system

Abstract

To provide a system.SOLUTION: A system includes: means for acquiring information inputted by a user; means for generating a form of a predetermined language using a generative AI model based on the information; means for translating the generated form into a language different from the predetermined language; and means for performing error check on whether the content of the generated form is appropriate or not based on the rule defined beforehand.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method comprising the steps of: receiving a user utterance; adding the user utterance to a prompt including an instruction associated with a description of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance responsive to the user utterance.

Prior Art Literature

Patent Literature

[0003]

Patent Document 1

Summary of the Invention

Problem to be Solved by the Invention

[0004] Document preparation at city halls often requires resubmission due to language problems, missing documents, and errors, which degrades citizens' experience of using public services.

Means for Solving the Problem

[0005] A system including automatic document generation means for documents required at city halls, multi-language support means, and automatic error check means is provided. With this system, AI understands the information required by a user and automatically generates a required form. Furthermore, missing parts and errors in documents are pointed out in real time, eliminating the trouble of resubmission.

Brief Description of Drawings

[0006] [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] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Form Example 1 when an emotion engine is combined. [Figure 18] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 of Form Example 2 when an emotion engine is combined. [Figure 20] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Form Example 3 when an emotion engine is combined. [Figure 22] It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. [Mode for Carrying Out the Invention]

[0007] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, terms used in the following description will be explained.

[0009] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic device, or may be a combination of a plurality of arithmetic devices. Further, the processor may be one type of arithmetic device, or may be a combination of a plurality of types of arithmetic devices. Examples of arithmetic devices include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), TPU (TENSOR PROCESSING UNIT (Registered Trademark)), and the like.

[0010] In the following embodiments, a labeled RAM (Random Access Memory) is a memory that temporarily stores information, and is used as a working memory by the processor.

[0011] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), magnetic tape, and the like.

[0012] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (Registered Trademark), Bluetooth (Registered Trademark), and the like.

[0013] 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."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] Embodiments of the present invention include a system comprising means for automatically generating necessary documents at a city hall, means for multi-language support, and means for automatic error checking. Specifically, the AI ​​understands the information required by the user and automatically generates the necessary forms. For example, when creating an application form required at a city hall, the user inputs the necessary information (e.g., name, address, application details, etc.), the AI ​​understands that information, and automatically generates an appropriate form.

[0029] "Example of form 2"

[0030] Furthermore, embodiments of the present invention provide multi-language support. Specifically, regardless of the language used by the user, the system understands that language and generates appropriate forms. For example, if the user uses English, the system understands English information and generates English forms.

[0031] "Example of form 3"

[0032] Furthermore, an embodiment of the present invention provides an automated error checking means. Specifically, the system identifies missing parts and errors in documents in real time, saving the user the trouble of resubmission. For example, if a user has omitted part of an application form, the system will identify the missing part and prompt the user to correct it.

[0033] The following describes the processing flow for each example of the form.

[0034] "Example of form 1"

[0035] Step 1: The user enters the necessary information into the system (e.g., name, address, application details, etc.).

[0036] Step 2: The AI ​​understands the information provided by the user and automatically generates the appropriate form.

[0037] Step 3: The generated form is presented to the user, who can make corrections or confirmations as needed.

[0038] "Example of form 2"

[0039] Step 1: Enter the language the user will be using into the system.

[0040] Step 2: The system understands the user's language and generates an appropriate form corresponding to that language.

[0041] Step 3: The generated form is presented to the user, who can make corrections or confirmations as needed.

[0042] "Example of form 3"

[0043] Step 1: The user submits the document to the system.

[0044] Step 2: The system checks the submitted documents for missing parts and errors in real time.

[0045] Step 3: If an error is detected, the system will notify the user of the error and prompt them to correct it.

[0046] (Example 1)

[0047] Next, we will describe Example 1 of Form 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."

[0048] Creating necessary documents at city hall is time-consuming and prone to errors for users. Furthermore, the complexity increases when multilingual support is required. As a result, users often waste time and effort resubmitting documents. An efficient system is needed to solve these problems.

[0049] 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.

[0050] In this invention, the server includes means for the user to input necessary information, means for analyzing the input information, means for automatically generating documents based on the analysis results, means for providing the generated documents in multiple languages, and means for performing error checking on the generated documents. As a result, users can easily create the necessary documents and use them across language barriers as they are provided in multiple languages. Furthermore, error checking eliminates the need for resubmission.

[0051] A "user" refers to an individual or organization that uses the system to create necessary documents at the city hall.

[0052] "Means of inputting information" refers to the interface through which users enter necessary information such as their name, address, and application details into the system.

[0053] "Means of analyzing information" refers to technologies such as generative AI models used to analyze input information and generate appropriate documents.

[0054] "Means for automatically generating documents" refers to a function that automatically creates necessary documents based on analyzed information.

[0055] "Means of providing in multiple languages" refers to technologies for translating generated documents into multiple languages ​​and providing them to users.

[0056] "Means of error checking" refers to a function that verifies the accuracy and appropriateness of the input content in the generated document and detects errors.

[0057] A "generative AI model" refers to artificial intelligence technology used to analyze information entered by a user and generate appropriate documents.

[0058] "Means of verifying the existence of an address" refers to technology used to confirm whether the entered address actually exists.

[0059] "Error checking rules" refer to predefined criteria and conditions used to verify the appropriateness of an application.

[0060] This invention is a system for efficiently creating necessary documents at a city hall. The system allows users to input the required information, analyzes that information to automatically generate appropriate documents, provides them in multiple languages, and also includes error checking capabilities.

[0061] Hardware and software to be used

[0062] hardware

[0063] Server: A central processing unit used for information analysis, document generation, multilingual translation, and error checking.

[0064] Terminal: A device used by the user to input information and review the generated documents (e.g., a personal computer or smartphone).

[0065] software

[0066] Generative AI model: Artificial intelligence technology used to analyze user-inputted information and generate appropriate documents (e.g., GPT-4®).

[0067] Translation API: A service for translating generated documents into multiple languages ​​(e.g., Google® Translate API).

[0068] Map API: A service used to verify the existence of an entered address (e.g., Google Maps API).

[0069] Program processing

[0070] User information entry

[0071] The user accesses the system's web interface and enters the necessary information, such as their name, address, and application details. For example, the user might enter "Taro Tanaka, Shinjuku Ward, Tokyo, application for a copy of resident registration."

[0072] Information transmission

[0073] The terminal sends the user-entered information to the server in JSON format. The data sent is in the following format:

[0074] json

[0075] {

[0076] "name": "Taro Tanaka",

[0077] "address": "Shinjuku-ku, Tokyo",

[0078] "application_content": "Application for a copy of the resident registration certificate"

[0079] }

[0080] Information analysis

[0081] The server inputs the received JSON data into a generating AI model (e.g., GPT-4). The generating AI model analyzes the user's input and selects an appropriate application form template.

[0082] Form generation

[0083] The server automatically generates application forms based on the analysis results obtained from the generated AI model. For example, it generates a form that includes the items necessary for "applying for a copy of a resident registration certificate."

[0084] Multilingual support

[0085] The server translates the generated Japanese form into English and Spanish using the Google Translate API. The translated form is then provided in multiple languages ​​as follows:

[0086] Japanese: Application for a copy of the resident registration certificate

[0087] English: Application for a Copy of Resident Certificate

[0088] Spanish: Solicitud de Copia del Certificado de Residencia

[0089] Error check

[0090] The server uses the Google Maps API to verify that the entered address exists. It also checks whether the application is valid based on predefined rules. For example, if the address does not exist or the application is invalid, it generates an error message.

[0091] Form provision

[0092] The server sends the form, after error checking is complete, to the user's device. The user can then review the form provided through the device and make corrections as needed. For example, if the user corrects their address, another error check will be performed.

[0093] Specific example

[0094] Example 1: When a user creates an application form in Japanese

[0095] 1. The user accesses the system and enters their name, address, and application details in Japanese.

[0096] 2. The server receives the input information and analyzes it using a generative AI model (e.g., GPT-4).

[0097] 3. The server automatically generates a Japanese application form based on the analysis results.

[0098] 4. The server also translates the generated form into English using the Google Translate API.

[0099] 5. The server uses the Google Maps API to verify the existence of the address and performs other error checks.

[0100] 6. The server provides the user with a form that has been error-checked.

[0101] Example 2: Example of a prompt message

[0102] "The user entered their name, address, and application details to create an application form required by the city hall. Based on this information, automatically generate the appropriate form and provide it in multiple languages. Also, perform error checking on the entered information."

[0103] In this way, a system is created that allows users to easily create the necessary documents at the city hall.

[0104] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0105] Step 1:

[0106] Users access the system's web interface and enter necessary information such as their name, address, and application details. The entered information is then sent from the terminal to the server. The input data consists of text information such as name, address, and application details.

[0107] Step 2:

[0108] The terminal sends the user-entered information to the server in JSON format. The data sent is in the following format:

[0109] json

[0110] {

[0111] "name": "Taro Tanaka",

[0112] "address": "Shinjuku-ku, Tokyo",

[0113] "application_content": "Application for a copy of the resident registration certificate"

[0114] }

[0115] The server receives this JSON data.

[0116] Step 3:

[0117] The server inputs the received JSON data into a generating AI model (e.g., GPT-4). The generating AI model analyzes the user's input and selects an appropriate application form template. As a result of the analysis, an application form template containing the necessary fields is generated.

[0118] Step 4:

[0119] The server automatically generates application forms based on the analysis results obtained from the generated AI model. For example, a form containing the necessary items for "applying for a copy of a resident registration certificate" is generated. The generated form is customized based on the information entered by the user.

[0120] Step 5:

[0121] The server uses a translation API (e.g., Google Translate API) to translate the generated Japanese forms into multiple languages. The translated forms are provided in several languages, such as English and Spanish. For example, the Japanese phrase "Jinmyō no hitsu no yūshū" (Application for a Copy of Resident Certificate) is translated into English as "Application for a Copy of Resident Certificate".

[0122] Step 6:

[0123] The server performs automatic error checking on the generated form. It uses the Google Maps API to verify that the entered address exists. It also checks whether the application content is appropriate based on predefined rules. For example, if the address does not exist or the application content is inappropriate, it generates an error message.

[0124] Step 7:

[0125] The server sends the form, after error checking is complete, to the user's device. The user can then review the form provided through the device and make corrections as needed. For example, if the user corrects their address, another error check will be performed.

[0126] (Application Example 1)

[0127] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0128] In autonomous vehicles, it is essential to quickly and accurately prepare the necessary documents for passengers to arrive at their destination. However, current systems require passengers to prepare these documents manually, which presents problems such as language barriers and errors. This reduces passenger convenience and can delay procedures at the destination. Therefore, a system is needed that automatically generates the necessary documents before passengers arrive at their destination and checks for errors in real time.

[0129] 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.

[0130] In this invention, the server includes means for automatically generating documents required at a city hall, means for multi-language support, means for automatic error checking, means for automatically generating documents required by passengers before they arrive at their destination in the infotainment system of an autonomous vehicle, means for passengers to input necessary information using a smart device, and means for AI to generate appropriate documents based on the input information. This makes it possible to quickly and accurately prepare the necessary documents for passengers before they arrive at their destination.

[0131] The "automatic document generation method required by the city hall" is a function that automatically generates various documents required by the city hall based on information entered by the user.

[0132] "Multi-language support" refers to a function that supports multiple languages ​​and translates user-inputted information into the appropriate language.

[0133] An "automatic error checking mechanism" is a function that detects in real time whether there are any missing parts or errors in the generated document and notifies the user.

[0134] An "infotainment system for autonomous vehicles" is a system installed inside an autonomous vehicle that provides passengers with information and entertainment.

[0135] "A means of automatically generating necessary documents for passengers before they arrive at their destination" refers to a function that automatically generates necessary documents for passengers before they arrive at their destination.

[0136] "Means for passengers to input necessary information using smart devices" refers to a function that allows passengers to input necessary information using devices such as smartphones or tablets.

[0137] "A means by which AI generates appropriate documents based on input information" refers to a function in which artificial intelligence analyzes passenger input information and generates appropriate documents based on that analysis.

[0138] The system for implementing this invention is configured as follows: First, the server includes means for automatically generating necessary documents at the city hall, means for multi-language support, means for automatic error checking, means for automatically generating necessary documents for passengers before they arrive at their destination in the infotainment system of an autonomous vehicle, means for passengers to input necessary information using a smart device, and means for an AI to generate appropriate documents based on the input information.

[0139] Hardware and software configuration

[0140] Hardware: Infotainment systems for autonomous vehicles, smartphones, tablets, touchscreens

[0141] Software: Python, Google Translate API, Langdetect library, generative AI model

[0142] Data processing and data calculation

[0143] 1. Language detection: The server uses the Langdetect library to detect the language of the text entered by the user using a smart device.

[0144] 2. Translation: If necessary, use the Google Translate API to translate the text into the target language.

[0145] 3. Form Generation: The server automatically generates the necessary document forms based on the information entered by the user. It uses a generation AI model to analyze the input information and generate the appropriate documents.

[0146] 4. Error checking: The server detects in real time whether the generated document contains any missing parts or errors and notifies the user.

[0147] Specific example

[0148] For example, when generating immigration documents required for a passenger visiting Japan for tourism purposes, the following prompt message would be used:

[0149] Example of a prompt:

[0150] Please enter your user information:

[0151] Name: Taro Yamada

[0152] Address: Shinjuku-ku, Tokyo

[0153] Purpose: Tourism

[0154] By inputting this prompt into the AI ​​generation model, the necessary documents are automatically generated. The server generates the appropriate documents and performs error checks based on the information entered by the user. This makes it possible to quickly and accurately prepare the necessary documents before passengers arrive at their destination.

[0155] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0156] Step 1:

[0157] The user enters the necessary information using a smart device.

[0158] Input: Users enter information such as their name, address, and purpose using a smartphone or tablet.

[0159] Data processing: The entered information is sent to the server.

[0160] Output: User input information is saved on the server.

[0161] Step 2:

[0162] The server detects the language of the input information.

[0163] Input: Text information entered by the user.

[0164] Data calculation: Use the Langdetect library to detect the language of the input text.

[0165] Output: Detected language information.

[0166] Step 3:

[0167] The server translates the input information as needed.

[0168] Input: User input information and detected language information.

[0169] Data processing: Use the Google Translate API to translate the input information into the target language.

[0170] Output: Translated text information.

[0171] Step 4:

[0172] The server generates the necessary document forms based on the input information.

[0173] Input: User input information (including translated information).

[0174] Data processing: Using a generative AI model, analyze input information and automatically generate appropriate document forms.

[0175] Output: The form of the generated document.

[0176] Step 5:

[0177] The server performs error checking on the generated documents.

[0178] Input: The form of the generated document.

[0179] Data processing: Detects missing parts and errors in documents in real time.

[0180] Output: Error check results (error message if there are errors).

[0181] Step 6:

[0182] The server notifies the user of the results of the error check.

[0183] Input: Error check results.

[0184] Data processing: Generate error messages and notify the user.

[0185] Output: Error message notified to the user.

[0186] Step 7:

[0187] The user corrects the error and re-enters the information.

[0188] Input: The user corrects the information based on the error message and re-enters it.

[0189] Data processing: The corrected information is sent back to the server.

[0190] Output: The corrected information stored on the server.

[0191] Step 8:

[0192] The server regenerates the document based on the corrected information and performs error checking.

[0193] Input: Corrected information.

[0194] Data processing: Regenerate documents using the AI ​​model and perform error checking.

[0195] Output: The final document form without errors.

[0196] Step 9:

[0197] The server provides the final document to the user.

[0198] Input: The final document form without errors.

[0199] Data processing: Format the final document for the user.

[0200] Output: The final document provided to the user.

[0201] (Example 2)

[0202] Next, we will describe Example 2 of Form 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".

[0203] Traditional systems required manual generation of forms in the user's language, making multilingual support difficult. Furthermore, they lacked features to identify missing or incorrect data in real time, resulting in the cumbersome process of resubmission.

[0204] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for detecting the language used by the user, means for generating an appropriate form based on the detected language, means for sending the generated form to the user's terminal, means for receiving and processing the data entered by the user, and means for sending the processing results to the user's terminal. This makes it easy to support multiple languages ​​and enables real-time identification of missing parts or errors in the data entered by the user.

[0205] "User" refers to an individual or group that uses the system.

[0206] "Language" refers to the natural language used by the user, including English, Japanese, Spanish, and others.

[0207] "Detection means" refers to technical means for identifying the language used by the user, and includes natural language processing techniques.

[0208] A "form" refers to an electronic document used by users to input necessary information.

[0209] "Generating means" refers to technical means for automatically creating appropriate forms based on a specified language.

[0210] "Means of transmission" refers to the technical means of sending the generated form or processing results to the user's device.

[0211] "Terminal" refers to an electronic device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[0212] "Means of receiving" refers to the technical means by which the server receives data entered by the user.

[0213] "Means of processing" refers to the technical means for analyzing received data and generating the necessary results.

[0214] "Missing information" refers to areas where the user is missing information that they should have entered.

[0215] An "error" refers to a mistake in the data entered by the user.

[0216] "Real-time" refers to the fact that data is processed instantly as soon as the user enters it.

[0217] "Resubmission" refers to the act of a user correcting errors or omissions and then resubmitting the data.

[0218] This invention is a system that automatically detects the language used by the user, generates an appropriate form, and provides it to the user. The system consists of three main elements: a server, a terminal, and a user.

[0219] The server uses natural language processing techniques to detect the language the user is using. Specifically, it utilizes services such as the Google Cloud Natural Language API and the Microsoft® Azure® Text Analytics API. The server analyzes the user's input data to identify the language being used.

[0220] Next, the server generates the appropriate form based on the specified language. The server retrieves a template corresponding to the user's language from the database and generates the form based on that template. For example, if the user is using English, the server will create the form using an English template.

[0221] The generated form is sent from the server to the user's device. The server sends the data using the HTTP protocol, specifically via a RESTful API. The device parses the received form data and displays it in the user interface. The device renders the form using a frontend framework such as React or Vue.js.

[0222] The user enters the required information into the displayed form and presses the submit button. The device then generates another HTTP request to send the data entered by the user to the server. The server processes the received data and generates the necessary results, such as saving them to a database or integrating with other services.

[0223] The processing results are sent from the server to the terminal, which then displays the results to the user. This allows the user to verify whether their input was processed correctly.

[0224] As a concrete example, consider a case where a user enters "I need a registration form" in English. The terminal sends this input data to the server, which parses the input data to determine that the user is using English. The server generates a registration form using an English template and sends the generated English registration form to the terminal. The terminal displays the received English registration form to the user, who enters the necessary information and submits it. The server processes the received data, generates a result, and sends it to the terminal, which then displays the result to the user.

[0225] Examples of prompt messages include the following:

[0226] "Please analyze the data entered by the user in English and generate an English form."

[0227] "Please analyze the data entered by the user in Japanese and generate a form in Japanese."

[0228] In this way, the system can generate and provide the appropriate form to the user, regardless of the language the user is using.

[0229] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0230] Step 1:

[0231] The user selects or enters a language.

[0232] Input: The language the user will be using (e.g., English, Japanese)

[0233] Operation: The user selects their preferred language on the system interface or enters it directly.

[0234] Output: Selected or entered language information

[0235] Step 2:

[0236] The terminal sends user input to the server.

[0237] Input: Language information selected or entered by the user.

[0238] Operation: The terminal sends the language information selected or entered by the user to the server. Specifically, it generates an HTTP POST request.

[0239] Output: Language information sent to the server

[0240] Step 3:

[0241] The server detects the user's language.

[0242] Input: Language information sent to the server

[0243] Operation: The server analyzes the received data and identifies the language the user is using. Specifically, it uses natural language processing techniques.

[0244] Output: Identified language information

[0245] Step 4:

[0246] The server generates the appropriate form.

[0247] Input: Identified language information

[0248] Operation: The server generates the appropriate form based on the specified language. Specifically, it retrieves the corresponding template from the database and creates the form based on that template.

[0249] Output: Generated form data

[0250] Step 5:

[0251] The server generates a form and sends it to the terminal.

[0252] Input: Generated form data

[0253] Operation: The server sends the generated form data to the terminal. Specifically, it sends the data via a RESTful API using the HTTP protocol.

[0254] Output: Form data sent to the terminal

[0255] Step 6:

[0256] The terminal displays the form to the user.

[0257] Input: Form data sent to the device

[0258] Operation: The terminal parses the received form data and displays it in the user interface. Specifically, it renders the form using a frontend framework such as React or Vue.js.

[0259] Output: Form displayed to the user

[0260] Step 7:

[0261] The user enters the required information into the form and submits it.

[0262] Input: Information entered by the user (e.g., name, email address)

[0263] Action: The user enters the required information into the displayed form and presses the submit button.

[0264] Output: Input information

[0265] Step 8:

[0266] The terminal sends the input data to the server.

[0267] Input: Entered information

[0268] Operation: The terminal generates an HTTP POST request to send the data entered by the user to the server.

[0269] Output: Input data sent to the server

[0270] Step 9:

[0271] The server processes the input data and generates the results.

[0272] Input: Input data sent to the server

[0273] Operation: the server parses the received data and generates required results. Specifically, it stores the data in a database and cooperates with other services.

[0274] Output: generated result data

[0275] Step 10:

[0276] The server transmits the result to the terminal.

[0277] Input: generated result data

[0278] Operation: the server transmits the generated result data to the terminal. Specifically, the data is transmitted again in JSON format through the RESTful API.

[0279] Output: result data transmitted to the terminal

[0280] Step 11:

[0281] The terminal displays the result to the user.

[0282] Input: result data transmitted to the terminal

[0283] Operation: the terminal parses the result data received from the server and displays it on the user interface. Specifically, React is used to render the result.

[0284] Output: result displayed to the user

[0285] (Application Example 2)

[0286] Next, Application Example 2 of Embodiment 2 will be described. In the following description, the data processing apparatus 12 is referred to as a "server", and the smart device 14 is referred to as a "terminal".

[0287] Traditional systems struggled to automatically generate appropriate forms based on the user's language, leading to a poor user experience, especially in environments requiring multilingual support. Furthermore, they lacked the ability to identify missing or incorrect documents in real time, often resulting in the need for resubmissions.

[0288] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatic document generation, means for multi-language support, means for automatic error checking, means for detecting the user's input language, and means for generating an appropriate form based on the detected language. This enables the automatic generation of an appropriate form according to the language used by the user, and by pointing out missing parts or errors in the document in real time, it is possible to reduce the effort of resubmission.

