into the form and presses the submit button. The terminal sends this data to the server.

[0656] Step 4:

[0657] Receipt of application and start of processing

[0658] The server receives the application content sent from the device and passes the received data to the generation AI and emotion engine, which then starts the application content review process.

[0659] Input: Application data sent from the terminal

[0660] Output: Providing data to generative AI and emotion engines

[0661] Specific operation: The server receives the application data and converts it into the appropriate data format to be passed to the generation AI and emotion engine.

[0662] Step 5:

[0663] Emotion recognition with emotion engine

[0664] The emotion engine collects and analyzes emotional data in real time while the user is filling out the application form. If the user shows any anxiety or tension, that information is provided to the generation AI.

[0665] Input: Application data and emotion data provided by the server

[0666] Output: User emotion data provided to the generative AI

[0667] Specific operation: The emotion engine collects data on the speed at which the user fills out the application form and analysis of the user's facial expressions, and sends information such as "I am worried about the content of the application" to the generation AI.

[0668] Step 6:

[0669] Generative AI generates revision suggestions

[0670] The generative AI evaluates the current application form based on past application data and emotional data sent from the emotion engine. The generative AI identifies avoidable issues and generates appropriate corrections.

[0671] Input: Emotion data from the emotion engine and past approval / disapproval data

[0672] Output: Revision proposal

[0673] Specific operation: The generative AI analyzes the input data and generates a correction suggestion such as, "Please provide the specific dates of your business trip and a breakdown of accommodation costs."

[0674] Step 7:

[0675] Return and view proposed revisions

[0676] The server receives the proposed amendments generated by the AI ​​and returns them to the user's device. The user's device then displays the proposed amendments, allowing the user to review the application contents.

[0677] Input: Correction suggestions from the generative AI

[0678] Output: Send and display suggested revisions to the user's device

[0679] Specific operation: The server receives the proposed revision and sends it to the user's terminal, which then displays the proposed revision to the user.

[0680] Step 8:

[0681] Review of application contents based on proposed amendments

[0682] The user reviews the proposed revisions, returns to the application form, makes any necessary revisions, and finally clicks the submit button again to submit the application.

[0683] Input: User makes corrections to the application based on the proposed corrections

[0684] Output: Resubmit corrected application data

[0685] Specific actions: The user adds specific information such as "Specific dates of business trip to New York: October 1st - October 5th, 2023" and "Breakdown of accommodation expenses: $800, transportation expenses: $200" and resubmits the application.

[0686] (Application example 2)

[0687] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0688] The present invention relates to a system that reduces the anxiety users feel when submitting application forms. Currently, when filling out and submitting application forms, users often feel anxious about whether the content is appropriate. In particular, in electronic payment services, where money is involved, there is a risk that the application will be rejected if the applicant enters incorrect or incomplete information. There is a need to reduce such anxiety and stress and provide an environment in which users can apply with peace of mind.

[0689] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for using a generation AI to learn the approval / denial history of the application form, means for scrutinizing the application content entered by the applicant, means for generating and presenting amendment proposals based on the scrutinized application content, means for using an emotion engine to recognize the applicant's emotions in real time and provide that information to the generation AI, and means for providing feedback that takes into consideration the applicant's emotional state. As a result, the applicant's emotional state is recognized in real time by the emotion engine, and the generation AI provides appropriate amendment proposals based on that information, so the applicant can be assured of the accuracy of the application content and can apply with confidence.

[0690] "Generative AI" is artificial intelligence that learns from past data and is used to evaluate and make suggestions for new data.

[0691] An "emotion engine" is a system that analyzes a user's facial expressions, tone of voice, and other physical signals to recognize their emotional state in real time.

[0692] "Applicant" means a person who completes and submits an application form for a particular purpose.

[0693] "Application Content" refers to all the information entered by the applicant in the application form.

[0694] "Proposed amendments" are changes proposed by the generation AI in response to points in the application that it determines require improvement after evaluating the application.

[0695] The "server" is a computing system that receives data sent from the user's device, processes and analyzes the data using the generative AI and emotion engine, and returns the results to the user.

[0696] "User terminal" refers to the device (e.g., a smartphone or computer) on which the applicant enters and submits the application form.

[0697] "Feedback" refers to advice and information provided to the Applicant based on the evaluation of the Generative AI and Emotion Engine.

[0698] "Approval / denial history" refers to data on whether applications have been approved or denied in the past.

[0699] "Scrutiny" refers to the act of examining the application in detail and determining its applicability and accuracy.

[0700] "Real-time" refers to processing or reaction occurring immediately, without delay.

[0701] The present invention is a system for reducing the anxiety felt by applicants when submitting application forms, and combines generative AI and an emotion engine to provide a user-friendly application experience. Detailed embodiments are described below.

[0702] System configuration

[0703] The system mainly consists of the following components:

[0704] 1. User device: The device on which the applicant fills out and submits the application form, such as a smartphone or computer.

[0705] 2. Server: Receives the application and analyzes it using generative AI and emotion engines.

[0706] 3. Generative AI: Learns from past approval and rejection history and generates evaluations and revision proposals for new applications.

[0707] 4. Emotion engine: Recognizes user emotions in real time and provides that information to the generative AI.

[0708] Hardware and software used

[0709] Hardware:

[0710] Smartphones and computers: Used as user terminals to fill out and submit application forms.

[0711] Camera: Captures the user's face and is used for emotion recognition by the emotion engine.

[0712] software:

[0713] DeepFace library: Used by the emotion engine to recognize user emotions in real time.

[0714] The transformers library: Used as a generative AI, specifically using the GPT-2 model to evaluate submissions and generate revisions.

[0715] Flask: A framework for building APIs on the server side and communicating with user devices.

[0716] Data processing and calculation

[0717] emotion recognition

[0718] The server analyzes the facial image sent from the user's device using the DeepFace library to identify the user's emotional state. The result of emotion recognition is returned as a major emotion such as "sad," "anxiety," or "anger."

[0719] Generate correction suggestions

[0720] The server examines the application content using the emotion information provided by the emotion engine and the generation AI. The generation AI generates correction suggestions and additional information for the application content based on past approval / denial history. In this case, the generation AI uses the Transformers library, specifically the GPT-2 model.

[0721] User Feedback

[0722] The server provides feedback to the applicant based on the generated revision proposal and sentiment information. This feedback is sent to the user's terminal, where the applicant can review, revise, and resubmit the application.

[0723] Examples of concrete examples and prompts

[0724] Specific examples

[0725] 1. Situation: A user is filling out an application form to make a large payment.

[0726] 2. User emotions: Anxiety.

[0727] 3. Correction suggestion: Please provide more details about the payment and the reason for it. If you have any questions, please contact our support center.

[0728] Prompt Sentence Examples

[0729] plaintext

[0730] The user is concerned. Please provide additional suggestions for the following request: Payment amount 50,000 yen, Purpose Travel

[0731] In this way, applicants can receive appropriate revision suggestions based on their feelings in real time, allowing them to submit applications with confidence. This is expected to improve the accuracy of applications and reduce the hassle of resubmissions.

[0732] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0733] Step 1:

[0734] The user enters the necessary information into the application form, attaches a photo of their face, and submits it via their terminal. The input data includes the specific application details (e.g., payment amount and reason for application) and a photo of their face. This sends the application information to the server.

[0735] Step 2:

[0736] The server receives the request and facial image sent by the user. The received facial image is passed to the emotion engine and used to analyze the user's emotional state. The emotion engine uses the DeepFace library to analyze the user's facial expressions and identify the dominant emotion. For example, the user's emotion may be identified as "anxiety."

[0737] Step 3:

[0738] The server creates a prompt for the generation AI based on the user's emotional information obtained from the emotion engine. Here, the emotional information and the application details are combined to generate a prompt. For example, a prompt such as "The user is feeling anxious. Please make additional suggestions for the following application details: Payment amount: 50,000 yen, Purpose: Travel" can be generated.

[0739] Step 4:

[0740] The generative AI receives a prompt as input and generates appropriate revision suggestions based on its content. The generative AI model used here is the GPT-2 model from the Transformers library. The model receives the prompt and generates specific revision suggestions for the application based on past approval and rejection history. For example, a revision suggestion might be generated such as, "Please provide a detailed explanation of the specific reason for the payment."

[0741] Step 5:

[0742] The generated revision proposal is sent to the user's terminal via the server. The revision proposal is displayed to the user in real time, allowing the user to review the application contents based on the revision proposal and make any necessary revisions.

[0743] Step 6:

[0744] The user then sends the revised application details back to the server via their device. The server receives the resent application details and performs a final check using the generation AI again. This allows the user to submit their application with peace of mind.

[0745] Step 7:

[0746] After the final confirmation, the server receives the complete application and generates appropriate feedback for the user. The feedback is provided by the AI ​​based on the user's emotional state and the application content. This completes the application process and allows the user to use the service with peace of mind.

[0747] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0748] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0749] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0750] [Third embodiment]

[0751] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0752] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0753] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0754] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0755] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0756] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0757] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0758] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0759] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0760] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0761] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0762] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0763] The present invention relates to a system that uses a generation AI to eliminate or reduce anxiety about submitting an unfamiliar application form. An embodiment of this system will be described in detail.

[0764] System Overview

[0765] The system consists of three main components:

[0766] 1. User terminal: The device on which the applicant fills out and submits the application form.

[0767] 2. Server: Receives the application content and passes it to the generation AI for review and generation of correction proposals.

[0768] 3. Generative AI: Learns from past approval and rejection history, evaluates the application content, and generates revision proposals.

[0769] Embodiment

[0770] Initializing the Server

[0771] When the server is initialized, a generation AI object is automatically generated. The generation AI is used to learn the approval and rejection history of past application forms. This AI carefully examines the application details entered by the applicant and generates appropriate correction suggestions, thereby reducing the user's anxiety.

[0772] Initializing the user device

[0773] The user terminal has an interface for communicating with the server. The user fills out an application form and sends it to the server through this terminal. The terminal has the function of receiving the proposed revisions sent from the server and presenting them to the user.

[0774] Submitting a form and generating suggested revisions

[0775] When a user submits an application form via their device, the server receives it and passes the content of the request to the generation AI. The generation AI evaluates the application content based on past approval / denial history and generates an appropriate revision proposal. This revision proposal is sent back to the user device via the server.

[0776] Proposal of amendments and review of application contents

[0777] By receiving and confirming the proposed revisions, users can review the content of their application. This allows users to submit applications with greater confidence. As a result, the hassle of having to reject or resubmit applications is reduced, reducing both psychological and time burdens.

[0778] Example

[0779] As an example, consider the following scenario.

