system

Abstract

Provide a system. 【Solution means】 Means for obtaining data of application forms and request forms received from users, Means for formatting the obtained data and converting it into a format that is easy for the AI model to process, Means for inputting the formatted data into the AI model and detecting deficiencies, Means for notifying the user of the detected deficiencies and the correction methods, Means for inputting the corrected data or data without deficiencies from the beginning into the AI model again for inspection, Means for automatically registering the data that has passed the inspection into the business system, Means for performing a final check on the registered data, Means for notifying the user that the registration has been completed, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In mobile registration, if there are deficiencies in the application forms and requests submitted by users, it takes time and effort to correct them, resulting in a delay in the registration process. Also, manual registration work is prone to human errors, and the accuracy of registration information may not be ensured. Furthermore, these problems are factors that deteriorate the user experience (UX). The present invention aims to solve these problems and provide a system for efficiently and accurately performing the mobile registration process.

Means for Solving the Problems

[0005] This invention solves these problems by providing a system that includes means for acquiring data from application forms and request forms received from users. The system includes means for formatting the acquired data and converting it into a format that is easy for an AI model to process. Furthermore, it includes means for inputting the formatted data into the AI ​​model and detecting defects, and means for notifying the user of the detected defects and how to correct them. The data with corrected defects or data that was defect-free from the beginning is input into the AI ​​model again and inspected. Subsequently, the system provides means for automatically registering the data that has passed the inspection into the business system. Finally, it includes means for performing a final check of the registered data and notifying the user that the registration is complete. This makes the mobile registration process efficient and accurate, reduces the effort required from the user, and minimizes errors.

[0006] An "application form" is a document containing necessary information that users submit when registering via mobile.

[0007] A "request form" is a document submitted by a user to request specific procedures regarding mobile registration.

[0008] "Data" refers to electronic information, including user information, as stated in the application form and request form.

[0009] "Formatting" is the process of converting received raw data into a format that is easy for the AI ​​model to process.

[0010] An "AI model" is an algorithm and program that uses artificial intelligence technology to analyze data and perform tasks such as detecting defects and other processing.

[0011] "Deficiencies" refer to problems such as omissions, inaccuracies, or contradictions in application forms and request forms.

[0012] "Notification" refers to the act of electronically communicating information to a user regarding defects and how to correct them.

[0013] "Correction" refers to the user changing data that has been identified as having errors to the correct information.

[0014] "Inspection" is the process of verifying whether formatted data has been entered correctly.

[0015] A "business system" refers to an internal computer system used within a company to receive and process the information necessary for mobile registration.

[0016] "Automatically" refers to a process performed by a computer system without human intervention.

[0017] "Final check" refers to the final verification process that confirms the accuracy of the data registered in the business system.

[0018] "User" refers to an individual or legal entity that submits a mobile registration application form and request form. [Brief explanation of the drawing]

[0019] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the language used in the following description will be described.

[0022] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Further, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention is a system that automatically detects deficiencies in application forms and request forms submitted by users for mobile registration, recommends corrections as necessary, and then automatically inputs the information into the registration system. This section describes specific embodiments of the system.

[0041] System Overview

[0042] This system consists primarily of servers, terminals, and users, and provides the following functions:

[0043] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[0044] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[0045] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[0046] 4. If a defect is detected, the server will notify the user of the defect and how to correct it.

[0047] 5. Correct the deficiencies pointed out by the user and resubmit.

[0048] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the business system.

[0049] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed.

[0050] Program Processing Description

[0051] 1. The user submits the application form and request form using a terminal. At this point, all necessary information should be entered, but there may be input errors or omissions.

[0052] 2. The server receives the application and request form data and first formats the data. This includes properly mapping the data to individual fields. For example, it distributes information to fields such as name, address, and phone number, and removes unnecessary spaces and special characters.

[0053] 3. The server inputs the formatted data into the AI ​​model. This AI model has learned from past data and can detect patterns of errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[0054] 4. If a problem is detected, the server will notify the user. The notification field will include the nature of the problem and specific instructions on how to fix it. For example, a message such as "The phone number is invalid. Please enter a number with 8 or more digits." might be sent.

[0055] 5. The user receives a notification from the server and revises the application form and request form. After revision, they resubmit them using their device.

[0056] 6. The server receives the corrected data again and performs a re-check. If the deficiencies are resolved, it proceeds to the next step.

[0057] 7. The server automatically inputs the data that has passed inspection into the business system. This process involves integrating with the business system using an API (Application Programming Interface).

[0058] 8. The server performs a final check of the data registered in the business system to verify the integrity and accuracy of the data.

[0059] 9. After the final check is complete, the server will notify the user that the registration has been successfully completed. This notification will also include a summary of the registered information.

[0060] Specific example

[0061] Examples of detecting and correcting errors in phone numbers

[0062] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[0063] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[0064] 3. The server uses an AI model to perform an inspection and detects an error in the phone number. It then sends a notification to the user stating, "The phone number must have at least 8 digits."

[0065] 4. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[0066] 5. The server receives the corrected data again and checks it using the AI ​​model once more.

[0067] 6. The server automatically inputs the data that has passed inspection into the business system.

[0068] 7. The server performs a final check to ensure that all data is accurate.

[0069] 8. The server notifies the user that mobile registration is complete.

[0070] In this way, the entire system operates efficiently, allowing for rapid detection, correction, and registration of user errors. This significantly reduces user effort and improves the efficiency and accuracy of the registration process.

[0071] The following describes the processing flow.

[0072] Step 1:

[0073] Users submit mobile registration application forms and requests via an online portal or mobile app using their devices. At this point, users enter all necessary information, but errors or omissions may occur.

[0074] Step 2:

[0075] The server receives application and request forms submitted by users. The received data includes names, addresses, phone numbers, and desired mobile plans.

[0076] Step 3:

[0077] The server analyzes the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[0078] Step 4:

[0079] The server inputs the formatted data into an AI model, which then detects errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect incorrect phone number formats or blank required fields.

[0080] Step 5:

[0081] The server notifies the user of any errors detected by the AI ​​model, including specific instructions on how to correct them. For example, a message might be sent such as, "The phone number is invalid. Please enter a number with 8 or more digits."

[0082] Step 6:

[0083] The user receives a notification from the server and corrects the identified deficiencies. After making the corrections, the user resubmits the application form and request form from their device.

[0084] Step 7:

[0085] The server receives the corrected data again and performs a re-examination. At this point, the AI ​​model is used again to check for any flaws in the formatted data.

[0086] Step 8:

[0087] The server automatically inputs data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[0088] Step 9:

[0089] The server re-imports the data registered in the business system and performs a final accuracy check. This confirms that all data is accurate.

[0090] Step 10:

[0091] The server confirms that all processing has been completed successfully and notifies the user that registration is complete. This notification also includes a summary of the registration details.

[0092] (Example 1)

[0093] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] Current online registration systems frequently suffer from input errors and missing information when users submit application forms and requests, often requiring manual corrections and resubmissions. This process is not only cumbersome and time-consuming for users but also places a burden on system administrators. Furthermore, manual checking and correction carries the risk of human error. Therefore, there is a growing demand for systems that automatically detect deficiencies and recommend corrections.

[0095] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0096] In this invention, the server includes means for acquiring application and request form data received from a user; means for formatting the acquired data and converting it into a format that is easy for an AI model to process; means for inputting the formatted data into the AI ​​model and detecting defects; means for notifying the user of the detected defects and how to correct them; means for inputting the corrected data or data that was free of defects from the beginning into the AI ​​model again and performing an inspection; means for automatically registering the data that has passed the inspection into a data management system; means for performing a final check of the registered data; and means for notifying the user that the registration is complete. This makes it possible to quickly and accurately detect defects in the data entered by the user and provide instructions for correction, thereby significantly improving the efficiency and accuracy of the registration process.

[0097] A "user" refers to an individual or legal entity that uses the system to submit application forms and requests.

[0098] "Application forms and request forms" refer to formal documents that users submit when registering or applying for services.

[0099] "Terminal" refers to a mobile device or computer used by a user to input application forms and requests and send them to the server.

[0100] A "server" refers to computing resources used to receive, process, and store data sent by users.

[0101] "Data" refers to the collection of information that users input and that servers process.

[0102] "Formatting" refers to the process of converting data received from a user into an appropriate format.

[0103] An "AI model" refers to a program that uses artificial intelligence technology to detect defects and analyze data.

[0104] "Deficiencies" refer to errors or missing information contained in application forms and request forms.

[0105] A "notification" refers to a message sent from a server to a user informing them of the nature of a problem and how to fix it.

[0106] "Re-examination" refers to the process in which corrected data is checked again by the AI ​​model.

[0107] A "data management system" refers to a system used to register and manage data that has passed inspection.

[0108] "Final check" refers to the inspection process to verify the accuracy and consistency of data registered in the data management system.

[0109] A "registration completion notification" refers to a message sent from the server to the user informing them that data registration has been successfully completed.

[0110] This invention is a system that automatically detects deficiencies in application forms and request forms submitted by users for mobile registration, recommends corrections as necessary, and then automatically inputs the information into the registration system. This section describes specific embodiments of the system.

[0111] System Configuration

[0112] This system consists primarily of servers, terminals, and users, and provides the following functions:

[0113] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[0114] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[0115] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[0116] 4. If a defect is detected, the server will notify the user of the defect and how to correct it.

[0117] 5. Correct the deficiencies pointed out by the user and resubmit.

[0118] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the data management system.

[0119] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed.

[0120] Hardware and software to be used

[0121] Device: Smartphone, tablet, or personal computer. This allows users to submit application forms and requests via an online portal or mobile app.

[0122] Server: Uses high-performance computing resources to receive, format, input into AI models, detect errors, send notifications, perform final checks, and automatically register data to the data management system.

[0123] AI model: A model trained using machine learning algorithms, which learns from past data and detects patterns of defects with high accuracy.

[0124] Data Management System: A database system that manages data after registration. It interacts with the server via an API.

[0125] Specific example

[0126] Examples of detecting and correcting errors in phone numbers

[0127] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[0128] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[0129] 3. The server uses an AI model to perform an inspection and detects any deficiencies in the phone number. It then sends a notification to the user stating, "The phone number must have at least 8 digits."

[0130] 4. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[0131] 5. The server receives the corrected data again and performs a re-inspection.

[0132] 6. The server automatically inputs the data that has passed the inspection into the data management system.

[0133] 7. The server performs a final check to ensure that all data is accurate.

[0134] 8. The server notifies the user that registration has been successfully completed.

[0135] Example of a prompt

[0136] Please enter the following information: Full Name: [First Name], Address: [Address], Phone Number: [Phone Number]. If there are any errors, please provide detailed feedback.

[0137] This system allows for the rapid detection, correction, and registration of user errors, significantly reducing user effort and improving the efficiency and accuracy of the registration process.

[0138] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0139] Step 1:

[0140] Users submit application and request forms from their devices via online portals or mobile apps. Input includes information such as name, address, and phone number. Specifically, they enter the required data into a form on a GUI (Graphical User Interface) and press the submit button.

[0141] Input: Information such as name, address, and phone number.

[0142] Output: Data transmission from terminal to server

[0143] Step 2:

[0144] The server receives data from the user. Specifically, a data receiving module captures the data sent from the terminal and temporarily stores it within the server.

[0145] Input: Application form and request form data sent from the terminal.

[0146] Output: Temporary storage of received data within the server.

[0147] Step 3:

[0148] The server formats the received data. Specifically, it distributes information into fields such as name, address, and phone number, and removes unnecessary spaces and special characters. It also converts the data into JSON or XML format.

[0149] Input: Temporarily saved application and request form data

[0150] Output: Formatted data (in JSON or XML format)

[0151] Step 4:

[0152] The server inputs the formatted data into an AI model, which then detects any errors. The AI ​​model has learned from past data and can detect things like whether the phone number format is correct and whether required fields are not left blank.

[0153] Input: Formatted data

[0154] Output: Results of defect detection (presence or absence of defects and details)

[0155] Step 5:

[0156] The server notifies the user of any detected vulnerabilities and how to correct them. For example, a message such as "Your phone number is invalid. Please enter a number with 8 or more digits." might be generated.

[0157] Input: Results of detecting defects

[0158] Output: Notification message to send to the user

[0159] Step 6:

[0160] The user receives a notification from the server and corrects any errors in the application and request forms. After correction, they resubmit the data. Specifically, this involves re-entering the correct information into the GUI form and pressing the submit button.

[0161] Input: Error notification from server

[0162] Output: Resubmission of corrected data

[0163] Step 7:

[0164] The server receives the corrected data again and performs another inspection using the AI ​​model. The same process is repeated to check for any further defects.

[0165] Input: Correction data resubmitted by the user

[0166] Output: Re-inspection results (presence or absence of defects)

[0167] Step 8:

[0168] The server automatically registers data that has passed re-inspection into the data management system. The data is then transferred to the management system using an API to complete the registration.

[0169] Input: Data that passed retesting

[0170] Output: Data registered in the data management system

[0171] Step 9:

[0172] The server performs a final check of the data registered in the data management system. It verifies the integrity and accuracy of the data and confirms that all data has been registered properly.

[0173] Input: Data registered in the data management system

[0174] Output: Final check results

[0175] Step 10:

[0176] The server notifies the user that registration is complete. The notification may include a message such as, "Registration completed successfully."

[0177] Input: Final check result

[0178] Output: Registration completion notification to send to the user

[0179] (Application Example 1)

[0180] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0181] In conventional systems, errors frequently occurred when users submitted online applications, and correcting these errors was time-consuming and cumbersome. Furthermore, the complex process of detecting and correcting errors raised concerns about a degraded user experience. There was also a need for an efficient system specifically designed for registration applications in a virtual environment.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0183] In this invention, the server includes means for a user to submit a registration application in a virtual environment using an electronic device, means for formatting the acquired information and converting it into a format that is easy for an artificial intelligence model to process, and means for inputting the formatted information into the artificial intelligence model and detecting errors. This makes it possible for users to submit online applications in a virtual environment easily and quickly.

[0184] A "user" is an individual or legal entity that uses the system to submit application documents.

[0185] An "application document" is a document submitted by a user for registration applications and requests.

[0186] "Information" refers to the data and content included in the application document.

[0187] "Formatting" refers to the process of converting acquired information into a format that is easily processed by artificial intelligence models.

[0188] An "artificial intelligence model" is a computer program used for machine learning and data analysis, and is particularly used to detect flaws and errors.

[0189] An "error" refers to inaccurate or incomplete information contained in the application document.

[0190] A "business platform" is a system that centrally manages registration application information and automates and streamlines related operations.

[0191] "Final check" is the process of ultimately verifying the consistency and accuracy of the registered information.

[0192] "Electronic devices" refer to electronic devices such as smartphones, tablets, or personal computers.

[0193] A "virtual environment" is an online environment that utilizes the internet and virtual reality technology.

[0194] "Message notifications" are a means of communicating information or correction instructions to users, and often involve using email or mobile apps.

[0195] This invention is a system that automatically detects the content of application documents submitted by users in a virtual environment, recommends corrections if errors are found, and automatically reflects the corrected information in the business platform once accurate information has been entered. The details of the system and its processes are described below.

[0196] System Configuration

[0197] This system consists of the following elements:

[0198] hardware

[0199] 1. Electronic devices: Users access the virtual environment and submit registration applications using electronic devices such as smartphones, tablets, or personal computers.

[0200] 2. Server: The central device that receives application document data and performs formatting, inspection, correction, registration, and final checks.

[0201] software

[0202] 1. Mobile application: An application used by users on electronic devices. It provides an interface for submitting and modifying application documents.

[0203] 2. Artificial Intelligence Models: AI models used to detect flaws. Specific examples include natural language processing models such as BERT and GPT.

[0204] 3. Business Platform: A system for managing registration application information and streamlining operations.

[0205] Details of the implementation

[0206] User submission of application documents

[0207] Users register in a virtual environment using electronic devices such as smartphones. Specifically, they input application documents through a mobile application and submit them to the server.

[0208] Data formatting and processing using AI models

[0209] The server formats the data from the application documents received from the user. Specifically, it appropriately allocates information to each field and removes unnecessary spaces and special characters. This formatted data is then input into an artificial intelligence model and checked for errors and deficiencies.

[0210] Detecting and recommending corrections for defects

[0211] Artificial intelligence models perform highly accurate error detection by learning from past data. For example, if the phone number format is incorrect, a notification will be sent to the user stating, "The phone number is invalid. Please enter a number with 8 or more digits." The user then corrects the data according to the instructions and resubmits it.

[0212] Automatic registration and final check

[0213] The server automatically registers data that has passed inspection into the business platform. After registration, a final check is performed to confirm that all data is accurate. This notifies the user that registration is complete.

[0214] Specific examples and prompt statements

[0215] Specific example:

[0216] 1. The user submits an application document with "example@com" entered in the email field.

[0217] 2. The server receives the data and inputs the formatted data into the artificial intelligence model.

[0218] 3. The server uses an AI model to perform an inspection and detects an error in the email address. It then sends a notification to the user stating, "The format of your email address is incorrect. Please enter the correct format."

[0219] 4. The user receives a notification from the server, corrects the email address to "example@example.com", and resubmits.

[0220] 5. The server receives the corrected data again and checks it using the AI ​​model once more.

[0221] 6. The server automatically inputs the data that has passed inspection into the business platform.

[0222] 7. The server performs a final check to ensure that all data is accurate.

[0223] 8. The server notifies the user that registration is complete.

[0224] Example of a prompt:

[0225] Please review the application forms submitted by users and detect any deficiencies. If deficiencies are found, please notify the user of the specific steps required to correct them.

[0226] These measures will enable users to submit online applications efficiently and quickly in a virtual environment. Furthermore, it is expected that the overall accuracy and reliability of the system will improve, contributing to a better user experience.

[0227] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0228] Step 1:

[0229] Users access a mobile application in a virtual environment using an electronic device (smartphone, tablet, or PC), and fill out and submit application documents.

[0230] Input: Data from the application document entered by the user.

[0231] Output: Data from application documents sent to the server via the mobile application.

[0232] Step 2:

[0233] The server formats the data from the received application documents. First, it appropriately allocates information to each field and removes unnecessary spaces and special characters.

[0234] Input: Raw data from the application document received from the user.

[0235] Output: Formatted data.

[0236] Specific actions: Information mapping to fields, removal of unnecessary spaces and special characters.

[0237] Step 3:

[0238] The server inputs the formatted data into an artificial intelligence model to detect errors. By learning from past data, the AI ​​model can identify flaws such as incorrect formatting of phone numbers and email addresses.

[0239] Input: Formatted data.

[0240] Output: Whether or not there are errors and details of the errors.

[0241] Specific actions: Data inspection and analysis using AI models.

[0242] Step 4:

[0243] The server will notify the user if any problems are detected. The notification will include specific instructions on how to fix the problem.

[0244] Input: Details of errors detected by the AI ​​model.

[0245] Output: Correction notification sent to the user.

[0246] Specific action: Sending notifications via email or mobile app.

[0247] Step 5:

[0248] The user receives a notification from the server and corrects any deficiencies in the application document. After correction, they resubmit it through the mobile application.

[0249] Input: Correction notification from the server.

[0250] Output: Resubmission of the revised application document.

[0251] Specific actions: Users can modify and resubmit application documents.

[0252] Step 6:

[0253] The server reformats the corrected data that has been re-received, inputs it into the AI ​​model, and performs a re-examination. If the deficiencies are resolved during the re-examination, it proceeds to the next step.

[0254] Input: User-submitted modification data.

[0255] Output: Data with errors corrected as a result of re-examination.

[0256] Specific actions: Data reshaping and re-examination using an AI model.

[0257] Step 7:

[0258] The server automatically registers data that passes inspection into the business platform. It interacts with the business platform via an API.

[0259] Input: Accurate data with all errors corrected.

[0260] Output: Data registered on the business platform.

[0261] Specific operation: Data registration to a business platform using an API.

[0262] Step 8:

[0263] The server performs a final check on the registered data to verify its integrity and accuracy. After the final check is complete, it sends a registration completion notification to the user.

[0264] Input: Data registered on the business platform.

[0265] Output: Registration completion notification sent to the user.

