System

Abstract

A system is provided.SOLUTION: A system for use in immigration control, comprising: scanning means for reading passport AI of an entrant; camera means for capturing a facial image of the entrant; recognition means for matching the passport and facial images; matching means for matching a previous immigration history of the entrant; query analysis means for generating data-driven queries and analyzing responses; analysis means for analyzing the responses and recognition to detect suspicious patterns; and notifying means for notifying a professional examiner if a suspicious pattern is detected.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the conventional immigration inspection system, communication difficulties due to language barriers and a shortage of immigration inspectors often lead to long wait times. Furthermore, at international airports where 24 / 7 operation is essential, this places a heavy burden on human resources and tends to lead to a worsening working environment. It is necessary to solve these issues and realize more efficient and less stressful immigration inspections. [Means for solving the problem]

[0005] The present invention includes a scanning means for reading the passport data of an entrant and a camera means for capturing a facial image of the entrant. It also includes a recognition means for comparing the passport data with the facial image, a matching means for matching the entrant's past entry history, and a question analysis means for analyzing questions posed by a generating AI and their responses. The system further includes an analysis means for analyzing the responses and recognition data to detect suspicious patterns, and a notification means for notifying a specialist inspector if a suspicious pattern is detected. This system enables effective and efficient immigration inspections.

[0006] "Scanning means" is a general term for devices and technologies used to electronically read passport data of entrants.

[0007] "Camera Means" refers to a camera and its associated technology for capturing facial images of entrants.

[0008] "Recognition means" refers to the algorithms and software used to match acquired passport data with captured facial images to verify identity.

[0009] "Matching means" refers to a function or device for retrieving an entrant's past entry history from a database and comparing and evaluating it with current entry data.

[0010] "Question analysis means" refers to a function that includes software and algorithms for displaying the questions that the generation AI will ask entrants and analyzing their responses.

[0011] "Analysis tools" refers to technologies and algorithms used to detect anomalies or suspicious patterns based on the response and recognition data of entrants.

[0012] "Notification methods" refer to systems and technologies that send alerts to expert reviewers when suspicious patterns are detected. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] This invention aims to improve the efficiency of immigration inspection procedures by using a self-service system that uses image recognition and generation AI. The operation of this system is explained in detail below.

[0035] System Overview

[0036] This system consists of an airport immigration terminal, a server, and a user (entrant). The user uses the terminal to go through immigration themselves, and the server processes the information as needed in the backend.

[0037] Program processing

[0038] 1. User scans passport

[0039] When the user arrives at the immigration terminal at the airport, they follow the instructions on the terminal to scan their passport.

[0040] The terminal electronically reads the passport information and stores it as data.

[0041] 2. Facial image capture and recognition

[0042] The device activates the camera and captures an image of the user's face.

[0043] The device compares the passport photo with the captured facial image to verify identity.

[0044] 3. Checking past immigration history

[0045] The server retrieves the user's past entry history from the database.

[0046] The server compares the historical data it has acquired with the current data to determine whether the person is a frequent entrant.

[0047] 4. Analysis of Questions and Answers by Generative AI

[0048] The device uses generative AI to display questions to the user.

[0049] The user answers the questions by voice or text input.

[0050] The terminal transmits the answer to the server, and the server analyzes the answer using the question analysis means.

[0051] 5. Suspicious Subject Detection

[0052] The server analyzes the answers and recognition data to detect suspicious patterns.

[0053] If a suspicious pattern is detected, the server notifies the examiner.

[0054] 6. Notification and Response by Specialist Examiners

[0055] The server sends an alert to the examiner's device and displays the analysis results.

[0056] The examiner will direct the user to a separate examination window for a detailed examination.

[0057] 7. Completion of the procedure

[0058] If there are no problems, the terminal displays "Entry Permitted" to the user.

[0059] If necessary, the terminal prints out an entry confirmation and provides it to the user.

[0060] Specific examples

[0061] Example 1: Frequent visitors

[0062] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[0063] The terminal reads the passport information.

[0064] 2. The device captures your face to verify your identity.

[0065] Identity verification is completed through facial recognition.

[0066] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[0067] Since it matches the existing data, it is determined that there is no problem.

[0068] 4. The device asks questions generated by the AI, and the user answers.

[0069] You answer the questions briefly and your answers are analyzed.

[0070] 5. The terminal will display "Entry Permitted" and the procedure will be complete.

[0071] The user receives confirmation and is allowed into the country.

[0072] Example 2: Suspicious Subject

[0073] 1. The user arrives at the terminal and scans their passport.

[0074] The terminal reads the passport information.

[0075] 2. The device captures your face to verify your identity.

[0076] Identity verification is completed through facial recognition.

[0077] 3. The server checks the past entry history and detects an anomaly.

[0078] 4. The device asks questions generated by the AI, and the user answers.

[0079] The answers are analyzed to detect suspicious patterns.

[0080] 5. The server notifies the examiner and sends the analysis results.

[0081] Examiners will conduct a detailed examination of users.

[0082] These processes will make immigration inspections more efficient and reduce stress for entrants.

[0083] The processing flow will be explained below.

[0084] Step 1:

[0085] The user arrives at the immigration terminal at the airport and scans their passport following the instructions on the terminal's screen.

[0086] Step 2:

[0087] The device scans the passport, reads the passport information (name, passport number, nationality, photo, etc.), and temporarily stores the data.

[0088] Step 3:

[0089] The device activates the camera and captures the user's face. The user faces forward as instructed by the camera.

[0090] Step 4:

[0091] The device compares the captured facial image with the passport photo data, and uses a recognition method to calculate the degree of match and verify the identity of the person.

[0092] Step 5:

[0093] The server retrieves the user's past entry history from the database. The device sends the retrieved data to the server, which then compares the past data with the current data.

[0094] Step 6:

[0095] The server checks the entry history and determines whether the person is a frequent entrant. If so, the server proceeds with the process as a "frequent entrant."

[0096] Step 7:

[0097] The device uses the generated AI to ask questions to the user, who responds by voice or text input to the questions displayed on the device screen.

[0098] Step 8:

[0099] The terminal transmits the user's answer to the server, which uses question analysis means to analyze the content of the user's answer.

[0100] Step 9:

[0101] The server analyzes the responses and recognition data to detect anomalies or suspicious patterns, and performs additional data processing if necessary.

[0102] Step 10:

[0103] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[0104] Step 11:

[0105] If no suspicious patterns are detected, the device displays "Entry Permitted" to the user, who then confirms the permission screen and the process is complete.

[0106] Step 12:

[0107] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[0108] The above processing steps allow users to complete immigration inspections quickly and efficiently. The introduction of this system is expected to reduce immigration inspection times and improve working conditions.

[0109] Example 1

[0110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0111] As the number of users increases, immigration inspection procedures become more time-consuming and labor-intensive, and efficient procedures are required. Identity verification and the detection of suspicious individuals are also important issues. Current systems require manual inspections, placing a heavy burden on human resources. Therefore, it is necessary to introduce a self-service system to improve the efficiency and reliability of immigration inspections.

[0112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0113] In this invention, the server includes optical character recognition means for reading passports, image capture means for acquiring facial images, and matching means, which enables automation and efficiency of immigration inspections.

[0114] "Optical character recognition" is a technology for analyzing input text data and reading it as electronic data.

[0115] "Image capture means" is a technology that uses a camera or a sensor to capture an image of the user's face.

[0116] "Facial recognition means" is a technology that identifies specific patterns based on acquired facial images and verifies identity.

[0117] "Database matching means" is a technology that searches for past entry history from an existing database and matches it with current data.

[0118] "Question analysis means" is a technology that uses generation AI to generate questions for users and analyze their answers.

[0119] "Data analysis means" refers to technology that detects suspicious patterns based on user responses and recognition data.

[0120] An "alert notification means" is a technology that sends a warning to a specialist examiner when a suspicious pattern is detected.

[0121] "Display means" refers to technology that displays entry permits and other information to the user.

[0122] "Printing means" refers to the technology used to physically print the entry confirmation and other required documents.

[0123] The present invention is a self-service system for streamlining immigration procedures, and is composed of an immigration terminal, a server, and a user (entrant). The following describes the specific hardware and software configurations and processing procedures for realizing the present invention.

[0124] System configuration

[0125] Hardware

[0126] 1. Immigration Terminal: A terminal used by people entering the country. It is equipped with a scanner for scanning passports, a camera for capturing facial images, a display for displaying questions, input devices (keyboard and microphone) for the user to enter their answers, and a CPU and memory for performing the necessary processing.

[0127] 2. Server: A high-performance computer for processing and analyzing large amounts of data. It runs various software such as databases, generative AI models, query analysis, and data analysis.

[0128] software

[0129] 1. Optical Character Recognition (OCR) software: Used to read passport data electronically. A common OCR tool is Adobe OCR.

[0130] 2. Facial recognition software: Used to match the captured facial image with the passport photo. Facial recognition libraries such as OpenCV are typically used.

[0131] 3. Database Management System (DBMS): Used to store information such as immigration history and retrieve and verify data as needed. Examples include MySQL and PostgreSQL.

[0132] 4. Generative AI models: Used to generate questions for users and analyze their answers. Examples include GPT-4 and other natural language processing (NLP) models.

[0133] 5. Speech recognition software: Used to convert user voice input into text data, such as the Google Speech-to-Text API.

[0134] Specific examples of operations

[0135] Frequent visitors

[0136] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[0137] The terminal reads the passport information using optical character recognition (OCR) software.

[0138] 2. The device captures your face to verify your identity.

[0139] It uses facial recognition software to match your passport photo with a real-time captured facial image.

[0140] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[0141] Obtain past immigration history from existing databases and match it with current data.

[0142] 4. The device asks questions based on the generated AI model, and the user answers.

[0143] Example: "What is your purpose for entering Japan?", user answers "Tourism."

[0144] 5. The terminal will display "Entry Permitted" and the procedure will be complete.

[0145] If necessary, print out the entry confirmation and provide it to the user.

[0146] Suspicious Subjects

[0147] 1. The user arrives at the terminal and scans their passport.

[0148] The terminal reads the passport information using optical character recognition (OCR) software.

[0149] 2. The device captures your face to verify your identity.

[0150] It uses facial recognition software to match your passport photo with a real-time captured facial image.

[0151] 3. The server checks the past entry history and detects an anomaly.

[0152] Obtain past immigration history from existing databases and match it with current data.

[0153] 4. The device asks questions based on the generated AI model, and the user answers.

[0154] Example: "How many times have you visited Japan in the past six months?" The user answers "Three times." The answer is detected as an abnormal pattern.

[0155] 5. The server notifies the examiner and sends the analysis results.

[0156] An alert is sent to the examiner's terminal and a detailed examination of the user is conducted.

[0157] Through the above process, this system will streamline immigration procedures and reduce stress for users. It will also improve security by enabling early detection of suspicious individuals and professional screening.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1:

[0160] The user scans their passport.

[0161] Input: The user inserts the passport into the terminal's scanner.

[0162] What it does: The terminal uses optical character recognition (OCR software) to read the passport information.

[0163] Data processing: OCR software converts the text information on the passport into electronic data.

[0164] Output: Save the scanned passport data in the device's memory.

[0165] Step 2:

[0166] Capture a facial image of the user.

[0167] Input: The user follows the instructions on the device and faces the camera.

[0168] Specific operation: The device camera captures an image of the user's face.

[0169] Data processing: The captured facial images are analyzed using facial recognition software (e.g., OpenCV).

[0170] Output: The analyzed facial image data is saved in the device's memory.

[0171] Step 3:

[0172] Perform facial recognition.

[0173] Input: Passport data obtained in step 1 and facial image data obtained in step 2.

[0174] What it does: The device uses facial recognition software to match the passport photo with the captured facial image in real time.

[0175] Data arithmetic: The face recognition algorithm compares the two using pattern matching.

[0176] Output: Save the identity verification result in the device's memory.

[0177] Step 4:

[0178] Check past immigration history.

[0179] Input: Passport data from step 1 and identity verification result from step 3.

[0180] Specific operation: The terminal sends passport data and identity verification results to the server, and the server queries the database.

[0181] Data processing: The server uses SQL queries to retrieve past entry history from the database.

[0182] Output: The acquired entry history data is sent to the terminal and the matching results are saved.

[0183] Step 5:

[0184] The generative AI asks questions to the user.

[0185] Input: Matching result from step 4.

[0186] Specific operation: The device uses a generative AI model (e.g., GPT-4) to generate questions for the user and displays them on the display.

[0187] Data processing: The generative AI model creates questions based on the prompt.

[0188] Output: The question is displayed on the display.

[0189] Step 6:

[0190] The user answers.

[0191] Input: The user answers the question by speaking or entering text.

[0192] What it does: The device converts voice input into text using speech recognition software (for example, Google Speech-to-Text API).

[0193] Data processing: Speech recognition software converts speech into text data.

[0194] Output: The user's answer as text data is saved in the device's memory.

[0195] Step 7:

[0196] The server analyzes the user's answers.

[0197] Input: User response data from step 6.

[0198] How it works: The device sends the answer data to the server, which then analyzes the answer using a generative AI model.

[0199] Data Computing: Generative AI models use natural language processing to analyze the content of responses and detect suspicious patterns.

[0200] Output: Sends the analysis results to the terminal.

[0201] Step 8:

[0202] Suspicious subject detection and notification.

[0203] Input: Analysis results from step 7.

[0204] Specific operation: If the server detects a suspicious pattern based on the analysis results, it sends an alert to the expert examiner's terminal.

[0205] Data calculation: Evaluate the analysis results and generate alert messages.

[0206] Output: An alert is displayed on the examiner's terminal.

[0207] Step 9:

[0208] Response from specialist examiners.

[0209] Input: Alert notification from step 8.

[0210] Specific operation: A specialist examiner will direct the user to another examination window for a detailed examination.

[0211] Output: The detailed review results are entered into the system and saved.

[0212] Step 10:

[0213] The procedure is complete.

[0214] Input: All preprocessing results.

[0215] Specific operation: The terminal displays the entry permit and prints an entry confirmation if necessary.

[0216] Output: The user's entry permit is displayed and a confirmation is provided.

[0217] This not only makes immigration procedures more efficient and improves convenience for users, but also strengthens security.

[0218] (Application example 1)

[0219] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0220] Traditional immigration inspection procedures are often cumbersome and time-consuming. Furthermore, because the identity of entrants and the verification of their past entry history are done manually, this can be inefficient and inaccurate. This increases security risks and places significant stress on entrants. Furthermore, it can take a long time for specialized inspectors to respond, and there are still challenges in detecting and responding to suspicious individuals.

[0221] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0222] In this invention, the server includes a scanning means for reading the passport data of an entrant, a camera means for capturing a facial image of the entrant, a recognition means for comparing the passport data with the facial image, a matching means for matching the entrant's past entry history, a question analysis means for asking questions using a generation AI and analyzing responses, an analysis means for analyzing the responses and recognition data and detecting suspicious patterns, a recording means for analyzing the passport image using the scanning means and recording the matching results, and a notification means for notifying a specialist inspector if a suspicious pattern is detected. This makes it possible to improve the efficiency and accuracy of immigration inspection operations, reduce security risks, and ease the stress of entrants.

[0223] "Scanning means" refers to a device that has the function of electronically reading the passport data of entrants and storing it as data.

[0224] The "camera means" is a device for capturing an image of the face of a person entering the country and obtaining the image data.

[0225] "Recognition means" refers to a device or software that collates passport data with a facial image to verify identity.

[0226] A "verification means" is a device or software that retrieves an entrant's past entry history from a database and compares it with current data.

[0227] A "question analysis means" is a device or software that outputs questions posed by the generation AI and receives and analyzes the user's responses in voice or text.

[0228] An "analysis means" is a device or software for analyzing response and recognition data and detecting suspicious patterns.

[0229] The "recording means" is a device or software that analyzes the passport image using the scanning means and records the results in a database.

[0230] "Notification means" means a device or software that sends an alert or notification to a specialist examiner when a suspicious pattern is detected.

[0231] The present invention is a system for streamlining immigration inspections and improving security. This system is realized using the following specific processing steps and techniques.

[0232] Hardware and software used

[0233] Hardware: Smartphones, camera devices

[0234] Software: OpenCV (image processing library), Requests (HTTP request library), Transformers (generative AI library)

[0235] Program processing explanation

[0236] This system starts when the user launches the smartphone application and scans their passport. The passport data is electronically read and saved using the scanning means. The data acquired by this scanning means is sent to the verification means and verified against past entry history.

[0237] Next, a camera is used to capture a facial image of the user. The facial image is compared with a passport photo by a recognition means to verify the user's identity. After that, a generative AI model uses a question analysis means to generate a question and display it to the user. The user then inputs an answer by voice or text, and the answer data is sent to the server.

[0238] The server uses its analysis means to analyze the responses and other recognition data to detect suspicious patterns. If a suspicious pattern is detected, its notification means sends an alert to a specialist examiner, who can then conduct a detailed review.

[0239] Specific examples

[0240] For example, when managing access to a sensitive facility, employees and visitors can use a smartphone app to complete the entry procedures. First, the user scans their passport with their smartphone and captures their facial image. The passport information is then matched with the facial image by the scanning and recognition means.

[0241] Next, the generative AI model displays the question "What is the purpose of your visit?" to the user. If the user answers "It's a business meeting," this response data is analyzed by the analysis means. If there are no problems with the analysis results, "Entry permitted" is displayed and the user is allowed to enter the facility.

[0242] Example prompt for a generative AI model:

[0243] Please tell us the purpose of your visit.

[0244] In this way, the present invention can improve the efficiency of immigration inspections and entry / exit management for important facilities, thereby reducing security risks.

[0245] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0246] Step 1:

[0247] The user launches the smartphone app and starts scanning their passport. The passport is read using a scanning device, and the acquired passport data is temporarily saved in the application. The input is an image of the passport taken using the smartphone, and the output is the passport information acquired through image analysis.

[0248] Step 2:

[0249] The device activates the built-in camera and captures the user's facial image. The captured facial image is analyzed using a facial recognition algorithm and temporarily saved. The input is the user's facial image, and the output is the analyzed facial data.

[0250] Step 3:

[0251] The terminal again sends the passport data and facial data to the recognition means for matching. Through this matching, the terminal performs identity verification. The input is the passport data and facial data, and the output is the result of identity verification. The specific operation of matching is to compare facial feature points using a facial recognition algorithm.

[0252] Step 4:

[0253] The server retrieves the entrant's past entry history from the database. The retrieved data is compared with the current entry information using a matching means. The input is the user's entry history data, and the output is the result of the entry history matching.

[0254] Step 5:

[0255] The terminal uses a question analysis means to input a prompt sentence into the generative AI model. The generative AI model generates a question and displays it to the user. The input is the prompt sentence, and the output is the generated question. Specifically, the generative AI model performs natural language generation.

[0256] Step 6:

[0257] The user answers questions generated by voice or text. The device captures the answers and sends them to the server. The input is the user's answer data, and the output is the answer data used for analysis.

[0258] Step 7:

[0259] The server analyzes the response data and the recognition data using an analysis means. This analysis determines whether a suspicious pattern is detected. The input is the response data and the recognition data, and the output is the result of the analysis. Specifically, a pattern recognition algorithm is used.

[0260] Step 8:

[0261] If the server detects a suspicious pattern based on the analysis results, it sends an alert to the expert examiner using a notification means. The input is the analysis result, and the output is a notification to the examiner. The specific operation uses an alert sending system.

[0262] The above processing steps improve the efficiency and accuracy of immigration inspection procedures, reduce security risks, and ease stress for entrants.