[0780] Let's take the example of a user applying for "travel expenses" for business purposes. The user enters "Category: Travel Expenses," "Amount: $1,000," "Comments: Expenses for a business trip to New York," etc. into the terminal and submits it. When this application content arrives at the server, the server passes it to the generation AI and requests it to generate a revision proposal.

[0781] The generation AI evaluates this and generates suggested corrections, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested corrections to the user's device, and the user can review them, revise the application, and resubmit it.

[0782] In this way, by using the system of the present invention, applicants can increase the accuracy of their application content and submit applications with fewer deficiencies.

[0783] The processing flow will be explained below.

[0784] Step 1:

[0785] Initializing the Server

[0786] When the server is initialized, it creates a generated AI object, which learns the approval / denial history of past application forms and is used to scrutinize the application content.

[0787] Step 2:

[0788] Initializing the user device

[0789] The user device is initialized and configured with a reference to the server instance (server object), which allows communication between the user and the server.

[0790] Step 3:

[0791] Fill in and submit the application form

[0792] The user enters the necessary information into the application form and sends it to the server via the terminal. For example, the input contents are "Category: Travel expenses", "Amount: 1,000 dollars", and "Comments: Expenses for business trip to New York".

[0793] Step 4:

[0794] The server receives the request

[0795] The server receives the application details sent from the user terminal. This received data is used as input for the subsequent AI generation process.

[0796] Step 5:

[0797] Request processing to generation AI

[0798] The server passes the received application content to the generation AI and requests it to generate a revision proposal. Here, the generation AI evaluates the application content based on past approval / rejection history.

[0799] Step 6:

[0800] Generate correction suggestions

[0801] The AI ​​generator then examines the application and generates suggested revisions, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs."

[0802] Step 7:

[0803] Sending proposed revisions back to the server

[0804] The AI ​​generates a proposed revision and sends it back to the server. The server receives the proposed revision and performs the following steps:

[0805] Step 8:

[0806] Sending proposed revisions to the user's device

[0807] The server then sends the received revision proposal to the user's terminal, allowing the user to confirm the revision proposal.

[0808] Step 9:

[0809] Confirmation of amendments and review of application details

[0810] The user can check the proposed revisions through their device and make any necessary changes to the application. By sending the revised application details back to the server, the accuracy of the application is improved and rework is reduced.

[0811] Example 1

[0812] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0813] When submitting conventional application forms, applicants often submit them without realizing that there are incomplete information, resulting in a high risk of the application being rejected. Furthermore, when corrections to the application content are necessary, there is a lack of guidance on how to correct inappropriate information. This often results in psychological stress for applicants and the loss of time required to resubmit. This invention solves these problems by utilizing generative AI to scrutinize application content and automatically generate suggested corrections.

[0814] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0815] In this invention, the server includes means for generating a generation AI object when initialized and training the generation AI using past application form data, means for a user terminal to communicate with the server and input and send an application form, means for passing the application data received by the server to the generation AI in prompt format and causing the generation AI to generate a revision proposal, and means for sending the generated revision proposal to the user terminal via the server and reviewing and correcting the application content. This enables the applicant to quickly check for deficiencies in the application content and review and correct it according to the appropriate revision proposal.

[0816] A "generative AI object" is an artificial intelligence program that learns from past data and generates outputs for specific tasks.

[0817] "Application form data" is a collection of information contained in past applications, including approval and denial history.

[0818] A "user terminal" is a device that allows a user to enter information into an application form and communicate with a server.

[0819] A "prompt format" is formatted text data used to give instructions to the generating AI.

[0820] "Proposed amendments" are suggestions made by the generating AI after evaluating the application and indicating any necessary amendments or additional information.

[0821] The "server" is a computer system that has the function of receiving application content from a user terminal, passing the data to the generation AI, generating a revision proposal, and returning it to the user terminal.

[0822] "Learning" is the process by which generative AI uses past data to improve its performance.

[0823] A "communication interface" is a means by which a user terminal and a server send and receive data to and from each other.

[0824] MODE FOR CARRYING OUT THE INVENTION

[0825] This invention relates to a system that uses generative AI to eliminate or reduce anxiety about submitting unfamiliar application forms. This system consists of the following three components:

[0826] User terminal

[0827] The user terminal is the device on which the applicant enters and submits the application form. The user terminal can be a PC, tablet, smartphone, or other device. This terminal has an interface for communicating with the server, and has the function of sending data to the server when the user enters and submits the application form. The user terminal also has the function of receiving proposed revisions sent from the server and presenting them to the user.

[0828] server

[0829] The server is a device that receives the application details sent from the user terminal and passes them to the generation AI to generate revision proposals. The server uses a high-performance computer (e.g., a cloud-based service). When the server is initialized, a generation AI object is created. This generation AI learns using a dataset of past application forms (approval and denial history). Specifically, it retrieves data from a database and trains a model using a machine learning algorithm.

[0830] Generation AI

[0831] Generative AI is an artificial intelligence program that evaluates application content and generates appropriate revision suggestions. A text generation model such as GPT-4 is used as the generative AI model. This model learns from past approval and denial history and generates appropriate revision suggestions if there are any deficiencies in the application content.

[0832] Specific scenario example

[0833] Let's take the example of a user applying for "travel expenses" for business purposes. The user enters "Category: Travel Expenses," "Amount: $1,000," and "Comment: Expenses for a business trip to New York" into the terminal and submits it. When this application content arrives at the server, the server sends it to the generation AI.

[0834] The generation AI evaluates the application content and generates suggested revisions, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, and the user can review them, revise the application content, and resubmit it.

[0835] Prompt Sentence Examples

[0836] Below is an example of a prompt sentence to input to the generative AI model.

[0837] "User's request: Category: Travel expenses, Amount: $1,000, Comments: Expenses for a business trip to New York. Please generate an appropriate amendment based on this information."

[0838] This prompt allows the generation AI to generate appropriate revision suggestions based on the user's request.

[0839] In this way, the system of the present invention alleviates applicants' anxieties and improves the accuracy of application content, enabling faster and more reliable applications.

[0840] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0841] Program processing flow

[0842] Step 1:

[0843] When the server is initialized, it first creates a Generative AI object. The server retrieves past application form data (approval / denial history) from the database and provides this data to the Generative AI. The Generative AI learns from this data and acquires the knowledge necessary to evaluate the application content and generate revision proposals. The input is the past application form data, and the output is the trained Generative AI model.

[0844] Step 2:

[0845] The user enters the necessary information into the application form. The user terminal provides the interface for the application form, and the user enters information such as "Category: Travel expenses," "Amount: $1,000," and "Comments: Expenses for business trip to New York." With this information as input, the user terminal sends it to the server in the form of an HTTP request. The output is the request data sent to the server.

[0846] Step 3:

[0847] The server receives application data from the user. The server analyzes the received data and generates a prompt for the generation AI. For example, it generates a prompt that reads, "User's application details: Category: Travel expenses, Amount: 1,000 dollars, Comments: Expenses for a business trip to New York. Please generate an appropriate amendment based on this information." The input is the application data, and the output is the prompt to be passed to the generation AI.

[0848] Step 4:

[0849] The generation AI receives a prompt and evaluates the application content. It detects any deficiencies in the application content based on past data and generates suggested corrections. For example, for a travel expense application, it might create a suggested correction such as, "Please include the specific dates of the trip and a breakdown of accommodation costs." The input is the prompt, and the output is the suggested correction.

[0850] Step 5:

[0851] The server receives the proposed revision from the generation AI. The server sends this proposed revision to the user terminal in the form of an HTTP response. The input is the generated proposed revision, and the output is the response data sent to the user terminal.

[0852] Step 6:

[0853] The user terminal receives the proposed amendments from the server and displays them on the user interface. For example, the proposed amendments are displayed as a pop-up window or a notification message. The user checks the proposed amendments and amends the application content as necessary. The input is the response data from the server, and the output is the displayed proposed amendments.

[0854] Step 7:

[0855] The user reviews the application based on the proposed revisions and resubmits it. Once the revisions are complete, the user terminal again sends the application data to the server. This process is repeated until the application is finally approved. The input is the revised application data, and the output is the resent request data.

[0856] (Application example 1)

[0857] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0858] When using electronic payment services, many users are unfamiliar with filling out application forms for financial transactions and payment procedures, and are therefore prone to input errors and incomplete information. This can lead to applications being rejected or requiring reapplication, which can cause problems of psychological stress and increased time burden for users. In addition, electronic payment service providers incur time and costs in processing incomplete applications, reducing the efficiency of the overall service.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0860] In this invention, the server includes means for correcting in advance any deficiencies in application forms for financial transactions or payment procedures using a generation AI, means for transmitting application details from a user terminal to the server and passing them from the server to the generation AI to generate a correction proposal, and means for returning the generated correction proposal to the user terminal and presenting it to the user. This allows the user to correct input deficiencies in the application form in advance, reducing the hassle of rejecting applications and having to resubmit them.

[0861] "Generative AI" is an artificial intelligence system that generates new information based on past data.

[0862] "Request Form" means a form or web form that a user fills out to request a particular service or permission.

[0863] "Approval / denial history" refers to data showing the results of past applications, including both approved and denied applications.

[0864] "User terminal" refers to the digital device used by the user to input the application form and send it to the server. Examples include smartphones and tablets.

[0865] A "server" is a computer system that provides services to other computers on a network.

[0866] "Suggested fixes" are data or information created by generative AI that suggests ways to improve input.

[0867] "Financial transactions" is a concept that refers to all transactions involving the transfer or exchange of funds.

[0868] A "payment transaction" is a formal process used to pay for goods or services.

[0869] "Defects" refer to missing information or errors in input or procedures.

[0870] This invention relates to a system that reduces psychological stress and time burdens by allowing users to correct incomplete application forms in advance when using electronic payment services, thereby improving efficiency on the part of electronic payment service providers.

[0871] System Overview

[0872] The system consists of the following main components:

[0873] 1. User device: A device such as a smartphone or tablet that the user uses to fill out and submit the application form.

[0874] 2. Server: A relay system that receives the application details, passes them to the generation AI, generates revision proposals, and returns them to the user's terminal.

[0875] 3. Generative AI: An artificial intelligence system that learns from past approval and denial history, evaluates the application content, and generates revision proposals.

[0876] Hardware and software used

[0877] User devices: smartphones (iOS, Android), tablets (iOS, Android)

[0878] Server: Remote server (Linux, Windows Server)

[0879] Generative AI: AI models that perform advanced natural language processing (e.g., GPT-3, BERT)

[0880] Specific implementation methods

[0881] 1. User device operation: The user uses a smartphone or tablet to fill out application forms for financial transactions and payment procedures. For example, they enter information such as "Category: Remittance," "Amount: 5,000 yen," and "Description: Rent payment."

[0882] 2. Data transmission: The entered application details are sent to a server via the Internet.