[0266] Specific actions: Final check of data and sending registration completion notifications to users.

[0267] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0268] This invention is a system that automatically detects whether there are any deficiencies in the application and request forms for mobile registration submitted by users, recommends corrections as necessary, and further provides feedback and support tailored to the user's emotional state. This section describes specific embodiments of the system.

[0269] System Overview

[0270] This system consists primarily of a server, terminal, user, and emotion engine, and provides the following functions:

[0271] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[0272] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[0273] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[0274] 4. If a defect is detected, the server will notify the user of the defect and how to correct it. At this time, the emotion engine will be used to analyze the user's emotional state and provide appropriate feedback.

[0275] 5. Correct the deficiencies pointed out by the user and resubmit.

[0276] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the business system.

[0277] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed. At this time, the emotion engine is also used to provide appropriate feedback.

[0278] Program Processing Description

[0279] 1. The user submits the application form and request form using a terminal. While all necessary information is entered, input errors or omissions may occur.

[0280] 2. The server receives the data from the application and request forms. The received data includes name, address, phone number, desired mobile plan, etc.

[0281] 3. The server formats the received data and appropriately maps it to each field (e.g., name, address, phone number, etc.). Also, data cleaning is performed to remove unnecessary spaces and special characters.

[0282] 4. The server inputs the formatted data into the AI model to detect deficiencies. The AI model has learned past data and can accurately discover deficiencies. For example, it detects cases where the phone number format is incorrect or where mandatory fields are blank.

[0283] 5. The server notifies the user of the deficiencies detected by the AI model. At this time, feedback including the content of the deficiencies and specific correction methods is notified, and the emotional state of the user is analyzed using an emotion engine to provide feedback accordingly. For example, when the user is feeling stressed, a considerate message such as "If the procedure is unclear, please refer to this detailed guide." is sent.

[0284] 6. The user receives the notification from the server and modifies the application form and request form. After modification, the user submits the application form and request form again from the terminal.

[0285] 7. The server receives the modified data again and performs a recheck. At this time, the AI model is used again to confirm whether there are any deficiencies in the formatted data.

[0286] 8. The server automatically registers the data that has passed the inspection into the business system. For this input process, an API (Application Programming Interface) is used to communicate with the business system.

[0287] 9. The server performs a final check on the data registered in the business system to confirm the integrity and accuracy of the data.

[0288] 10. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback based on the user's emotional state using the emotion engine.

[0289] Specific example

[0290] Examples of detecting errors in phone numbers and providing emotion-responsive feedback.

[0291] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[0292] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[0293] 3. The server uses an AI model to perform an inspection and detects any discrepancies in the phone number.

[0294] 4. The server uses an emotion engine to determine from the user's input process that the user is confused. In this case, it sends a helpful message such as, "Your phone number must be at least 8 digits long. Having trouble? You can find detailed instructions at this link."

[0295] 5. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[0296] 6. The server receives the corrected data again and checks it using the AI ​​model once more.

[0297] 7. The server automatically inputs the data that has passed inspection into the business system.

[0298] 8. The server performs a final check to ensure that all data is accurate.

[0299] 9. The server uses the emotion engine to determine that the user is in a secure state and sends a simple message saying, "Mobile registration has been completed. Thank you for your cooperation."

[0300] In this way, the system can operate efficiently, not only quickly detect, correct, and register user errors, but also improve the user experience by flexibly responding to the user's emotional state.

[0301] The following describes the processing flow.

[0302] Step 1:

[0303] The user uses the terminal to submit the mobile registration application form and request form through an online portal or mobile app. The user enters all the necessary information, but there may be input mistakes or omissions.

[0304] Step 2:

[0305] The server receives the data of the application form and request form submitted by the user. The received data includes name, address, phone number, desired mobile plan, etc.

[0306] Step 3:

[0307] The server analyzes the received data and appropriately maps it to each field (e.g., name, address, phone number, etc.). Also, data cleaning is performed to remove unnecessary spaces and special characters.

[0308] Step 4:

[0309] The server inputs the formatted data into the AI model to detect deficiencies. The AI model has learned past data and can discover deficiencies with high precision. For example, it detects cases where the phone number format is incorrect or where mandatory fields are blank.

[0310] Step 5:

[0311] The server notifies the user of any issues detected by the AI ​​model, including specific correction methods. During this process, an emotion engine is used to analyze the user's emotional state and generate appropriate feedback. For example, if the user is irritated, the message might be rephrased to be more polite and helpful.

[0312] Step 6:

[0313] The user receives a notification from the server and corrects the identified deficiencies. After making the corrections, they resubmit the application form and request form from their device.

[0314] Step 7:

[0315] The server receives the corrected data again and performs a re-examination. The AI ​​model is used again to check for any flaws in the formatted data.

[0316] Step 8:

[0317] The server automatically registers data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[0318] Step 9:

[0319] The server re-imports the data registered in the business system and performs a final accuracy check. This confirms that all data is accurate.

[0320] Step 10:

[0321] The server confirms that all processing has been completed successfully and notifies the user that registration is complete. This notification includes a summary of the registration details, but also uses an emotion engine to provide appropriate feedback based on the user's emotional state. For example, if the user is feeling reassured, a simple message such as "Mobile registration complete. Thank you for your cooperation." is sent.

[0322] (Example 2)

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

[0324] In conventional systems, if there were errors in application forms or requests submitted by users, corrections had to be made manually, which was time-consuming and laborious, and sometimes did not provide users with appropriate feedback. Furthermore, the lack of consideration for the user's emotional state resulted in a poor user experience. A system is needed to address these issues.

[0325] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0326] In this invention, the server includes means for acquiring application and request form data received from a user; means for formatting the acquired data and converting it into a format easily processed by a generation AI model; means for inputting the formatted data into the generation AI model and detecting defects; means for notifying the user of the detected defects and how to correct them; means for the notification means to analyze the user's emotional state in response to the detected defects and provide appropriate feedback; means for inputting the data corrected by the user or data that has been defect-free from the beginning into the generation AI model again and performing an inspection; means for automatically registering the data that has passed the inspection into the business system; means for performing a final check of the registered data; and means for notifying the user that the registration is complete. This makes it possible to quickly detect and correct defects in the user's application and request forms and to provide flexible feedback that corresponds to the user's emotional state.

[0327] A "user" refers to an individual or legal entity that accesses the system and submits application forms and requests.

[0328] A "server" refers to a computing system that processes data received from users, detects errors, provides feedback, registers data, and performs final checks.

[0329] An "application form" refers to an official document submitted by a user to apply for services such as mobile registration.

[0330] A "request form" refers to an official document submitted by a user to request a specific service or action.

[0331] "Means of acquiring data" refers to the function or process for receiving information from users in application forms and request forms.

[0332] "Means of formatting data" refers to a function or process for converting received data into a format that is easy for the generating AI model to process.

[0333] A "generative AI model" refers to an algorithmic model that uses machine learning and artificial intelligence technologies to detect data flaws with high accuracy.

[0334] "Means of inputting data into an AI model" refers to a function or process that sends formatted data to a generating AI model to detect defects.

[0335] "Means for detecting defects" refers to a function or process for finding defects in data using a generative AI model.

[0336] "Means of notifying users of how to correct defects" refers to a function or process for informing users of the nature of the detected defects and how to correct them.

[0337] "Means for analyzing emotional states" refers to a function or process for evaluating a user's emotional state and providing appropriate feedback based on that state.

[0338] "Means of re-inputting data into the AI ​​model and performing inspection" refers to a function or process of re-inputting corrected data into the generated AI model and performing a re-inspection.

[0339] "Means of automatically registering data in a business system" refers to a function or process for automatically inputting and saving data that has passed inspection into a business system.

[0340] "Means of final data checking" refers to a function or process for finally verifying the data registered in a business system to confirm its consistency and accuracy.

[0341] "Means of notifying that registration is complete" refers to a function or process that informs the user that data registration has been successfully completed.

[0342] Modes for carrying out the invention

[0343] This invention is a system that automatically detects whether there are any deficiencies in the application forms and request forms submitted by users, recommends corrections as necessary, and further provides feedback and support tailored to the user's emotional state.

[0344] This system primarily consists of a server, terminals, users, and an emotion engine. The roles and functions of each component are explained in detail below.

[0345] User

[0346] Users submit application and request forms through online portals or mobile applications using internet-connected devices (such as PCs and smartphones). While they enter all necessary information, errors or omissions may occur.

[0347] server

[0348] The server receives data submitted by users and performs a series of processes including data formatting, AI model detection of errors, sentiment analysis and feedback provision, data re-examination, automated registration, final check, and completion notification. The specific virtual machines and cloud services used are not platform-dependent, but AWS® and Microsoft® Azure® are commonly used.

[0349] 1. Receiving data: The server receives user input data via an HTTP request. This data includes name, address, phone number, desired mobile plan, etc.

[0350] 2. Data Formatting and Cleaning: Format the received data, map it appropriately to each field, and remove unnecessary spaces and special characters. Specifically, clean the data using string manipulation and regular expressions.

[0351] 3. Detection of errors: The formatted data is input into an AI model to detect any errors. Because the AI ​​model has learned from past data, it can detect errors such as misspellings in names, incorrect phone number formats, and missing required fields with high accuracy.

[0352] 4. Notification of defects and corrective methods: Users will be notified of defects detected by the AI ​​model. Feedback will be provided that includes the nature of the defect and specific corrective methods. The emotion engine will be used to analyze the user's emotional state and provide appropriate feedback. For example, if it is determined that the user is confused, consideration will be given to providing detailed guidance.

[0353] 5. Re-examination: The user's corrected data is received again and re-examined. The AI ​​model is used again for re-formatting and checking for errors in the data.

[0354] 6. Automatic Data Registration: Data that passes inspection is automatically registered in the business system. An API (Application Programming Interface) is used for integration with the business system.

[0355] 7. Final Check: Perform a final check of the data registered in the business system to confirm data integrity and accuracy.

[0356] 8. Registration Completion Notification: After the final check is complete, the server notifies the user that registration is complete. Using the emotion engine, appropriate feedback is provided according to the user's emotional state.

[0357] Specific example

[0358] Examples of detecting errors in phone numbers and providing emotion-responsive feedback.

[0359] 1. The user enters "12345" in the phone number field and submits the application form.

[0360] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[0361] 3. The server uses an AI model to perform an inspection and detects any discrepancies in the phone number.

[0362] 4. The server uses an emotion engine to determine from the user's input process that the user is confused. In this case, it sends a helpful message such as, "Your phone number must be at least 8 digits long. Having trouble? You can find detailed instructions at this link."

[0363] 5. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[0364] 6. The server receives the corrected data again and checks it using the AI ​​model once more.

[0365] 7. The server automatically inputs the data that has passed inspection into the business system.

[0366] 8. The server performs a final check to ensure that all data is accurate.

[0367] 9. The server uses an emotion engine to determine that the user is in a comfortable state and sends a simple message: "Mobile registration complete. Thank you for your cooperation."

[0368] Examples of prompt statements

[0369] "There is an error in the phone number field on your application form. Please enter a phone number with 8 or more digits."

[0370] As described above, the system operates efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Program processing flow

[0373] (Step 1:)

[0374] Users fill out application and request forms using their devices and submit them through an online portal or mobile application.

[0375] Input: Name, address, phone number, desired mobile plan, and other information.

[0376] Output: Data from application forms and request forms submitted by the user.

[0377] Specific actions:

[0378] The user enters the necessary information on the device and clicks the "Submit" button.

[0379] (Step 2:)

[0380] The server receives data submitted by the user.

[0381] Input: Data from application forms and request forms submitted by the user.

[0382] Output: Received data

[0383] Specific actions:

[0384] The server parses the HTTP request and retrieves the user input data contained within it.

[0385] (Step 3:)

[0386] The server formats and cleans the data it receives.

[0387] Input: Received data

[0388] Output: Formatted and cleaned data

[0389] Specific actions:

[0390] The server splits the data into fields and removes unnecessary spaces and special characters. For example, it converts "123-456-7890" to "1234567890" and maps each field appropriately.

[0391] (Step 4:)

[0392] The server generates formatted data, which is then input into an AI model for error detection.

[0393] Input: Formatted and cleaned data

[0394] Output: Results indicating whether or not there are defects.

[0395] Specific actions:

[0396] The server passes the formatted data to the AI ​​model, which checks for typos in names, formatting errors in phone numbers, and missing required fields.

[0397] (Step 5:)

[0398] The server notifies users of any detected vulnerabilities and how to fix them, and provides emotionally responsive feedback.

[0399] Input: Result indicating whether or not there are defects.

[0400] Output: User notification of the deficiency and how to fix it, and sentiment feedback.

[0401] Specific actions:

[0402] The server notifies the user of any problems, providing information on the detected problems and how to fix them. An emotion engine is used to analyze the user's emotional state and provide appropriate feedback.

[0403] (Step 6:)

[0404] The user corrects the reported deficiencies and resubmits the application and request forms.

[0405] Input: Data from the revised application and request forms.

[0406] Output: Resubmitted data

[0407] Specific actions:

[0408] The user receives a notification from the server, makes the necessary corrections, and clicks the "Submit" button again.

[0409] (Step 7:)

[0410] The server receives the corrected data again and performs a re-examination.

[0411] Input: Resubmitted data

[0412] Output: Re-examined data

[0413] Specific actions:

[0414] The server receives the corrected data again and uses the AI ​​model to check for any flaws in the formatted data once more.

[0415] (Step 8:)

[0416] The server automatically registers data that has passed inspection into the business system.

[0417] Input: Retested data

[0418] Output: Data registered in the business system

[0419] Specific actions:

[0420] Use an API to send data to a business system and register it automatically.

[0421] (Step 9:)

[0422] The server performs a final check on the data registered in the business system.

[0423] Input: Data registered in the business system

[0424] Output: Final check results

[0425] Specific actions:

[0426] The registered information in the database will be checked again for consistency and accuracy.

[0427] (Step 10:)

[0428] The server notifies the user that registration is complete and provides emotionally appropriate feedback.

[0429] Input: Final check result

[0430] Output: Completion message notified to the user

[0431] Specific actions:

[0432] The server sends the user a success message and appropriate feedback based on the emotion engine. For example, it might send a message like, "Mobile registration complete. Thank you for your cooperation."

[0433] (Application Example 2)

[0434] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0435] Traditional user registration systems perform error checking on user-entered information, but the feedback provided when errors occur is insufficient, and support that takes into account the user's emotional state is not offered, resulting in a poor user experience. Furthermore, the accuracy of error checking and the methods for providing corrections remain as challenges.

[0436] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring application form and request form data received from the user, means for formatting the acquired data and converting it into a format that is easy for the generating AI model to process, means for inputting the formatted data into the generating AI model and detecting defects, means for notifying the user of the detected defects and how to correct them, means for sentiment analysis for analyzing the user's emotional state, means for providing feedback according to the user's emotional state using the sentiment analysis means, means for inputting the corrected data or data that has no defects from the beginning into the generating AI model again and performing an inspection, means for automatically registering the data that has passed the inspection into the business system, means for performing a final check of the registered data, and means for notifying the user that the registration is complete. As a result, the accuracy of error checking and error correction during user input is improved, and the user experience can be improved by providing appropriate feedback according to the user's emotional state.

[0437] "User" refers to a person who uses the system to submit an application form or request form.

[0438] An "application form" refers to a document containing the information necessary for a user to register for the service.

[0439] A "request form" refers to a document submitted by a user to request a specific service or product.

[0440] A "server" is a computer system that processes data received from users and interacts with other systems.

[0441] A "generative AI model" refers to an artificial intelligence model that learns from past data and is used to detect flaws in new input data.

[0442] "Formatting" refers to the process of converting acquired data into a format that is easy for the generating AI model to process.

[0443] "Emotional analysis methods" refer to methods for analyzing a user's emotional state based on their input information and behavior.

[0444] A "business system" refers to a system that uses registered data to perform actual business operations.

[0445] "Notification methods" refer to means of communicating information to users, such as defects or how to correct them.

[0446] This invention is a system that automatically detects the contents of application forms and request forms submitted by users, recommends revisions as necessary, and further provides feedback and support tailored to the user's emotional state.

[0447] System Overview

[0448] This system consists primarily of a server, terminals, and sentiment analysis tools, and implements the following functions.

[0449] 1. Users submit application forms and requests via online portals or mobile apps using their devices. While users enter all necessary information, input errors or omissions may occur.

[0450] 2. The server retrieves application and request forms received from users, formats the data, and converts it into a format that is easy for the generating AI model to process. This data includes names, addresses, phone numbers, and desired services.

[0451] 3. The server inputs the formatted data into a generation AI model to detect errors. The generation AI model has learned from past data and is capable of detecting errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[0452] 4. The server analyzes the user's emotional state using sentiment analysis tools and provides appropriate feedback if any issues are detected. For example, if it determines that the user is experiencing stress, it will send a considerate message such as, "If the procedure is unclear, please refer to this detailed guide."

[0453] 5. The user receives a notification from the server and revises the application form and request form. After revision, the user resubmits the application form and request form from their device.

[0454] 6. The server receives the corrected data again and performs a re-examination. At this time, it checks whether there are any defects in the data that has been formatted using the generated AI model again.

[0455] 7. The server automatically registers the data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[0456] 8. The server performs a final check to ensure that all data is accurate. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback tailored to the user's emotional state using sentiment analysis tools.

[0457] Hardware and software to be used

[0458] This system uses the following hardware and software.

[0459] Server: This server collects, formats, and generates data, runs AI models, and notifies the results. Specifically, it uses a web server framework such as Flask.

[0460] Generative AI model: Detects defects based on data received from the user. Specifically, it utilizes the BERT or BART model from the transformers library.

[0461] Sentiment analysis method: The emotional state is analyzed from the user's input information and actions. Specifically, the sentiment analysis model (facebook / bart-large-mnli) from the transformers library is used.

[0462] Terminal: A device used by users to submit application forms and requests. Specifically, this includes smartphones and PCs.

[0463] Specific example

[0464] For example, if the user enters the following:

[0465] Name: Taro Yamada

[0466] Email: yamada@example

[0467] Phone: 12345

[0468] The server receives the data, formats it, and inputs it into a generative AI model. The generative AI model detects any errors in the phone number and analyzes the user's state using sentiment analysis. If the sentiment analysis determines that the user is confused, the server sends a considerate message such as, "The phone number must be at least 10 digits long. Are you having trouble? You can find detailed instructions at this link."

[0469] Example of a prompt

[0470] text

[0471] emotion_analyzer('Username: Taro Yamada, Email: yamada@example, Phone: 12345')

[0472] In this way, the system can operate efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[0473] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0474] Step 1:

[0475] Users submit application and request forms via an online portal or mobile app using their device (smartphone or PC). At this stage, users enter all necessary information, including their name, address, phone number, and desired services. The device then transmits this information to the server.

[0476] Step 2:

[0477] The server retrieves application and request form data received from users. The retrieved data is received in a format such as JSON. The server then formats this data and converts it into a format that is easy for the generating AI model to process. Specifically, it performs data cleaning, such as appropriately mapping the data fields and removing unnecessary spaces and special characters.

[0478] Step 3:

[0479] The server inputs the formatted data into a generative AI model for error detection. The generative AI model has learned from past data and can detect errors with high accuracy. At this stage, the input is formatted data, and the output is information about the presence or absence of errors and their details. For example, it can detect incorrect phone number formats or blank required fields.

[0480] Step 4:

[0481] The server analyzes the user's emotional state using an emotion analysis tool. Specifically, it inputs the user's input string as a prompt into an emotion analysis model (facebook / bart-large-mnli) to determine whether the emotion is positive, negative, or neutral. The input is the user's input string, and the output is the result of the emotion determination.

[0482] Example of a prompt

[0483] text

[0484] emotion_analyzer('Username: Taro Yamada, Email: yamada@example, Phone: 12345')

[0485] Step 5:

[0486] The server generates appropriate feedback and notifies the user based on the details of the detected flaws and the results of sentiment analysis. This specific feedback includes instructions on how to correct the flaws and messages tailored to the user's emotional state (e.g., "Phone numbers must be at least 10 digits long. Need help? You can find detailed instructions at this link."). The input is the flaw details and sentiment analysis results, and the output is the appropriate feedback message.