[0263] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0264] This invention improves the efficiency of immigration procedures by using a self-service system that uses image recognition and generation AI, and also combines it with an emotion engine that recognizes the user's emotions. The operation of this system is explained in detail below.

[0265] System Overview

[0266] This system consists of an immigration inspection terminal, a server, a user (entrant), and an emotion engine. The user uses the terminal to conduct immigration inspection themselves, and the server and emotion engine perform processing as needed on the backend.

[0267] Program processing

[0268] 1. User scans passport

[0269] When the user arrives at the immigration terminal at the airport, they follow the instructions on the terminal to scan their passport.

[0270] The terminal electronically reads the passport information and temporarily stores the data.

[0271] 2. Facial image capture and recognition

[0272] The device activates the camera and captures an image of the user's face.

[0273] The device compares the passport photo with the captured facial image to verify identity.

[0274] 3. Checking past immigration history

[0275] The server retrieves the user's past entry history from the database.

[0276] The data acquired by the terminal is sent to the server, which then compares the past data with the current data.

[0277] 4. Analysis of Questions and Answers by Generative AI

[0278] The device uses the generative AI to ask questions to the user, who then responds by voice or text input to the questions displayed on the device.

[0279] The terminal transmits the answer to the server, and the server uses the question analysis means to analyze the content of the user's answer.

[0280] 5. Emotional Recognition

[0281] The device activates an emotion engine that analyzes the user's voice and facial expressions in real time.

[0282] The emotion engine detects the user's emotion data and transmits the data to the analysis means.

[0283] 6. Suspicious Subject Detection

[0284] The server analyzes the user's responses, cognitive data, and emotional data to detect suspicious patterns.

[0285] If necessary, further data processing is performed.

[0286] 7. Notification and Response by Specialist Examiners

[0287] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[0288] The examiner will direct the user to a separate examination window for a detailed examination.

[0289] 8. Completion of the procedure

[0290] If there are no problems, the terminal will display "Entry Permitted" to the user. The user confirms the screen displaying the permission and the procedure is complete.

[0291] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[0292] Specific examples

[0293] Example 1: Frequent visitors

[0294] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[0295] The terminal reads the passport information.

[0296] 2. The device captures your face to verify your identity.

[0297] Identity verification is completed through facial recognition.

[0298] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[0299] Since it matches the existing data, it is determined that there is no problem.

[0300] 4. The device asks questions generated by the AI, and the user answers.

[0301] You answer the questions briefly and your answers are analyzed.

[0302] 5. The device uses the emotion engine to analyze the user's emotions.

[0303] Emotion data is generated and sent to the analysis means.

[0304] 6. The terminal will display "Entry Permitted" and the procedure will be completed.

[0305] The user receives confirmation and is allowed into the country.

[0306] Example 2: Suspicious Subject

[0307] 1. The user arrives at the terminal and scans their passport.

[0308] The terminal reads the passport information.

[0309] 2. The device captures your face to verify your identity.

[0310] Identity verification is completed through facial recognition.

[0311] 3. The server checks the past entry history and detects an anomaly.

[0312] 4. The device asks questions generated by the AI, and the user answers.

[0313] The answers are analyzed to detect suspicious patterns.

[0314] 5. The device uses the emotion engine to analyze the user's emotions.

[0315] Emotional data such as anxiety and tension is detected.

[0316] 6. The server notifies the examiner and sends the analysis results.

[0317] Examiners will conduct a detailed examination of users.

[0318] The above process will improve the efficiency and security of immigration inspections. Furthermore, analysis of emotion data will enable more accurate judgments.

[0319] The processing flow will be explained below.

[0320] Step 1:

[0321] The user arrives at the immigration terminal at the airport and scans their passport following the instructions on the terminal's screen.

[0322] Step 2:

[0323] The device scans the passport and reads passport information (name, passport number, nationality, photo, etc.) and temporarily stores the read data.

[0324] Step 3:

[0325] The device activates the camera and captures the user's face. The user faces forward as instructed by the camera.

[0326] Step 4:

[0327] The facial image captured by the device is compared with the passport photo data, and the matching is carried out using a recognition method to calculate the degree of match and verify the identity of the person.

[0328] Step 5:

[0329] The server retrieves the user's past entry history from the database. The device sends the retrieved data to the server, which then compares the past data with the current data.

[0330] Step 6:

[0331] The server checks the entry history and determines whether the person is a frequent entrant. If so, the server proceeds with the process as a "frequent entrant."

[0332] Step 7:

[0333] The device uses the generated AI to ask questions to the user, who responds by voice or text input to the questions displayed on the device screen.

[0334] Step 8:

[0335] The terminal transmits the user's answer to the server, which uses question analysis means to analyze the content of the user's answer.

[0336] Step 9:

[0337] The device activates an emotion engine that analyzes the user's voice and facial expressions in real time. The emotion engine detects changes in the user's voice tone and facial expressions.

[0338] Step 10:

[0339] The emotion engine generates user emotion data and sends it to the analysis means, which incorporates the emotion data into the analysis of the user's responses.

[0340] Step 11:

[0341] The server performs analysis based on the user's responses, facial recognition data, and emotion data to detect anomalies or suspicious patterns.

[0342] Step 12:

[0343] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[0344] Step 13:

[0345] If no suspicious patterns are detected, the device displays "Entry Permitted" to the user, who then confirms the permission screen and the process is complete.

[0346] Step 14:

[0347] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[0348] The above processing steps allow users to complete immigration inspections quickly and efficiently. The introduction of this system is expected to shorten immigration inspection times and improve working conditions. Furthermore, the addition of an emotion engine allows for more accurate inspections that take the user's psychological state into account, improving security.

[0349] Example 2

[0350] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0351] Conventional immigration inspection systems require a lot of time for immigration procedures, and there is a need for efficient handling, especially for frequent entrants and those entering under specific conditions. Furthermore, there is a lack of methods for quickly and accurately detecting and responding to entrants' fraudulent or suspicious behavior. Furthermore, there is a need to analyze emotions to more accurately assess the state of entrants and improve safety.

[0352] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a scanning means for reading the passport data of the entrant via the terminal, a photographing means for capturing a facial image of the entrant via the terminal, a recognition means for comparing the passport data with the facial image via the server, a matching means for comparing the entrant's past entry history via the server, a question analysis means for asking questions using the generative AI model via the terminal and analyzing responses via the server, anomaly detection means for analyzing the responses and recognition data via the server and detecting suspicious patterns, and a notification means for notifying a specialist inspector when a suspicious pattern is detected by the server. This makes it possible to improve the efficiency and security of immigration inspections.

[0353] "Scanning means" refers to devices and methods for reading a user's passport data, including devices that capture information from the machine-readable area or IC chip on a passport.

[0354] "Capture Means" refers to devices and methods for capturing a user's facial image, including a camera and its control software.

[0355] "Recognition means" refers to devices and methods for matching passport data with captured facial images to verify identity, including image processing algorithms and database matching techniques.

[0356] "Verification means" refers to devices and methods for retrieving an entrant's past immigration history from a database and comparing it with current immigration data. This means includes database query systems and verification algorithms.

[0357] "Question Analysis Means" refers to devices and methods for using generative AI models to ask users questions and analyze their responses, including natural language processing and speech recognition technologies.

[0358] "Anomaly detection means" refers to devices and methods for analyzing user response and perception data to detect suspicious patterns. This means includes anomaly detection algorithms and multivariate analysis techniques.

[0359] "Notification means" refers to devices and methods for notifying expert reviewers when suspicious patterns are detected, including alert systems and communication networks.

[0360] This invention provides a system for improving security by streamlining immigration procedures at airports and other locations. This system is comprised of interactions between a server, a terminal, and a user. Specific examples of each function are described below.

[0361] The system consists of an immigration inspection terminal, a server, a user (entrant), and a generation AI module. The user uses the terminal to conduct their own immigration inspection, and the server and generation AI module process the information as needed in the backend.

[0362] First, the terminal scans the user's passport. The passport scanner device acquires data from the passport's machine-readable zone (MRZ) and IC chip. The terminal temporarily stores the scanned data in memory and proceeds.

[0363] The device then activates the camera to capture the user's facial image, which is then compared with the passport photo by the server to verify identity. The facial recognition algorithm uses OpenCV or TensorFlow, for example.

[0364] The server then retrieves the user's past entry history from a database, and compares the retrieved history data with the current entry data using an existing database query system.

[0365] The device then uses a generation AI to ask the user questions. The generation AI, for example, uses OpenAI's GPT-3, which randomly generates questions from a specific question list. The user responds by voice or text input, which is converted into text by speech recognition software (e.g., Google Speech-to-Text). The server then analyzes the content of the answers using a question analysis method to check for any suspicious points.

[0366] In addition, the device activates an emotion engine to analyze the user's voice and facial expressions in real time. Voice analysis software (e.g., Praat) is used for voice analysis, and facial recognition software (e.g., Affectiva) is used for facial expression analysis. The detected emotion data is sent to the server.

[0367] All information is analyzed, and the server evaluates the user's responses and sentiment data holistically, applying anomaly detection algorithms to detect suspicious patterns. If an anomaly is detected, a notification system is activated, sending an alert and analysis results to expert reviewers.

[0368] If there are no problems, the terminal will display "Entry Permitted" to the user and print an entry confirmation form if necessary, officially completing the procedure.

[0369] As a concrete example, when a frequent entrant arrives at the terminal, their passport is scanned and facial recognition is performed, their past entry history is confirmed, and after answering questions posed by the AI ​​generation system, if the emotion engine determines that there are no problems, the terminal will display "Entry Permitted."

[0370] Examples of prompts for a generative AI model might include questions like:

[0371] "As someone integrated into the airport immigration system, how would you question a high-frequency immigrant who is somewhat nervous?"

[0372] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0373] Step 1:

[0374] The user scans the passport into the terminal.

[0375] Input: The user places the passport in the passport scanner.

[0376] Specific operation: The terminal activates the passport scanner and obtains data from the passport's MRZ (Machine Readable Zone) and IC chip.

[0377] Data processing: The acquired data is temporarily stored in memory as electronic data.

[0378] Output: Passport data is saved on the device.

[0379] Step 2:

[0380] The device captures a facial image of the user.

[0381] Input: The user stands in front of the device's camera.

[0382] Specific operation: The device activates the camera and captures an image of the user's face.

[0383] Data processing: The captured facial image is saved as digital data.

[0384] Output: The facial image data is saved to the device.

[0385] Step 3:

[0386] The server compares the passport data with the facial image.

[0387] Input: Passport data and facial image data are sent from the terminal to the server.

[0388] What it does: The server uses a facial recognition algorithm (e.g. OpenCV or TensorFlow) to compare the passport photo with the captured face image.

[0389] Data calculation: Feature points of the facial image are extracted and matched using a recognition algorithm.

[0390] Output: Matching result is generated on the server (e.g. match success / failure).

[0391] Step 4:

[0392] The server checks past entry history.

[0393] Input: The server queries the database based on the passport data.

[0394] Specific operation: The server retrieves past entry history from the database.

[0395] Data calculation: Compare the acquired historical data with the current entry data.

[0396] Output: The matching result is generated on the server (e.g., whether or not there is an entry history).

[0397] Step 5:

[0398] The device uses the generative AI model to ask the user questions.

[0399] Input: Instructions from the server and user data.

[0400] Specific operation: The device uses generative AI (e.g., GPT-3) to generate questions and display them to the user.

[0401] Data calculation: Generate appropriate questions based on the user's past answers and current situation.

[0402] Output: The generated question is displayed on the terminal.

[0403] Step 6:

[0404] The user inputs the answer to the question into the terminal.

[0405] Input: The user enters the answer into the device by voice or text.

[0406] What happens: Your device uses speech recognition software (e.g., Google Speech-to-Text) to convert speech to text.

[0407] Data processing: Converts audio data into text data.

[0408] Output: The textual answers are saved to your device.

[0409] Step 7:

[0410] The server analyzes the user's answers.

[0411] Input: The textual response is sent from the device to the server.

[0412] What happens: The server uses natural language processing (NLP) tools to analyze the answers.

[0413] Data calculation: Analyze the intent and content of the response and check for any abnormalities.

[0414] Output: The analysis results are generated on the server (e.g. normal / abnormal).

[0415] Step 8:

[0416] The device uses an emotion engine to analyze the user's emotions.

[0417] Input: User's facial expressions and voice data.

[0418] Specific operation: The device analyzes voice and facial expressions using emotion analysis software (e.g. Praat, Affectiva).

[0419] Data computation: Extracting and analyzing emotion features.

[0420] Output: Sentiment analysis data is saved on the device.

[0421] Step 9:

[0422] The server detects a suspicious pattern.

[0423] Input: Answer data and sentiment data parsed by the server.

[0424] What happens: The server applies an anomaly detection algorithm and performs pattern analysis.

[0425] Data Calculation: Detecting suspicious patterns based on various parameters.

[0426] Output: Detection results are generated on the server (e.g., suspicious / not suspicious).

[0427] Step 10:

[0428] The server notifies the expert examiner.

[0429] Input: The server's verdict when a suspicious pattern is detected.

[0430] Specific operation: The server sends an alert to the examiner's terminal.

[0431] Data processing: Generate alert data and send it over the communication network.

[0432] Output: An alert notification will be displayed on the examiner's terminal.

[0433] Step 11:

[0434] The terminal completes immigration procedures.

[0435] Input: Final decision from the server (e.g., entry allowed / denied).

[0436] Specific operation: The terminal displays the entry permit and prints an entry confirmation if necessary.

[0437] Data processing: Output entry permit data on screen and in print.

[0438] Output: The user confirms the entry permit and the process is completed.

[0439] (Application example 2)

[0440] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0441] Conventional immigration inspection systems require a large amount of manual checking work, are inefficient, and pose a high security risk, so there is a need for a means to perform immigration inspections quickly and accurately. Furthermore, there are insufficient means to improve safety by performing more accurate risk assessments by recognizing visitor emotions. Furthermore, similar issues exist in security checks at large-scale events and important facilities, so there is a need for efficient and highly accurate security checks.

[0442] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0443] In this invention, the server includes a scanning means for reading passport data of entrants, a camera means for capturing facial images of entrants, and an emotion recognition means for recognizing and analyzing the emotions of entrants, thereby enabling quick and accurate immigration inspection and security checks.

[0444] "Scanning means" refers to a device or group of devices that have the function of reading passport data of entrants.

[0445] "Camera means" refers to a device or group of devices that have the function of capturing facial images of entrants.

[0446] "Recognition means" refers to a device or program that has the function of matching the read passport data with the captured facial image.

[0447] "Verification means" refers to a device or program that has the function of acquiring the past entry history of an entrant and comparing it with current data.

[0448] "Question analysis means" refers to a device or program that has the function of using generation AI to ask questions to entrants and analyze their responses.

[0449] "Analysis means" refers to a device or program that has the functionality to analyze acquired response and recognition data and detect suspicious patterns.

[0450] The "notification means" is a device or group of devices that has the function of notifying a specialist examiner when a suspicious pattern is detected.

[0451] "Emotion recognition means" refers to a device or program that has the function of recognizing the emotions of entrants and analyzing the data.

[0452] This invention relates to a system that improves the efficiency and accuracy of security checks at immigration and large-scale events. The system includes passport scanning, facial recognition, past entry history matching, question and answer analysis using generative AI, emotion recognition, suspicious pattern detection, and notification functions.

[0453] Program processing description

[0454] 1. The server electronically stores the passport data when the user scans the passport using a scanning method, which can be a dedicated scanner or a camera in smart glasses.

[0455] 2. To capture the user's face image, the camera means is activated and the face image is acquired in real time, which is then matched with the passport photo by the recognition means.

[0456] 3. The server retrieves the entrant's or visitor's past entry history from the database and uses a matching means to compare it with the current data.

[0457] 4. The question analysis means uses the generative AI model to ask the user a question and receives the user's response via voice or text input. The acquired response data is analyzed by the analysis means.

[0458] 5. The facial image and voice data of the user captured by the camera means are analyzed by the emotion recognition means to grasp the user's emotional state in real time.

[0459] 6. The analysis means comprehensively analyzes the acquired response data, facial image data, and emotion data to perform risk assessment. If a suspicious pattern is detected, an alert is sent to the expert examiner by the notification means.

[0460] 7. The examiner will conduct a detailed check based on the alert and will carry out additional checks as necessary.

[0461] Hardware and software used

[0462] Scanner and camera means: smart glasses (e.g., Google Glass) and dedicated scanners

[0463] Analysis software: face recognition library (e.g., face_recognition), emotion recognition library (e.g., EmotionRecognizer)

[0464] Server: Back-end processing for question analysis and notification functions

[0465] Adding specific examples

[0466] For example, when used at a large music festival, when attendees arrive at the entrance, security guards wearing smart glasses scan their passports and capture their faces. The generative AI then asks, "What is the purpose of your visit?" If the attendee answers, "I'm here to enjoy the concert," the application analyzes this and records it as a secure answer. Meanwhile, an emotion recognition engine analyzes the attendee's facial expressions in real time, and if it detects any abnormalities, it notifies the security guard. If suspicious, the attendee will be directed to an additional checkpoint.

[0467] Prompt Sentence Examples

[0468] "Please tell us the purpose of your visit. Then, please explain the purpose of your visit in one sentence."

[0469] The above system will make it possible to improve the efficiency and accuracy of immigration and security checks.

[0470] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0471] Step 1:

[0472] When a user scans their passport, the server uses a scanning method to electronically store the passport data. Specifically, the server reads the passport information using a dedicated scanner or a camera mounted on smart glasses and temporarily stores it as digital data. The input is the passport image data, and the output is the scanned passport information.

[0473] Step 2:

[0474] The terminal activates the camera means to capture the user's facial image and acquires the facial image in real time. The acquired facial image is compared with the scanned passport photo by the recognition means. The input is the real-time facial image data, and the output is the matching result, i.e., a match judgment.

[0475] Step 3:

[0476] The server retrieves the past entry history of the entrant or visitor from the database and uses a matching method to compare it with the current data. The input is the passport ID information, and the output is the matching result of the past entry history. The server compares these data and determines whether they match or differ.

[0477] Step 4:

[0478] The question analysis means uses the generative AI model to ask the user a question and receives a response via voice or text input. The terminal displays the question and waits for the user's response. The input is a prompt from the generative AI model and the user's voice or text response, and the output is the analyzed response. An example of a prompt is: "Please tell us the purpose of your visit."

[0479] Step 5:

[0480] The emotion recognition means analyzes the user's facial image and audio data captured by the camera means to grasp the user's emotional state in real time. The input is the facial image and audio data, and the output is the analysis result of the emotional state. Specifically, the analysis is performed using the EmotionRecognizer library.

[0481] Step 6:

[0482] The analysis means performs an integrated analysis of the acquired response data, facial image data, and emotional data to perform a risk assessment. The inputs are response data, facial image data, and emotional data, and the output is the detection results of suspicious patterns. The system uses an anomaly detection algorithm to determine the risk level based on the analysis results.

[0483] Step 7:

[0484] If a suspicious pattern is detected, the notification means sends an alert to the expert examiner. The terminal displays the alert on the terminal of the security response officer, prompting further confirmation. The input is the detection result of the suspicious pattern, and the output is an alert notification. This allows the expert examiner to carry out further confirmation work.

[0485] Step 8:

[0486] If there are no problems, the terminal displays a message to the user granting permission to enter. Specifically, once the verification process is complete, the user is given permission to proceed to the next step. The input is the final analysis result, and the output is a message granting permission to enter.