[0883] 3. Server processing: The server passes the received application content to the generative AI model, which analyzes the application content based on past approval / rejection history and generates suggestions for correcting deficiencies.

[0884] 4. Generate suggested amendments: The AI ​​generator suggests additional information or amendments that the user should fill in, such as, "Please specify the specific period for rent payments (e.g., October 1st to October 31st, 2023)."

[0885] 5. Return and presentation to the user's device: The proposed revisions generated by the generation AI are returned to the user's device via the server and presented to the user. The user reviews the application based on the proposed revisions and makes any necessary revisions.

[0886] Examples and prompts

[0887] Specific examples

[0888] If a user is trying to request a rent payment:

[0889] Original input:

[0890] Category:Remittance

[0891] Price: 5,000 yen

[0892] Description: Rent payment

[0893] AI's suggested correction: "Please specify the specific period for rent payment (e.g., October 1st to October 31st, 2023)."

[0894] Prompt example

[0895] text

[0896] user_form = {

[0897] "Category": "Remittance",

[0898] "Amount": "5000 yen",

[0899] "Description": "Rent payment"

[0900] }

[0901] correction_suggestions = send_application_form(user_form)

[0902] Using this prompt as an example, the user device sends the application details to the server, and the server uses a generative AI to generate an appropriate correction proposal and sends it back to the user device, completing a series of processes.

[0903] This allows users to prevent input errors in application forms and increase the accuracy of applications, while also enabling electronic payment service providers to provide services more efficiently.

[0904] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0905] Step 1:

[0906] The user fills out the application form using a terminal. For example, the user enters information such as "Category: Remittance," "Amount: 5,000 yen," and "Explanation: Rent payment." This input itself becomes the application form data and is sent to the server.

[0907] Step 2:

[0908] The terminal sends the entered application form data to the server. Specifically, it sends the application details to the server via the Internet using an HTTP request. The input data is encoded in JSON format and passed to the server.

[0909] Step 3:

[0910] The server receives the submitted application, which is first checked for format and required fields, eliminating basic input errors.

[0911] Step 4:

[0912] The server passes the received request to the generation AI, which evaluates the request using a model trained on past approval and denial history. The input data is sent to the generation AI model as a prompt.

[0913] Step 5:

[0914] The generative AI analyzes the application content and detects deficiencies and areas for improvement. Specifically, it uses natural language processing technology to analyze text data and generate revision proposals by referencing past approval and rejection patterns. The data operations performed during this process include text pattern matching, probability evaluation, and calculation of a generation score.

[0915] Step 6:

[0916] The proposed revisions generated by the generation AI are sent back to the server. The server receives the proposed revisions and prepares them to send back to the user device. The proposed revisions are also encoded in JSON format and sent to the user device as an HTTP response.

[0917] Step 7:

[0918] The terminal displays the proposed amendment received from the server to the user. The user confirms the proposed amendment and amends the application. Specifically, the proposed amendment includes specific instructions such as "Please specify the specific period for rent payment (e.g., October 1 to October 31, 2023)."

[0919] Step 8:

[0920] The user then modifies the application form based on the proposed modifications. The modified data is then sent to the server as in the first step. This process is repeated until the user is satisfied with the modified application.

[0921] This allows users to accurately correct their input data and increase the accuracy of their applications, while electronic payment service providers can reduce the number of incomplete applications from users and improve processing efficiency.

[0922] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0923] The present invention relates to a system that uses a generative AI and an emotion engine in combination to eliminate or reduce anxiety about submitting an application form. An embodiment of this system will be specifically described.

[0924] System Overview

[0925] The system consists of four main components:

[0926] 1. User terminal: The device on which the applicant fills out and submits the application form.

[0927] 2. Server: Receives the application content and passes it to the generation AI for review and generation of correction proposals.

[0928] 3. Generative AI: Learns from past approval and rejection history, evaluates the application content, and generates revision proposals.

[0929] 4. Emotion engine: Recognizes user emotions in real time and provides that information to the generative AI.

[0930] Embodiment

[0931] Initializing the Server

[0932] When the server is initialized, it creates a generation AI object and an emotion engine object. The generation AI is used to learn the approval / denial history of past application forms, and the emotion engine recognizes the user's emotions in real time and provides that information to the generation AI.

[0933] Initializing the user device

[0934] The user terminal has an interface for communicating with the server. The user fills out the application form and sends it to the server through this terminal. The terminal has the function of receiving the proposed revisions sent from the server and presenting them to the user.

[0935] Submitting a form and generating suggested revisions

[0936] When a user submits an application form via their device, the server receives it and passes the application details to the generation AI and emotion engine. The emotion engine recognizes the user's emotions in real time and provides this information to the generation AI. The generation AI evaluates the application details based on past approval / denial history and information obtained from the emotion engine, and generates appropriate revision suggestions. These revision suggestions are sent back to the user's device via the server.

[0937] Proposal of amendments and review of application contents

[0938] By receiving and confirming the proposed revisions, users can review the content of their application. By utilizing information from the emotion engine, revisions are generated that reflect the user's emotions, improving the user experience. This allows users to submit applications with greater confidence, reducing the hassle of having to reject or resubmit applications, and also reducing the psychological and time burden.

[0939] Example

[0940] As an example, consider the following scenario.

[0941] When a user applies for "travel expenses," they input "Category: Travel Expenses," "Amount: $1,000," "Comments: Expenses for a business trip to New York," etc. into their device and submit it. When this application content arrives at the server, the server passes it to the generation AI and emotion engine and requests them to generate a revision proposal.

[0942] The emotion engine recognizes the user's emotions in real time and provides the generation AI with emotions such as "I'm worried about the application content." The generation AI evaluates the application content based on past approval / denial history and information from the emotion engine, and generates suggested revisions such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, allowing the user to review and resubmit the application.

[0943] By using the emotion engine, the system can provide revision suggestions that take the user's emotions into consideration, allowing for more effective revision of application content. This allows applicants to submit applications with confidence and confidence, resulting in improved application accuracy and reduced resubmission efforts.

[0944] The processing flow will be explained below.

[0945] Step 1:

[0946] Initializing the Server

[0947] When the server is initialized, it creates a generation AI object and an emotion engine object. The generation AI object learns the approval / denial history of past application forms and is used to scrutinize the application content. The emotion engine is used to recognize the user's emotions in real time.

[0948] Step 2:

[0949] Initializing the user device

[0950] The user device is initialized and configured with a reference to the server instance (server object), which allows communication between the user and the server.

[0951] Step 3:

[0952] Fill in and submit the application form

[0953] The user enters the necessary information into the application form and sends it to the server via the terminal. For example, the input contents are "Category: Travel Expenses", "Amount: $1,000", and "Comment: Expenses for a business trip to New York". The terminal also sends the user's emotions to the emotion engine.

[0954] Step 4:

[0955] The server receives the request

[0956] The server receives the application details and emotion information sent from the user terminal, and this received data is used as input for subsequent processing of the generation AI and emotion engine.

[0957] Step 5:

[0958] Submitting to generative AI and emotion engines

[0959] The server passes the received application details and emotion engine information to the generation AI and requests it to generate a revision proposal. The emotion engine analyzes the user's emotions in real time and provides that information to the generation AI.

[0960] Step 6:

[0961] Generate correction suggestions

[0962] The generative AI scrutinizes the application content based on past approval and rejection history, and generates appropriate revision suggestions based on the user's emotional information provided by the emotion engine. Suggested revisions include, for example, "Please include the specific dates of the business trip and a breakdown of accommodation costs."

[0963] Step 7:

[0964] Sending proposed revisions back to the server

[0965] The AI ​​generates a proposed revision and sends it back to the server. The server receives the proposed revision and performs the following steps:

[0966] Step 8:

[0967] Sending proposed revisions to the user's device

[0968] The server then sends the received revision proposal to the user's terminal, allowing the user to confirm the revision proposal.

[0969] Step 9:

[0970] Confirmation of amendments and review of application details

[0971] The user checks the proposed revisions through their device and makes any necessary changes to the application. By sending the revised application details back to the server, the accuracy of the application is improved and rework is reduced. Specifically, the user can add details about the business trip schedule and accommodation costs based on the proposed revisions. This makes the application more specific and accurate, increasing the likelihood of approval.

[0972] Example 2

[0973] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0974] The present invention aims to reduce the anxiety and psychological burden felt by applicants when submitting application forms. Conventional systems examine application content and provide suggested revisions, but do not consider the applicant's feelings, often resulting in significant stress for the applicant. Furthermore, rejection of applications due to inappropriate content and the hassle of resubmissions have also become an issue. There is a need for a system that solves these issues, reduces applicants' psychological burden, and improves application accuracy.

[0975] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0976] In this invention, the server includes means for using a generation AI to learn the approval / denial history of the application form, means for scrutinizing the application content entered by the applicant, means for generating and presenting proposed revisions based on the scrutinized application content, means for using an emotion engine to recognize the applicant's emotions in real time and providing that information to the generation AI, and means for reducing psychological burden by generating proposed revisions that reflect the applicant's emotional information. This makes it possible to create more accurate application content while taking the applicant's emotions into consideration, thereby reducing psychological burden and increasing the success rate of applications.

[0977] "Generative AI" is artificial intelligence that learns from past data and generates new information and suggestions based on that data.

[0978] An "application form" is a format in which a user applies for some service or approval by inputting and submitting specific information.

[0979] "Approval / denial history" refers to the record of approvals or denials of applications submitted in the past.

[0980] An "emotion engine" is a technology that recognizes a user's emotions in real time and provides that information to other systems.

[0981] "Proposed amendments" are suggested changes or improvements to the current application.

[0982] "Server" means a computer system that receives, processes, and transmits data over a network to other devices.

[0983] A "terminal" is a device that allows a user to input data and exchanges information with a server through communication.

[0984] "Verification" is the process of examining data or information in detail based on specific criteria.

[0985] "Training" is the process by which generative AI learns from past data and improves its accuracy.

[0986] "Psychological burden" refers to the mental stress and anxiety experienced by the user.

[0987] This invention is a system that combines generative AI and an emotion engine to reduce the anxiety and psychological burden felt by users when submitting application forms. This system is mainly composed of four components: a user terminal, a server, generative AI, and an emotion engine.

[0988] User terminal

[0989] A user terminal is a device on which a user enters and submits an application form. The terminal is equipped with an interface for communicating with the server and has the function of sending the application details entered by the user to the server. The terminal also has a screen that receives proposed revisions returned from the server and presents them to the user. Specific devices include PCs, tablets, and smartphones.

[0990] server

[0991] The role of the server is to receive the application content sent from the user terminal and pass it to the generation AI and emotion engine to generate a revision proposal. The server instantiates a generation AI object and an emotion engine object during initialization. This generation AI is used to learn from past application data and evaluate the application content. Meanwhile, the emotion engine recognizes the user's emotions in real time and provides that information to the generation AI.