[0289] "Automatic document generation methods" refer to functions that automatically create appropriate documents and forms based on the information required by the user.

[0290] "Multi-language support" refers to a function that supports multiple languages ​​used by the user and provides appropriate information in each language.

[0291] An "automatic error checking mechanism" is a function that detects missing parts or errors in a document in real time and notifies the user.

[0292] "Means for detecting the user's input language" refers to a function that automatically identifies the language of the text entered by the user.

[0293] "Means for generating appropriate forms based on detected language" refers to a function that automatically generates appropriate forms in a language easily understood by the user, depending on the detected language.

[0294] A system for carrying out this invention includes means for automatically generating documents, means for multi-language support, means for automatic error checking, means for detecting the user's input language, and means for generating an appropriate form based on the detected language.

[0295] The server uses a language detection library (e.g., langdetect) to automatically identify the language of the text entered by the user. If the text entered by the user is in Japanese, a Japanese form is generated; if it is in English, an English form is generated. This process uses Google's translation API (googletrans library) to translate the text as needed.

[0296] Specifically, when a user types "search for products" using their smartphone, the server receives this input and uses a language detection library to identify that it is in Japanese. It then generates a Japanese form and provides it to the user. This allows the user to search for products in their own language and proceed with the purchase process.

[0297] Furthermore, the server uses automated error checking mechanisms to detect missing parts and errors in the generated form in real time and notify the user. This saves the user the trouble of resubmitting the form.

[0298] The hardware used is a smartphone, and the software consists of Python, the langdetect library, and the googletrans library.

[0299] For example, if a user enters "Search for products," a form in Japanese will be generated. An example of this prompt is as follows:

[0300] When a user enters "Search for products," please generate a form in Japanese.

[0301] In this way, it becomes possible to automatically generate an appropriate form according to the language used by the user, and the effort of resubmission can be saved by pointing out missing portions and errors in documents in real time.

[0302] The flow of specific processing in Application Example 2 will be described with reference to FIG. 14.

[0303] Step 1:

[0304] A user inputs text using a smartphone. For example, the user inputs "search for product". This input text is transmitted to the system.

[0305] Step 2:

[0306] The server analyzes the received input text using the language detection library (langdetect) to identify the language of the input text. The input is the user's text, and the output is the identified language (Japanese in this case).

[0307] Step 3:

[0308] The server generates an appropriate form based on the identified language. Here, templates for form generation are prepared, and a template corresponding to the language is selected. The input is the identified language, and the output is the generated form.

[0309] Step 4:

[0310] The server transmits the generated form to the user's smartphone. The user can check the form displayed in their own language. The input is the generated form, and the output is the form displayed on the user's smartphone.

[0311] Step 5:

[0312] The user enters information into the form and presses the submit button. The entered data is sent to the server. The input is the data entered by the user into the form, and the output is the data sent to the server.

[0313] Step 6:

[0314] The server uses automated error checking mechanisms to detect missing data and errors in real time. The input is the data sent by the user, and the output is the detected error information.

[0315] Step 7:

[0316] The server notifies the user of any detected errors. The user receives instructions to correct the errors and can then modify and resubmit the form. The input is the detected error information, and the output is the error information notified to the user.

[0317] Step 8:

[0318] The user corrects the error and resubmits the form. The server receives the data again and performs error checking. If there are no errors, the data is processed successfully. The input is the corrected data, and the output is the successfully processed data.

[0319] In this way, appropriate forms are automatically generated according to the language used by the user, and error checking is performed in real time.

[0320] (Example 3)

[0321] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0322] Traditional document submission systems had problems such as requiring users to manually check and correct missing parts and errors in documents, resulting in the cumbersome process of resubmission. Furthermore, they lacked sufficient multilingual support, making them difficult for users of different languages ​​to use. Additionally, the lack of technology to accurately analyze document content resulted in low accuracy in error detection.

[0323] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0324] In this invention, the server includes means for automatic document generation, means for multilingual support, means for automatic error checking, means for optical character recognition, means for analysis using a generation AI model, and means for real-time notification. This makes it possible to detect missing parts and errors in documents submitted by users in real time and prompt them to correct them quickly. Furthermore, multilingual support makes it easy for users who speak different languages ​​to use the service. In addition, analysis using a generation AI model can analyze the contents of documents with high accuracy and improve the accuracy of error detection.

[0325] "Automatic document generation methods" refer to functions that automatically generate necessary forms based on the information required by the user.

[0326] "Multilingual support" refers to a function that provides system functions and interfaces in multiple languages ​​for users who speak different languages.

[0327] An "automatic error checking mechanism" is a function that detects missing parts or errors in submitted documents in real time and notifies the user.

[0328] "Optical character recognition means" refers to a technology for converting uploaded documents into text data.

[0329] "Analysis method using a generative AI model" refers to a function that uses a generative AI model to analyze text data and understand the content of a document.

[0330] A "real-time notification method" is a function that immediately notifies the user of any detected missing parts or errors.

[0331] This invention is a system that detects missing parts and errors in user-submitted documents in real time and prompts for their rapid correction. The system includes means for automatic document generation, multilingual support, automatic error checking, optical character recognition, analysis using a generation AI model, and real-time notification.

[0332] First, the user accesses the system interface using a terminal and uploads documents such as application forms. The documents to be uploaded can be in PDF or image format. The server receives the documents uploaded by the user and temporarily stores them on the server.

[0333] Next, the server uses optical character recognition (e.g., "Tesseract OCR") to convert the uploaded documents into text data. This process transforms images and PDF documents into parseable text data.

[0334] The server analyzes the converted text data using a generative AI model (e.g., "OpenAI® GPT-4"). The purpose of the analysis is to understand the content of the document and identify any missing parts or errors. Examples of prompts to be input to the generative AI model include the following:

[0335] Analyze the contents of the application form submitted by the user and detect any missing information or errors. Please analyze the following text data:

[0336] [Text data]

[0337] The server detects missing parts and errors in documents based on the analysis results of the generated AI model. For example, it identifies cases where required fields are not filled in or the format is incorrect.

[0338] Any detected missing sections or errors are notified to the user using real-time notification methods. These notifications are displayed in real time on the system interface. Notifications can also be sent via email or other means as needed.

[0339] For example, suppose a user uploads a mortgage application. This application is missing the income field. When the user uploads the application, the server receives the document and converts it into text data using Tesseract OCR. Next, it analyzes the text data using a generative AI model (e.g., OpenAI GPT-4) and detects that the income field is missing. Based on this information, the server notifies the user, "The income field is missing. Please correct it." The user receives this notification, fills in the income field, and uploads the application again.

[0340] In this way, users can quickly correct missing parts or errors in documents, saving the trouble of resubmission. Furthermore, multilingual support makes it easy for users who speak different languages ​​to use. Additionally, analysis using a generative AI model allows for highly accurate analysis of document content, improving the accuracy of error detection. The specific processing flow in Example 3 will be explained using Figure 15.

[0341] Step 1:

[0342] The user uploads the document.

[0343] Users access the system interface using a terminal and upload documents such as application forms. Input documents are in PDF or image format, and output documents are sent to the server as data.

[0344] Step 2:

[0345] The server receives the documents

[0346] The server receives documents uploaded by users and temporarily stores them on the server. The input is the document data sent by the user, and the output is the document data stored on the server.

[0347] Step 3:

[0348] The server uses OCR software to convert documents into text data.

[0349] The server uses optical character recognition (e.g., "Tesseract OCR") to convert uploaded documents into text data. The input is document data stored on the server, and the output is text data. Specifically, the OCR software analyzes the image of the document and extracts the text information.

[0350] Step 4:

[0351] The server analyzes text data using a generative AI model.

[0352] The server uses a generative AI model (e.g., "OpenAI GPT-4") to analyze the converted text data. The input is text data, and the output is the analysis result. Specifically, the generative AI model analyzes the text data based on prompts to understand the content of the document.

[0353] Step 5:

[0354] The server detects missing parts and errors based on the analysis results.

[0355] The server detects missing parts and errors in a document based on the analysis results of the generated AI model. The input is the analysis results, and the output is a list of missing parts and errors. Specifically, it compares the analysis results to identify missing required items and formatting errors.

[0356] Step 6:

[0357] The server notifies the user of any missing parts or errors.

[0358] The server notifies the user of any detected missing parts or errors. The input is a list of missing parts or errors, and the output is a notification message to the user. Specifically, it displays notifications in real time on the system interface and sends notifications via email or other means as needed.

[0359] Step 7:

[0360] The user corrects and resubmits the document.

[0361] The user receives a notification from the server and corrects any missing parts or errors indicated. They then re-upload and resubmit the corrected document. The input is the notification message from the server, and the output is the corrected document data. Specifically, the user corrects the document and re-uploads it to the system.

[0362] (Application Example 3)

[0363] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0364] Conventional systems lacked sufficient automatic document generation and multilingual support for city hall applications. Furthermore, they lacked the ability to identify missing or incorrect documents in real time, often forcing users to resubmit documents. Additionally, inputting electronic payment information was prone to errors and omissions, leading to frequent payment errors. A system is needed to address these challenges.

[0365] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0366] In this invention, the server includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, and means for pointing out missing parts or errors in electronic payment information in real time and prompting the user to make corrections. This makes it possible to point out missing parts or errors in documents in real time and eliminate the need for resubmission. Furthermore, when inputting electronic payment information, it is possible to reduce payment errors by pointing out missing parts or errors in real time and prompting the user to make corrections.

[0367] A "city hall" is an administrative body of a local government, a facility that provides various administrative services to citizens.

[0368] An "automatic document generation method" is a system that has the function of automatically creating necessary documents and forms based on the information required by the user.

[0369] A "multi-language support system" is a system that supports multiple languages ​​and has the function of providing information in the language selected by the user.

[0370] An "automatic error checking system" is a system that has the function of detecting missing parts or errors in documents and input information in real time and prompting the user to make corrections.

[0371] "Electronic payment information" refers to information such as credit card details, address, and name that is necessary for online payments.

[0372] "Missing information" refers to the portion where necessary information has not been entered.

[0373] An "error" refers to a state where the entered information is incorrect or does not conform to the specified format.

[0374] "Real-time" refers to the fact that information is processed and feedback is provided immediately as the user inputs it.

[0375] "User" refers to an individual or group that uses the system.

[0376] "Prompting for correction" refers to pointing out errors or missing information in the user's input and prompting them to re-enter the correct information.

[0377] A system for carrying out this invention includes a server, user terminals, and a network. The server includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, and means for pointing out missing parts or errors in electronic payment information in real time and prompting the user to make corrections.

[0378] A user terminal is a device such as a smartphone, tablet, or personal computer that provides an interface for the user to input information. When the user uses the terminal to input the necessary information, that information is sent to the server.

[0379] The server first receives the information entered by the user and then automatically creates the necessary documents and forms using an automated document generation system. During this process, it uses a generation AI model to analyze the user's input and generate appropriate documents.

[0380] Next, information is provided in the user's chosen language using multi-language support. This makes it possible to accommodate users who speak different languages.

[0381] Furthermore, automated error checking mechanisms are used to detect missing or incorrect information entered by the user in real time. For example, it checks whether information such as credit card numbers, expiration dates, security codes, names, and addresses are entered correctly. If an error is detected, the server immediately provides feedback to the user prompting them to correct it.

[0382] As a concrete example, consider a case where a user omits part of their credit card number when entering their credit card information. In this case, the server displays an error message stating, "Invalid credit card number," and prompts the user to re-enter the correct information.

[0383] Examples of prompts to input into a generative AI model:

[0384] Create a program that checks the credit card information entered by the user and points out any missing information or errors in real time. The following fields should be checked: credit card number, expiration date, security code, name, and address. If there are errors in any field, add them to a list and provide feedback to the user.

[0385] This system allows for real-time identification of missing or incorrect information in documents, eliminating the need for resubmission. Furthermore, it can reduce payment errors by prompting users to correct any missing or incorrect information during electronic payment data entry.

[0386] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0387] Step 1:

[0388] The user enters the necessary information using their device.

[0389] Input: Information entered by the user through the terminal interface (e.g., credit card number, expiration date, security code, name, address).

[0390] Output: The input information is sent from the terminal to the server.

[0391] Step 2:

[0392] The server analyzes the received information and automatically creates the necessary documents and forms using an automated document generation system.

[0393] Input: User input information sent from the terminal.

[0394] Data processing: Use a generative AI model to analyze user input and generate appropriate documents.

[0395] Output: The generated documents and forms.

[0396] Step 3:

[0397] The server uses multi-language support to provide information in the language selected by the user.

[0398] Input: User input information and selected language.

[0399] Data processing: Translate information based on the selected language and provide it to the user.

[0400] Output: Information displayed in the language selected by the user.

[0401] Step 4:

[0402] The server uses an automated error checking mechanism to detect missing information or errors in user-entered data in real time.

[0403] Input: User input information.

[0404] Data processing: Regular expressions and other validation algorithms are used to detect missing parts and errors in the input information.

[0405] Output: A list of detected errors and missing parts.

[0406] Step 5:

[0407] The server provides feedback to the user, prompting them to correct any errors or missing parts detected.

[0408] Input: A list of detected errors or missing parts.

[0409] Data processing: Generate error messages and display them to the user.

[0410] Output: Error messages displayed to the user.

[0411] Step 6:

[0412] The user corrects the information based on the error message and re-enters it.

[0413] Input: Information modified by the user.

[0414] Output: The corrected information is sent back to the server.

[0415] Step 7:

[0416] The server will analyze the information again and confirm that there are no errors.

[0417] Input: Corrected information.

[0418] Data processing: The input information is validated again using regular expressions and other validation algorithms.

[0419] Output: If there are no errors, the process is complete. If there are errors, return to step 5.

[0420] 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.

[0421] "Example of form 1"

[0422] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts its operation according to that emotional state. Specifically, if the system recognizes that the user is feeling stressed, it adjusts the operation of automatic generation means and automatic error checking means to reduce the user's stress. For example, it may change the way error messages are displayed to a gentler tone or make the automatic generation of necessary documents smoother.

[0423] "Example of form 2"

[0424] Furthermore, the emotion engine recognizes user satisfaction and delight, and uses that information to improve system performance. For example, if the system recognizes that a user is satisfied with the automatic generation of a particular form, it learns how to generate that form and improves form generation in similar situations.

[0425] "Example of form 3"

[0426] Furthermore, the emotion engine adjusts the operation of multi-language support mechanisms according to the user's emotional state. For example, if the system detects that the user is confused, it enhances support in the user's native language. This allows the user to receive support in their own language, resolve their confusion, and smoothly proceed with creating the necessary documents.

[0427] The following describes the processing flow for each example of the form.

[0428] "Example of form 1"

[0429] Step 1: The system recognizes the user's emotions using an emotion engine.

[0430] Step 2: If the emotion engine recognizes the user's stress, the system adjusts the operation of the automatic generation and automatic error checking mechanisms.

[0431] Step 3: Specifically, this involves changing the way error messages are displayed to a gentler tone and making the automatic generation of necessary documents smoother.

[0432] "Example of form 2"

[0433] Step 1: The emotion engine recognizes the user's joy and satisfaction.

[0434] Step 2: If the system recognizes that the user is satisfied with the automatic generation of a particular form, it learns how to generate that form.

[0435] Step 3: Use the learned information to improve form generation in similar situations.

[0436] "Example of form 3"

[0437] Step 1: The emotion engine adjusts the operation of the multi-language support means according to the user's emotional state.

[0438] Step 2: If the system detects that the user is confused, it will enhance support in the user's native language.

[0439] Step 3: This allows users to receive support in their own language, resolving confusion and enabling them to smoothly create the necessary documents.

[0440] (Example 1)

[0441] Next, we will describe Example 1 of Form 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."

[0442] Creating necessary documents at city hall is a cumbersome and time-consuming task for users. Furthermore, the process becomes even more complicated when multilingual support or error checking is required. Additionally, system operation can become difficult when users are under stress. An efficient system is needed to solve these problems.

[0443] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes an automatic generation means, a multilingual support means, an automatic error checking means, an emotion recognition means, a user information input means, an information transmission means, and a feedback provision means. This makes it possible to quickly and accurately automatically generate the documents required by the user, perform multilingual support and error checking, and further adjust the operation of the system according to the user's emotional state.

[0444] An "automatic generation method" is a function that automatically generates necessary documents and forms based on information entered by the user.

[0445] "Multilingual support" refers to a function that translates generated documents and forms into multiple languages ​​and provides them in the language selected by the user.

[0446] An "automatic error checking mechanism" is a function that automatically detects missing parts and errors in generated documents and forms and points them out in real time.

[0447] "Emotion recognition means" refers to a function that analyzes the user's emotional state and adjusts the system's operation according to that emotional state.

[0448] A "user information input means" is a function that provides an interface for users to input the information they need.

[0449] "Information transmission means" refers to the function that sends information entered by the user to the server.

[0450] A "feedback provision method" is a function that displays information and error messages received from the server to the user and provides necessary feedback.

[0451] Modes for carrying out the invention

[0452] This invention relates to a system for automatically generating necessary documents at a city hall. The system aims to quickly and accurately generate the required documents and forms based on information entered by the user. This system includes automatic generation means, multilingual support means, automatic error checking means, emotion recognition means, user information input means, information transmission means, and feedback provision means.

[0453] Hardware and software to be used

[0454] Hardware: Terminals for users to input information (e.g., personal computers, smartphones, tablets) and servers for processing that information.

[0455] Software: Generative AI models (e.g., OpenAI's GPT-4), translation software (e.g., Google Translate API), emotion recognition AI models, rule-based error checking algorithms.

[0456] Program processing

[0457] 1. Enter user information

[0458] The user uses their device to enter the required information (e.g., name, address, application details, etc.).

[0459] The terminal temporarily stores the entered information and prepares for the next processing step.

[0460] 2. Sending information

[0461] The device sends the stored user information to the server.

[0462] The server analyzes the received information and converts it into the required data format.

[0463] 3. Understanding information and automatically generating forms

[0464] The server generates prompt messages for inputting the received information into the AI ​​model.

[0465] The server sends prompt messages to the generating AI model, which then automatically generates the appropriate form.

[0466] The generative AI model generates the necessary document forms based on the prompt text and returns them to the server.

[0467] 4. Multilingual support

[0468] The server checks the user's language settings.

[0469] The server uses translation software to translate the generated forms to make them multilingual.

[0470] The server sends the translated form to the terminal.

[0471] 5. Automatic error checking

[0472] The server validates the entered information and the generated form using automated error checking mechanisms.

[0473] If an error is detected, the server generates an error message and sends it to the terminal.

[0474] 6. Operation adjustment using the emotion engine

[0475] The server analyzes the user's emotional state using an emotion recognition AI model.

[0476] If the server detects that the user is experiencing stress, it will soften the tone of the error message.

[0477] The server will make adjustments as needed to streamline the form generation process.

[0478] 7. Providing feedback

[0479] The terminal displays the final form and error messages received from the server to the user.

[0480] The user reviews the displayed information and makes corrections or re-enters it as needed.

[0481] Specific example

[0482] For example, consider a case where a user applies for a copy of their resident registration at the city hall. The user enters their name "Taro Yamada," address "Shinjuku Ward, Tokyo," and application details "Issuance of a copy of resident registration" into the terminal. The terminal sends this information to the server. The server sends the following prompt message to the AI ​​model:

[0483] Please generate a form for a user to apply for a copy of their resident registration certificate at the city hall. The user's name is "Taro Yamada", their address is "Shinjuku Ward, Tokyo", and the application content is "Issuance of a copy of resident registration certificate".

[0484] The generation AI model generates an application form for a copy of the resident registration certificate based on this prompt and returns it to the server. The server translates the form using the Google Translate API to make it multilingual and performs error checking. If an error is detected, the emotion engine recognizes the user's stress and generates an error message in a gentle tone. Finally, the terminal displays the generated form and error message to the user.

[0485] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0486] Step 1: Enter user information

[0487] The user uses their device to enter the required information (e.g., name, address, application details, etc.).

[0488] Input: Information entered by the user (name, address, application details).

[0489] The terminal temporarily stores the entered information and prepares for the next processing step.

[0490] Output: Saved user information.

[0491] Step 2: Sending Information

[0492] The device sends the stored user information to the server.

[0493] Input: Saved user information.

[0494] The server analyzes the received information and converts it into the required data format.

[0495] Output: Analyzed user information.

[0496] Step 3: Understanding the information and automatically generating the form

[0497] The server generates prompt messages for inputting the received information into the AI ​​model.

[0498] Input: Analyzed user information.

[0499] The server sends prompt messages to the generating AI model, which then automatically generates the appropriate form.

[0500] The generative AI model generates the necessary document forms based on the prompt text and returns them to the server.

[0501] Output: The generated form.

[0502] Step 4: Multilingual support

[0503] The server checks the user's language settings.

[0504] Input: Generated form, user's language settings.

[0505] The server uses translation software to translate the generated forms to make them multilingual.

[0506] The server sends the translated form to the terminal.

[0507] Output: Translated form.

[0508] Step 5: Automatic Error Check

[0509] The server validates the entered information and the generated form using automated error checking mechanisms.

[0510] Input: Translated form, user information.

[0511] If an error is detected, the server generates an error message and sends it to the terminal.

[0512] Output: Error message (if necessary).

[0513] Step 6: Behavior adjustment using the emotion engine

[0514] The server analyzes the user's emotional state using an emotion recognition AI model.

[0515] Input: User's emotional state.

[0516] If the server detects that the user is experiencing stress, it will soften the tone of the error message.

[0517] The server will make adjustments as needed to streamline the form generation process.

[0518] Output: Adjusted error messages, smooth form generation process.

[0519] Step 7: Provide feedback

[0520] The terminal displays the final form and error messages received from the server to the user.

[0521] Input: Final form, error message.

[0522] The user reviews the displayed information and makes corrections or re-enters it as needed.

[0523] Output: User review and correction information.

[0524] (Application Example 1)

[0525] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0526] The paperwork procedures at city halls are cumbersome for users, and become even more complicated when multilingual support and error checking are inadequate. Furthermore, users often experience stress, leading to decreased efficiency in the process. A system is needed to address these issues and reduce the burden on users.

[0527] 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. In this invention, the server includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, means for emotion recognition, means for interface adjustment, and means for automatic document generation. This enables the AI ​​to understand the information the user needs and automatically generate the necessary forms. Furthermore, by recognizing the user's emotional state and adjusting the system's operation according to that emotional state, user stress can be reduced and the efficiency of procedures can be improved.

[0528] A "city hall" is an administrative body of a local government, a facility that provides various administrative services to citizens.