[0487] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0488] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0489] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0490] [Second embodiment]

[0491] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0492] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0493] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0494] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0495] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0496] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0497] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0498] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0499] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0500] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0501] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0502] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0503] This invention aims to improve the efficiency of immigration inspection procedures by using a self-service system that uses image recognition and generation AI. The operation of this system is explained in detail below.

[0504] System Overview

[0505] This system consists of an airport immigration terminal, a server, and a user (entrant). The user uses the terminal to go through immigration themselves, and the server processes the information as needed in the backend.

[0506] Program processing

[0507] 1. User scans passport

[0508] When the user arrives at the immigration terminal at the airport, they follow the instructions on the terminal to scan their passport.

[0509] The terminal electronically reads the passport information and stores it as data.

[0510] 2. Facial image capture and recognition

[0511] The device activates the camera and captures an image of the user's face.

[0512] The device compares the passport photo with the captured facial image to verify identity.

[0513] 3. Checking past immigration history

[0514] The server retrieves the user's past entry history from the database.

[0515] The server compares the historical data it has acquired with the current data to determine whether the person is a frequent entrant.

[0516] 4. Analysis of Questions and Answers by Generative AI

[0517] The device uses generative AI to display questions to the user.

[0518] The user answers the questions by voice or text input.

[0519] The terminal transmits the answer to the server, and the server analyzes the answer using the question analysis means.

[0520] 5. Suspicious Subject Detection

[0521] The server analyzes the answers and recognition data to detect suspicious patterns.

[0522] If a suspicious pattern is detected, the server notifies the examiner.

[0523] 6. Notification and Response by Specialist Examiners

[0524] The server sends an alert to the examiner's device and displays the analysis results.

[0525] The examiner will direct the user to a separate examination window for a detailed examination.

[0526] 7. Completion of the procedure

[0527] If there are no problems, the terminal displays "Entry Permitted" to the user.

[0528] If necessary, the terminal prints out an entry confirmation and provides it to the user.

[0529] Specific examples

[0530] Example 1: Frequent visitors

[0531] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[0532] The terminal reads the passport information.

[0533] 2. The device captures your face to verify your identity.

[0534] Identity verification is completed through facial recognition.

[0535] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[0536] Since it matches the existing data, it is determined that there is no problem.

[0537] 4. The device asks questions generated by the AI, and the user answers.

[0538] You answer the questions briefly and your answers are analyzed.

[0539] 5. The terminal will display "Entry Permitted" and the procedure will be complete.

[0540] The user receives confirmation and is allowed into the country.

[0541] Example 2: Suspicious Subject

[0542] 1. The user arrives at the terminal and scans their passport.

[0543] The terminal reads the passport information.

[0544] 2. The device captures your face to verify your identity.

[0545] Identity verification is completed through facial recognition.

[0546] 3. The server checks the past entry history and detects an anomaly.

[0547] 4. The device asks questions generated by the AI, and the user answers.

[0548] The answers are analyzed to detect suspicious patterns.

[0549] 5. The server notifies the examiner and sends the analysis results.

[0550] Examiners will conduct a detailed examination of users.

[0551] These processes will make immigration inspections more efficient and reduce stress for entrants.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The user arrives at the immigration terminal at the airport and scans their passport following the instructions on the terminal's screen.

[0555] Step 2:

[0556] The device scans the passport, reads the passport information (name, passport number, nationality, photo, etc.), and temporarily stores the data.

[0557] Step 3:

[0558] The device activates the camera and captures the user's face. The user faces forward as instructed by the camera.

[0559] Step 4:

[0560] The device compares the captured facial image with the passport photo data, and uses a recognition method to calculate the degree of match and verify the identity of the person.

[0561] Step 5:

[0562] The server retrieves the user's past entry history from the database. The device sends the retrieved data to the server, which then compares the past data with the current data.

[0563] Step 6:

[0564] The server checks the entry history and determines whether the person is a frequent entrant. If so, the server proceeds with the process as a "frequent entrant."

[0565] Step 7:

[0566] The device uses the generated AI to ask questions to the user, who responds by voice or text input to the questions displayed on the device screen.

[0567] Step 8:

[0568] The terminal transmits the user's answer to the server, which uses question analysis means to analyze the content of the user's answer.

[0569] Step 9:

[0570] The server analyzes the responses and recognition data to detect anomalies or suspicious patterns, and performs additional data processing if necessary.

[0571] Step 10:

[0572] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[0573] Step 11:

[0574] If no suspicious patterns are detected, the device displays "Entry Permitted" to the user, who then confirms the permission screen and the process is complete.

[0575] Step 12:

[0576] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[0577] The above processing steps allow users to complete immigration inspections quickly and efficiently. The introduction of this system is expected to reduce immigration inspection times and improve working conditions.

[0578] Example 1

[0579] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0580] As the number of users increases, immigration inspection procedures become more time-consuming and labor-intensive, and efficient procedures are required. Identity verification and the detection of suspicious individuals are also important issues. Current systems require manual inspections, placing a heavy burden on human resources. Therefore, it is necessary to introduce a self-service system to improve the efficiency and reliability of immigration inspections.

[0581] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0582] In this invention, the server includes optical character recognition means for reading passports, image capture means for acquiring facial images, and matching means, which enables automation and efficiency of immigration inspections.

[0583] "Optical character recognition" is a technology for analyzing input text data and reading it as electronic data.

[0584] "Image capture means" is a technology that uses a camera or a sensor to capture an image of the user's face.

[0585] "Facial recognition means" is a technology that identifies specific patterns based on acquired facial images and verifies identity.

[0586] "Database matching means" is a technology that searches for past entry history from an existing database and matches it with current data.

[0587] "Question analysis means" is a technology that uses generation AI to generate questions for users and analyze their answers.

[0588] "Data analysis means" refers to technology that detects suspicious patterns based on user responses and recognition data.

[0589] An "alert notification means" is a technology that sends a warning to a specialist examiner when a suspicious pattern is detected.

[0590] "Display means" refers to technology that displays entry permits and other information to the user.

[0591] "Printing means" refers to the technology used to physically print the entry confirmation and other required documents.

[0592] The present invention is a self-service system for streamlining immigration procedures, and is composed of an immigration terminal, a server, and a user (entrant). The following describes the specific hardware and software configurations and processing procedures for realizing the present invention.

[0593] System configuration

[0594] Hardware

[0595] 1. Immigration Terminal: A terminal used by people entering the country. It is equipped with a scanner for scanning passports, a camera for capturing facial images, a display for displaying questions, input devices (keyboard and microphone) for the user to enter their answers, and a CPU and memory for performing the necessary processing.

[0596] 2. Server: A high-performance computer for processing and analyzing large amounts of data. It runs various software such as databases, generative AI models, query analysis, and data analysis.

[0597] software

[0598] 1. Optical Character Recognition (OCR) software: Used to read passport data electronically. A common OCR tool is Adobe OCR.

[0599] 2. Facial recognition software: Used to match the captured facial image with the passport photo. Facial recognition libraries such as OpenCV are typically used.

[0600] 3. Database Management System (DBMS): Used to store information such as immigration history and retrieve and verify data as needed. Examples include MySQL and PostgreSQL.

[0601] 4. Generative AI models: Used to generate questions for users and analyze their answers. Examples include GPT-4 and other natural language processing (NLP) models.

[0602] 5. Speech recognition software: Used to convert user voice input into text data, such as the Google Speech-to-Text API.

[0603] Specific examples of operations

[0604] Frequent visitors

[0605] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[0606] The terminal reads the passport information using optical character recognition (OCR) software.

[0607] 2. The device captures your face to verify your identity.

[0608] It uses facial recognition software to match your passport photo with a real-time captured facial image.

[0609] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[0610] Obtain past immigration history from existing databases and match it with current data.

[0611] 4. The device asks questions based on the generated AI model, and the user answers.

[0612] Example: "What is your purpose for entering Japan?", user answers "Tourism."

[0613] 5. The terminal will display "Entry Permitted" and the procedure will be complete.

[0614] If necessary, print out the entry confirmation and provide it to the user.

[0615] Suspicious Subjects

[0616] 1. The user arrives at the terminal and scans their passport.

[0617] The terminal reads the passport information using optical character recognition (OCR) software.

[0618] 2. The device captures your face to verify your identity.

[0619] It uses facial recognition software to match your passport photo with a real-time captured facial image.

[0620] 3. The server checks the past entry history and detects an anomaly.

[0621] Obtain past immigration history from existing databases and match it with current data.

[0622] 4. The device asks questions based on the generated AI model, and the user answers.

[0623] Example: "How many times have you visited Japan in the past six months?" The user answers "Three times." The answer is detected as an abnormal pattern.

[0624] 5. The server notifies the examiner and sends the analysis results.

[0625] An alert is sent to the examiner's terminal and a detailed examination of the user is conducted.

[0626] Through the above process, this system will streamline immigration procedures and reduce stress for users. It will also improve security by enabling early detection of suspicious individuals and professional screening.

[0627] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0628] Step 1:

[0629] The user scans their passport.

[0630] Input: The user inserts the passport into the terminal's scanner.

[0631] What it does: The terminal uses optical character recognition (OCR software) to read the passport information.

[0632] Data processing: OCR software converts the text information on the passport into electronic data.

[0633] Output: Save the scanned passport data in the device's memory.

[0634] Step 2:

[0635] Capture a facial image of the user.

[0636] Input: The user follows the instructions on the device and faces the camera.

[0637] Specific operation: The device camera captures an image of the user's face.

[0638] Data processing: The captured facial images are analyzed using facial recognition software (e.g., OpenCV).

[0639] Output: The analyzed facial image data is saved in the device's memory.

[0640] Step 3:

[0641] Perform facial recognition.

[0642] Input: Passport data obtained in step 1 and facial image data obtained in step 2.

[0643] What it does: The device uses facial recognition software to match the passport photo with the captured facial image in real time.

[0644] Data arithmetic: The face recognition algorithm compares the two using pattern matching.

[0645] Output: Save the identity verification result in the device's memory.

[0646] Step 4:

[0647] Check past immigration history.

[0648] Input: Passport data from step 1 and identity verification result from step 3.

[0649] Specific operation: The terminal sends passport data and identity verification results to the server, and the server queries the database.

[0650] Data processing: The server uses SQL queries to retrieve past entry history from the database.

[0651] Output: The acquired entry history data is sent to the terminal and the matching results are saved.

[0652] Step 5:

[0653] The generative AI asks questions to the user.

[0654] Input: Matching result from step 4.

[0655] Specific operation: The device uses a generative AI model (e.g., GPT-4) to generate questions for the user and displays them on the display.

[0656] Data processing: The generative AI model creates questions based on the prompt.

[0657] Output: The question is displayed on the display.

[0658] Step 6:

[0659] The user answers.

[0660] Input: The user answers the question by speaking or entering text.

[0661] What it does: The device converts voice input into text using speech recognition software (for example, Google Speech-to-Text API).

[0662] Data processing: Speech recognition software converts speech into text data.

[0663] Output: The user's answer as text data is saved in the device's memory.

[0664] Step 7:

[0665] The server analyzes the user's answers.

[0666] Input: User response data from step 6.

[0667] How it works: The device sends the answer data to the server, which then analyzes the answer using a generative AI model.

[0668] Data Computing: Generative AI models use natural language processing to analyze the content of responses and detect suspicious patterns.

[0669] Output: Sends the analysis results to the terminal.

[0670] Step 8:

[0671] Suspicious subject detection and notification.

[0672] Input: Analysis results from step 7.

[0673] Specific operation: If the server detects a suspicious pattern based on the analysis results, it sends an alert to the expert examiner's terminal.

[0674] Data calculation: Evaluate the analysis results and generate alert messages.

[0675] Output: An alert is displayed on the examiner's terminal.

[0676] Step 9:

[0677] Response from specialist examiners.

[0678] Input: Alert notification from step 8.

[0679] Specific operation: A specialist examiner will direct the user to another examination window for a detailed examination.

[0680] Output: The detailed review results are entered into the system and saved.

[0681] Step 10:

[0682] The procedure is complete.

[0683] Input: All preprocessing results.

[0684] Specific operation: The terminal displays the entry permit and prints an entry confirmation if necessary.

[0685] Output: The user's entry permit is displayed and a confirmation is provided.

[0686] This not only makes immigration procedures more efficient and improves convenience for users, but also strengthens security.

[0687] (Application example 1)

[0688] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0689] Traditional immigration inspection procedures are often cumbersome and time-consuming. Furthermore, because the identity of entrants and the verification of their past entry history are done manually, this can be inefficient and inaccurate. This increases security risks and places significant stress on entrants. Furthermore, it can take a long time for specialized inspectors to respond, and there are still challenges in detecting and responding to suspicious individuals.

[0690] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0691] In this invention, the server includes a scanning means for reading the passport data of an entrant, a camera means for capturing a facial image of the entrant, a recognition means for comparing the passport data with the facial image, a matching means for matching the entrant's past entry history, a question analysis means for asking questions using a generation AI and analyzing responses, an analysis means for analyzing the responses and recognition data and detecting suspicious patterns, a recording means for analyzing the passport image using the scanning means and recording the matching results, and a notification means for notifying a specialist inspector if a suspicious pattern is detected. This makes it possible to improve the efficiency and accuracy of immigration inspection operations, reduce security risks, and ease the stress of entrants.

[0692] "Scanning means" refers to a device that has the function of electronically reading the passport data of entrants and storing it as data.

[0693] The "camera means" is a device for capturing an image of the face of a person entering the country and obtaining the image data.

[0694] "Recognition means" refers to a device or software that collates passport data with a facial image to verify identity.

[0695] A "verification means" is a device or software that retrieves an entrant's past entry history from a database and compares it with current data.

[0696] A "question analysis means" is a device or software that outputs questions posed by the generation AI and receives and analyzes the user's responses in voice or text.

[0697] An "analysis means" is a device or software for analyzing response and recognition data and detecting suspicious patterns.

[0698] The "recording means" is a device or software that analyzes the passport image using the scanning means and records the results in a database.

[0699] "Notification means" means a device or software that sends an alert or notification to a specialist examiner when a suspicious pattern is detected.

[0700] The present invention is a system for streamlining immigration inspections and improving security. This system is realized using the following specific processing steps and techniques.

[0701] Hardware and software used

[0702] Hardware: Smartphones, camera devices

[0703] Software: OpenCV (image processing library), Requests (HTTP request library), Transformers (generative AI library)

[0704] Program processing explanation

[0705] This system starts when the user launches the smartphone application and scans their passport. The passport data is electronically read and saved using the scanning means. The data acquired by this scanning means is sent to the verification means and verified against past entry history.

[0706] Next, a camera is used to capture a facial image of the user. The facial image is compared with a passport photo by a recognition means to verify the user's identity. After that, a generative AI model uses a question analysis means to generate a question and display it to the user. The user then inputs an answer by voice or text, and the answer data is sent to the server.

[0707] The server uses its analysis means to analyze the responses and other recognition data to detect suspicious patterns. If a suspicious pattern is detected, its notification means sends an alert to a specialist examiner, who can then conduct a detailed review.

[0708] Specific examples

[0709] For example, when managing access to a sensitive facility, employees and visitors can use a smartphone app to complete the entry procedures. First, the user scans their passport with their smartphone and captures their facial image. The passport information is then matched with the facial image by the scanning and recognition means.

[0710] Next, the generative AI model displays the question "What is the purpose of your visit?" to the user. If the user answers "It's a business meeting," this response data is analyzed by the analysis means. If there are no problems with the analysis results, "Entry permitted" is displayed and the user is allowed to enter the facility.

[0711] Example prompt for a generative AI model:

[0712] Please tell us the purpose of your visit.

[0713] In this way, the present invention can improve the efficiency of immigration inspections and entry / exit management for important facilities, thereby reducing security risks.

[0714] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0715] Step 1:

[0716] The user launches the smartphone app and starts scanning their passport. The passport is read using a scanning device, and the acquired passport data is temporarily saved in the application. The input is an image of the passport taken using the smartphone, and the output is the passport information acquired through image analysis.

[0717] Step 2:

[0718] The device activates the built-in camera and captures the user's facial image. The captured facial image is analyzed using a facial recognition algorithm and temporarily saved. The input is the user's facial image, and the output is the analyzed facial data.

[0719] Step 3:

[0720] The terminal again sends the passport data and facial data to the recognition means for matching. Through this matching, the terminal performs identity verification. The input is the passport data and facial data, and the output is the result of identity verification. The specific operation of matching is to compare facial feature points using a facial recognition algorithm.

[0721] Step 4:

[0722] The server retrieves the entrant's past entry history from the database. The retrieved data is compared with the current entry information using a matching means. The input is the user's entry history data, and the output is the result of the entry history matching.

[0723] Step 5:

[0724] The terminal uses a question analysis means to input a prompt sentence into the generative AI model. The generative AI model generates a question and displays it to the user. The input is the prompt sentence, and the output is the generated question. Specifically, the generative AI model performs natural language generation.

[0725] Step 6:

[0726] The user answers questions generated by voice or text. The device captures the answers and sends them to the server. The input is the user's answer data, and the output is the answer data used for analysis.

[0727] Step 7:

[0728] The server analyzes the response data and the recognition data using an analysis means. This analysis determines whether a suspicious pattern is detected. The input is the response data and the recognition data, and the output is the result of the analysis. Specifically, a pattern recognition algorithm is used.

[0729] Step 8:

[0730] If the server detects a suspicious pattern based on the analysis results, it sends an alert to the expert examiner using a notification means. The input is the analysis result, and the output is a notification to the examiner. The specific operation uses an alert sending system.

[0731] The above processing steps improve the efficiency and accuracy of immigration inspection procedures, reduce security risks, and ease stress for entrants.

[0732] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0733] This invention improves the efficiency of immigration procedures by using a self-service system that uses image recognition and generation AI, and also combines it with an emotion engine that recognizes the user's emotions. The operation of this system is explained in detail below.

[0734] System Overview

[0735] This system consists of an immigration inspection terminal, a server, a user (entrant), and an emotion engine. The user uses the terminal to conduct immigration inspection themselves, and the server and emotion engine perform processing as needed on the backend.

[0736] Program processing

[0737] 1. User scans passport

[0738] When the user arrives at the immigration terminal at the airport, they follow the instructions on the terminal to scan their passport.

[0739] The terminal electronically reads the passport information and temporarily stores the data.

[0740] 2. Facial image capture and recognition

[0741] The device activates the camera and captures an image of the user's face.

[0742] The device compares the passport photo with the captured facial image to verify identity.

[0743] 3. Checking past immigration history

[0744] The server retrieves the user's past entry history from the database.

[0745] The data acquired by the terminal is sent to the server, which then compares the past data with the current data.

[0746] 4. Analysis of Questions and Answers by Generative AI

[0747] The device uses the generative AI to ask questions to the user, who then responds by voice or text input to the questions displayed on the device.

[0748] The terminal transmits the answer to the server, and the server uses the question analysis means to analyze the content of the user's answer.

[0749] 5. Emotional Recognition

[0750] The device activates an emotion engine that analyzes the user's voice and facial expressions in real time.

[0751] The emotion engine detects the user's emotion data and transmits the data to the analysis means.

[0752] 6. Suspicious Subject Detection

[0753] The server analyzes the user's responses, cognitive data, and emotional data to detect suspicious patterns.

[0754] If necessary, further data processing is performed.

[0755] 7. Notification and Response by Specialist Examiners

[0756] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[0757] The examiner will direct the user to a separate examination window for a detailed examination.

[0758] 8. Completion of the procedure

[0759] If there are no problems, the terminal will display "Entry Permitted" to the user. The user confirms the screen displaying the permission and the procedure is complete.