[0992] Generation AI

[0993] Generative AI is an AI model that learns from the approval and rejection history of past application forms, examines new application content, and generates suggested revisions. Generative AI runs on the server and compares the received application content with past data to identify areas for improvement and potential problems in the application content. This allows for the generation of more appropriate application content.

[0994] Emotion Engine

[0995] The emotion engine is a technology that recognizes the user's emotions in real time while they are filling out an application form. The emotion engine analyzes the user's facial expressions, voice, input speed, etc. to infer the user's psychological state. This information is provided to the generative AI and used to evaluate the application content and generate revision suggestions.

[0996] Example

[0997] When a user applies for "travel expenses," they enter information such as "Category: Travel Expenses," "Amount: $1,000," and "Comments: Expenses for a business trip to New York" into their device and press the send button. When this application information arrives at the server, the server passes it to the generation AI and emotion engine and requests them to generate a revision proposal.

[0998] The emotion engine recognizes emotions such as "the user is anxious about the application content" and provides this information to the generation AI. The generation AI evaluates the application content based on past approval / denial history and emotional data, and generates suggested revisions such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, where the user can confirm them. Through this process, the user can review the application content based on accurate information, eliminating any anxiety and enabling them to submit a reliable application.

[0999] An example of a specific prompt for a generative AI model is, "Travel expense request subject: Business trip to New York, October 1, 2023 - October 5, 2023. Add details: Accommodation $800, Transportation $200."

[1000] In this way, by combining a generative AI and an emotion engine, the system of the present invention has the effect of reducing the psychological burden on users while improving the accuracy of application content.

[1001] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1002] Step 1:

[1003] Initializing the Server

[1004] When the server is initialized, it instantiates a generation AI object and an emotion engine object. The generation AI object learns from past application data and is used to evaluate the application content. The emotion engine object recognizes the user's emotions in real time and provides that information to the generation AI.

[1005] Input: Server startup sequence

[1006] Output: Generate AI objects and emotion engine objects

[1007] Specific operation: The server program executes the startup sequence, and each object of the generation AI and emotion engine is loaded into memory.

[1008] Step 2:

[1009] Initializing the user device

[1010] The user terminal has an interface for communicating with the server. When the terminal is initialized, a form is displayed in which the user can enter the application details. The terminal also has the function of receiving and displaying proposed revisions sent from the server.

[1011] Input: Start user terminal

[1012] Output: Display of application form, initialization of communication interface

[1013] Specific operation: The user terminal starts up, the application form is initialized and displayed, and at the same time, a network connection is established to communicate with the server.

[1014] Step 3:

[1015] Fill in and submit the application form

[1016] The user enters the necessary information into the application form on the terminal. For example, the category, amount, comments, etc. Once the input is complete, the user clicks the "Submit" button. The terminal then sends the entered data to the server.

[1017] Input: User inputs application details (category, amount, comments)

[1018] Output: Application data sent to the server

[1019] Specific operation: The user enters "Category: Travel expenses", "Amount: $1,000", and "Comment: Expenses for business trip to New York" into the form and presses the submit button. The terminal sends this data to the server.

[1020] Step 4:

[1021] Receipt of application and start of processing

[1022] The server receives the application content sent from the device and passes the received data to the generation AI and emotion engine, which then starts the application content review process.

[1023] Input: Application data sent from the terminal

[1024] Output: Providing data to generative AI and emotion engines

[1025] Specific operation: The server receives the application data and converts it into the appropriate data format to be passed to the generation AI and emotion engine.

[1026] Step 5:

[1027] Emotion recognition with emotion engine

[1028] The emotion engine collects and analyzes emotional data in real time while the user is filling out the application form. If the user shows any anxiety or tension, that information is provided to the generation AI.

[1029] Input: Application data and emotion data provided by the server

[1030] Output: User emotion data provided to the generative AI

[1031] Specific operation: The emotion engine collects data on the speed at which the user fills out the application form and analysis of the user's facial expressions, and sends information such as "I am worried about the content of the application" to the generation AI.

[1032] Step 6:

[1033] Generative AI generates revision suggestions

[1034] The generative AI evaluates the current application form based on past application data and emotional data sent from the emotion engine. The generative AI identifies avoidable issues and generates appropriate corrections.

[1035] Input: Emotion data from the emotion engine and past approval / disapproval data

[1036] Output: Revision proposal

[1037] Specific operation: The generative AI analyzes the input data and generates a correction suggestion such as, "Please provide the specific dates of your business trip and a breakdown of accommodation costs."

[1038] Step 7:

[1039] Return and view proposed revisions

[1040] The server receives the proposed amendments generated by the AI ​​and returns them to the user's device. The user's device then displays the proposed amendments, allowing the user to review the application contents.

[1041] Input: Correction suggestions from the generative AI

[1042] Output: Send and display suggested revisions to the user's device

[1043] Specific operation: The server receives the proposed revision and sends it to the user's terminal, which then displays the proposed revision to the user.

[1044] Step 8:

[1045] Review of application contents based on proposed amendments

[1046] The user reviews the proposed revisions, returns to the application form, makes any necessary revisions, and finally clicks the submit button again to submit the application.

[1047] Input: User makes corrections to the application based on the proposed corrections

[1048] Output: Resubmit corrected application data

[1049] Specific actions: The user adds specific information such as "Specific dates of business trip to New York: October 1st - October 5th, 2023" and "Breakdown of accommodation expenses: $800, transportation expenses: $200" and resubmits the application.

[1050] (Application example 2)

[1051] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1052] The present invention relates to a system that reduces the anxiety users feel when submitting application forms. Currently, when filling out and submitting application forms, users often feel anxious about whether the content is appropriate. In particular, in electronic payment services, where money is involved, there is a risk that the application will be rejected if the applicant enters incorrect or incomplete information. There is a need to reduce such anxiety and stress and provide an environment in which users can apply with peace of mind.

[1053] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for using a generation AI to learn the approval / denial history of the application form, means for scrutinizing the application content entered by the applicant, means for generating and presenting amendment proposals based on the scrutinized application content, means for using an emotion engine to recognize the applicant's emotions in real time and provide that information to the generation AI, and means for providing feedback that takes into consideration the applicant's emotional state. As a result, the applicant's emotional state is recognized in real time by the emotion engine, and the generation AI provides appropriate amendment proposals based on that information, so the applicant can be assured of the accuracy of the application content and can apply with confidence.

[1054] "Generative AI" is artificial intelligence that learns from past data and is used to evaluate and make suggestions for new data.

[1055] An "emotion engine" is a system that analyzes a user's facial expressions, tone of voice, and other physical signals to recognize their emotional state in real time.

[1056] "Applicant" means a person who completes and submits an application form for a particular purpose.

[1057] "Application Content" refers to all the information entered by the applicant in the application form.

[1058] "Proposed amendments" are changes proposed by the generation AI in response to points in the application that it determines require improvement after evaluating the application.

[1059] The "server" is a computing system that receives data sent from the user's device, processes and analyzes the data using the generative AI and emotion engine, and returns the results to the user.

[1060] "User terminal" refers to the device (e.g., a smartphone or computer) on which the applicant enters and submits the application form.

[1061] "Feedback" refers to advice and information provided to the Applicant based on the evaluation of the Generative AI and Emotion Engine.

[1062] "Approval / denial history" refers to data on whether applications have been approved or denied in the past.

[1063] "Scrutiny" refers to the act of examining the application in detail and determining its applicability and accuracy.

[1064] "Real-time" refers to processing or reaction occurring immediately, without delay.

[1065] The present invention is a system for reducing the anxiety felt by applicants when submitting application forms, and combines generative AI and an emotion engine to provide a user-friendly application experience. Detailed embodiments are described below.

[1066] System configuration

[1067] The system mainly consists of the following components:

[1068] 1. User device: The device on which the applicant fills out and submits the application form, such as a smartphone or computer.

[1069] 2. Server: Receives the application and analyzes it using generative AI and emotion engines.

[1070] 3. Generative AI: Learns from past approval and rejection history and generates evaluations and revision proposals for new applications.

[1071] 4. Emotion engine: Recognizes user emotions in real time and provides that information to the generative AI.

[1072] Hardware and software used

[1073] Hardware:

[1074] Smartphones and computers: Used as user terminals to fill out and submit application forms.

[1075] Camera: Captures the user's face and is used for emotion recognition by the emotion engine.

[1076] software:

[1077] DeepFace library: Used by the emotion engine to recognize user emotions in real time.

[1078] The transformers library: Used as a generative AI, specifically using the GPT-2 model to evaluate submissions and generate revisions.

[1079] Flask: A framework for building APIs on the server side and communicating with user devices.

[1080] Data processing and calculation

[1081] emotion recognition

[1082] The server analyzes the facial image sent from the user's device using the DeepFace library to identify the user's emotional state. The result of emotion recognition is returned as a major emotion such as "sad," "anxiety," or "anger."

[1083] Generate correction suggestions

[1084] The server examines the application content using the emotion information provided by the emotion engine and the generation AI. The generation AI generates correction suggestions and additional information for the application content based on past approval / denial history. In this case, the generation AI uses the Transformers library, specifically the GPT-2 model.

[1085] User Feedback

[1086] The server provides feedback to the applicant based on the generated revision proposal and sentiment information. This feedback is sent to the user's terminal, where the applicant can review, revise, and resubmit the application.

[1087] Examples of concrete examples and prompts

[1088] Specific examples

[1089] 1. Situation: A user is filling out an application form to make a large payment.

[1090] 2. User emotions: Anxiety.

[1091] 3. Correction suggestion: Please provide more details about the payment and the reason for it. If you have any questions, please contact our support center.

[1092] Prompt Sentence Examples

[1093] plaintext

[1094] The user is concerned. Please provide additional suggestions for the following request: Payment amount 50,000 yen, Purpose Travel

[1095] In this way, applicants can receive appropriate revision suggestions based on their feelings in real time, allowing them to submit applications with confidence. This is expected to improve the accuracy of applications and reduce the hassle of resubmissions.

[1096] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1097] Step 1:

[1098] The user enters the necessary information into the application form, attaches a photo of their face, and submits it via their terminal. The input data includes the specific application details (e.g., payment amount and reason for application) and a photo of their face. This sends the application information to the server.

[1099] Step 2:

[1100] The server receives the request and facial image sent by the user. The received facial image is passed to the emotion engine and used to analyze the user's emotional state. The emotion engine uses the DeepFace library to analyze the user's facial expressions and identify the dominant emotion. For example, the user's emotion may be identified as "anxiety."

