system

Abstract

Provide a system. [Solution] means for collecting and verifying biometric information of a user; A means including a generation AI that generates questions based on user attributes; A means for presenting a question by voice and analyzing a user's response using voice recognition technology; A means for conducting a risk assessment based on the analyzed responses; A means to complete self-service when risk is low and escalate when risk is high; A system including:

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] ---

[0005] Conventional immigration inspection systems have faced challenges such as stress due to language barriers, long waiting times due to a shortage of immigration inspectors, and poor working conditions due to the need to operate at international airports 24 hours a day, 365 days a year. These challenges hinder the efficiency of immigration inspection operations and increase the burden on immigration inspectors. The purpose of this invention is to solve these problems and improve the efficiency and accuracy of immigration inspection operations. [Means for solving the problem]

[0006] The present invention solves the problems by the following means.

[0007] The system includes a means for collecting and verifying a user's biometric information, a means including a generation AI that generates questions based on the user's attributes, a means for presenting questions by voice and analyzing the user's answers using voice recognition technology, a means for performing a risk assessment based on the analyzed answers, and a means for completing self-service if the risk is low and escalating if the risk is high, thereby achieving efficiency in immigration inspections and reducing the man-hours required for the work.In addition, the system includes a means for presenting the generated questions to the user in multiple languages ​​and a means for escalating high-risk users to a responsible person, thereby improving the accuracy and flexibility of the inspection.

[0008] ---

[0009] "Biometric information" refers to information for identifying a user's biological characteristics, and specifically includes a facial photograph, fingerprints, iris, and the like.

[0010] "Generative AI" refers to artificial intelligence technology that automatically generates appropriate questions based on the user's attributes.

[0011] "Speech recognition technology" refers to technology that converts a user's voice into text data.

[0012] "Risk assessment" refers to the process of determining the entry risk based on the user's answers, and this determination is made by the generating AI.

[0013] "Self-service" refers to a system that allows users who are assessed as low risk to complete immigration procedures themselves.

[0014] "Escalation" refers to the process of transferring information about users who are assessed as high risk to a responsible person for a final decision.

[0015] "Multilingual" refers to the fact that the generated questions are presented to the user in multiple languages.

[0016] "Verification" refers to the process of comparing collected biometric information with a database to verify a user's identity. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] ---

[0039] The immigration inspection system of the present invention integrates several key modules and technologies to efficiently and accurately inspect users for immigration, including biometric information collection, question generation using generative AI, response analysis using voice recognition technology, risk assessment, and self-service or escalation measures.

[0040] Specifically, the immigration inspection system of the present invention is realized by the following functional modules.

[0041] User Check-in and Identification

[0042] Biometric information collection and matching

[0043] When a user checks in at an airport terminal, the terminal collects the user's biometric information (such as a photo of their face, fingerprints, or iris), which is then sent to a server where it is matched with existing data in a database. The user's identity is then confirmed by comparing it with past immigration records and blacklists.

[0044] Question Generation

[0045] Question creation using generative AI

[0046] The server is equipped with a generation AI that automatically generates appropriate questions based on the user's attributes (such as nationality or purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[0047] Question and answer exchange

[0048] Speech synthesis and speech recognition

[0049] The device uses speech synthesis technology to present the generated questions to the user. When the user responds by voice, the device analyzes the response using speech recognition technology, converts it into text data, and sends it to the server.

[0050] Risk Assessment

[0051] Analysis and evaluation of answers

[0052] The server analyzes the received text data using the AI ​​generator to evaluate the accuracy and consistency of the answer. For example, if a user answers "I want to go to Tokyo Disneyland," the server determines whether the answer is consistent with the tourist purpose and natural.

[0053] Risk Classification

[0054] The user's risk is assessed based on the analysis results. If the risk is determined to be low, the user can complete immigration procedures through self-service. On the other hand, if the risk is high, the server will escalate the process to the appropriate person depending on the level of urgency.

[0055] Escalation and Final Decision

[0056] Review by the person in charge

[0057] If the issue is escalated, the person in charge will ask additional questions and conduct further inspections based on the user's details and the risk assessment made by the generating AI. The person in charge will make a final decision, enter the result into the server, and notify the user.

[0058] Specific examples

[0059] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The terminal scans the user's passport and takes a photo of their face. The server uses this information to identify the user, and a generation AI generates questions such as "Where are you planning to visit?" and "How long will you be staying?", which are then presented to the user using voice synthesis technology. When the user answers "Tokyo Disneyland," voice recognition technology analyzes the answer, and the server performs a risk assessment. If the risk is low, the user automatically completes immigration procedures, and the terminal notifies the user that "immigration processing has been completed." If the risk is deemed high, the matter is escalated to a responsible official for additional screening.

[0060] As a result, the system of the present invention can significantly improve the efficiency and accuracy of immigration inspections.

[0061] The processing flow will be explained below.

[0062] ---

[0063] Step 1:

[0064] User check-in and biometric collection

[0065] Terminal: When the user arrives at the airport, they scan their passport at the terminal, which collects biometric information such as a photo, fingerprint, and iris.

[0066] Terminal: Sends collected biometric information to the server.

[0067] Step 2:

[0068] User Identification

[0069] Server: The received biometric information is compared with a database to verify the user's identity. It also compares the information with past immigration records and blacklists to make an initial assessment.

[0070] Step 3:

[0071] Question generation

[0072] Server: The generation AI generates appropriate questions based on the user's attributes (nationality, purpose of entry, etc.). For example, it generates questions in Japanese and other languages ​​such as "Which tourist spots do you want to visit?" and "How many days do you plan to stay?"

[0073] Server: Sends the generated question to the terminal.

[0074] Step 4:

[0075] Posing the Question

[0076] Terminal: Using speech synthesis technology, the generated question is presented to the user aloud. For example, it reads out in Japanese, "What tourist spots would you like to visit?"

[0077] User: Answers questions by voice.

[0078] Step 5:

[0079] Response collection and analysis

[0080] Terminal: The user's responses are analyzed using voice recognition technology and converted into text data.

[0081] Terminal: Sends the converted text data to the server.

[0082] Step 6:

[0083] Analysis and evaluation of answers

[0084] Server: The generation AI analyzes the received text data and evaluates the naturalness and consistency of the answers. For example, it evaluates whether the answer "Tokyo Disneyland" matches the tourist destination.

[0085] Step 7:

[0086] Risk Classification

[0087] Server: The generating AI performs a risk assessment of the responses and classifies entrants as low-risk or high-risk.

[0088] Step 8:

[0089] Low risk actions

[0090] Server: If the risk is determined to be low, notify the terminal that "entry processing has been completed."

[0091] Terminal: Upon receiving the notification, the user completes the immigration procedure.

[0092] Step 9:

[0093] High-risk escalation

[0094] Server: If the risk is deemed high, an escalation notice is sent to the appropriate person.

[0095] Person in charge: Asks additional questions or clarifies based on the escalated information.

[0096] Person in charge: Enters the final review results into the server and notifies the user.

[0097] ---

[0098] Through this processing step, the system of the present invention can conduct immigration inspections efficiently and fairly.

[0099] Example 1

[0100] 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."

[0101] Conventional immigration inspection systems require a lot of time and effort for manual passport inspections and questioning, resulting in problems with efficiency and accuracy. They also lack multilingual support and risk assessments often rely on subjective judgment. As a result, the risk of fraudulent entry and illegal residence can increase. To solve these problems, automation and highly accurate risk assessment are required.

[0102] 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.

[0103] In this invention, the server includes means for collecting and verifying a user's biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting questions by voice and analyzing the user's answers using voice recognition technology, means for evaluating the consistency and precision of the answers, means for allowing the user to complete the procedure self-service if the risk is low, means for escalating the process by sending information to an official if the risk is high, and means for notifying the user of the final decision. This significantly improves the efficiency and accuracy of immigration inspections and reduces the risk of fraudulent entry and illegal residence.

[0104] "Biometric information" is data that indicates the personal characteristics of a user, and includes physiological information such as a facial photograph, fingerprints, and iris.

[0105] "Attributes" are information related to a user, including nationality, purpose of entry, length of stay, and the like.

[0106] "Generative AI" is artificial intelligence that automatically generates appropriate questions based on the user's attribute information.

[0107] "Speech recognition technology" is a technology that converts a user's voice into text data and is used to analyze the voice response.

[0108] "Risk assessment" is the process of assessing a user's risk level based on collected and analyzed data.

[0109] "Self-service" refers to a means by which users can automatically complete a process themselves.

[0110] "Escalation" is a process in which, for high-risk users, the system sends information to a responsible person to prompt further review or action.

[0111] The "final decision" is the decision made by the person in charge after further questioning and inspection, and is notified to the user.

[0112] "Presenting in multiple languages" refers to a means by which the questions created by the generative AI can be communicated to the user in multiple languages.

[0113] "Evaluating consistency and elaboration" is the process of evaluating whether the answers are logical, consistent, and specific.

[0114] ---

[0115] The immigration inspection system of the present invention performs immigration inspection efficiently and accurately based on the user's biometric information. This system integrates biometric information collection, question generation using AI, answer analysis using voice recognition technology, risk assessment, self-service, and escalation methods.

[0116] User Check-in and Identification

[0117] Biometric information collection and matching

[0118] When a user goes to a check-in terminal at an airport, the terminal collects the user's biometric information (such as a photo of their face, fingerprints, or iris), for example, using the terminal's built-in camera and fingerprint sensor. This collected information is sent to a server and matched with existing data in a database. The server then verifies the user's identity by comparing it with past entry records and blacklists.

[0119] Question Generation

[0120] Question creation using generative AI

[0121] The server is equipped with a generative AI (for example, OpenAI's GPT-3 (registered trademark)), which automatically generates appropriate questions based on the user's attributes (for example, nationality, purpose of entry, etc.). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[0122] Question and answer exchange

[0123] Speech synthesis and speech recognition

[0124] The device presents the generated question to the user using speech synthesis technology (e.g., Google® Cloud Text-to-Speech). When the user answers verbally, the device analyzes the answer using speech recognition technology (e.g., Google Cloud Speech-to-Text), converts it into text data, and sends it to the server.

[0125] Risk Assessment

[0126] Analysis and evaluation of answers

[0127] The server analyzes the received text data and uses a generative AI to evaluate the consistency and precision of the answer. For example, if a user answers "I want to go to Tokyo Disneyland," it determines whether the answer is consistent with the tourist purpose and sounds natural.

[0128] Risk Classification

[0129] The server evaluates the user's risk based on the analysis results. If the evaluation score meets certain criteria, it is determined to be low risk. If the risk is low, the user can complete immigration procedures self-service. If the risk is high, information is sent to an official for escalation.

[0130] Escalation and Final Decision

[0131] Review by the person in charge

[0132] If the issue is escalated, the person in charge will confirm the detailed information, ask additional questions, and conduct further inspections. The person in charge will make a final decision and enter the result into the server. The server will then notify the user of the final decision.

[0133] Specific examples

[0134] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The user scans their passport with a device, takes a photo of their face, and provides fingerprints. The device sends this biometric information to a server, which verifies the information. The server uses generative AI to generate questions such as "Where are your tourist destinations?" and "How long do you plan to stay?" and sends them to the device. The device presents the questions to the user using speech synthesis technology. When the user answers "Tokyo Disneyland," the device analyzes the answer using speech recognition technology, converts it into text data, and sends it to the server. The server analyzes the answer and performs a risk assessment. If the risk is low, the user automatically completes immigration procedures, and the device notifies them that "immigration processing has been completed." If the risk is determined to be high, the case is escalated to a responsible official for additional screening.

[0135] Prompt Sentence Examples

[0136] A scenario in which a user checks in to Japan for tourism purposes and plans to visit Tokyo Disneyland.

[0137] As a result, the system of the present invention can significantly improve the efficiency and accuracy of immigration inspections.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Processing Steps

[0140] Step 1:

[0141] Input A user goes to an airport check-in kiosk, scans their passport, takes a photo of their face, and provides their fingerprints.

[0142] concrete action

[0143] When a user inserts their passport into the terminal, the terminal's camera automatically takes a photo of the user's face and the fingerprint sensor captures their fingerprint.

[0144] Output Collected biometric information (e.g., facial photo, fingerprint, iris) is stored on the device and sent to a server for processing.

[0145] Step 2:

[0146] The input server receives the biometric information sent from the terminal.

[0147] concrete action

[0148] The server compares the biometric information with existing data in a database, using facial recognition and fingerprint matching algorithms to verify the user's identity.