[0760] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[0761] Specific examples

[0762] Example 1: Frequent visitors

[0763] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[0764] The terminal reads the passport information.

[0765] 2. The device captures your face to verify your identity.

[0766] Identity verification is completed through facial recognition.

[0767] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[0768] Since it matches the existing data, it is determined that there is no problem.

[0769] 4. The device asks questions generated by the AI, and the user answers.

[0770] You answer the questions briefly and your answers are analyzed.

[0771] 5. The device uses the emotion engine to analyze the user's emotions.

[0772] Emotion data is generated and sent to the analysis means.

[0773] 6. The terminal will display "Entry Permitted" and the procedure will be completed.

[0774] The user receives confirmation and is allowed into the country.

[0775] Example 2: Suspicious Subject

[0776] 1. The user arrives at the terminal and scans their passport.

[0777] The terminal reads the passport information.

[0778] 2. The device captures your face to verify your identity.

[0779] Identity verification is completed through facial recognition.

[0780] 3. The server checks the past entry history and detects an anomaly.

[0781] 4. The device asks questions generated by the AI, and the user answers.

[0782] The answers are analyzed to detect suspicious patterns.

[0783] 5. The device uses the emotion engine to analyze the user's emotions.

[0784] Emotional data such as anxiety and tension is detected.

[0785] 6. The server notifies the examiner and sends the analysis results.

[0786] Examiners will conduct a detailed examination of users.

[0787] The above process will improve the efficiency and security of immigration inspections. Furthermore, analysis of emotion data will enable more accurate judgments.

[0788] The processing flow will be explained below.

[0789] Step 1:

[0790] The user arrives at the immigration terminal at the airport and scans their passport following the instructions on the terminal's screen.

[0791] Step 2:

[0792] The device scans the passport and reads passport information (name, passport number, nationality, photo, etc.) and temporarily stores the read data.

[0793] Step 3:

[0794] The device activates the camera and captures the user's face. The user faces forward as instructed by the camera.

[0795] Step 4:

[0796] The facial image captured by the device is compared with the passport photo data, and the matching is carried out using a recognition method to calculate the degree of match and verify the identity of the person.

[0797] Step 5:

[0798] The server retrieves the user's past entry history from the database. The device sends the retrieved data to the server, which then compares the past data with the current data.

[0799] Step 6:

[0800] The server checks the entry history and determines whether the person is a frequent entrant. If so, the server proceeds with the process as a "frequent entrant."

[0801] Step 7:

[0802] The device uses the generated AI to ask questions to the user, who responds by voice or text input to the questions displayed on the device screen.

[0803] Step 8:

[0804] The terminal transmits the user's answer to the server, which uses question analysis means to analyze the content of the user's answer.

[0805] Step 9:

[0806] The device activates an emotion engine that analyzes the user's voice and facial expressions in real time. The emotion engine detects changes in the user's voice tone and facial expressions.

[0807] Step 10:

[0808] The emotion engine generates user emotion data and sends it to the analysis means, which incorporates the emotion data into the analysis of the user's responses.

[0809] Step 11:

[0810] The server performs analysis based on the user's responses, facial recognition data, and emotion data to detect anomalies or suspicious patterns.

[0811] Step 12:

[0812] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[0813] Step 13:

[0814] If no suspicious patterns are detected, the device displays "Entry Permitted" to the user, who then confirms the permission screen and the process is complete.

[0815] Step 14:

[0816] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[0817] The above processing steps allow users to complete immigration inspections quickly and efficiently. The introduction of this system is expected to shorten immigration inspection times and improve working conditions. Furthermore, the addition of an emotion engine allows for more accurate inspections that take the user's psychological state into account, improving security.

[0818] Example 2

[0819] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0820] Conventional immigration inspection systems require a lot of time for immigration procedures, and there is a need for efficient handling, especially for frequent entrants and those entering under specific conditions. Furthermore, there is a lack of methods for quickly and accurately detecting and responding to entrants' fraudulent or suspicious behavior. Furthermore, there is a need to analyze emotions to more accurately assess the state of entrants and improve safety.

[0821] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a scanning means for reading the passport data of the entrant via the terminal, a photographing means for capturing a facial image of the entrant via the terminal, a recognition means for comparing the passport data with the facial image via the server, a matching means for comparing the entrant's past entry history via the server, a question analysis means for asking questions using the generative AI model via the terminal and analyzing responses via the server, anomaly detection means for analyzing the responses and recognition data via the server and detecting suspicious patterns, and a notification means for notifying a specialist inspector when a suspicious pattern is detected by the server. This makes it possible to improve the efficiency and security of immigration inspections.

[0822] "Scanning means" refers to devices and methods for reading a user's passport data, including devices that capture information from the machine-readable area or IC chip on a passport.

[0823] "Capture Means" refers to devices and methods for capturing a user's facial image, including a camera and its control software.

[0824] "Recognition means" refers to devices and methods for matching passport data with captured facial images to verify identity, including image processing algorithms and database matching techniques.

[0825] "Verification means" refers to devices and methods for retrieving an entrant's past immigration history from a database and comparing it with current immigration data. This means includes database query systems and verification algorithms.

[0826] "Question Analysis Means" refers to devices and methods for using generative AI models to ask users questions and analyze their responses, including natural language processing and speech recognition technologies.

[0827] "Anomaly detection means" refers to devices and methods for analyzing user response and perception data to detect suspicious patterns. This means includes anomaly detection algorithms and multivariate analysis techniques.

[0828] "Notification means" refers to devices and methods for notifying expert reviewers when suspicious patterns are detected, including alert systems and communication networks.

[0829] This invention provides a system for improving security by streamlining immigration procedures at airports and other locations. This system is comprised of interactions between a server, a terminal, and a user. Specific examples of each function are described below.

[0830] The system consists of an immigration inspection terminal, a server, a user (entrant), and a generation AI module. The user uses the terminal to conduct their own immigration inspection, and the server and generation AI module process the information as needed in the backend.

[0831] First, the terminal scans the user's passport. The passport scanner device acquires data from the passport's machine-readable zone (MRZ) and IC chip. The terminal temporarily stores the scanned data in memory and proceeds.

[0832] The device then activates the camera to capture the user's facial image, which is then compared with the passport photo by the server to verify identity. The facial recognition algorithm uses OpenCV or TensorFlow, for example.

[0833] The server then retrieves the user's past entry history from a database, and compares the retrieved history data with the current entry data using an existing database query system.

[0834] The device then uses a generation AI to ask the user questions. The generation AI, for example, uses OpenAI's GPT-3, which randomly generates questions from a specific question list. The user responds by voice or text input, which is converted into text by speech recognition software (e.g., Google Speech-to-Text). The server then analyzes the content of the answers using a question analysis method to check for any suspicious points.

[0835] In addition, the device activates an emotion engine to analyze the user's voice and facial expressions in real time. Voice analysis software (e.g., Praat) is used for voice analysis, and facial recognition software (e.g., Affectiva) is used for facial expression analysis. The detected emotion data is sent to the server.

[0836] All information is analyzed, and the server evaluates the user's responses and sentiment data holistically, applying anomaly detection algorithms to detect suspicious patterns. If an anomaly is detected, a notification system is activated, sending an alert and analysis results to expert reviewers.

[0837] If there are no problems, the terminal will display "Entry Permitted" to the user and print an entry confirmation form if necessary, officially completing the procedure.

[0838] As a concrete example, when a frequent entrant arrives at the terminal, their passport is scanned and facial recognition is performed, their past entry history is confirmed, and after answering questions posed by the AI ​​generation system, if the emotion engine determines that there are no problems, the terminal will display "Entry Permitted."

[0839] Examples of prompts for a generative AI model might include questions like:

[0840] "As someone integrated into the airport immigration system, how would you question a high-frequency immigrant who is somewhat nervous?"

[0841] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0842] Step 1:

[0843] The user scans the passport into the terminal.

[0844] Input: The user places the passport in the passport scanner.

[0845] Specific operation: The terminal activates the passport scanner and obtains data from the passport's MRZ (Machine Readable Zone) and IC chip.

[0846] Data processing: The acquired data is temporarily stored in memory as electronic data.

[0847] Output: Passport data is saved on the device.

[0848] Step 2:

[0849] The device captures a facial image of the user.

[0850] Input: The user stands in front of the device's camera.

[0851] Specific operation: The device activates the camera and captures an image of the user's face.

[0852] Data processing: The captured facial image is saved as digital data.

[0853] Output: The facial image data is saved to the device.

[0854] Step 3:

[0855] The server compares the passport data with the facial image.

[0856] Input: Passport data and facial image data are sent from the terminal to the server.

[0857] What it does: The server uses a facial recognition algorithm (e.g. OpenCV or TensorFlow) to compare the passport photo with the captured face image.

[0858] Data calculation: Feature points of the facial image are extracted and matched using a recognition algorithm.

[0859] Output: Matching result is generated on the server (e.g. match success / failure).

[0860] Step 4:

[0861] The server checks past entry history.

[0862] Input: The server queries the database based on the passport data.

[0863] Specific operation: The server retrieves past entry history from the database.

[0864] Data calculation: Compare the acquired historical data with the current entry data.

[0865] Output: The matching result is generated on the server (e.g., whether or not there is an entry history).

[0866] Step 5:

[0867] The device uses the generative AI model to ask the user questions.

[0868] Input: Instructions from the server and user data.

[0869] Specific operation: The device uses generative AI (e.g., GPT-3) to generate questions and display them to the user.

[0870] Data calculation: Generate appropriate questions based on the user's past answers and current situation.

[0871] Output: The generated question is displayed on the terminal.

[0872] Step 6:

[0873] The user inputs the answer to the question into the terminal.

[0874] Input: The user enters the answer into the device by voice or text.

[0875] What happens: Your device uses speech recognition software (e.g., Google Speech-to-Text) to convert speech to text.

[0876] Data processing: Converts audio data into text data.

[0877] Output: The textual answers are saved to your device.

[0878] Step 7:

[0879] The server analyzes the user's answers.

[0880] Input: The textual response is sent from the device to the server.

[0881] What happens: The server uses natural language processing (NLP) tools to analyze the answers.

[0882] Data calculation: Analyze the intent and content of the response and check for any abnormalities.

[0883] Output: The analysis results are generated on the server (e.g. normal / abnormal).

[0884] Step 8:

[0885] The device uses an emotion engine to analyze the user's emotions.

[0886] Input: User's facial expressions and voice data.

[0887] Specific operation: The device analyzes voice and facial expressions using emotion analysis software (e.g. Praat, Affectiva).

[0888] Data computation: Extracting and analyzing emotion features.

[0889] Output: Sentiment analysis data is saved on the device.

[0890] Step 9:

[0891] The server detects a suspicious pattern.

[0892] Input: Answer data and sentiment data parsed by the server.

[0893] What happens: The server applies an anomaly detection algorithm and performs pattern analysis.

[0894] Data Calculation: Detecting suspicious patterns based on various parameters.

[0895] Output: Detection results are generated on the server (e.g., suspicious / not suspicious).

[0896] Step 10:

[0897] The server notifies the expert examiner.

[0898] Input: The server's verdict when a suspicious pattern is detected.

[0899] Specific operation: The server sends an alert to the examiner's terminal.

[0900] Data processing: Generate alert data and send it over the communication network.

[0901] Output: An alert notification will be displayed on the examiner's terminal.

[0902] Step 11:

[0903] The terminal completes immigration procedures.

[0904] Input: Final decision from the server (e.g., entry allowed / denied).

[0905] Specific operation: The terminal displays the entry permit and prints an entry confirmation if necessary.

[0906] Data processing: Output entry permit data on screen and in print.

[0907] Output: The user confirms the entry permit and the process is completed.

[0908] (Application example 2)

[0909] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0910] Conventional immigration inspection systems require a large amount of manual checking work, are inefficient, and pose a high security risk, so there is a need for a means to perform immigration inspections quickly and accurately. Furthermore, there are insufficient means to improve safety by performing more accurate risk assessments by recognizing visitor emotions. Furthermore, similar issues exist in security checks at large-scale events and important facilities, so there is a need for efficient and highly accurate security checks.

[0911] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0912] In this invention, the server includes a scanning means for reading passport data of entrants, a camera means for capturing facial images of entrants, and an emotion recognition means for recognizing and analyzing the emotions of entrants, thereby enabling quick and accurate immigration inspection and security checks.

[0913] "Scanning means" refers to a device or group of devices that have the function of reading passport data of entrants.

[0914] "Camera means" refers to a device or group of devices that have the function of capturing facial images of entrants.

[0915] "Recognition means" refers to a device or program that has the function of matching the read passport data with the captured facial image.

[0916] "Verification means" refers to a device or program that has the function of acquiring the past entry history of an entrant and comparing it with current data.

[0917] "Question analysis means" refers to a device or program that has the function of using generation AI to ask questions to entrants and analyze their responses.

[0918] "Analysis means" refers to a device or program that has the functionality to analyze acquired response and recognition data and detect suspicious patterns.

[0919] The "notification means" is a device or group of devices that has the function of notifying a specialist examiner when a suspicious pattern is detected.

[0920] "Emotion recognition means" refers to a device or program that has the function of recognizing the emotions of entrants and analyzing the data.

[0921] This invention relates to a system that improves the efficiency and accuracy of security checks at immigration and large-scale events. The system includes passport scanning, facial recognition, past entry history matching, question and answer analysis using generative AI, emotion recognition, suspicious pattern detection, and notification functions.

[0922] Program processing description

[0923] 1. The server electronically stores the passport data when the user scans the passport using a scanning method, which can be a dedicated scanner or a camera in smart glasses.

[0924] 2. To capture the user's face image, the camera means is activated and the face image is acquired in real time, which is then matched with the passport photo by the recognition means.

[0925] 3. The server retrieves the entrant's or visitor's past entry history from the database and uses a matching means to compare it with the current data.

[0926] 4. The question analysis means uses the generative AI model to ask the user a question and receives the user's response via voice or text input. The acquired response data is analyzed by the analysis means.

[0927] 5. The facial image and voice data of the user captured by the camera means are analyzed by the emotion recognition means to grasp the user's emotional state in real time.

[0928] 6. The analysis means comprehensively analyzes the acquired response data, facial image data, and emotion data to perform risk assessment. If a suspicious pattern is detected, an alert is sent to the expert examiner by the notification means.

[0929] 7. The examiner will conduct a detailed check based on the alert and will carry out additional checks as necessary.

[0930] Hardware and software used

[0931] Scanner and camera means: smart glasses (e.g., Google Glass) and dedicated scanners

[0932] Analysis software: face recognition library (e.g., face_recognition), emotion recognition library (e.g., EmotionRecognizer)

[0933] Server: Back-end processing for question analysis and notification functions

[0934] Adding specific examples

[0935] For example, when used at a large music festival, when attendees arrive at the entrance, security guards wearing smart glasses scan their passports and capture their faces. The generative AI then asks, "What is the purpose of your visit?" If the attendee answers, "I'm here to enjoy the concert," the application analyzes this and records it as a secure answer. Meanwhile, an emotion recognition engine analyzes the attendee's facial expressions in real time, and if it detects any abnormalities, it notifies the security guard. If suspicious, the attendee will be directed to an additional checkpoint.

[0936] Prompt Sentence Examples

[0937] "Please tell us the purpose of your visit. Then, please explain the purpose of your visit in one sentence."

[0938] The above system will make it possible to improve the efficiency and accuracy of immigration and security checks.

[0939] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0940] Step 1:

[0941] When a user scans their passport, the server uses a scanning method to electronically store the passport data. Specifically, the server reads the passport information using a dedicated scanner or a camera mounted on smart glasses and temporarily stores it as digital data. The input is the passport image data, and the output is the scanned passport information.

[0942] Step 2:

[0943] The terminal activates the camera means to capture the user's facial image and acquires the facial image in real time. The acquired facial image is compared with the scanned passport photo by the recognition means. The input is the real-time facial image data, and the output is the matching result, i.e., a match judgment.

[0944] Step 3:

[0945] The server retrieves the past entry history of the entrant or visitor from the database and uses a matching method to compare it with the current data. The input is the passport ID information, and the output is the matching result of the past entry history. The server compares these data and determines whether they match or differ.

[0946] Step 4:

[0947] The question analysis means uses the generative AI model to ask the user a question and receives a response via voice or text input. The terminal displays the question and waits for the user's response. The input is a prompt from the generative AI model and the user's voice or text response, and the output is the analyzed response. An example of a prompt is: "Please tell us the purpose of your visit."

[0948] Step 5:

[0949] The emotion recognition means analyzes the user's facial image and audio data captured by the camera means to grasp the user's emotional state in real time. The input is the facial image and audio data, and the output is the analysis result of the emotional state. Specifically, the analysis is performed using the EmotionRecognizer library.

[0950] Step 6:

[0951] The analysis means performs an integrated analysis of the acquired response data, facial image data, and emotional data to perform a risk assessment. The inputs are response data, facial image data, and emotional data, and the output is the detection results of suspicious patterns. The system uses an anomaly detection algorithm to determine the risk level based on the analysis results.

[0952] Step 7:

[0953] If a suspicious pattern is detected, the notification means sends an alert to the expert examiner. The terminal displays the alert on the terminal of the security response officer, prompting further confirmation. The input is the detection result of the suspicious pattern, and the output is an alert notification. This allows the expert examiner to carry out further confirmation work.

[0954] Step 8:

[0955] If there are no problems, the terminal displays a message to the user granting permission to enter. Specifically, once the verification process is complete, the user is given permission to proceed to the next step. The input is the final analysis result, and the output is a message granting permission to enter.

[0956] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0957] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0958] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0959] [Third embodiment]

[0960] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0961] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0962] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0963] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0964] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0965] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0966] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0967] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0968] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0969] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0970] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0971] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0972] This invention aims to improve the efficiency of immigration inspection procedures by using a self-service system that uses image recognition and generation AI. The operation of this system is explained in detail below.

[0973] System Overview

[0974] This system consists of an airport immigration terminal, a server, and a user (entrant). The user uses the terminal to go through immigration themselves, and the server processes the information as needed in the backend.

[0975] Program processing

[0976] 1. User scans passport

[0977] When the user arrives at the immigration terminal at the airport, they follow the instructions on the terminal to scan their passport.

[0978] The terminal electronically reads the passport information and stores it as data.

[0979] 2. Facial image capture and recognition

[0980] The device activates the camera and captures an image of the user's face.

[0981] The device compares the passport photo with the captured facial image to verify identity.

[0982] 3. Checking past immigration history

[0983] The server retrieves the user's past entry history from the database.

[0984] The server compares the historical data it has acquired with the current data to determine whether the person is a frequent entrant.

[0985] 4. Analysis of Questions and Answers by Generative AI

[0986] The device uses generative AI to display questions to the user.

[0987] The user answers the questions by voice or text input.

[0988] The terminal transmits the answer to the server, and the server analyzes the answer using the question analysis means.

[0989] 5. Suspicious Subject Detection

[0990] The server analyzes the answers and recognition data to detect suspicious patterns.

[0991] If a suspicious pattern is detected, the server notifies the examiner.

[0992] 6. Notification and Response by Specialist Examiners

[0993] The server sends an alert to the examiner's device and displays the analysis results.

[0994] The examiner will direct the user to a separate examination window for a detailed examination.

[0995] 7. Completion of the procedure

[0996] If there are no problems, the terminal displays "Entry Permitted" to the user.

[0997] If necessary, the terminal prints out an entry confirmation and provides it to the user.