[1101] Step 3:

[1102] The server creates a prompt for the generation AI based on the user's emotional information obtained from the emotion engine. Here, the emotional information and the application details are combined to generate a prompt. For example, a prompt such as "The user is feeling anxious. Please make additional suggestions for the following application details: Payment amount: 50,000 yen, Purpose: Travel" can be generated.

[1103] Step 4:

[1104] The generative AI receives a prompt as input and generates appropriate revision suggestions based on its content. The generative AI model used here is the GPT-2 model from the Transformers library. The model receives the prompt and generates specific revision suggestions for the application based on past approval and rejection history. For example, a revision suggestion might be generated such as, "Please provide a detailed explanation of the specific reason for the payment."

[1105] Step 5:

[1106] The generated revision proposal is sent to the user's terminal via the server. The revision proposal is displayed to the user in real time, allowing the user to review the application contents based on the revision proposal and make any necessary revisions.

[1107] Step 6:

[1108] The user then sends the revised application details back to the server via their device. The server receives the resent application details and performs a final check using the generation AI again. This allows the user to submit their application with peace of mind.

[1109] Step 7:

[1110] After the final confirmation, the server receives the complete application and generates appropriate feedback for the user. The feedback is provided by the AI ​​based on the user's emotional state and the application content. This completes the application process and allows the user to use the service with peace of mind.

[1111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1113] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1114] [Fourth embodiment]

[1115] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1119] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1122] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1124] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1126] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1127] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1128] The present invention relates to a system that uses a generation AI to eliminate or reduce anxiety about submitting an unfamiliar application form. An embodiment of this system will be described in detail.

[1129] System Overview

[1130] The system consists of three main components:

[1131] 1. User terminal: The device on which the applicant fills out and submits the application form.

[1132] 2. Server: Receives the application content and passes it to the generation AI for review and generation of correction proposals.

[1133] 3. Generative AI: Learns from past approval and rejection history, evaluates the application content, and generates revision proposals.

[1134] Embodiment

[1135] Initializing the Server

[1136] When the server is initialized, a generation AI object is automatically generated. The generation AI is used to learn the approval and rejection history of past application forms. This AI carefully examines the application details entered by the applicant and generates appropriate correction suggestions, thereby reducing the user's anxiety.

[1137] Initializing the user device

[1138] The user terminal has an interface for communicating with the server. The user fills out an application form and sends it to the server through this terminal. The terminal has the function of receiving the proposed revisions sent from the server and presenting them to the user.

[1139] Submitting a form and generating suggested revisions

[1140] When a user submits an application form via their device, the server receives it and passes the content of the request to the generation AI. The generation AI evaluates the application content based on past approval / denial history and generates an appropriate revision proposal. This revision proposal is sent back to the user device via the server.

[1141] Proposal of amendments and review of application contents

[1142] By receiving and confirming the proposed revisions, users can review the content of their application. This allows users to submit applications with greater confidence. As a result, the hassle of having to reject or resubmit applications is reduced, reducing both psychological and time burdens.

[1143] Example

[1144] As an example, consider the following scenario.

[1145] Let's take the example of a user applying for "travel expenses" for business purposes. The user enters "Category: Travel Expenses," "Amount: $1,000," "Comments: Expenses for a business trip to New York," etc. into the terminal and submits it. When this application content arrives at the server, the server passes it to the generation AI and requests it to generate a revision proposal.

[1146] The generation AI evaluates this and generates suggested corrections, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested corrections to the user's device, and the user can review them, revise the application, and resubmit it.

[1147] In this way, by using the system of the present invention, applicants can increase the accuracy of their application content and submit applications with fewer deficiencies.

[1148] The processing flow will be explained below.

[1149] Step 1:

[1150] Initializing the Server

[1151] When the server is initialized, it creates a generated AI object, which learns the approval / denial history of past application forms and is used to scrutinize the application content.

[1152] Step 2:

[1153] Initializing the user device

[1154] The user device is initialized and configured with a reference to the server instance (server object), which allows communication between the user and the server.

[1155] Step 3:

[1156] Fill in and submit the application form

[1157] The user enters the necessary information into the application form and sends it to the server via the terminal. For example, the input contents are "Category: Travel expenses", "Amount: 1,000 dollars", and "Comments: Expenses for business trip to New York".

[1158] Step 4:

[1159] The server receives the request

[1160] The server receives the application details sent from the user terminal. This received data is used as input for the subsequent AI generation process.

[1161] Step 5:

[1162] Request processing to generation AI

[1163] The server passes the received application content to the generation AI and requests it to generate a revision proposal. Here, the generation AI evaluates the application content based on past approval / rejection history.

[1164] Step 6:

[1165] Generate correction suggestions

[1166] The AI ​​generator then examines the application and generates suggested revisions, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs."

[1167] Step 7:

[1168] Sending proposed revisions back to the server

[1169] The AI ​​generates a proposed revision and sends it back to the server. The server receives the proposed revision and performs the following steps:

[1170] Step 8:

[1171] Sending proposed revisions to the user's device

[1172] The server then sends the received revision proposal to the user's terminal, allowing the user to confirm the revision proposal.

[1173] Step 9:

[1174] Confirmation of amendments and review of application details

[1175] The user can check the proposed revisions through their device and make any necessary changes to the application. By sending the revised application details back to the server, the accuracy of the application is improved and rework is reduced.

[1176] Example 1

[1177] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1178] When submitting conventional application forms, applicants often submit them without realizing that there are incomplete information, resulting in a high risk of the application being rejected. Furthermore, when corrections to the application content are necessary, there is a lack of guidance on how to correct inappropriate information. This often results in psychological stress for applicants and the loss of time required to resubmit. This invention solves these problems by utilizing generative AI to scrutinize application content and automatically generate suggested corrections.

[1179] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1180] In this invention, the server includes means for generating a generation AI object when initialized and training the generation AI using past application form data, means for a user terminal to communicate with the server and input and send an application form, means for passing the application data received by the server to the generation AI in prompt format and causing the generation AI to generate a revision proposal, and means for sending the generated revision proposal to the user terminal via the server and reviewing and correcting the application content. This enables the applicant to quickly check for deficiencies in the application content and review and correct it according to the appropriate revision proposal.

[1181] A "generative AI object" is an artificial intelligence program that learns from past data and generates outputs for specific tasks.

[1182] "Application form data" is a collection of information contained in past applications, including approval and denial history.

[1183] A "user terminal" is a device that allows a user to enter information into an application form and communicate with a server.

[1184] A "prompt format" is formatted text data used to give instructions to the generating AI.

[1185] "Proposed amendments" are suggestions made by the generating AI after evaluating the application and indicating any necessary amendments or additional information.

[1186] The "server" is a computer system that has the function of receiving application content from a user terminal, passing the data to the generation AI, generating a revision proposal, and returning it to the user terminal.

[1187] "Learning" is the process by which generative AI uses past data to improve its performance.

[1188] A "communication interface" is a means by which a user terminal and a server send and receive data to and from each other.

[1189] MODE FOR CARRYING OUT THE INVENTION

[1190] This invention relates to a system that uses generative AI to eliminate or reduce anxiety about submitting unfamiliar application forms. This system consists of the following three components:

[1191] User terminal

[1192] The user terminal is the device on which the applicant enters and submits the application form. The user terminal can be a PC, tablet, smartphone, or other device. This terminal has an interface for communicating with the server, and has the function of sending data to the server when the user enters and submits the application form. The user terminal also has the function of receiving proposed revisions sent from the server and presenting them to the user.

[1193] server

[1194] The server is a device that receives the application details sent from the user terminal and passes them to the generation AI to generate revision proposals. The server uses a high-performance computer (e.g., a cloud-based service). When the server is initialized, a generation AI object is created. This generation AI learns using a dataset of past application forms (approval and denial history). Specifically, it retrieves data from a database and trains a model using a machine learning algorithm.

[1195] Generation AI

[1196] Generative AI is an artificial intelligence program that evaluates application content and generates appropriate revision suggestions. A text generation model such as GPT-4 is used as the generative AI model. This model learns from past approval and denial history and generates appropriate revision suggestions if there are any deficiencies in the application content.

[1197] Specific scenario example

[1198] Let's take the example of a user applying for "travel expenses" for business purposes. The user enters "Category: Travel Expenses," "Amount: $1,000," and "Comment: Expenses for a business trip to New York" into the terminal and submits it. When this application content arrives at the server, the server sends it to the generation AI.

[1199] The generation AI evaluates the application content and generates suggested revisions, such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, and the user can review them, revise the application content, and resubmit it.

[1200] Prompt Sentence Examples

[1201] Below is an example of a prompt sentence to input to the generative AI model.

[1202] "User's request: Category: Travel expenses, Amount: $1,000, Comments: Expenses for a business trip to New York. Please generate an appropriate amendment based on this information."

[1203] This prompt allows the generation AI to generate appropriate revision suggestions based on the user's request.

[1204] In this way, the system of the present invention alleviates applicants' anxieties and improves the accuracy of application content, enabling faster and more reliable applications.

[1205] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1206] Program processing flow

[1207] Step 1:

[1208] When the server is initialized, it first creates a Generative AI object. The server retrieves past application form data (approval / denial history) from the database and provides this data to the Generative AI. The Generative AI learns from this data and acquires the knowledge necessary to evaluate the application content and generate revision proposals. The input is the past application form data, and the output is the trained Generative AI model.

[1209] Step 2:

[1210] The user enters the necessary information into the application form. The user terminal provides the interface for the application form, and the user enters information such as "Category: Travel expenses," "Amount: $1,000," and "Comments: Expenses for business trip to New York." With this information as input, the user terminal sends it to the server in the form of an HTTP request. The output is the request data sent to the server.

[1211] Step 3:

[1212] The server receives application data from the user. The server analyzes the received data and generates a prompt for the generation AI. For example, it generates a prompt that reads, "User's application details: Category: Travel expenses, Amount: 1,000 dollars, Comments: Expenses for a business trip to New York. Please generate an appropriate amendment based on this information." The input is the application data, and the output is the prompt to be passed to the generation AI.

[1213] Step 4:

[1214] The generation AI receives a prompt and evaluates the application content. It detects any deficiencies in the application content based on past data and generates suggested corrections. For example, for a travel expense application, it might create a suggested correction such as, "Please include the specific dates of the trip and a breakdown of accommodation costs." The input is the prompt, and the output is the suggested correction.

[1215] Step 5:

[1216] The server receives the proposed revision from the generation AI. The server sends this proposed revision to the user terminal in the form of an HTTP response. The input is the generated proposed revision, and the output is the response data sent to the user terminal.

[1217] Step 6:

[1218] The user terminal receives the proposed amendments from the server and displays them on the user interface. For example, the proposed amendments are displayed as a pop-up window or a notification message. The user checks the proposed amendments and amends the application content as necessary. The input is the response data from the server, and the output is the displayed proposed amendments.