[0149] Output The matching results include the user's identification information, past entry records, and blacklist matching results.

[0150] Step 3:

[0151] Input User identification information and attribute information (nationality, purpose of entry, etc.).

[0152] concrete action

[0153] The server uses a generative AI model (e.g., GPT-3) to generate appropriate questions based on the user's attributes.

[0154] Output The generated questions are generated in multiple languages ​​and sent to the terminal.

[0155] Step 4:

[0156] An input terminal receives the generated query from the server.

[0157] concrete action

[0158] The device uses speech synthesis technology (e.g., Google Cloud Text-to-Speech) to present questions to the user aloud.

[0159] Output The question is presented to the user aloud.

[0160] Step 5:

[0161] Input The user answers the question by voice.

[0162] concrete action

[0163] When the user responds, the device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the voice into text data.

[0164] Output The answer converted into text data is sent from the terminal to the server.

[0165] Step 6:

[0166] The input server receives the text data of the user's answer.

[0167] concrete action

[0168] The server uses generative AI to evaluate the consistency and precision of the answers, for example, analyzing whether the answers match tourist objectives.

[0169] Output The answer analysis results in the user's risk assessment.

[0170] Step 7:

[0171] Input Answer analysis results and risk assessment.

[0172] concrete action

[0173] Based on the risk assessment, the server allows users to complete the procedure self-service if the risk is low, or sends an escalation notification to the responsible party if the risk is high.

[0174] Output The user is notified that immigration procedures have been completed, or an escalation notification is sent to the person in charge.

[0175] Step 8:

[0176] Input Escalated user details and risk assessment.

[0177] concrete action

[0178] The person in charge will review the details, ask additional questions or carry out additional tests as necessary, make a final decision, and enter the results into the server.

[0179] Output The final decision result is notified to the user via the server.

[0180] This allows the system of the present invention to achieve efficient and accurate immigration inspection.

[0181] (Application example 1)

[0182] 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."

[0183] In recent years, immigration inspections have become increasingly complex due to the large number of users involved, resulting in a demand for greater efficiency and accuracy. However, traditional immigration inspection systems often require manual inspections, consuming significant amounts of time and resources. Furthermore, there were challenges in generating appropriate questions for users who speak different languages ​​and analyzing their answers. Furthermore, there was no established method for accurately identifying high-risk users and promptly notifying immigration officials. This compromised the reliability and efficiency of the immigration inspection process.

[0184] 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.

[0185] In this invention, the server includes means for collecting and verifying a user's biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting questions aloud, analyzing the user's answers using voice recognition technology, and converting them into text data, means for conducting risk assessment based on the analyzed answers, and means for completing self-service if the risk is low, or escalating the process and notifying an official if the risk is high. This improves the efficiency and accuracy of immigration inspections. Furthermore, this system supports different languages ​​and supports biometric collection and answer input using smartphones, making it suitable for users with different backgrounds.

[0186] "User biometric information" refers to biometric information for identifying an individual, such as a facial photograph, fingerprint, or iris.

[0187] "Generative AI" is an artificial intelligence technology that automatically generates appropriate questions based on user attribute data.

[0188] "Speech recognition technology" is a technology that converts a user's voice into a digital signal and analyzes it as text data.

[0189] "Risk assessment" is a process of analyzing the user's answers and assessing the level of risk in immigration inspection.

[0190] "Escalation" is the process of automatically notifying high-risk users for further review.

[0191] "Self-service" is a system that allows users to automatically complete immigration procedures.

[0192] The "server" is a central computer system that analyzes the user's biometric information and response data and performs the necessary processing.

[0193] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0194] The system that realizes this application integrates the following main modules and technologies: First, the user's smartphone is used. The user goes through immigration procedures using the smartphone application "Secure Travel Check-In."

[0195] 1. Collection and matching of biometric information

[0196] Hardware: Smartphone built-in camera, fingerprint sensor

[0197] Software: OpenCV, face_recognition library

[0198] Processing: When a user provides biometric information (face photo, fingerprint) to a smartphone application, the application collects it and sends it to a server, where it matches the collected information with existing data in a database.

[0199] 2. Question Generation

[0200] Software: OpenAI GPT-3 model

[0201] Processing: The server automatically generates appropriate questions using a generative AI model based on the user's attribute data (e.g., nationality and purpose of entry). The generated questions are then sent from the server to the smartphone application.

[0202] Example prompt: "The user is a tourist. Please generate questions suitable for immigration."

[0203] 3. Question and answer exchanges

[0204] Hardware: Smartphone microphone and speaker

[0205] Software: Google Speech-to-Text API

[0206] Processing: The smartphone application uses voice synthesis technology to present the questions sent from the server to the user. The user's voice response is converted into text data using voice recognition technology and sent to the server.

[0207] 4. Risk Assessment

[0208] Software: OpenAI GPT-3 model

[0209] Processing: The server analyzes the received text data using the generation AI and evaluates the accuracy and consistency of the answers. Based on the analysis results, the user's risk level is assessed.

[0210] Example prompt: "User answered: Tokyo Disneyland. Please rate the risk level based on this answer."

[0211] 5. Escalation and Final Decision

[0212] Processing: If the risk is low, the server allows the user to complete the immigration process on their own. If the risk is high, an escalation notification is sent to an officer who can conduct additional questioning or screening.

[0213] Specific examples

[0214] A foreign user wishes to enter Japan for tourism purposes using the smartphone application "Secure Travel Check-In." First, the user takes a photo of their face with their smartphone camera, scans their fingerprints, and provides them to the application. The server identifies the user based on this information, and a generative AI model generates questions such as "Where are your tourist destinations?" and "How long will you be staying?" These questions are presented to the user using speech synthesis technology. If the user answers "Tokyo Disneyland," speech recognition technology converts the answer into text data, and the server performs a risk assessment. If the risk is deemed low, the user automatically completes immigration procedures. However, if the risk is deemed high, an official is notified and additional screening is carried out.

[0215] In this way, the system of the invention significantly improves the efficiency and accuracy of immigration inspections.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] A user launches a smartphone application and provides a facial photograph and fingerprint. At this time, biometric information is collected using the smartphone's camera and fingerprint sensor. This information is sent from the smartphone application to a server. The input is the user's facial photograph and fingerprint data, and the output is the biometric information sent to the server.

[0219] Step 2:

[0220] The server compares the received biometric information with existing data in a database. Here, it performs face recognition using OpenCV and the face_recognition library. It also performs fingerprint authentication. The input is biometric information, and the output is the user's identification result. Specifically, the server analyzes the face image and fingerprint to check whether they match.

[0221] Step 3:

[0222] The server uses a generative AI model (OpenAI GPT-3) to generate appropriate questions based on the user's attribute data (e.g., nationality and purpose of entry). The input is the user's attribute data, and the output is the generated question. An example of a prompt sentence: "The user is a tourist. Please generate questions appropriate for immigration inspection."

[0223] Step 4:

[0224] The server sends the generated question to the smartphone application. The input is the generated question, and the output is the question sent to the smartphone application. Specifically, the server sends the generated question to the application via an API.

[0225] Step 5:

[0226] The smartphone application uses speech synthesis technology to present the received questions to the user. The input is the question sent from the server, and the output is a voice message suggested to the user. Specifically, the application converts text into speech and transmits it to the user through the smartphone's speaker.

[0227] Step 6:

[0228] The user responds verbally, and the smartphone application's microphone receives the response. The input is the user's verbal response, and the output is the smartphone's voice data.

[0229] Step 7:

[0230] The smartphone application uses speech recognition technology (Google Speech-to-Text API) to convert the voice response into text data and send it to the server. The input is the user's voice data and the output is text data. Specifically, the application analyzes the voice data, converts it into text, and sends that data to the server.

[0231] Step 8:

[0232] The server analyzes the received text data using a generative AI model and evaluates the precision and consistency of the user's answers. Based on the results of this analysis, the server evaluates the user's risk level. The input is text data, and the output is the result of the risk assessment. An example of a prompt is: "User's answer: Tokyo Disneyland. Please evaluate the risk level based on this answer."

[0233] Step 9:

[0234] If the server determines that the risk level is low, the user receives a notification to complete immigration procedures through self-service. If the risk level is high, an officer is notified and additional questions or inspections are carried out. The input is the result of the risk assessment, and the output is a notification of the completion of immigration procedures or a notification of escalation. Specifically, the server performs the appropriate action (self-service completion or officer notification) based on the risk assessment.

[0235] 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.

[0236] ---

[0237] This invention achieves efficient and accurate immigration screening by combining biometric information collection, question generation using generative AI, voice recognition technology, and an emotion engine. This system is composed of the following functional modules:

[0238] User Check-in and Identification

[0239] Biometric information collection and matching

[0240] When a user checks in at an airport terminal, the terminal collects the user's biometric information (e.g., facial photograph, fingerprint, iris, etc.), which is then sent to a server where it is matched with existing data in a database, comparing it with past immigration records and blacklists to confirm the user's identity.

[0241] Question Generation

[0242] Question creation using generative AI

[0243] The server is equipped with a generation AI that generates appropriate questions based on the user's attributes (such as nationality and purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[0244] Question and answer exchange

[0245] Speech synthesis and speech recognition

[0246] The device uses speech synthesis technology to present the generated questions to the user. When the user responds by voice, the device analyzes the response using speech recognition technology, converts it into text data, and sends it to the server.

[0247] Analysis by emotion engine

[0248] Emotional state analysis

[0249] The server includes an emotion engine that analyzes the user's emotional state from their voice and facial expressions. For example, it can detect emotions such as tension or anxiety by analyzing the tone of voice and facial expressions when the user answers.

[0250] Risk Assessment

[0251] Risk assessment based on response content and sentiment analysis

[0252] The server uses generative AI to assess risk based on the received text data and the analysis results of the emotion engine. For example, if a user answers "I want to go to Tokyo Disneyland," and the emotion engine recognizes an appropriate emotional response to that answer, it evaluates whether the response is consistent with the tourist purpose and whether it is a natural response.

[0253] Risk Classification

[0254] Classification by risk assessment

[0255] The user's risk is assessed based on the analysis results. If the risk is deemed low, the user is provided with a means to complete immigration procedures through self-service. On the other hand, if the risk is high, the server notifies the relevant person for escalation.

[0256] Escalation and Final Decision

[0257] Further review by personnel

[0258] If the issue is escalated, the person in charge will ask additional questions and confirm the details of the user and the risk assessment made by the generative AI and emotion engine. The person in charge will make a final decision, enter the result into the server, and notify the user.

[0259] Specific examples

[0260] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The terminal scans the user's passport and takes a photo of the user's face. The server uses this information to identify the user, and a generation AI generates questions such as "Where are you planning to visit?" and "How long will you be staying?", which are then presented to the user using speech synthesis technology. When the user answers "Tokyo Disneyland," the terminal uses voice recognition technology to analyze the answer and sends it to the server. At the same time, an emotion engine analyzes the user's tone of voice and facial expressions to detect signs of tension or anxiety. The server performs a risk assessment based on this information, and if the risk is low, the immigration process is completed, and the terminal notifies the user that "immigration processing has been completed." If the risk is deemed high, the case is escalated to a responsible official for additional screening.

[0261] This allows the system of the present invention to significantly improve the efficiency and accuracy of immigration inspections. In addition, by incorporating an emotion engine, the system can grasp the user's emotional state and perform more accurate risk assessments.

[0262] The processing flow will be explained below.

[0263] ---

[0264] Step 1:

[0265] Biometric collection and check-in

[0266] User: Upon arrival at the airport, scan your passport at the terminal and check in.

[0267] Terminal: Collects biometric information such as the user's face photo, fingerprint, and iris.

[0268] Terminal: Sends collected biometric information to the server.

[0269] Step 2:

[0270] User Identification

[0271] Server: The received biometric information is compared with a database, including past immigration records and blacklists, to verify the user's identity.

[0272] Step 3:

[0273] Question generation

[0274] Server: Based on the user's attributes (nationality, purpose of entry, etc.), the AI ​​generates appropriate questions. For example, it generates questions in Japanese and other languages, such as "Which tourist spots do you want to visit?" and "How many days do you plan to stay?"

[0275] Server: Sends the generated question to the terminal.

[0276] Step 4:

[0277] Posing the Question

[0278] Terminal: The generated question is presented to the user using speech synthesis technology. For example, it reads out in Japanese, "What tourist spots would you like to visit?"

[0279] User: Answers questions by voice.

[0280] Step 5:

[0281] Response collection and analysis

[0282] Terminal: Converts the user's response into text data using voice recognition technology.