[0998] Specific examples

[0999] Example 1: Frequent visitors

[1000] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[1001] The terminal reads the passport information.

[1002] 2. The device captures your face to verify your identity.

[1003] Identity verification is completed through facial recognition.

[1004] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[1005] Since it matches the existing data, it is determined that there is no problem.

[1006] 4. The device asks questions generated by the AI, and the user answers.

[1007] You answer the questions briefly and your answers are analyzed.

[1008] 5. The terminal will display "Entry Permitted" and the procedure will be complete.

[1009] The user receives confirmation and is allowed into the country.

[1010] Example 2: Suspicious Subject

[1011] 1. The user arrives at the terminal and scans their passport.

[1012] The terminal reads the passport information.

[1013] 2. The device captures your face to verify your identity.

[1014] Identity verification is completed through facial recognition.

[1015] 3. The server checks the past entry history and detects an anomaly.

[1016] 4. The device asks questions generated by the AI, and the user answers.

[1017] The answers are analyzed to detect suspicious patterns.

[1018] 5. The server notifies the examiner and sends the analysis results.

[1019] Examiners will conduct a detailed examination of users.

[1020] These processes will make immigration inspections more efficient and reduce stress for entrants.

[1021] The processing flow will be explained below.

[1022] Step 1:

[1023] The user arrives at the immigration terminal at the airport and scans their passport following the instructions on the terminal's screen.

[1024] Step 2:

[1025] The device scans the passport, reads the passport information (name, passport number, nationality, photo, etc.), and temporarily stores the data.

[1026] Step 3:

[1027] The device activates the camera and captures the user's face. The user faces forward as instructed by the camera.

[1028] Step 4:

[1029] The device compares the captured facial image with the passport photo data, and uses a recognition method to calculate the degree of match and verify the identity of the person.

[1030] Step 5:

[1031] The server retrieves the user's past entry history from the database. The device sends the retrieved data to the server, which then compares the past data with the current data.

[1032] Step 6:

[1033] The server checks the entry history and determines whether the person is a frequent entrant. If so, the server proceeds with the process as a "frequent entrant."

[1034] Step 7:

[1035] The device uses the generated AI to ask questions to the user, who responds by voice or text input to the questions displayed on the device screen.

[1036] Step 8:

[1037] The terminal transmits the user's answer to the server, which uses question analysis means to analyze the content of the user's answer.

[1038] Step 9:

[1039] The server analyzes the responses and recognition data to detect anomalies or suspicious patterns, and performs additional data processing if necessary.

[1040] Step 10:

[1041] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[1042] Step 11:

[1043] If no suspicious patterns are detected, the device displays "Entry Permitted" to the user, who then confirms the permission screen and the process is complete.

[1044] Step 12:

[1045] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[1046] The above processing steps allow users to complete immigration inspections quickly and efficiently. The introduction of this system is expected to reduce immigration inspection times and improve working conditions.

[1047] Example 1

[1048] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1049] As the number of users increases, immigration inspection procedures become more time-consuming and labor-intensive, and efficient procedures are required. Identity verification and the detection of suspicious individuals are also important issues. Current systems require manual inspections, placing a heavy burden on human resources. Therefore, it is necessary to introduce a self-service system to improve the efficiency and reliability of immigration inspections.

[1050] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1051] In this invention, the server includes optical character recognition means for reading passports, image capture means for acquiring facial images, and matching means, which enables automation and efficiency of immigration inspections.

[1052] "Optical character recognition" is a technology for analyzing input text data and reading it as electronic data.

[1053] "Image capture means" is a technology that uses a camera or a sensor to capture an image of the user's face.

[1054] "Facial recognition means" is a technology that identifies specific patterns based on acquired facial images and verifies identity.

[1055] "Database matching means" is a technology that searches for past entry history from an existing database and matches it with current data.

[1056] "Question analysis means" is a technology that uses generation AI to generate questions for users and analyze their answers.

[1057] "Data analysis means" refers to technology that detects suspicious patterns based on user responses and recognition data.

[1058] An "alert notification means" is a technology that sends a warning to a specialist examiner when a suspicious pattern is detected.

[1059] "Display means" refers to technology that displays entry permits and other information to the user.

[1060] "Printing means" refers to the technology used to physically print the entry confirmation and other required documents.

[1061] The present invention is a self-service system for streamlining immigration procedures, and is composed of an immigration terminal, a server, and a user (entrant). The following describes the specific hardware and software configurations and processing procedures for realizing the present invention.

[1062] System configuration

[1063] Hardware

[1064] 1. Immigration Terminal: A terminal used by people entering the country. It is equipped with a scanner for scanning passports, a camera for capturing facial images, a display for displaying questions, input devices (keyboard and microphone) for the user to enter their answers, and a CPU and memory for performing the necessary processing.

[1065] 2. Server: A high-performance computer for processing and analyzing large amounts of data. It runs various software such as databases, generative AI models, query analysis, and data analysis.

[1066] software

[1067] 1. Optical Character Recognition (OCR) software: Used to read passport data electronically. A common OCR tool is Adobe OCR.

[1068] 2. Facial recognition software: Used to match the captured facial image with the passport photo. Facial recognition libraries such as OpenCV are typically used.

[1069] 3. Database Management System (DBMS): Used to store information such as immigration history and retrieve and verify data as needed. Examples include MySQL and PostgreSQL.

[1070] 4. Generative AI models: Used to generate questions for users and analyze their answers. Examples include GPT-4 and other natural language processing (NLP) models.

[1071] 5. Speech recognition software: Used to convert user voice input into text data, such as the Google Speech-to-Text API.

[1072] Specific examples of operations

[1073] Frequent visitors

[1074] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[1075] The terminal reads the passport information using optical character recognition (OCR) software.

[1076] 2. The device captures your face to verify your identity.

[1077] It uses facial recognition software to match your passport photo with a real-time captured facial image.

[1078] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[1079] Obtain past immigration history from existing databases and match it with current data.

[1080] 4. The device asks questions based on the generated AI model, and the user answers.

[1081] Example: "What is your purpose for entering Japan?", user answers "Tourism."

[1082] 5. The terminal will display "Entry Permitted" and the procedure will be complete.

[1083] If necessary, print out the entry confirmation and provide it to the user.

[1084] Suspicious Subjects

[1085] 1. The user arrives at the terminal and scans their passport.

[1086] The terminal reads the passport information using optical character recognition (OCR) software.

[1087] 2. The device captures your face to verify your identity.

[1088] It uses facial recognition software to match your passport photo with a real-time captured facial image.

[1089] 3. The server checks the past entry history and detects an anomaly.

[1090] Obtain past immigration history from existing databases and match it with current data.

[1091] 4. The device asks questions based on the generated AI model, and the user answers.

[1092] Example: "How many times have you visited Japan in the past six months?" The user answers "Three times." The answer is detected as an abnormal pattern.

[1093] 5. The server notifies the examiner and sends the analysis results.

[1094] An alert is sent to the examiner's terminal and a detailed examination of the user is conducted.

[1095] Through the above process, this system will streamline immigration procedures and reduce stress for users. It will also improve security by enabling early detection of suspicious individuals and professional screening.

[1096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1097] Step 1:

[1098] The user scans their passport.

[1099] Input: The user inserts the passport into the terminal's scanner.

[1100] What it does: The terminal uses optical character recognition (OCR software) to read the passport information.

[1101] Data processing: OCR software converts the text information on the passport into electronic data.

[1102] Output: Save the scanned passport data in the device's memory.

[1103] Step 2:

[1104] Capture a facial image of the user.

[1105] Input: The user follows the instructions on the device and faces the camera.

[1106] Specific operation: The device camera captures an image of the user's face.

[1107] Data processing: The captured facial images are analyzed using facial recognition software (e.g., OpenCV).

[1108] Output: The analyzed facial image data is saved in the device's memory.

[1109] Step 3:

[1110] Perform facial recognition.

[1111] Input: Passport data obtained in step 1 and facial image data obtained in step 2.

[1112] What it does: The device uses facial recognition software to match the passport photo with the captured facial image in real time.

[1113] Data arithmetic: The face recognition algorithm compares the two using pattern matching.

[1114] Output: Save the identity verification result in the device's memory.

[1115] Step 4:

[1116] Check past immigration history.

[1117] Input: Passport data from step 1 and identity verification result from step 3.

[1118] Specific operation: The terminal sends passport data and identity verification results to the server, and the server queries the database.

[1119] Data processing: The server uses SQL queries to retrieve past entry history from the database.

[1120] Output: The acquired entry history data is sent to the terminal and the matching results are saved.

[1121] Step 5:

[1122] The generative AI asks questions to the user.

[1123] Input: Matching result from step 4.

[1124] Specific operation: The device uses a generative AI model (e.g., GPT-4) to generate questions for the user and displays them on the display.

[1125] Data processing: The generative AI model creates questions based on the prompt.

[1126] Output: The question is displayed on the display.

[1127] Step 6:

[1128] The user answers.

[1129] Input: The user answers the question by speaking or entering text.

[1130] What it does: The device converts voice input into text using speech recognition software (for example, Google Speech-to-Text API).

[1131] Data processing: Speech recognition software converts speech into text data.

[1132] Output: The user's answer as text data is saved in the device's memory.

[1133] Step 7:

[1134] The server analyzes the user's answers.

[1135] Input: User response data from step 6.

[1136] How it works: The device sends the answer data to the server, which then analyzes the answer using a generative AI model.

[1137] Data Computing: Generative AI models use natural language processing to analyze the content of responses and detect suspicious patterns.

[1138] Output: Sends the analysis results to the terminal.

[1139] Step 8:

[1140] Suspicious subject detection and notification.

[1141] Input: Analysis results from step 7.

[1142] Specific operation: If the server detects a suspicious pattern based on the analysis results, it sends an alert to the expert examiner's terminal.

[1143] Data calculation: Evaluate the analysis results and generate alert messages.

[1144] Output: An alert is displayed on the examiner's terminal.

[1145] Step 9:

[1146] Response from specialist examiners.

[1147] Input: Alert notification from step 8.

[1148] Specific operation: A specialist examiner will direct the user to another examination window for a detailed examination.

[1149] Output: The detailed review results are entered into the system and saved.

[1150] Step 10:

[1151] The procedure is complete.

[1152] Input: All preprocessing results.

[1153] Specific operation: The terminal displays the entry permit and prints an entry confirmation if necessary.

[1154] Output: The user's entry permit is displayed and a confirmation is provided.

[1155] This not only makes immigration procedures more efficient and improves convenience for users, but also strengthens security.

[1156] (Application example 1)

[1157] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1158] Traditional immigration inspection procedures are often cumbersome and time-consuming. Furthermore, because the identity of entrants and the verification of their past entry history are done manually, this can be inefficient and inaccurate. This increases security risks and places significant stress on entrants. Furthermore, it can take a long time for specialized inspectors to respond, and there are still challenges in detecting and responding to suspicious individuals.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1160] In this invention, the server includes a scanning means for reading the passport data of an entrant, a camera means for capturing a facial image of the entrant, a recognition means for comparing the passport data with the facial image, a matching means for matching the entrant's past entry history, a question analysis means for asking questions using a generation AI and analyzing responses, an analysis means for analyzing the responses and recognition data and detecting suspicious patterns, a recording means for analyzing the passport image using the scanning means and recording the matching results, and a notification means for notifying a specialist inspector if a suspicious pattern is detected. This makes it possible to improve the efficiency and accuracy of immigration inspection operations, reduce security risks, and ease the stress of entrants.

[1161] "Scanning means" refers to a device that has the function of electronically reading the passport data of entrants and storing it as data.

[1162] The "camera means" is a device for capturing an image of the face of a person entering the country and obtaining the image data.

[1163] "Recognition means" refers to a device or software that collates passport data with a facial image to verify identity.

[1164] A "verification means" is a device or software that retrieves an entrant's past entry history from a database and compares it with current data.

[1165] A "question analysis means" is a device or software that outputs questions posed by the generation AI and receives and analyzes the user's responses in voice or text.

[1166] An "analysis means" is a device or software for analyzing response and recognition data and detecting suspicious patterns.

[1167] The "recording means" is a device or software that analyzes the passport image using the scanning means and records the results in a database.

[1168] "Notification means" means a device or software that sends an alert or notification to a specialist examiner when a suspicious pattern is detected.

[1169] The present invention is a system for streamlining immigration inspections and improving security. This system is realized using the following specific processing steps and techniques.

[1170] Hardware and software used

[1171] Hardware: Smartphones, camera devices

[1172] Software: OpenCV (image processing library), Requests (HTTP request library), Transformers (generative AI library)

[1173] Program processing explanation

[1174] This system starts when the user launches the smartphone application and scans their passport. The passport data is electronically read and saved using the scanning means. The data acquired by this scanning means is sent to the verification means and verified against past entry history.

[1175] Next, a camera is used to capture a facial image of the user. The facial image is compared with a passport photo by a recognition means to verify the user's identity. After that, a generative AI model uses a question analysis means to generate a question and display it to the user. The user then inputs an answer by voice or text, and the answer data is sent to the server.

[1176] The server uses its analysis means to analyze the responses and other recognition data to detect suspicious patterns. If a suspicious pattern is detected, its notification means sends an alert to a specialist examiner, who can then conduct a detailed review.

[1177] Specific examples

[1178] For example, when managing access to a sensitive facility, employees and visitors can use a smartphone app to complete the entry procedures. First, the user scans their passport with their smartphone and captures their facial image. The passport information is then matched with the facial image by the scanning and recognition means.

[1179] Next, the generative AI model displays the question "What is the purpose of your visit?" to the user. If the user answers "It's a business meeting," this response data is analyzed by the analysis means. If there are no problems with the analysis results, "Entry permitted" is displayed and the user is allowed to enter the facility.

[1180] Example prompt for a generative AI model:

[1181] Please tell us the purpose of your visit.

[1182] In this way, the present invention can improve the efficiency of immigration inspections and entry / exit management for important facilities, thereby reducing security risks.

[1183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1184] Step 1:

[1185] The user launches the smartphone app and starts scanning their passport. The passport is read using a scanning device, and the acquired passport data is temporarily saved in the application. The input is an image of the passport taken using the smartphone, and the output is the passport information acquired through image analysis.

[1186] Step 2:

[1187] The device activates the built-in camera and captures the user's facial image. The captured facial image is analyzed using a facial recognition algorithm and temporarily saved. The input is the user's facial image, and the output is the analyzed facial data.

[1188] Step 3:

[1189] The terminal again sends the passport data and facial data to the recognition means for matching. Through this matching, the terminal performs identity verification. The input is the passport data and facial data, and the output is the result of identity verification. The specific operation of matching is to compare facial feature points using a facial recognition algorithm.

[1190] Step 4:

[1191] The server retrieves the entrant's past entry history from the database. The retrieved data is compared with the current entry information using a matching means. The input is the user's entry history data, and the output is the result of the entry history matching.

[1192] Step 5:

[1193] The terminal uses a question analysis means to input a prompt sentence into the generative AI model. The generative AI model generates a question and displays it to the user. The input is the prompt sentence, and the output is the generated question. Specifically, the generative AI model performs natural language generation.

[1194] Step 6:

[1195] The user answers questions generated by voice or text. The device captures the answers and sends them to the server. The input is the user's answer data, and the output is the answer data used for analysis.

[1196] Step 7:

[1197] The server analyzes the response data and the recognition data using an analysis means. This analysis determines whether a suspicious pattern is detected. The input is the response data and the recognition data, and the output is the result of the analysis. Specifically, a pattern recognition algorithm is used.

[1198] Step 8:

[1199] If the server detects a suspicious pattern based on the analysis results, it sends an alert to the expert examiner using a notification means. The input is the analysis result, and the output is a notification to the examiner. The specific operation uses an alert sending system.

[1200] The above processing steps improve the efficiency and accuracy of immigration inspection procedures, reduce security risks, and ease stress for entrants.

[1201] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1202] This invention improves the efficiency of immigration procedures by using a self-service system that uses image recognition and generation AI, and also combines it with an emotion engine that recognizes the user's emotions. The operation of this system is explained in detail below.

[1203] System Overview

[1204] This system consists of an immigration inspection terminal, a server, a user (entrant), and an emotion engine. The user uses the terminal to conduct immigration inspection themselves, and the server and emotion engine perform processing as needed on the backend.

[1205] Program processing

[1206] 1. User scans passport

[1207] When the user arrives at the immigration terminal at the airport, they follow the instructions on the terminal to scan their passport.

[1208] The terminal electronically reads the passport information and temporarily stores the data.

[1209] 2. Facial image capture and recognition

[1210] The device activates the camera and captures an image of the user's face.

[1211] The device compares the passport photo with the captured facial image to verify identity.

[1212] 3. Checking past immigration history

[1213] The server retrieves the user's past entry history from the database.

[1214] The data acquired by the terminal is sent to the server, which then compares the past data with the current data.

[1215] 4. Analysis of Questions and Answers by Generative AI

[1216] The device uses the generative AI to ask questions to the user, who then responds by voice or text input to the questions displayed on the device.

[1217] The terminal transmits the answer to the server, and the server uses the question analysis means to analyze the content of the user's answer.

[1218] 5. Emotional Recognition

[1219] The device activates an emotion engine that analyzes the user's voice and facial expressions in real time.

[1220] The emotion engine detects the user's emotion data and transmits the data to the analysis means.

[1221] 6. Suspicious Subject Detection

[1222] The server analyzes the user's responses, cognitive data, and emotional data to detect suspicious patterns.

[1223] If necessary, further data processing is performed.

[1224] 7. Notification and Response by Specialist Examiners

[1225] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[1226] The examiner will direct the user to a separate examination window for a detailed examination.

[1227] 8. Completion of the procedure

[1228] If there are no problems, the terminal will display "Entry Permitted" to the user. The user confirms the screen displaying the permission and the procedure is complete.

[1229] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[1230] Specific examples

[1231] Example 1: Frequent visitors

[1232] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[1233] The terminal reads the passport information.

[1234] 2. The device captures your face to verify your identity.

[1235] Identity verification is completed through facial recognition.

[1236] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[1237] Since it matches the existing data, it is determined that there is no problem.

[1238] 4. The device asks questions generated by the AI, and the user answers.

[1239] You answer the questions briefly and your answers are analyzed.

[1240] 5. The device uses the emotion engine to analyze the user's emotions.

[1241] Emotion data is generated and sent to the analysis means.

[1242] 6. The terminal will display "Entry Permitted" and the procedure will be completed.

[1243] The user receives confirmation and is allowed into the country.

[1244] Example 2: Suspicious Subject

[1245] 1. The user arrives at the terminal and scans their passport.

[1246] The terminal reads the passport information.

[1247] 2. The device captures your face to verify your identity.

[1248] Identity verification is completed through facial recognition.

[1249] 3. The server checks the past entry history and detects an anomaly.

[1250] 4. The device asks questions generated by the AI, and the user answers.

[1251] The answers are analyzed to detect suspicious patterns.

[1252] 5. The device uses the emotion engine to analyze the user's emotions.

[1253] Emotional data such as anxiety and tension is detected.

[1254] 6. The server notifies the examiner and sends the analysis results.

[1255] Examiners will conduct a detailed examination of users.

[1256] The above process will improve the efficiency and security of immigration inspections. Furthermore, analysis of emotion data will enable more accurate judgments.

[1257] The processing flow will be explained below.

[1258] Step 1:

[1259] The user arrives at the immigration terminal at the airport and scans their passport following the instructions on the terminal's screen.

[1260] Step 2:

[1261] The device scans the passport and reads passport information (name, passport number, nationality, photo, etc.) and temporarily stores the read data.