[1219] Step 7:

[1220] The user reviews the application based on the proposed revisions and resubmits it. Once the revisions are complete, the user terminal again sends the application data to the server. This process is repeated until the application is finally approved. The input is the revised application data, and the output is the resent request data.

[1221] (Application example 1)

[1222] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1223] When using electronic payment services, many users are unfamiliar with filling out application forms for financial transactions and payment procedures, and are therefore prone to input errors and incomplete information. This can lead to applications being rejected or requiring reapplication, which can cause problems of psychological stress and increased time burden for users. In addition, electronic payment service providers incur time and costs in processing incomplete applications, reducing the efficiency of the overall service.

[1224] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1225] In this invention, the server includes means for correcting in advance any deficiencies in application forms for financial transactions or payment procedures using a generation AI, means for transmitting application details from a user terminal to the server and passing them from the server to the generation AI to generate a correction proposal, and means for returning the generated correction proposal to the user terminal and presenting it to the user. This allows the user to correct input deficiencies in the application form in advance, reducing the hassle of rejecting applications and having to resubmit them.

[1226] "Generative AI" is an artificial intelligence system that generates new information based on past data.

[1227] "Request Form" means a form or web form that a user fills out to request a particular service or permission.

[1228] "Approval / denial history" refers to data showing the results of past applications, including both approved and denied applications.

[1229] "User terminal" refers to the digital device used by the user to input the application form and send it to the server. Examples include smartphones and tablets.

[1230] A "server" is a computer system that provides services to other computers on a network.

[1231] "Suggested fixes" are data or information created by generative AI that suggests ways to improve input.

[1232] "Financial transactions" is a concept that refers to all transactions involving the transfer or exchange of funds.

[1233] A "payment transaction" is a formal process used to pay for goods or services.

[1234] "Defects" refer to missing information or errors in input or procedures.

[1235] This invention relates to a system that reduces psychological stress and time burdens by allowing users to correct incomplete application forms in advance when using electronic payment services, thereby improving efficiency on the part of electronic payment service providers.

[1236] System Overview

[1237] The system consists of the following main components:

[1238] 1. User device: A device such as a smartphone or tablet that the user uses to fill out and submit the application form.

[1239] 2. Server: A relay system that receives the application details, passes them to the generation AI, generates revision proposals, and returns them to the user's terminal.

[1240] 3. Generative AI: An artificial intelligence system that learns from past approval and denial history, evaluates the application content, and generates revision proposals.

[1241] Hardware and software used

[1242] User devices: smartphones (iOS, Android), tablets (iOS, Android)

[1243] Server: Remote server (Linux, Windows Server)

[1244] Generative AI: AI models that perform advanced natural language processing (e.g., GPT-3, BERT)

[1245] Specific implementation methods

[1246] 1. User device operation: The user uses a smartphone or tablet to fill out application forms for financial transactions and payment procedures. For example, they enter information such as "Category: Remittance," "Amount: 5,000 yen," and "Description: Rent payment."

[1247] 2. Data transmission: The entered application details are sent to a server via the Internet.

[1248] 3. Server processing: The server passes the received application content to the generative AI model, which analyzes the application content based on past approval / rejection history and generates suggestions for correcting deficiencies.

[1249] 4. Generate suggested amendments: The AI ​​generator suggests additional information or amendments that the user should fill in, such as, "Please specify the specific period for rent payments (e.g., October 1st to October 31st, 2023)."

[1250] 5. Return and presentation to the user's device: The proposed revisions generated by the generation AI are returned to the user's device via the server and presented to the user. The user reviews the application based on the proposed revisions and makes any necessary revisions.

[1251] Examples and prompts

[1252] Specific examples

[1253] If a user is trying to request a rent payment:

[1254] Original input:

[1255] Category:Remittance

[1256] Price: 5,000 yen

[1257] Description: Rent payment

[1258] AI's suggested correction: "Please specify the specific period for rent payment (e.g., October 1st to October 31st, 2023)."

[1259] Prompt example

[1260] text

[1261] user_form = {

[1262] "Category": "Remittance",

[1263] "Amount": "5000 yen",

[1264] "Description": "Rent payment"

[1265] }

[1266] correction_suggestions = send_application_form(user_form)

[1267] Using this prompt as an example, the user device sends the application details to the server, and the server uses a generative AI to generate an appropriate correction proposal and sends it back to the user device, completing a series of processes.

[1268] This allows users to prevent input errors in application forms and increase the accuracy of applications, while also enabling electronic payment service providers to provide services more efficiently.

[1269] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1270] Step 1:

[1271] The user fills out the application form using a terminal. For example, the user enters information such as "Category: Remittance," "Amount: 5,000 yen," and "Explanation: Rent payment." This input itself becomes the application form data and is sent to the server.

[1272] Step 2:

[1273] The terminal sends the entered application form data to the server. Specifically, it sends the application details to the server via the Internet using an HTTP request. The input data is encoded in JSON format and passed to the server.

[1274] Step 3:

[1275] The server receives the submitted application, which is first checked for format and required fields, eliminating basic input errors.

[1276] Step 4:

[1277] The server passes the received request to the generation AI, which evaluates the request using a model trained on past approval and denial history. The input data is sent to the generation AI model as a prompt.

[1278] Step 5:

[1279] The generative AI analyzes the application content and detects deficiencies and areas for improvement. Specifically, it uses natural language processing technology to analyze text data and generate revision proposals by referencing past approval and rejection patterns. The data operations performed during this process include text pattern matching, probability evaluation, and calculation of a generation score.

[1280] Step 6:

[1281] The proposed revisions generated by the generation AI are sent back to the server. The server receives the proposed revisions and prepares them to send back to the user device. The proposed revisions are also encoded in JSON format and sent to the user device as an HTTP response.

[1282] Step 7:

[1283] The terminal displays the proposed amendment received from the server to the user. The user confirms the proposed amendment and amends the application. Specifically, the proposed amendment includes specific instructions such as "Please specify the specific period for rent payment (e.g., October 1 to October 31, 2023)."

[1284] Step 8:

[1285] The user then modifies the application form based on the proposed modifications. The modified data is then sent to the server as in the first step. This process is repeated until the user is satisfied with the modified application.

[1286] This allows users to accurately correct their input data and increase the accuracy of their applications, while electronic payment service providers can reduce the number of incomplete applications from users and improve processing efficiency.

[1287] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1288] The present invention relates to a system that uses a generative AI and an emotion engine in combination to eliminate or reduce anxiety about submitting an application form. An embodiment of this system will be specifically described.

[1289] System Overview

[1290] The system consists of four main components:

[1291] 1. User terminal: The device on which the applicant fills out and submits the application form.

[1292] 2. Server: Receives the application content and passes it to the generation AI for review and generation of correction proposals.

[1293] 3. Generative AI: Learns from past approval and rejection history, evaluates the application content, and generates revision proposals.

[1294] 4. Emotion engine: Recognizes user emotions in real time and provides that information to the generative AI.

[1295] Embodiment

[1296] Initializing the Server

[1297] When the server is initialized, it creates a generation AI object and an emotion engine object. The generation AI is used to learn the approval / denial history of past application forms, and the emotion engine recognizes the user's emotions in real time and provides that information to the generation AI.

[1298] Initializing the user device

[1299] The user terminal has an interface for communicating with the server. The user fills out the application form and sends it to the server through this terminal. The terminal has the function of receiving the proposed revisions sent from the server and presenting them to the user.

[1300] Submitting a form and generating suggested revisions

[1301] When a user submits an application form via their device, the server receives it and passes the application details to the generation AI and emotion engine. The emotion engine recognizes the user's emotions in real time and provides this information to the generation AI. The generation AI evaluates the application details based on past approval / denial history and information obtained from the emotion engine, and generates appropriate revision suggestions. These revision suggestions are sent back to the user's device via the server.

[1302] Proposal of amendments and review of application contents

[1303] By receiving and confirming the proposed revisions, users can review the content of their application. By utilizing information from the emotion engine, revisions are generated that reflect the user's emotions, improving the user experience. This allows users to submit applications with greater confidence, reducing the hassle of having to reject or resubmit applications, and also reducing the psychological and time burden.

[1304] Example

[1305] As an example, consider the following scenario.

[1306] When a user applies for "travel expenses," they input "Category: Travel Expenses," "Amount: $1,000," "Comments: Expenses for a business trip to New York," etc. into their device and submit it. When this application content arrives at the server, the server passes it to the generation AI and emotion engine and requests them to generate a revision proposal.

[1307] The emotion engine recognizes the user's emotions in real time and provides the generation AI with emotions such as "I'm worried about the application content." The generation AI evaluates the application content based on past approval / denial history and information from the emotion engine, and generates suggested revisions such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, allowing the user to review and resubmit the application.

[1308] By using the emotion engine, the system can provide revision suggestions that take the user's emotions into consideration, allowing for more effective revision of application content. This allows applicants to submit applications with confidence and confidence, resulting in improved application accuracy and reduced resubmission efforts.

[1309] The processing flow will be explained below.

[1310] Step 1:

[1311] Initializing the Server

[1312] When the server is initialized, it creates a generation AI object and an emotion engine object. The generation AI object learns the approval / denial history of past application forms and is used to scrutinize the application content. The emotion engine is used to recognize the user's emotions in real time.

[1313] Step 2:

[1314] Initializing the user device

[1315] The user device is initialized and configured with a reference to the server instance (server object), which allows communication between the user and the server.

[1316] Step 3:

[1317] Fill in and submit the application form

[1318] The user enters the necessary information into the application form and sends it to the server via the terminal. For example, the input contents are "Category: Travel Expenses", "Amount: $1,000", and "Comment: Expenses for a business trip to New York". The terminal also sends the user's emotions to the emotion engine.

[1319] Step 4:

[1320] The server receives the request

[1321] The server receives the application details and emotion information sent from the user terminal, and this received data is used as input for subsequent processing of the generation AI and emotion engine.

[1322] Step 5:

[1323] Submitting to generative AI and emotion engines

[1324] The server passes the received application details and emotion engine information to the generation AI and requests it to generate a revision proposal. The emotion engine analyzes the user's emotions in real time and provides that information to the generation AI.

[1325] Step 6:

[1326] Generate correction suggestions

[1327] The generative AI scrutinizes the application content based on past approval and rejection history, and generates appropriate revision suggestions based on the user's emotional information provided by the emotion engine. Suggested revisions include, for example, "Please include the specific dates of the business trip and a breakdown of accommodation costs."