[0283] Terminal: Sends the converted text data to the server.

[0284] Step 6:

[0285] Emotional state analysis

[0286] Server: Uses an emotion engine to analyze the user's emotional state (e.g., tension, anxiety, etc.) from their voice and facial expressions.

[0287] Step 7:

[0288] Risk assessment based on response content and sentiment analysis

[0289] Server: The generation AI performs a risk assessment based on the response content and the analysis results of the emotion engine. For example, if a user responds "I want to go to Tokyo Disneyland" and the emotion engine detects a natural emotional response, it evaluates whether the response matches the tourist purpose.

[0290] Step 8:

[0291] Risk Classification

[0292] Server: If the user is at low risk, it sends instructions to the terminal to complete the self-service. If the user is at high risk, it sends instructions for escalation.

[0293] Step 9:

[0294] Low risk actions

[0295] Terminal: Receives instructions from the server and notifies the user that "entry processing has been completed."

[0296] User: Complete immigration procedures.

[0297] Step 10:

[0298] High-risk escalation

[0299] Server: If the risk is deemed high, an escalation notice is sent to the appropriate person.

[0300] Person in charge: Asks additional questions or clarifies based on the escalated information.

[0301] Person in charge: Enters the final review results into the server and notifies the user.

[0302] ---

[0303] Through the above processing steps, the system of the present invention realizes efficient and accurate immigration inspection. In addition, by incorporating an emotion engine, it is possible to grasp the user's emotional state and perform more accurate risk assessment.

[0304] Example 2

[0305] 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."

[0306] Conventional immigration inspection systems take a lot of time to identify users and answer questions, and because they do not take the user's emotional state into account, risk assessment is often inaccurate. This results in problems such as reduced inspection efficiency and an increased risk of illegal entry. Therefore, a new system that can achieve efficient and accurate immigration inspection is needed.

[0307] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and verifying biometric information of a user, means including a generation AI for generating questions based on user attributes, means for presenting questions by voice and analyzing the user's answers using voice recognition technology, means for performing risk assessment based on the analyzed answers and emotion analysis results, and means for completing self-service if the risk is low and escalating if the risk is high. This enables highly accurate risk assessment that takes the user's emotional state into consideration, improving the efficiency and accuracy of immigration inspections.

[0308] "Biometric information" refers to information that indicates an individual's physical characteristics, such as a photograph of their face, fingerprints, or iris, for use in biometric authentication.

[0309] "Generative AI" is an artificial intelligence technology that automatically generates appropriate questions based on the user's attribute information.

[0310] "Speech recognition technology" is a technology that analyzes a user's voice and converts it into text data.

[0311] The "server" is a central computer system that centrally processes tasks such as matching biometric information, generating questions using AI, and assessing the risk of answers.

[0312] A "terminal" is a device that is directly operated by a user to collect biometric information, pose questions, and input answers.

[0313] An "emotion engine" is software or hardware that analyzes a user's voice and facial expressions and evaluates their emotional state.

[0314] "Risk assessment" is a process of assessing the risk of entry into a country on a three-point scale: "low," "medium," or "high" based on the user's responses and the results of sentiment analysis.

[0315] "Self-service" means that users can complete immigration procedures on their own.

[0316] "Escalation" is the process of requesting further review from a responsible party when a risk is deemed high.

[0317] The "person in charge" is the person in charge or the inspector who makes the final decision on entry permission.

[0318] MODE FOR CARRYING OUT THE INVENTION

[0319] This invention is a system that combines biometric information collection, question generation using generative AI, voice recognition technology, and an emotion engine to efficiently and accurately conduct immigration inspections for users. In this system, the server and terminal work together to collect and authenticate the user's biometric information and perform risk assessment based on the generated questions.

[0320] Hardware and Software Configuration

[0321] Device:

[0322] It is equipped with a camera, fingerprint sensor, and iris recognition sensor to collect biometric information.

[0323] Includes a microphone and speaker for audio interface with the user.

[0324] It has a display that displays the user interface.

[0325] server:

[0326] It connects to the database and verifies the biometric information.

[0327] It is equipped with generative AI that generates questions based on the user's attributes.

[0328] Voice recognition technology is used to convert voice data into text.

[0329] An emotion engine is used to analyze the user's voice and facial expressions to assess their emotional state.

[0330] Data processing and calculation

[0331] When a user checks in at an airport terminal, the terminal collects biometric information such as a facial photo, fingerprints, and iris scan, and sends it to a server. The server compares the received biometric information with existing data in a database. Next, a generation AI generates questions based on the user's attribute information (for example, purpose of entry and nationality). These questions are presented to the user from the terminal using speech synthesis technology. When the user responds verbally, the terminal analyzes the response using speech recognition technology, converts it into text data, and sends it to the server. The server then uses an emotion engine to analyze the user's voice data and facial expressions to evaluate their emotional state. A risk assessment is performed based on this data, and depending on the result, the self-service is either completed or escalated.

[0332] Specific examples

[0333] For example, consider a foreign user entering Japan for tourism. The user scans their passport and takes a photo of their face at an airport terminal. The device's camera acts as a facial recognition sensor, and biometric information is collected using a fingerprint sensor and iris recognition sensor. This information is then sent to a server and compared against an existing database.

[0334] The generation AI confirms that the user is traveling and generates questions such as, "Which tourist spots do you want to visit?" and "How many days do you plan to stay?" These questions are presented to the user using voice synthesis technology. When the user answers, "I want to go to Tokyo Disneyland," the device analyzes the voice, converts it into text data, and sends it to the server.

[0335] The server then uses an emotion engine to analyze the user's tone of voice and facial expressions to assess their emotional state. Based on this information, the server performs a risk assessment, and if the risk is deemed low, the device notifies the user that entry processing has been completed. If the risk is deemed high, the process is escalated to a staff member who will ask additional questions and provide confirmation before making a final decision.

[0336] Prompt Sentence Examples

[0337] "If you are visiting Japan for tourism purposes, please tell us the specific places you will be visiting and the length of your stay."

[0338] In this way, this system improves the efficiency and accuracy of immigration inspections by combining biometric information matching technology, generative AI, voice recognition technology, and an emotion engine.

[0339] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0340] Program flow divided into processing steps

[0341] Step 1: Collect user biometric information

[0342] Specific actions

[0343] Users check in at airport terminals and provide a photo of their face, fingerprints, and iris information.

[0344] The terminal collects this biometric information, encrypts it, and sends it to the server.

[0345] Input and Output

[0346] Input: User's photo, fingerprint, and iris information.

[0347] Output: Encrypted biometric information is sent to the server.

[0348] Step 2: Verify biometric information

[0349] Specific actions

[0350] The server compares the received biometric information with existing data in a database.

[0351] Compare with past entry records and blacklists to find matching data.

[0352] Input and Output

[0353] Input: Encrypted biometric information.

[0354] Output: Matching results (matching rate and blacklist flags) are generated.

[0355] Step 3: Generate questions

[0356] Specific actions

[0357] The server uses a generation AI to generate appropriate questions based on the user's attribute information (purpose of entry, nationality, etc.).

[0358] The questions are translated into multiple languages ​​and sent to the device.

[0359] Input and Output

[0360] Input: User's demographic information (purpose of entry, nationality, etc.).

[0361] Output: The generated question (e.g., "Which tourist destination would you like to visit?").

[0362] Step 4: Posing the Question

[0363] Specific actions

[0364] The terminal presents the generated question to the user using voice synthesis technology.

[0365] The question is also displayed on the display so that the user can check it.

[0366] Input and Output

[0367] Input: The generated question and its translation.

[0368] Output: The question is presented to the user in audio and text form.

[0369] Step 5: Collect user responses

[0370] Specific actions

[0371] The user answers by voice into the microphone of the terminal.

[0372] The device receives the voice, analyzes it using voice recognition technology, and converts it into text data.

[0373] Input and Output

[0374] Input: The user's spoken response.

[0375] Output: The user's answer converted to text is sent to the server.

[0376] Step 6: Analyze emotional state

[0377] Specific actions

[0378] The server analyzes the received audio data and video feed with an emotion engine to assess the user's emotional state.

[0379] Detects emotions such as tension and anxiety and saves the evaluation results.

[0380] Input and Output

[0381] Input: User's voice data, video feed.

[0382] Output: Emotion analysis results (tension, anxiety, neutral, etc.) are generated.

[0383] Step 7: Risk Assessment

[0384] Specific actions

[0385] The server uses generative AI to perform risk assessment based on the user's responses and the results of sentiment analysis.

[0386] Risk levels are classified into three categories: "low," "medium," and "high."

[0387] Input and Output

[0388] Input: Translated user responses, sentiment analysis results.

[0389] Output: Risk level (low, medium, high).

[0390] Step 8: Determine risk treatment

[0391] Specific actions

[0392] Based on the risk assessment results, the server decides whether to continue or escalate the user's processing and sends instructions to the terminal.

[0393] If it's low risk, complete the self-service process. If it's high risk, escalate to a responsible person.

[0394] Input and Output

[0395] Input: Risk level.

[0396] Output: Action flag (self-service completed or escalated).

[0397] Step 9: Final decision and notification

[0398] Specific actions

[0399] The agent will review the escalated case and ask additional questions or clarify as needed.

[0400] The person in charge makes the final decision, enters the result into the server, and notifies the user from the server.

[0401] Input and Output

[0402] Input: Escalated user details, risk assessment results.

[0403] Output: Final assessment result (entry allowed or denied) and notification to the user.

[0404] Through these steps, the system achieves efficient and accurate immigration inspection.

[0405] (Application example 2)

[0406] 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."

[0407] Improving the efficiency and accuracy of access control is a key challenge for modern security systems. Current systems rely on biometric verification and simple questions to verify access, but do not consider the user's emotional state for risk assessment. This results in insufficient accuracy in detecting unauthorized access attempts, resulting in security risks. Furthermore, unclear escalation criteria result in a waste of human resources. Therefore, there is a need to improve security and efficiency by combining prompt generation based on the user's biometric information and attributes with emotion analysis and generative AI.

[0408] 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.

[0409] In this invention, the server includes means for collecting and verifying user biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting the questions by voice and analyzing the user's answers using voice recognition technology, means for analyzing the user's emotional state, means for reflecting the results of the emotion analysis in risk assessment, and means for the generation AI to create prompt sentences based on the user's attributes. This enables highly accurate risk assessment that takes the emotional state into account and appropriate escalation according to the situation.

[0410] A "user" is an individual using the system.

[0411] "Biometric information" is biological information used to identify an individual, such as a facial photograph, fingerprint, or retinal scan.

[0412] "Verification" is the process of comparing collected biometric information with an existing database to see if there is a match.

[0413] "Generative AI" is an algorithm that uses artificial intelligence to automatically generate questions and prompts based on user attribute information.

[0414] "Question generation" is the process of creating appropriate questions based on user attribute information.

[0415] "Speech recognition technology" is a technology for converting a user's voice into text data.

[0416] "Risk assessment" is the process of determining whether a user's behavior poses a security risk based on collected data and information.

[0417] "Self-service" is a means by which users can self-complete their own actions or procedures.

[0418] "Escalation" is the process of notifying a human reviewer when the system determines that a risk is high.

[0419] An "emotional state" is a psychological state that a user expresses through their voice and facial expressions.

[0420] "Emotion analysis" is a technology that determines a user's emotions based on collected audio and image data.

[0421] A "prompt sentence" is an instruction or question sentence created by generative AI based on the user's attributes and situation.

[0422] This invention is a system for efficiently and accurately managing access to facilities. This system collects user biometric information and combines voice recognition and emotion analysis to assess the risk of access and escalate the situation to the appropriate personnel as necessary.

[0423] Component Description

[0424] The system consists of the following main components:

[0425] 1. Collection of Biometric Information

[0426] When a user enters a facility, biometric information such as a facial photograph and fingerprints is collected using a smartphone. Specifically, a facial photograph is taken using the smartphone's camera, and a fingerprint is acquired using the fingerprint sensor.

[0427] 2. Question Generation

[0428] The server is equipped with a generative AI that generates appropriate questions based on the user's attribute information (e.g., age, gender, nationality, purpose of visit, etc.). These questions are presented to the user via voice or text. The generated questions vary depending on the situation.

[0429] 3. Voice Recognition

[0430] When the user responds verbally, the smartphone's voice recognition technology is used to convert the voice data into text data, which is then sent to a server where it is analyzed.