[1262] Step 3:

[1263] The device activates the camera and captures the user's face. The user faces forward as instructed by the camera.

[1264] Step 4:

[1265] The facial image captured by the device is compared with the passport photo data, and the matching is carried out using a recognition method to calculate the degree of match and verify the identity of the person.

[1266] Step 5:

[1267] The server retrieves the user's past entry history from the database. The device sends the retrieved data to the server, which then compares the past data with the current data.

[1268] Step 6:

[1269] The server checks the entry history and determines whether the person is a frequent entrant. If so, the server proceeds with the process as a "frequent entrant."

[1270] Step 7:

[1271] The device uses the generated AI to ask questions to the user, who responds by voice or text input to the questions displayed on the device screen.

[1272] Step 8:

[1273] The terminal transmits the user's answer to the server, which uses question analysis means to analyze the content of the user's answer.

[1274] Step 9:

[1275] The device activates an emotion engine that analyzes the user's voice and facial expressions in real time. The emotion engine detects changes in the user's voice tone and facial expressions.

[1276] Step 10:

[1277] The emotion engine generates user emotion data and sends it to the analysis means, which incorporates the emotion data into the analysis of the user's responses.

[1278] Step 11:

[1279] The server performs analysis based on the user's responses, facial recognition data, and emotion data to detect anomalies or suspicious patterns.

[1280] Step 12:

[1281] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[1282] Step 13:

[1283] If no suspicious patterns are detected, the device displays "Entry Permitted" to the user, who then confirms the permission screen and the process is complete.

[1284] Step 14:

[1285] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[1286] The above processing steps allow users to complete immigration inspections quickly and efficiently. The introduction of this system is expected to shorten immigration inspection times and improve working conditions. Furthermore, the addition of an emotion engine allows for more accurate inspections that take the user's psychological state into account, improving security.

[1287] Example 2

[1288] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1289] Conventional immigration inspection systems require a lot of time for immigration procedures, and there is a need for efficient handling, especially for frequent entrants and those entering under specific conditions. Furthermore, there is a lack of methods for quickly and accurately detecting and responding to entrants' fraudulent or suspicious behavior. Furthermore, there is a need to analyze emotions to more accurately assess the state of entrants and improve safety.

[1290] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a scanning means for reading the passport data of the entrant via the terminal, a photographing means for capturing a facial image of the entrant via the terminal, a recognition means for comparing the passport data with the facial image via the server, a matching means for comparing the entrant's past entry history via the server, a question analysis means for asking questions using the generative AI model via the terminal and analyzing responses via the server, anomaly detection means for analyzing the responses and recognition data via the server and detecting suspicious patterns, and a notification means for notifying a specialist inspector when a suspicious pattern is detected by the server. This makes it possible to improve the efficiency and security of immigration inspections.

[1291] "Scanning means" refers to devices and methods for reading a user's passport data, including devices that capture information from the machine-readable area or IC chip on a passport.

[1292] "Capture Means" refers to devices and methods for capturing a user's facial image, including a camera and its control software.

[1293] "Recognition means" refers to devices and methods for matching passport data with captured facial images to verify identity, including image processing algorithms and database matching techniques.

[1294] "Verification means" refers to devices and methods for retrieving an entrant's past immigration history from a database and comparing it with current immigration data. This means includes database query systems and verification algorithms.

[1295] "Question Analysis Means" refers to devices and methods for using generative AI models to ask users questions and analyze their responses, including natural language processing and speech recognition technologies.

[1296] "Anomaly detection means" refers to devices and methods for analyzing user response and perception data to detect suspicious patterns. This means includes anomaly detection algorithms and multivariate analysis techniques.

[1297] "Notification means" refers to devices and methods for notifying expert reviewers when suspicious patterns are detected, including alert systems and communication networks.

[1298] This invention provides a system for improving security by streamlining immigration procedures at airports and other locations. This system is comprised of interactions between a server, a terminal, and a user. Specific examples of each function are described below.

[1299] The system consists of an immigration inspection terminal, a server, a user (entrant), and a generation AI module. The user uses the terminal to conduct their own immigration inspection, and the server and generation AI module process the information as needed in the backend.

[1300] First, the terminal scans the user's passport. The passport scanner device acquires data from the passport's machine-readable zone (MRZ) and IC chip. The terminal temporarily stores the scanned data in memory and proceeds.

[1301] The device then activates the camera to capture the user's facial image, which is then compared with the passport photo by the server to verify identity. The facial recognition algorithm uses OpenCV or TensorFlow, for example.

[1302] The server then retrieves the user's past entry history from a database, and compares the retrieved history data with the current entry data using an existing database query system.

[1303] The device then uses a generation AI to ask the user questions. The generation AI, for example, uses OpenAI's GPT-3, which randomly generates questions from a specific question list. The user responds by voice or text input, which is converted into text by speech recognition software (e.g., Google Speech-to-Text). The server then analyzes the content of the answers using a question analysis method to check for any suspicious points.

[1304] In addition, the device activates an emotion engine to analyze the user's voice and facial expressions in real time. Voice analysis software (e.g., Praat) is used for voice analysis, and facial recognition software (e.g., Affectiva) is used for facial expression analysis. The detected emotion data is sent to the server.

[1305] All information is analyzed, and the server evaluates the user's responses and sentiment data holistically, applying anomaly detection algorithms to detect suspicious patterns. If an anomaly is detected, a notification system is activated, sending an alert and analysis results to expert reviewers.

[1306] If there are no problems, the terminal will display "Entry Permitted" to the user and print an entry confirmation form if necessary, officially completing the procedure.

[1307] As a concrete example, when a frequent entrant arrives at the terminal, their passport is scanned and facial recognition is performed, their past entry history is confirmed, and after answering questions posed by the AI ​​generation system, if the emotion engine determines that there are no problems, the terminal will display "Entry Permitted."

[1308] Examples of prompts for a generative AI model might include questions like:

[1309] "As someone integrated into the airport immigration system, how would you question a high-frequency immigrant who is somewhat nervous?"

[1310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1311] Step 1:

[1312] The user scans the passport into the terminal.

[1313] Input: The user places the passport in the passport scanner.

[1314] Specific operation: The terminal activates the passport scanner and obtains data from the passport's MRZ (Machine Readable Zone) and IC chip.

[1315] Data processing: The acquired data is temporarily stored in memory as electronic data.

[1316] Output: Passport data is saved on the device.

[1317] Step 2:

[1318] The device captures a facial image of the user.

[1319] Input: The user stands in front of the device's camera.

[1320] Specific operation: The device activates the camera and captures an image of the user's face.

[1321] Data processing: The captured facial image is saved as digital data.

[1322] Output: The facial image data is saved to the device.

[1323] Step 3:

[1324] The server compares the passport data with the facial image.

[1325] Input: Passport data and facial image data are sent from the terminal to the server.

[1326] What it does: The server uses a facial recognition algorithm (e.g. OpenCV or TensorFlow) to compare the passport photo with the captured face image.

[1327] Data calculation: Feature points of the facial image are extracted and matched using a recognition algorithm.

[1328] Output: Matching result is generated on the server (e.g. match success / failure).

[1329] Step 4:

[1330] The server checks past entry history.

[1331] Input: The server queries the database based on the passport data.

[1332] Specific operation: The server retrieves past entry history from the database.

[1333] Data calculation: Compare the acquired historical data with the current entry data.

[1334] Output: The matching result is generated on the server (e.g., whether or not there is an entry history).

[1335] Step 5:

[1336] The device uses the generative AI model to ask the user questions.

[1337] Input: Instructions from the server and user data.

[1338] Specific operation: The device uses generative AI (e.g., GPT-3) to generate questions and display them to the user.

[1339] Data calculation: Generate appropriate questions based on the user's past answers and current situation.

[1340] Output: The generated question is displayed on the terminal.

[1341] Step 6:

[1342] The user inputs the answer to the question into the terminal.

[1343] Input: The user enters the answer into the device by voice or text.

[1344] What happens: Your device uses speech recognition software (e.g., Google Speech-to-Text) to convert speech to text.

[1345] Data processing: Converts audio data into text data.

[1346] Output: The textual answers are saved to your device.

[1347] Step 7:

[1348] The server analyzes the user's answers.

[1349] Input: The textual response is sent from the device to the server.

[1350] What happens: The server uses natural language processing (NLP) tools to analyze the answers.

[1351] Data calculation: Analyze the intent and content of the response and check for any abnormalities.

[1352] Output: The analysis results are generated on the server (e.g. normal / abnormal).

[1353] Step 8:

[1354] The device uses an emotion engine to analyze the user's emotions.

[1355] Input: User's facial expressions and voice data.

[1356] Specific operation: The device analyzes voice and facial expressions using emotion analysis software (e.g. Praat, Affectiva).

[1357] Data computation: Extracting and analyzing emotion features.

[1358] Output: Sentiment analysis data is saved on the device.

[1359] Step 9:

[1360] The server detects a suspicious pattern.

[1361] Input: Answer data and sentiment data parsed by the server.

[1362] What happens: The server applies an anomaly detection algorithm and performs pattern analysis.

[1363] Data Calculation: Detecting suspicious patterns based on various parameters.

[1364] Output: Detection results are generated on the server (e.g., suspicious / not suspicious).

[1365] Step 10:

[1366] The server notifies the expert examiner.

[1367] Input: The server's verdict when a suspicious pattern is detected.

[1368] Specific operation: The server sends an alert to the examiner's terminal.

[1369] Data processing: Generate alert data and send it over the communication network.

[1370] Output: An alert notification will be displayed on the examiner's terminal.

[1371] Step 11:

[1372] The terminal completes immigration procedures.

[1373] Input: Final decision from the server (e.g., entry allowed / denied).

[1374] Specific operation: The terminal displays the entry permit and prints an entry confirmation if necessary.

[1375] Data processing: Output entry permit data on screen and in print.

[1376] Output: The user confirms the entry permit and the process is completed.

[1377] (Application example 2)

[1378] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1379] Conventional immigration inspection systems require a large amount of manual checking work, are inefficient, and pose a high security risk, so there is a need for a means to perform immigration inspections quickly and accurately. Furthermore, there are insufficient means to improve safety by performing more accurate risk assessments by recognizing visitor emotions. Furthermore, similar issues exist in security checks at large-scale events and important facilities, so there is a need for efficient and highly accurate security checks.

[1380] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1381] In this invention, the server includes a scanning means for reading passport data of entrants, a camera means for capturing facial images of entrants, and an emotion recognition means for recognizing and analyzing the emotions of entrants, thereby enabling quick and accurate immigration inspection and security checks.

[1382] "Scanning means" refers to a device or group of devices that have the function of reading passport data of entrants.

[1383] "Camera means" refers to a device or group of devices that have the function of capturing facial images of entrants.

[1384] "Recognition means" refers to a device or program that has the function of matching the read passport data with the captured facial image.

[1385] "Verification means" refers to a device or program that has the function of acquiring the past entry history of an entrant and comparing it with current data.

[1386] "Question analysis means" refers to a device or program that has the function of using generation AI to ask questions to entrants and analyze their responses.

[1387] "Analysis means" refers to a device or program that has the functionality to analyze acquired response and recognition data and detect suspicious patterns.

[1388] The "notification means" is a device or group of devices that has the function of notifying a specialist examiner when a suspicious pattern is detected.

[1389] "Emotion recognition means" refers to a device or program that has the function of recognizing the emotions of entrants and analyzing the data.

[1390] This invention relates to a system that improves the efficiency and accuracy of security checks at immigration and large-scale events. The system includes passport scanning, facial recognition, past entry history matching, question and answer analysis using generative AI, emotion recognition, suspicious pattern detection, and notification functions.

[1391] Program processing description

[1392] 1. The server electronically stores the passport data when the user scans the passport using a scanning method, which can be a dedicated scanner or a camera in smart glasses.

[1393] 2. To capture the user's face image, the camera means is activated and the face image is acquired in real time, which is then matched with the passport photo by the recognition means.

[1394] 3. The server retrieves the entrant's or visitor's past entry history from the database and uses a matching means to compare it with the current data.

[1395] 4. The question analysis means uses the generative AI model to ask the user a question and receives the user's response via voice or text input. The acquired response data is analyzed by the analysis means.

[1396] 5. The facial image and voice data of the user captured by the camera means are analyzed by the emotion recognition means to grasp the user's emotional state in real time.

[1397] 6. The analysis means comprehensively analyzes the acquired response data, facial image data, and emotion data to perform risk assessment. If a suspicious pattern is detected, an alert is sent to the expert examiner by the notification means.

[1398] 7. The examiner will conduct a detailed check based on the alert and will carry out additional checks as necessary.

[1399] Hardware and software used

[1400] Scanner and camera means: smart glasses (e.g., Google Glass) and dedicated scanners

[1401] Analysis software: face recognition library (e.g., face_recognition), emotion recognition library (e.g., EmotionRecognizer)

[1402] Server: Back-end processing for question analysis and notification functions

[1403] Adding specific examples

[1404] For example, when used at a large music festival, when attendees arrive at the entrance, security guards wearing smart glasses scan their passports and capture their faces. The generative AI then asks, "What is the purpose of your visit?" If the attendee answers, "I'm here to enjoy the concert," the application analyzes this and records it as a secure answer. Meanwhile, an emotion recognition engine analyzes the attendee's facial expressions in real time, and if it detects any abnormalities, it notifies the security guard. If suspicious, the attendee will be directed to an additional checkpoint.

[1405] Prompt Sentence Examples

[1406] "Please tell us the purpose of your visit. Then, please explain the purpose of your visit in one sentence."

[1407] The above system will make it possible to improve the efficiency and accuracy of immigration and security checks.

[1408] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1409] Step 1:

[1410] When a user scans their passport, the server uses a scanning method to electronically store the passport data. Specifically, the server reads the passport information using a dedicated scanner or a camera mounted on smart glasses and temporarily stores it as digital data. The input is the passport image data, and the output is the scanned passport information.

[1411] Step 2:

[1412] The terminal activates the camera means to capture the user's facial image and acquires the facial image in real time. The acquired facial image is compared with the scanned passport photo by the recognition means. The input is the real-time facial image data, and the output is the matching result, i.e., a match judgment.

[1413] Step 3:

[1414] The server retrieves the past entry history of the entrant or visitor from the database and uses a matching method to compare it with the current data. The input is the passport ID information, and the output is the matching result of the past entry history. The server compares these data and determines whether they match or differ.

[1415] Step 4:

[1416] The question analysis means uses the generative AI model to ask the user a question and receives a response via voice or text input. The terminal displays the question and waits for the user's response. The input is a prompt from the generative AI model and the user's voice or text response, and the output is the analyzed response. An example of a prompt is: "Please tell us the purpose of your visit."

[1417] Step 5:

[1418] The emotion recognition means analyzes the user's facial image and audio data captured by the camera means to grasp the user's emotional state in real time. The input is the facial image and audio data, and the output is the analysis result of the emotional state. Specifically, the analysis is performed using the EmotionRecognizer library.

[1419] Step 6:

[1420] The analysis means performs an integrated analysis of the acquired response data, facial image data, and emotional data to perform a risk assessment. The inputs are response data, facial image data, and emotional data, and the output is the detection results of suspicious patterns. The system uses an anomaly detection algorithm to determine the risk level based on the analysis results.

[1421] Step 7:

[1422] If a suspicious pattern is detected, the notification means sends an alert to the expert examiner. The terminal displays the alert on the terminal of the security response officer, prompting further confirmation. The input is the detection result of the suspicious pattern, and the output is an alert notification. This allows the expert examiner to carry out further confirmation work.

[1423] Step 8:

[1424] If there are no problems, the terminal displays a message to the user granting permission to enter. Specifically, once the verification process is complete, the user is given permission to proceed to the next step. The input is the final analysis result, and the output is a message granting permission to enter.

[1425] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1426] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1427] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1428] [Fourth embodiment]

[1429] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1430] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1431] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1432] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1433] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1434] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1435] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1436] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1437] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1438] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1439] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1440] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1441] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1442] This invention aims to improve the efficiency of immigration inspection procedures by using a self-service system that uses image recognition and generation AI. The operation of this system is explained in detail below.

[1443] System Overview

[1444] This system consists of an airport immigration terminal, a server, and a user (entrant). The user uses the terminal to go through immigration themselves, and the server processes the information as needed in the backend.

[1445] Program processing

[1446] 1. User scans passport

[1447] When the user arrives at the immigration terminal at the airport, they follow the instructions on the terminal to scan their passport.

[1448] The terminal electronically reads the passport information and stores it as data.

[1449] 2. Facial image capture and recognition

[1450] The device activates the camera and captures an image of the user's face.

[1451] The device compares the passport photo with the captured facial image to verify identity.

[1452] 3. Checking past immigration history

[1453] The server retrieves the user's past entry history from the database.

[1454] The server compares the historical data it has acquired with the current data to determine whether the person is a frequent entrant.

[1455] 4. Analysis of Questions and Answers by Generative AI

[1456] The device uses generative AI to display questions to the user.

[1457] The user answers the questions by voice or text input.

[1458] The terminal transmits the answer to the server, and the server analyzes the answer using the question analysis means.

[1459] 5. Suspicious Subject Detection

[1460] The server analyzes the answers and recognition data to detect suspicious patterns.

[1461] If a suspicious pattern is detected, the server notifies the examiner.

[1462] 6. Notification and Response by Specialist Examiners

[1463] The server sends an alert to the examiner's device and displays the analysis results.

[1464] The examiner will direct the user to a separate examination window for a detailed examination.

[1465] 7. Completion of the procedure

[1466] If there are no problems, the terminal displays "Entry Permitted" to the user.

[1467] If necessary, the terminal prints out an entry confirmation and provides it to the user.

[1468] Specific examples

[1469] Example 1: Frequent visitors

[1470] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[1471] The terminal reads the passport information.

[1472] 2. The device captures your face to verify your identity.

[1473] Identity verification is completed through facial recognition.

[1474] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[1475] Since it matches the existing data, it is determined that there is no problem.

[1476] 4. The device asks questions generated by the AI, and the user answers.

[1477] You answer the questions briefly and your answers are analyzed.

[1478] 5. The terminal will display "Entry Permitted" and the procedure will be complete.

[1479] The user receives confirmation and is allowed into the country.

[1480] Example 2: Suspicious Subject

[1481] 1. The user arrives at the terminal and scans their passport.

[1482] The terminal reads the passport information.

[1483] 2. The device captures your face to verify your identity.

[1484] Identity verification is completed through facial recognition.

[1485] 3. The server checks the past entry history and detects an anomaly.

[1486] 4. The device asks questions generated by the AI, and the user answers.

[1487] The answers are analyzed to detect suspicious patterns.

[1488] 5. The server notifies the examiner and sends the analysis results.

[1489] Examiners will conduct a detailed examination of users.

[1490] These processes will make immigration inspections more efficient and reduce stress for entrants.

[1491] The processing flow will be explained below.

[1492] Step 1:

[1493] The user arrives at the immigration terminal at the airport and scans their passport following the instructions on the terminal's screen.

[1494] Step 2:

[1495] The device scans the passport, reads the passport information (name, passport number, nationality, photo, etc.), and temporarily stores the data.

[1496] Step 3:

[1497] The device activates the camera and captures the user's face. The user faces forward as instructed by the camera.

[1498] Step 4:

[1499] The device compares the captured facial image with the passport photo data, and uses a recognition method to calculate the degree of match and verify the identity of the person.

[1500] Step 5:

[1501] The server retrieves the user's past entry history from the database. The device sends the retrieved data to the server, which then compares the past data with the current data.