[0487] Step 6:

[0488] The user receives a notification from the server and modifies the application and request forms. After modification, the user resubmits the application and request forms from the terminal. The terminal then resends the modified data to the server.

[0489] Step 7:

[0490] The server receives the corrected data again and performs a re-examination. At this stage, it checks for any flaws in the formatted data using the generation AI model again. The input is the corrected data, and the output is the result of the second flaw detection.

[0491] Step 8:

[0492] The server automatically registers data that has passed inspection into the business system. This process utilizes an API (Application Programming Interface) to interact with the business system. The input is the data that has passed inspection, and the output is the result of registration into the business system.

[0493] Step 9:

[0494] The server performs a final check of the data registered in the business system to verify its integrity and accuracy. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback tailored to the user's emotional state using sentiment analysis tools. The input is the result of the data registered in the business system, and the output is the registration completion notification.

[0495] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0496] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0497] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0498] [Second Embodiment]

[0499] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0500] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0501] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0502] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0503] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0504] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0505] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0506] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0507] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0508] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0509] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0510] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0511] This invention is a system that automatically detects deficiencies in application forms and request forms submitted by users for mobile registration, recommends corrections as necessary, and then automatically inputs the information into the registration system. This section describes specific embodiments of the system.

[0512] System Overview

[0513] This system consists primarily of servers, terminals, and users, and provides the following functions:

[0514] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[0515] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[0516] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[0517] 4. If a defect is detected, the server will notify the user of the defect and how to correct it.

[0518] 5. Correct the deficiencies pointed out by the user and resubmit.

[0519] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the business system.

[0520] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed.

[0521] Program Processing Description

[0522] 1. The user submits the application form and request form using a terminal. At this point, all necessary information should be entered, but there may be input errors or omissions.

[0523] 2. The server receives the application and request form data and first formats the data. This includes properly mapping the data to individual fields. For example, it distributes information to fields such as name, address, and phone number, and removes unnecessary spaces and special characters.

[0524] 3. The server inputs the formatted data into the AI ​​model. This AI model has learned from past data and can detect patterns of errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[0525] 4. If a problem is detected, the server will notify the user. The notification field will include the nature of the problem and specific instructions on how to fix it. For example, a message such as "The phone number is invalid. Please enter a number with 8 or more digits." might be sent.

[0526] 5. The user receives a notification from the server and revises the application form and request form. After revision, they resubmit them using their device.

[0527] 6. The server receives the corrected data again and performs a re-check. If the deficiencies are resolved, it proceeds to the next step.

[0528] 7. The server automatically inputs the data that has passed inspection into the business system. This process involves integrating with the business system using an API (Application Programming Interface).

[0529] 8. The server performs a final check of the data registered in the business system to verify the integrity and accuracy of the data.

[0530] 9. After the final check is complete, the server will notify the user that the registration has been successfully completed. This notification will also include a summary of the registered information.

[0531] Specific example

[0532] Examples of detecting and correcting errors in phone numbers

[0533] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[0534] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[0535] 3. The server uses an AI model to perform an inspection and detects an error in the phone number. It then sends a notification to the user stating, "The phone number must have at least 8 digits."

[0536] 4. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[0537] 5. The server receives the corrected data again and checks it using the AI ​​model once more.

[0538] 6. The server automatically inputs the data that has passed inspection into the business system.

[0539] 7. The server performs a final check to ensure that all data is accurate.

[0540] 8. The server notifies the user that mobile registration is complete.

[0541] In this way, the entire system operates efficiently, allowing for rapid detection, correction, and registration of user errors. This significantly reduces user effort and improves the efficiency and accuracy of the registration process.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] Users submit mobile registration application forms and requests via an online portal or mobile app using their devices. At this point, users enter all necessary information, but errors or omissions may occur.

[0545] Step 2:

[0546] The server receives application and request forms submitted by users. The received data includes names, addresses, phone numbers, and desired mobile plans.

[0547] Step 3:

[0548] The server analyzes the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[0549] Step 4:

[0550] The server inputs the formatted data into an AI model, which then detects errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect incorrect phone number formats or blank required fields.

[0551] Step 5:

[0552] The server notifies the user of any errors detected by the AI ​​model, including specific instructions on how to correct them. For example, a message might be sent such as, "The phone number is invalid. Please enter a number with 8 or more digits."

[0553] Step 6:

[0554] The user receives a notification from the server and corrects the identified deficiencies. After making the corrections, the user resubmits the application form and request form from their device.

[0555] Step 7:

[0556] The server receives the corrected data again and performs a re-examination. At this point, the AI ​​model is used again to check for any flaws in the formatted data.

[0557] Step 8:

[0558] The server automatically inputs data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[0559] Step 9:

[0560] The server re-imports the data registered in the business system and performs a final accuracy check. This confirms that all data is accurate.

[0561] Step 10:

[0562] The server confirms that all processing has been completed successfully and notifies the user that registration is complete. This notification also includes a summary of the registration details.

[0563] (Example 1)

[0564] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0565] Current online registration systems frequently suffer from input errors and missing information when users submit application forms and requests, often requiring manual corrections and resubmissions. This process is not only cumbersome and time-consuming for users but also places a burden on system administrators. Furthermore, manual checking and correction carries the risk of human error. Therefore, there is a growing demand for systems that automatically detect deficiencies and recommend corrections.

[0566] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0567] In this invention, the server includes means for acquiring application and request form data received from a user; means for formatting the acquired data and converting it into a format that is easy for an AI model to process; means for inputting the formatted data into the AI ​​model and detecting defects; means for notifying the user of the detected defects and how to correct them; means for inputting the corrected data or data that was free of defects from the beginning into the AI ​​model again and performing an inspection; means for automatically registering the data that has passed the inspection into a data management system; means for performing a final check of the registered data; and means for notifying the user that the registration is complete. This makes it possible to quickly and accurately detect defects in the data entered by the user and provide instructions for correction, thereby significantly improving the efficiency and accuracy of the registration process.

[0568] A "user" refers to an individual or legal entity that uses the system to submit application forms and requests.

[0569] "Application forms and request forms" refer to formal documents that users submit when registering or applying for services.

[0570] "Terminal" refers to a mobile device or computer used by a user to input application forms and requests and send them to the server.

[0571] A "server" refers to computing resources used to receive, process, and store data sent by users.

[0572] "Data" refers to the collection of information that users input and that servers process.

[0573] "Formatting" refers to the process of converting data received from a user into an appropriate format.

[0574] An "AI model" refers to a program that uses artificial intelligence technology to detect defects and analyze data.

[0575] "Deficiencies" refer to errors or missing information contained in application forms and request forms.

[0576] A "notification" refers to a message sent from a server to a user informing them of the nature of a problem and how to fix it.

[0577] "Re-examination" refers to the process in which corrected data is checked again by the AI ​​model.

[0578] A "data management system" refers to a system used to register and manage data that has passed inspection.

[0579] "Final check" refers to the inspection process to verify the accuracy and consistency of data registered in the data management system.

[0580] A "registration completion notification" refers to a message sent from the server to the user informing them that data registration has been successfully completed.

[0581] This invention is a system that automatically detects deficiencies in application forms and request forms submitted by users for mobile registration, recommends corrections as necessary, and then automatically inputs the information into the registration system. This section describes specific embodiments of the system.

[0582] System Configuration

[0583] This system consists primarily of servers, terminals, and users, and provides the following functions:

[0584] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[0585] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[0586] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[0587] 4. If a defect is detected, the server will notify the user of the defect and how to correct it.

[0588] 5. Correct the deficiencies pointed out by the user and resubmit.

[0589] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the data management system.

[0590] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed.

[0591] Hardware and software to be used

[0592] Device: Smartphone, tablet, or personal computer. This allows users to submit application forms and requests via an online portal or mobile app.

[0593] Server: Uses high-performance computing resources to receive, format, input into AI models, detect errors, send notifications, perform final checks, and automatically register data to the data management system.

[0594] AI model: A model trained using machine learning algorithms, which learns from past data and detects patterns of defects with high accuracy.

[0595] Data Management System: A database system that manages data after registration. It interacts with the server via an API.

[0596] Specific example

[0597] Examples of detecting and correcting errors in phone numbers

[0598] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[0599] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[0600] 3. The server uses an AI model to perform an inspection and detects any deficiencies in the phone number. It then sends a notification to the user stating, "The phone number must have at least 8 digits."

[0601] 4. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[0602] 5. The server receives the corrected data again and performs a re-inspection.

[0603] 6. The server automatically inputs the data that has passed the inspection into the data management system.

[0604] 7. The server performs a final check to ensure that all data is accurate.

[0605] 8. The server notifies the user that registration has been successfully completed.

[0606] Example of a prompt

[0607] Please enter the following information: Full Name: [First Name], Address: [Address], Phone Number: [Phone Number]. If there are any errors, please provide detailed feedback.

[0608] This system allows for the rapid detection, correction, and registration of user errors, significantly reducing user effort and improving the efficiency and accuracy of the registration process.

[0609] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0610] Step 1:

[0611] Users submit application and request forms from their devices via online portals or mobile apps. Input includes information such as name, address, and phone number. Specifically, they enter the required data into a form on a GUI (Graphical User Interface) and press the submit button.

[0612] Input: Information such as name, address, and phone number.

[0613] Output: Data transmission from terminal to server

[0614] Step 2:

[0615] The server receives data from the user. Specifically, a data receiving module captures the data sent from the terminal and temporarily stores it within the server.

[0616] Input: Application form and request form data sent from the terminal.

[0617] Output: Temporary storage of received data within the server.

[0618] Step 3:

[0619] The server formats the received data. Specifically, it distributes information into fields such as name, address, and phone number, and removes unnecessary spaces and special characters. It also converts the data into JSON or XML format.

[0620] Input: Temporarily saved application and request form data

[0621] Output: Formatted data (in JSON or XML format)

[0622] Step 4:

[0623] The server inputs the formatted data into an AI model, which then detects any errors. The AI ​​model has learned from past data and can detect things like whether the phone number format is correct and whether required fields are not left blank.

[0624] Input: Formatted data

[0625] Output: Results of defect detection (presence or absence of defects and details)

[0626] Step 5:

[0627] The server notifies the user of any detected vulnerabilities and how to correct them. For example, a message such as "Your phone number is invalid. Please enter a number with 8 or more digits." might be generated.

[0628] Input: Results of detecting defects

[0629] Output: Notification message to send to the user

[0630] Step 6:

[0631] The user receives a notification from the server and corrects any errors in the application and request forms. After correction, they resubmit the data. Specifically, this involves re-entering the correct information into the GUI form and pressing the submit button.

[0632] Input: Error notification from server

[0633] Output: Resubmission of corrected data

[0634] Step 7:

[0635] The server receives the corrected data again and performs another inspection using the AI ​​model. The same process is repeated to check for any further defects.

[0636] Input: Correction data resubmitted by the user

[0637] Output: Re-inspection results (presence or absence of defects)

[0638] Step 8:

[0639] The server automatically registers data that has passed re-inspection into the data management system. The data is then transferred to the management system using an API to complete the registration.

[0640] Input: Data that passed retesting

[0641] Output: Data registered in the data management system

[0642] Step 9:

[0643] The server performs a final check of the data registered in the data management system. It verifies the integrity and accuracy of the data and confirms that all data has been registered properly.

[0644] Input: Data registered in the data management system

[0645] Output: Final check results

[0646] Step 10:

[0647] The server notifies the user that registration is complete. The notification may include a message such as, "Registration completed successfully."

[0648] Input: Final check result

[0649] Output: Registration completion notification to send to the user

[0650] (Application Example 1)

[0651] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0652] In conventional systems, errors frequently occurred when users submitted online applications, and correcting these errors was time-consuming and cumbersome. Furthermore, the complex process of detecting and correcting errors raised concerns about a degraded user experience. There was also a need for an efficient system specifically designed for registration applications in a virtual environment.

[0653] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0654] In this invention, the server includes means for a user to submit a registration application in a virtual environment using an electronic device, means for formatting the acquired information and converting it into a format that is easy for an artificial intelligence model to process, and means for inputting the formatted information into the artificial intelligence model and detecting errors. This makes it possible for users to submit online applications in a virtual environment easily and quickly.

[0655] A "user" is an individual or legal entity that uses the system to submit application documents.

[0656] An "application document" is a document submitted by a user for registration applications and requests.

[0657] "Information" refers to the data and content included in the application document.

[0658] "Formatting" refers to the process of converting acquired information into a format that is easily processed by artificial intelligence models.

[0659] An "artificial intelligence model" is a computer program used for machine learning and data analysis, and is particularly used to detect flaws and errors.

[0660] An "error" refers to inaccurate or incomplete information contained in the application document.

[0661] A "business platform" is a system that centrally manages registration application information and automates and streamlines related operations.

[0662] "Final check" is the process of ultimately verifying the consistency and accuracy of the registered information.

[0663] "Electronic devices" refer to electronic devices such as smartphones, tablets, or personal computers.

[0664] A "virtual environment" is an online environment that utilizes the internet and virtual reality technology.

[0665] "Message notifications" are a means of communicating information or correction instructions to users, and often involve using email or mobile apps.

[0666] This invention is a system that automatically detects the content of application documents submitted by users in a virtual environment, recommends corrections if errors are found, and automatically reflects the corrected information in the business platform once accurate information has been entered. The details of the system and its processes are described below.

[0667] System Configuration

[0668] This system consists of the following elements:

[0669] hardware

[0670] 1. Electronic devices: Users access the virtual environment and submit registration applications using electronic devices such as smartphones, tablets, or personal computers.

[0671] 2. Server: The central device that receives application document data and performs formatting, inspection, correction, registration, and final checks.

[0672] software

[0673] 1. Mobile application: An application used by users on electronic devices. It provides an interface for submitting and modifying application documents.

[0674] 2. Artificial Intelligence Models: AI models used to detect flaws. Specific examples include natural language processing models such as BERT and GPT.

[0675] 3. Business Platform: A system for managing registration application information and streamlining operations.

[0676] Details of the implementation

[0677] User submission of application documents

[0678] Users register in a virtual environment using electronic devices such as smartphones. Specifically, they input application documents through a mobile application and submit them to the server.

[0679] Data formatting and processing using AI models

[0680] The server formats the data from the application documents received from the user. Specifically, it appropriately allocates information to each field and removes unnecessary spaces and special characters. This formatted data is then input into an artificial intelligence model and checked for errors and deficiencies.

[0681] Detecting and recommending corrections for defects

[0682] Artificial intelligence models perform highly accurate error detection by learning from past data. For example, if the phone number format is incorrect, a notification will be sent to the user stating, "The phone number is invalid. Please enter a number with 8 or more digits." The user then corrects the data according to the instructions and resubmits it.

[0683] Automatic registration and final check

[0684] The server automatically registers data that has passed inspection into the business platform. After registration, a final check is performed to confirm that all data is accurate. This notifies the user that registration is complete.

[0685] Specific examples and prompt statements

[0686] Specific example:

[0687] 1. The user submits an application document with "example@com" entered in the email field.

[0688] 2. The server receives the data and inputs the formatted data into the artificial intelligence model.

[0689] 3. The server uses an AI model to perform an inspection and detects an error in the email address. It then sends a notification to the user stating, "The format of your email address is incorrect. Please enter the correct format."

[0690] 4. The user receives a notification from the server, corrects the email address to "example@example.com", and resubmits.

[0691] 5. The server receives the corrected data again and checks it using the AI ​​model once more.

[0692] 6. The server automatically inputs the data that has passed inspection into the business platform.

[0693] 7. The server performs a final check to ensure that all data is accurate.

[0694] 8. The server notifies the user that registration is complete.

[0695] Example of a prompt:

[0696] Please review the application forms submitted by users and detect any deficiencies. If deficiencies are found, please notify the user of the specific steps required to correct them.

[0697] These measures will enable users to submit online applications efficiently and quickly in a virtual environment. Furthermore, it is expected that the overall accuracy and reliability of the system will improve, contributing to a better user experience.

[0698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0699] Step 1:

[0700] Users access a mobile application in a virtual environment using an electronic device (smartphone, tablet, or PC), and fill out and submit application documents.

[0701] Input: Data from the application document entered by the user.

[0702] Output: Data from application documents sent to the server via the mobile application.

[0703] Step 2:

[0704] The server formats the data from the received application documents. First, it appropriately allocates information to each field and removes unnecessary spaces and special characters.

[0705] Input: Raw data from the application document received from the user.

[0706] Output: Formatted data.

[0707] Specific actions: Information mapping to fields, removal of unnecessary spaces and special characters.

[0708] Step 3:

[0709] The server inputs the formatted data into an artificial intelligence model to detect errors. By learning from past data, the AI ​​model can identify flaws such as incorrect formatting of phone numbers and email addresses.

[0710] Input: Formatted data.

[0711] Output: Whether or not there are errors and details of the errors.

[0712] Specific actions: Data inspection and analysis using AI models.

[0713] Step 4:

[0714] The server will notify the user if any problems are detected. The notification will include specific instructions on how to fix the problem.

[0715] Input: Details of errors detected by the AI ​​model.

[0716] Output: Correction notification sent to the user.

[0717] Specific action: Sending notifications via email or mobile app.

[0718] Step 5:

[0719] The user receives a notification from the server and corrects any deficiencies in the application document. After correction, they resubmit it through the mobile application.

[0720] Input: Correction notification from the server.

[0721] Output: Resubmission of the revised application document.

[0722] Specific actions: Users can modify and resubmit application documents.

[0723] Step 6:

[0724] The server reformats the corrected data that has been re-received, inputs it into the AI ​​model, and performs a re-examination. If the deficiencies are resolved during the re-examination, it proceeds to the next step.

[0725] Input: User-submitted modification data.

[0726] Output: Data with errors corrected as a result of re-examination.

[0727] Specific actions: Data reshaping and re-examination using an AI model.

[0728] Step 7:

[0729] The server automatically registers data that passes inspection into the business platform. It interacts with the business platform via an API.

[0730] Input: Accurate data with all errors corrected.

[0731] Output: Data registered on the business platform.

[0732] Specific operation: Data registration to a business platform using an API.

[0733] Step 8:

[0734] The server performs a final check on the registered data to verify its integrity and accuracy. After the final check is complete, it sends a registration completion notification to the user.

[0735] Input: Data registered on the business platform.

[0736] Output: Registration completion notification sent to the user.

[0737] Specific actions: Final check of data and sending registration completion notifications to users.

[0738] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0739] This invention is a system that automatically detects whether there are any deficiencies in the application and request forms for mobile registration submitted by users, recommends corrections as necessary, and further provides feedback and support tailored to the user's emotional state. This section describes specific embodiments of the system.

[0740] System Overview

[0741] This system consists primarily of a server, terminal, user, and emotion engine, and provides the following functions:

[0742] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[0743] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[0744] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[0745] 4. If a defect is detected, the server will notify the user of the defect and how to correct it. At this time, the emotion engine will be used to analyze the user's emotional state and provide appropriate feedback.

[0746] 5. Correct the deficiencies pointed out by the user and resubmit.

[0747] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the business system.

[0748] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed. At this time, the emotion engine is also used to provide appropriate feedback.

[0749] Program Processing Description

[0750] 1. The user submits the application form and request form using a terminal. While all necessary information is entered, input errors or omissions may occur.

[0751] 2. The server receives the data from the application and request forms. The received data includes name, address, phone number, desired mobile plan, etc.

[0752] 3. The server formats the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[0753] 4. The server inputs the formatted data into the AI ​​model to detect errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[0754] 5. The server notifies the user of any deficiencies detected by the AI ​​model. This notification includes feedback detailing the deficiency and specific correction methods. Furthermore, the system uses an emotion engine to analyze the user's emotional state and provide appropriate feedback. For example, if the user is feeling stressed, a considerate message such as, "If the steps are unclear, please refer to this detailed guide," is sent.

[0755] 6. The user receives a notification from the server and revises the application form and request form. After revision, the user resubmits the application form and request form from their device.

[0756] 7. The server receives the corrected data again and performs a re-examination. At this time, the AI ​​model is used again to check for any defects in the formatted data.