[0431] 4. Emotion analysis

[0432] The server includes an emotion analysis engine that analyzes the user's emotional state from their voice and facial expressions. For example, it analyzes the tone of voice and facial expressions when the user answers to detect emotions such as tension or anxiety.

[0433] 5. Risk Assessment

[0434] The server performs a risk assessment based on the received text data and the results of sentiment analysis, using generative AI to determine whether the user's reaction is natural and consistent with the purpose of entering the facility.

[0435] 6. Escalation and Notification

[0436] If the risk assessment determines that the risk is low, the visitor can complete the self-service entry procedure and receive a notification on their smartphone. If the risk is high, the procedure is escalated to a staff member who will conduct additional checks and ask questions.

[0437] Processing example

[0438] For example:

[0439] When foreign tourists enter a facility, they take a photo of their face with their smartphone and scan their fingerprints. The server uses this information to verify their identity. Next, a generation AI automatically generates a question such as "Where is your tourist destination?" and presents it to the user aloud. If the user replies "Tokyo Disneyland," this is converted into text data using voice recognition technology and sent to the server. At the same time, an emotion analysis engine analyzes the user's tone of voice and facial expressions to detect signs of tension or anxiety. The server comprehensively evaluates this information and, if the risk is low, sends a notification to the smartphone stating, "Entry permitted." If the risk is high, a notification is sent to a responsible person to ask additional questions and conduct further confirmation.

[0440] Hardware and software used

[0441] The system uses the following hardware and software:

[0442] Smartphone: A device that collects biometric information and recognizes voice.

[0443] Server: A central management system that runs generative AI, sentiment analysis engines, and risk assessments.

[0444] Open source libraries:

[0445] OpenCV: Facial photo capture and analysis.

[0446] TENSORFLOW(R) / Keras: Building a sentiment analysis engine.

[0447] SpeechRecognition: Speech recognition.

[0448] Hugging Face Transformers: Using generative AI models.

[0449] Prompt Sentence Examples

[0450] Here are some example prompts the generator AI might create:

[0451] "Generate appropriate questions for users with American nationality."

[0452] "Ask users who are visiting for sightseeing what kind of tourist spots they would like to visit."

[0453] This system enables highly accurate risk assessment that takes emotional states into account and appropriate escalation according to the situation, significantly improving the security and efficiency of access control.

[0454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0455] Step 1:

[0456] When a user enters a facility, the device (smartphone) collects the user's facial photograph and fingerprints. First, the smartphone's camera is activated and a photograph of the user's face is taken. This image is saved as a temporary file. Next, the fingerprint is scanned using the smartphone's fingerprint sensor and saved as digital data. This biometric information is sent to a server. The input is the user's facial photograph and fingerprint information, and the output is the matching result on the server.

[0457] Step 2:

[0458] The server matches the submitted biometric information with existing data in a database. Specifically, it compares the facial photo and fingerprint data against the existing database using a matching algorithm. This process verifies the user's identity. The input is the user's facial photo and fingerprint data, and the output is the matching result.

[0459] Step 3:

[0460] The generative AI model on the server generates appropriate questions based on the user's attribute information (e.g., age, gender, nationality, purpose of visit). The generated questions are sent to the terminal in the form of prompt sentences. Specifically, the generative AI takes user data as input and outputs the question as a prompt sentence. For example, a prompt sentence might be generated that reads, "Please ask users who are visiting for sightseeing what kind of tourist spots they would like to visit."

[0461] Step 4:

[0462] The device uses speech synthesis technology to present the generated question to the user aloud. The speech synthesis engine converts the text-based question into speech output, which is played through the smartphone's speaker. The user responds verbally. The input is the text of the generated question, and the output is the spoken question presented to the user.

[0463] Step 5:

[0464] When the user responds verbally, the device records the voice. The recorded voice data is analyzed by a voice recognition engine and converted into text data. Specifically, the smartphone's microphone captures the voice, and the voice data is analyzed by a voice recognition algorithm. The input is the user's voice data, and the output is text data that has been recognized by voice.

[0465] Step 6:

[0466] The server receives and analyzes the voice-recognized text data and the user's emotional state (e.g., tension, anxiety, excitement, etc.) provided by the emotion analysis engine. In emotion analysis, facial photos and voice data are processed as input to detect the user's emotional state from their tone of voice and facial expressions, and the results are passed to the risk assessment system. The inputs are the voice-recognized text data and emotional state data, and the output is the risk assessment result.

[0467] Step 7:

[0468] The server calculates the user's risk level based on the risk assessment results and decides on a response. If the risk is determined to be low, the self-service entry procedure is completed and a notification to that effect is sent to the terminal. If the risk is high, an escalation notification is sent to the person in charge, who will ask for additional confirmation or questions. The input is the risk assessment result, and the output is a self-service completion notification or an escalation notification.

[0469] 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.

[0470] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0471] 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.

[0472] [Second embodiment]

[0473] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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).

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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."

[0485] ---

[0486] The immigration inspection system of the present invention integrates several key modules and technologies to efficiently and accurately inspect users for immigration, including biometric information collection, question generation using generative AI, response analysis using voice recognition technology, risk assessment, and self-service or escalation measures.

[0487] Specifically, the immigration inspection system of the present invention is realized by the following functional modules.

[0488] User Check-in and Identification

[0489] Biometric information collection and matching

[0490] When a user checks in at an airport terminal, the terminal collects the user's biometric information (such as a photo of their face, fingerprints, or iris), which is then sent to a server where it is matched with existing data in a database. The user's identity is then confirmed by comparing it with past immigration records and blacklists.

[0491] Question Generation

[0492] Question creation using generative AI

[0493] The server is equipped with a generation AI that automatically generates appropriate questions based on the user's attributes (such as nationality or purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[0494] Question and answer exchange

[0495] Speech synthesis and speech recognition

[0496] The device uses speech synthesis technology to present the generated questions to the user. When the user responds by voice, the device analyzes the response using speech recognition technology, converts it into text data, and sends it to the server.

[0497] Risk Assessment

[0498] Analysis and evaluation of answers

[0499] The server analyzes the received text data using the AI ​​generator to evaluate the accuracy and consistency of the answer. For example, if a user answers "I want to go to Tokyo Disneyland," the server determines whether the answer is consistent with the tourist purpose and natural.

[0500] Risk Classification

[0501] The user's risk is assessed based on the analysis results. If the risk is determined to be low, the user can complete immigration procedures through self-service. On the other hand, if the risk is high, the server will escalate the process to the appropriate person depending on the level of urgency.

[0502] Escalation and Final Decision

[0503] Review by the person in charge

[0504] If the issue is escalated, the person in charge will ask additional questions and conduct further inspections based on the user's details and the risk assessment made by the generating AI. The person in charge will make a final decision, enter the result into the server, and notify the user.

[0505] Specific examples

[0506] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The terminal scans the user's passport and takes a photo of their face. The server uses this information to identify the user, and a generation AI generates questions such as "Where are you planning to visit?" and "How long will you be staying?", which are then presented to the user using voice synthesis technology. When the user answers "Tokyo Disneyland," voice recognition technology analyzes the answer, and the server performs a risk assessment. If the risk is low, the user automatically completes immigration procedures, and the terminal notifies the user that "immigration processing has been completed." If the risk is deemed high, the matter is escalated to a responsible official for additional screening.

[0507] As a result, the system of the present invention can significantly improve the efficiency and accuracy of immigration inspections.

[0508] The processing flow will be explained below.

[0509] ---

[0510] Step 1:

[0511] User check-in and biometric collection

[0512] Terminal: When the user arrives at the airport, they scan their passport at the terminal, which collects biometric information such as a photo, fingerprint, and iris.

[0513] Terminal: Sends collected biometric information to the server.

[0514] Step 2:

[0515] User Identification

[0516] Server: The received biometric information is compared with a database to verify the user's identity. It also compares the information with past immigration records and blacklists to make an initial assessment.

[0517] Step 3:

[0518] Question generation

[0519] Server: The generation AI generates appropriate questions based on the user's attributes (nationality, purpose of entry, etc.). For example, it generates questions in Japanese and other languages ​​such as "Which tourist spots do you want to visit?" and "How many days do you plan to stay?"

[0520] Server: Sends the generated question to the terminal.

[0521] Step 4:

[0522] Posing the Question

[0523] Terminal: Using speech synthesis technology, the generated question is presented to the user aloud. For example, it reads out in Japanese, "What tourist spots would you like to visit?"

[0524] User: Answers questions by voice.

[0525] Step 5:

[0526] Response collection and analysis

[0527] Terminal: The user's responses are analyzed using voice recognition technology and converted into text data.

[0528] Terminal: Sends the converted text data to the server.

[0529] Step 6:

[0530] Analysis and evaluation of answers

[0531] Server: The generation AI analyzes the received text data and evaluates the naturalness and consistency of the answers. For example, it evaluates whether the answer "Tokyo Disneyland" matches the tourist destination.

[0532] Step 7:

[0533] Risk Classification

[0534] Server: The generating AI performs a risk assessment of the responses and classifies entrants as low-risk or high-risk.

[0535] Step 8:

[0536] Low risk actions

[0537] Server: If the risk is determined to be low, notify the terminal that "entry processing has been completed."

[0538] Terminal: Upon receiving the notification, the user completes the immigration procedure.

[0539] Step 9:

[0540] High-risk escalation

[0541] Server: If the risk is deemed high, an escalation notice is sent to the appropriate person.

[0542] Person in charge: Asks additional questions or clarifies based on the escalated information.

[0543] Person in charge: Enters the final review results into the server and notifies the user.

[0544] ---

[0545] Through this processing step, the system of the present invention can conduct immigration inspections efficiently and fairly.

[0546] Example 1

[0547] 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."

[0548] Conventional immigration inspection systems require a lot of time and effort for manual passport inspections and questioning, resulting in problems with efficiency and accuracy. They also lack multilingual support and risk assessments often rely on subjective judgment. As a result, the risk of fraudulent entry and illegal residence can increase. To solve these problems, automation and highly accurate risk assessment are required.

[0549] 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.

[0550] In this invention, the server includes means for collecting and verifying a user's biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting questions by voice and analyzing the user's answers using voice recognition technology, means for evaluating the consistency and precision of the answers, means for allowing the user to complete the procedure self-service if the risk is low, means for escalating the process by sending information to an official if the risk is high, and means for notifying the user of the final decision. This significantly improves the efficiency and accuracy of immigration inspections and reduces the risk of fraudulent entry and illegal residence.

[0551] "Biometric information" is data that indicates the personal characteristics of a user, and includes physiological information such as a facial photograph, fingerprints, and iris.

[0552] "Attributes" are information related to a user, including nationality, purpose of entry, length of stay, and the like.

[0553] "Generative AI" is artificial intelligence that automatically generates appropriate questions based on the user's attribute information.

[0554] "Speech recognition technology" is a technology that converts a user's voice into text data and is used to analyze the voice response.

[0555] "Risk assessment" is the process of assessing a user's risk level based on collected and analyzed data.

[0556] "Self-service" refers to a means by which users can automatically complete a process themselves.

[0557] "Escalation" is a process in which, for high-risk users, the system sends information to a responsible person to prompt further review or action.

[0558] The "final decision" is the decision made by the person in charge after further questioning and inspection, and is notified to the user.

[0559] "Presenting in multiple languages" refers to a means by which the questions created by the generative AI can be communicated to the user in multiple languages.

[0560] "Evaluating consistency and elaboration" is the process of evaluating whether the answers are logical, consistent, and specific.

[0561] ---

[0562] The immigration inspection system of the present invention performs immigration inspection efficiently and accurately based on the user's biometric information. This system integrates biometric information collection, question generation using AI, answer analysis using voice recognition technology, risk assessment, self-service, and escalation methods.

[0563] User Check-in and Identification

[0564] Biometric information collection and matching

[0565] When a user goes to a check-in terminal at an airport, the terminal collects the user's biometric information (such as a photo of their face, fingerprints, or iris), for example, using the terminal's built-in camera and fingerprint sensor. This collected information is sent to a server and matched with existing data in a database. The server then verifies the user's identity by comparing it with past entry records and blacklists.

[0566] Question Generation

[0567] Question creation using generative AI

[0568] The server is equipped with a generative AI (for example, OpenAI's GPT-3) that automatically generates appropriate questions based on the user's attributes (for example, nationality or purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[0569] Question and answer exchange

[0570] Speech synthesis and speech recognition

[0571] The device uses voice synthesis technology (e.g., Google Cloud Text-to-Speech) to present the generated question to the user. When the user answers verbally, the device analyzes the answer using voice recognition technology (e.g., Google Cloud Speech-to-Text), converts it into text data, and sends it to the server.