[0529] An "automatic document generation method" is a system that has the function of automatically creating necessary documents based on information entered by the user.

[0530] A "multi-language support system" is a system that supports multiple languages ​​and has the functionality to provide services in the language selected by the user.

[0531] An "automatic error checking system" is a system that has the function of detecting missing parts or errors in documents in real time and notifying the user.

[0532] An "emotion recognition system" is a system that recognizes the user's emotional state and adjusts its operation based on that information.

[0533] An "interface adjustment mechanism" is a system that has the function of adjusting the system's user interface according to the user's emotional state, thereby reducing user stress.

[0534] "AI" is an abbreviation for artificial intelligence, which is a technology in which machines imitate human intelligence to learn and reason.

[0535] A "form" refers to a template or document used by users to input necessary information.

[0536] "Real-time" means that data processing and information provision are performed immediately.

[0537] "User" refers to an individual or group that uses the system.

[0538] The system for implementing this invention includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, means for emotion recognition, and means for interface adjustment. Specific embodiments of each means are described below.

[0539] 1. Method for automatic document generation

[0540] The server automatically creates the necessary documents based on the information entered by the user. Specifically, when a user enters information such as their name, address, and application details through a smartphone application, the AI ​​understands the information and automatically generates the appropriate form. In this process, the FPDF library is used to generate the document in PDF format.

[0541] 2. Multi-language support means

[0542] The server supports multiple languages ​​and provides services in the language selected by the user. For example, if a user selects English, Spanish, or Chinese, the system will generate and provide documents in that language. This allows many users to access the system, overcoming language barriers.

[0543] 3. Automatic error checking means

[0544] The server detects missing parts and errors in documents in real time and notifies the user. Specifically, it checks each field of the document based on the information entered by the user and immediately notifies the user if there are any omissions or errors. This eliminates the need for resubmission.

[0545] 4. Emotion recognition means

[0546] The server uses the smartphone's camera to recognize the user's emotional state. Specifically, it analyzes the user's facial image using the OpenCV library and determines the user's emotions using an emotion recognition model (Transformers). This allows the server to determine whether the user is experiencing stress.

[0547] 5. Interface adjustment means

[0548] The server adjusts the system's user interface based on the user's emotional state, as determined by emotion recognition. For example, if the server determines that the user is stressed, it changes the interface tone to a gentler one and makes error messages more user-friendly. This reduces user stress and improves the efficiency of the process.

[0549] Specific example

[0550] The user opens the smartphone app and enters the required information (name, address, application details). The smartphone camera takes a picture of the user's face and performs emotion recognition. Based on the emotion recognition results, the interface tone is adjusted. Based on the entered information, an application form in PDF format is automatically generated and provided to the user.

[0551] Example of a prompt

[0552] The user opens the smartphone app and enters the required information (name, address, application details). The smartphone camera takes a picture of the user's face and performs emotion recognition. Based on the emotion recognition results, the interface tone is adjusted. Based on the entered information, an application form in PDF format is automatically generated and provided to the user.

[0553] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0554] Step 1:

[0555] The user opens the smartphone app and enters the required information (name, address, application details). The entered information is sent from the device to the server. The server receives this information and stores it in a database. The input data consists of text data such as name, address, and application details.

[0556] Step 2:

[0557] The server automatically generates documents based on the information it receives. Specifically, it uses a generation AI model to analyze the information entered by the user and automatically generate the appropriate form. It uses the FPDF library to generate the document in PDF format. The output is an application form in PDF format that reflects the user's information.

[0558] Step 3:

[0559] The user takes a picture of their face using their smartphone camera. The captured image is sent from the device to the server. The server receives this image and analyzes the user's emotional state using emotion recognition technology. Specifically, the OpenCV library is used to preprocess the face image, and an emotion recognition model (Transformers) is used to determine the emotion. The input is face image data, and the output is an emotional state (e.g., stress, relaxation).

[0560] Step 4:

[0561] The server adjusts the interface based on the emotion recognition results. Specifically, if it determines that the user is experiencing stress, it changes the interface tone to a gentler one and makes error messages more user-friendly. This reduces user stress. The input is emotion state data, and the output is the adjusted interface settings.

[0562] Step 5:

[0563] The server provides the user with the generated application form in PDF format. Specifically, it sends the generated PDF to the user's device, allowing the user to download it. The user reviews the provided application form and prints or submits it as needed. The input is the generated PDF data, and the output is the PDF file sent to the user's device.

[0564] (Example 2)

[0565] Next, we will describe Example 2 of Form 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".

[0566] Conventional automated document generation systems may not support the language used by the user, and they have not been improved to take into account user emotions and feedback. This results in a poor user experience and insufficient system performance.

[0567] 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.

[0568] In this invention, the server includes means for automatic document generation, means for multi-language support, means for automatic error checking, means for sentiment analysis, and means for improving system performance based on user feedback. This makes it possible to generate appropriate forms regardless of the language used by the user and to improve system performance by taking user sentiment and feedback into consideration.

[0569] An "automatic document generation method" is a method that has the function of automatically generating appropriate documents and forms based on the information required by the user.

[0570] "Multi-language support means" refers to a means that allows a system to understand a user's language and generate documents and forms in the appropriate language, regardless of the language the user is using.

[0571] An "automatic error checking method" is a system that checks generated documents and forms in real time for any missing parts or errors and notifies the user of any issues.

[0572] An "emotion analysis tool" is a tool that analyzes user input and feedback to obtain information about the user's emotions.

[0573] "Methods for improving system performance based on user feedback" refer to methods that improve the system's algorithms and functions based on acquired user sentiment information and feedback, thereby improving performance in subsequent uses.

[0574] Modes for carrying out the invention

[0575] This invention is a system that automatically generates appropriate documents and forms regardless of the language used by the user, and improves the system's performance based on the user's emotions and feedback. Specific embodiments of this system are described below.

[0576] System Configuration

[0577] The system consists of three main elements: a server, terminals, and users. The server includes means for automatic document generation, multi-language support, automatic error checking, sentiment analysis, and means for improving system performance based on user feedback.

[0578] Hardware and software to be used

[0579] The server uses the following hardware and software:

[0580] Natural Language Processing Engine: Google Cloud Natural Language API

[0581] Template engine: Handlebars.js

[0582] Sentiment analysis engine: IBM Watson® Tone Analyzer

[0583] The terminal provides an interface for receiving user input and sending it to the server. Users access the system through the terminal and enter the necessary information.

[0584] Program processing

[0585] The server analyzes the information entered by the user to determine the language being used. Specifically, it uses the Google Cloud Natural Language API to analyze the entered text data. From the analysis results, it identifies the language of the entered text.

[0586] Next, the server generates the appropriate documents and forms based on the specified language. This involves using Handlebars.js to dynamically create forms containing the information requested by the user.

[0587] The generated form is sent from the server to the terminal and displayed to the user. The user reviews the displayed form and enters the required information.

[0588] When a user submits feedback through a form, the device sends that feedback to the server. The server uses IBM Watson Tone Analyzer to analyze the user's feedback and obtain information about the user's sentiment.

[0589] Based on the acquired sentiment information, the server learns to improve system performance. Specifically, it improves the form generation algorithm and applies the improvements to subsequent form generation.

[0590] Specific example

[0591] For example, if a user enters "I need a registration form for a new user" in English, the server will process it as follows:

[0592] 1. The server uses the Google Cloud Natural Language API to determine that the input text is in English.

[0593] 2. The server uses Handlebars.js to generate the registration form in English.

[0594] 3. The server sends the generated English registration form to the terminal.

[0595] 4. The terminal displays the received form to the user.

[0596] 5. The user provides feedback on the ease of use of the form, stating, "This form is very user-friendly."

[0597] 6. The device sends user feedback to the server.

[0598] 7. The server uses IBM Watson Tone Analyzer to recognize that the user is satisfied with the form.

[0599] 8. The server improves the form generation algorithm based on the acquired sentiment information.

[0600] Example of a prompt:

[0601] Please explain how the system processes a user's input in English, such as "I need a registration form for a new user."

[0602] In this way, servers, terminals, and users can work together to improve system performance.

[0603] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0604] Step 1:

[0605] The user enters the information.

[0606] The user enters "I need a registration form for a new user" into the input field on the terminal. The input data is in text format.

[0607] Step 2:

[0608] The terminal sends the input to the server.

[0609] The terminal sends text data entered by the user to the server. The HTTPS protocol is used for transmission to ensure data security. Input is text data, and output is the transmission of data to the server.

[0610] Step 3:

[0611] The server analyzes the input and identifies the language being used.

[0612] The server uses the Google Cloud Natural Language API to parse the received text data. From the analysis results, it identifies that the input text is in English. The input is text data, and the output is the language identification result.

[0613] Step 4:

[0614] The server generates the form based on the specified language.

[0615] The server uses Handlebars.js to generate an English registration form. A template engine is used to dynamically create a form containing the information requested by the user. Inputs are language identification results and template data, and output is the generated form.

[0616] Step 5:

[0617] The server sends the generated form to the terminal.

[0618] The server sends the generated English registration form to the terminal. It again uses the HTTPS protocol to securely transmit the data. The input is the generated form, and the output is the data transmission to the terminal.

[0619] Step 6:

[0620] The terminal displays the form to the user.

[0621] The terminal displays the received form to the user. The user reviews the displayed form and enters the necessary information. The input is the received form, and the output is what is displayed to the user.

[0622] Step 7:

[0623] Users provide feedback through a form.

[0624] Users provide feedback on the usability and satisfaction level of the form. For example, they might enter a comment such as "This form is very user-friendly." The input is feedback text, and the output is input to the terminal.

[0625] Step 8:

[0626] The device sends feedback to the server.

[0627] The device sends user feedback to the server. The data is securely transmitted again using the HTTPS protocol. The input is the feedback text, and the output is the data transmission to the server.

[0628] Step 9:

[0629] The server analyzes the feedback and obtains emotional information.

[0630] The server uses IBM Watson Tone Analyzer to analyze the received feedback. From the analysis results, it recognizes that the user is satisfied with the form. The input is the feedback text, and the output is the sentiment analysis result.

[0631] Step 10:

[0632] The server improves system performance based on emotional information.

[0633] The server improves its form generation algorithm based on the acquired sentiment information. It then learns to generate better forms in similar situations in the future. The input is the sentiment analysis result, and the output is the improved algorithm.

[0634] (Application Example 2)

[0635] Next, we will describe Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0636] Traditional e-commerce sites have struggled to provide personalized experiences based on the language and emotions users use. In particular, they lack multilingual support and product recommendations that consider user emotions, highlighting the need for improved user experience.

[0637] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0638] In this invention, the server includes means for automatic document generation, means for multi-language support, means for automatic error checking, means for sentiment recognition, and means for personalized recommendations. This enables the provision of an interface according to the user's language and personalized product recommendations based on the user's sentiment.

[0639] "Automatic document generation methods" refer to a function in which artificial intelligence understands the information a user needs and automatically generates the necessary forms.

[0640] "Multilingual support" refers to a function that understands the user's language and provides appropriate forms and interfaces, regardless of the language the user is using.

[0641] An "automatic error checking mechanism" is a function that identifies missing parts or errors in documents in real time, saving the trouble of resubmission.

[0642] "Emotion recognition means" refers to a function that analyzes the user's emotions and uses that information to improve the system's performance.

[0643] "Personalized recommendation methods" refer to features that recommend appropriate products and services based on the user's emotions and language of use.

[0644] To implement this invention, a system including a server, a user terminal, and an artificial intelligence model is required. Specific embodiments of this system are described below.

[0645] System Configuration

[0646] 1. Server: The server includes means for automatic document generation, multi-language support, automatic error checking, sentiment recognition, and personalized recommendation.

[0647] 2. User terminal: A user terminal is a device such as a smartphone, tablet, or personal computer that provides an interface for the user to access the system.

[0648] 3. Artificial Intelligence Model: A generative AI model is used to analyze user input and generate appropriate forms and recommendations.

[0649] Program processing

[0650] The server receives input from the user terminal and performs the following processing:

[0651] 1. Language detection and translation: The server uses the langdetect library to detect the user's input language and the TextBlob library to translate it into English as needed.

[0652] 2. Sentiment Analysis: The server uses the transformers library pipeline to analyze the sentiment of the user's input text.

[0653] 3. Form generation: The server generates a form according to the user's language.

[0654] 4. Personalized Recommendations: The server recommends appropriate products and services based on the user's emotions and language of use.

[0655] Hardware and software to be used

[0656] Hardware: Servers, user terminals (smartphones, tablets, PCs)

[0657] Software: langdetect library, TextBlob library, transformers library

[0658] Specific example

[0659] For example, if a user enters "I really liked this product!", the server translates the text, analyzes the sentiment, and generates an appropriate form. It also recommends related products and services based on the user's sentiment.

[0660] Example of a prompt

[0661] If a user enters "I really love this product!", the system should translate that text, analyze the sentiment, and generate an appropriate form.

[0662] In this way, it is possible to provide a personalized experience based on the user's language and emotions.

[0663] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0664] Step 1:

[0665] The user accesses the system using a terminal and enters text into an input form. For example, they might enter "I really like this product!". The input data is then sent to the server.

[0666] Step 2:

[0667] The server uses the langdetect library to detect the language of the received text data. The input is user text, and the output is the detected language code (e.g., 'ja').

[0668] Step 3:

[0669] If the detected language is not English, the server uses the TextBlob library to translate the text into English. The input is the user's text and the detected language code, and the output is the translated English text.

[0670] Step 4:

[0671] The server performs sentiment analysis on the translated text using the transformers library's pipeline. The input is the translated English text, and the output is the result of the sentiment analysis (e.g., 'POSITIVE').

[0672] Step 5:

[0673] The server generates a form according to the user's language. For example, if the user's language is Japanese, it generates a Japanese form. The input is the detected language code, and the output is a form in the corresponding language.

[0674] Step 6:

[0675] The server recommends personalized products and services based on the sentiment analysis results and the user's language. The input is the sentiment analysis results and the detected language code, and the output is a list of recommended products and services.

[0676] Step 7:

[0677] The server sends the generated form and a list of recommended products and services to the user's device. The user can then review this information on their device and select their next action.

[0678] (Example 3)

[0679] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0680] Traditional document creation systems had the problem of being time-consuming and cumbersome, as users had to manually input the necessary information and check for errors and missing parts themselves. Furthermore, insufficient multilingual support made it difficult to address user confusion. Additionally, the lack of support that considered the user's emotional state could potentially degrade the user experience.

[0681] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0682] In this invention, the server includes means for automatic document generation, means for multilingual support, means for automatic error checking, means for emotional state analysis, and means for support adjustment based on emotional state. This eliminates the need for users to manually input necessary information and allows for real-time identification of missing parts or errors in documents. Furthermore, by providing multilingual support according to the user's emotional state, users can receive appropriate support even when confused, thereby improving the overall user experience.

[0683] "Automatic document generation methods" refer to a function in which artificial intelligence understands the information a user needs and automatically generates the necessary forms.

[0684] "Multilingual support means" refers to a function that assists users in multiple languages, and can provide support in the user's native language.

[0685] An "automatic error checking mechanism" is a function that detects missing parts or errors in a document in real time and notifies the user.

[0686] "Emotional state analysis means" refers to a function that monitors user input and behavior and analyzes the user's emotional state using emotion recognition technology.

[0687] "Support adjustment based on emotional state" refers to a function that adjusts the operation of multilingual support means according to the user's emotional state, providing support in the user's native language.

[0688] This invention relates to a system that includes means for automatic document generation, multilingual support, automatic error checking, emotional state analysis, and support adjustment based on emotional state. Specific embodiments of this system are described below.

[0689] First, the user uploads documents such as application forms to the system. The server receives the uploaded documents and saves them to a database. At this time, the server uses the Google Cloud Vision API to convert the contents of the documents into text data using OCR (Optical Character Recognition) technology. The converted text data is then analyzed to check if all the necessary fields are filled in.

[0690] Next, the server detects missing parts and errors based on the analysis results. For example, if the address field is blank, the server will list the error as "Address field is blank." The detected errors are notified to the user. The notification is sent via email or in-app notification. The user receives the notification, checks the indicated parts, and makes corrections. The corrected document is uploaded again, and the server analyzes the resubmitted document again to check if the errors have been corrected.

[0691] Furthermore, the device monitors user input and behavior and analyzes their emotional state using the Microsoft Azure Emotion API. If the device detects that the user is confused, this information is sent to the server. Based on the received emotional state, the server uses the Google Translate API to adjust the multilingual support. For example, if the user's native language is Japanese, the support content is translated into Japanese. The translated support content is then provided to the user, allowing them to receive support in their native language.

[0692] For example, if a user submits an application form but the address field is left blank, the server uses the Google Cloud Vision API to detect the omission and notifies the user, "The address field is blank. Please fill it in." The user then fills in the address and resubmits the document. Also, if a user is receiving support in English but is perceived as confused, the server uses the Google Translate API to provide support in the user's native language, Japanese.

[0693] Examples of prompts include, "Detect and notify the user of any missing parts in the documents they have submitted," and "Provide support in the user's native language if they are confused."

[0694] The above describes specific embodiments for carrying out this invention. The flow of the specific processing in Example 3 will be explained with reference to Figure 21.

[0695] Step 1:

[0696] Upload documents

[0697] Users upload documents such as application forms to the system.

[0698] Input: Document file uploaded by the user.

[0699] Output: Document files saved on the server.

[0700] Specific operation: The user selects a document through the web interface and clicks the upload button.

[0701] Step 2:

[0702] Document storage

[0703] The server receives the uploaded documents and stores them in the database.

[0704] Input: Document file uploaded by the user.

[0705] Output: Document data stored in the database.

[0706] Specific operation: The server receives the document file and creates an entry for saving it to the database.

[0707] Step 3:

[0708] Document analysis

[0709] The server uses the Google Cloud Vision API to convert the document contents into text data using OCR technology.

[0710] Input: Document data stored in the database.

[0711] Output: Document contents converted to text data.

[0712] Specific operation: The server calls the Google Cloud Vision API to convert the image data of the document into text data.

[0713] Step 4:

[0714] Error detected

[0715] The server parses the converted text data and verifies that all necessary fields are filled in.

[0716] Input: Document contents converted to text data.

[0717] Output: A list of detected missing parts and errors.

[0718] Specific operation: The server parses the text data and checks whether predefined required fields are filled in.

[0719] Step 5:

[0720] Error notification

[0721] The server sends emails or in-app notifications to inform users of any detected errors.

[0722] Input: A list of detected missing parts or errors.

[0723] Output: Error message sent to the user.

[0724] Specific action: The server generates an error message and sends a notification to the user.

[0725] Step 6:

[0726] Corrections and resubmissions

[0727] The user corrects the errors pointed out and re-uploads the document.

[0728] Input: A document file modified by the user.

[0729] Output: The revised document file saved again on the server.

[0730] Specific action: The user checks the error message, corrects the document, and uploads it again.

[0731] Step 7:

[0732] Analysis of emotional states

[0733] The device monitors user input and behavior and analyzes emotional states using the Microsoft Azure Emotion API.

[0734] Input: User input and behavioral data.

[0735] Output: Analyzed user emotional state.

[0736] Specific operation: The device monitors user input and actions in real time and calls APIs to analyze the user's emotional state.

[0737] Step 8:

[0738] Sending emotional states

[0739] The device sends the user's emotional state to the server.

[0740] Input: Analyzed user emotional state.

[0741] Output: Emotional state data sent to the server.

[0742] Specific action: The terminal makes a request to send the analysis results to the server.

[0743] Step 9:

[0744] Support adjustments

[0745] The server uses the Google Translate API to adjust its multilingual support based on the emotional state it receives.

[0746] Input: User's emotional state data.

[0747] Output: Adjusted multilingual support content.

[0748] Specific operation: The server translates support content based on the emotional state and provides it in the appropriate language.

[0749] Step 10:

[0750] Support

[0751] The server provides the translated support content to the user.

[0752] Input: Adjusted multilingual support content.

[0753] Output: Support provided to the user.

[0754] Specific operation: The server sends translated support content to the user, ensuring the user receives appropriate support.

[0755] (Application Example 3)

[0756] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0757] Traditional electronic payment systems often made it difficult for users to spot input errors or missing information, leading to frequent resubmissions. Furthermore, the lack of support tailored to the user's emotional state meant that the process was cumbersome and stressful, especially for anxious or confused users.

[0758] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes an automatic generation means, a multi-language support means, an automatic error checking means, an emotion engine means, and a support provision means according to the user's emotional state. This makes it possible to point out input errors and missing parts in real time when the user makes an electronic payment and to provide appropriate support messages according to the user's emotional state.

[0759] "Automatic generation means" refers to a function in which artificial intelligence understands the information a user needs and automatically generates the necessary forms.

[0760] "Multilingual support" refers to a function that supports users in multiple languages.

[0761] An "automatic error checking mechanism" is a function that identifies missing parts or errors in documents in real time, saving the trouble of resubmission.

[0762] The "emotional engine" is a function that analyzes the user's emotional state.

[0763] "Means of providing support tailored to the user's emotional state" refers to a function that provides appropriate support messages according to the user's emotional state.

[0764] The system for carrying out this invention consists of a server and a user terminal. The server includes an automatic generation means, a multi-language support means, an automatic error checking means, an emotion engine means, and a support provision means according to the user's emotional state.

[0765] The user terminal is a device such as a smartphone, tablet, or personal computer, and is used when the user makes an electronic payment. The information entered by the user into the terminal is sent to the server.

[0766] The server first uses an automated generation mechanism to allow artificial intelligence to understand the information the user needs and automatically generate the necessary forms. Next, an automated error checking mechanism points out any missing parts or errors in the document in real time and prompts the user to make corrections.

[0767] Furthermore, an emotion engine analyzes the user's emotional state. The emotion engine uses natural language processing libraries such as TextBlob to detect emotions from the user's input text. Once an emotional state is detected, a support system tailored to the user's emotional state provides appropriate support messages. For example, if the user is anxious, the system will explain things in simpler terms to help the user relax.

[0768] For example, if a user enters "12345abc", the server will determine that the input is correct and prompt the user to proceed to the next step. If there is an error in the input, the sentiment engine will analyze the user's emotional state and provide an appropriate support message.

[0769] Examples of prompts to input into a generative AI model:

[0770] Create a program that, when a user makes an electronic payment, will point out input errors or missing information in real time and provide support tailored to the user's emotional state using an emotion engine. If the user is anxious, the system will explain things in simpler language to help them relax.