[1502] Step 6:

[1503] The server checks the entry history and determines whether the person is a frequent entrant. If so, the server proceeds with the process as a "frequent entrant."

[1504] Step 7:

[1505] The device uses the generated AI to ask questions to the user, who responds by voice or text input to the questions displayed on the device screen.

[1506] Step 8:

[1507] The terminal transmits the user's answer to the server, which uses question analysis means to analyze the content of the user's answer.

[1508] Step 9:

[1509] The server analyzes the responses and recognition data to detect anomalies or suspicious patterns, and performs additional data processing if necessary.

[1510] Step 10:

[1511] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[1512] Step 11:

[1513] If no suspicious patterns are detected, the device displays "Entry Permitted" to the user, who then confirms the permission screen and the process is complete.

[1514] Step 12:

[1515] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[1516] The above processing steps allow users to complete immigration inspections quickly and efficiently. The introduction of this system is expected to reduce immigration inspection times and improve working conditions.

[1517] Example 1

[1518] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1519] As the number of users increases, immigration inspection procedures become more time-consuming and labor-intensive, and efficient procedures are required. Identity verification and the detection of suspicious individuals are also important issues. Current systems require manual inspections, placing a heavy burden on human resources. Therefore, it is necessary to introduce a self-service system to improve the efficiency and reliability of immigration inspections.

[1520] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1521] In this invention, the server includes optical character recognition means for reading passports, image capture means for acquiring facial images, and matching means, which enables automation and efficiency of immigration inspections.

[1522] "Optical character recognition" is a technology for analyzing input text data and reading it as electronic data.

[1523] "Image capture means" is a technology that uses a camera or a sensor to capture an image of the user's face.

[1524] "Facial recognition means" is a technology that identifies specific patterns based on acquired facial images and verifies identity.

[1525] "Database matching means" is a technology that searches for past entry history from an existing database and matches it with current data.

[1526] "Question analysis means" is a technology that uses generation AI to generate questions for users and analyze their answers.

[1527] "Data analysis means" refers to technology that detects suspicious patterns based on user responses and recognition data.

[1528] An "alert notification means" is a technology that sends a warning to a specialist examiner when a suspicious pattern is detected.

[1529] "Display means" refers to technology that displays entry permits and other information to the user.

[1530] "Printing means" refers to the technology used to physically print the entry confirmation and other required documents.

[1531] The present invention is a self-service system for streamlining immigration procedures, and is composed of an immigration terminal, a server, and a user (entrant). The following describes the specific hardware and software configurations and processing procedures for realizing the present invention.

[1532] System configuration

[1533] Hardware

[1534] 1. Immigration Terminal: A terminal used by people entering the country. It is equipped with a scanner for scanning passports, a camera for capturing facial images, a display for displaying questions, input devices (keyboard and microphone) for the user to enter their answers, and a CPU and memory for performing the necessary processing.

[1535] 2. Server: A high-performance computer for processing and analyzing large amounts of data. It runs various software such as databases, generative AI models, query analysis, and data analysis.

[1536] software

[1537] 1. Optical Character Recognition (OCR) software: Used to read passport data electronically. A common OCR tool is Adobe OCR.

[1538] 2. Facial recognition software: Used to match the captured facial image with the passport photo. Facial recognition libraries such as OpenCV are typically used.

[1539] 3. Database Management System (DBMS): Used to store information such as immigration history and retrieve and verify data as needed. Examples include MySQL and PostgreSQL.

[1540] 4. Generative AI models: Used to generate questions for users and analyze their answers. Examples include GPT-4 and other natural language processing (NLP) models.

[1541] 5. Speech recognition software: Used to convert user voice input into text data, such as the Google Speech-to-Text API.

[1542] Specific examples of operations

[1543] Frequent visitors

[1544] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[1545] The terminal reads the passport information using optical character recognition (OCR) software.

[1546] 2. The device captures your face to verify your identity.

[1547] It uses facial recognition software to match your passport photo with a real-time captured facial image.

[1548] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[1549] Obtain past immigration history from existing databases and match it with current data.

[1550] 4. The device asks questions based on the generated AI model, and the user answers.

[1551] Example: "What is your purpose for entering Japan?", user answers "Tourism."

[1552] 5. The terminal will display "Entry Permitted" and the procedure will be complete.

[1553] If necessary, print out the entry confirmation and provide it to the user.

[1554] Suspicious Subjects

[1555] 1. The user arrives at the terminal and scans their passport.

[1556] The terminal reads the passport information using optical character recognition (OCR) software.

[1557] 2. The device captures your face to verify your identity.

[1558] It uses facial recognition software to match your passport photo with a real-time captured facial image.

[1559] 3. The server checks the past entry history and detects an anomaly.

[1560] Obtain past immigration history from existing databases and match it with current data.

[1561] 4. The device asks questions based on the generated AI model, and the user answers.

[1562] Example: "How many times have you visited Japan in the past six months?" The user answers "Three times." The answer is detected as an abnormal pattern.

[1563] 5. The server notifies the examiner and sends the analysis results.

[1564] An alert is sent to the examiner's terminal and a detailed examination of the user is conducted.

[1565] Through the above process, this system will streamline immigration procedures and reduce stress for users. It will also improve security by enabling early detection of suspicious individuals and professional screening.

[1566] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1567] Step 1:

[1568] The user scans their passport.

[1569] Input: The user inserts the passport into the terminal's scanner.

[1570] What it does: The terminal uses optical character recognition (OCR software) to read the passport information.

[1571] Data processing: OCR software converts the text information on the passport into electronic data.

[1572] Output: Save the scanned passport data in the device's memory.

[1573] Step 2:

[1574] Capture a facial image of the user.

[1575] Input: The user follows the instructions on the device and faces the camera.

[1576] Specific operation: The device camera captures an image of the user's face.

[1577] Data processing: The captured facial images are analyzed using facial recognition software (e.g., OpenCV).

[1578] Output: The analyzed facial image data is saved in the device's memory.

[1579] Step 3:

[1580] Perform facial recognition.

[1581] Input: Passport data obtained in step 1 and facial image data obtained in step 2.

[1582] What it does: The device uses facial recognition software to match the passport photo with the captured facial image in real time.

[1583] Data arithmetic: The face recognition algorithm compares the two using pattern matching.

[1584] Output: Save the identity verification result in the device's memory.

[1585] Step 4:

[1586] Check past immigration history.

[1587] Input: Passport data from step 1 and identity verification result from step 3.

[1588] Specific operation: The terminal sends passport data and identity verification results to the server, and the server queries the database.

[1589] Data processing: The server uses SQL queries to retrieve past entry history from the database.

[1590] Output: The acquired entry history data is sent to the terminal and the matching results are saved.

[1591] Step 5:

[1592] The generative AI asks questions to the user.

[1593] Input: Matching result from step 4.

[1594] Specific operation: The device uses a generative AI model (e.g., GPT-4) to generate questions for the user and displays them on the display.

[1595] Data processing: The generative AI model creates questions based on the prompt.

[1596] Output: The question is displayed on the display.

[1597] Step 6:

[1598] The user answers.

[1599] Input: The user answers the question by speaking or entering text.

[1600] What it does: The device converts voice input into text using speech recognition software (for example, Google Speech-to-Text API).

[1601] Data processing: Speech recognition software converts speech into text data.

[1602] Output: The user's answer as text data is saved in the device's memory.

[1603] Step 7:

[1604] The server analyzes the user's answers.

[1605] Input: User response data from step 6.

[1606] How it works: The device sends the answer data to the server, which then analyzes the answer using a generative AI model.

[1607] Data Computing: Generative AI models use natural language processing to analyze the content of responses and detect suspicious patterns.

[1608] Output: Sends the analysis results to the terminal.

[1609] Step 8:

[1610] Suspicious subject detection and notification.

[1611] Input: Analysis results from step 7.

[1612] Specific operation: If the server detects a suspicious pattern based on the analysis results, it sends an alert to the expert examiner's terminal.

[1613] Data calculation: Evaluate the analysis results and generate alert messages.

[1614] Output: An alert is displayed on the examiner's terminal.

[1615] Step 9:

[1616] Response from specialist examiners.

[1617] Input: Alert notification from step 8.

[1618] Specific operation: A specialist examiner will direct the user to another examination window for a detailed examination.

[1619] Output: The detailed review results are entered into the system and saved.

[1620] Step 10:

[1621] The procedure is complete.

[1622] Input: All preprocessing results.

[1623] Specific operation: The terminal displays the entry permit and prints an entry confirmation if necessary.

[1624] Output: The user's entry permit is displayed and a confirmation is provided.

[1625] This not only makes immigration procedures more efficient and improves convenience for users, but also strengthens security.

[1626] (Application example 1)

[1627] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1628] Traditional immigration inspection procedures are often cumbersome and time-consuming. Furthermore, because the identity of entrants and the verification of their past entry history are done manually, this can be inefficient and inaccurate. This increases security risks and places significant stress on entrants. Furthermore, it can take a long time for specialized inspectors to respond, and there are still challenges in detecting and responding to suspicious individuals.

[1629] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1630] In this invention, the server includes a scanning means for reading the passport data of an entrant, a camera means for capturing a facial image of the entrant, a recognition means for comparing the passport data with the facial image, a matching means for matching the entrant's past entry history, a question analysis means for asking questions using a generation AI and analyzing responses, an analysis means for analyzing the responses and recognition data and detecting suspicious patterns, a recording means for analyzing the passport image using the scanning means and recording the matching results, and a notification means for notifying a specialist inspector if a suspicious pattern is detected. This makes it possible to improve the efficiency and accuracy of immigration inspection operations, reduce security risks, and ease the stress of entrants.

[1631] "Scanning means" refers to a device that has the function of electronically reading the passport data of entrants and storing it as data.

[1632] The "camera means" is a device for capturing an image of the face of a person entering the country and obtaining the image data.

[1633] "Recognition means" refers to a device or software that collates passport data with a facial image to verify identity.

[1634] A "verification means" is a device or software that retrieves an entrant's past entry history from a database and compares it with current data.

[1635] A "question analysis means" is a device or software that outputs questions posed by the generation AI and receives and analyzes the user's responses in voice or text.

[1636] An "analysis means" is a device or software for analyzing response and recognition data and detecting suspicious patterns.

[1637] The "recording means" is a device or software that analyzes the passport image using the scanning means and records the results in a database.

[1638] "Notification means" means a device or software that sends an alert or notification to a specialist examiner when a suspicious pattern is detected.

[1639] The present invention is a system for streamlining immigration inspections and improving security. This system is realized using the following specific processing steps and techniques.

[1640] Hardware and software used

[1641] Hardware: Smartphones, camera devices

[1642] Software: OpenCV (image processing library), Requests (HTTP request library), Transformers (generative AI library)

[1643] Program processing explanation

[1644] This system starts when the user launches the smartphone application and scans their passport. The passport data is electronically read and saved using the scanning means. The data acquired by this scanning means is sent to the verification means and verified against past entry history.

[1645] Next, a camera is used to capture a facial image of the user. The facial image is compared with a passport photo by a recognition means to verify the user's identity. After that, a generative AI model uses a question analysis means to generate a question and display it to the user. The user then inputs an answer by voice or text, and the answer data is sent to the server.

[1646] The server uses its analysis means to analyze the responses and other recognition data to detect suspicious patterns. If a suspicious pattern is detected, its notification means sends an alert to a specialist examiner, who can then conduct a detailed review.

[1647] Specific examples

[1648] For example, when managing access to a sensitive facility, employees and visitors can use a smartphone app to complete the entry procedures. First, the user scans their passport with their smartphone and captures their facial image. The passport information is then matched with the facial image by the scanning and recognition means.

[1649] Next, the generative AI model displays the question "What is the purpose of your visit?" to the user. If the user answers "It's a business meeting," this response data is analyzed by the analysis means. If there are no problems with the analysis results, "Entry permitted" is displayed and the user is allowed to enter the facility.

[1650] Example prompt for a generative AI model:

[1651] Please tell us the purpose of your visit.

[1652] In this way, the present invention can improve the efficiency of immigration inspections and entry / exit management for important facilities, thereby reducing security risks.

[1653] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1654] Step 1:

[1655] The user launches the smartphone app and starts scanning their passport. The passport is read using a scanning device, and the acquired passport data is temporarily saved in the application. The input is an image of the passport taken using the smartphone, and the output is the passport information acquired through image analysis.

[1656] Step 2:

[1657] The device activates the built-in camera and captures the user's facial image. The captured facial image is analyzed using a facial recognition algorithm and temporarily saved. The input is the user's facial image, and the output is the analyzed facial data.

[1658] Step 3:

[1659] The terminal again sends the passport data and facial data to the recognition means for matching. Through this matching, the terminal performs identity verification. The input is the passport data and facial data, and the output is the result of identity verification. The specific operation of matching is to compare facial feature points using a facial recognition algorithm.

[1660] Step 4:

[1661] The server retrieves the entrant's past entry history from the database. The retrieved data is compared with the current entry information using a matching means. The input is the user's entry history data, and the output is the result of the entry history matching.

[1662] Step 5:

[1663] The terminal uses a question analysis means to input a prompt sentence into the generative AI model. The generative AI model generates a question and displays it to the user. The input is the prompt sentence, and the output is the generated question. Specifically, the generative AI model performs natural language generation.

[1664] Step 6:

[1665] The user answers questions generated by voice or text. The device captures the answers and sends them to the server. The input is the user's answer data, and the output is the answer data used for analysis.

[1666] Step 7:

[1667] The server analyzes the response data and the recognition data using an analysis means. This analysis determines whether a suspicious pattern is detected. The input is the response data and the recognition data, and the output is the result of the analysis. Specifically, a pattern recognition algorithm is used.

[1668] Step 8:

[1669] If the server detects a suspicious pattern based on the analysis results, it sends an alert to the expert examiner using a notification means. The input is the analysis result, and the output is a notification to the examiner. The specific operation uses an alert sending system.

[1670] The above processing steps improve the efficiency and accuracy of immigration inspection procedures, reduce security risks, and ease stress for entrants.

[1671] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1672] This invention improves the efficiency of immigration procedures by using a self-service system that uses image recognition and generation AI, and also combines it with an emotion engine that recognizes the user's emotions. The operation of this system is explained in detail below.

[1673] System Overview

[1674] This system consists of an immigration inspection terminal, a server, a user (entrant), and an emotion engine. The user uses the terminal to conduct immigration inspection themselves, and the server and emotion engine perform processing as needed on the backend.

[1675] Program processing

[1676] 1. User scans passport

[1677] When the user arrives at the immigration terminal at the airport, they follow the instructions on the terminal to scan their passport.

[1678] The terminal electronically reads the passport information and temporarily stores the data.

[1679] 2. Facial image capture and recognition

[1680] The device activates the camera and captures an image of the user's face.

[1681] The device compares the passport photo with the captured facial image to verify identity.

[1682] 3. Checking past immigration history

[1683] The server retrieves the user's past entry history from the database.

[1684] The data acquired by the terminal is sent to the server, which then compares the past data with the current data.

[1685] 4. Analysis of Questions and Answers by Generative AI

[1686] The device uses the generative AI to ask questions to the user, who then responds by voice or text input to the questions displayed on the device.

[1687] The terminal transmits the answer to the server, and the server uses the question analysis means to analyze the content of the user's answer.

[1688] 5. Emotional Recognition

[1689] The device activates an emotion engine that analyzes the user's voice and facial expressions in real time.

[1690] The emotion engine detects the user's emotion data and transmits the data to the analysis means.

[1691] 6. Suspicious Subject Detection

[1692] The server analyzes the user's responses, cognitive data, and emotional data to detect suspicious patterns.

[1693] If necessary, further data processing is performed.

[1694] 7. Notification and Response by Specialist Examiners

[1695] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[1696] The examiner will direct the user to a separate examination window for a detailed examination.

[1697] 8. Completion of the procedure

[1698] If there are no problems, the terminal will display "Entry Permitted" to the user. The user confirms the screen displaying the permission and the procedure is complete.

[1699] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[1700] Specific examples

[1701] Example 1: Frequent visitors

[1702] 1. A user (frequent traveller) arrives at the terminal and scans their passport.

[1703] The terminal reads the passport information.

[1704] 2. The device captures your face to verify your identity.

[1705] Identity verification is completed through facial recognition.

[1706] 3. The server checks the person's past entry history and determines that they are a frequent entrant.

[1707] Since it matches the existing data, it is determined that there is no problem.

[1708] 4. The device asks questions generated by the AI, and the user answers.

[1709] You answer the questions briefly and your answers are analyzed.

[1710] 5. The device uses the emotion engine to analyze the user's emotions.

[1711] Emotion data is generated and sent to the analysis means.

[1712] 6. The terminal will display "Entry Permitted" and the procedure will be completed.

[1713] The user receives confirmation and is allowed into the country.

[1714] Example 2: Suspicious Subject

[1715] 1. The user arrives at the terminal and scans their passport.

[1716] The terminal reads the passport information.

[1717] 2. The device captures your face to verify your identity.

[1718] Identity verification is completed through facial recognition.

[1719] 3. The server checks the past entry history and detects an anomaly.

[1720] 4. The device asks questions generated by the AI, and the user answers.

[1721] The answers are analyzed to detect suspicious patterns.

[1722] 5. The device uses the emotion engine to analyze the user's emotions.

[1723] Emotional data such as anxiety and tension is detected.

[1724] 6. The server notifies the examiner and sends the analysis results.

[1725] Examiners will conduct a detailed examination of users.

[1726] The above process will improve the efficiency and security of immigration inspections. Furthermore, analysis of emotion data will enable more accurate judgments.

[1727] The processing flow will be explained below.

[1728] Step 1:

[1729] The user arrives at the immigration terminal at the airport and scans their passport following the instructions on the terminal's screen.

[1730] Step 2:

[1731] The device scans the passport and reads passport information (name, passport number, nationality, photo, etc.) and temporarily stores the read data.

[1732] Step 3:

[1733] The device activates the camera and captures the user's face. The user faces forward as instructed by the camera.

[1734] Step 4:

[1735] The facial image captured by the device is compared with the passport photo data, and the matching is carried out using a recognition method to calculate the degree of match and verify the identity of the person.

[1736] Step 5:

[1737] The server retrieves the user's past entry history from the database. The device sends the retrieved data to the server, which then compares the past data with the current data.

[1738] Step 6:

[1739] The server checks the entry history and determines whether the person is a frequent entrant. If so, the server proceeds with the process as a "frequent entrant."

[1740] Step 7:

[1741] The device uses the generated AI to ask questions to the user, who responds by voice or text input to the questions displayed on the device screen.

[1742] Step 8:

[1743] The terminal transmits the user's answer to the server, which uses question analysis means to analyze the content of the user's answer.

[1744] Step 9:

[1745] The device activates an emotion engine that analyzes the user's voice and facial expressions in real time. The emotion engine detects changes in the user's voice tone and facial expressions.

[1746] Step 10:

[1747] The emotion engine generates user emotion data and sends it to the analysis means, which incorporates the emotion data into the analysis of the user's responses.

[1748] Step 11:

[1749] The server performs analysis based on the user's responses, facial recognition data, and emotion data to detect anomalies or suspicious patterns.

[1750] Step 12:

[1751] If the server detects a suspicious pattern, it notifies the examiner, who then sends an alert to the examiner's device and displays the analysis results.

[1752] Step 13:

[1753] If no suspicious patterns are detected, the device displays "Entry Permitted" to the user, who then confirms the permission screen and the process is complete.

[1754] Step 14:

[1755] The terminal prints out an entry confirmation as needed and provides it to the user, who then receives the confirmation and checks its contents.