[0572] Risk Assessment

[0573] Analysis and evaluation of answers

[0574] The server analyzes the received text data and uses a generative AI to evaluate the consistency and precision of the answer. For example, if a user answers "I want to go to Tokyo Disneyland," it determines whether the answer is consistent with the tourist purpose and sounds natural.

[0575] Risk Classification

[0576] The server evaluates the user's risk based on the analysis results. If the evaluation score meets certain criteria, it is determined to be low risk. If the risk is low, the user can complete immigration procedures self-service. If the risk is high, information is sent to an official for escalation.

[0577] Escalation and Final Decision

[0578] Review by the person in charge

[0579] If the issue is escalated, the person in charge will confirm the detailed information, ask additional questions, and conduct further inspections. The person in charge will make a final decision and enter the result into the server. The server will then notify the user of the final decision.

[0580] Specific examples

[0581] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The user scans their passport with a device, takes a photo of their face, and provides fingerprints. The device sends this biometric information to a server, which verifies the information. The server uses generative AI to generate questions such as "Where are your tourist destinations?" and "How long do you plan to stay?" and sends them to the device. The device presents the questions to the user using speech synthesis technology. When the user answers "Tokyo Disneyland," the device analyzes the answer using speech recognition technology, converts it into text data, and sends it to the server. The server analyzes the answer and performs a risk assessment. If the risk is low, the user automatically completes immigration procedures, and the device notifies them that "immigration processing has been completed." If the risk is determined to be high, the case is escalated to a responsible official for additional screening.

[0582] Prompt Sentence Examples

[0583] A scenario in which a user checks in to Japan for tourism purposes and plans to visit Tokyo Disneyland.

[0584] As a result, the system of the present invention can significantly improve the efficiency and accuracy of immigration inspections.

[0585] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0586] Processing Steps

[0587] Step 1:

[0588] Input A user goes to an airport check-in kiosk, scans their passport, takes a photo of their face, and provides their fingerprints.

[0589] concrete action

[0590] When a user inserts their passport into the terminal, the terminal's camera automatically takes a photo of the user's face and the fingerprint sensor captures their fingerprint.

[0591] Output Collected biometric information (e.g., facial photo, fingerprint, iris) is stored on the device and sent to a server for processing.

[0592] Step 2:

[0593] The input server receives the biometric information sent from the terminal.

[0594] concrete action

[0595] The server compares the biometric information with existing data in a database, using facial recognition and fingerprint matching algorithms to verify the user's identity.

[0596] Output The matching results include the user's identification information, past entry records, and blacklist matching results.

[0597] Step 3:

[0598] Input User identification information and attribute information (nationality, purpose of entry, etc.).

[0599] concrete action

[0600] The server uses a generative AI model (e.g., GPT-3) to generate appropriate questions based on the user's attributes.

[0601] Output The generated questions are generated in multiple languages ​​and sent to the terminal.

[0602] Step 4:

[0603] An input terminal receives the generated query from the server.

[0604] concrete action

[0605] The device uses speech synthesis technology (e.g., Google Cloud Text-to-Speech) to present questions to the user aloud.

[0606] Output The question is presented to the user aloud.

[0607] Step 5:

[0608] Input The user answers the question by voice.

[0609] concrete action

[0610] When the user responds, the device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the voice into text data.

[0611] Output The answer converted into text data is sent from the terminal to the server.

[0612] Step 6:

[0613] The input server receives the text data of the user's answer.

[0614] concrete action

[0615] The server uses generative AI to evaluate the consistency and precision of the answers, for example, analyzing whether the answers match tourist objectives.

[0616] Output The answer analysis results in the user's risk assessment.

[0617] Step 7:

[0618] Input Answer analysis results and risk assessment.

[0619] concrete action

[0620] Based on the risk assessment, the server allows users to complete the procedure self-service if the risk is low, or sends an escalation notification to the responsible party if the risk is high.

[0621] Output The user is notified that immigration procedures have been completed, or an escalation notification is sent to the person in charge.

[0622] Step 8:

[0623] Input Escalated user details and risk assessment.

[0624] concrete action

[0625] The person in charge will review the details, ask additional questions or carry out additional tests as necessary, make a final decision, and enter the results into the server.

[0626] Output The final decision result is notified to the user via the server.

[0627] This allows the system of the present invention to achieve efficient and accurate immigration inspection.

[0628] (Application example 1)

[0629] 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."

[0630] In recent years, immigration inspections have become increasingly complex due to the large number of users involved, resulting in a demand for greater efficiency and accuracy. However, traditional immigration inspection systems often require manual inspections, consuming significant amounts of time and resources. Furthermore, there were challenges in generating appropriate questions for users who speak different languages ​​and analyzing their answers. Furthermore, there was no established method for accurately identifying high-risk users and promptly notifying immigration officials. This compromised the reliability and efficiency of the immigration inspection process.

[0631] 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.

[0632] In this invention, the server includes means for collecting and verifying a user's biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting questions aloud, analyzing the user's answers using voice recognition technology, and converting them into text data, means for conducting risk assessment based on the analyzed answers, and means for completing self-service if the risk is low, or escalating the process and notifying an official if the risk is high. This improves the efficiency and accuracy of immigration inspections. Furthermore, this system supports different languages ​​and supports biometric collection and answer input using smartphones, making it suitable for users with different backgrounds.

[0633] "User biometric information" refers to biometric information for identifying an individual, such as a facial photograph, fingerprint, or iris.

[0634] "Generative AI" is an artificial intelligence technology that automatically generates appropriate questions based on user attribute data.

[0635] "Speech recognition technology" is a technology that converts a user's voice into a digital signal and analyzes it as text data.

[0636] "Risk assessment" is a process of analyzing the user's answers and assessing the level of risk in immigration inspection.

[0637] "Escalation" is the process of automatically notifying high-risk users for further review.

[0638] "Self-service" is a system that allows users to automatically complete immigration procedures.

[0639] The "server" is a central computer system that analyzes the user's biometric information and response data and performs the necessary processing.

[0640] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0641] The system that realizes this application integrates the following main modules and technologies: First, the user's smartphone is used. The user goes through immigration procedures using the smartphone application "Secure Travel Check-In."

[0642] 1. Collection and matching of biometric information

[0643] Hardware: Smartphone built-in camera, fingerprint sensor

[0644] Software: OpenCV, face_recognition library

[0645] Processing: When a user provides biometric information (face photo, fingerprint) to a smartphone application, the application collects it and sends it to a server, where it matches the collected information with existing data in a database.

[0646] 2. Question Generation

[0647] Software: OpenAI GPT-3 model

[0648] Processing: The server automatically generates appropriate questions using a generative AI model based on the user's attribute data (e.g., nationality and purpose of entry). The generated questions are then sent from the server to the smartphone application.

[0649] Example prompt: "The user is a tourist. Please generate questions suitable for immigration."

[0650] 3. Question and answer exchanges

[0651] Hardware: Smartphone microphone and speaker

[0652] Software: Google Speech-to-Text API

[0653] Processing: The smartphone application uses voice synthesis technology to present the questions sent from the server to the user. The user's voice response is converted into text data using voice recognition technology and sent to the server.

[0654] 4. Risk Assessment

[0655] Software: OpenAI GPT-3 model

[0656] Processing: The server analyzes the received text data using the generation AI and evaluates the accuracy and consistency of the answers. Based on the analysis results, the user's risk level is assessed.

[0657] Example prompt: "User answered: Tokyo Disneyland. Please rate the risk level based on this answer."

[0658] 5. Escalation and Final Decision

[0659] Processing: If the risk is low, the server allows the user to complete the immigration process on their own. If the risk is high, an escalation notification is sent to an officer who can conduct additional questioning or screening.

[0660] Specific examples

[0661] A foreign user wishes to enter Japan for tourism purposes using the smartphone application "Secure Travel Check-In." First, the user takes a photo of their face with their smartphone camera, scans their fingerprints, and provides them to the application. The server identifies the user based on this information, and a generative AI model generates questions such as "Where are your tourist destinations?" and "How long will you be staying?" These questions are presented to the user using speech synthesis technology. If the user answers "Tokyo Disneyland," speech recognition technology converts the answer into text data, and the server performs a risk assessment. If the risk is deemed low, the user automatically completes immigration procedures. However, if the risk is deemed high, an official is notified and additional screening is carried out.

[0662] In this way, the system of the invention significantly improves the efficiency and accuracy of immigration inspections.

[0663] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0664] Step 1:

[0665] A user launches a smartphone application and provides a facial photograph and fingerprint. At this time, biometric information is collected using the smartphone's camera and fingerprint sensor. This information is sent from the smartphone application to a server. The input is the user's facial photograph and fingerprint data, and the output is the biometric information sent to the server.

[0666] Step 2:

[0667] The server compares the received biometric information with existing data in a database. Here, it performs face recognition using OpenCV and the face_recognition library. It also performs fingerprint authentication. The input is biometric information, and the output is the user's identification result. Specifically, the server analyzes the face image and fingerprint to check whether they match.

[0668] Step 3:

[0669] The server uses a generative AI model (OpenAI GPT-3) to generate appropriate questions based on the user's attribute data (e.g., nationality and purpose of entry). The input is the user's attribute data, and the output is the generated question. An example of a prompt sentence: "The user is a tourist. Please generate questions appropriate for immigration inspection."

[0670] Step 4:

[0671] The server sends the generated question to the smartphone application. The input is the generated question, and the output is the question sent to the smartphone application. Specifically, the server sends the generated question to the application via an API.

[0672] Step 5:

[0673] The smartphone application uses speech synthesis technology to present the received questions to the user. The input is the question sent from the server, and the output is a voice message suggested to the user. Specifically, the application converts text into speech and transmits it to the user through the smartphone's speaker.

[0674] Step 6:

[0675] The user responds verbally, and the smartphone application's microphone receives the response. The input is the user's verbal response, and the output is the smartphone's voice data.

[0676] Step 7:

[0677] The smartphone application uses speech recognition technology (Google Speech-to-Text API) to convert the voice response into text data and send it to the server. The input is the user's voice data and the output is text data. Specifically, the application analyzes the voice data, converts it into text, and sends that data to the server.

[0678] Step 8:

[0679] The server analyzes the received text data using a generative AI model and evaluates the precision and consistency of the user's answers. Based on the results of this analysis, the server evaluates the user's risk level. The input is text data, and the output is the result of the risk assessment. An example of a prompt is: "User's answer: Tokyo Disneyland. Please evaluate the risk level based on this answer."

[0680] Step 9:

[0681] If the server determines that the risk level is low, the user receives a notification to complete immigration procedures through self-service. If the risk level is high, an officer is notified and additional questions or inspections are carried out. The input is the result of the risk assessment, and the output is a notification of the completion of immigration procedures or a notification of escalation. Specifically, the server performs the appropriate action (self-service completion or officer notification) based on the risk assessment.

[0682] 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.

[0683] ---

[0684] This invention achieves efficient and accurate immigration screening by combining biometric information collection, question generation using generative AI, voice recognition technology, and an emotion engine. This system is composed of the following functional modules:

[0685] User Check-in and Identification

[0686] Biometric information collection and matching

[0687] When a user checks in at an airport terminal, the terminal collects the user's biometric information (e.g., facial photograph, fingerprint, iris, etc.), which is then sent to a server where it is matched with existing data in a database, comparing it with past immigration records and blacklists to confirm the user's identity.

[0688] Question Generation

[0689] Question creation using generative AI

[0690] The server is equipped with a generation AI that generates appropriate questions based on the user's attributes (such as nationality and purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[0691] Question and answer exchange

[0692] Speech synthesis and speech recognition

[0693] The device uses speech synthesis technology to present the generated questions to the user. When the user responds by voice, the device analyzes the response using speech recognition technology, converts it into text data, and sends it to the server.

[0694] Analysis by emotion engine

[0695] Emotional state analysis

[0696] The server includes an emotion engine that analyzes the user's emotional state from their voice and facial expressions. For example, it can detect emotions such as tension or anxiety by analyzing the tone of voice and facial expressions when the user answers.

[0697] Risk Assessment

[0698] Risk assessment based on response content and sentiment analysis

[0699] The server uses generative AI to assess risk based on the received text data and the analysis results of the emotion engine. For example, if a user answers "I want to go to Tokyo Disneyland," and the emotion engine recognizes an appropriate emotional response to that answer, it evaluates whether the response is consistent with the tourist purpose and whether it is a natural response.