[0771] In this way, users can make electronic payments smoothly, and it becomes easier to correct input errors or missing information. Furthermore, support tailored to the user's emotional state is provided, reducing stress and offering a more comfortable user experience.

[0772] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0773] Step 1:

[0774] The user enters their electronic payment information into the terminal. The entered information is then transmitted from the user's terminal to the server. The entered data includes the payment amount, payee, and the user's personal information.

[0775] Step 2:

[0776] The server uses automated generation methods to allow artificial intelligence to understand the information the user needs and automatically generate the necessary forms. It analyzes the input data and processes it to generate appropriate forms. The generated forms are then displayed to the user.

[0777] Step 3:

[0778] The user reviews the generated form and enters the required information. The entered information is sent back to the server. The server receives the input data and proceeds to the next processing step.

[0779] Step 4:

[0780] The server uses automated error checking mechanisms to identify missing parts and errors in documents in real time. It analyzes the input data and performs data calculations to detect missing parts and errors. If errors are detected, a message prompting the user to correct them is displayed.

[0781] Step 5:

[0782] The user makes corrections according to the error message. The corrected information is sent back to the server. The server receives the corrected data and performs another error check.

[0783] Step 6:

[0784] The server analyzes the user's emotional state using an emotion engine. It parses the user's input text and performs data calculations to detect emotions. The emotion engine uses natural language processing libraries such as TextBlob.

[0785] Step 7:

[0786] The server provides appropriate support messages using support delivery methods tailored to the user's emotional state. It processes data to select the message to display to the user based on their emotional state. For example, if the user is anxious, the system will explain things in simpler terms to help the user relax.

[0787] Step 8:

[0788] The user follows the support messages to perform final verification and corrections. If all information is correct, the payment is completed. The server receives the final data and processes the payment. The user is notified that the payment is complete.

[0789] The above processing steps enable users to make electronic payments smoothly and easily correct input errors or missing information. Furthermore, support tailored to the user's emotional state is provided, reducing stress and offering a more comfortable user experience.

[0790] 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.

[0791] Data generation model 58 is a form of 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> 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.

[0792] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0793] 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.

[0794] [Second Embodiment]

[0795] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0796] 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.

[0797] 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).

[0798] 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.

[0799] 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.

[0800] 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).

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0807] "Example of form 1"

[0808] Embodiments of the present invention include a system comprising means for automatically generating necessary documents at a city hall, means for multi-language support, and means for automatic error checking. Specifically, the AI ​​understands the information required by the user and automatically generates the necessary forms. For example, when creating an application form required at a city hall, the user inputs the necessary information (e.g., name, address, application details, etc.), the AI ​​understands that information, and automatically generates an appropriate form.

[0809] "Example of form 2"

[0810] Furthermore, embodiments of the present invention provide multi-language support. Specifically, regardless of the language used by the user, the system understands that language and generates appropriate forms. For example, if the user uses English, the system understands English information and generates English forms.

[0811] "Example of form 3"

[0812] Furthermore, an embodiment of the present invention provides an automated error checking means. Specifically, the system identifies missing parts and errors in documents in real time, saving the user the trouble of resubmission. For example, if a user has omitted part of an application form, the system will identify the missing part and prompt the user to correct it.

[0813] The following describes the processing flow for each example of the form.

[0814] "Example of form 1"

[0815] Step 1: The user enters the necessary information into the system (e.g., name, address, application details, etc.).

[0816] Step 2: The AI ​​understands the information provided by the user and automatically generates the appropriate form.

[0817] Step 3: The generated form is presented to the user, who can make corrections or confirmations as needed.

[0818] "Example of form 2"

[0819] Step 1: Enter the language the user will be using into the system.

[0820] Step 2: The system understands the user's language and generates an appropriate form corresponding to that language.

[0821] Step 3: The generated form is presented to the user, who can make corrections or confirmations as needed.

[0822] "Example of form 3"

[0823] Step 1: The user submits the document to the system.

[0824] Step 2: The system checks the submitted documents for missing parts and errors in real time.

[0825] Step 3: If an error is detected, the system will notify the user of the error and prompt them to correct it.

[0826] (Example 1)

[0827] Next, we will describe Example 1 of Form 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".

[0828] Creating necessary documents at city hall is time-consuming and prone to errors for users. Furthermore, the complexity increases when multilingual support is required. As a result, users often waste time and effort resubmitting documents. An efficient system is needed to solve these problems.

[0829] 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.

[0830] In this invention, the server includes means for the user to input necessary information, means for analyzing the input information, means for automatically generating documents based on the analysis results, means for providing the generated documents in multiple languages, and means for performing error checking on the generated documents. As a result, users can easily create the necessary documents and use them across language barriers as they are provided in multiple languages. Furthermore, error checking eliminates the need for resubmission.

[0831] A "user" refers to an individual or organization that uses the system to create necessary documents at the city hall.

[0832] "Means of inputting information" refers to the interface through which users enter necessary information such as their name, address, and application details into the system.

[0833] "Means of analyzing information" refers to technologies such as generative AI models used to analyze input information and generate appropriate documents.

[0834] "Means for automatically generating documents" refers to a function that automatically creates necessary documents based on analyzed information.

[0835] "Means of providing in multiple languages" refers to technologies for translating generated documents into multiple languages ​​and providing them to users.

[0836] "Means of error checking" refers to a function that verifies the accuracy and appropriateness of the input content in the generated document and detects errors.

[0837] A "generative AI model" refers to artificial intelligence technology used to analyze information entered by a user and generate appropriate documents.

[0838] "Means of verifying the existence of an address" refers to technology used to confirm whether the entered address actually exists.

[0839] "Error checking rules" refer to predefined criteria and conditions used to verify the appropriateness of an application.

[0840] This invention is a system for efficiently creating necessary documents at a city hall. The system allows users to input the required information, analyzes that information to automatically generate appropriate documents, provides them in multiple languages, and also includes error checking capabilities.

[0841] Hardware and software to be used

[0842] hardware

[0843] Server: A central processing unit used for information analysis, document generation, multilingual translation, and error checking.

[0844] Terminal: A device used by the user to input information and review the generated documents (e.g., a personal computer or smartphone).

[0845] software

[0846] Generative AI model: Artificial intelligence technology used to analyze user-inputted information and generate appropriate documents (e.g., GPT-4).

[0847] Translation API: A service for translating generated documents into multiple languages ​​(e.g., Google Translate API).

[0848] Map API: A service used to verify the existence of an entered address (e.g., Google Maps API).

[0849] Program processing

[0850] User information entry

[0851] The user accesses the system's web interface and enters the necessary information, such as their name, address, and application details. For example, the user might enter "Taro Tanaka, Shinjuku Ward, Tokyo, application for a copy of resident registration."

[0852] Information transmission

[0853] The terminal sends the user-entered information to the server in JSON format. The data sent is in the following format:

[0854] json

[0855] {

[0856] "name": "Taro Tanaka",

[0857] "address": "Shinjuku-ku, Tokyo",

[0858] "application_content": "Application for a copy of the resident registration certificate"

[0859] }

[0860] Information analysis

[0861] The server inputs the received JSON data into a generating AI model (e.g., GPT-4). The generating AI model analyzes the user's input and selects an appropriate application form template.

[0862] Form generation

[0863] The server automatically generates application forms based on the analysis results obtained from the generated AI model. For example, it generates a form that includes the items necessary for "applying for a copy of a resident registration certificate."

[0864] Multilingual support

[0865] The server translates the generated Japanese form into English and Spanish using the Google Translate API. The translated form is then provided in multiple languages ​​as follows:

[0866] Japanese: Application for a copy of the resident registration certificate

[0867] English: Application for a Copy of Resident Certificate

[0868] Spanish: Solicitud de Copia del Certificado de Residencia

[0869] Error check

[0870] The server uses the Google Maps API to verify that the entered address exists. It also checks whether the application is valid based on predefined rules. For example, if the address does not exist or the application is invalid, it generates an error message.

[0871] Form provision

[0872] The server sends the form, after error checking is complete, to the user's device. The user can then review the form provided through the device and make corrections as needed. For example, if the user corrects their address, another error check will be performed.

[0873] Specific example

[0874] Example 1: When a user creates an application form in Japanese

[0875] 1. The user accesses the system and enters their name, address, and application details in Japanese.

[0876] 2. The server receives the input information and analyzes it using a generative AI model (e.g., GPT-4).

[0877] 3. The server automatically generates a Japanese application form based on the analysis results.

[0878] 4. The server also translates the generated form into English using the Google Translate API.

[0879] 5. The server uses the Google Maps API to verify the existence of the address and performs other error checks.

[0880] 6. The server provides the user with a form that has been error-checked.

[0881] Example 2: Example of a prompt message

[0882] "The user entered their name, address, and application details to create an application form required by the city hall. Based on this information, automatically generate the appropriate form and provide it in multiple languages. Also, perform error checking on the entered information."

[0883] In this way, a system is created that allows users to easily create the necessary documents at the city hall.

[0884] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0885] Step 1:

[0886] Users access the system's web interface and enter necessary information such as their name, address, and application details. The entered information is then sent from the terminal to the server. The input data consists of text information such as name, address, and application details.

[0887] Step 2:

[0888] The terminal sends the user-entered information to the server in JSON format. The data sent is in the following format:

[0889] json

[0890] {

[0891] "name": "Taro Tanaka",

[0892] "address": "Shinjuku-ku, Tokyo",

[0893] "application_content": "Application for a copy of the resident registration certificate"

[0894] }

[0895] The server receives this JSON data.

[0896] Step 3:

[0897] The server inputs the received JSON data into a generating AI model (e.g., GPT-4). The generating AI model analyzes the user's input and selects an appropriate application form template. As a result of the analysis, an application form template containing the necessary fields is generated.

[0898] Step 4:

[0899] The server automatically generates application forms based on the analysis results obtained from the generated AI model. For example, a form containing the necessary items for "applying for a copy of a resident registration certificate" is generated. The generated form is customized based on the information entered by the user.

[0900] Step 5:

[0901] The server uses a translation API (e.g., Google Translate API) to translate the generated Japanese forms into multiple languages. The translated forms are provided in several languages, such as English and Spanish. For example, the Japanese phrase "Jinmyō no hitsu no yūshū" (Application for a Copy of Resident Certificate) is translated into English as "Application for a Copy of Resident Certificate".

[0902] Step 6:

[0903] The server performs automatic error checking on the generated form. It uses the Google Maps API to verify that the entered address exists. It also checks whether the application content is appropriate based on predefined rules. For example, if the address does not exist or the application content is inappropriate, it generates an error message.

[0904] Step 7:

[0905] The server sends the form, after error checking is complete, to the user's device. The user can then review the form provided through the device and make corrections as needed. For example, if the user corrects their address, another error check will be performed.

[0906] (Application Example 1)

[0907] Next, we will describe Application Example 1 of Form 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."

[0908] In autonomous vehicles, it is essential to quickly and accurately prepare the necessary documents for passengers to arrive at their destination. However, current systems require passengers to prepare these documents manually, which presents problems such as language barriers and errors. This reduces passenger convenience and can delay procedures at the destination. Therefore, a system is needed that automatically generates the necessary documents before passengers arrive at their destination and checks for errors in real time.

[0909] 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.

[0910] In this invention, the server includes means for automatically generating documents required at a city hall, means for multi-language support, means for automatic error checking, means for automatically generating documents required by passengers before they arrive at their destination in the infotainment system of an autonomous vehicle, means for passengers to input necessary information using a smart device, and means for AI to generate appropriate documents based on the input information. This makes it possible to quickly and accurately prepare the necessary documents for passengers before they arrive at their destination.

[0911] The "automatic document generation method required by the city hall" is a function that automatically generates various documents required by the city hall based on information entered by the user.

[0912] "Multi-language support" refers to a function that supports multiple languages ​​and translates user-inputted information into the appropriate language.

[0913] An "automatic error checking mechanism" is a function that detects in real time whether there are any missing parts or errors in the generated document and notifies the user.

[0914] An "infotainment system for autonomous vehicles" is a system installed inside an autonomous vehicle that provides passengers with information and entertainment.

[0915] "A means of automatically generating necessary documents for passengers before they arrive at their destination" refers to a function that automatically generates necessary documents for passengers before they arrive at their destination.

[0916] "Means for passengers to input necessary information using smart devices" refers to a function that allows passengers to input necessary information using devices such as smartphones or tablets.

[0917] "A means by which AI generates appropriate documents based on input information" refers to a function in which artificial intelligence analyzes passenger input information and generates appropriate documents based on that analysis.

[0918] The system for implementing this invention is configured as follows: First, the server includes means for automatically generating necessary documents at the city hall, means for multi-language support, means for automatic error checking, means for automatically generating necessary documents for passengers before they arrive at their destination in the infotainment system of an autonomous vehicle, means for passengers to input necessary information using a smart device, and means for an AI to generate appropriate documents based on the input information.

[0919] Hardware and software configuration

[0920] Hardware: Infotainment systems for autonomous vehicles, smartphones, tablets, touchscreens

[0921] Software: Python, Google Translate API, Langdetect library, generative AI model

[0922] Data processing and data calculation

[0923] 1. Language detection: The server uses the Langdetect library to detect the language of the text entered by the user using a smart device.

[0924] 2. Translation: If necessary, use the Google Translate API to translate the text into the target language.

[0925] 3. Form Generation: The server automatically generates the necessary document forms based on the information entered by the user. It uses a generation AI model to analyze the input information and generate the appropriate documents.

[0926] 4. Error checking: The server detects in real time whether the generated document contains any missing parts or errors and notifies the user.

[0927] Specific example

[0928] For example, when generating immigration documents required for a passenger visiting Japan for tourism purposes, the following prompt message would be used:

[0929] Example of a prompt:

[0930] Please enter your user information:

[0931] Name: Taro Yamada

[0932] Address: Shinjuku-ku, Tokyo

[0933] Purpose: Tourism

[0934] By inputting this prompt into the AI ​​generation model, the necessary documents are automatically generated. The server generates the appropriate documents and performs error checks based on the information entered by the user. This makes it possible to quickly and accurately prepare the necessary documents before passengers arrive at their destination.

[0935] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0936] Step 1:

[0937] The user enters the necessary information using a smart device.

[0938] Input: Users enter information such as their name, address, and purpose using a smartphone or tablet.

[0939] Data processing: The entered information is sent to the server.

[0940] Output: User input information is saved on the server.

[0941] Step 2:

[0942] The server detects the language of the input information.

[0943] Input: Text information entered by the user.

[0944] Data calculation: Use the Langdetect library to detect the language of the input text.

[0945] Output: Detected language information.

[0946] Step 3:

[0947] The server translates the input information as needed.

[0948] Input: User input information and detected language information.

[0949] Data processing: Use the Google Translate API to translate the input information into the target language.

[0950] Output: Translated text information.

[0951] Step 4:

[0952] The server generates the necessary document forms based on the input information.

[0953] Input: User input information (including translated information).

[0954] Data processing: Using a generative AI model, analyze input information and automatically generate appropriate document forms.

[0955] Output: The form of the generated document.

[0956] Step 5:

[0957] The server performs error checking on the generated documents.

[0958] Input: The form of the generated document.

[0959] Data processing: Detects missing parts and errors in documents in real time.

[0960] Output: Error check results (error message if there are errors).

[0961] Step 6:

[0962] The server notifies the user of the results of the error check.

[0963] Input: Error check results.

[0964] Data processing: Generate error messages and notify the user.

[0965] Output: Error message notified to the user.

[0966] Step 7:

[0967] The user corrects the error and re-enters the information.

[0968] Input: The user corrects the information based on the error message and re-enters it.

[0969] Data processing: The corrected information is sent back to the server.

[0970] Output: The corrected information stored on the server.

[0971] Step 8:

[0972] The server regenerates the document based on the corrected information and performs error checking.

[0973] Input: Corrected information.

[0974] Data processing: Regenerate documents using the AI ​​model and perform error checking.

[0975] Output: The final document form without errors.

[0976] Step 9:

[0977] The server provides the final document to the user.

[0978] Input: The final document form without errors.

[0979] Data processing: Format the final document for the user.

[0980] Output: The final document provided to the user.

[0981] (Example 2)

[0982] Next, we will describe Example 2 of Form 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".

[0983] Traditional systems required manual generation of forms in the user's language, making multilingual support difficult. Furthermore, they lacked features to identify missing or incorrect data in real time, resulting in the cumbersome process of resubmission.

[0984] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for detecting the language used by the user, means for generating an appropriate form based on the detected language, means for sending the generated form to the user's terminal, means for receiving and processing the data entered by the user, and means for sending the processing results to the user's terminal. This makes it easy to support multiple languages ​​and enables real-time identification of missing parts or errors in the data entered by the user.

[0985] "User" refers to an individual or group that uses the system.

[0986] "Language" refers to the natural language used by the user, including English, Japanese, Spanish, and others.

[0987] "Detection means" refers to technical means for identifying the language used by the user, and includes natural language processing techniques.

[0988] A "form" refers to an electronic document used by users to input necessary information.

[0989] "Generating means" refers to technical means for automatically creating appropriate forms based on a specified language.

[0990] "Means of transmission" refers to the technical means of sending the generated form or processing results to the user's device.

[0991] "Terminal" refers to an electronic device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[0992] "Means of receiving" refers to the technical means by which the server receives data entered by the user.

[0993] "Means of processing" refers to the technical means for analyzing received data and generating the necessary results.

[0994] "Missing information" refers to areas where the user is missing information that they should have entered.

[0995] An "error" refers to a mistake in the data entered by the user.

[0996] "Real-time" refers to the fact that data is processed instantly as soon as the user enters it.

[0997] "Resubmission" refers to the act of a user correcting errors or omissions and then resubmitting the data.

[0998] This invention is a system that automatically detects the language used by the user, generates an appropriate form, and provides it to the user. The system consists of three main elements: a server, a terminal, and a user.

[0999] The server uses natural language processing techniques to detect the language the user is using. Specifically, it utilizes services such as the Google Cloud Natural Language API and the Microsoft Azure Text Analytics API. The server analyzes the user's input data to identify the language being used.

[1000] Next, the server generates the appropriate form based on the specified language. The server retrieves a template corresponding to the user's language from the database and generates the form based on that template. For example, if the user is using English, the server will create the form using an English template.

[1001] The generated form is sent from the server to the user's device. The server sends the data using the HTTP protocol, specifically via a RESTful API. The device parses the received form data and displays it in the user interface. The device renders the form using a frontend framework such as React or Vue.js.

[1002] The user enters the required information into the displayed form and presses the submit button. The device then generates another HTTP request to send the data entered by the user to the server. The server processes the received data and generates the necessary results, such as saving them to a database or integrating with other services.

[1003] The processing results are sent from the server to the terminal, which then displays the results to the user. This allows the user to verify whether their input was processed correctly.

[1004] As a concrete example, consider a case where a user enters "I need a registration form" in English. The terminal sends this input data to the server, which parses the input data to determine that the user is using English. The server generates a registration form using an English template and sends the generated English registration form to the terminal. The terminal displays the received English registration form to the user, who enters the necessary information and submits it. The server processes the received data, generates a result, and sends it to the terminal, which then displays the result to the user.

[1005] Examples of prompt messages include the following:

[1006] "Please analyze the data entered by the user in English and generate an English form."

[1007] "Please analyze the data entered by the user in Japanese and generate a form in Japanese."

[1008] In this way, the system can generate and provide the appropriate form to the user, regardless of the language the user is using.

[1009] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1010] Step 1:

[1011] The user selects or enters a language.

[1012] Input: The language the user will be using (e.g., English, Japanese)

[1013] Operation: The user selects their preferred language on the system interface or enters it directly.

[1014] Output: Selected or entered language information

[1015] Step 2:

[1016] The terminal sends user input to the server.

[1017] Input: Language information selected or entered by the user.

[1018] Operation: The terminal sends the language information selected or entered by the user to the server. Specifically, it generates an HTTP POST request.

[1019] Output: Language information sent to the server

[1020] Step 3:

[1021] The server detects the user's language.

[1022] Input: Language information sent to the server

[1023] Operation: The server analyzes the received data and identifies the language the user is using. Specifically, it uses natural language processing techniques.

[1024] Output: Identified language information

[1025] Step 4:

[1026] The server generates the appropriate form.

[1027] Input: Identified language information

[1028] Operation: The server generates the appropriate form based on the specified language. Specifically, it retrieves the corresponding template from the database and creates the form based on that template.

[1029] Output: Generated form data

[1030] Step 5:

[1031] The server generates a form and sends it to the terminal.

[1032] Input: Generated form data

[1033] Operation: The server sends the generated form data to the terminal. Specifically, it sends the data via a RESTful API using the HTTP protocol.

[1034] Output: Form data sent to the terminal

[1035] Step 6:

[1036] The terminal displays the form to the user.

[1037] Input: Form data sent to the device

[1038] Operation: The terminal parses the received form data and displays it in the user interface. Specifically, it renders the form using a frontend framework such as React or Vue.js.

[1039] Output: Form displayed to the user

[1040] Step 7:

[1041] The user enters the required information into the form and submits it.

[1042] Input: Information entered by the user (e.g., name, email address)

[1043] Action: The user enters the required information into the displayed form and presses the submit button.

[1044] Output: Input information

[1045] Step 8:

[1046] The terminal sends the input data to the server.

[1047] Input: Entered information

[1048] Operation: The terminal generates an HTTP POST request to send the data entered by the user to the server.

[1049] Output: Input data sent to the server

[1050] Step 9:

[1051] The server processes the input data and generates the results.

[1052] Input: Input data sent to the server

[1053] Operation: The server analyzes the received data and generates the necessary results. Specifically, it stores them in a database or interacts with other services.

[1054] Output: Generated result data

[1055] Step 10:

[1056] The server sends the results to the terminal.

[1057] Input: Generated result data

[1058] Operation: The server sends the generated result data to the terminal. Specifically, it sends the data again in JSON format via a RESTful API.

[1059] Output: Result data sent to the terminal

[1060] Step 11:

[1061] The device displays the results to the user.

[1062] Input: Result data sent to the terminal

[1063] Operation: The terminal parses the result data received from the server and displays it in the user interface. Specifically, it uses React to render the results.

[1064] Output: Results displayed to the user

[1065] (Application Example 2)

[1066] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1067] Traditional systems struggled to automatically generate appropriate forms based on the user's language, leading to a poor user experience, especially in environments requiring multilingual support. Furthermore, they lacked the ability to identify missing or incorrect documents in real time, often resulting in the need for resubmissions.