[1328] Step 7:

[1329] Sending proposed revisions back to the server

[1330] The AI ​​generates a proposed revision and sends it back to the server. The server receives the proposed revision and performs the following steps:

[1331] Step 8:

[1332] Sending proposed revisions to the user's device

[1333] The server then sends the received revision proposal to the user's terminal, allowing the user to confirm the revision proposal.

[1334] Step 9:

[1335] Confirmation of amendments and review of application details

[1336] The user checks the proposed revisions through their device and makes any necessary changes to the application. By sending the revised application details back to the server, the accuracy of the application is improved and rework is reduced. Specifically, the user can add details about the business trip schedule and accommodation costs based on the proposed revisions. This makes the application more specific and accurate, increasing the likelihood of approval.

[1337] Example 2

[1338] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1339] The present invention aims to reduce the anxiety and psychological burden felt by applicants when submitting application forms. Conventional systems examine application content and provide suggested revisions, but do not consider the applicant's feelings, often resulting in significant stress for the applicant. Furthermore, rejection of applications due to inappropriate content and the hassle of resubmissions have also become an issue. There is a need for a system that solves these issues, reduces applicants' psychological burden, and improves application accuracy.

[1340] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1341] In this invention, the server includes means for using a generation AI to learn the approval / denial history of the application form, means for scrutinizing the application content entered by the applicant, means for generating and presenting proposed revisions based on the scrutinized application content, means for using an emotion engine to recognize the applicant's emotions in real time and providing that information to the generation AI, and means for reducing psychological burden by generating proposed revisions that reflect the applicant's emotional information. This makes it possible to create more accurate application content while taking the applicant's emotions into consideration, thereby reducing psychological burden and increasing the success rate of applications.

[1342] "Generative AI" is artificial intelligence that learns from past data and generates new information and suggestions based on that data.

[1343] An "application form" is a format in which a user applies for some service or approval by inputting and submitting specific information.

[1344] "Approval / denial history" refers to the record of approvals or denials of applications submitted in the past.

[1345] An "emotion engine" is a technology that recognizes a user's emotions in real time and provides that information to other systems.

[1346] "Proposed amendments" are suggested changes or improvements to the current application.

[1347] "Server" means a computer system that receives, processes, and transmits data over a network to other devices.

[1348] A "terminal" is a device that allows a user to input data and exchanges information with a server through communication.

[1349] "Verification" is the process of examining data or information in detail based on specific criteria.

[1350] "Training" is the process by which generative AI learns from past data and improves its accuracy.

[1351] "Psychological burden" refers to the mental stress and anxiety experienced by the user.

[1352] This invention is a system that combines generative AI and an emotion engine to reduce the anxiety and psychological burden felt by users when submitting application forms. This system is mainly composed of four components: a user terminal, a server, generative AI, and an emotion engine.

[1353] User terminal

[1354] A user terminal is a device on which a user enters and submits an application form. The terminal is equipped with an interface for communicating with the server and has the function of sending the application details entered by the user to the server. The terminal also has a screen that receives proposed revisions returned from the server and presents them to the user. Specific devices include PCs, tablets, and smartphones.

[1355] server

[1356] The role of the server is to receive the application content sent from the user terminal and pass it to the generation AI and emotion engine to generate a revision proposal. The server instantiates a generation AI object and an emotion engine object during initialization. This generation AI is used to learn from past application data and evaluate the application content. Meanwhile, the emotion engine recognizes the user's emotions in real time and provides that information to the generation AI.

[1357] Generation AI

[1358] Generative AI is an AI model that learns from the approval and rejection history of past application forms, examines new application content, and generates suggested revisions. Generative AI runs on the server and compares the received application content with past data to identify areas for improvement and potential problems in the application content. This allows for the generation of more appropriate application content.

[1359] Emotion Engine

[1360] The emotion engine is a technology that recognizes the user's emotions in real time while they are filling out an application form. The emotion engine analyzes the user's facial expressions, voice, input speed, etc. to infer the user's psychological state. This information is provided to the generative AI and used to evaluate the application content and generate revision suggestions.

[1361] Example

[1362] When a user applies for "travel expenses," they enter information such as "Category: Travel Expenses," "Amount: $1,000," and "Comments: Expenses for a business trip to New York" into their device and press the send button. When this application information arrives at the server, the server passes it to the generation AI and emotion engine and requests them to generate a revision proposal.

[1363] The emotion engine recognizes emotions such as "the user is anxious about the application content" and provides this information to the generation AI. The generation AI evaluates the application content based on past approval / denial history and emotional data, and generates suggested revisions such as "Please include the specific dates of the business trip and a breakdown of accommodation costs." The server returns these suggested revisions to the user's device, where the user can confirm them. Through this process, the user can review the application content based on accurate information, eliminating any anxiety and enabling them to submit a reliable application.

[1364] An example of a specific prompt for a generative AI model is, "Travel expense request subject: Business trip to New York, October 1, 2023 - October 5, 2023. Add details: Accommodation $800, Transportation $200."

[1365] In this way, by combining a generative AI and an emotion engine, the system of the present invention has the effect of reducing the psychological burden on users while improving the accuracy of application content.

[1366] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1367] Step 1:

[1368] Initializing the Server

[1369] When the server is initialized, it instantiates a generation AI object and an emotion engine object. The generation AI object learns from past application data and is used to evaluate the application content. The emotion engine object recognizes the user's emotions in real time and provides that information to the generation AI.

[1370] Input: Server startup sequence

[1371] Output: Generate AI objects and emotion engine objects

[1372] Specific operation: The server program executes the startup sequence, and each object of the generation AI and emotion engine is loaded into memory.

[1373] Step 2:

[1374] Initializing the user device

[1375] The user terminal has an interface for communicating with the server. When the terminal is initialized, a form is displayed in which the user can enter the application details. The terminal also has the function of receiving and displaying proposed revisions sent from the server.

[1376] Input: Start user terminal

[1377] Output: Display of application form, initialization of communication interface

[1378] Specific operation: The user terminal starts up, the application form is initialized and displayed, and at the same time, a network connection is established to communicate with the server.

[1379] Step 3:

[1380] Fill in and submit the application form

[1381] The user enters the necessary information into the application form on the terminal. For example, the category, amount, comments, etc. Once the input is complete, the user clicks the "Submit" button. The terminal then sends the entered data to the server.

[1382] Input: User inputs application details (category, amount, comments)

[1383] Output: Application data sent to the server

[1384] Specific operation: The user enters "Category: Travel expenses", "Amount: $1,000", and "Comment: Expenses for business trip to New York" into the form and presses the submit button. The terminal sends this data to the server.

[1385] Step 4:

[1386] Receipt of application and start of processing

[1387] The server receives the application content sent from the device and passes the received data to the generation AI and emotion engine, which then starts the application content review process.

[1388] Input: Application data sent from the terminal

[1389] Output: Providing data to generative AI and emotion engines

[1390] Specific operation: The server receives the application data and converts it into the appropriate data format to be passed to the generation AI and emotion engine.

[1391] Step 5:

[1392] Emotion recognition with emotion engine

[1393] The emotion engine collects and analyzes emotional data in real time while the user is filling out the application form. If the user shows any anxiety or tension, that information is provided to the generation AI.

[1394] Input: Application data and emotion data provided by the server

[1395] Output: User emotion data provided to the generative AI

[1396] Specific operation: The emotion engine collects data on the speed at which the user fills out the application form and analysis of the user's facial expressions, and sends information such as "I am worried about the content of the application" to the generation AI.

[1397] Step 6:

[1398] Generative AI generates revision suggestions

[1399] The generative AI evaluates the current application form based on past application data and emotional data sent from the emotion engine. The generative AI identifies avoidable issues and generates appropriate corrections.

[1400] Input: Emotion data from the emotion engine and past approval / disapproval data

[1401] Output: Revision proposal

[1402] Specific operation: The generative AI analyzes the input data and generates a correction suggestion such as, "Please provide the specific dates of your business trip and a breakdown of accommodation costs."

[1403] Step 7:

[1404] Return and view proposed revisions

[1405] The server receives the proposed amendments generated by the AI ​​and returns them to the user's device. The user's device then displays the proposed amendments, allowing the user to review the application contents.

[1406] Input: Correction suggestions from the generative AI

[1407] Output: Send and display suggested revisions to the user's device

[1408] Specific operation: The server receives the proposed revision and sends it to the user's terminal, which then displays the proposed revision to the user.

[1409] Step 8:

[1410] Review of application contents based on proposed amendments

[1411] The user reviews the proposed revisions, returns to the application form, makes any necessary revisions, and finally clicks the submit button again to submit the application.

[1412] Input: User makes corrections to the application based on the proposed corrections

[1413] Output: Resubmit corrected application data

[1414] Specific actions: The user adds specific information such as "Specific dates of business trip to New York: October 1st - October 5th, 2023" and "Breakdown of accommodation expenses: $800, transportation expenses: $200" and resubmits the application.

[1415] (Application example 2)

[1416] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1417] The present invention relates to a system that reduces the anxiety users feel when submitting application forms. Currently, when filling out and submitting application forms, users often feel anxious about whether the content is appropriate. In particular, in electronic payment services, where money is involved, there is a risk that the application will be rejected if the applicant enters incorrect or incomplete information. There is a need to reduce such anxiety and stress and provide an environment in which users can apply with peace of mind.

[1418] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for using a generation AI to learn the approval / denial history of the application form, means for scrutinizing the application content entered by the applicant, means for generating and presenting amendment proposals based on the scrutinized application content, means for using an emotion engine to recognize the applicant's emotions in real time and provide that information to the generation AI, and means for providing feedback that takes into consideration the applicant's emotional state. As a result, the applicant's emotional state is recognized in real time by the emotion engine, and the generation AI provides appropriate amendment proposals based on that information, so the applicant can be assured of the accuracy of the application content and can apply with confidence.

[1419] "Generative AI" is artificial intelligence that learns from past data and is used to evaluate and make suggestions for new data.

[1420] An "emotion engine" is a system that analyzes a user's facial expressions, tone of voice, and other physical signals to recognize their emotional state in real time.

[1421] "Applicant" means a person who completes and submits an application form for a particular purpose.

[1422] "Application Content" refers to all the information entered by the applicant in the application form.

[1423] "Proposed amendments" are changes proposed by the generation AI in response to points in the application that it determines require improvement after evaluating the application.

[1424] The "server" is a computing system that receives data sent from the user's device, processes and analyzes the data using the generative AI and emotion engine, and returns the results to the user.

[1425] "User terminal" refers to the device (e.g., a smartphone or computer) on which the applicant enters and submits the application form.

[1426] "Feedback" refers to advice and information provided to the Applicant based on the evaluation of the Generative AI and Emotion Engine.

[1427] "Approval / denial history" refers to data on whether applications have been approved or denied in the past.

[1428] "Scrutiny" refers to the act of examining the application in detail and determining its applicability and accuracy.