[0700] Risk Classification

[0701] Classification by risk assessment

[0702] The user's risk is assessed based on the analysis results. If the risk is deemed low, the user is provided with a means to complete immigration procedures through self-service. On the other hand, if the risk is high, the server notifies the relevant person for escalation.

[0703] Escalation and Final Decision

[0704] Further review by personnel

[0705] If the issue is escalated, the person in charge will ask additional questions and confirm the details of the user and the risk assessment made by the generative AI and emotion engine. The person in charge will make a final decision, enter the result into the server, and notify the user.

[0706] Specific examples

[0707] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The terminal scans the user's passport and takes a photo of the user's face. The server uses this information to identify the user, and a generation AI generates questions such as "Where are you planning to visit?" and "How long will you be staying?", which are then presented to the user using speech synthesis technology. When the user answers "Tokyo Disneyland," the terminal uses voice recognition technology to analyze the answer and sends it to the server. At the same time, an emotion engine analyzes the user's tone of voice and facial expressions to detect signs of tension or anxiety. The server performs a risk assessment based on this information, and if the risk is low, the immigration process is completed, and the terminal notifies the user that "immigration processing has been completed." If the risk is deemed high, the case is escalated to a responsible official for additional screening.

[0708] This allows the system of the present invention to significantly improve the efficiency and accuracy of immigration inspections. In addition, by incorporating an emotion engine, the system can grasp the user's emotional state and perform more accurate risk assessments.

[0709] The processing flow will be explained below.

[0710] ---

[0711] Step 1:

[0712] Biometric collection and check-in

[0713] User: Upon arrival at the airport, scan your passport at the terminal and check in.

[0714] Terminal: Collects biometric information such as the user's face photo, fingerprint, and iris.

[0715] Terminal: Sends collected biometric information to the server.

[0716] Step 2:

[0717] User Identification

[0718] Server: The received biometric information is compared with a database, including past immigration records and blacklists, to verify the user's identity.

[0719] Step 3:

[0720] Question generation

[0721] Server: Based on the user's attributes (nationality, purpose of entry, etc.), the AI ​​generates appropriate questions. For example, it generates questions in Japanese and other languages, such as "Which tourist spots do you want to visit?" and "How many days do you plan to stay?"

[0722] Server: Sends the generated question to the terminal.

[0723] Step 4:

[0724] Posing the Question

[0725] Terminal: The generated question is presented to the user using speech synthesis technology. For example, it reads out in Japanese, "What tourist spots would you like to visit?"

[0726] User: Answers questions by voice.

[0727] Step 5:

[0728] Response collection and analysis

[0729] Terminal: Converts the user's response into text data using voice recognition technology.

[0730] Terminal: Sends the converted text data to the server.

[0731] Step 6:

[0732] Emotional state analysis

[0733] Server: Uses an emotion engine to analyze the user's emotional state (e.g., tension, anxiety, etc.) from their voice and facial expressions.

[0734] Step 7:

[0735] Risk assessment based on response content and sentiment analysis

[0736] Server: The generation AI performs a risk assessment based on the response content and the analysis results of the emotion engine. For example, if a user responds "I want to go to Tokyo Disneyland" and the emotion engine detects a natural emotional response, it evaluates whether the response matches the tourist purpose.

[0737] Step 8:

[0738] Risk Classification

[0739] Server: If the user is at low risk, it sends instructions to the terminal to complete the self-service. If the user is at high risk, it sends instructions for escalation.

[0740] Step 9:

[0741] Low risk actions

[0742] Terminal: Receives instructions from the server and notifies the user that "entry processing has been completed."

[0743] User: Complete immigration procedures.

[0744] Step 10:

[0745] High-risk escalation

[0746] Server: If the risk is deemed high, an escalation notice is sent to the appropriate person.

[0747] Person in charge: Asks additional questions or clarifies based on the escalated information.

[0748] Person in charge: Enters the final review results into the server and notifies the user.

[0749] ---

[0750] Through the above processing steps, the system of the present invention realizes efficient and accurate immigration inspection. In addition, by incorporating an emotion engine, it is possible to grasp the user's emotional state and perform more accurate risk assessment.

[0751] Example 2

[0752] 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."

[0753] Conventional immigration inspection systems take a lot of time to identify users and answer questions, and because they do not take the user's emotional state into account, risk assessment is often inaccurate. This results in problems such as reduced inspection efficiency and an increased risk of illegal entry. Therefore, a new system that can achieve efficient and accurate immigration inspection is needed.

[0754] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and verifying biometric information of a user, means including a generation AI for generating questions based on user attributes, means for presenting questions by voice and analyzing the user's answers using voice recognition technology, means for performing risk assessment based on the analyzed answers and emotion analysis results, and means for completing self-service if the risk is low and escalating if the risk is high. This enables highly accurate risk assessment that takes the user's emotional state into consideration, improving the efficiency and accuracy of immigration inspections.

[0755] "Biometric information" refers to information that indicates an individual's physical characteristics, such as a photograph of their face, fingerprints, or iris, for use in biometric authentication.

[0756] "Generative AI" is an artificial intelligence technology that automatically generates appropriate questions based on the user's attribute information.

[0757] "Speech recognition technology" is a technology that analyzes a user's voice and converts it into text data.

[0758] The "server" is a central computer system that centrally processes tasks such as matching biometric information, generating questions using AI, and assessing the risk of answers.

[0759] A "terminal" is a device that is directly operated by a user to collect biometric information, pose questions, and input answers.

[0760] An "emotion engine" is software or hardware that analyzes a user's voice and facial expressions and evaluates their emotional state.

[0761] "Risk assessment" is a process of assessing the risk of entry into a country on a three-point scale: "low," "medium," or "high" based on the user's responses and the results of sentiment analysis.

[0762] "Self-service" means that users can complete immigration procedures on their own.

[0763] "Escalation" is the process of requesting further review from a responsible party when a risk is deemed high.

[0764] The "person in charge" is the person in charge or the inspector who makes the final decision on entry permission.

[0765] MODE FOR CARRYING OUT THE INVENTION

[0766] This invention is a system that combines biometric information collection, question generation using generative AI, voice recognition technology, and an emotion engine to efficiently and accurately conduct immigration inspections for users. In this system, the server and terminal work together to collect and authenticate the user's biometric information and perform risk assessment based on the generated questions.

[0767] Hardware and Software Configuration

[0768] Device:

[0769] It is equipped with a camera, fingerprint sensor, and iris recognition sensor to collect biometric information.

[0770] Includes a microphone and speaker for audio interface with the user.

[0771] It has a display that displays the user interface.

[0772] server:

[0773] It connects to the database and verifies the biometric information.

[0774] It is equipped with generative AI that generates questions based on the user's attributes.

[0775] Voice recognition technology is used to convert voice data into text.

[0776] An emotion engine is used to analyze the user's voice and facial expressions to assess their emotional state.

[0777] Data processing and calculation

[0778] When a user checks in at an airport terminal, the terminal collects biometric information such as a facial photo, fingerprints, and iris scan, and sends it to a server. The server compares the received biometric information with existing data in a database. Next, a generation AI generates questions based on the user's attribute information (for example, purpose of entry and nationality). These questions are presented to the user from the terminal using speech synthesis technology. When the user responds verbally, the terminal analyzes the response using speech recognition technology, converts it into text data, and sends it to the server. The server then uses an emotion engine to analyze the user's voice data and facial expressions to evaluate their emotional state. A risk assessment is performed based on this data, and depending on the result, the self-service is either completed or escalated.

[0779] Specific examples

[0780] For example, consider a foreign user entering Japan for tourism. The user scans their passport and takes a photo of their face at an airport terminal. The device's camera acts as a facial recognition sensor, and biometric information is collected using a fingerprint sensor and iris recognition sensor. This information is then sent to a server and compared against an existing database.

[0781] The generation AI confirms that the user is traveling and generates questions such as, "Which tourist spots do you want to visit?" and "How many days do you plan to stay?" These questions are presented to the user using voice synthesis technology. When the user answers, "I want to go to Tokyo Disneyland," the device analyzes the voice, converts it into text data, and sends it to the server.

[0782] The server then uses an emotion engine to analyze the user's tone of voice and facial expressions to assess their emotional state. Based on this information, the server performs a risk assessment, and if the risk is deemed low, the device notifies the user that entry processing has been completed. If the risk is deemed high, the process is escalated to a staff member who will ask additional questions and provide confirmation before making a final decision.

[0783] Prompt Sentence Examples

[0784] "If you are visiting Japan for tourism purposes, please tell us the specific places you will be visiting and the length of your stay."

[0785] In this way, this system improves the efficiency and accuracy of immigration inspections by combining biometric information matching technology, generative AI, voice recognition technology, and an emotion engine.

[0786] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0787] Program flow divided into processing steps

[0788] Step 1: Collect user biometric information

[0789] Specific actions

[0790] Users check in at airport terminals and provide a photo of their face, fingerprints, and iris information.

[0791] The terminal collects this biometric information, encrypts it, and sends it to the server.

[0792] Input and Output

[0793] Input: User's photo, fingerprint, and iris information.

[0794] Output: Encrypted biometric information is sent to the server.

[0795] Step 2: Verify biometric information

[0796] Specific actions

[0797] The server compares the received biometric information with existing data in a database.

[0798] Compare with past entry records and blacklists to find matching data.

[0799] Input and Output

[0800] Input: Encrypted biometric information.

[0801] Output: Matching results (matching rate and blacklist flags) are generated.

[0802] Step 3: Generate questions

[0803] Specific actions

[0804] The server uses a generation AI to generate appropriate questions based on the user's attribute information (purpose of entry, nationality, etc.).

[0805] The questions are translated into multiple languages ​​and sent to the device.

[0806] Input and Output

[0807] Input: User's demographic information (purpose of entry, nationality, etc.).

[0808] Output: The generated question (e.g., "Which tourist destination would you like to visit?").

[0809] Step 4: Posing the Question

[0810] Specific actions

[0811] The terminal presents the generated question to the user using voice synthesis technology.

[0812] The question is also displayed on the display so that the user can check it.

[0813] Input and Output

[0814] Input: The generated question and its translation.

[0815] Output: The question is presented to the user in audio and text form.

[0816] Step 5: Collect user responses

[0817] Specific actions

[0818] The user answers by voice into the microphone of the terminal.

[0819] The device receives the voice, analyzes it using voice recognition technology, and converts it into text data.

[0820] Input and Output

[0821] Input: The user's spoken response.

[0822] Output: The user's answer converted to text is sent to the server.

[0823] Step 6: Analyze emotional state

[0824] Specific actions

[0825] The server analyzes the received audio data and video feed with an emotion engine to assess the user's emotional state.

[0826] Detects emotions such as tension and anxiety and saves the evaluation results.

[0827] Input and Output

[0828] Input: User's voice data, video feed.

[0829] Output: Emotion analysis results (tension, anxiety, neutral, etc.) are generated.

[0830] Step 7: Risk Assessment

[0831] Specific actions

[0832] The server uses generative AI to perform risk assessment based on the user's responses and the results of sentiment analysis.

[0833] Risk levels are classified into three categories: "low," "medium," and "high."

[0834] Input and Output

[0835] Input: Translated user responses, sentiment analysis results.

[0836] Output: Risk level (low, medium, high).

[0837] Step 8: Determine risk treatment

[0838] Specific actions

[0839] Based on the risk assessment results, the server decides whether to continue or escalate the user's processing and sends instructions to the terminal.

[0840] If it's low risk, complete the self-service process. If it's high risk, escalate to a responsible person.

[0841] Input and Output

[0842] Input: Risk level.

[0843] Output: Action flag (self-service completed or escalated).

[0844] Step 9: Final decision and notification

[0845] Specific actions

[0846] The agent will review the escalated case and ask additional questions or clarify as needed.

[0847] The person in charge makes the final decision, enters the result into the server, and notifies the user from the server.

[0848] Input and Output

[0849] Input: Escalated user details, risk assessment results.

[0850] Output: Final assessment result (entry allowed or denied) and notification to the user.

[0851] Through these steps, the system achieves efficient and accurate immigration inspection.

[0852] (Application example 2)

[0853] 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."

[0854] Improving the efficiency and accuracy of access control is a key challenge for modern security systems. Current systems rely on biometric verification and simple questions to verify access, but do not consider the user's emotional state for risk assessment. This results in insufficient accuracy in detecting unauthorized access attempts, resulting in security risks. Furthermore, unclear escalation criteria result in a waste of human resources. Therefore, there is a need to improve security and efficiency by combining prompt generation based on the user's biometric information and attributes with emotion analysis and generative AI.