[1068] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatic document generation, means for multi-language support, means for automatic error checking, means for detecting the user's input language, and means for generating an appropriate form based on the detected language. This enables the automatic generation of an appropriate form according to the language used by the user, and by pointing out missing parts or errors in the document in real time, it is possible to reduce the effort of resubmission.

[1069] "Automatic document generation methods" refer to functions that automatically create appropriate documents and forms based on the information required by the user.

[1070] "Multi-language support" refers to a function that supports multiple languages ​​used by the user and provides appropriate information in each language.

[1071] An "automatic error checking mechanism" is a function that detects missing parts or errors in a document in real time and notifies the user.

[1072] "Means for detecting the user's input language" refers to a function that automatically identifies the language of the text entered by the user.

[1073] "Means for generating appropriate forms based on detected language" refers to a function that automatically generates appropriate forms in a language easily understood by the user, depending on the detected language.

[1074] A system for carrying out this invention includes means for automatically generating documents, means for multi-language support, means for automatic error checking, means for detecting the user's input language, and means for generating an appropriate form based on the detected language.

[1075] The server uses a language detection library (e.g., langdetect) to automatically identify the language of the text entered by the user. If the text entered by the user is in Japanese, a Japanese form is generated; if it is in English, an English form is generated. This process uses Google's translation API (googletrans library) to translate the text as needed.

[1076] Specifically, when a user types "search for products" using their smartphone, the server receives this input and uses a language detection library to identify that it is in Japanese. It then generates a Japanese form and provides it to the user. This allows the user to search for products in their own language and proceed with the purchase process.

[1077] Furthermore, the server uses automated error checking mechanisms to detect missing parts and errors in the generated form in real time and notify the user. This saves the user the trouble of resubmitting the form.

[1078] The hardware used is a smartphone, and the software consists of Python, the langdetect library, and the googletrans library.

[1079] For example, if a user enters "Search for products," a form in Japanese will be generated. An example of this prompt is as follows:

[1080] When a user enters "Search for products," please generate a form in Japanese.

[1081] In this way, it becomes possible to automatically generate appropriate forms according to the language used by the user, and by pointing out missing parts or errors in the document in real time, the trouble of resubmission can be avoided.

[1082] The flow of the specific processing in Application Example 2 will be explained using Figure 14.

[1083] Step 1:

[1084] The user enters text using their smartphone. For example, they might type "search for products." This entered text is then sent to the system.

[1085] Step 2:

[1086] The server analyzes the received input text using a language detection library (langdetect) to identify the language of the input text. The input is the user's text, and the output is in the identified language (in this case, Japanese).

[1087] Step 3:

[1088] The server generates an appropriate form based on the identified language. A template for form generation is provided, and the appropriate template is selected according to the language. The input is the identified language, and the output is the generated form.

[1089] Step 4:

[1090] The server sends the generated form to the user's smartphone. The user can then view the form displayed in their own language. The input is the generated form, and the output is the form displayed on the user's smartphone.

[1091] Step 5:

[1092] The user enters information into the form and presses the submit button. The entered data is sent to the server. The input is the data entered by the user into the form, and the output is the data sent to the server.

[1093] Step 6:

[1094] The server uses automated error checking mechanisms to detect missing data and errors in real time. The input is the data sent by the user, and the output is the detected error information.

[1095] Step 7:

[1096] The server notifies the user of any detected errors. The user receives instructions to correct the errors and can then modify and resubmit the form. The input is the detected error information, and the output is the error information notified to the user.

[1097] Step 8:

[1098] The user corrects the error and resubmits the form. The server receives the data again and performs error checking. If there are no errors, the data is processed successfully. The input is the corrected data, and the output is the successfully processed data.

[1099] In this way, appropriate forms are automatically generated according to the language used by the user, and error checking is performed in real time.

[1100] (Example 3)

[1101] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[1102] Traditional document submission systems had problems such as requiring users to manually check and correct missing parts and errors in documents, resulting in the cumbersome process of resubmission. Furthermore, they lacked sufficient multilingual support, making them difficult for users of different languages ​​to use. Additionally, the lack of technology to accurately analyze document content resulted in low accuracy in error detection.

[1103] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1104] In this invention, the server includes means for automatic document generation, means for multilingual support, means for automatic error checking, means for optical character recognition, means for analysis using a generation AI model, and means for real-time notification. This makes it possible to detect missing parts and errors in documents submitted by users in real time and prompt them to correct them quickly. Furthermore, multilingual support makes it easy for users who speak different languages ​​to use the service. In addition, analysis using a generation AI model can analyze the contents of documents with high accuracy and improve the accuracy of error detection.

[1105] "Automatic document generation methods" refer to functions that automatically generate necessary forms based on the information required by the user.

[1106] "Multilingual support" refers to a function that provides system functions and interfaces in multiple languages ​​for users who speak different languages.

[1107] An "automatic error checking mechanism" is a function that detects missing parts or errors in submitted documents in real time and notifies the user.

[1108] "Optical character recognition means" refers to a technology for converting uploaded documents into text data.

[1109] "Analysis method using a generative AI model" refers to a function that uses a generative AI model to analyze text data and understand the content of a document.

[1110] A "real-time notification method" is a function that immediately notifies the user of any detected missing parts or errors.

[1111] This invention is a system that detects missing parts and errors in user-submitted documents in real time and prompts for their rapid correction. The system includes means for automatic document generation, multilingual support, automatic error checking, optical character recognition, analysis using a generation AI model, and real-time notification.

[1112] First, the user accesses the system interface using a terminal and uploads documents such as application forms. The documents to be uploaded can be in PDF or image format. The server receives the documents uploaded by the user and temporarily stores them on the server.

[1113] Next, the server uses optical character recognition (e.g., "Tesseract OCR") to convert the uploaded documents into text data. This process transforms images and PDF documents into parseable text data.

[1114] The server analyzes the converted text data using a generative AI model (e.g., "OpenAI GPT-4"). The purpose of the analysis is to understand the content of the document and identify any missing parts or errors. Examples of prompts to be input to the generative AI model include the following:

[1115] Analyze the contents of the application form submitted by the user and detect any missing information or errors. Please analyze the following text data:

[1116] [Text data]

[1117] The server detects missing parts and errors in documents based on the analysis results of the generated AI model. For example, it identifies cases where required fields are not filled in or the format is incorrect.

[1118] Any detected missing sections or errors are notified to the user using real-time notification methods. These notifications are displayed in real time on the system interface. Notifications can also be sent via email or other means as needed.

[1119] For example, suppose a user uploads a mortgage application. This application is missing the income field. When the user uploads the application, the server receives the document and converts it into text data using Tesseract OCR. Next, it analyzes the text data using a generative AI model (e.g., OpenAI GPT-4) and detects that the income field is missing. Based on this information, the server notifies the user, "The income field is missing. Please correct it." The user receives this notification, fills in the income field, and uploads the application again.

[1120] In this way, users can quickly correct missing parts or errors in documents, saving the trouble of resubmission. Furthermore, multilingual support makes it easy for users who speak different languages ​​to use. Additionally, analysis using a generative AI model allows for highly accurate analysis of document content, improving the accuracy of error detection. The specific processing flow in Example 3 will be explained using Figure 15.

[1121] Step 1:

[1122] The user uploads the document.

[1123] Users access the system interface using a terminal and upload documents such as application forms. Input documents are in PDF or image format, and output documents are sent to the server as data.

[1124] Step 2:

[1125] The server receives the documents

[1126] The server receives documents uploaded by users and temporarily stores them on the server. The input is the document data sent by the user, and the output is the document data stored on the server.

[1127] Step 3:

[1128] The server uses OCR software to convert documents into text data.

[1129] The server uses optical character recognition (e.g., "Tesseract OCR") to convert uploaded documents into text data. The input is document data stored on the server, and the output is text data. Specifically, the OCR software analyzes the image of the document and extracts the text information.

[1130] Step 4:

[1131] The server analyzes text data using a generative AI model.

[1132] The server uses a generative AI model (e.g., "OpenAI GPT-4") to analyze the converted text data. The input is text data, and the output is the analysis result. Specifically, the generative AI model analyzes the text data based on prompts to understand the content of the document.

[1133] Step 5:

[1134] The server detects missing parts and errors based on the analysis results.

[1135] The server detects missing parts and errors in a document based on the analysis results of the generated AI model. The input is the analysis results, and the output is a list of missing parts and errors. Specifically, it compares the analysis results to identify missing required items and formatting errors.

[1136] Step 6:

[1137] The server notifies the user of any missing parts or errors.

[1138] The server notifies the user of any detected missing parts or errors. The input is a list of missing parts or errors, and the output is a notification message to the user. Specifically, it displays notifications in real time on the system interface and sends notifications via email or other means as needed.

[1139] Step 7:

[1140] The user corrects and resubmits the document.

[1141] The user receives a notification from the server and corrects any missing parts or errors indicated. They then re-upload and resubmit the corrected document. The input is the notification message from the server, and the output is the corrected document data. Specifically, the user corrects the document and re-uploads it to the system.

[1142] (Application Example 3)

[1143] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 as a "terminal".

[1144] Conventional systems lacked sufficient automatic document generation and multilingual support for city hall applications. Furthermore, they lacked the ability to identify missing or incorrect documents in real time, often forcing users to resubmit documents. Additionally, inputting electronic payment information was prone to errors and omissions, leading to frequent payment errors. A system is needed to address these challenges.

[1145] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1146] In this invention, the server includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, and means for pointing out missing parts or errors in electronic payment information in real time and prompting the user to make corrections. This makes it possible to point out missing parts or errors in documents in real time and eliminate the need for resubmission. Furthermore, when inputting electronic payment information, it is possible to reduce payment errors by pointing out missing parts or errors in real time and prompting the user to make corrections.

[1147] A "city hall" is an administrative body of a local government, a facility that provides various administrative services to citizens.

[1148] An "automatic document generation method" is a system that has the function of automatically creating necessary documents and forms based on the information required by the user.

[1149] A "multi-language support system" is a system that supports multiple languages ​​and has the function of providing information in the language selected by the user.

[1150] An "automatic error checking system" is a system that has the function of detecting missing parts or errors in documents and input information in real time and prompting the user to make corrections.

[1151] "Electronic payment information" refers to information such as credit card details, address, and name that is necessary for online payments.

[1152] "Missing information" refers to the portion where necessary information has not been entered.

[1153] An "error" refers to a state where the entered information is incorrect or does not conform to the specified format.

[1154] "Real-time" refers to the fact that information is processed and feedback is provided immediately as the user inputs it.

[1155] "User" refers to an individual or group that uses the system.

[1156] "Prompting for correction" refers to pointing out errors or missing information in the user's input and prompting them to re-enter the correct information.

[1157] A system for carrying out this invention includes a server, user terminals, and a network. The server includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, and means for pointing out missing parts or errors in electronic payment information in real time and prompting the user to make corrections.

[1158] A user terminal is a device such as a smartphone, tablet, or personal computer that provides an interface for the user to input information. When the user uses the terminal to input the necessary information, that information is sent to the server.

[1159] The server first receives the information entered by the user and then automatically creates the necessary documents and forms using an automated document generation system. During this process, it uses a generation AI model to analyze the user's input and generate appropriate documents.

[1160] Next, information is provided in the user's chosen language using multi-language support. This makes it possible to accommodate users who speak different languages.

[1161] Furthermore, automated error checking mechanisms are used to detect missing or incorrect information entered by the user in real time. For example, it checks whether information such as credit card numbers, expiration dates, security codes, names, and addresses are entered correctly. If an error is detected, the server immediately provides feedback to the user prompting them to correct it.

[1162] As a concrete example, consider a case where a user omits part of their credit card number when entering their credit card information. In this case, the server displays an error message stating, "Invalid credit card number," and prompts the user to re-enter the correct information.

[1163] Examples of prompts to input into a generative AI model:

[1164] Create a program that checks the credit card information entered by the user and points out any missing information or errors in real time. The following fields should be checked: credit card number, expiration date, security code, name, and address. If there are errors in any field, add them to a list and provide feedback to the user.

[1165] This system allows for real-time identification of missing or incorrect information in documents, eliminating the need for resubmission. Furthermore, it can reduce payment errors by prompting users to correct any missing or incorrect information during electronic payment data entry.

[1166] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1167] Step 1:

[1168] The user enters the necessary information using their device.

[1169] Input: Information entered by the user through the terminal interface (e.g., credit card number, expiration date, security code, name, address).

[1170] Output: The input information is sent from the terminal to the server.

[1171] Step 2:

[1172] The server analyzes the received information and automatically creates the necessary documents and forms using an automated document generation system.

[1173] Input: User input information sent from the terminal.

[1174] Data processing: Use a generative AI model to analyze user input and generate appropriate documents.

[1175] Output: The generated documents and forms.

[1176] Step 3:

[1177] The server uses multi-language support to provide information in the language selected by the user.

[1178] Input: User input information and selected language.

[1179] Data processing: Translate information based on the selected language and provide it to the user.

[1180] Output: Information displayed in the language selected by the user.

[1181] Step 4:

[1182] The server uses an automated error checking mechanism to detect missing information or errors in user-entered data in real time.

[1183] Input: User input information.

[1184] Data processing: Regular expressions and other validation algorithms are used to detect missing parts and errors in the input information.

[1185] Output: A list of detected errors and missing parts.

[1186] Step 5:

[1187] The server provides feedback to the user, prompting them to correct any errors or missing parts detected.

[1188] Input: A list of detected errors or missing parts.

[1189] Data processing: Generate error messages and display them to the user.

[1190] Output: Error messages displayed to the user.

[1191] Step 6:

[1192] The user corrects the information based on the error message and re-enters it.

[1193] Input: Information modified by the user.

[1194] Output: The corrected information is sent back to the server.

[1195] Step 7:

[1196] The server will analyze the information again and confirm that there are no errors.

[1197] Input: Corrected information.

[1198] Data processing: The input information is validated again using regular expressions and other validation algorithms.

[1199] Output: If there are no errors, the process is complete. If there are errors, return to step 5.

[1200] 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.

[1201] "Example of form 1"

[1202] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts its operation according to that emotional state. Specifically, if the system recognizes that the user is feeling stressed, it adjusts the operation of automatic generation means and automatic error checking means to reduce the user's stress. For example, it may change the way error messages are displayed to a gentler tone or make the automatic generation of necessary documents smoother.

[1203] "Example of form 2"

[1204] Furthermore, the emotion engine recognizes user satisfaction and delight, and uses that information to improve system performance. For example, if the system recognizes that a user is satisfied with the automatic generation of a particular form, it learns how to generate that form and improves form generation in similar situations.

[1205] "Example of form 3"

[1206] Furthermore, the emotion engine adjusts the operation of multi-language support mechanisms according to the user's emotional state. For example, if the system detects that the user is confused, it enhances support in the user's native language. This allows the user to receive support in their own language, resolve their confusion, and smoothly proceed with creating the necessary documents.

[1207] The following describes the processing flow for each example of the form.

[1208] "Example of form 1"

[1209] Step 1: The system recognizes the user's emotions using an emotion engine.

[1210] Step 2: If the emotion engine recognizes the user's stress, the system adjusts the operation of the automatic generation and automatic error checking mechanisms.

[1211] Step 3: Specifically, this involves changing the way error messages are displayed to a gentler tone and making the automatic generation of necessary documents smoother.

[1212] "Example of form 2"

[1213] Step 1: The emotion engine recognizes the user's joy and satisfaction.

[1214] Step 2: If the system recognizes that the user is satisfied with the automatic generation of a particular form, it learns how to generate that form.

[1215] Step 3: Use the learned information to improve form generation in similar situations.

[1216] "Example of form 3"

[1217] Step 1: The emotion engine adjusts the operation of the multi-language support means according to the user's emotional state.

[1218] Step 2: If the system detects that the user is confused, it will enhance support in the user's native language.

[1219] Step 3: This allows users to receive support in their own language, resolving confusion and enabling them to smoothly create the necessary documents.

[1220] (Example 1)

[1221] Next, we will describe Example 1 of Form 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".

[1222] Creating necessary documents at city hall is a cumbersome and time-consuming task for users. Furthermore, the process becomes even more complicated when multilingual support or error checking is required. Additionally, system operation can become difficult when users are under stress. An efficient system is needed to solve these problems.

[1223] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes an automatic generation means, a multilingual support means, an automatic error checking means, an emotion recognition means, a user information input means, an information transmission means, and a feedback provision means. This makes it possible to quickly and accurately automatically generate the documents required by the user, perform multilingual support and error checking, and further adjust the operation of the system according to the user's emotional state.

[1224] An "automatic generation method" is a function that automatically generates necessary documents and forms based on information entered by the user.

[1225] "Multilingual support" refers to a function that translates generated documents and forms into multiple languages ​​and provides them in the language selected by the user.

[1226] An "automatic error checking mechanism" is a function that automatically detects missing parts and errors in generated documents and forms and points them out in real time.

[1227] "Emotion recognition means" refers to a function that analyzes the user's emotional state and adjusts the system's operation according to that emotional state.

[1228] A "user information input means" is a function that provides an interface for users to input the information they need.

[1229] "Information transmission means" refers to the function that sends information entered by the user to the server.

[1230] A "feedback provision method" is a function that displays information and error messages received from the server to the user and provides necessary feedback.

[1231] Modes for carrying out the invention

[1232] This invention relates to a system for automatically generating necessary documents at a city hall. The system aims to quickly and accurately generate the required documents and forms based on information entered by the user. This system includes automatic generation means, multilingual support means, automatic error checking means, emotion recognition means, user information input means, information transmission means, and feedback provision means.

[1233] Hardware and software to be used

[1234] Hardware: Terminals for users to input information (e.g., personal computers, smartphones, tablets) and servers for processing that information.

[1235] Software: Generative AI models (e.g., OpenAI's GPT-4), translation software (e.g., Google Translate API), emotion recognition AI models, rule-based error checking algorithms.

[1236] Program processing

[1237] 1. Enter user information

[1238] The user uses their device to enter the required information (e.g., name, address, application details, etc.).

[1239] The terminal temporarily stores the entered information and prepares for the next processing step.

[1240] 2. Sending information

[1241] The device sends the stored user information to the server.

[1242] The server analyzes the received information and converts it into the required data format.

[1243] 3. Understanding information and automatically generating forms

[1244] The server generates prompt messages for inputting the received information into the AI ​​model.

[1245] The server sends prompt messages to the generating AI model, which then automatically generates the appropriate form.

[1246] The generative AI model generates the necessary document forms based on the prompt text and returns them to the server.

[1247] 4. Multilingual support

[1248] The server checks the user's language settings.

[1249] The server uses translation software to translate the generated forms to make them multilingual.

[1250] The server sends the translated form to the terminal.

[1251] 5. Automatic error checking

[1252] The server validates the entered information and the generated form using automated error checking mechanisms.

[1253] If an error is detected, the server generates an error message and sends it to the terminal.

[1254] 6. Operation adjustment using the emotion engine

[1255] The server analyzes the user's emotional state using an emotion recognition AI model.

[1256] If the server detects that the user is experiencing stress, it will soften the tone of the error message.

[1257] The server will make adjustments as needed to streamline the form generation process.

[1258] 7. Providing feedback

[1259] The terminal displays the final form and error messages received from the server to the user.

[1260] The user reviews the displayed information and makes corrections or re-enters it as needed.

[1261] Specific example

[1262] For example, consider a case where a user applies for a copy of their resident registration at the city hall. The user enters their name "Taro Yamada," address "Shinjuku Ward, Tokyo," and application details "Issuance of a copy of resident registration" into the terminal. The terminal sends this information to the server. The server sends the following prompt message to the AI ​​model:

[1263] Please generate a form for a user to apply for a copy of their resident registration certificate at the city hall. The user's name is "Taro Yamada", their address is "Shinjuku Ward, Tokyo", and the application content is "Issuance of a copy of resident registration certificate".

[1264] The generation AI model generates an application form for a copy of the resident registration certificate based on this prompt and returns it to the server. The server translates the form using the Google Translate API to make it multilingual and performs error checking. If an error is detected, the emotion engine recognizes the user's stress and generates an error message in a gentle tone. Finally, the terminal displays the generated form and error message to the user.

[1265] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1266] Step 1: Enter user information

[1267] The user uses their device to enter the required information (e.g., name, address, application details, etc.).

[1268] Input: Information entered by the user (name, address, application details).

[1269] The terminal temporarily stores the entered information and prepares for the next processing step.

[1270] Output: Saved user information.

[1271] Step 2: Sending Information

[1272] The device sends the stored user information to the server.

[1273] Input: Saved user information.

[1274] The server analyzes the received information and converts it into the required data format.

[1275] Output: Analyzed user information.

[1276] Step 3: Understanding the information and automatically generating the form

[1277] The server generates prompt messages for inputting the received information into the AI ​​model.

[1278] Input: Analyzed user information.

[1279] The server sends prompt messages to the generating AI model, which then automatically generates the appropriate form.

[1280] The generative AI model generates the necessary document forms based on the prompt text and returns them to the server.

[1281] Output: The generated form.

[1282] Step 4: Multilingual support

[1283] The server checks the user's language settings.

[1284] Input: Generated form, user's language settings.

[1285] The server uses translation software to translate the generated forms to make them multilingual.

[1286] The server sends the translated form to the terminal.

[1287] Output: Translated form.

[1288] Step 5: Automatic Error Check

[1289] The server validates the entered information and the generated form using automated error checking mechanisms.

[1290] Input: Translated form, user information.

[1291] If an error is detected, the server generates an error message and sends it to the terminal.

[1292] Output: Error message (if necessary).

[1293] Step 6: Behavior adjustment using the emotion engine

[1294] The server analyzes the user's emotional state using an emotion recognition AI model.

[1295] Input: User's emotional state.

[1296] If the server detects that the user is experiencing stress, it will soften the tone of the error message.

[1297] The server will make adjustments as needed to streamline the form generation process.

[1298] Output: Adjusted error messages, smooth form generation process.

[1299] Step 7: Provide feedback

[1300] The terminal displays the final form and error messages received from the server to the user.

[1301] Input: Final form, error message.

[1302] The user reviews the displayed information and makes corrections or re-enters it as needed.

[1303] Output: User review and correction information.

[1304] (Application Example 1)

[1305] Next, we will describe Application Example 1 of Form 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."

[1306] The paperwork procedures at city halls are cumbersome for users, and become even more complicated when multilingual support and error checking are inadequate. Furthermore, users often experience stress, leading to decreased efficiency in the process. A system is needed to address these issues and reduce the burden on users.