[0757] 8. The server automatically registers the data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[0758] 9. The server performs a final check of the data registered in the business system to verify the integrity and accuracy of the data.

[0759] 10. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback based on the user's emotional state using the emotion engine.

[0760] Specific example

[0761] Examples of detecting errors in phone numbers and providing emotion-responsive feedback.

[0762] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[0763] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[0764] 3. The server uses an AI model to perform an inspection and detects any discrepancies in the phone number.

[0765] 4. The server uses an emotion engine to determine from the user's input process that the user is confused. In this case, it sends a helpful message such as, "Your phone number must be at least 8 digits long. Having trouble? You can find detailed instructions at this link."

[0766] 5. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[0767] 6. The server receives the corrected data again and checks it using the AI ​​model once more.

[0768] 7. The server automatically inputs the data that has passed inspection into the business system.

[0769] 8. The server performs a final check to ensure that all data is accurate.

[0770] 9. The server uses an emotion engine to determine that the user is in a comfortable state and sends a simple message: "Mobile registration complete. Thank you for your cooperation."

[0771] In this way, the system can operate efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[0772] The following describes the processing flow.

[0773] Step 1:

[0774] Users submit mobile registration application forms and requests via an online portal or mobile app using their devices. While users enter all necessary information, errors or omissions may occur.

[0775] Step 2:

[0776] The server receives application and request forms submitted by users. The received data includes names, addresses, phone numbers, and desired mobile plans.

[0777] Step 3:

[0778] The server analyzes the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[0779] Step 4:

[0780] The server inputs the formatted data into an AI model, which then detects errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect incorrect phone number formats or blank required fields.

[0781] Step 5:

[0782] The server notifies the user of any issues detected by the AI ​​model, including specific correction methods. During this process, an emotion engine is used to analyze the user's emotional state and generate appropriate feedback. For example, if the user is irritated, the message might be rephrased to be more polite and helpful.

[0783] Step 6:

[0784] The user receives a notification from the server and corrects the identified deficiencies. After making the corrections, they resubmit the application form and request form from their device.

[0785] Step 7:

[0786] The server receives the corrected data again and performs a re-examination. The AI ​​model is used again to check for any flaws in the formatted data.

[0787] Step 8:

[0788] The server automatically registers data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[0789] Step 9:

[0790] The server re-imports the data registered in the business system and performs a final accuracy check. This confirms that all data is accurate.

[0791] Step 10:

[0792] The server confirms that all processing has been completed successfully and notifies the user that registration is complete. This notification includes a summary of the registration details, but also uses an emotion engine to provide appropriate feedback based on the user's emotional state. For example, if the user is feeling reassured, a simple message such as "Mobile registration complete. Thank you for your cooperation." is sent.

[0793] (Example 2)

[0794] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0795] In conventional systems, if there were errors in application forms or requests submitted by users, corrections had to be made manually, which was time-consuming and laborious, and sometimes did not provide users with appropriate feedback. Furthermore, the lack of consideration for the user's emotional state resulted in a poor user experience. A system is needed to address these issues.

[0796] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0797] In this invention, the server includes means for acquiring application and request form data received from a user; means for formatting the acquired data and converting it into a format easily processed by a generation AI model; means for inputting the formatted data into the generation AI model and detecting defects; means for notifying the user of the detected defects and how to correct them; means for the notification means to analyze the user's emotional state in response to the detected defects and provide appropriate feedback; means for inputting the data corrected by the user or data that has been defect-free from the beginning into the generation AI model again and performing an inspection; means for automatically registering the data that has passed the inspection into the business system; means for performing a final check of the registered data; and means for notifying the user that the registration is complete. This makes it possible to quickly detect and correct defects in the user's application and request forms and to provide flexible feedback that corresponds to the user's emotional state.

[0798] A "user" refers to an individual or legal entity that accesses the system and submits application forms and requests.

[0799] A "server" refers to a computing system that processes data received from users, detects errors, provides feedback, registers data, and performs final checks.

[0800] An "application form" refers to an official document submitted by a user to apply for services such as mobile registration.

[0801] A "request form" refers to an official document submitted by a user to request a specific service or action.

[0802] "Means of acquiring data" refers to the function or process for receiving information from users in application forms and request forms.

[0803] "Means of formatting data" refers to a function or process for converting received data into a format that is easy for the generating AI model to process.

[0804] A "generative AI model" refers to an algorithmic model that uses machine learning and artificial intelligence technologies to detect data flaws with high accuracy.

[0805] "Means of inputting data into an AI model" refers to a function or process that sends formatted data to a generating AI model to detect defects.

[0806] "Means for detecting defects" refers to a function or process for finding defects in data using a generative AI model.

[0807] "Means of notifying users of how to correct defects" refers to a function or process for informing users of the nature of the detected defects and how to correct them.

[0808] "Means for analyzing emotional states" refers to a function or process for evaluating a user's emotional state and providing appropriate feedback based on that state.

[0809] "Means of re-inputting data into the AI ​​model and performing inspection" refers to a function or process of re-inputting corrected data into the generated AI model and performing a re-inspection.

[0810] "Means of automatically registering data in a business system" refers to a function or process for automatically inputting and saving data that has passed inspection into a business system.

[0811] "Means of final data checking" refers to a function or process for finally verifying the data registered in a business system to confirm its consistency and accuracy.

[0812] "Means of notifying that registration is complete" refers to a function or process that informs the user that data registration has been successfully completed.

[0813] Modes for carrying out the invention

[0814] This invention is a system that automatically detects whether there are any deficiencies in the application forms and request forms submitted by users, recommends corrections as necessary, and further provides feedback and support tailored to the user's emotional state.

[0815] This system primarily consists of a server, terminals, users, and an emotion engine. The roles and functions of each component are explained in detail below.

[0816] User

[0817] Users submit application and request forms through online portals or mobile applications using internet-connected devices (such as PCs and smartphones). While they enter all necessary information, errors or omissions may occur.

[0818] server

[0819] The server receives data submitted by users and performs a series of processes including data formatting, AI model detection of errors, sentiment analysis and feedback provision, data re-examination, automated registration, final check, and completion notification. The specific virtual machines and cloud services used are not platform-dependent, but AWS and Microsoft Azure are commonly used.

[0820] 1. Receiving data: The server receives user input data via an HTTP request. This data includes name, address, phone number, desired mobile plan, etc.

[0821] 2. Data Formatting and Cleaning: Format the received data, map it appropriately to each field, and remove unnecessary spaces and special characters. Specifically, clean the data using string manipulation and regular expressions.

[0822] 3. Detection of errors: The formatted data is input into an AI model to detect any errors. Because the AI ​​model has learned from past data, it can detect errors such as misspellings in names, incorrect phone number formats, and missing required fields with high accuracy.

[0823] 4. Notification of defects and corrective methods: Users will be notified of defects detected by the AI ​​model. Feedback will be provided that includes the nature of the defect and specific corrective methods. The emotion engine will be used to analyze the user's emotional state and provide appropriate feedback. For example, if it is determined that the user is confused, consideration will be given to providing detailed guidance.

[0824] 5. Re-examination: The user's corrected data is received again and re-examined. The AI ​​model is used again for re-formatting and checking for errors in the data.

[0825] 6. Automatic Data Registration: Data that passes inspection is automatically registered in the business system. An API (Application Programming Interface) is used for integration with the business system.

[0826] 7. Final Check: Perform a final check of the data registered in the business system to confirm data integrity and accuracy.

[0827] 8. Registration Completion Notification: After the final check is complete, the server notifies the user that registration is complete. Using the emotion engine, appropriate feedback is provided according to the user's emotional state.

[0828] Specific example

[0829] Examples of detecting errors in phone numbers and providing emotion-responsive feedback.

[0830] 1. The user enters "12345" in the phone number field and submits the application form.

[0831] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[0832] 3. The server uses an AI model to perform an inspection and detects any discrepancies in the phone number.

[0833] 4. The server uses an emotion engine to determine from the user's input process that the user is confused. In this case, it sends a helpful message such as, "Your phone number must be at least 8 digits long. Having trouble? You can find detailed instructions at this link."

[0834] 5. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[0835] 6. The server receives the corrected data again and checks it using the AI ​​model once more.

[0836] 7. The server automatically inputs the data that has passed inspection into the business system.

[0837] 8. The server performs a final check to ensure that all data is accurate.

[0838] 9. The server uses an emotion engine to determine that the user is in a comfortable state and sends a simple message: "Mobile registration complete. Thank you for your cooperation."

[0839] Examples of prompt statements

[0840] "There is an error in the phone number field on your application form. Please enter a phone number with 8 or more digits."

[0841] As described above, the system operates efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[0842] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0843] Program processing flow

[0844] (Step 1:)

[0845] Users fill out application and request forms using their devices and submit them through an online portal or mobile application.

[0846] Input: Name, address, phone number, desired mobile plan, and other information.

[0847] Output: Data from application forms and request forms submitted by the user.

[0848] Specific actions:

[0849] The user enters the necessary information on the device and clicks the "Submit" button.

[0850] (Step 2:)

[0851] The server receives data submitted by the user.

[0852] Input: Data from application forms and request forms submitted by the user.

[0853] Output: Received data

[0854] Specific actions:

[0855] The server parses the HTTP request and retrieves the user input data contained within it.

[0856] (Step 3:)

[0857] The server formats and cleans the data it receives.

[0858] Input: Received data

[0859] Output: Formatted and cleaned data

[0860] Specific actions:

[0861] The server splits the data into fields and removes unnecessary spaces and special characters. For example, it converts "123-456-7890" to "1234567890" and maps each field appropriately.

[0862] (Step 4:)

[0863] The server generates formatted data, which is then input into an AI model for error detection.

[0864] Input: Formatted and cleaned data

[0865] Output: Results indicating whether or not there are defects.

[0866] Specific actions:

[0867] The server passes the formatted data to the AI ​​model, which checks for typos in names, formatting errors in phone numbers, and missing required fields.

[0868] (Step 5:)

[0869] The server notifies users of any detected vulnerabilities and how to fix them, and provides emotionally responsive feedback.

[0870] Input: Result indicating whether or not there are defects.

[0871] Output: User notification of the deficiency and how to fix it, and sentiment feedback.

[0872] Specific actions:

[0873] The server notifies the user of any problems, providing information on the detected problems and how to fix them. An emotion engine is used to analyze the user's emotional state and provide appropriate feedback.

[0874] (Step 6:)

[0875] The user corrects the reported deficiencies and resubmits the application and request forms.

[0876] Input: Data from the revised application and request forms.

[0877] Output: Resubmitted data

[0878] Specific actions:

[0879] The user receives a notification from the server, makes the necessary corrections, and clicks the "Submit" button again.

[0880] (Step 7:)

[0881] The server receives the corrected data again and performs a re-examination.

[0882] Input: Resubmitted data

[0883] Output: Re-examined data

[0884] Specific actions:

[0885] The server receives the corrected data again and uses the AI ​​model to check for any flaws in the formatted data once more.

[0886] (Step 8:)

[0887] The server automatically registers data that has passed inspection into the business system.

[0888] Input: Retested data

[0889] Output: Data registered in the business system

[0890] Specific actions:

[0891] Use an API to send data to a business system and register it automatically.

[0892] (Step 9:)

[0893] The server performs a final check on the data registered in the business system.

[0894] Input: Data registered in the business system

[0895] Output: Final check results

[0896] Specific actions:

[0897] The registered information in the database will be checked again for consistency and accuracy.

[0898] (Step 10:)

[0899] The server notifies the user that registration is complete and provides emotionally appropriate feedback.

[0900] Input: Final check result

[0901] Output: Completion message notified to the user

[0902] Specific actions:

[0903] The server sends the user a success message and appropriate feedback based on the emotion engine. For example, it might send a message like, "Mobile registration complete. Thank you for your cooperation."

[0904] (Application Example 2)

[0905] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0906] Traditional user registration systems perform error checking on user-entered information, but the feedback provided when errors occur is insufficient, and support that takes into account the user's emotional state is not offered, resulting in a poor user experience. Furthermore, the accuracy of error checking and the methods for providing corrections remain as challenges.

[0907] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring application form and request form data received from the user, means for formatting the acquired data and converting it into a format that is easy for the generating AI model to process, means for inputting the formatted data into the generating AI model and detecting defects, means for notifying the user of the detected defects and how to correct them, means for sentiment analysis for analyzing the user's emotional state, means for providing feedback according to the user's emotional state using the sentiment analysis means, means for inputting the corrected data or data that has no defects from the beginning into the generating AI model again and performing an inspection, means for automatically registering the data that has passed the inspection into the business system, means for performing a final check of the registered data, and means for notifying the user that the registration is complete. As a result, the accuracy of error checking and error correction during user input is improved, and the user experience can be improved by providing appropriate feedback according to the user's emotional state.

[0908] "User" refers to a person who uses the system to submit an application form or request form.

[0909] An "application form" refers to a document containing the information necessary for a user to register for the service.

[0910] A "request form" refers to a document submitted by a user to request a specific service or product.

[0911] A "server" is a computer system that processes data received from users and interacts with other systems.

[0912] A "generative AI model" refers to an artificial intelligence model that learns from past data and is used to detect flaws in new input data.

[0913] "Formatting" refers to the process of converting acquired data into a format that is easy for the generating AI model to process.

[0914] "Emotional analysis methods" refer to methods for analyzing a user's emotional state based on their input information and behavior.

[0915] A "business system" refers to a system that uses registered data to perform actual business operations.

[0916] "Notification methods" refer to means of communicating information to users, such as defects or how to correct them.

[0917] This invention is a system that automatically detects the contents of application forms and request forms submitted by users, recommends revisions as necessary, and further provides feedback and support tailored to the user's emotional state.

[0918] System Overview

[0919] This system consists primarily of a server, terminals, and sentiment analysis tools, and implements the following functions.

[0920] 1. Users submit application forms and requests via online portals or mobile apps using their devices. While users enter all necessary information, input errors or omissions may occur.

[0921] 2. The server retrieves application and request forms received from users, formats the data, and converts it into a format that is easy for the generating AI model to process. This data includes names, addresses, phone numbers, and desired services.

[0922] 3. The server inputs the formatted data into a generation AI model to detect errors. The generation AI model has learned from past data and is capable of detecting errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[0923] 4. The server analyzes the user's emotional state using sentiment analysis tools and provides appropriate feedback if any issues are detected. For example, if it determines that the user is experiencing stress, it will send a considerate message such as, "If the procedure is unclear, please refer to this detailed guide."

[0924] 5. The user receives a notification from the server and revises the application form and request form. After revision, the user resubmits the application form and request form from their device.

[0925] 6. The server receives the corrected data again and performs a re-examination. At this time, it checks whether there are any defects in the data that has been formatted using the generated AI model again.

[0926] 7. The server automatically registers the data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[0927] 8. The server performs a final check to ensure that all data is accurate. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback tailored to the user's emotional state using sentiment analysis tools.

[0928] Hardware and software to be used

[0929] This system uses the following hardware and software.

[0930] Server: This server collects, formats, and generates data, runs AI models, and notifies the results. Specifically, it uses a web server framework such as Flask.

[0931] Generative AI model: Detects defects based on data received from the user. Specifically, it utilizes the BERT or BART model from the transformers library.

[0932] Sentiment analysis method: The emotional state is analyzed from the user's input information and actions. Specifically, the sentiment analysis model (facebook / bart-large-mnli) from the transformers library is used.

[0933] Terminal: A device used by users to submit application forms and requests. Specifically, this includes smartphones and PCs.

[0934] Specific example

[0935] For example, if the user enters the following:

[0936] Name: Taro Yamada

[0937] Email: yamada@example

[0938] Phone: 12345

[0939] The server receives the data, formats it, and inputs it into a generative AI model. The generative AI model detects any errors in the phone number and analyzes the user's state using sentiment analysis. If the sentiment analysis determines that the user is confused, the server sends a considerate message such as, "The phone number must be at least 10 digits long. Are you having trouble? You can find detailed instructions at this link."

[0940] Example of a prompt

[0941] text

[0942] emotion_analyzer('Username: Taro Yamada, Email: yamada@example, Phone: 12345')

[0943] In this way, the system can operate efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[0944] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0945] Step 1:

[0946] Users submit application and request forms via an online portal or mobile app using their device (smartphone or PC). At this stage, users enter all necessary information, including their name, address, phone number, and desired services. The device then transmits this information to the server.

[0947] Step 2:

[0948] The server retrieves application and request form data received from users. The retrieved data is received in a format such as JSON. The server then formats this data and converts it into a format that is easy for the generating AI model to process. Specifically, it performs data cleaning, such as appropriately mapping the data fields and removing unnecessary spaces and special characters.

[0949] Step 3:

[0950] The server inputs the formatted data into a generative AI model for error detection. The generative AI model has learned from past data and can detect errors with high accuracy. At this stage, the input is formatted data, and the output is information about the presence or absence of errors and their details. For example, it can detect incorrect phone number formats or blank required fields.

[0951] Step 4:

[0952] The server analyzes the user's emotional state using an emotion analysis tool. Specifically, it inputs the user's input string as a prompt into an emotion analysis model (facebook / bart-large-mnli) to determine whether the emotion is positive, negative, or neutral. The input is the user's input string, and the output is the result of the emotion determination.

[0953] Example of a prompt

[0954] text

[0955] emotion_analyzer('Username: Taro Yamada, Email: yamada@example, Phone: 12345')

[0956] Step 5:

[0957] The server generates appropriate feedback and notifies the user based on the details of the detected flaws and the results of sentiment analysis. This specific feedback includes instructions on how to correct the flaws and messages tailored to the user's emotional state (e.g., "Phone numbers must be at least 10 digits long. Need help? You can find detailed instructions at this link."). The input is the flaw details and sentiment analysis results, and the output is the appropriate feedback message.

[0958] Step 6:

[0959] The user receives a notification from the server and modifies the application and request forms. After modification, the user resubmits the application and request forms from the terminal. The terminal then resends the modified data to the server.

[0960] Step 7:

[0961] The server receives the corrected data again and performs a re-examination. At this stage, it checks for any flaws in the formatted data using the generation AI model again. The input is the corrected data, and the output is the result of the second flaw detection.

[0962] Step 8:

[0963] The server automatically registers data that has passed inspection into the business system. This process utilizes an API (Application Programming Interface) to interact with the business system. The input is the data that has passed inspection, and the output is the result of registration into the business system.

[0964] Step 9:

[0965] The server performs a final check of the data registered in the business system to verify its integrity and accuracy. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback tailored to the user's emotional state using sentiment analysis tools. The input is the result of the data registered in the business system, and the output is the registration completion notification.

[0966] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0967] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0968] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0969] [Third Embodiment]

[0970] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0971] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0972] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0973] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0974] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0975] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0976] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0977] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0978] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0979] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0980] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0981] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0982] This invention is a system that automatically detects deficiencies in application forms and request forms submitted by users for mobile registration, recommends corrections as necessary, and then automatically inputs the information into the registration system. This section describes specific embodiments of the system.

[0983] System Overview

[0984] This system consists primarily of servers, terminals, and users, and provides the following functions:

[0985] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[0986] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[0987] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[0988] 4. If a defect is detected, the server will notify the user of the defect and how to correct it.

[0989] 5. Correct the deficiencies pointed out by the user and resubmit.

[0990] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the business system.

[0991] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed.

[0992] Program Processing Description

[0993] 1. The user submits the application form and request form using a terminal. At this point, all necessary information should be entered, but there may be input errors or omissions.

[0994] 2. The server receives the application and request form data and first formats the data. This includes properly mapping the data to individual fields. For example, it distributes information to fields such as name, address, and phone number, and removes unnecessary spaces and special characters.

[0995] 3. The server inputs the formatted data into the AI ​​model. This AI model has learned from past data and can detect patterns of errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[0996] 4. If a problem is detected, the server will notify the user. The notification field will include the nature of the problem and specific instructions on how to fix it. For example, a message such as "The phone number is invalid. Please enter a number with 8 or more digits." might be sent.

[0997] 5. The user receives a notification from the server and revises the application form and request form. After revision, they resubmit them using their device.