[0855] 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.

[0856] In this invention, the server includes means for collecting and verifying user biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting the questions by voice and analyzing the user's answers using voice recognition technology, means for analyzing the user's emotional state, means for reflecting the results of the emotion analysis in risk assessment, and means for the generation AI to create prompt sentences based on the user's attributes. This enables highly accurate risk assessment that takes the emotional state into account and appropriate escalation according to the situation.

[0857] A "user" is an individual using the system.

[0858] "Biometric information" is biological information used to identify an individual, such as a facial photograph, fingerprint, or retinal scan.

[0859] "Verification" is the process of comparing collected biometric information with an existing database to see if there is a match.

[0860] "Generative AI" is an algorithm that uses artificial intelligence to automatically generate questions and prompts based on user attribute information.

[0861] "Question generation" is the process of creating appropriate questions based on user attribute information.

[0862] "Speech recognition technology" is a technology for converting a user's voice into text data.

[0863] "Risk assessment" is the process of determining whether a user's behavior poses a security risk based on collected data and information.

[0864] "Self-service" is a means by which users can self-complete their own actions or procedures.

[0865] "Escalation" is the process of notifying a human reviewer when the system determines that a risk is high.

[0866] An "emotional state" is a psychological state that a user expresses through their voice and facial expressions.

[0867] "Emotion analysis" is a technology that determines a user's emotions based on collected audio and image data.

[0868] A "prompt sentence" is an instruction or question sentence created by generative AI based on the user's attributes and situation.

[0869] This invention is a system for efficiently and accurately managing access to facilities. This system collects user biometric information and combines voice recognition and emotion analysis to assess the risk of access and escalate the situation to the appropriate personnel as necessary.

[0870] Component Description

[0871] The system consists of the following main components:

[0872] 1. Collection of Biometric Information

[0873] When a user enters a facility, biometric information such as a facial photograph and fingerprints is collected using a smartphone. Specifically, a facial photograph is taken using the smartphone's camera, and a fingerprint is acquired using the fingerprint sensor.

[0874] 2. Question Generation

[0875] The server is equipped with a generative AI that generates appropriate questions based on the user's attribute information (e.g., age, gender, nationality, purpose of visit, etc.). These questions are presented to the user via voice or text. The generated questions vary depending on the situation.

[0876] 3. Voice Recognition

[0877] When the user responds verbally, the smartphone's voice recognition technology is used to convert the voice data into text data, which is then sent to a server where it is analyzed.

[0878] 4. Emotion analysis

[0879] The server includes an emotion analysis engine that analyzes the user's emotional state from their voice and facial expressions. For example, it analyzes the tone of voice and facial expressions when the user answers to detect emotions such as tension or anxiety.

[0880] 5. Risk Assessment

[0881] The server performs a risk assessment based on the received text data and the results of sentiment analysis, using generative AI to determine whether the user's reaction is natural and consistent with the purpose of entering the facility.

[0882] 6. Escalation and Notification

[0883] If the risk assessment determines that the risk is low, the visitor can complete the self-service entry procedure and receive a notification on their smartphone. If the risk is high, the procedure is escalated to a staff member who will conduct additional checks and ask questions.

[0884] Processing example

[0885] For example:

[0886] When foreign tourists enter a facility, they take a photo of their face with their smartphone and scan their fingerprints. The server uses this information to verify their identity. Next, a generation AI automatically generates a question such as "Where is your tourist destination?" and presents it to the user aloud. If the user replies "Tokyo Disneyland," this is converted into text data using voice recognition technology and sent to the server. At the same time, an emotion analysis engine analyzes the user's tone of voice and facial expressions to detect signs of tension or anxiety. The server comprehensively evaluates this information and, if the risk is low, sends a notification to the smartphone stating, "Entry permitted." If the risk is high, a notification is sent to a responsible person to ask additional questions and conduct further confirmation.

[0887] Hardware and software used

[0888] The system uses the following hardware and software:

[0889] Smartphone: A device that collects biometric information and recognizes voice.

[0890] Server: A central management system that runs generative AI, sentiment analysis engines, and risk assessments.

[0891] Open source libraries:

[0892] OpenCV: Facial photo capture and analysis.

[0893] TensorFlow / Keras: Building a sentiment analysis engine.

[0894] SpeechRecognition: Speech recognition.

[0895] Hugging Face Transformers: Using generative AI models.

[0896] Prompt Sentence Examples

[0897] Here are some example prompts the generator AI might create:

[0898] "Generate appropriate questions for users with American nationality."

[0899] "Ask users who are visiting for sightseeing what kind of tourist spots they would like to visit."

[0900] This system enables highly accurate risk assessment that takes emotional states into account and appropriate escalation according to the situation, significantly improving the security and efficiency of access control.

[0901] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0902] Step 1:

[0903] When a user enters a facility, the device (smartphone) collects the user's facial photograph and fingerprints. First, the smartphone's camera is activated and a photograph of the user's face is taken. This image is saved as a temporary file. Next, the fingerprint is scanned using the smartphone's fingerprint sensor and saved as digital data. This biometric information is sent to a server. The input is the user's facial photograph and fingerprint information, and the output is the matching result on the server.

[0904] Step 2:

[0905] The server matches the submitted biometric information with existing data in a database. Specifically, it compares the facial photo and fingerprint data against the existing database using a matching algorithm. This process verifies the user's identity. The input is the user's facial photo and fingerprint data, and the output is the matching result.

[0906] Step 3:

[0907] The generative AI model on the server generates appropriate questions based on the user's attribute information (e.g., age, gender, nationality, purpose of visit). The generated questions are sent to the terminal in the form of prompt sentences. Specifically, the generative AI takes user data as input and outputs the question as a prompt sentence. For example, a prompt sentence might be generated that reads, "Please ask users who are visiting for sightseeing what kind of tourist spots they would like to visit."

[0908] Step 4:

[0909] The device uses speech synthesis technology to present the generated question to the user aloud. The speech synthesis engine converts the text-based question into speech output, which is played through the smartphone's speaker. The user responds verbally. The input is the text of the generated question, and the output is the spoken question presented to the user.

[0910] Step 5:

[0911] When the user responds verbally, the device records the voice. The recorded voice data is analyzed by a voice recognition engine and converted into text data. Specifically, the smartphone's microphone captures the voice, and the voice data is analyzed by a voice recognition algorithm. The input is the user's voice data, and the output is text data that has been recognized by voice.

[0912] Step 6:

[0913] The server receives and analyzes the voice-recognized text data and the user's emotional state (e.g., tension, anxiety, excitement, etc.) provided by the emotion analysis engine. In emotion analysis, facial photos and voice data are processed as input to detect the user's emotional state from their tone of voice and facial expressions, and the results are passed to the risk assessment system. The inputs are the voice-recognized text data and emotional state data, and the output is the risk assessment result.

[0914] Step 7:

[0915] The server calculates the user's risk level based on the risk assessment results and decides on a response. If the risk is determined to be low, the self-service entry procedure is completed and a notification to that effect is sent to the terminal. If the risk is high, an escalation notification is sent to the person in charge, who will ask for additional confirmation or questions. The input is the risk assessment result, and the output is a self-service completion notification or an escalation notification.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] [Third embodiment]

[0920] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0921] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0922] 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).

[0923] 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.

[0924] 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.

[0925] 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).

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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."

[0932] ---

[0933] The immigration inspection system of the present invention integrates several key modules and technologies to efficiently and accurately inspect users for immigration, including biometric information collection, question generation using generative AI, response analysis using voice recognition technology, risk assessment, and self-service or escalation measures.

[0934] Specifically, the immigration inspection system of the present invention is realized by the following functional modules.

[0935] User Check-in and Identification

[0936] Biometric information collection and matching

[0937] When a user checks in at an airport terminal, the terminal collects the user's biometric information (such as a photo of their face, fingerprints, or iris), which is then sent to a server where it is matched with existing data in a database. The user's identity is then confirmed by comparing it with past immigration records and blacklists.

[0938] Question Generation

[0939] Question creation using generative AI

[0940] The server is equipped with a generation AI that automatically generates appropriate questions based on the user's attributes (such as nationality or purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[0941] Question and answer exchange

[0942] Speech synthesis and speech recognition

[0943] The device uses speech synthesis technology to present the generated questions to the user. When the user responds by voice, the device analyzes the response using speech recognition technology, converts it into text data, and sends it to the server.

[0944] Risk Assessment

[0945] Analysis and evaluation of answers

[0946] The server analyzes the received text data using the AI ​​generator to evaluate the accuracy and consistency of the answer. For example, if a user answers "I want to go to Tokyo Disneyland," the server determines whether the answer is consistent with the tourist purpose and natural.

[0947] Risk Classification

[0948] The user's risk is assessed based on the analysis results. If the risk is determined to be low, the user can complete immigration procedures through self-service. On the other hand, if the risk is high, the server will escalate the process to the appropriate person depending on the level of urgency.

[0949] Escalation and Final Decision

[0950] Review by the person in charge

[0951] If the issue is escalated, the person in charge will ask additional questions and conduct further inspections based on the user's details and the risk assessment made by the generating AI. The person in charge will make a final decision, enter the result into the server, and notify the user.

[0952] Specific examples

[0953] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The terminal scans the user's passport and takes a photo of their face. The server uses this information to identify the user, and a generation AI generates questions such as "Where are you planning to visit?" and "How long will you be staying?", which are then presented to the user using voice synthesis technology. When the user answers "Tokyo Disneyland," voice recognition technology analyzes the answer, and the server performs a risk assessment. If the risk is low, the user automatically completes immigration procedures, and the terminal notifies the user that "immigration processing has been completed." If the risk is deemed high, the matter is escalated to a responsible official for additional screening.

[0954] As a result, the system of the present invention can significantly improve the efficiency and accuracy of immigration inspections.

[0955] The processing flow will be explained below.

[0956] ---

[0957] Step 1:

[0958] User check-in and biometric collection

[0959] Terminal: When the user arrives at the airport, they scan their passport at the terminal, which collects biometric information such as a photo, fingerprint, and iris.

[0960] Terminal: Sends collected biometric information to the server.

[0961] Step 2:

[0962] User Identification

[0963] Server: The received biometric information is compared with a database to verify the user's identity. It also compares the information with past immigration records and blacklists to make an initial assessment.

[0964] Step 3:

[0965] Question generation

[0966] Server: The generation AI generates appropriate questions based on the user's attributes (nationality, purpose of entry, etc.). For example, it generates questions in Japanese and other languages ​​such as "Which tourist spots do you want to visit?" and "How many days do you plan to stay?"

[0967] Server: Sends the generated question to the terminal.

[0968] Step 4:

[0969] Posing the Question

[0970] Terminal: Using speech synthesis technology, the generated question is presented to the user aloud. For example, it reads out in Japanese, "What tourist spots would you like to visit?"

[0971] User: Answers questions by voice.

[0972] Step 5:

[0973] Response collection and analysis

[0974] Terminal: The user's responses are analyzed using voice recognition technology and converted into text data.

[0975] Terminal: Sends the converted text data to the server.

[0976] Step 6:

[0977] Analysis and evaluation of answers

[0978] Server: The generation AI analyzes the received text data and evaluates the naturalness and consistency of the answers. For example, it evaluates whether the answer "Tokyo Disneyland" matches the tourist destination.

[0979] Step 7:

[0980] Risk Classification

[0981] Server: The generating AI performs a risk assessment of the responses and classifies entrants as low-risk or high-risk.

[0982] Step 8:

[0983] Low risk actions

[0984] Server: If the risk is determined to be low, notify the terminal that "entry processing has been completed."

[0985] Terminal: Upon receiving the notification, the user completes the immigration procedure.

[0986] Step 9:

[0987] High-risk escalation

[0988] Server: If the risk is deemed high, an escalation notice is sent to the appropriate person.

[0989] Person in charge: Asks additional questions or clarifies based on the escalated information.

[0990] Person in charge: Enters the final review results into the server and notifies the user.

[0991] ---

[0992] Through this processing step, the system of the present invention can conduct immigration inspections efficiently and fairly.

[0993] Example 1

[0994] 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."

[0995] Conventional immigration inspection systems require a lot of time and effort for manual passport inspections and questioning, resulting in problems with efficiency and accuracy. They also lack multilingual support and risk assessments often rely on subjective judgment. As a result, the risk of fraudulent entry and illegal residence can increase. To solve these problems, automation and highly accurate risk assessment are required.