[1307] 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. In this invention, the server includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, means for emotion recognition, means for interface adjustment, and means for automatic document generation. This enables the AI ​​to understand the information the user needs and automatically generate the necessary forms. Furthermore, by recognizing the user's emotional state and adjusting the system's operation according to that emotional state, user stress can be reduced and the efficiency of procedures can be improved.

[1308] A "city hall" is an administrative body of a local government, a facility that provides various administrative services to citizens.

[1309] An "automatic document generation method" is a system that has the function of automatically creating necessary documents based on information entered by the user.

[1310] A "multi-language support system" is a system that supports multiple languages ​​and has the functionality to provide services in the language selected by the user.

[1311] An "automatic error checking system" is a system that has the function of detecting missing parts or errors in documents in real time and notifying the user.

[1312] An "emotion recognition system" is a system that recognizes the user's emotional state and adjusts its operation based on that information.

[1313] An "interface adjustment mechanism" is a system that has the function of adjusting the system's user interface according to the user's emotional state, thereby reducing user stress.

[1314] "AI" is an abbreviation for artificial intelligence, which is a technology in which machines imitate human intelligence to learn and reason.

[1315] A "form" refers to a template or document used by users to input necessary information.

[1316] "Real-time" means that data processing and information provision are performed immediately.

[1317] "User" refers to an individual or group that uses the system.

[1318] The system for implementing this invention includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, means for emotion recognition, and means for interface adjustment. Specific embodiments of each means are described below.

[1319] 1. Method for automatic document generation

[1320] The server automatically creates the necessary documents based on the information entered by the user. Specifically, when a user enters information such as their name, address, and application details through a smartphone application, the AI ​​understands the information and automatically generates the appropriate form. In this process, the FPDF library is used to generate the document in PDF format.

[1321] 2. Multi-language support means

[1322] The server supports multiple languages ​​and provides services in the language selected by the user. For example, if a user selects English, Spanish, or Chinese, the system will generate and provide documents in that language. This allows many users to access the system, overcoming language barriers.

[1323] 3. Automatic error checking means

[1324] The server detects missing parts and errors in documents in real time and notifies the user. Specifically, it checks each field of the document based on the information entered by the user and immediately notifies the user if there are any omissions or errors. This eliminates the need for resubmission.

[1325] 4. Emotion recognition means

[1326] The server uses the smartphone's camera to recognize the user's emotional state. Specifically, it analyzes the user's facial image using the OpenCV library and determines the user's emotions using an emotion recognition model (Transformers). This allows the server to determine whether the user is experiencing stress.

[1327] 5. Interface adjustment means

[1328] The server adjusts the system's user interface based on the user's emotional state, as determined by emotion recognition. For example, if the server determines that the user is stressed, it changes the interface tone to a gentler one and makes error messages more user-friendly. This reduces user stress and improves the efficiency of the process.

[1329] Specific example

[1330] The user opens the smartphone app and enters the required information (name, address, application details). The smartphone camera takes a picture of the user's face and performs emotion recognition. Based on the emotion recognition results, the interface tone is adjusted. Based on the entered information, an application form in PDF format is automatically generated and provided to the user.

[1331] Example of a prompt

[1332] The user opens the smartphone app and enters the required information (name, address, application details). The smartphone camera takes a picture of the user's face and performs emotion recognition. Based on the emotion recognition results, the interface tone is adjusted. Based on the entered information, an application form in PDF format is automatically generated and provided to the user.

[1333] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1334] Step 1:

[1335] The user opens the smartphone app and enters the required information (name, address, application details). The entered information is sent from the device to the server. The server receives this information and stores it in a database. The input data consists of text data such as name, address, and application details.

[1336] Step 2:

[1337] The server automatically generates documents based on the information it receives. Specifically, it uses a generation AI model to analyze the information entered by the user and automatically generate the appropriate form. It uses the FPDF library to generate the document in PDF format. The output is an application form in PDF format that reflects the user's information.

[1338] Step 3:

[1339] The user takes a picture of their face using their smartphone camera. The captured image is sent from the device to the server. The server receives this image and analyzes the user's emotional state using emotion recognition technology. Specifically, the OpenCV library is used to preprocess the face image, and an emotion recognition model (Transformers) is used to determine the emotion. The input is face image data, and the output is an emotional state (e.g., stress, relaxation).

[1340] Step 4:

[1341] The server adjusts the interface based on the emotion recognition results. Specifically, if it determines that the user is experiencing stress, it changes the interface tone to a gentler one and makes error messages more user-friendly. This reduces user stress. The input is emotion state data, and the output is the adjusted interface settings.

[1342] Step 5:

[1343] The server provides the user with the generated application form in PDF format. Specifically, it sends the generated PDF to the user's device, allowing the user to download it. The user reviews the provided application form and prints or submits it as needed. The input is the generated PDF data, and the output is the PDF file sent to the user's device.

[1344] (Example 2)

[1345] Next, we will describe Example 2 of Form 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".

[1346] Conventional automated document generation systems may not support the language used by the user, and they have not been improved to take into account user emotions and feedback. This results in a poor user experience and insufficient system performance.

[1347] 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.

[1348] In this invention, the server includes means for automatic document generation, means for multi-language support, means for automatic error checking, means for sentiment analysis, and means for improving system performance based on user feedback. This makes it possible to generate appropriate forms regardless of the language used by the user and to improve system performance by taking user sentiment and feedback into consideration.

[1349] An "automatic document generation method" is a method that has the function of automatically generating appropriate documents and forms based on the information required by the user.

[1350] "Multi-language support means" refers to a means that allows a system to understand a user's language and generate documents and forms in the appropriate language, regardless of the language the user is using.

[1351] An "automatic error checking method" is a system that checks generated documents and forms in real time for any missing parts or errors and notifies the user of any issues.

[1352] An "emotion analysis tool" is a tool that analyzes user input and feedback to obtain information about the user's emotions.

[1353] "Methods for improving system performance based on user feedback" refer to methods that improve the system's algorithms and functions based on acquired user sentiment information and feedback, thereby improving performance in subsequent uses.

[1354] Modes for carrying out the invention

[1355] This invention is a system that automatically generates appropriate documents and forms regardless of the language used by the user, and improves the system's performance based on the user's emotions and feedback. Specific embodiments of this system are described below.

[1356] System Configuration

[1357] The system consists of three main elements: a server, terminals, and users. The server includes means for automatic document generation, multi-language support, automatic error checking, sentiment analysis, and means for improving system performance based on user feedback.

[1358] Hardware and software to be used

[1359] The server uses the following hardware and software:

[1360] Natural Language Processing Engine: Google Cloud Natural Language API

[1361] Template engine: Handlebars.js

[1362] Sentiment analysis engine: IBM Watson Tone Analyzer

[1363] The terminal provides an interface for receiving user input and sending it to the server. Users access the system through the terminal and enter the necessary information.

[1364] Program processing

[1365] The server analyzes the information entered by the user to determine the language being used. Specifically, it uses the Google Cloud Natural Language API to analyze the entered text data. From the analysis results, it identifies the language of the entered text.

[1366] Next, the server generates the appropriate documents and forms based on the specified language. This involves using Handlebars.js to dynamically create forms containing the information requested by the user.

[1367] The generated form is sent from the server to the terminal and displayed to the user. The user reviews the displayed form and enters the required information.

[1368] When a user submits feedback through a form, the device sends that feedback to the server. The server uses IBM Watson Tone Analyzer to analyze the user's feedback and obtain information about the user's sentiment.

[1369] Based on the acquired sentiment information, the server learns to improve system performance. Specifically, it improves the form generation algorithm and applies the improvements to subsequent form generation.

[1370] Specific example

[1371] For example, if a user enters "I need a registration form for a new user" in English, the server will process it as follows:

[1372] 1. The server uses the Google Cloud Natural Language API to determine that the input text is in English.

[1373] 2. The server uses Handlebars.js to generate the registration form in English.

[1374] 3. The server sends the generated English registration form to the terminal.

[1375] 4. The terminal displays the received form to the user.

[1376] 5. The user provides feedback on the ease of use of the form, stating, "This form is very user-friendly."

[1377] 6. The device sends user feedback to the server.

[1378] 7. The server uses IBM Watson Tone Analyzer to recognize that the user is satisfied with the form.

[1379] 8. The server improves the form generation algorithm based on the acquired sentiment information.

[1380] Example of a prompt:

[1381] Please explain how the system processes a user's input in English, such as "I need a registration form for a new user."

[1382] In this way, servers, terminals, and users can work together to improve system performance.

[1383] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1384] Step 1:

[1385] The user enters the information.

[1386] The user enters "I need a registration form for a new user" into the input field on the terminal. The input data is in text format.

[1387] Step 2:

[1388] The terminal sends the input to the server.

[1389] The terminal sends text data entered by the user to the server. The HTTPS protocol is used for transmission to ensure data security. Input is text data, and output is the transmission of data to the server.

[1390] Step 3:

[1391] The server analyzes the input and identifies the language being used.

[1392] The server uses the Google Cloud Natural Language API to parse the received text data. From the analysis results, it identifies that the input text is in English. The input is text data, and the output is the language identification result.

[1393] Step 4:

[1394] The server generates the form based on the specified language.

[1395] The server uses Handlebars.js to generate an English registration form. A template engine is used to dynamically create a form containing the information requested by the user. Inputs are language identification results and template data, and output is the generated form.

[1396] Step 5:

[1397] The server sends the generated form to the terminal.

[1398] The server sends the generated English registration form to the terminal. It again uses the HTTPS protocol to securely transmit the data. The input is the generated form, and the output is the data transmission to the terminal.

[1399] Step 6:

[1400] The terminal displays the form to the user.

[1401] The terminal displays the received form to the user. The user reviews the displayed form and enters the necessary information. The input is the received form, and the output is what is displayed to the user.

[1402] Step 7:

[1403] Users provide feedback through a form.

[1404] Users provide feedback on the usability and satisfaction level of the form. For example, they might enter a comment such as "This form is very user-friendly." The input is feedback text, and the output is input to the terminal.

[1405] Step 8:

[1406] The device sends feedback to the server.

[1407] The device sends user feedback to the server. The data is securely transmitted again using the HTTPS protocol. The input is the feedback text, and the output is the data transmission to the server.

[1408] Step 9:

[1409] The server analyzes the feedback and obtains emotional information.

[1410] The server uses IBM Watson Tone Analyzer to analyze the received feedback. From the analysis results, it recognizes that the user is satisfied with the form. The input is the feedback text, and the output is the sentiment analysis result.

[1411] Step 10:

[1412] The server improves system performance based on emotional information.

[1413] The server improves its form generation algorithm based on the acquired sentiment information. It then learns to generate better forms in similar situations in the future. The input is the sentiment analysis result, and the output is the improved algorithm.

[1414] (Application Example 2)

[1415] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1416] Traditional e-commerce sites have struggled to provide personalized experiences based on the language and emotions users use. In particular, they lack multilingual support and product recommendations that consider user emotions, highlighting the need for improved user experience.

[1417] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1418] In this invention, the server includes means for automatic document generation, means for multi-language support, means for automatic error checking, means for sentiment recognition, and means for personalized recommendations. This enables the provision of an interface according to the user's language and personalized product recommendations based on the user's sentiment.

[1419] "Automatic document generation methods" refer to a function in which artificial intelligence understands the information a user needs and automatically generates the necessary forms.

[1420] "Multilingual support" refers to a function that understands the user's language and provides appropriate forms and interfaces, regardless of the language the user is using.

[1421] An "automatic error checking mechanism" is a function that identifies missing parts or errors in documents in real time, saving the trouble of resubmission.

[1422] "Emotion recognition means" refers to a function that analyzes the user's emotions and uses that information to improve the system's performance.

[1423] "Personalized recommendation methods" refer to features that recommend appropriate products and services based on the user's emotions and language of use.

[1424] To implement this invention, a system including a server, a user terminal, and an artificial intelligence model is required. Specific embodiments of this system are described below.

[1425] System Configuration

[1426] 1. Server: The server includes means for automatic document generation, multi-language support, automatic error checking, sentiment recognition, and personalized recommendation.

[1427] 2. User terminal: A user terminal is a device such as a smartphone, tablet, or personal computer that provides an interface for the user to access the system.

[1428] 3. Artificial Intelligence Model: A generative AI model is used to analyze user input and generate appropriate forms and recommendations.

[1429] Program processing

[1430] The server receives input from the user terminal and performs the following processing:

[1431] 1. Language detection and translation: The server uses the langdetect library to detect the user's input language and the TextBlob library to translate it into English as needed.

[1432] 2. Sentiment Analysis: The server uses the transformers library pipeline to analyze the sentiment of the user's input text.

[1433] 3. Form generation: The server generates a form according to the user's language.

[1434] 4. Personalized Recommendations: The server recommends appropriate products and services based on the user's emotions and language of use.

[1435] Hardware and software to be used

[1436] Hardware: Servers, user terminals (smartphones, tablets, PCs)

[1437] Software: langdetect library, TextBlob library, transformers library

[1438] Specific example

[1439] For example, if a user enters "I really liked this product!", the server translates the text, analyzes the sentiment, and generates an appropriate form. It also recommends related products and services based on the user's sentiment.

[1440] Example of a prompt

[1441] If a user enters "I really love this product!", the system should translate that text, analyze the sentiment, and generate an appropriate form.

[1442] In this way, it is possible to provide a personalized experience based on the user's language and emotions.

[1443] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1444] Step 1:

[1445] The user accesses the system using a terminal and enters text into an input form. For example, they might enter "I really like this product!". The input data is then sent to the server.

[1446] Step 2:

[1447] The server uses the langdetect library to detect the language of the received text data. The input is user text, and the output is the detected language code (e.g., 'ja').

[1448] Step 3:

[1449] If the detected language is not English, the server uses the TextBlob library to translate the text into English. The input is the user's text and the detected language code, and the output is the translated English text.

[1450] Step 4:

[1451] The server performs sentiment analysis on the translated text using the transformers library's pipeline. The input is the translated English text, and the output is the result of the sentiment analysis (e.g., 'POSITIVE').

[1452] Step 5:

[1453] The server generates a form according to the user's language. For example, if the user's language is Japanese, it generates a Japanese form. The input is the detected language code, and the output is a form in the corresponding language.

[1454] Step 6:

[1455] The server recommends personalized products and services based on the sentiment analysis results and the user's language. The input is the sentiment analysis results and the detected language code, and the output is a list of recommended products and services.

[1456] Step 7:

[1457] The server sends the generated form and a list of recommended products and services to the user's device. The user can then review this information on their device and select their next action.

[1458] (Example 3)

[1459] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[1460] Traditional document creation systems had the problem of being time-consuming and cumbersome, as users had to manually input the necessary information and check for errors and missing parts themselves. Furthermore, insufficient multilingual support made it difficult to address user confusion. Additionally, the lack of support that considered the user's emotional state could potentially degrade the user experience.

[1461] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1462] In this invention, the server includes means for automatic document generation, means for multilingual support, means for automatic error checking, means for emotional state analysis, and means for support adjustment based on emotional state. This eliminates the need for users to manually input necessary information and allows for real-time identification of missing parts or errors in documents. Furthermore, by providing multilingual support according to the user's emotional state, users can receive appropriate support even when confused, thereby improving the overall user experience.

[1463] "Automatic document generation methods" refer to a function in which artificial intelligence understands the information a user needs and automatically generates the necessary forms.

[1464] "Multilingual support means" refers to a function that assists users in multiple languages, and can provide support in the user's native language.

[1465] An "automatic error checking mechanism" is a function that detects missing parts or errors in a document in real time and notifies the user.

[1466] "Emotional state analysis means" refers to a function that monitors user input and behavior and analyzes the user's emotional state using emotion recognition technology.

[1467] "Support adjustment based on emotional state" refers to a function that adjusts the operation of multilingual support means according to the user's emotional state, providing support in the user's native language.

[1468] This invention relates to a system that includes means for automatic document generation, multilingual support, automatic error checking, emotional state analysis, and support adjustment based on emotional state. Specific embodiments of this system are described below.

[1469] First, the user uploads documents such as application forms to the system. The server receives the uploaded documents and saves them to a database. At this time, the server uses the Google Cloud Vision API to convert the contents of the documents into text data using OCR (Optical Character Recognition) technology. The converted text data is then analyzed to check if all the necessary fields are filled in.

[1470] Next, the server detects missing parts and errors based on the analysis results. For example, if the address field is blank, the server will list the error as "Address field is blank." The detected errors are notified to the user. The notification is sent via email or in-app notification. The user receives the notification, checks the indicated parts, and makes corrections. The corrected document is uploaded again, and the server analyzes the resubmitted document again to check if the errors have been corrected.

[1471] Furthermore, the device monitors user input and behavior and analyzes their emotional state using the Microsoft Azure Emotion API. If the device detects that the user is confused, this information is sent to the server. Based on the received emotional state, the server uses the Google Translate API to adjust the multilingual support. For example, if the user's native language is Japanese, the support content is translated into Japanese. The translated support content is then provided to the user, allowing them to receive support in their native language.

[1472] For example, if a user submits an application form but the address field is left blank, the server uses the Google Cloud Vision API to detect the omission and notifies the user, "The address field is blank. Please fill it in." The user then fills in the address and resubmits the document. Also, if a user is receiving support in English but is perceived as confused, the server uses the Google Translate API to provide support in the user's native language, Japanese.

[1473] Examples of prompts include, "Detect and notify the user of any missing parts in the documents they have submitted," and "Provide support in the user's native language if they are confused."

[1474] The above describes specific embodiments for carrying out this invention. The flow of the specific processing in Example 3 will be explained with reference to Figure 21.

[1475] Step 1:

[1476] Upload documents

[1477] Users upload documents such as application forms to the system.

[1478] Input: Document file uploaded by the user.

[1479] Output: Document files saved on the server.

[1480] Specific operation: The user selects a document through the web interface and clicks the upload button.

[1481] Step 2:

[1482] Document storage

[1483] The server receives the uploaded documents and stores them in the database.

[1484] Input: Document file uploaded by the user.

[1485] Output: Document data stored in the database.

[1486] Specific operation: The server receives the document file and creates an entry for saving it to the database.

[1487] Step 3:

[1488] Document analysis

[1489] The server uses the Google Cloud Vision API to convert the document contents into text data using OCR technology.

[1490] Input: Document data stored in the database.

[1491] Output: Document contents converted to text data.

[1492] Specific operation: The server calls the Google Cloud Vision API to convert the image data of the document into text data.

[1493] Step 4:

[1494] Error detected

[1495] The server parses the converted text data and verifies that all necessary fields are filled in.

[1496] Input: Document contents converted to text data.

[1497] Output: A list of detected missing parts and errors.

[1498] Specific operation: The server parses the text data and checks whether predefined required fields are filled in.

[1499] Step 5:

[1500] Error notification

[1501] The server sends emails or in-app notifications to inform users of any detected errors.

[1502] Input: A list of detected missing parts or errors.

[1503] Output: Error message sent to the user.

[1504] Specific action: The server generates an error message and sends a notification to the user.

[1505] Step 6:

[1506] Corrections and resubmissions

[1507] The user corrects the errors pointed out and re-uploads the document.

[1508] Input: A document file modified by the user.

[1509] Output: The revised document file saved again on the server.

[1510] Specific action: The user checks the error message, corrects the document, and uploads it again.

[1511] Step 7:

[1512] Analysis of emotional states

[1513] The device monitors user input and behavior and analyzes emotional states using the Microsoft Azure Emotion API.

[1514] Input: User input and behavioral data.

[1515] Output: Analyzed user emotional state.

[1516] Specific operation: The device monitors user input and actions in real time and calls APIs to analyze the user's emotional state.

[1517] Step 8:

[1518] Sending emotional states

[1519] The device sends the user's emotional state to the server.

[1520] Input: Analyzed user emotional state.

[1521] Output: Emotional state data sent to the server.

[1522] Specific action: The terminal makes a request to send the analysis results to the server.

[1523] Step 9:

[1524] Support adjustments

[1525] The server uses the Google Translate API to adjust its multilingual support based on the emotional state it receives.

[1526] Input: User's emotional state data.

[1527] Output: Adjusted multilingual support content.

[1528] Specific operation: The server translates support content based on the emotional state and provides it in the appropriate language.

[1529] Step 10:

[1530] Support

[1531] The server provides the translated support content to the user.

[1532] Input: Adjusted multilingual support content.

[1533] Output: Support provided to the user.

[1534] Specific operation: The server sends translated support content to the user, ensuring the user receives appropriate support.

[1535] (Application Example 3)

[1536] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 as a "terminal".

[1537] Traditional electronic payment systems often made it difficult for users to spot input errors or missing information, leading to frequent resubmissions. Furthermore, the lack of support tailored to the user's emotional state meant that the process was cumbersome and stressful, especially for anxious or confused users.

[1538] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes an automatic generation means, a multi-language support means, an automatic error checking means, an emotion engine means, and a support provision means according to the user's emotional state. This makes it possible to point out input errors and missing parts in real time when the user makes an electronic payment and to provide appropriate support messages according to the user's emotional state.

[1539] "Automatic generation means" refers to a function in which artificial intelligence understands the information a user needs and automatically generates the necessary forms.

[1540] "Multilingual support" refers to a function that supports users in multiple languages.

[1541] An "automatic error checking mechanism" is a function that identifies missing parts or errors in documents in real time, saving the trouble of resubmission.

[1542] The "emotional engine" is a function that analyzes the user's emotional state.

[1543] "Means of providing support tailored to the user's emotional state" refers to a function that provides appropriate support messages according to the user's emotional state.

[1544] The system for carrying out this invention consists of a server and a user terminal. The server includes an automatic generation means, a multi-language support means, an automatic error checking means, an emotion engine means, and a support provision means according to the user's emotional state.

[1545] The user terminal is a device such as a smartphone, tablet, or personal computer, and is used when the user makes an electronic payment. The information entered by the user into the terminal is sent to the server.

[1546] The server first uses an automated generation mechanism to allow artificial intelligence to understand the information the user needs and automatically generate the necessary forms. Next, an automated error checking mechanism points out any missing parts or errors in the document in real time and prompts the user to make corrections.

[1547] Furthermore, an emotion engine analyzes the user's emotional state. The emotion engine uses natural language processing libraries such as TextBlob to detect emotions from the user's input text. Once an emotional state is detected, a support system tailored to the user's emotional state provides appropriate support messages. For example, if the user is anxious, the system will explain things in simpler terms to help the user relax.

[1548] For example, if a user enters "12345abc", the server will determine that the input is correct and prompt the user to proceed to the next step. If there is an error in the input, the sentiment engine will analyze the user's emotional state and provide an appropriate support message.