[1756] The above processing steps allow users to complete immigration inspections quickly and efficiently. The introduction of this system is expected to shorten immigration inspection times and improve working conditions. Furthermore, the addition of an emotion engine allows for more accurate inspections that take the user's psychological state into account, improving security.

[1757] Example 2

[1758] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1759] Conventional immigration inspection systems require a lot of time for immigration procedures, and there is a need for efficient handling, especially for frequent entrants and those entering under specific conditions. Furthermore, there is a lack of methods for quickly and accurately detecting and responding to entrants' fraudulent or suspicious behavior. Furthermore, there is a need to analyze emotions to more accurately assess the state of entrants and improve safety.

[1760] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a scanning means for reading the passport data of the entrant via the terminal, a photographing means for capturing a facial image of the entrant via the terminal, a recognition means for comparing the passport data with the facial image via the server, a matching means for comparing the entrant's past entry history via the server, a question analysis means for asking questions using the generative AI model via the terminal and analyzing responses via the server, anomaly detection means for analyzing the responses and recognition data via the server and detecting suspicious patterns, and a notification means for notifying a specialist inspector when a suspicious pattern is detected by the server. This makes it possible to improve the efficiency and security of immigration inspections.

[1761] "Scanning means" refers to devices and methods for reading a user's passport data, including devices that capture information from the machine-readable area or IC chip on a passport.

[1762] "Capture Means" refers to devices and methods for capturing a user's facial image, including a camera and its control software.

[1763] "Recognition means" refers to devices and methods for matching passport data with captured facial images to verify identity, including image processing algorithms and database matching techniques.

[1764] "Verification means" refers to devices and methods for retrieving an entrant's past immigration history from a database and comparing it with current immigration data. This means includes database query systems and verification algorithms.

[1765] "Question Analysis Means" refers to devices and methods for using generative AI models to ask users questions and analyze their responses, including natural language processing and speech recognition technologies.

[1766] "Anomaly detection means" refers to devices and methods for analyzing user response and perception data to detect suspicious patterns. This means includes anomaly detection algorithms and multivariate analysis techniques.

[1767] "Notification means" refers to devices and methods for notifying expert reviewers when suspicious patterns are detected, including alert systems and communication networks.

[1768] This invention provides a system for improving security by streamlining immigration procedures at airports and other locations. This system is comprised of interactions between a server, a terminal, and a user. Specific examples of each function are described below.

[1769] The system consists of an immigration inspection terminal, a server, a user (entrant), and a generation AI module. The user uses the terminal to conduct their own immigration inspection, and the server and generation AI module process the information as needed in the backend.

[1770] First, the terminal scans the user's passport. The passport scanner device acquires data from the passport's machine-readable zone (MRZ) and IC chip. The terminal temporarily stores the scanned data in memory and proceeds.

[1771] The device then activates the camera to capture the user's facial image, which is then compared with the passport photo by the server to verify identity. The facial recognition algorithm uses OpenCV or TensorFlow, for example.

[1772] The server then retrieves the user's past entry history from a database, and compares the retrieved history data with the current entry data using an existing database query system.

[1773] The device then uses a generation AI to ask the user questions. The generation AI, for example, uses OpenAI's GPT-3, which randomly generates questions from a specific question list. The user responds by voice or text input, which is converted into text by speech recognition software (e.g., Google Speech-to-Text). The server then analyzes the content of the answers using a question analysis method to check for any suspicious points.

[1774] In addition, the device activates an emotion engine to analyze the user's voice and facial expressions in real time. Voice analysis software (e.g., Praat) is used for voice analysis, and facial recognition software (e.g., Affectiva) is used for facial expression analysis. The detected emotion data is sent to the server.

[1775] All information is analyzed, and the server evaluates the user's responses and sentiment data holistically, applying anomaly detection algorithms to detect suspicious patterns. If an anomaly is detected, a notification system is activated, sending an alert and analysis results to expert reviewers.

[1776] If there are no problems, the terminal will display "Entry Permitted" to the user and print an entry confirmation form if necessary, officially completing the procedure.

[1777] As a concrete example, when a frequent entrant arrives at the terminal, their passport is scanned and facial recognition is performed, their past entry history is confirmed, and after answering questions posed by the AI ​​generation system, if the emotion engine determines that there are no problems, the terminal will display "Entry Permitted."

[1778] Examples of prompts for a generative AI model might include questions like:

[1779] "As someone integrated into the airport immigration system, how would you question a high-frequency immigrant who is somewhat nervous?"

[1780] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1781] Step 1:

[1782] The user scans the passport into the terminal.

[1783] Input: The user places the passport in the passport scanner.

[1784] Specific operation: The terminal activates the passport scanner and obtains data from the passport's MRZ (Machine Readable Zone) and IC chip.

[1785] Data processing: The acquired data is temporarily stored in memory as electronic data.

[1786] Output: Passport data is saved on the device.

[1787] Step 2:

[1788] The device captures a facial image of the user.

[1789] Input: The user stands in front of the device's camera.

[1790] Specific operation: The device activates the camera and captures an image of the user's face.

[1791] Data processing: The captured facial image is saved as digital data.

[1792] Output: The facial image data is saved to the device.

[1793] Step 3:

[1794] The server compares the passport data with the facial image.

[1795] Input: Passport data and facial image data are sent from the terminal to the server.

[1796] What it does: The server uses a facial recognition algorithm (e.g. OpenCV or TensorFlow) to compare the passport photo with the captured face image.

[1797] Data calculation: Feature points of the facial image are extracted and matched using a recognition algorithm.

[1798] Output: Matching result is generated on the server (e.g. match success / failure).

[1799] Step 4:

[1800] The server checks past entry history.

[1801] Input: The server queries the database based on the passport data.

[1802] Specific operation: The server retrieves past entry history from the database.

[1803] Data calculation: Compare the acquired historical data with the current entry data.

[1804] Output: The matching result is generated on the server (e.g., whether or not there is an entry history).

[1805] Step 5:

[1806] The device uses the generative AI model to ask the user questions.

[1807] Input: Instructions from the server and user data.

[1808] Specific operation: The device uses generative AI (e.g., GPT-3) to generate questions and display them to the user.

[1809] Data calculation: Generate appropriate questions based on the user's past answers and current situation.

[1810] Output: The generated question is displayed on the terminal.

[1811] Step 6:

[1812] The user inputs the answer to the question into the terminal.

[1813] Input: The user enters the answer into the device by voice or text.

[1814] What happens: Your device uses speech recognition software (e.g., Google Speech-to-Text) to convert speech to text.

[1815] Data processing: Converts audio data into text data.

[1816] Output: The textual answers are saved to your device.

[1817] Step 7:

[1818] The server analyzes the user's answers.

[1819] Input: The textual response is sent from the device to the server.

[1820] What happens: The server uses natural language processing (NLP) tools to analyze the answers.

[1821] Data calculation: Analyze the intent and content of the response and check for any abnormalities.

[1822] Output: The analysis results are generated on the server (e.g. normal / abnormal).

[1823] Step 8:

[1824] The device uses an emotion engine to analyze the user's emotions.

[1825] Input: User's facial expressions and voice data.

[1826] Specific operation: The device analyzes voice and facial expressions using emotion analysis software (e.g. Praat, Affectiva).

[1827] Data computation: Extracting and analyzing emotion features.

[1828] Output: Sentiment analysis data is saved on the device.

[1829] Step 9:

[1830] The server detects a suspicious pattern.

[1831] Input: Answer data and sentiment data parsed by the server.

[1832] What happens: The server applies an anomaly detection algorithm and performs pattern analysis.

[1833] Data Calculation: Detecting suspicious patterns based on various parameters.

[1834] Output: Detection results are generated on the server (e.g., suspicious / not suspicious).

[1835] Step 10:

[1836] The server notifies the expert examiner.

[1837] Input: The server's verdict when a suspicious pattern is detected.

[1838] Specific operation: The server sends an alert to the examiner's terminal.

[1839] Data processing: Generate alert data and send it over the communication network.

[1840] Output: An alert notification will be displayed on the examiner's terminal.

[1841] Step 11:

[1842] The terminal completes immigration procedures.

[1843] Input: Final decision from the server (e.g., entry allowed / denied).

[1844] Specific operation: The terminal displays the entry permit and prints an entry confirmation if necessary.

[1845] Data processing: Output entry permit data on screen and in print.

[1846] Output: The user confirms the entry permit and the process is completed.

[1847] (Application example 2)

[1848] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1849] Conventional immigration inspection systems require a large amount of manual checking work, are inefficient, and pose a high security risk, so there is a need for a means to perform immigration inspections quickly and accurately. Furthermore, there are insufficient means to improve safety by performing more accurate risk assessments by recognizing visitor emotions. Furthermore, similar issues exist in security checks at large-scale events and important facilities, so there is a need for efficient and highly accurate security checks.

[1850] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1851] In this invention, the server includes a scanning means for reading passport data of entrants, a camera means for capturing facial images of entrants, and an emotion recognition means for recognizing and analyzing the emotions of entrants, thereby enabling quick and accurate immigration inspection and security checks.

[1852] "Scanning means" refers to a device or group of devices that have the function of reading passport data of entrants.

[1853] "Camera means" refers to a device or group of devices that have the function of capturing facial images of entrants.

[1854] "Recognition means" refers to a device or program that has the function of matching the read passport data with the captured facial image.

[1855] "Verification means" refers to a device or program that has the function of acquiring the past entry history of an entrant and comparing it with current data.

[1856] "Question analysis means" refers to a device or program that has the function of using generation AI to ask questions to entrants and analyze their responses.

[1857] "Analysis means" refers to a device or program that has the functionality to analyze acquired response and recognition data and detect suspicious patterns.

[1858] The "notification means" is a device or group of devices that has the function of notifying a specialist examiner when a suspicious pattern is detected.

[1859] "Emotion recognition means" refers to a device or program that has the function of recognizing the emotions of entrants and analyzing the data.

[1860] This invention relates to a system that improves the efficiency and accuracy of security checks at immigration and large-scale events. The system includes passport scanning, facial recognition, past entry history matching, question and answer analysis using generative AI, emotion recognition, suspicious pattern detection, and notification functions.

[1861] Program processing description

[1862] 1. The server electronically stores the passport data when the user scans the passport using a scanning method, which can be a dedicated scanner or a camera in smart glasses.

[1863] 2. To capture the user's face image, the camera means is activated and the face image is acquired in real time, which is then matched with the passport photo by the recognition means.

[1864] 3. The server retrieves the entrant's or visitor's past entry history from the database and uses a matching means to compare it with the current data.

[1865] 4. The question analysis means uses the generative AI model to ask the user a question and receives the user's response via voice or text input. The acquired response data is analyzed by the analysis means.

[1866] 5. The facial image and voice data of the user captured by the camera means are analyzed by the emotion recognition means to grasp the user's emotional state in real time.

[1867] 6. The analysis means comprehensively analyzes the acquired response data, facial image data, and emotion data to perform risk assessment. If a suspicious pattern is detected, an alert is sent to the expert examiner by the notification means.

[1868] 7. The examiner will conduct a detailed check based on the alert and will carry out additional checks as necessary.

[1869] Hardware and software used

[1870] Scanner and camera means: smart glasses (e.g., Google Glass) and dedicated scanners

[1871] Analysis software: face recognition library (e.g., face_recognition), emotion recognition library (e.g., EmotionRecognizer)

[1872] Server: Back-end processing for question analysis and notification functions

[1873] Adding specific examples

[1874] For example, when used at a large music festival, when attendees arrive at the entrance, security guards wearing smart glasses scan their passports and capture their faces. The generative AI then asks, "What is the purpose of your visit?" If the attendee answers, "I'm here to enjoy the concert," the application analyzes this and records it as a secure answer. Meanwhile, an emotion recognition engine analyzes the attendee's facial expressions in real time, and if it detects any abnormalities, it notifies the security guard. If suspicious, the attendee will be directed to an additional checkpoint.

[1875] Prompt Sentence Examples

[1876] "Please tell us the purpose of your visit. Then, please explain the purpose of your visit in one sentence."

[1877] The above system will make it possible to improve the efficiency and accuracy of immigration and security checks.

[1878] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1879] Step 1:

[1880] When a user scans their passport, the server uses a scanning method to electronically store the passport data. Specifically, the server reads the passport information using a dedicated scanner or a camera mounted on smart glasses and temporarily stores it as digital data. The input is the passport image data, and the output is the scanned passport information.

[1881] Step 2:

[1882] The terminal activates the camera means to capture the user's facial image and acquires the facial image in real time. The acquired facial image is compared with the scanned passport photo by the recognition means. The input is the real-time facial image data, and the output is the matching result, i.e., a match judgment.

[1883] Step 3:

[1884] The server retrieves the past entry history of the entrant or visitor from the database and uses a matching method to compare it with the current data. The input is the passport ID information, and the output is the matching result of the past entry history. The server compares these data and determines whether they match or differ.

[1885] Step 4:

[1886] The question analysis means uses the generative AI model to ask the user a question and receives a response via voice or text input. The terminal displays the question and waits for the user's response. The input is a prompt from the generative AI model and the user's voice or text response, and the output is the analyzed response. An example of a prompt is: "Please tell us the purpose of your visit."

[1887] Step 5:

[1888] The emotion recognition means analyzes the user's facial image and audio data captured by the camera means to grasp the user's emotional state in real time. The input is the facial image and audio data, and the output is the analysis result of the emotional state. Specifically, the analysis is performed using the EmotionRecognizer library.

[1889] Step 6:

[1890] The analysis means performs an integrated analysis of the acquired response data, facial image data, and emotional data to perform a risk assessment. The inputs are response data, facial image data, and emotional data, and the output is the detection results of suspicious patterns. The system uses an anomaly detection algorithm to determine the risk level based on the analysis results.

[1891] Step 7:

[1892] If a suspicious pattern is detected, the notification means sends an alert to the expert examiner. The terminal displays the alert on the terminal of the security response officer, prompting further confirmation. The input is the detection result of the suspicious pattern, and the output is an alert notification. This allows the expert examiner to carry out further confirmation work.

[1893] Step 8:

[1894] If there are no problems, the terminal displays a message to the user granting permission to enter. Specifically, once the verification process is complete, the user is given permission to proceed to the next step. The input is the final analysis result, and the output is a message granting permission to enter.

[1895] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1896] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1897] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1898] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1899] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1900] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1901] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1902] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1903] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1904] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1905] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1906] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1907] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1908] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1909] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1910] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1911] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1912] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1913] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1914] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1915] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1916] The following is further disclosed regarding the above embodiment.

[1917] (Claim 1)

[1918] A system used in immigration inspection,

[1919] a scanning means for reading passport data of entrants;

[1920] camera means for capturing facial images of entrants;

[1921] recognition means for comparing the passport data with a facial image;

[1922] a means for verifying the past entry history of the entrant;

[1923] a question analysis means for asking questions by the generation AI and analyzing responses;

[1924] analysis means for analyzing the response and recognition data to detect suspicious patterns;

[1925] a notification means for notifying expert reviewers when suspicious patterns are detected;

[1926] A system including:

[1927] (Claim 2)

[1928] 2. The system of claim 1, wherein the notification means includes means for sending an alert to an examiner's terminal.

[1929] (Claim 3)

[1930] 2. The system of claim 1, wherein the question analysis means includes means for accepting voice input and text input.

[1931] "Example 1"

[1932] (Claim 1)

[1933] A system used in immigration inspection,

[1934] Optical character recognition means for reading passport data of entrants;

[1935] image capture means for capturing a facial image of an entrant;

[1936] a face recognition means for comparing the passport data with a face image;

[1937] a database matching means for obtaining and matching the past entry history of an entrant;

[1938] a question analysis means for asking questions by the generation AI and analyzing responses;

[1939] data analysis means for analyzing the response and recognition data to detect suspicious patterns;

[1940] an alert notification means for notifying expert reviewers when suspicious patterns are detected;

[1941] a displaying and printing means for permitting entry;

[1942] A system including:

[1943] (Claim 2)

[1944] 2. The system according to claim 1, wherein the alert notification means includes means for sending a warning to an examiner's terminal.

[1945] (Claim 3)

[1946] 2. The system of claim 1, wherein the question analysis means includes means for accepting voice input and text input.

[1947] "Application Example 1"

[1948] (Claim 1)

[1949] A system used in immigration inspection,

[1950] a scanning means for reading passport data of entrants;

[1951] camera means for capturing facial images of entrants;

[1952] recognition means for comparing the passport data with a facial image;

[1953] a means for verifying the past entry history of the entrant;

[1954] a question analysis means for asking questions by the generation AI and analyzing responses;

[1955] analysis means for analyzing the response and recognition data to detect suspicious patterns;

[1956] a recording means for analyzing the passport image using the scanning means and recording the result of the comparison;

[1957] a notification means for notifying expert reviewers when suspicious patterns are detected;

[1958] A system including:

[1959] (Claim 2)

[1960] 2. The system of claim 1, wherein the notification means includes means for sending an alert to an examiner's terminal.

[1961] (Claim 3)

[1962] 2. The system of claim 1, wherein the question analysis means includes means for accepting voice input and text input.

[1963] "Example 2: Combining Emotion Engines"

[1964] (Claim 1)

[1965] a scanning means for reading passport data of an entrant by a terminal;

[1966] a photographing means for capturing a facial image of an entrant by a terminal;

[1967] a recognition means for matching the passport data with a facial image by a server;

[1968] A verification means for verifying the past entry history of the entrant by the server;

[1969] A question analysis means for asking a question using the generated AI model by the terminal and analyzing the response by the server;

[1970] anomaly detection means by the server for analyzing the response and recognition data to detect suspicious patterns;

[1971] a notification means for notifying the expert examiner when a suspicious pattern is detected by the server;

[1972] A system including:

[1973] (Claim 2)

[1974] 2. The system of claim 1, wherein the notification means includes means for sending an alert to an examiner's terminal.

[1975] (Claim 3)

[1976] 2. The system of claim 1, wherein the question analysis means includes means for accepting voice input and text input.

[1977] "Application example 2 when combining emotion engines"

[1978] (Claim 1)

[1979] A system used in immigration inspection,

[1980] a scanning means for reading passport data of entrants;

[1981] camera means for capturing facial images of entrants;

[1982] recognition means for comparing the passport data with a facial image;

[1983] a means for verifying the past entry history of the entrant;

[1984] a question analysis means for asking questions by the generation AI and analyzing responses;

[1985] analysis means for analyzing the response and recognition data to detect suspicious patterns;

[1986] a notification means for notifying expert reviewers when suspicious patterns are detected;

[1987] emotion recognition means for recognizing and analyzing emotions of visitors;

[1988] A system including:

[1989] (Claim 2)

[1990] 2. The system of claim 1, wherein the notification means includes means for sending an alert to an examiner's terminal.

[1991] (Claim 3)

[1992] 2. The system of claim 1, wherein the question analysis means includes means for accepting voice input and text input. [Explanation of symbols]

[1993] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system used in immigration inspection, a scanning means for reading passport data of entrants; camera means for capturing facial images of entrants; recognition means for comparing the passport data with a facial image; a means for verifying the past entry history of the entrant; a question analysis means for asking questions by the generation AI and analyzing responses; analysis means for analyzing the response and recognition data to detect suspicious patterns; a notification means for notifying expert reviewers when suspicious patterns are detected; A system including:

2. 2. The system of claim 1, wherein the notification means includes means for sending an alert to an examiner's terminal.

3. 2. The system of claim 1, wherein the question analysis means includes means for accepting voice and text input.

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