[0998] 6. The server receives the corrected data again and performs a re-check. If the deficiencies are resolved, it proceeds to the next step.

[0999] 7. The server automatically inputs the data that has passed inspection into the business system. This process involves integrating with the business system using an API (Application Programming Interface).

[1000] 8. The server performs a final check of the data registered in the business system to verify the integrity and accuracy of the data.

[1001] 9. After the final check is complete, the server will notify the user that the registration has been successfully completed. This notification will also include a summary of the registered information.

[1002] Specific example

[1003] Examples of detecting and correcting errors in phone numbers

[1004] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[1005] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[1006] 3. The server uses an AI model to perform an inspection and detects an error in the phone number. It then sends a notification to the user stating, "The phone number must have at least 8 digits."

[1007] 4. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[1008] 5. The server receives the corrected data again and checks it using the AI ​​model once more.

[1009] 6. The server automatically inputs the data that has passed inspection into the business system.

[1010] 7. The server performs a final check to ensure that all data is accurate.

[1011] 8. The server notifies the user that mobile registration is complete.

[1012] In this way, the entire system operates efficiently, allowing for rapid detection, correction, and registration of user errors. This significantly reduces user effort and improves the efficiency and accuracy of the registration process.

[1013] The following describes the processing flow.

[1014] Step 1:

[1015] Users submit mobile registration application forms and requests via an online portal or mobile app using their devices. At this point, users enter all necessary information, but errors or omissions may occur.

[1016] Step 2:

[1017] The server receives application and request forms submitted by users. The received data includes names, addresses, phone numbers, and desired mobile plans.

[1018] Step 3:

[1019] The server analyzes the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[1020] Step 4:

[1021] The server inputs the formatted data into an AI model, which then detects errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect incorrect phone number formats or blank required fields.

[1022] Step 5:

[1023] The server notifies the user of any errors detected by the AI ​​model, including specific instructions on how to correct them. For example, a message might be sent such as, "The phone number is invalid. Please enter a number with 8 or more digits."

[1024] Step 6:

[1025] The user receives a notification from the server and corrects the identified deficiencies. After making the corrections, the user resubmits the application form and request form from their device.

[1026] Step 7:

[1027] The server receives the corrected data again and performs a re-examination. At this point, the AI ​​model is used again to check for any flaws in the formatted data.

[1028] Step 8:

[1029] The server automatically inputs data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[1030] Step 9:

[1031] The server re-imports the data registered in the business system and performs a final accuracy check. This confirms that all data is accurate.

[1032] Step 10:

[1033] The server confirms that all processing has been completed successfully and notifies the user that registration is complete. This notification also includes a summary of the registration details.

[1034] (Example 1)

[1035] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1036] Current online registration systems frequently suffer from input errors and missing information when users submit application forms and requests, often requiring manual corrections and resubmissions. This process is not only cumbersome and time-consuming for users but also places a burden on system administrators. Furthermore, manual checking and correction carries the risk of human error. Therefore, there is a growing demand for systems that automatically detect deficiencies and recommend corrections.

[1037] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1038] In this invention, the server includes means for acquiring application and request form data received from a user; means for formatting the acquired data and converting it into a format that is easy for an AI model to process; means for inputting the formatted data into the AI ​​model and detecting defects; means for notifying the user of the detected defects and how to correct them; means for inputting the corrected data or data that was free of defects from the beginning into the AI ​​model again and performing an inspection; means for automatically registering the data that has passed the inspection into a data management system; means for performing a final check of the registered data; and means for notifying the user that the registration is complete. This makes it possible to quickly and accurately detect defects in the data entered by the user and provide instructions for correction, thereby significantly improving the efficiency and accuracy of the registration process.

[1039] A "user" refers to an individual or legal entity that uses the system to submit application forms and requests.

[1040] "Application forms and request forms" refer to formal documents that users submit when registering or applying for services.

[1041] "Terminal" refers to a mobile device or computer used by a user to input application forms and requests and send them to the server.

[1042] A "server" refers to computing resources used to receive, process, and store data sent by users.

[1043] "Data" refers to the collection of information that users input and that servers process.

[1044] "Formatting" refers to the process of converting data received from a user into an appropriate format.

[1045] An "AI model" refers to a program that uses artificial intelligence technology to detect defects and analyze data.

[1046] "Deficiencies" refer to errors or missing information contained in application forms and request forms.

[1047] A "notification" refers to a message sent from a server to a user informing them of the nature of a problem and how to fix it.

[1048] "Re-examination" refers to the process in which corrected data is checked again by the AI ​​model.

[1049] A "data management system" refers to a system used to register and manage data that has passed inspection.

[1050] "Final check" refers to the inspection process to verify the accuracy and consistency of data registered in the data management system.

[1051] A "registration completion notification" refers to a message sent from the server to the user informing them that data registration has been successfully completed.

[1052] This invention is a system that automatically detects deficiencies in application forms and request forms submitted by users for mobile registration, recommends corrections as necessary, and then automatically inputs the information into the registration system. This section describes specific embodiments of the system.

[1053] System Configuration

[1054] This system consists primarily of servers, terminals, and users, and provides the following functions:

[1055] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[1056] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[1057] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[1058] 4. If a defect is detected, the server will notify the user of the defect and how to correct it.

[1059] 5. Correct the deficiencies pointed out by the user and resubmit.

[1060] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the data management system.

[1061] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed.

[1062] Hardware and software to be used

[1063] Device: Smartphone, tablet, or personal computer. This allows users to submit application forms and requests via an online portal or mobile app.

[1064] Server: Uses high-performance computing resources to receive, format, input into AI models, detect errors, send notifications, perform final checks, and automatically register data to the data management system.

[1065] AI model: A model trained using machine learning algorithms, which learns from past data and detects patterns of defects with high accuracy.

[1066] Data Management System: A database system that manages data after registration. It interacts with the server via an API.

[1067] Specific example

[1068] Examples of detecting and correcting errors in phone numbers

[1069] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[1070] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[1071] 3. The server uses an AI model to perform an inspection and detects any deficiencies in the phone number. It then sends a notification to the user stating, "The phone number must have at least 8 digits."

[1072] 4. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[1073] 5. The server receives the corrected data again and performs a re-inspection.

[1074] 6. The server automatically inputs the data that has passed the inspection into the data management system.

[1075] 7. The server performs a final check to ensure that all data is accurate.

[1076] 8. The server notifies the user that registration has been successfully completed.

[1077] Example of a prompt

[1078] Please enter the following information: Full Name: [First Name], Address: [Address], Phone Number: [Phone Number]. If there are any errors, please provide detailed feedback.

[1079] This system allows for the rapid detection, correction, and registration of user errors, significantly reducing user effort and improving the efficiency and accuracy of the registration process.

[1080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1081] Step 1:

[1082] Users submit application and request forms from their devices via online portals or mobile apps. Input includes information such as name, address, and phone number. Specifically, they enter the required data into a form on a GUI (Graphical User Interface) and press the submit button.

[1083] Input: Information such as name, address, and phone number.

[1084] Output: Data transmission from terminal to server

[1085] Step 2:

[1086] The server receives data from the user. Specifically, a data receiving module captures the data sent from the terminal and temporarily stores it within the server.

[1087] Input: Application form and request form data sent from the terminal.

[1088] Output: Temporary storage of received data within the server.

[1089] Step 3:

[1090] The server formats the received data. Specifically, it distributes information into fields such as name, address, and phone number, and removes unnecessary spaces and special characters. It also converts the data into JSON or XML format.

[1091] Input: Temporarily saved application and request form data

[1092] Output: Formatted data (in JSON or XML format)

[1093] Step 4:

[1094] The server inputs the formatted data into an AI model, which then detects any errors. The AI ​​model has learned from past data and can detect things like whether the phone number format is correct and whether required fields are not left blank.

[1095] Input: Formatted data

[1096] Output: Results of defect detection (presence or absence of defects and details)

[1097] Step 5:

[1098] The server notifies the user of any detected vulnerabilities and how to correct them. For example, a message such as "Your phone number is invalid. Please enter a number with 8 or more digits." might be generated.

[1099] Input: Results of detecting defects

[1100] Output: Notification message to send to the user

[1101] Step 6:

[1102] The user receives a notification from the server and corrects any errors in the application and request forms. After correction, they resubmit the data. Specifically, this involves re-entering the correct information into the GUI form and pressing the submit button.

[1103] Input: Error notification from server

[1104] Output: Resubmission of corrected data

[1105] Step 7:

[1106] The server receives the corrected data again and performs another inspection using the AI ​​model. The same process is repeated to check for any further defects.

[1107] Input: Correction data resubmitted by the user

[1108] Output: Re-inspection results (presence or absence of defects)

[1109] Step 8:

[1110] The server automatically registers data that has passed re-inspection into the data management system. The data is then transferred to the management system using an API to complete the registration.

[1111] Input: Data that passed retesting

[1112] Output: Data registered in the data management system

[1113] Step 9:

[1114] The server performs a final check of the data registered in the data management system. It verifies the integrity and accuracy of the data and confirms that all data has been registered properly.

[1115] Input: Data registered in the data management system

[1116] Output: Final check results

[1117] Step 10:

[1118] The server notifies the user that registration is complete. The notification may include a message such as, "Registration completed successfully."

[1119] Input: Final check result

[1120] Output: Registration completion notification to send to the user

[1121] (Application Example 1)

[1122] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1123] In conventional systems, errors frequently occurred when users submitted online applications, and correcting these errors was time-consuming and cumbersome. Furthermore, the complex process of detecting and correcting errors raised concerns about a degraded user experience. There was also a need for an efficient system specifically designed for registration applications in a virtual environment.

[1124] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1125] In this invention, the server includes means for a user to submit a registration application in a virtual environment using an electronic device, means for formatting the acquired information and converting it into a format that is easy for an artificial intelligence model to process, and means for inputting the formatted information into the artificial intelligence model and detecting errors. This makes it possible for users to submit online applications in a virtual environment easily and quickly.

[1126] A "user" is an individual or legal entity that uses the system to submit application documents.

[1127] An "application document" is a document submitted by a user for registration applications and requests.

[1128] "Information" refers to the data and content included in the application document.

[1129] "Formatting" refers to the process of converting acquired information into a format that is easily processed by artificial intelligence models.

[1130] An "artificial intelligence model" is a computer program used for machine learning and data analysis, and is particularly used to detect flaws and errors.

[1131] An "error" refers to inaccurate or incomplete information contained in the application document.

[1132] A "business platform" is a system that centrally manages registration application information and automates and streamlines related operations.

[1133] "Final check" is the process of ultimately verifying the consistency and accuracy of the registered information.

[1134] "Electronic devices" refer to electronic devices such as smartphones, tablets, or personal computers.

[1135] A "virtual environment" is an online environment that utilizes the internet and virtual reality technology.

[1136] "Message notifications" are a means of communicating information or correction instructions to users, and often involve using email or mobile apps.

[1137] This invention is a system that automatically detects the content of application documents submitted by users in a virtual environment, recommends corrections if errors are found, and automatically reflects the corrected information in the business platform once accurate information has been entered. The details of the system and its processes are described below.

[1138] System Configuration

[1139] This system consists of the following elements:

[1140] hardware

[1141] 1. Electronic devices: Users access the virtual environment and submit registration applications using electronic devices such as smartphones, tablets, or personal computers.

[1142] 2. Server: The central device that receives application document data and performs formatting, inspection, correction, registration, and final checks.

[1143] software

[1144] 1. Mobile application: An application used by users on electronic devices. It provides an interface for submitting and modifying application documents.

[1145] 2. Artificial Intelligence Models: AI models used to detect flaws. Specific examples include natural language processing models such as BERT and GPT.

[1146] 3. Business Platform: A system for managing registration application information and streamlining operations.

[1147] Details of the implementation

[1148] User submission of application documents

[1149] Users register in a virtual environment using electronic devices such as smartphones. Specifically, they input application documents through a mobile application and submit them to the server.

[1150] Data formatting and processing using AI models

[1151] The server formats the data from the application documents received from the user. Specifically, it appropriately allocates information to each field and removes unnecessary spaces and special characters. This formatted data is then input into an artificial intelligence model and checked for errors and deficiencies.

[1152] Detecting and recommending corrections for defects

[1153] Artificial intelligence models perform highly accurate error detection by learning from past data. For example, if the phone number format is incorrect, a notification will be sent to the user stating, "The phone number is invalid. Please enter a number with 8 or more digits." The user then corrects the data according to the instructions and resubmits it.

[1154] Automatic registration and final check

[1155] The server automatically registers data that has passed inspection into the business platform. After registration, a final check is performed to confirm that all data is accurate. This notifies the user that registration is complete.

[1156] Specific examples and prompt statements

[1157] Specific example:

[1158] 1. The user submits an application document with "example@com" entered in the email field.

[1159] 2. The server receives the data and inputs the formatted data into the artificial intelligence model.

[1160] 3. The server uses an AI model to perform an inspection and detects an error in the email address. It then sends a notification to the user stating, "The format of your email address is incorrect. Please enter the correct format."

[1161] 4. The user receives a notification from the server, corrects the email address to "example@example.com", and resubmits.

[1162] 5. The server receives the corrected data again and checks it using the AI ​​model once more.

[1163] 6. The server automatically inputs the data that has passed inspection into the business platform.

[1164] 7. The server performs a final check to ensure that all data is accurate.

[1165] 8. The server notifies the user that registration is complete.

[1166] Example of a prompt:

[1167] Please review the application forms submitted by users and detect any deficiencies. If deficiencies are found, please notify the user of the specific steps required to correct them.

[1168] These measures will enable users to submit online applications efficiently and quickly in a virtual environment. Furthermore, it is expected that the overall accuracy and reliability of the system will improve, contributing to a better user experience.

[1169] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1170] Step 1:

[1171] Users access a mobile application in a virtual environment using an electronic device (smartphone, tablet, or PC), and fill out and submit application documents.

[1172] Input: Data from the application document entered by the user.

[1173] Output: Data from application documents sent to the server via the mobile application.

[1174] Step 2:

[1175] The server formats the data from the received application documents. First, it appropriately allocates information to each field and removes unnecessary spaces and special characters.

[1176] Input: Raw data from the application document received from the user.

[1177] Output: Formatted data.

[1178] Specific actions: Information mapping to fields, removal of unnecessary spaces and special characters.

[1179] Step 3:

[1180] The server inputs the formatted data into an artificial intelligence model to detect errors. By learning from past data, the AI ​​model can identify flaws such as incorrect formatting of phone numbers and email addresses.

[1181] Input: Formatted data.

[1182] Output: Whether or not there are errors and details of the errors.

[1183] Specific actions: Data inspection and analysis using AI models.

[1184] Step 4:

[1185] The server will notify the user if any problems are detected. The notification will include specific instructions on how to fix the problem.

[1186] Input: Details of errors detected by the AI ​​model.

[1187] Output: Correction notification sent to the user.

[1188] Specific action: Sending notifications via email or mobile app.

[1189] Step 5:

[1190] The user receives a notification from the server and corrects any deficiencies in the application document. After correction, they resubmit it through the mobile application.

[1191] Input: Correction notification from the server.

[1192] Output: Resubmission of the revised application document.

[1193] Specific actions: Users can modify and resubmit application documents.

[1194] Step 6:

[1195] The server reformats the corrected data that has been re-received, inputs it into the AI ​​model, and performs a re-examination. If the deficiencies are resolved during the re-examination, it proceeds to the next step.

[1196] Input: User-submitted modification data.

[1197] Output: Data with errors corrected as a result of re-examination.

[1198] Specific actions: Data reshaping and re-examination using an AI model.

[1199] Step 7:

[1200] The server automatically registers data that passes inspection into the business platform. It interacts with the business platform via an API.

[1201] Input: Accurate data with all errors corrected.

[1202] Output: Data registered on the business platform.

[1203] Specific operation: Data registration to a business platform using an API.

[1204] Step 8:

[1205] The server performs a final check on the registered data to verify its integrity and accuracy. After the final check is complete, it sends a registration completion notification to the user.

[1206] Input: Data registered on the business platform.

[1207] Output: Registration completion notification sent to the user.

[1208] Specific actions: Final check of data and sending registration completion notifications to users.

[1209] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1210] This invention is a system that automatically detects whether there are any deficiencies in the application and request forms for mobile registration submitted by users, recommends corrections as necessary, and further provides feedback and support tailored to the user's emotional state. This section describes specific embodiments of the system.

[1211] System Overview

[1212] This system consists primarily of a server, terminal, user, and emotion engine, and provides the following functions:

[1213] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[1214] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[1215] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[1216] 4. If a defect is detected, the server will notify the user of the defect and how to correct it. At this time, the emotion engine will be used to analyze the user's emotional state and provide appropriate feedback.

[1217] 5. Correct the deficiencies pointed out by the user and resubmit.

[1218] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the business system.

[1219] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed. At this time, the emotion engine is also used to provide appropriate feedback.

[1220] Program Processing Description

[1221] 1. The user submits the application form and request form using a terminal. While all necessary information is entered, input errors or omissions may occur.

[1222] 2. The server receives the data from the application and request forms. The received data includes name, address, phone number, desired mobile plan, etc.

[1223] 3. The server formats the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[1224] 4. The server inputs the formatted data into the AI ​​model to detect errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[1225] 5. The server notifies the user of any deficiencies detected by the AI ​​model. This notification includes feedback detailing the deficiency and specific correction methods. Furthermore, the system uses an emotion engine to analyze the user's emotional state and provide appropriate feedback. For example, if the user is feeling stressed, a considerate message such as, "If the steps are unclear, please refer to this detailed guide," is sent.

[1226] 6. The user receives a notification from the server and revises the application form and request form. After revision, the user resubmits the application form and request form from their device.

[1227] 7. The server receives the corrected data again and performs a re-examination. At this time, the AI ​​model is used again to check for any defects in the formatted data.

[1228] 8. The server automatically registers the data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[1229] 9. The server performs a final check of the data registered in the business system to verify the integrity and accuracy of the data.

[1230] 10. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback based on the user's emotional state using the emotion engine.

[1231] Specific example

[1232] Examples of detecting errors in phone numbers and providing emotion-responsive feedback.

[1233] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[1234] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[1235] 3. The server uses an AI model to perform an inspection and detects any discrepancies in the phone number.

[1236] 4. The server uses an emotion engine to determine from the user's input process that the user is confused. In this case, it sends a helpful message such as, "Your phone number must be at least 8 digits long. Having trouble? You can find detailed instructions at this link."

[1237] 5. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[1238] 6. The server receives the corrected data again and checks it using the AI ​​model once more.

[1239] 7. The server automatically inputs the data that has passed inspection into the business system.

[1240] 8. The server performs a final check to ensure that all data is accurate.

[1241] 9. The server uses an emotion engine to determine that the user is in a comfortable state and sends a simple message: "Mobile registration complete. Thank you for your cooperation."

[1242] In this way, the system can operate efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[1243] The following describes the processing flow.

[1244] Step 1:

[1245] Users submit mobile registration application forms and requests via an online portal or mobile app using their devices. While users enter all necessary information, errors or omissions may occur.

[1246] Step 2:

[1247] The server receives application and request forms submitted by users. The received data includes names, addresses, phone numbers, and desired mobile plans.

[1248] Step 3:

[1249] The server analyzes the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[1250] Step 4:

[1251] The server inputs the formatted data into an AI model, which then detects errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect incorrect phone number formats or blank required fields.

[1252] Step 5:

[1253] The server notifies the user of any issues detected by the AI ​​model, including specific correction methods. During this process, an emotion engine is used to analyze the user's emotional state and generate appropriate feedback. For example, if the user is irritated, the message might be rephrased to be more polite and helpful.

[1254] Step 6:

[1255] The user receives a notification from the server and corrects the identified deficiencies. After making the corrections, they resubmit the application form and request form from their device.

[1256] Step 7:

[1257] The server receives the corrected data again and performs a re-examination. The AI ​​model is used again to check for any flaws in the formatted data.

[1258] Step 8:

[1259] The server automatically registers data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[1260] Step 9:

[1261] The server re-imports the data registered in the business system and performs a final accuracy check. This confirms that all data is accurate.