[1549] Examples of prompts to input into a generative AI model:

[1550] Create a program that, when a user makes an electronic payment, will point out input errors or missing information in real time and provide support tailored to the user's emotional state using an emotion engine. If the user is anxious, the system will explain things in simpler language to help them relax.

[1551] In this way, users can make electronic payments smoothly, and it becomes easier to correct input errors or missing information. Furthermore, support tailored to the user's emotional state is provided, reducing stress and offering a more comfortable user experience.

[1552] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1553] Step 1:

[1554] The user enters their electronic payment information into the terminal. The entered information is then transmitted from the user's terminal to the server. The entered data includes the payment amount, payee, and the user's personal information.

[1555] Step 2:

[1556] The server uses automated generation methods to allow artificial intelligence to understand the information the user needs and automatically generate the necessary forms. It analyzes the input data and processes it to generate appropriate forms. The generated forms are then displayed to the user.

[1557] Step 3:

[1558] The user reviews the generated form and enters the required information. The entered information is sent back to the server. The server receives the input data and proceeds to the next processing step.

[1559] Step 4:

[1560] The server uses automated error checking mechanisms to identify missing parts and errors in documents in real time. It analyzes the input data and performs data calculations to detect missing parts and errors. If errors are detected, a message prompting the user to correct them is displayed.

[1561] Step 5:

[1562] The user makes corrections according to the error message. The corrected information is sent back to the server. The server receives the corrected data and performs another error check.

[1563] Step 6:

[1564] The server analyzes the user's emotional state using an emotion engine. It parses the user's input text and performs data calculations to detect emotions. The emotion engine uses natural language processing libraries such as TextBlob.

[1565] Step 7:

[1566] The server provides appropriate support messages using support delivery methods tailored to the user's emotional state. It processes data to select the message to display to the user based on their emotional state. For example, if the user is anxious, the system will explain things in simpler terms to help the user relax.

[1567] Step 8:

[1568] The user follows the support messages to perform final verification and corrections. If all information is correct, the payment is completed. The server receives the final data and processes the payment. The user is notified that the payment is complete.

[1569] The above processing steps enable users to make electronic payments smoothly and easily correct input errors or missing information. Furthermore, support tailored to the user's emotional state is provided, reducing stress and offering a more comfortable user experience.

[1570] 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.

[1571] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> 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.

[1572] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1573] 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.

[1574] [Third Embodiment]

[1575] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1576] 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.

[1577] 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).

[1578] 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.

[1579] 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.

[1580] 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).

[1581] 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.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[1587] "Example of form 1"

[1588] Embodiments of the present invention include a system comprising means for automatically generating necessary documents at a city hall, means for multi-language support, and means for automatic error checking. Specifically, the AI ​​understands the information required by the user and automatically generates the necessary forms. For example, when creating an application form required at a city hall, the user inputs the necessary information (e.g., name, address, application details, etc.), the AI ​​understands that information, and automatically generates an appropriate form.

[1589] "Example of form 2"

[1590] Furthermore, embodiments of the present invention provide multi-language support. Specifically, regardless of the language used by the user, the system understands that language and generates appropriate forms. For example, if the user uses English, the system understands English information and generates English forms.

[1591] "Example of form 3"

[1592] Furthermore, an embodiment of the present invention provides an automated error checking means. Specifically, the system identifies missing parts and errors in documents in real time, saving the user the trouble of resubmission. For example, if a user has omitted part of an application form, the system will identify the missing part and prompt the user to correct it.

[1593] The following describes the processing flow for each example of the form.

[1594] "Example of form 1"

[1595] Step 1: The user enters the necessary information into the system (e.g., name, address, application details, etc.).

[1596] Step 2: The AI ​​understands the information provided by the user and automatically generates the appropriate form.

[1597] Step 3: The generated form is presented to the user, who can make corrections or confirmations as needed.

[1598] "Example of form 2"

[1599] Step 1: Enter the language the user will be using into the system.

[1600] Step 2: The system understands the user's language and generates an appropriate form corresponding to that language.

[1601] Step 3: The generated form is presented to the user, who can make corrections or confirmations as needed.

[1602] "Example of form 3"

[1603] Step 1: The user submits the document to the system.

[1604] Step 2: The system checks the submitted documents for missing parts and errors in real time.

[1605] Step 3: If an error is detected, the system will notify the user of the error and prompt them to correct it.

[1606] (Example 1)

[1607] Next, we will describe Embodiment 1 of 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."

[1608] Creating necessary documents at city hall is time-consuming and prone to errors for users. Furthermore, the complexity increases when multilingual support is required. As a result, users often waste time and effort resubmitting documents. An efficient system is needed to solve these problems.

[1609] 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.

[1610] In this invention, the server includes means for the user to input necessary information, means for analyzing the input information, means for automatically generating documents based on the analysis results, means for providing the generated documents in multiple languages, and means for performing error checking on the generated documents. As a result, users can easily create the necessary documents and use them across language barriers as they are provided in multiple languages. Furthermore, error checking eliminates the need for resubmission.

[1611] A "user" refers to an individual or organization that uses the system to create necessary documents at the city hall.

[1612] "Means of inputting information" refers to the interface through which users enter necessary information such as their name, address, and application details into the system.

[1613] "Means of analyzing information" refers to technologies such as generative AI models used to analyze input information and generate appropriate documents.

[1614] "Means for automatically generating documents" refers to a function that automatically creates necessary documents based on analyzed information.

[1615] "Means of providing in multiple languages" refers to technologies for translating generated documents into multiple languages ​​and providing them to users.

[1616] "Means of error checking" refers to a function that verifies the accuracy and appropriateness of the input content in the generated document and detects errors.

[1617] A "generative AI model" refers to artificial intelligence technology used to analyze information entered by a user and generate appropriate documents.

[1618] "Means of verifying the existence of an address" refers to technology used to confirm whether the entered address actually exists.

[1619] "Error checking rules" refer to predefined criteria and conditions used to verify the appropriateness of an application.

[1620] This invention is a system for efficiently creating necessary documents at a city hall. The system allows users to input the required information, analyzes that information to automatically generate appropriate documents, provides them in multiple languages, and also includes error checking capabilities.

[1621] Hardware and software to be used

[1622] hardware

[1623] Server: A central processing unit used for information analysis, document generation, multilingual translation, and error checking.

[1624] Terminal: A device used by the user to input information and review the generated documents (e.g., a personal computer or smartphone).

[1625] software

[1626] Generative AI model: Artificial intelligence technology used to analyze user-inputted information and generate appropriate documents (e.g., GPT-4).

[1627] Translation API: A service for translating generated documents into multiple languages ​​(e.g., Google Translate API).

[1628] Map API: A service used to verify the existence of an entered address (e.g., Google Maps API).

[1629] Program processing

[1630] User information entry

[1631] The user accesses the system's web interface and enters the necessary information, such as their name, address, and application details. For example, the user might enter "Taro Tanaka, Shinjuku Ward, Tokyo, application for a copy of resident registration."

[1632] Information transmission

[1633] The terminal sends the user-entered information to the server in JSON format. The data sent is in the following format:

[1634] json

[1635] {

[1636] "name": "Taro Tanaka",

[1637] "address": "Shinjuku-ku, Tokyo",

[1638] "application_content": "Application for a copy of the resident registration certificate"

[1639] }

[1640] Information analysis

[1641] The server inputs the received JSON data into a generating AI model (e.g., GPT-4). The generating AI model analyzes the user's input and selects an appropriate application form template.

[1642] Form generation

[1643] The server automatically generates application forms based on the analysis results obtained from the generated AI model. For example, it generates a form that includes the items necessary for "applying for a copy of a resident registration certificate."

[1644] Multilingual support

[1645] The server translates the generated Japanese form into English and Spanish using the Google Translate API. The translated form is then provided in multiple languages ​​as follows:

[1646] Japanese: Application for a copy of the resident registration certificate

[1647] English: Application for a Copy of Resident Certificate

[1648] Spanish: Solicitud de Copia del Certificado de Residencia

[1649] Error check

[1650] The server uses the Google Maps API to verify that the entered address exists. It also checks whether the application is valid based on predefined rules. For example, if the address does not exist or the application is invalid, it generates an error message.

[1651] Form provision

[1652] The server sends the form, after error checking is complete, to the user's device. The user can then review the form provided through the device and make corrections as needed. For example, if the user corrects their address, another error check will be performed.

[1653] Specific example

[1654] Example 1: When a user creates an application form in Japanese

[1655] 1. The user accesses the system and enters their name, address, and application details in Japanese.

[1656] 2. The server receives the input information and analyzes it using a generative AI model (e.g., GPT-4).

[1657] 3. The server automatically generates a Japanese application form based on the analysis results.

[1658] 4. The server also translates the generated form into English using the Google Translate API.

[1659] 5. The server uses the Google Maps API to verify the existence of the address and performs other error checks.

[1660] 6. The server provides the user with a form that has been error-checked.

[1661] Example 2: Example of a prompt message

[1662] "The user entered their name, address, and application details to create an application form required by the city hall. Based on this information, automatically generate the appropriate form and provide it in multiple languages. Also, perform error checking on the entered information."

[1663] In this way, a system is created that allows users to easily create the necessary documents at the city hall.

[1664] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1665] Step 1:

[1666] Users access the system's web interface and enter necessary information such as their name, address, and application details. The entered information is then sent from the terminal to the server. The input data consists of text information such as name, address, and application details.

[1667] Step 2:

[1668] The terminal sends the user-entered information to the server in JSON format. The data sent is in the following format:

[1669] json

[1670] {

[1671] "name": "Taro Tanaka",

[1672] "address": "Shinjuku-ku, Tokyo",

[1673] "application_content": "Application for a copy of the resident registration certificate"

[1674] }

[1675] The server receives this JSON data.

[1676] Step 3:

[1677] The server inputs the received JSON data into a generating AI model (e.g., GPT-4). The generating AI model analyzes the user's input and selects an appropriate application form template. As a result of the analysis, an application form template containing the necessary fields is generated.

[1678] Step 4:

[1679] The server automatically generates application forms based on the analysis results obtained from the generated AI model. For example, a form containing the necessary items for "applying for a copy of a resident registration certificate" is generated. The generated form is customized based on the information entered by the user.

[1680] Step 5:

[1681] The server uses a translation API (e.g., Google Translate API) to translate the generated Japanese forms into multiple languages. The translated forms are provided in several languages, such as English and Spanish. For example, the Japanese phrase "Jinmyō no hitsu no yūshū" (Application for a Copy of Resident Certificate) is translated into English as "Application for a Copy of Resident Certificate".

[1682] Step 6:

[1683] The server performs automatic error checking on the generated form. It uses the Google Maps API to verify that the entered address exists. It also checks whether the application content is appropriate based on predefined rules. For example, if the address does not exist or the application content is inappropriate, it generates an error message.

[1684] Step 7:

[1685] The server sends the form, after error checking is complete, to the user's device. The user can then review the form provided through the device and make corrections as needed. For example, if the user corrects their address, another error check will be performed.

[1686] (Application Example 1)

[1687] Next, we will describe Application Example 1 of Form 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."

[1688] In autonomous vehicles, it is essential to quickly and accurately prepare the necessary documents for passengers to arrive at their destination. However, current systems require passengers to prepare these documents manually, which presents problems such as language barriers and errors. This reduces passenger convenience and can delay procedures at the destination. Therefore, a system is needed that automatically generates the necessary documents before passengers arrive at their destination and checks for errors in real time.

[1689] 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.

[1690] In this invention, the server includes means for automatically generating documents required at a city hall, means for multi-language support, means for automatic error checking, means for automatically generating documents required by passengers before they arrive at their destination in the infotainment system of an autonomous vehicle, means for passengers to input necessary information using a smart device, and means for AI to generate appropriate documents based on the input information. This makes it possible to quickly and accurately prepare the necessary documents for passengers before they arrive at their destination.

[1691] The "automatic document generation method required by the city hall" is a function that automatically generates various documents required by the city hall based on information entered by the user.

[1692] "Multi-language support" refers to a function that supports multiple languages ​​and translates user-inputted information into the appropriate language.

[1693] An "automatic error checking mechanism" is a function that detects in real time whether there are any missing parts or errors in the generated document and notifies the user.

[1694] An "infotainment system for autonomous vehicles" is a system installed inside an autonomous vehicle that provides passengers with information and entertainment.

[1695] "A means of automatically generating necessary documents for passengers before they arrive at their destination" refers to a function that automatically generates necessary documents for passengers before they arrive at their destination.

[1696] "Means for passengers to input necessary information using smart devices" refers to a function that allows passengers to input necessary information using devices such as smartphones or tablets.

[1697] "A means by which AI generates appropriate documents based on input information" refers to a function in which artificial intelligence analyzes passenger input information and generates appropriate documents based on that analysis.

[1698] The system for implementing this invention is configured as follows: First, the server includes means for automatically generating necessary documents at the city hall, means for multi-language support, means for automatic error checking, means for automatically generating necessary documents for passengers before they arrive at their destination in the infotainment system of an autonomous vehicle, means for passengers to input necessary information using a smart device, and means for an AI to generate appropriate documents based on the input information.

[1699] Hardware and software configuration

[1700] Hardware: Infotainment systems for autonomous vehicles, smartphones, tablets, touchscreens

[1701] Software: Python, Google Translate API, Langdetect library, generative AI model

[1702] Data processing and data calculation

[1703] 1. Language detection: The server uses the Langdetect library to detect the language of the text entered by the user using a smart device.

[1704] 2. Translation: If necessary, use the Google Translate API to translate the text into the target language.

[1705] 3. Form Generation: The server automatically generates the necessary document forms based on the information entered by the user. It uses a generation AI model to analyze the input information and generate the appropriate documents.

[1706] 4. Error checking: The server detects in real time whether the generated document contains any missing parts or errors and notifies the user.

[1707] Specific example

[1708] For example, when generating immigration documents required for a passenger visiting Japan for tourism purposes, the following prompt message would be used:

[1709] Example of a prompt:

[1710] Please enter your user information:

[1711] Name: Taro Yamada

[1712] Address: Shinjuku-ku, Tokyo

[1713] Purpose: Tourism

[1714] By inputting this prompt into the AI ​​generation model, the necessary documents are automatically generated. The server generates the appropriate documents and performs error checks based on the information entered by the user. This makes it possible to quickly and accurately prepare the necessary documents before passengers arrive at their destination.

[1715] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1716] Step 1:

[1717] The user enters the necessary information using a smart device.

[1718] Input: Users enter information such as their name, address, and purpose using a smartphone or tablet.

[1719] Data processing: The entered information is sent to the server.

[1720] Output: User input information is saved on the server.

[1721] Step 2:

[1722] The server detects the language of the input information.

[1723] Input: Text information entered by the user.

[1724] Data calculation: Use the Langdetect library to detect the language of the input text.

[1725] Output: Detected language information.

[1726] Step 3:

[1727] The server translates the input information as needed.

[1728] Input: User input information and detected language information.

[1729] Data processing: Use the Google Translate API to translate the input information into the target language.

[1730] Output: Translated text information.

[1731] Step 4:

[1732] The server generates the necessary document forms based on the input information.

[1733] Input: User input information (including translated information).

[1734] Data processing: Using a generative AI model, analyze input information and automatically generate appropriate document forms.

[1735] Output: The form of the generated document.

[1736] Step 5:

[1737] The server performs error checking on the generated documents.

[1738] Input: The form of the generated document.

[1739] Data processing: Detects missing parts and errors in documents in real time.

[1740] Output: Error check results (error message if there are errors).

[1741] Step 6:

[1742] The server notifies the user of the results of the error check.

[1743] Input: Error check results.

[1744] Data processing: Generate error messages and notify the user.

[1745] Output: Error message notified to the user.

[1746] Step 7:

[1747] The user corrects the error and re-enters the information.

[1748] Input: The user corrects the information based on the error message and re-enters it.

[1749] Data processing: The corrected information is sent back to the server.

[1750] Output: The corrected information stored on the server.

[1751] Step 8:

[1752] The server regenerates the document based on the corrected information and performs error checking.

[1753] Input: Corrected information.

[1754] Data processing: Regenerate documents using the AI ​​model and perform error checking.

[1755] Output: The final document form without errors.

[1756] Step 9:

[1757] The server provides the final document to the user.

[1758] Input: The final document form without errors.

[1759] Data processing: Format the final document for the user.

[1760] Output: The final document provided to the user.

[1761] (Example 2)

[1762] Next, we will describe Example 2 of the morphological example. 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."

[1763] Traditional systems required manual generation of forms in the user's language, making multilingual support difficult. Furthermore, they lacked features to identify missing or incorrect data in real time, resulting in the cumbersome process of resubmission.

[1764] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for detecting the language used by the user, means for generating an appropriate form based on the detected language, means for sending the generated form to the user's terminal, means for receiving and processing the data entered by the user, and means for sending the processing results to the user's terminal. This makes it easy to support multiple languages ​​and enables real-time identification of missing parts or errors in the data entered by the user.

[1765] "User" refers to an individual or group that uses the system.

[1766] "Language" refers to the natural language used by the user, including English, Japanese, Spanish, and others.

[1767] "Detection means" refers to technical means for identifying the language used by the user, and includes natural language processing techniques.

[1768] A "form" refers to an electronic document used by users to input necessary information.

[1769] "Generating means" refers to technical means for automatically creating appropriate forms based on a specified language.

[1770] "Means of transmission" refers to the technical means of sending the generated form or processing results to the user's device.

[1771] "Terminal" refers to an electronic device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[1772] "Means of receiving" refers to the technical means by which the server receives data entered by the user.

[1773] "Means of processing" refers to the technical means for analyzing received data and generating the necessary results.

[1774] "Missing information" refers to areas where the user is missing information that they should have entered.

[1775] An "error" refers to a mistake in the data entered by the user.

[1776] "Real-time" refers to the fact that data is processed instantly as soon as the user enters it.

[1777] "Resubmission" refers to the act of a user correcting errors or omissions and then resubmitting the data.

[1778] This invention is a system that automatically detects the language used by the user, generates an appropriate form, and provides it to the user. The system consists of three main elements: a server, a terminal, and a user.

[1779] The server uses natural language processing techniques to detect the language the user is using. Specifically, it utilizes services such as the Google Cloud Natural Language API and the Microsoft Azure Text Analytics API. The server analyzes the user's input data to identify the language being used.

[1780] Next, the server generates the appropriate form based on the specified language. The server retrieves a template corresponding to the user's language from the database and generates the form based on that template. For example, if the user is using English, the server will create the form using an English template.

[1781] The generated form is sent from the server to the user's device. The server sends the data using the HTTP protocol, specifically via a RESTful API. The device parses the received form data and displays it in the user interface. The device renders the form using a frontend framework such as React or Vue.js.

[1782] The user enters the required information into the displayed form and presses the submit button. The device then generates another HTTP request to send the data entered by the user to the server. The server processes the received data and generates the necessary results, such as saving them to a database or integrating with other services.

[1783] The processing results are sent from the server to the terminal, which then displays the results to the user. This allows the user to verify whether their input was processed correctly.

[1784] As a concrete example, consider a case where a user enters "I need a registration form" in English. The terminal sends this input data to the server, which parses the input data to determine that the user is using English. The server generates a registration form using an English template and sends the generated English registration form to the terminal. The terminal displays the received English registration form to the user, who enters the necessary information and submits it. The server processes the received data, generates a result, and sends it to the terminal, which then displays the result to the user.

[1785] Examples of prompt messages include the following:

[1786] "Please analyze the data entered by the user in English and generate an English form."

[1787] "Please analyze the data entered by the user in Japanese and generate a form in Japanese."

[1788] In this way, the system can generate and provide the appropriate form to the user, regardless of the language the user is using.

[1789] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1790] Step 1:

[1791] The user selects or enters a language.

[1792] Input: The language the user will be using (e.g., English, Japanese)

[1793] Operation: The user selects their preferred language on the system interface or enters it directly.

[1794] Output: Selected or entered language information

[1795] Step 2:

[1796] The terminal sends user input to the server.

[1797] Input: Language information selected or entered by the user.

[1798] Operation: The terminal sends the language information selected or entered by the user to the server. Specifically, it generates an HTTP POST request.

[1799] Output: Language information sent to the server

[1800] Step 3:

[1801] The server detects the user's language.

[1802] Input: Language information sent to the server

[1803] Operation: The server analyzes the received data and identifies the language the user is using. Specifically, it uses natural language processing techniques.

[1804] Output: Identified language information

[1805] Step 4:

[1806] The server generates the appropriate form.

[1807] Input: Identified language information

[1808] Operation: The server generates the appropriate form based on the specified language. Specifically, it retrieves the corresponding template from the database and creates the form based on that template.

[1809] Output: Generated form data

[1810] Step 5:

[1811] The server generates a form and sends it to the terminal.

[1812] Input: Generated form data

[1813] Operation: The server sends the generated form data to the terminal. Specifically, it sends the data via a RESTful API using the HTTP protocol.

[1814] Output: Form data sent to the terminal

[1815] Step 6:

[1816] The terminal displays the form to the user.

[1817] Input: Form data sent to the device

[1818] Operation: The terminal parses the received form data and displays it in the user interface. Specifically, it renders the form using a frontend framework such as React or Vue.js.

[1819] Output: Form displayed to the user

[1820] Step 7:

[1821] The user enters the required information into the form and submits it.

[1822] Input: Information entered by the user (e.g., name, email address)

[1823] Action: The user enters the required information into the displayed form and presses the submit button.

[1824] Output: Input information

[1825] Step 8:

[1826] The terminal sends the input data to the server.

[1827] Input: Entered information

[1828] Operation: The terminal generates an HTTP POST request to send the data entered by the user to the server.

[1829] Output: Input data sent to the server

[1830] Step 9:

[1831] The server processes the input data and generates the results.

[1832] Input: Input data sent to the server

[1833] Operation: The server analyzes the received data and generates the necessary results. Specifically, it stores them in a database or interacts with other services.

[1834] Output: Generated result data

[1835] Step 10:

[1836] The server sends the results to the terminal.

[1837] Input: Generated result data

[1838] Operation: The server sends the generated result data to the terminal. Specifically, it sends the data again in JSON format via a RESTful API.

[1839] Output: Result data sent to the terminal

[1840] Step 11:

[1841] The device displays the results to the user.

[1842] Input: Result data sent to the terminal

[1843] Operation: The terminal parses the result data received from the server and displays it in the user interface. Specifically, it uses React to render the results.

[1844] Output: Results displayed to the user

[1845] (Application Example 2)

[1846] Next, we will describe application example 2 of form 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."