[1429] "Real-time" refers to processing or reaction occurring immediately, without delay.

[1430] The present invention is a system for reducing the anxiety felt by applicants when submitting application forms, and combines generative AI and an emotion engine to provide a user-friendly application experience. Detailed embodiments are described below.

[1431] System configuration

[1432] The system mainly consists of the following components:

[1433] 1. User device: The device on which the applicant fills out and submits the application form, such as a smartphone or computer.

[1434] 2. Server: Receives the application and analyzes it using generative AI and emotion engines.

[1435] 3. Generative AI: Learns from past approval and rejection history and generates evaluations and revision proposals for new applications.

[1436] 4. Emotion engine: Recognizes user emotions in real time and provides that information to the generative AI.

[1437] Hardware and software used

[1438] Hardware:

[1439] Smartphones and computers: Used as user terminals to fill out and submit application forms.

[1440] Camera: Captures the user's face and is used for emotion recognition by the emotion engine.

[1441] software:

[1442] DeepFace library: Used by the emotion engine to recognize user emotions in real time.

[1443] The transformers library: Used as a generative AI, specifically using the GPT-2 model to evaluate submissions and generate revisions.

[1444] Flask: A framework for building APIs on the server side and communicating with user devices.

[1445] Data processing and calculation

[1446] emotion recognition

[1447] The server analyzes the facial image sent from the user's device using the DeepFace library to identify the user's emotional state. The result of emotion recognition is returned as a major emotion such as "sad," "anxiety," or "anger."

[1448] Generate correction suggestions

[1449] The server examines the application content using the emotion information provided by the emotion engine and the generation AI. The generation AI generates correction suggestions and additional information for the application content based on past approval / denial history. In this case, the generation AI uses the Transformers library, specifically the GPT-2 model.

[1450] User Feedback

[1451] The server provides feedback to the applicant based on the generated revision proposal and sentiment information. This feedback is sent to the user's terminal, where the applicant can review, revise, and resubmit the application.

[1452] Examples of concrete examples and prompts

[1453] Specific examples

[1454] 1. Situation: A user is filling out an application form to make a large payment.

[1455] 2. User emotions: Anxiety.

[1456] 3. Correction suggestion: Please provide more details about the payment and the reason for it. If you have any questions, please contact our support center.

[1457] Prompt Sentence Examples

[1458] plaintext

[1459] The user is concerned. Please provide additional suggestions for the following request: Payment amount 50,000 yen, Purpose Travel

[1460] In this way, applicants can receive appropriate revision suggestions based on their feelings in real time, allowing them to submit applications with confidence. This is expected to improve the accuracy of applications and reduce the hassle of resubmissions.

[1461] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1462] Step 1:

[1463] The user enters the necessary information into the application form, attaches a photo of their face, and submits it via their terminal. The input data includes the specific application details (e.g., payment amount and reason for application) and a photo of their face. This sends the application information to the server.

[1464] Step 2:

[1465] The server receives the request and facial image sent by the user. The received facial image is passed to the emotion engine and used to analyze the user's emotional state. The emotion engine uses the DeepFace library to analyze the user's facial expressions and identify the dominant emotion. For example, the user's emotion may be identified as "anxiety."

[1466] Step 3:

[1467] The server creates a prompt for the generation AI based on the user's emotional information obtained from the emotion engine. Here, the emotional information and the application details are combined to generate a prompt. For example, a prompt such as "The user is feeling anxious. Please make additional suggestions for the following application details: Payment amount: 50,000 yen, Purpose: Travel" can be generated.

[1468] Step 4:

[1469] The generative AI receives a prompt as input and generates appropriate revision suggestions based on its content. The generative AI model used here is the GPT-2 model from the Transformers library. The model receives the prompt and generates specific revision suggestions for the application based on past approval and rejection history. For example, a revision suggestion might be generated such as, "Please provide a detailed explanation of the specific reason for the payment."

[1470] Step 5:

[1471] The generated revision proposal is sent to the user's terminal via the server. The revision proposal is displayed to the user in real time, allowing the user to review the application contents based on the revision proposal and make any necessary revisions.

[1472] Step 6:

[1473] The user then sends the revised application details back to the server via their device. The server receives the resent application details and performs a final check using the generation AI again. This allows the user to submit their application with peace of mind.

[1474] Step 7:

[1475] After the final confirmation, the server receives the complete application and generates appropriate feedback for the user. The feedback is provided by the AI ​​based on the user's emotional state and the application content. This completes the application process and allows the user to use the service with peace of mind.

[1476] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1477] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1478] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1479] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1480] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1481] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1482] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1483] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1484] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1485] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1486] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1487] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1488] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1489] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1490] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1491] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1492] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1493] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1494] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1495] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1496] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1497] The following is further disclosed regarding the above embodiment.

[1498] (Claim 1)

[1499] Using generative AI, we can learn the approval and denial history of application forms,

[1500] A means for reviewing the application details entered by the applicant;

[1501] a means for generating and presenting amendments based on the reviewed application;

[1502] A system including a means for amending the application based on the proposed amendments.

[1503] (Claim 2)

[1504] A means to collect the approval / denial history of past application forms and use it to train the generation AI,

[1505] A means of using generative AI to evaluate applications; and

[1506] 10. The system of claim 1, further comprising means for generating and transmitting a revision suggestion to the user terminal.

[1507] (Claim 3)

[1508] a terminal through which the applicant transmits the application details;

[1509] A server that receives the submitted application content and passes it to a generation AI to generate a revision proposal;

[1510] 10. The system of claim 1, further comprising means for returning the generated amendment proposal to the applicant.

[1511] "Example 1"

[1512] (Claim 1)

[1513] A means for generating a generation AI object when the server is initialized and training the generation AI using past application form data;

[1514] A means for a user terminal to communicate with a server, input an application form, and send it;

[1515] A means for the server to pass the received application data to the generation AI in a prompt format and have the generation AI generate a correction proposal;

[1516] The system includes a means for transmitting the generated revision proposal to a user terminal via a server, and for reviewing and revising the application contents.

[1517] (Claim 2)

[1518] A means to collect approval and denial data from past application forms and use it to train the generation AI;

[1519] A means of using generative AI to evaluate applications; and

[1520] 10. The system of claim 1, further comprising means for transmitting the generated revision proposal to a user terminal.

[1521] (Claim 3)

[1522] a terminal to which a user transmits application details;

[1523] A server that receives the submitted application content and passes it to a generation AI to generate a revision proposal;

[1524] 10. The system of claim 1, further comprising means for returning the generated revision suggestions to the user terminal.

[1525] "Application Example 1"

[1526] (Claim 1)

[1527] Using generative AI, we can learn the approval and denial history of application forms,

[1528] A means for reviewing the application details entered by the applicant;

[1529] a means for generating and presenting amendments based on the reviewed application;

[1530] a means of amending the application based on the proposed amendment;

[1531] A means of using generative AI to proactively correct deficiencies in application forms for financial transactions and payment procedures, and

[1532] A means for transmitting the application content from the user terminal to a server, and then passing it from the server to a generation AI to generate a revision proposal;

[1533] The system includes means for returning the generated revision proposal to the user terminal and presenting it to the user.

[1534] (Claim 2)

[1535] A means to collect the approval / denial history of past application forms and use it to train the generation AI,

[1536] A means of using generative AI to evaluate applications; and

[1537] 10. The system of claim 1, further comprising means for generating and transmitting a revision suggestion to the user terminal.

[1538] (Claim 3)

[1539] a terminal through which the applicant transmits the application details;

[1540] A server that receives the submitted application content and passes it to a generation AI to generate a revision proposal;

[1541] 10. The system of claim 1, further comprising means for returning the generated amendment proposal to the applicant.

[1542] "Example 2: Combining Emotion Engines"

[1543] (Claim 1)

[1544] Using generative AI, we can learn the approval and denial history of application forms,

[1545] A means for reviewing the application details entered by the applicant;

[1546] a means for generating and presenting amendments based on the reviewed application;

[1547] a means of amending the application based on the proposed amendment;

[1548] A means of using an emotion engine to recognize the applicant's emotions in real time and provide that information to the generative AI;

[1549] A system including a means for reducing psychological burden by generating revision proposals that reflect the applicant's emotional information.

[1550] (Claim 2)

[1551] A means to collect the approval / denial history of past application forms and use it to train the generation AI,

[1552] A means of using generative AI to evaluate applications; and

[1553] 10. The system of claim 1, further comprising means for generating and transmitting a revision suggestion to the user terminal.

[1554] (Claim 3)

[1555] a terminal through which the applicant transmits the application details;

[1556] A server that receives the submitted application content and passes it to a generation AI to generate a revision proposal;

[1557] a means for returning the generated amendment to the applicant;

[1558] 10. The system of claim 1, further comprising means for utilizing an emotion engine to recognize emotions of the applicant and providing that information to the generating AI.

[1559] "Application example 2 when combining emotion engines"

[1560] (Claim 1)

[1561] Using generative AI, we can learn the approval and denial history of application forms,

[1562] A means for reviewing the application details entered by the applicant;

[1563] a means for generating and presenting amendments based on the reviewed application;

[1564] a means of amending the application based on the proposed amendment;

[1565] A means of using an emotion engine to recognize the applicant's emotions in real time and provide that information to the generative AI;

[1566] a means of providing feedback that is sensitive to the applicant's emotional state;

[1567] A system including:

[1568] (Claim 2)

[1569] A means to collect the approval / denial history of past application forms and use it to train the generation AI,

[1570] A means of using generative AI to evaluate applications; and

[1571] means for generating and transmitting a revision proposal to a user terminal;

[1572] 10. The system of claim 1, further comprising means for acquiring the applicant's emotional state and providing appropriate feedback based on that information.

[1573] (Claim 3)

[1574] a terminal through which the applicant transmits the application details;

[1575] A server that receives the submitted application content and passes it to a generation AI to generate a revision proposal;

[1576] a means for returning the generated amendment to the applicant;

[1577] 10. The system of claim 1, comprising a device for detecting emotions of the applicant and means for transmitting the emotion information to a server. [Explanation of symbols]

[1578] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. Using generative AI, we can learn the approval / denial history of application forms, A means for reviewing the application details entered by the applicant; a means for generating and presenting amendments based on the reviewed application; A system including a means for amending the application based on the proposed amendments.

2. A means to collect the approval / denial history of past application forms and use it to train the generation AI, A means of using generative AI to evaluate applications; and 10. The system of claim 1, further comprising means for generating revision suggestions and transmitting them to the user terminal.

3. a terminal through which the applicant transmits the application details; A server that receives the submitted application content and passes it to a generation AI to generate a revision proposal; 10. The system of claim 1, further comprising means for returning the generated revision proposal to the applicant.

Images