[1262] Step 10:

[1263] The server confirms that all processing has been completed successfully and notifies the user that registration is complete. This notification includes a summary of the registration details, but also uses an emotion engine to provide appropriate feedback based on the user's emotional state. For example, if the user is feeling reassured, a simple message such as "Mobile registration complete. Thank you for your cooperation." is sent.

[1264] (Example 2)

[1265] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1266] In conventional systems, if there were errors in application forms or requests submitted by users, corrections had to be made manually, which was time-consuming and laborious, and sometimes did not provide users with appropriate feedback. Furthermore, the lack of consideration for the user's emotional state resulted in a poor user experience. A system is needed to address these issues.

[1267] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1268] In this invention, the server includes means for acquiring application and request form data received from a user; means for formatting the acquired data and converting it into a format easily processed by a generation AI model; means for inputting the formatted data into the generation AI model and detecting defects; means for notifying the user of the detected defects and how to correct them; means for the notification means to analyze the user's emotional state in response to the detected defects and provide appropriate feedback; means for inputting the data corrected by the user or data that has been defect-free from the beginning into the generation AI model again and performing an inspection; means for automatically registering the data that has passed the inspection into the business system; means for performing a final check of the registered data; and means for notifying the user that the registration is complete. This makes it possible to quickly detect and correct defects in the user's application and request forms and to provide flexible feedback that corresponds to the user's emotional state.

[1269] A "user" refers to an individual or legal entity that accesses the system and submits application forms and requests.

[1270] A "server" refers to a computing system that processes data received from users, detects errors, provides feedback, registers data, and performs final checks.

[1271] An "application form" refers to an official document submitted by a user to apply for services such as mobile registration.

[1272] A "request form" refers to an official document submitted by a user to request a specific service or action.

[1273] "Means of acquiring data" refers to the function or process for receiving information from users in application forms and request forms.

[1274] "Means of formatting data" refers to a function or process for converting received data into a format that is easy for the generating AI model to process.

[1275] A "generative AI model" refers to an algorithmic model that uses machine learning and artificial intelligence technologies to detect data flaws with high accuracy.

[1276] "Means of inputting data into an AI model" refers to a function or process that sends formatted data to a generating AI model to detect defects.

[1277] "Means for detecting defects" refers to a function or process for finding defects in data using a generative AI model.

[1278] "Means of notifying users of how to correct defects" refers to a function or process for informing users of the nature of the detected defects and how to correct them.

[1279] "Means for analyzing emotional states" refers to a function or process for evaluating a user's emotional state and providing appropriate feedback based on that state.

[1280] "Means of re-inputting data into the AI ​​model and performing inspection" refers to a function or process of re-inputting corrected data into the generated AI model and performing a re-inspection.

[1281] "Means of automatically registering data in a business system" refers to a function or process for automatically inputting and saving data that has passed inspection into a business system.

[1282] "Means of final data checking" refers to a function or process for finally verifying the data registered in a business system to confirm its consistency and accuracy.

[1283] "Means of notifying that registration is complete" refers to a function or process that informs the user that data registration has been successfully completed.

[1284] Modes for carrying out the invention

[1285] This invention is a system that automatically detects whether there are any deficiencies in the application forms and request forms submitted by users, recommends corrections as necessary, and further provides feedback and support tailored to the user's emotional state.

[1286] This system primarily consists of a server, terminals, users, and an emotion engine. The roles and functions of each component are explained in detail below.

[1287] User

[1288] Users submit application and request forms through online portals or mobile applications using internet-connected devices (such as PCs and smartphones). While they enter all necessary information, errors or omissions may occur.

[1289] server

[1290] The server receives data submitted by users and performs a series of processes including data formatting, AI model detection of errors, sentiment analysis and feedback provision, data re-examination, automated registration, final check, and completion notification. The specific virtual machines and cloud services used are not platform-dependent, but AWS and Microsoft Azure are commonly used.

[1291] 1. Receiving data: The server receives user input data via an HTTP request. This data includes name, address, phone number, desired mobile plan, etc.

[1292] 2. Data Formatting and Cleaning: Format the received data, map it appropriately to each field, and remove unnecessary spaces and special characters. Specifically, clean the data using string manipulation and regular expressions.

[1293] 3. Detection of errors: The formatted data is input into an AI model to detect any errors. Because the AI ​​model has learned from past data, it can detect errors such as misspellings in names, incorrect phone number formats, and missing required fields with high accuracy.

[1294] 4. Notification of defects and corrective methods: Users will be notified of defects detected by the AI ​​model. Feedback will be provided that includes the nature of the defect and specific corrective methods. The emotion engine will be used to analyze the user's emotional state and provide appropriate feedback. For example, if it is determined that the user is confused, consideration will be given to providing detailed guidance.

[1295] 5. Re-examination: The user's corrected data is received again and re-examined. The AI ​​model is used again for re-formatting and checking for errors in the data.

[1296] 6. Automatic Data Registration: Data that passes inspection is automatically registered in the business system. An API (Application Programming Interface) is used for integration with the business system.

[1297] 7. Final Check: Perform a final check of the data registered in the business system to confirm data integrity and accuracy.

[1298] 8. Registration Completion Notification: After the final check is complete, the server notifies the user that registration is complete. Using the emotion engine, appropriate feedback is provided according to the user's emotional state.

[1299] Specific example

[1300] Examples of detecting errors in phone numbers and providing emotion-responsive feedback.

[1301] 1. The user enters "12345" in the phone number field and submits the application form.

[1302] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[1303] 3. The server uses an AI model to perform an inspection and detects any discrepancies in the phone number.

[1304] 4. The server uses an emotion engine to determine from the user's input process that the user is confused. In this case, it sends a helpful message such as, "Your phone number must be at least 8 digits long. Having trouble? You can find detailed instructions at this link."

[1305] 5. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[1306] 6. The server receives the corrected data again and checks it using the AI ​​model once more.

[1307] 7. The server automatically inputs the data that has passed inspection into the business system.

[1308] 8. The server performs a final check to ensure that all data is accurate.

[1309] 9. The server uses an emotion engine to determine that the user is in a comfortable state and sends a simple message: "Mobile registration complete. Thank you for your cooperation."

[1310] Examples of prompt statements

[1311] "There is an error in the phone number field on your application form. Please enter a phone number with 8 or more digits."

[1312] As described above, the system operates efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[1313] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1314] Program processing flow

[1315] (Step 1:)

[1316] Users fill out application and request forms using their devices and submit them through an online portal or mobile application.

[1317] Input: Name, address, phone number, desired mobile plan, and other information.

[1318] Output: Data from application forms and request forms submitted by the user.

[1319] Specific actions:

[1320] The user enters the necessary information on the device and clicks the "Submit" button.

[1321] (Step 2:)

[1322] The server receives data submitted by the user.

[1323] Input: Data from application forms and request forms submitted by the user.

[1324] Output: Received data

[1325] Specific actions:

[1326] The server parses the HTTP request and retrieves the user input data contained within it.

[1327] (Step 3:)

[1328] The server formats and cleans the data it receives.

[1329] Input: Received data

[1330] Output: Formatted and cleaned data

[1331] Specific actions:

[1332] The server splits the data into fields and removes unnecessary spaces and special characters. For example, it converts "123-456-7890" to "1234567890" and maps each field appropriately.

[1333] (Step 4:)

[1334] The server generates formatted data, which is then input into an AI model for error detection.

[1335] Input: Formatted and cleaned data

[1336] Output: Results indicating whether or not there are defects.

[1337] Specific actions:

[1338] The server passes the formatted data to the AI ​​model, which checks for typos in names, formatting errors in phone numbers, and missing required fields.

[1339] (Step 5:)

[1340] The server notifies users of any detected vulnerabilities and how to fix them, and provides emotionally responsive feedback.

[1341] Input: Result indicating whether or not there are defects.

[1342] Output: User notification of the deficiency and how to fix it, and sentiment feedback.

[1343] Specific actions:

[1344] The server notifies the user of any problems, providing information on the detected problems and how to fix them. An emotion engine is used to analyze the user's emotional state and provide appropriate feedback.

[1345] (Step 6:)

[1346] The user corrects the reported deficiencies and resubmits the application and request forms.

[1347] Input: Data from the revised application and request forms.

[1348] Output: Resubmitted data

[1349] Specific actions:

[1350] The user receives a notification from the server, makes the necessary corrections, and clicks the "Submit" button again.

[1351] (Step 7:)

[1352] The server receives the corrected data again and performs a re-examination.

[1353] Input: Resubmitted data

[1354] Output: Re-examined data

[1355] Specific actions:

[1356] The server receives the corrected data again and uses the AI ​​model to check for any flaws in the formatted data once more.

[1357] (Step 8:)

[1358] The server automatically registers data that has passed inspection into the business system.

[1359] Input: Retested data

[1360] Output: Data registered in the business system

[1361] Specific actions:

[1362] Use an API to send data to a business system and register it automatically.

[1363] (Step 9:)

[1364] The server performs a final check on the data registered in the business system.

[1365] Input: Data registered in the business system

[1366] Output: Final check results

[1367] Specific actions:

[1368] The registered information in the database will be checked again for consistency and accuracy.

[1369] (Step 10:)

[1370] The server notifies the user that registration is complete and provides emotionally appropriate feedback.

[1371] Input: Final check result

[1372] Output: Completion message notified to the user

[1373] Specific actions:

[1374] The server sends the user a success message and appropriate feedback based on the emotion engine. For example, it might send a message like, "Mobile registration complete. Thank you for your cooperation."

[1375] (Application Example 2)

[1376] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1377] Traditional user registration systems perform error checking on user-entered information, but the feedback provided when errors occur is insufficient, and support that takes into account the user's emotional state is not offered, resulting in a poor user experience. Furthermore, the accuracy of error checking and the methods for providing corrections remain as challenges.

[1378] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring application form and request form data received from the user, means for formatting the acquired data and converting it into a format that is easy for the generating AI model to process, means for inputting the formatted data into the generating AI model and detecting defects, means for notifying the user of the detected defects and how to correct them, means for sentiment analysis for analyzing the user's emotional state, means for providing feedback according to the user's emotional state using the sentiment analysis means, means for inputting the corrected data or data that has no defects from the beginning into the generating AI model again and performing an inspection, means for automatically registering the data that has passed the inspection into the business system, means for performing a final check of the registered data, and means for notifying the user that the registration is complete. As a result, the accuracy of error checking and error correction during user input is improved, and the user experience can be improved by providing appropriate feedback according to the user's emotional state.

[1379] "User" refers to a person who uses the system to submit an application form or request form.

[1380] An "application form" refers to a document containing the information necessary for a user to register for the service.

[1381] A "request form" refers to a document submitted by a user to request a specific service or product.

[1382] A "server" is a computer system that processes data received from users and interacts with other systems.

[1383] A "generative AI model" refers to an artificial intelligence model that learns from past data and is used to detect flaws in new input data.

[1384] "Formatting" refers to the process of converting acquired data into a format that is easy for the generating AI model to process.

[1385] "Emotional analysis methods" refer to methods for analyzing a user's emotional state based on their input information and behavior.

[1386] A "business system" refers to a system that uses registered data to perform actual business operations.

[1387] "Notification methods" refer to means of communicating information to users, such as defects or how to correct them.

[1388] This invention is a system that automatically detects the contents of application forms and request forms submitted by users, recommends revisions as necessary, and further provides feedback and support tailored to the user's emotional state.

[1389] System Overview

[1390] This system consists primarily of a server, terminals, and sentiment analysis tools, and implements the following functions.

[1391] 1. Users submit application forms and requests via online portals or mobile apps using their devices. While users enter all necessary information, input errors or omissions may occur.

[1392] 2. The server retrieves application and request forms received from users, formats the data, and converts it into a format that is easy for the generating AI model to process. This data includes names, addresses, phone numbers, and desired services.

[1393] 3. The server inputs the formatted data into a generation AI model to detect errors. The generation AI model has learned from past data and is capable of detecting errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[1394] 4. The server analyzes the user's emotional state using sentiment analysis tools and provides appropriate feedback if any issues are detected. For example, if it determines that the user is experiencing stress, it will send a considerate message such as, "If the procedure is unclear, please refer to this detailed guide."

[1395] 5. The user receives a notification from the server and revises the application form and request form. After revision, the user resubmits the application form and request form from their device.

[1396] 6. The server receives the corrected data again and performs a re-examination. At this time, it checks whether there are any defects in the data that has been formatted using the generated AI model again.

[1397] 7. The server automatically registers the data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[1398] 8. The server performs a final check to ensure that all data is accurate. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback tailored to the user's emotional state using sentiment analysis tools.

[1399] Hardware and software to be used

[1400] This system uses the following hardware and software.

[1401] Server: This server collects, formats, and generates data, runs AI models, and notifies the results. Specifically, it uses a web server framework such as Flask.

[1402] Generative AI model: Detects defects based on data received from the user. Specifically, it utilizes the BERT or BART model from the transformers library.

[1403] Sentiment analysis method: The emotional state is analyzed from the user's input information and actions. Specifically, the sentiment analysis model (facebook / bart-large-mnli) from the transformers library is used.

[1404] Terminal: A device used by users to submit application forms and requests. Specifically, this includes smartphones and PCs.

[1405] Specific example

[1406] For example, if the user enters the following:

[1407] Name: Taro Yamada

[1408] Email: yamada@example

[1409] Phone: 12345

[1410] The server receives the data, formats it, and inputs it into a generative AI model. The generative AI model detects any errors in the phone number and analyzes the user's state using sentiment analysis. If the sentiment analysis determines that the user is confused, the server sends a considerate message such as, "The phone number must be at least 10 digits long. Are you having trouble? You can find detailed instructions at this link."

[1411] Example of a prompt

[1412] text

[1413] emotion_analyzer('Username: Taro Yamada, Email: yamada@example, Phone: 12345')

[1414] In this way, the system can operate efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[1415] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1416] Step 1:

[1417] Users submit application and request forms via an online portal or mobile app using their device (smartphone or PC). At this stage, users enter all necessary information, including their name, address, phone number, and desired services. The device then transmits this information to the server.

[1418] Step 2:

[1419] The server retrieves application and request form data received from users. The retrieved data is received in a format such as JSON. The server then formats this data and converts it into a format that is easy for the generating AI model to process. Specifically, it performs data cleaning, such as appropriately mapping the data fields and removing unnecessary spaces and special characters.

[1420] Step 3:

[1421] The server inputs the formatted data into a generative AI model for error detection. The generative AI model has learned from past data and can detect errors with high accuracy. At this stage, the input is formatted data, and the output is information about the presence or absence of errors and their details. For example, it can detect incorrect phone number formats or blank required fields.

[1422] Step 4:

[1423] The server analyzes the user's emotional state using an emotion analysis tool. Specifically, it inputs the user's input string as a prompt into an emotion analysis model (facebook / bart-large-mnli) to determine whether the emotion is positive, negative, or neutral. The input is the user's input string, and the output is the result of the emotion determination.

[1424] Example of a prompt

[1425] text

[1426] emotion_analyzer('Username: Taro Yamada, Email: yamada@example, Phone: 12345')

[1427] Step 5:

[1428] The server generates appropriate feedback and notifies the user based on the details of the detected flaws and the results of sentiment analysis. This specific feedback includes instructions on how to correct the flaws and messages tailored to the user's emotional state (e.g., "Phone numbers must be at least 10 digits long. Need help? You can find detailed instructions at this link."). The input is the flaw details and sentiment analysis results, and the output is the appropriate feedback message.

[1429] Step 6:

[1430] The user receives a notification from the server and modifies the application and request forms. After modification, the user resubmits the application and request forms from the terminal. The terminal then resends the modified data to the server.

[1431] Step 7:

[1432] The server receives the corrected data again and performs a re-examination. At this stage, it checks for any flaws in the formatted data using the generation AI model again. The input is the corrected data, and the output is the result of the second flaw detection.

[1433] Step 8:

[1434] The server automatically registers data that has passed inspection into the business system. This process utilizes an API (Application Programming Interface) to interact with the business system. The input is the data that has passed inspection, and the output is the result of registration into the business system.

[1435] Step 9:

[1436] The server performs a final check of the data registered in the business system to verify its integrity and accuracy. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback tailored to the user's emotional state using sentiment analysis tools. The input is the result of the data registered in the business system, and the output is the registration completion notification.

[1437] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1438] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1439] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1440] [Fourth Embodiment]

[1441] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1442] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1443] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1444] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1445] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1446] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1447] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1448] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1449] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1450] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1451] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1452] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1453] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1454] This invention is a system that automatically detects deficiencies in application forms and request forms submitted by users for mobile registration, recommends corrections as necessary, and then automatically inputs the information into the registration system. This section describes specific embodiments of the system.

[1455] System Overview

[1456] This system consists primarily of servers, terminals, and users, and provides the following functions:

[1457] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[1458] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[1459] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[1460] 4. If a defect is detected, the server will notify the user of the defect and how to correct it.

[1461] 5. Correct the deficiencies pointed out by the user and resubmit.

[1462] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the business system.

[1463] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed.

[1464] Program Processing Description

[1465] 1. The user submits the application form and request form using a terminal. At this point, all necessary information should be entered, but there may be input errors or omissions.

[1466] 2. The server receives the application and request form data and first formats the data. This includes properly mapping the data to individual fields. For example, it distributes information to fields such as name, address, and phone number, and removes unnecessary spaces and special characters.

[1467] 3. The server inputs the formatted data into the AI ​​model. This AI model has learned from past data and can detect patterns of errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[1468] 4. If a problem is detected, the server will notify the user. The notification field will include the nature of the problem and specific instructions on how to fix it. For example, a message such as "The phone number is invalid. Please enter a number with 8 or more digits." might be sent.

[1469] 5. The user receives a notification from the server and revises the application form and request form. After revision, they resubmit them using their device.

[1470] 6. The server receives the corrected data again and performs a re-check. If the deficiencies are resolved, it proceeds to the next step.

[1471] 7. The server automatically inputs the data that has passed inspection into the business system. This process involves integrating with the business system using an API (Application Programming Interface).

[1472] 8. The server performs a final check of the data registered in the business system to verify the integrity and accuracy of the data.

[1473] 9. After the final check is complete, the server will notify the user that the registration has been successfully completed. This notification will also include a summary of the registered information.

[1474] Specific example

[1475] Examples of detecting and correcting errors in phone numbers

[1476] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[1477] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[1478] 3. The server uses an AI model to perform an inspection and detects an error in the phone number. It then sends a notification to the user stating, "The phone number must have at least 8 digits."

[1479] 4. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[1480] 5. The server receives the corrected data again and checks it using the AI ​​model once more.

[1481] 6. The server automatically inputs the data that has passed inspection into the business system.

[1482] 7. The server performs a final check to ensure that all data is accurate.

[1483] 8. The server notifies the user that mobile registration is complete.

[1484] In this way, the entire system operates efficiently, allowing for rapid detection, correction, and registration of user errors. This significantly reduces user effort and improves the efficiency and accuracy of the registration process.

[1485] The following describes the processing flow.

[1486] Step 1:

[1487] Users submit mobile registration application forms and requests via an online portal or mobile app using their devices. At this point, users enter all necessary information, but errors or omissions may occur.

[1488] Step 2:

[1489] The server receives application and request forms submitted by users. The received data includes names, addresses, phone numbers, and desired mobile plans.

[1490] Step 3:

[1491] The server analyzes the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[1492] Step 4:

[1493] The server inputs the formatted data into an AI model, which then detects errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect incorrect phone number formats or blank required fields.

[1494] Step 5:

[1495] The server notifies the user of any errors detected by the AI ​​model, including specific instructions on how to correct them. For example, a message might be sent such as, "The phone number is invalid. Please enter a number with 8 or more digits."

[1496] Step 6:

[1497] The user receives a notification from the server and corrects the identified deficiencies. After making the corrections, the user resubmits the application form and request form from their device.

[1498] Step 7:

[1499] The server receives the corrected data again and performs a re-examination. At this point, the AI ​​model is used again to check for any flaws in the formatted data.

[1500] Step 8:

[1501] The server automatically inputs data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[1502] Step 9:

[1503] The server re-imports the data registered in the business system and performs a final accuracy check. This confirms that all data is accurate.