[1847] Traditional systems struggled to automatically generate appropriate forms based on the user's language, leading to a poor user experience, especially in environments requiring multilingual support. Furthermore, they lacked the ability to identify missing or incorrect documents in real time, often resulting in the need for resubmissions.

[1848] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatic document generation, means for multi-language support, means for automatic error checking, means for detecting the user's input language, and means for generating an appropriate form based on the detected language. This enables the automatic generation of an appropriate form according to the language used by the user, and by pointing out missing parts or errors in the document in real time, it is possible to reduce the effort of resubmission.

[1849] "Automatic document generation methods" refer to functions that automatically create appropriate documents and forms based on the information required by the user.

[1850] "Multi-language support" refers to a function that supports multiple languages ​​used by the user and provides appropriate information in each language.

[1851] An "automatic error checking mechanism" is a function that detects missing parts or errors in a document in real time and notifies the user.

[1852] "Means for detecting the user's input language" refers to a function that automatically identifies the language of the text entered by the user.

[1853] "Means for generating appropriate forms based on detected language" refers to a function that automatically generates appropriate forms in a language easily understood by the user, depending on the detected language.

[1854] A system for carrying out this invention includes means for automatically generating documents, means for multi-language support, means for automatic error checking, means for detecting the user's input language, and means for generating an appropriate form based on the detected language.

[1855] The server uses a language detection library (e.g., langdetect) to automatically identify the language of the text entered by the user. If the text entered by the user is in Japanese, a Japanese form is generated; if it is in English, an English form is generated. This process uses Google's translation API (googletrans library) to translate the text as needed.

[1856] Specifically, when a user types "search for products" using their smartphone, the server receives this input and uses a language detection library to identify that it is in Japanese. It then generates a Japanese form and provides it to the user. This allows the user to search for products in their own language and proceed with the purchase process.

[1857] Furthermore, the server uses automated error checking mechanisms to detect missing parts and errors in the generated form in real time and notify the user. This saves the user the trouble of resubmitting the form.

[1858] The hardware used is a smartphone, and the software consists of Python, the langdetect library, and the googletrans library.

[1859] For example, if a user enters "Search for products," a form in Japanese will be generated. An example of this prompt is as follows:

[1860] When a user enters "Search for products," please generate a form in Japanese.

[1861] In this way, it becomes possible to automatically generate appropriate forms according to the language used by the user, and by pointing out missing parts or errors in the document in real time, the trouble of resubmission can be avoided.

[1862] The flow of the specific processing in Application Example 2 will be explained using Figure 14.

[1863] Step 1:

[1864] The user enters text using their smartphone. For example, they might type "search for products." This entered text is then sent to the system.

[1865] Step 2:

[1866] The server analyzes the received input text using a language detection library (langdetect) to identify the language of the input text. The input is the user's text, and the output is in the identified language (in this case, Japanese).

[1867] Step 3:

[1868] The server generates an appropriate form based on the identified language. A template for form generation is provided, and the appropriate template is selected according to the language. The input is the identified language, and the output is the generated form.

[1869] Step 4:

[1870] The server sends the generated form to the user's smartphone. The user can then view the form displayed in their own language. The input is the generated form, and the output is the form displayed on the user's smartphone.

[1871] Step 5:

[1872] The user enters information into the form and presses the submit button. The entered data is sent to the server. The input is the data entered by the user into the form, and the output is the data sent to the server.

[1873] Step 6:

[1874] The server uses automated error checking mechanisms to detect missing data and errors in real time. The input is the data sent by the user, and the output is the detected error information.

[1875] Step 7:

[1876] The server notifies the user of any detected errors. The user receives instructions to correct the errors and can then modify and resubmit the form. The input is the detected error information, and the output is the error information notified to the user.

[1877] Step 8:

[1878] The user corrects the error and resubmits the form. The server receives the data again and performs error checking. If there are no errors, the data is processed successfully. The input is the corrected data, and the output is the successfully processed data.

[1879] In this way, appropriate forms are automatically generated according to the language used by the user, and error checking is performed in real time.

[1880] (Example 3)

[1881] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1882] Traditional document submission systems had problems such as requiring users to manually check and correct missing parts and errors in documents, resulting in the cumbersome process of resubmission. Furthermore, they lacked sufficient multilingual support, making them difficult for users of different languages ​​to use. Additionally, the lack of technology to accurately analyze document content resulted in low accuracy in error detection.

[1883] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1884] In this invention, the server includes means for automatic document generation, means for multilingual support, means for automatic error checking, means for optical character recognition, means for analysis using a generation AI model, and means for real-time notification. This makes it possible to detect missing parts and errors in documents submitted by users in real time and prompt them to correct them quickly. Furthermore, multilingual support makes it easy for users who speak different languages ​​to use the service. In addition, analysis using a generation AI model can analyze the contents of documents with high accuracy and improve the accuracy of error detection.

[1885] "Automatic document generation methods" refer to functions that automatically generate necessary forms based on the information required by the user.

[1886] "Multilingual support" refers to a function that provides system functions and interfaces in multiple languages ​​for users who speak different languages.

[1887] An "automatic error checking mechanism" is a function that detects missing parts or errors in submitted documents in real time and notifies the user.

[1888] "Optical character recognition means" refers to a technology for converting uploaded documents into text data.

[1889] "Analysis method using a generative AI model" refers to a function that uses a generative AI model to analyze text data and understand the content of a document.

[1890] A "real-time notification method" is a function that immediately notifies the user of any detected missing parts or errors.

[1891] This invention is a system that detects missing parts and errors in user-submitted documents in real time and prompts for their rapid correction. The system includes means for automatic document generation, multilingual support, automatic error checking, optical character recognition, analysis using a generation AI model, and real-time notification.

[1892] First, the user accesses the system interface using a terminal and uploads documents such as application forms. The documents to be uploaded can be in PDF or image format. The server receives the documents uploaded by the user and temporarily stores them on the server.

[1893] Next, the server uses optical character recognition (e.g., "Tesseract OCR") to convert the uploaded documents into text data. This process transforms images and PDF documents into parseable text data.

[1894] The server analyzes the converted text data using a generative AI model (e.g., "OpenAI GPT-4"). The purpose of the analysis is to understand the content of the document and identify any missing parts or errors. Examples of prompts to be input to the generative AI model include the following:

[1895] Analyze the contents of the application form submitted by the user and detect any missing information or errors. Please analyze the following text data:

[1896] [Text data]

[1897] The server detects missing parts and errors in documents based on the analysis results of the generated AI model. For example, it identifies cases where required fields are not filled in or the format is incorrect.

[1898] Any detected missing sections or errors are notified to the user using real-time notification methods. These notifications are displayed in real time on the system interface. Notifications can also be sent via email or other means as needed.

[1899] For example, suppose a user uploads a mortgage application. This application is missing the income field. When the user uploads the application, the server receives the document and converts it into text data using Tesseract OCR. Next, it analyzes the text data using a generative AI model (e.g., OpenAI GPT-4) and detects that the income field is missing. Based on this information, the server notifies the user, "The income field is missing. Please correct it." The user receives this notification, fills in the income field, and uploads the application again.

[1900] In this way, users can quickly correct missing parts or errors in documents, saving the trouble of resubmission. Furthermore, multilingual support makes it easy for users who speak different languages ​​to use. Additionally, analysis using a generative AI model allows for highly accurate analysis of document content, improving the accuracy of error detection. The specific processing flow in Example 3 will be explained using Figure 15.

[1901] Step 1:

[1902] The user uploads the document.

[1903] Users access the system interface using a terminal and upload documents such as application forms. Input documents are in PDF or image format, and output documents are sent to the server as data.

[1904] Step 2:

[1905] The server receives the documents

[1906] The server receives documents uploaded by users and temporarily stores them on the server. The input is the document data sent by the user, and the output is the document data stored on the server.

[1907] Step 3:

[1908] The server uses OCR software to convert documents into text data.

[1909] The server uses optical character recognition (e.g., "Tesseract OCR") to convert uploaded documents into text data. The input is document data stored on the server, and the output is text data. Specifically, the OCR software analyzes the image of the document and extracts the text information.

[1910] Step 4:

[1911] The server analyzes text data using a generative AI model.

[1912] The server uses a generative AI model (e.g., "OpenAI GPT-4") to analyze the converted text data. The input is text data, and the output is the analysis result. Specifically, the generative AI model analyzes the text data based on prompts to understand the content of the document.

[1913] Step 5:

[1914] The server detects missing parts and errors based on the analysis results.

[1915] The server detects missing parts and errors in a document based on the analysis results of the generated AI model. The input is the analysis results, and the output is a list of missing parts and errors. Specifically, it compares the analysis results to identify missing required items and formatting errors.

[1916] Step 6:

[1917] The server notifies the user of any missing parts or errors.

[1918] The server notifies the user of any detected missing parts or errors. The input is a list of missing parts or errors, and the output is a notification message to the user. Specifically, it displays notifications in real time on the system interface and sends notifications via email or other means as needed.

[1919] Step 7:

[1920] The user corrects and resubmits the document.

[1921] The user receives a notification from the server and corrects any missing parts or errors indicated. They then re-upload and resubmit the corrected document. The input is the notification message from the server, and the output is the corrected document data. Specifically, the user corrects the document and re-uploads it to the system.

[1922] (Application Example 3)

[1923] Next, we will describe application example 3 of form example 3. 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."

[1924] Conventional systems lacked sufficient automatic document generation and multilingual support for city hall applications. Furthermore, they lacked the ability to identify missing or incorrect documents in real time, often forcing users to resubmit documents. Additionally, inputting electronic payment information was prone to errors and omissions, leading to frequent payment errors. A system is needed to address these challenges.

[1925] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1926] In this invention, the server includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, and means for pointing out missing parts or errors in electronic payment information in real time and prompting the user to make corrections. This makes it possible to point out missing parts or errors in documents in real time and eliminate the need for resubmission. Furthermore, when inputting electronic payment information, it is possible to reduce payment errors by pointing out missing parts or errors in real time and prompting the user to make corrections.

[1927] A "city hall" is an administrative body of a local government, a facility that provides various administrative services to citizens.

[1928] An "automatic document generation method" is a system that has the function of automatically creating necessary documents and forms based on the information required by the user.

[1929] A "multi-language support system" is a system that supports multiple languages ​​and has the function of providing information in the language selected by the user.

[1930] An "automatic error checking system" is a system that has the function of detecting missing parts or errors in documents and input information in real time and prompting the user to make corrections.

[1931] "Electronic payment information" refers to information such as credit card details, address, and name that is necessary for online payments.

[1932] "Missing information" refers to the portion where necessary information has not been entered.

[1933] An "error" refers to a state where the entered information is incorrect or does not conform to the specified format.

[1934] "Real-time" refers to the fact that information is processed and feedback is provided immediately as the user inputs it.

[1935] "User" refers to an individual or group that uses the system.

[1936] "Prompting for correction" refers to pointing out errors or missing information in the user's input and prompting them to re-enter the correct information.

[1937] A system for carrying out this invention includes a server, user terminals, and a network. The server includes means for automatically generating documents required by the city hall, means for multi-language support, means for automatic error checking, and means for pointing out missing parts or errors in electronic payment information in real time and prompting the user to make corrections.

[1938] A user terminal is a device such as a smartphone, tablet, or personal computer that provides an interface for the user to input information. When the user uses the terminal to input the necessary information, that information is sent to the server.

[1939] The server first receives the information entered by the user and then automatically creates the necessary documents and forms using an automated document generation system. During this process, it uses a generation AI model to analyze the user's input and generate appropriate documents.

[1940] Next, information is provided in the user's chosen language using multi-language support. This makes it possible to accommodate users who speak different languages.

[1941] Furthermore, automated error checking mechanisms are used to detect missing or incorrect information entered by the user in real time. For example, it checks whether information such as credit card numbers, expiration dates, security codes, names, and addresses are entered correctly. If an error is detected, the server immediately provides feedback to the user prompting them to correct it.

[1942] As a concrete example, consider a case where a user omits part of their credit card number when entering their credit card information. In this case, the server displays an error message stating, "Invalid credit card number," and prompts the user to re-enter the correct information.

[1943] Examples of prompts to input into a generative AI model:

[1944] Create a program that checks the credit card information entered by the user and points out any missing information or errors in real time. The following fields should be checked: credit card number, expiration date, security code, name, and address. If there are errors in any field, add them to a list and provide feedback to the user.

[1945] This system allows for real-time identification of missing or incorrect information in documents, eliminating the need for resubmission. Furthermore, it can reduce payment errors by prompting users to correct any missing or incorrect information during electronic payment data entry.

[1946] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1947] Step 1:

[1948] The user enters the necessary information using their device.

[1949] Input: Information entered by the user through the terminal interface (e.g., credit card number, expiration date, security code, name, address).

[1950] Output: The input information is sent from the terminal to the server.

[1951] Step 2:

[1952] The server analyzes the received information and automatically creates the necessary documents and forms using an automated document generation system.

[1953] Input: User input information sent from the terminal.

[1954] Data processing: Use a generative AI model to analyze user input and generate appropriate documents.

[1955] Output: The generated documents and forms.

[1956] Step 3:

[1957] The server uses multi-language support to provide information in the language selected by the user.

[1958] Input: User input information and selected language.

[1959] Data processing: Translate information based on the selected language and provide it to the user.

[1960] Output: Information displayed in the language selected by the user.

[1961] Step 4:

[1962] The server uses an automated error checking mechanism to detect missing information or errors in user-entered data in real time.

[1963] Input: User input information.

[1964] Data processing: Regular expressions and other validation algorithms are used to detect missing parts and errors in the input information.

[1965] Output: A list of detected errors and missing parts.

[1966] Step 5:

[1967] The server provides feedback to the user, prompting them to correct any errors or missing parts detected.

[1968] Input: A list of detected errors or missing parts.

[1969] Data processing: Generate error messages and display them to the user.

[1970] Output: Error messages displayed to the user.

[1971] Step 6:

[1972] The user corrects the information based on the error message and re-enters it.

[1973] Input: Information modified by the user.

[1974] Output: The corrected information is sent back to the server.

[1975] Step 7:

[1976] The server will analyze the information again and confirm that there are no errors.

[1977] Input: Corrected information.

[1978] Data processing: The input information is validated again using regular expressions and other validation algorithms.

[1979] Output: If there are no errors, the process is complete. If there are errors, return to step 5.

[1980] 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.

[1981] "Example of form 1"

[1982] One embodiment of the present invention provides a system that incorporates an emotion engine. This system recognizes the user's emotions and adjusts its operation according to that emotional state. Specifically, if the system recognizes that the user is feeling stressed, it adjusts the operation of automatic generation means and automatic error checking means to reduce the user's stress. For example, it may change the way error messages are displayed to a gentler tone or make the automatic generation of necessary documents smoother.

[1983] "Example of form 2"

[1984] Furthermore, the emotion engine recognizes user satisfaction and delight, and uses that information to improve system performance. For example, if the system recognizes that a user is satisfied with the automatic generation of a particular form, it learns how to generate that form and improves form generation in similar situations.

[1985] "Example of form 3"

[1986] Furthermore, the emotion engine adjusts the operation of multi-language support mechanisms according to the user's emotional state. For example, if the system detects that the user is confused, it enhances support in the user's native language. This allows the user to receive support in their own language, resolve their confusion, and smoothly proceed with creating the necessary documents.

[1987] The following describes the processing flow for each example of the form.

[1988] "Example of form 1"

[1989] Step 1: The system recognizes the user's emotions using an emotion engine.

[1990] Step 2: If the emotion engine recognizes the user's stress, the system adjusts the operation of the automatic generation and automatic error checking mechanisms.

[1991] Step 3: Specifically, this involves changing the way error messages are displayed to a gentler tone and making the automatic generation of necessary documents smoother.

[1992] "Example of form 2"

[1993] Step 1: The emotion engine recognizes the user's joy and satisfaction.

[1994] Step 2: If the system recognizes that the user is satisfied with the automatic generation of a particular form, it learns how to generate that form.

[1995] Step 3: Use the learned information to improve form generation in similar situations.

[1996] "Example of form 3"

[1997] Step 1: The emotion engine adjusts the operation of the multi-language support means according to the user's emotional state.

[1998] Step 2: If the system detects that the user is confused, it will enhance support in the user's native language.

[1999] Step 3: This allows users to receive support in their own language, resolving confusion and enabling them to smoothly create the necessary documents.

[2000] (Example 1)

[2001] Next, we will describe Embodiment 1 of 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."

[2002] Creating necessary documents at city hall is a cumbersome and time-consuming task for users. Furthermore, the process becomes even more complicated when multilingual support or error checking is required. Additionally, system operation can become difficult when users are under stress. An efficient system is needed to solve these problems.

[2003] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes an automatic generation means, a multilingual support means, an automatic error checking means, an emotion recognition means, a user information input means, an information transmission means, and a feedback provision means. This makes it possible to quickly and accurately automatically generate the documents required by the user, perform multilingual support and error checking, and further adjust the operation of the system according to the user's emotional state.

[2004] An "automatic generation method" is a function that automatically generates necessary documents and forms based on information entered by the user.

[2005] "Multilingual support" refers to a function that translates generated documents and forms into multiple languages ​​and provides them in the language selected by the user.

[2006] An "automatic error checking mechanism" is a function that automatically detects missing parts and errors in generated documents and forms and points them out in real time.

[2007] "Emotion recognition means" refers to a function that analyzes the user's emotional state and adjusts the system's operation according to that emotional state.

[2008] A "user information input means" is a function that provides an interface for users to input the information they need.

[2009] "Information transmission means" refers to the function that sends information entered by the user to the server.

[2010] A "feedback provision method" is a function that displays information and error messages received from the server to the user and provides necessary feedback.

[2011] Modes for carrying out the invention

[2012] This invention relates to a system for automatically generating necessary documents at a city hall. The system aims to quickly and accurately generate the required documents and forms based on information entered by the user. This system includes automatic generation means, multilingual support means, automatic error checking means, emotion recognition means, user information input means, information transmission means, and feedback provision means.

[2013] Hardware and software to be used

[2014] Hardware: Terminals for users to input information (e.g., personal computers, smartphones, tablets) and servers for processing that information.

[2015] Software: Generative AI models (e.g., OpenAI's GPT-4), translation software (e.g., Google Translate API), emotion recognition AI models, rule-based error checking algorithms.

[2016] Program processing

[2017] 1. Enter user information

[2018] The user uses their device to enter the required information (e.g., name, address, application details, etc.).

[2019] The terminal temporarily stores the entered information and prepares for the next processing step.

[2020] 2. Sending information

[2021] The device sends the stored user information to the server.

[2022] The server analyzes the received information and converts it into the required data format.

[2023] 3. Understanding information and automatically generating forms

[2024] The server generates prompt messages for inputting the received information into the AI ​​model.

[2025] The server sends prompt messages to the generating AI model, which then automatically generates the appropriate form.

[2026] The generative AI model generates the necessary document forms based on the prompt text and returns them to the server.

[2027] 4. Multilingual support

[2028] The server checks the user's language settings.

[2029] The server uses translation software to translate the generated forms to make them multilingual.

[2030] The server sends the translated form to the terminal.

[2031] 5. Automatic error checking

[2032] The server validates the entered information and the generated form using automated error checking mechanisms.

[2033] If an error is detected, the server generates an error message and sends it to the terminal.

[2034] 6. Operation adjustment using the emotion engine

[2035] The server analyzes the user's emotional state using an emotion recognition AI model.

[2036] If the server detects that the user is experiencing stress, it will soften the tone of the error message.

[2037] The server will make adjustments as needed to streamline the form generation process.

[2038] 7. Providing feedback

[2039] The terminal displays the final form and error messages received from the server to the user.

[2040] The user reviews the displayed information and makes corrections or re-enters it as needed.

[2041] Specific example

[2042] For example, consider a case where a user applies for a copy of their resident registration at the city hall. The user enters their name "Taro Yamada," address "Shinjuku Ward, Tokyo," and application details "Issuance of a copy of resident registration" into the terminal. The terminal sends this information to the server. The server sends the following prompt message to the AI ​​model:

[2043] Please generate a form for a user to apply for a copy of their resident registration certificate at the city hall. The user's name is "Taro Yamada", their address is "Shinjuku Ward, Tokyo", and the application content is "Issuance of a copy of resident registration certificate".

[2044] The generation AI model generates an application form for a copy of the resident registration certificate based on this prompt and returns it to the server. The server translates the form using the Google Translate API to make it multilingual and performs error checking. If an error is detected, the emotion engine recognizes the user's stress and generates an error message in a gentle tone. Finally, the terminal displays the generated form and error message to the user.

[2045] The flow of the specific processing in Example 1 will be explained using Figure 17.

[2046] Step 1: Enter user information

[2047] The user uses their device to enter the required information (e.g., name, address, application details, etc.).

[2048] Input: Information entered by the user (name, address, application details).

[2049] The terminal temporarily stores the entered information and prepares for the next processing step.

[2050] Output: Saved user information.

[2051] Step 2: Sending Information

[2052] The device sends the stored user information to the server.

[2053] Input: Saved user information.

[2054] The server analyzes the received information and converts it into the required data format.

[2055] Output: Analyzed user information.

[2056] Step 3: Understanding the information and automatically generating the form

[2057] The server generates prompt messages for inputting the received information into the AI ​​model.

[2058] Input: Analyzed user information.

[2059] The server sends prompt messages to the generating AI model, which then automatically generates the appropriate form.

[2060] The generative AI model generates the necessary document forms based on the prompt text and returns them to the server.

[2061] Output: The generated form.

[2062] Step 4: Multilingual support

[2063] The server checks the user's language settings.

[2064] Input: Generated form, user's language settings.

[2065] The server uses translation software to translate the generated forms to make them multilingual.

[2066] The server sends the translated form to the terminal. 【2067...

Claims

1. A means of obtaining information including the name, address, and application details entered by the user, A means for analyzing the aforementioned information, selecting an application form template that corresponds to the aforementioned information and includes the necessary items based on the results of the analysis, and generating a form in a specified language for submission to the city hall using an AI model based on the aforementioned information, A means for translating the generated form into a language different from the predetermined language, Means for providing the user with a translated form in a language different from the predetermined language for user verification, A means for performing error checking on whether the contents of a generated form are appropriate based on predefined rules, wherein the error checking based on the predefined rules includes, at a minimum, verifying whether the address included in the entered information actually exists, and verifying whether the application contents conform to the predefined rules. A system that includes this.

2. Verification of whether the address included in the input information actually exists is performed using a map API. The system according to claim 1.

3. The form is customized based on the information entered. The system according to claim 1 or claim 2.

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