[1504] Step 10:

[1505] The server confirms that all processing has been completed successfully and notifies the user that registration is complete. This notification also includes a summary of the registration details.

[1506] (Example 1)

[1507] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1508] Current online registration systems frequently suffer from input errors and missing information when users submit application forms and requests, often requiring manual corrections and resubmissions. This process is not only cumbersome and time-consuming for users but also places a burden on system administrators. Furthermore, manual checking and correction carries the risk of human error. Therefore, there is a growing demand for systems that automatically detect deficiencies and recommend corrections.

[1509] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1510] In this invention, the server includes means for acquiring application and request form data received from a user; means for formatting the acquired data and converting it into a format that is easy for an AI model to process; means for inputting the formatted data into the AI ​​model and detecting defects; means for notifying the user of the detected defects and how to correct them; means for inputting the corrected data or data that was free of defects from the beginning into the AI ​​model again and performing an inspection; means for automatically registering the data that has passed the inspection into a data management system; means for performing a final check of the registered data; and means for notifying the user that the registration is complete. This makes it possible to quickly and accurately detect defects in the data entered by the user and provide instructions for correction, thereby significantly improving the efficiency and accuracy of the registration process.

[1511] A "user" refers to an individual or legal entity that uses the system to submit application forms and requests.

[1512] "Application forms and request forms" refer to formal documents that users submit when registering or applying for services.

[1513] "Terminal" refers to a mobile device or computer used by a user to input application forms and requests and send them to the server.

[1514] A "server" refers to computing resources used to receive, process, and store data sent by users.

[1515] "Data" refers to the collection of information that users input and that servers process.

[1516] "Formatting" refers to the process of converting data received from a user into an appropriate format.

[1517] An "AI model" refers to a program that uses artificial intelligence technology to detect defects and analyze data.

[1518] "Deficiencies" refer to errors or missing information contained in application forms and request forms.

[1519] A "notification" refers to a message sent from a server to a user informing them of the nature of a problem and how to fix it.

[1520] "Re-examination" refers to the process in which corrected data is checked again by the AI ​​model.

[1521] A "data management system" refers to a system used to register and manage data that has passed inspection.

[1522] "Final check" refers to the inspection process to verify the accuracy and consistency of data registered in the data management system.

[1523] A "registration completion notification" refers to a message sent from the server to the user informing them that data registration has been successfully completed.

[1524] This invention is a system that automatically detects deficiencies in application forms and request forms submitted by users for mobile registration, recommends corrections as necessary, and then automatically inputs the information into the registration system. This section describes specific embodiments of the system.

[1525] System Configuration

[1526] This system consists primarily of servers, terminals, and users, and provides the following functions:

[1527] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[1528] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[1529] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[1530] 4. If a defect is detected, the server will notify the user of the defect and how to correct it.

[1531] 5. Correct the deficiencies pointed out by the user and resubmit.

[1532] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the data management system.

[1533] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed.

[1534] Hardware and software to be used

[1535] Device: Smartphone, tablet, or personal computer. This allows users to submit application forms and requests via an online portal or mobile app.

[1536] Server: Uses high-performance computing resources to receive, format, input into AI models, detect errors, send notifications, perform final checks, and automatically register data to the data management system.

[1537] AI model: A model trained using machine learning algorithms, which learns from past data and detects patterns of defects with high accuracy.

[1538] Data Management System: A database system that manages data after registration. It interacts with the server via an API.

[1539] Specific example

[1540] Examples of detecting and correcting errors in phone numbers

[1541] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[1542] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[1543] 3. The server uses an AI model to perform an inspection and detects any deficiencies in the phone number. It then sends a notification to the user stating, "The phone number must have at least 8 digits."

[1544] 4. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[1545] 5. The server receives the corrected data again and performs a re-inspection.

[1546] 6. The server automatically inputs the data that has passed the inspection into the data management system.

[1547] 7. The server performs a final check to ensure that all data is accurate.

[1548] 8. The server notifies the user that registration has been successfully completed.

[1549] Example of a prompt

[1550] Please enter the following information: Full Name: [First Name], Address: [Address], Phone Number: [Phone Number]. If there are any errors, please provide detailed feedback.

[1551] This system allows for the rapid detection, correction, and registration of user errors, significantly reducing user effort and improving the efficiency and accuracy of the registration process.

[1552] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1553] Step 1:

[1554] Users submit application and request forms from their devices via online portals or mobile apps. Input includes information such as name, address, and phone number. Specifically, they enter the required data into a form on a GUI (Graphical User Interface) and press the submit button.

[1555] Input: Information such as name, address, and phone number.

[1556] Output: Data transmission from terminal to server

[1557] Step 2:

[1558] The server receives data from the user. Specifically, a data receiving module captures the data sent from the terminal and temporarily stores it within the server.

[1559] Input: Application form and request form data sent from the terminal.

[1560] Output: Temporary storage of received data within the server.

[1561] Step 3:

[1562] The server formats the received data. Specifically, it distributes information into fields such as name, address, and phone number, and removes unnecessary spaces and special characters. It also converts the data into JSON or XML format.

[1563] Input: Temporarily saved application and request form data

[1564] Output: Formatted data (in JSON or XML format)

[1565] Step 4:

[1566] The server inputs the formatted data into an AI model, which then detects any errors. The AI ​​model has learned from past data and can detect things like whether the phone number format is correct and whether required fields are not left blank.

[1567] Input: Formatted data

[1568] Output: Results of defect detection (presence or absence of defects and details)

[1569] Step 5:

[1570] The server notifies the user of any detected vulnerabilities and how to correct them. For example, a message such as "Your phone number is invalid. Please enter a number with 8 or more digits." might be generated.

[1571] Input: Results of detecting defects

[1572] Output: Notification message to send to the user

[1573] Step 6:

[1574] The user receives a notification from the server and corrects any errors in the application and request forms. After correction, they resubmit the data. Specifically, this involves re-entering the correct information into the GUI form and pressing the submit button.

[1575] Input: Error notification from server

[1576] Output: Resubmission of corrected data

[1577] Step 7:

[1578] The server receives the corrected data again and performs another inspection using the AI ​​model. The same process is repeated to check for any further defects.

[1579] Input: Correction data resubmitted by the user

[1580] Output: Re-inspection results (presence or absence of defects)

[1581] Step 8:

[1582] The server automatically registers data that has passed re-inspection into the data management system. The data is then transferred to the management system using an API to complete the registration.

[1583] Input: Data that passed retesting

[1584] Output: Data registered in the data management system

[1585] Step 9:

[1586] The server performs a final check of the data registered in the data management system. It verifies the integrity and accuracy of the data and confirms that all data has been registered properly.

[1587] Input: Data registered in the data management system

[1588] Output: Final check results

[1589] Step 10:

[1590] The server notifies the user that registration is complete. The notification may include a message such as, "Registration completed successfully."

[1591] Input: Final check result

[1592] Output: Registration completion notification to send to the user

[1593] (Application Example 1)

[1594] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1595] In conventional systems, errors frequently occurred when users submitted online applications, and correcting these errors was time-consuming and cumbersome. Furthermore, the complex process of detecting and correcting errors raised concerns about a degraded user experience. There was also a need for an efficient system specifically designed for registration applications in a virtual environment.

[1596] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1597] In this invention, the server includes means for a user to submit a registration application in a virtual environment using an electronic device, means for formatting the acquired information and converting it into a format that is easy for an artificial intelligence model to process, and means for inputting the formatted information into the artificial intelligence model and detecting errors. This makes it possible for users to submit online applications in a virtual environment easily and quickly.

[1598] A "user" is an individual or legal entity that uses the system to submit application documents.

[1599] An "application document" is a document submitted by a user for registration applications and requests.

[1600] "Information" refers to the data and content included in the application document.

[1601] "Formatting" refers to the process of converting acquired information into a format that is easily processed by artificial intelligence models.

[1602] An "artificial intelligence model" is a computer program used for machine learning and data analysis, and is particularly used to detect flaws and errors.

[1603] An "error" refers to inaccurate or incomplete information contained in the application document.

[1604] A "business platform" is a system that centrally manages registration application information and automates and streamlines related operations.

[1605] "Final check" is the process of ultimately verifying the consistency and accuracy of the registered information.

[1606] "Electronic devices" refer to electronic devices such as smartphones, tablets, or personal computers.

[1607] A "virtual environment" is an online environment that utilizes the internet and virtual reality technology.

[1608] "Message notifications" are a means of communicating information or correction instructions to users, and often involve using email or mobile apps.

[1609] This invention is a system that automatically detects the content of application documents submitted by users in a virtual environment, recommends corrections if errors are found, and automatically reflects the corrected information in the business platform once accurate information has been entered. The details of the system and its processes are described below.

[1610] System Configuration

[1611] This system consists of the following elements:

[1612] hardware

[1613] 1. Electronic devices: Users access the virtual environment and submit registration applications using electronic devices such as smartphones, tablets, or personal computers.

[1614] 2. Server: The central device that receives application document data and performs formatting, inspection, correction, registration, and final checks.

[1615] software

[1616] 1. Mobile application: An application used by users on electronic devices. It provides an interface for submitting and modifying application documents.

[1617] 2. Artificial Intelligence Models: AI models used to detect flaws. Specific examples include natural language processing models such as BERT and GPT.

[1618] 3. Business Platform: A system for managing registration application information and streamlining operations.

[1619] Details of the implementation

[1620] User submission of application documents

[1621] Users register in a virtual environment using electronic devices such as smartphones. Specifically, they input application documents through a mobile application and submit them to the server.

[1622] Data formatting and processing using AI models

[1623] The server formats the data from the application documents received from the user. Specifically, it appropriately allocates information to each field and removes unnecessary spaces and special characters. This formatted data is then input into an artificial intelligence model and checked for errors and deficiencies.

[1624] Detecting and recommending corrections for defects

[1625] Artificial intelligence models perform highly accurate error detection by learning from past data. For example, if the phone number format is incorrect, a notification will be sent to the user stating, "The phone number is invalid. Please enter a number with 8 or more digits." The user then corrects the data according to the instructions and resubmits it.

[1626] Automatic registration and final check

[1627] The server automatically registers data that has passed inspection into the business platform. After registration, a final check is performed to confirm that all data is accurate. This notifies the user that registration is complete.

[1628] Specific examples and prompt statements

[1629] Specific example:

[1630] 1. The user submits an application document with "example@com" entered in the email field.

[1631] 2. The server receives the data and inputs the formatted data into the artificial intelligence model.

[1632] 3. The server uses an AI model to perform an inspection and detects an error in the email address. It then sends a notification to the user stating, "The format of your email address is incorrect. Please enter the correct format."

[1633] 4. The user receives a notification from the server, corrects the email address to "example@example.com", and resubmits.

[1634] 5. The server receives the corrected data again and checks it using the AI ​​model once more.

[1635] 6. The server automatically inputs the data that has passed inspection into the business platform.

[1636] 7. The server performs a final check to ensure that all data is accurate.

[1637] 8. The server notifies the user that registration is complete.

[1638] Example of a prompt:

[1639] Please review the application forms submitted by users and detect any deficiencies. If deficiencies are found, please notify the user of the specific steps required to correct them.

[1640] These measures will enable users to submit online applications efficiently and quickly in a virtual environment. Furthermore, it is expected that the overall accuracy and reliability of the system will improve, contributing to a better user experience.

[1641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1642] Step 1:

[1643] Users access a mobile application in a virtual environment using an electronic device (smartphone, tablet, or PC), and fill out and submit application documents.

[1644] Input: Data from the application document entered by the user.

[1645] Output: Data from application documents sent to the server via the mobile application.

[1646] Step 2:

[1647] The server formats the data from the received application documents. First, it appropriately allocates information to each field and removes unnecessary spaces and special characters.

[1648] Input: Raw data from the application document received from the user.

[1649] Output: Formatted data.

[1650] Specific actions: Information mapping to fields, removal of unnecessary spaces and special characters.

[1651] Step 3:

[1652] The server inputs the formatted data into an artificial intelligence model to detect errors. By learning from past data, the AI ​​model can identify flaws such as incorrect formatting of phone numbers and email addresses.

[1653] Input: Formatted data.

[1654] Output: Whether or not there are errors and details of the errors.

[1655] Specific actions: Data inspection and analysis using AI models.

[1656] Step 4:

[1657] The server will notify the user if any problems are detected. The notification will include specific instructions on how to fix the problem.

[1658] Input: Details of errors detected by the AI ​​model.

[1659] Output: Correction notification sent to the user.

[1660] Specific action: Sending notifications via email or mobile app.

[1661] Step 5:

[1662] The user receives a notification from the server and corrects any deficiencies in the application document. After correction, they resubmit it through the mobile application.

[1663] Input: Correction notification from the server.

[1664] Output: Resubmission of the revised application document.

[1665] Specific actions: Users can modify and resubmit application documents.

[1666] Step 6:

[1667] The server reformats the corrected data that has been re-received, inputs it into the AI ​​model, and performs a re-examination. If the deficiencies are resolved during the re-examination, it proceeds to the next step.

[1668] Input: User-submitted modification data.

[1669] Output: Data with errors corrected as a result of re-examination.

[1670] Specific actions: Data reshaping and re-examination using an AI model.

[1671] Step 7:

[1672] The server automatically registers data that passes inspection into the business platform. It interacts with the business platform via an API.

[1673] Input: Accurate data with all errors corrected.

[1674] Output: Data registered on the business platform.

[1675] Specific operation: Data registration to a business platform using an API.

[1676] Step 8:

[1677] The server performs a final check on the registered data to verify its integrity and accuracy. After the final check is complete, it sends a registration completion notification to the user.

[1678] Input: Data registered on the business platform.

[1679] Output: Registration completion notification sent to the user.

[1680] Specific actions: Final check of data and sending registration completion notifications to users.

[1681] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1682] This invention is a system that automatically detects whether there are any deficiencies in the application and request forms for mobile registration submitted by users, recommends corrections as necessary, and further provides feedback and support tailored to the user's emotional state. This section describes specific embodiments of the system.

[1683] System Overview

[1684] This system consists primarily of a server, terminal, user, and emotion engine, and provides the following functions:

[1685] 1. Users submit application forms and request forms from their devices via an online portal or mobile app.

[1686] 2. The server receives data from the user, formats this data, and converts it into a format that is easy for the AI ​​model to process.

[1687] 3. The server inputs the formatted data into the AI ​​model and detects any deficiencies.

[1688] 4. If a defect is detected, the server will notify the user of the defect and how to correct it. At this time, the emotion engine will be used to analyze the user's emotional state and provide appropriate feedback.

[1689] 5. Correct the deficiencies pointed out by the user and resubmit.

[1690] 6. The server re-examines the corrected data and, if there are no problems, automatically registers it in the business system.

[1691] 7. The server performs a final check on the registered data and notifies the user that the registration has been successfully completed. At this time, the emotion engine is also used to provide appropriate feedback.

[1692] Program Processing Description

[1693] 1. The user submits the application form and request form using a terminal. While all necessary information is entered, input errors or omissions may occur.

[1694] 2. The server receives the data from the application and request forms. The received data includes name, address, phone number, desired mobile plan, etc.

[1695] 3. The server formats the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[1696] 4. The server inputs the formatted data into the AI ​​model to detect errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[1697] 5. The server notifies the user of any deficiencies detected by the AI ​​model. This notification includes feedback detailing the deficiency and specific correction methods. Furthermore, the system uses an emotion engine to analyze the user's emotional state and provide appropriate feedback. For example, if the user is feeling stressed, a considerate message such as, "If the steps are unclear, please refer to this detailed guide," is sent.

[1698] 6. The user receives a notification from the server and revises the application form and request form. After revision, the user resubmits the application form and request form from their device.

[1699] 7. The server receives the corrected data again and performs a re-examination. At this time, the AI ​​model is used again to check for any defects in the formatted data.

[1700] 8. The server automatically registers the data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[1701] 9. The server performs a final check of the data registered in the business system to verify the integrity and accuracy of the data.

[1702] 10. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback based on the user's emotional state using the emotion engine.

[1703] Specific example

[1704] Examples of detecting errors in phone numbers and providing emotion-responsive feedback.

[1705] 1. The user submits an application form in which they have entered "12345" in the phone number field.

[1706] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[1707] 3. The server uses an AI model to perform an inspection and detects any discrepancies in the phone number.

[1708] 4. The server uses an emotion engine to determine from the user's input process that the user is confused. In this case, it sends a helpful message such as, "Your phone number must be at least 8 digits long. Having trouble? You can find detailed instructions at this link."

[1709] 5. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[1710] 6. The server receives the corrected data again and checks it using the AI ​​model once more.

[1711] 7. The server automatically inputs the data that has passed inspection into the business system.

[1712] 8. The server performs a final check to ensure that all data is accurate.

[1713] 9. The server uses an emotion engine to determine that the user is in a comfortable state and sends a simple message: "Mobile registration complete. Thank you for your cooperation."

[1714] In this way, the system can operate efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[1715] The following describes the processing flow.

[1716] Step 1:

[1717] Users submit mobile registration application forms and requests via an online portal or mobile app using their devices. While users enter all necessary information, errors or omissions may occur.

[1718] Step 2:

[1719] The server receives application and request forms submitted by users. The received data includes names, addresses, phone numbers, and desired mobile plans.

[1720] Step 3:

[1721] The server analyzes the received data and maps it appropriately to each field (e.g., name, address, phone number, etc.). It also performs data cleaning to remove unnecessary spaces and special characters.

[1722] Step 4:

[1723] The server inputs the formatted data into an AI model, which then detects errors. The AI ​​model has learned from past data and can detect errors with high accuracy. For example, it can detect incorrect phone number formats or blank required fields.

[1724] Step 5:

[1725] The server notifies the user of any issues detected by the AI ​​model, including specific correction methods. During this process, an emotion engine is used to analyze the user's emotional state and generate appropriate feedback. For example, if the user is irritated, the message might be rephrased to be more polite and helpful.

[1726] Step 6:

[1727] The user receives a notification from the server and corrects the identified deficiencies. After making the corrections, they resubmit the application form and request form from their device.

[1728] Step 7:

[1729] The server receives the corrected data again and performs a re-examination. The AI ​​model is used again to check for any flaws in the formatted data.

[1730] Step 8:

[1731] The server automatically registers data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[1732] Step 9:

[1733] The server re-imports the data registered in the business system and performs a final accuracy check. This confirms that all data is accurate.

[1734] Step 10:

[1735] The server confirms that all processing has been completed successfully and notifies the user that registration is complete. This notification includes a summary of the registration details, but also uses an emotion engine to provide appropriate feedback based on the user's emotional state. For example, if the user is feeling reassured, a simple message such as "Mobile registration complete. Thank you for your cooperation." is sent.

[1736] (Example 2)

[1737] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1738] In conventional systems, if there were errors in application forms or requests submitted by users, corrections had to be made manually, which was time-consuming and laborious, and sometimes did not provide users with appropriate feedback. Furthermore, the lack of consideration for the user's emotional state resulted in a poor user experience. A system is needed to address these issues.

[1739] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1740] In this invention, the server includes means for acquiring application and request form data received from a user; means for formatting the acquired data and converting it into a format easily processed by a generation AI model; means for inputting the formatted data into the generation AI model and detecting defects; means for notifying the user of the detected defects and how to correct them; means for the notification means to analyze the user's emotional state in response to the detected defects and provide appropriate feedback; means for inputting the data corrected by the user or data that has been defect-free from the beginning into the generation AI model again and performing an inspection; means for automatically registering the data that has passed the inspection into the business system; means for performing a final check of the registered data; and means for notifying the user that the registration is complete. This makes it possible to quickly detect and correct defects in the user's application and request forms and to provide flexible feedback that corresponds to the user's emotional state.

[1741] A "user" refers to an individual or legal entity that accesses the system and submits application forms and requests.

[1742] A "server" refers to a computing system that processes data received from users, detects errors, provides feedback, registers data, and performs final checks.

[1743] An "application form" refers to an official document submitted by a user to apply for services such as mobile registration.

[1744] A "request form" refers to an official document submitted by a user to request a specific service or action.

[1745] "Means of acquiring data" refers to the function or process for receiving information from users in application forms and request forms.

[1746] "Means of formatting data" refers to a function or process for converting received data into a format that is easy for the generating AI model to process.

[1747] A "generative AI model" refers to an algorithmic model that uses machine learning and artificial intelligence technologies to detect data flaws with high accuracy.

[1748] "Means of inputting data into an AI model" refers to a function or process that sends formatted data to a generating AI model to detect defects.

[1749] "Means for detecting defects" refers to a function or process for finding defects in data using a generative AI model.

[1750] "Means of notifying users of how to correct defects" refers to a function or process for informing users of the nature of the detected defects and how to correct them.

[1751] "Means for analyzing emotional states" refers to a function or process for evaluating a user's emotional state and providing appropriate feedback based on that state.

[1752] "Means of re-inputting data into the AI ​​model and performing inspection" refers to a function or process of re-inputting corrected data into the generated AI model and performing a re-inspection.

[1753] "Means of automatically registering data in a business system" refers to a function or process for automatically inputting and saving data that has passed inspection into a business system.

[1754] "Means of final data checking" refers to a function or process for finally verifying the data registered in a business system to confirm its consistency and accuracy.

[1755] "Means of notifying that registration is complete" refers to a function or process that informs the user that data registration has been successfully completed.

[1756] Modes for carrying out the invention

[1757] This invention is a system that automatically detects whether there are any deficiencies in the application forms and request forms submitted by users, recommends corrections as necessary, and further provides feedback and support tailored to the user's emotional state.

[1758] This system primarily consists of a server, terminals, users, and an emotion engine. The roles and functions of each component are explained in detail below.

[1759] User

[1760] Users submit application and request forms through online portals or mobile applications using internet-connected devices (such as PCs and smartphones). While they enter all necessary information, errors or omissions may occur.

[1761] server

[1762] The server receives data submitted by users and performs a series of processes including data formatting, AI model detection of errors, sentiment analysis and feedback provision, data re-examination, automated registration, final check, and completion notification. The specific virtual machines and cloud services used are not platform-dependent, but AWS and Microsoft Azure are commonly used.

[1763] 1. Receiving data: The server receives user input data via an HTTP request. This data includes name, address, phone number, desired mobile plan, etc.

[1764] 2. Data Formatting and Cleaning: Format the received data, map it appropriately to each field, and remove unnecessary spaces and special characters. Specifically, clean the data using string manipulation and regular expressions.

[1765] 3. Detection of errors: The formatted data is input into an AI model to detect any errors. Because the AI ​​model has learned from past data, it can detect errors such as misspellings in names, incorrect phone number formats, and missing required fields with high accuracy.

[1766] 4. Notification of defects and corrective methods: Users will be notified of defects detected by the AI ​​model. Feedback will be provided that includes the nature of the defect and specific corrective methods. The emotion engine will be used to analyze the user's emotional state and provide appropriate feedback. For example, if it is determined that the user is confused, consideration will be given to providing detailed guidance.

[1767] 5. Re-examination: The user's corrected data is received again and re-examined. The AI ​​model is used again for re-formatting and checking for errors in the data.

[1768] 6. Automatic Data Registration: Data that passes inspection is automatically registered in the business system. An API (Application Programming Interface) is used for integration with the business system.

[1769] 7. Final Check: Perform a final check of the data registered in the business system to confirm data integrity and accuracy.

[1770] 8. Registration Completion Notification: After the final check is complete, the server notifies the user that registration is complete. Using the emotion engine, appropriate feedback is provided according to the user's emotional state.

[1771] Specific example

[1772] Examples of detecting errors in phone numbers and providing emotion-responsive feedback.

[1773] 1. The user enters "12345" in the phone number field and submits the application form.

[1774] 2. The server receives the data and inputs the formatted data into the AI ​​model.

[1775] 3. The server uses an AI model to perform an inspection and detects any discrepancies in the phone number.

[1776] 4. The server uses an emotion engine to determine from the user's input process that the user is confused. In this case, it sends a helpful message such as, "Your phone number must be at least 8 digits long. Having trouble? You can find detailed instructions at this link."

[1777] 5. The user receives a notification from the server, corrects the phone number to "12345678", and resubmits.

[1778] 6. The server receives the corrected data again and checks it using the AI ​​model once more.

[1779] 7. The server automatically inputs the data that has passed inspection into the business system.

[1780] 8. The server performs a final check to ensure that all data is accurate.

[1781] 9. The server uses an emotion engine to determine that the user is in a comfortable state and sends a simple message: "Mobile registration complete. Thank you for your cooperation."

[1782] Examples of prompt statements

[1783] "There is an error in the phone number field on your application form. Please enter a phone number with 8 or more digits."

[1784] As described above, the system operates efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[1785] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1786] Program processing flow

[1787] (Step 1:)

[1788] Users fill out application and request forms using their devices and submit them through an online portal or mobile application.

[1789] Input: Name, address, phone number, desired mobile plan, and other information.

[1790] Output: Data from application forms and request forms submitted by the user.

[1791] Specific actions:

[1792] The user enters the necessary information on the device and clicks the "Submit" button.

[1793] (Step 2:)

[1794] The server receives data submitted by the user.

[1795] Input: Data from application forms and request forms submitted by the user.

[1796] Output: Received data

[1797] Specific actions:

[1798] The server parses the HTTP request and retrieves the user input data contained within it.

[1799] (Step 3:)

[1800] The server formats and cleans the data it receives.

[1801] Input: Received data

[1802] Output: Formatted and cleaned data

[1803] Specific actions:

[1804] The server splits the data into fields and removes unnecessary spaces and special characters. For example, it converts "123-456-7890" to "1234567890" and maps each field appropriately.

[1805] (Step 4:)

[1806] The server generates formatted data, which is then input into an AI model for error detection.

[1807] Input: Formatted and cleaned data

[1808] Output: Results indicating whether or not there are defects.

[1809] Specific actions:

[1810] The server passes the formatted data to the AI ​​model, which checks for typos in names, formatting errors in phone numbers, and missing required fields.

[1811] (Step 5:)

[1812] The server notifies users of any detected vulnerabilities and how to fix them, and provides emotionally responsive feedback.

[1813] Input: Result indicating whether or not there are defects.

[1814] Output: User notification of the deficiency and how to fix it, and sentiment feedback.

[1815] Specific actions:

[1816] The server notifies the user of any problems, providing information on the detected problems and how to fix them. An emotion engine is used to analyze the user's emotional state and provide appropriate feedback.

[1817] (Step 6:)

[1818] The user corrects the reported deficiencies and resubmits the application and request forms.

[1819] Input: Data from the revised application and request forms.

[1820] Output: Resubmitted data

[1821] Specific actions:

[1822] The user receives a notification from the server, makes the necessary corrections, and clicks the "Submit" button again.

[1823] (Step 7:)

[1824] The server receives the corrected data again and performs a re-examination.

[1825] Input: Resubmitted data

[1826] Output: Re-examined data

[1827] Specific actions:

[1828] The server receives the corrected data again and uses the AI ​​model to check for any flaws in the formatted data once more.

[1829] (Step 8:)

[1830] The server automatically registers data that has passed inspection into the business system.

[1831] Input: Retested data

[1832] Output: Data registered in the business system

[1833] Specific actions:

[1834] Use an API to send data to a business system and register it automatically.

[1835] (Step 9:)

[1836] The server performs a final check on the data registered in the business system.

[1837] Input: Data registered in the business system

[1838] Output: Final check results

[1839] Specific actions:

[1840] The registered information in the database will be checked again for consistency and accuracy.

[1841] (Step 10:)

[1842] The server notifies the user that registration is complete and provides emotionally appropriate feedback.

[1843] Input: Final check result

[1844] Output: Completion message notified to the user

[1845] Specific actions:

[1846] The server sends the user a success message and appropriate feedback based on the emotion engine. For example, it might send a message like, "Mobile registration complete. Thank you for your cooperation."

[1847] (Application Example 2)

[1848] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1849] Traditional user registration systems perform error checking on user-entered information, but the feedback provided when errors occur is insufficient, and support that takes into account the user's emotional state is not offered, resulting in a poor user experience. Furthermore, the accuracy of error checking and the methods for providing corrections remain as challenges.

[1850] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring application form and request form data received from the user, means for formatting the acquired data and converting it into a format that is easy for the generating AI model to process, means for inputting the formatted data into the generating AI model and detecting defects, means for notifying the user of the detected defects and how to correct them, means for sentiment analysis for analyzing the user's emotional state, means for providing feedback according to the user's emotional state using the sentiment analysis means, means for inputting the corrected data or data that has no defects from the beginning into the generating AI model again and performing an inspection, means for automatically registering the data that has passed the inspection into the business system, means for performing a final check of the registered data, and means for notifying the user that the registration is complete. As a result, the accuracy of error checking and error correction during user input is improved, and the user experience can be improved by providing appropriate feedback according to the user's emotional state.

[1851] "User" refers to a person who uses the system to submit an application form or request form.

[1852] An "application form" refers to a document containing the information necessary for a user to register for the service.

[1853] A "request form" refers to a document submitted by a user to request a specific service or product.

[1854] A "server" is a computer system that processes data received from users and interacts with other systems.

[1855] A "generative AI model" refers to an artificial intelligence model that learns from past data and is used to detect flaws in new input data.

[1856] "Formatting" refers to the process of converting acquired data into a format that is easy for the generating AI model to process.

[1857] "Emotional analysis methods" refer to methods for analyzing a user's emotional state based on their input information and behavior.

[1858] A "business system" refers to a system that uses registered data to perform actual business operations.

[1859] "Notification methods" refer to means of communicating information to users, such as defects or how to correct them.

[1860] This invention is a system that automatically detects the contents of application forms and request forms submitted by users, recommends revisions as necessary, and further provides feedback and support tailored to the user's emotional state.

[1861] System Overview

[1862] This system consists primarily of a server, terminals, and sentiment analysis tools, and implements the following functions.

[1863] 1. Users submit application forms and requests via online portals or mobile apps using their devices. While users enter all necessary information, input errors or omissions may occur.

[1864] 2. The server retrieves application and request forms received from users, formats the data, and converts it into a format that is easy for the generating AI model to process. This data includes names, addresses, phone numbers, and desired services.

[1865] 3. The server inputs the formatted data into a generation AI model to detect errors. The generation AI model has learned from past data and is capable of detecting errors with high accuracy. For example, it can detect cases where the phone number format is incorrect or required fields are left blank.

[1866] 4. The server analyzes the user's emotional state using sentiment analysis tools and provides appropriate feedback if any issues are detected. For example, if it determines that the user is experiencing stress, it will send a considerate message such as, "If the procedure is unclear, please refer to this detailed guide."

[1867] 5. The user receives a notification from the server and revises the application form and request form. After revision, the user resubmits the application form and request form from their device.

[1868] 6. The server receives the corrected data again and performs a re-examination. At this time, it checks whether there are any defects in the data that has been formatted using the generated AI model again.

[1869] 7. The server automatically registers the data that has passed inspection into the business system. This input process utilizes an API (Application Programming Interface) to connect with the business system.

[1870] 8. The server performs a final check to ensure that all data is accurate. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback tailored to the user's emotional state using sentiment analysis tools.

[1871] Hardware and software to be used

[1872] This system uses the following hardware and software.

[1873] Server: This server collects, formats, and generates data, runs AI models, and notifies the results. Specifically, it uses a web server framework such as Flask.

[1874] Generative AI model: Detects defects based on data received from the user. Specifically, it utilizes the BERT or BART model from the transformers library.

[1875] Sentiment analysis method: The emotional state is analyzed from the user's input information and actions. Specifically, the sentiment analysis model (facebook / bart-large-mnli) from the transformers library is used.

[1876] Terminal: A device used by users to submit application forms and requests. Specifically, this includes smartphones and PCs.

[1877] Specific example

[1878] For example, if the user enters the following:

[1879] Name: Taro Yamada

[1880] Email: yamada@example

[1881] Phone: 12345

[1882] The server receives the data, formats it, and inputs it into a generative AI model. The generative AI model detects any errors in the phone number and analyzes the user's state using sentiment analysis. If the sentiment analysis determines that the user is confused, the server sends a considerate message such as, "The phone number must be at least 10 digits long. Are you having trouble? You can find detailed instructions at this link."

[1883] Example of a prompt

[1884] text

[1885] emotion_analyzer('Username: Taro Yamada, Email: yamada@example, Phone: 12345')

[1886] In this way, the system can operate efficiently, not only quickly detecting, correcting, and registering user errors, but also improving the user experience by responding flexibly to the user's emotional state.

[1887] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1888] Step 1:

[1889] Users submit application and request forms via an online portal or mobile app using their device (smartphone or PC). At this stage, users enter all necessary information, including their name, address, phone number, and desired services. The device then transmits this information to the server.

[1890] Step 2:

[1891] The server retrieves application and request form data received from users. The retrieved data is received in a format such as JSON. The server then formats this data and converts it into a format that is easy for the generating AI model to process. Specifically, it performs data cleaning, such as appropriately mapping the data fields and removing unnecessary spaces and special characters.

[1892] Step 3:

[1893] The server inputs the formatted data into a generative AI model for error detection. The generative AI model has learned from past data and can detect errors with high accuracy. At this stage, the input is formatted data, and the output is information about the presence or absence of errors and their details. For example, it can detect incorrect phone number formats or blank required fields.

[1894] Step 4:

[1895] The server analyzes the user's emotional state using an emotion analysis tool. Specifically, it inputs the user's input string as a prompt into an emotion analysis model (facebook / bart-large-mnli) to determine whether the emotion is positive, negative, or neutral. The input is the user's input string, and the output is the result of the emotion determination.

[1896] Example of a prompt

[1897] text

[1898] emotion_analyzer('Username: Taro Yamada, Email: yamada@example, Phone: 12345')

[1899] Step 5:

[1900] The server generates appropriate feedback and notifies the user based on the details of the detected flaws and the results of sentiment analysis. This specific feedback includes instructions on how to correct the flaws and messages tailored to the user's emotional state (e.g., "Phone numbers must be at least 10 digits long. Need help? You can find detailed instructions at this link."). The input is the flaw details and sentiment analysis results, and the output is the appropriate feedback message.

[1901] Step 6:

[1902] The user receives a notification from the server and modifies the application and request forms. After modification, the user resubmits the application and request forms from the terminal. The terminal then resends the modified data to the server.

[1903] Step 7:

[1904] The server receives the corrected data again and performs a re-examination. At this stage, it checks for any flaws in the formatted data using the generation AI model again. The input is the corrected data, and the output is the result of the second flaw detection.

[1905] Step 8:

[1906] The server automatically registers data that has passed inspection into the business system. This process utilizes an API (Application Programming Interface) to interact with the business system. The input is the data that has passed inspection, and the output is the result of registration into the business system.

[1907] Step 9:

[1908] The server performs a final check of the data registered in the business system to verify its integrity and accuracy. After the final check is complete, the server notifies the user that registration is complete. This notification includes a summary of the registration details, but also provides appropriate feedback tailored to the user's emotional state using sentiment analysis tools. The input is the result of the data registered in the business system, and the output is the registration completion notification.

[1909] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1910] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1911] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1912] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1913] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1914] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1915] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1916] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1917] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1918] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1919] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1920] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1921] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1922] 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.

[1923] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1924] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1925] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1926] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1927] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1928] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1929] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1930] The following is further disclosed regarding the embodiments described above.

[1931] (Claim 1)

[1932] A means of obtaining data from application forms and request forms received from users,

[1933] A means of formatting the acquired data and converting it into a format that is easy for the AI ​​model to process,

[1934] A method for inputting formatted data into an AI model and detecting defects,

[1935] A means of notifying the user of detected defects and how to correct them,

[1936] A method for re-inputting corrected data or data that was free of defects from the initial input into the AI ​​model and performing inspections,

[1937] A means of automatically registering data that has passed inspection into the business system,

[1938] A means of performing a final check on the registered data,

[1939] A means of notifying the user that registration is complete,

[1940] A system that includes this.

[1941] (Claim 2)

[1942] The system according to claim 1, wherein the AI ​​model learns from past data and improves the accuracy of detecting defects.

[1943] (Claim 3)

[1944] The system according to claim 1, wherein the notification means utilizes email or mobile app notifications to the user.

[1945] "Example 1"

[1946] (Claim 1)

[1947] A means of obtaining data from application forms and request forms received from users,

[1948] A means of formatting the acquired data and converting it into a format that is easy for the AI ​​model to process,

[1949] A method for inputting formatted data into an AI model and detecting defects,

[1950] A means of notifying the user of detected defects and how to correct them,

[1951] A method for re-inputting corrected data or data that was free of defects from the initial input into the AI ​​model and performing inspections,

[1952] A means of automatically registering data that has passed inspection into a data management system,

[1953] A means of performing a final check on the registered data,

[1954] A means of notifying the user that registration is complete,

[1955] A system that includes this.

[1956] (Claim 2)

[1957] The system according to claim 1, wherein the AI ​​model learns from past data and improves the accuracy of detecting defects.

[1958] (Claim 3)

[1959] The system according to claim 1, wherein the notification means utilizes email or mobile app notifications to the user.

[1960] "Application Example 1"

[1961] (Claim 1)

[1962] A means of obtaining information from application documents received from users,

[1963] A means of formatting the acquired information and converting it into a format that is easy for artificial intelligence models to process,

[1964] A means for inputting formatted information into an artificial intelligence model and detecting errors,

[1965] A means of notifying the user of detected errors and how to correct them,

[1966] A means of re-inputting corrected information or information that was error-free from the initial input into the artificial intelligence model and performing an inspection,

[1967] A means of automatically registering information that has passed inspection on the business platform,

[1968] A means of performing a final check on the registered information,

[1969] A means of notifying the user that registration is complete,

[1970] A means for users to submit registration applications in a virtual environment using electronic devices,

[1971] A system that includes this.

[1972] (Claim 2)

[1973] The system according to claim 1, wherein the artificial intelligence model learns from past data and improves the accuracy of error detection.

[1974] (Claim 3)

[1975] The system according to claim 1, wherein the notification means utilizes message notifications or mobile app notifications to the user.

[1976] "Example 2 of combining an emotion engine"

[1977] (Claim 1)

[1978] A means of obtaining data from application forms and request forms received from users,

[1979] A means of formatting the acquired data and converting it into a format that is easy for the generating AI model to process,

[1980] A means of inputting formatted data into a generating AI model and detecting defects,

[1981] A means of notifying the user of detected defects and how to correct them,

[1982] The notification means includes a means for analyzing the user's emotional state in response to detected defects and providing feedback accordingly.

[1983] A method for re-inputting user-corrected data or data that was flawless from the initial generation into the AI ​​model and performing inspections,

[1984] A means of automatically registering data that has passed inspection into the business system,

[1985] A means of performing a final check on the registered data,

[1986] A means of notifying the user that registration is complete,

[1987] A system that includes this.

[1988] (Claim 2)

[1989] The system according to claim 1, wherein the generating AI model learns from past data and improves the accuracy of detecting defects.

[1990] (Claim 3)

[1991] The system according to claim 1, wherein the notification means utilizes email or mobile app notifications to the user.

[1992] "Application example 2 when combining with an emotional engine"

[1993] (Claim 1)

[1994] A means of obtaining data from application forms and request forms received from users,

[1995] A means of formatting the acquired data and converting it into a format that is easy for the generating AI model to process,

[1996] A means of inputting formatted data into a generating AI model and detecting defects,

[1997] A means of notifying the user of detected defects and how to correct t...

Claims

1. A means of obtaining data from application forms and request forms received from users, A means of formatting the acquired data and converting it into a format that is easy for the AI ​​model to process, A method for inputting formatted data into an AI model and detecting defects, A means of notifying the user of detected defects and how to correct them, A method for re-inputting corrected data or data that was free of defects from the initial input into the AI ​​model and performing inspections, A means of automatically registering data that has passed inspection into the business system, A means of performing a final check on the registered data, A means of notifying the user that registration is complete, A system that includes this.

2. The system according to claim 1, wherein the AI ​​model learns from past data and improves the accuracy of detecting defects.

3. The system according to claim 1, wherein the notification means utilizes email or mobile app notifications to the user.

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