[0996] 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.

[0997] In this invention, the server includes means for collecting and verifying a user's biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting questions by voice and analyzing the user's answers using voice recognition technology, means for evaluating the consistency and precision of the answers, means for allowing the user to complete the procedure self-service if the risk is low, means for escalating the process by sending information to an official if the risk is high, and means for notifying the user of the final decision. This significantly improves the efficiency and accuracy of immigration inspections and reduces the risk of fraudulent entry and illegal residence.

[0998] "Biometric information" is data that indicates the personal characteristics of a user, and includes physiological information such as a facial photograph, fingerprints, and iris.

[0999] "Attributes" are information related to a user, including nationality, purpose of entry, length of stay, and the like.

[1000] "Generative AI" is artificial intelligence that automatically generates appropriate questions based on the user's attribute information.

[1001] "Speech recognition technology" is a technology that converts a user's voice into text data and is used to analyze the voice response.

[1002] "Risk assessment" is the process of assessing a user's risk level based on collected and analyzed data.

[1003] "Self-service" refers to a means by which users can automatically complete a process themselves.

[1004] "Escalation" is a process in which, for high-risk users, the system sends information to a responsible person to prompt further review or action.

[1005] The "final decision" is the decision made by the person in charge after further questioning and inspection, and is notified to the user.

[1006] "Presenting in multiple languages" refers to a means by which the questions created by the generative AI can be communicated to the user in multiple languages.

[1007] "Evaluating consistency and elaboration" is the process of evaluating whether the answers are logical, consistent, and specific.

[1008] ---

[1009] The immigration inspection system of the present invention performs immigration inspection efficiently and accurately based on the user's biometric information. This system integrates biometric information collection, question generation using AI, answer analysis using voice recognition technology, risk assessment, self-service, and escalation methods.

[1010] User Check-in and Identification

[1011] Biometric information collection and matching

[1012] When a user goes to a check-in terminal at an airport, the terminal collects the user's biometric information (such as a photo of their face, fingerprints, or iris), for example, using the terminal's built-in camera and fingerprint sensor. This collected information is sent to a server and matched with existing data in a database. The server then verifies the user's identity by comparing it with past entry records and blacklists.

[1013] Question Generation

[1014] Question creation using generative AI

[1015] The server is equipped with a generative AI (for example, OpenAI's GPT-3) that automatically generates appropriate questions based on the user's attributes (for example, nationality or purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[1016] Question and answer exchange

[1017] Speech synthesis and speech recognition

[1018] The device uses voice synthesis technology (e.g., Google Cloud Text-to-Speech) to present the generated question to the user. When the user answers verbally, the device analyzes the answer using voice recognition technology (e.g., Google Cloud Speech-to-Text), converts it into text data, and sends it to the server.

[1019] Risk Assessment

[1020] Analysis and evaluation of answers

[1021] The server analyzes the received text data and uses a generative AI to evaluate the consistency and precision of the answer. For example, if a user answers "I want to go to Tokyo Disneyland," it determines whether the answer is consistent with the tourist purpose and sounds natural.

[1022] Risk Classification

[1023] The server evaluates the user's risk based on the analysis results. If the evaluation score meets certain criteria, it is determined to be low risk. If the risk is low, the user can complete immigration procedures self-service. If the risk is high, information is sent to an official for escalation.

[1024] Escalation and Final Decision

[1025] Review by the person in charge

[1026] If the issue is escalated, the person in charge will confirm the detailed information, ask additional questions, and conduct further inspections. The person in charge will make a final decision and enter the result into the server. The server will then notify the user of the final decision.

[1027] Specific examples

[1028] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The user scans their passport with a device, takes a photo of their face, and provides fingerprints. The device sends this biometric information to a server, which verifies the information. The server uses generative AI to generate questions such as "Where are your tourist destinations?" and "How long do you plan to stay?" and sends them to the device. The device presents the questions to the user using speech synthesis technology. When the user answers "Tokyo Disneyland," the device analyzes the answer using speech recognition technology, converts it into text data, and sends it to the server. The server analyzes the answer and performs a risk assessment. If the risk is low, the user automatically completes immigration procedures, and the device notifies them that "immigration processing has been completed." If the risk is determined to be high, the case is escalated to a responsible official for additional screening.

[1029] Prompt Sentence Examples

[1030] A scenario in which a user checks in to Japan for tourism purposes and plans to visit Tokyo Disneyland.

[1031] As a result, the system of the present invention can significantly improve the efficiency and accuracy of immigration inspections.

[1032] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1033] Processing Steps

[1034] Step 1:

[1035] Input A user goes to an airport check-in kiosk, scans their passport, takes a photo of their face, and provides their fingerprints.

[1036] concrete action

[1037] When a user inserts their passport into the terminal, the terminal's camera automatically takes a photo of the user's face and the fingerprint sensor captures their fingerprint.

[1038] Output Collected biometric information (e.g., facial photo, fingerprint, iris) is stored on the device and sent to a server for processing.

[1039] Step 2:

[1040] The input server receives the biometric information sent from the terminal.

[1041] concrete action

[1042] The server compares the biometric information with existing data in a database, using facial recognition and fingerprint matching algorithms to verify the user's identity.

[1043] Output The matching results include the user's identification information, past entry records, and blacklist matching results.

[1044] Step 3:

[1045] Input User identification information and attribute information (nationality, purpose of entry, etc.).

[1046] concrete action

[1047] The server uses a generative AI model (e.g., GPT-3) to generate appropriate questions based on the user's attributes.

[1048] Output The generated questions are generated in multiple languages ​​and sent to the terminal.

[1049] Step 4:

[1050] An input terminal receives the generated query from the server.

[1051] concrete action

[1052] The device uses speech synthesis technology (e.g., Google Cloud Text-to-Speech) to present questions to the user aloud.

[1053] Output The question is presented to the user aloud.

[1054] Step 5:

[1055] Input The user answers the question by voice.

[1056] concrete action

[1057] When the user responds, the device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the voice into text data.

[1058] Output The answer converted into text data is sent from the terminal to the server.

[1059] Step 6:

[1060] The input server receives the text data of the user's answer.

[1061] concrete action

[1062] The server uses generative AI to evaluate the consistency and precision of the answers, for example, analyzing whether the answers match tourist objectives.

[1063] Output The answer analysis results in the user's risk assessment.

[1064] Step 7:

[1065] Input Answer analysis results and risk assessment.

[1066] concrete action

[1067] Based on the risk assessment, the server allows users to complete the procedure self-service if the risk is low, or sends an escalation notification to the responsible party if the risk is high.

[1068] Output The user is notified that immigration procedures have been completed, or an escalation notification is sent to the person in charge.

[1069] Step 8:

[1070] Input Escalated user details and risk assessment.

[1071] concrete action

[1072] The person in charge will review the details, ask additional questions or carry out additional tests as necessary, make a final decision, and enter the results into the server.

[1073] Output The final decision result is notified to the user via the server.

[1074] This allows the system of the present invention to achieve efficient and accurate immigration inspection.

[1075] (Application example 1)

[1076] 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."

[1077] In recent years, immigration inspections have become increasingly complex due to the large number of users involved, resulting in a demand for greater efficiency and accuracy. However, traditional immigration inspection systems often require manual inspections, consuming significant amounts of time and resources. Furthermore, there were challenges in generating appropriate questions for users who speak different languages ​​and analyzing their answers. Furthermore, there was no established method for accurately identifying high-risk users and promptly notifying immigration officials. This compromised the reliability and efficiency of the immigration inspection process.

[1078] 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.

[1079] In this invention, the server includes means for collecting and verifying a user's biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting questions aloud, analyzing the user's answers using voice recognition technology, and converting them into text data, means for conducting risk assessment based on the analyzed answers, and means for completing self-service if the risk is low, or escalating the process and notifying an official if the risk is high. This improves the efficiency and accuracy of immigration inspections. Furthermore, this system supports different languages ​​and supports biometric collection and answer input using smartphones, making it suitable for users with different backgrounds.

[1080] "User biometric information" refers to biometric information for identifying an individual, such as a facial photograph, fingerprint, or iris.

[1081] "Generative AI" is an artificial intelligence technology that automatically generates appropriate questions based on user attribute data.

[1082] "Speech recognition technology" is a technology that converts a user's voice into a digital signal and analyzes it as text data.

[1083] "Risk assessment" is a process of analyzing the user's answers and assessing the level of risk in immigration inspection.

[1084] "Escalation" is the process of automatically notifying high-risk users for further review.

[1085] "Self-service" is a system that allows users to automatically complete immigration procedures.

[1086] The "server" is a central computer system that analyzes the user's biometric information and response data and performs the necessary processing.

[1087] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[1088] The system that realizes this application integrates the following main modules and technologies: First, the user's smartphone is used. The user goes through immigration procedures using the smartphone application "Secure Travel Check-In."

[1089] 1. Collection and matching of biometric information

[1090] Hardware: Smartphone built-in camera, fingerprint sensor

[1091] Software: OpenCV, face_recognition library

[1092] Processing: When a user provides biometric information (face photo, fingerprint) to a smartphone application, the application collects it and sends it to a server, where it matches the collected information with existing data in a database.

[1093] 2. Question Generation

[1094] Software: OpenAI GPT-3 model

[1095] Processing: The server automatically generates appropriate questions using a generative AI model based on the user's attribute data (e.g., nationality and purpose of entry). The generated questions are then sent from the server to the smartphone application.

[1096] Example prompt: "The user is a tourist. Please generate questions suitable for immigration."

[1097] 3. Question and answer exchanges

[1098] Hardware: Smartphone microphone and speaker

[1099] Software: Google Speech-to-Text API

[1100] Processing: The smartphone application uses voice synthesis technology to present the questions sent from the server to the user. The user's voice response is converted into text data using voice recognition technology and sent to the server.

[1101] 4. Risk Assessment

[1102] Software: OpenAI GPT-3 model

[1103] Processing: The server analyzes the received text data using the generation AI and evaluates the accuracy and consistency of the answers. Based on the analysis results, the user's risk level is assessed.

[1104] Example prompt: "User answered: Tokyo Disneyland. Please rate the risk level based on this answer."

[1105] 5. Escalation and Final Decision

[1106] Processing: If the risk is low, the server allows the user to complete the immigration process on their own. If the risk is high, an escalation notification is sent to an officer who can conduct additional questioning or screening.

[1107] Specific examples

[1108] A foreign user wishes to enter Japan for tourism purposes using the smartphone application "Secure Travel Check-In." First, the user takes a photo of their face with their smartphone camera, scans their fingerprints, and provides them to the application. The server identifies the user based on this information, and a generative AI model generates questions such as "Where are your tourist destinations?" and "How long will you be staying?" These questions are presented to the user using speech synthesis technology. If the user answers "Tokyo Disneyland," speech recognition technology converts the answer into text data, and the server performs a risk assessment. If the risk is deemed low, the user automatically completes immigration procedures. However, if the risk is deemed high, an official is notified and additional screening is carried out.

[1109] In this way, the system of the invention significantly improves the efficiency and accuracy of immigration inspections.

[1110] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1111] Step 1:

[1112] A user launches a smartphone application and provides a facial photograph and fingerprint. At this time, biometric information is collected using the smartphone's camera and fingerprint sensor. This information is sent from the smartphone application to a server. The input is the user's facial photograph and fingerprint data, and the output is the biometric information sent to the server.

[1113] Step 2:

[1114] The server compares the received biometric information with existing data in a database. Here, it performs face recognition using OpenCV and the face_recognition library. It also performs fingerprint authentication. The input is biometric information, and the output is the user's identification result. Specifically, the server analyzes the face image and fingerprint to check whether they match.

[1115] Step 3:

[1116] The server uses a generative AI model (OpenAI GPT-3) to generate appropriate questions based on the user's attribute data (e.g., nationality and purpose of entry). The input is the user's attribute data, and the output is the generated question. An example of a prompt sentence: "The user is a tourist. Please generate questions appropriate for immigration inspection."

[1117] Step 4:

[1118] The server sends the generated question to the smartphone application. The input is the generated question, and the output is the question sent to the smartphone application. Specifically, the server sends the generated question to the application via an API.

[1119] Step 5:

[1120] The smartphone application uses speech synthesis technology to present the received questions to the user. The input is the question sent from the server, and the output is a voice message suggested to the user. Specifically, the application converts text into speech and transmits it to the user through the smartphone's speaker.

[1121] Step 6:

[1122] The user responds verbally, and the smartphone application's microphone receives the response. The input is the user's verbal response, and the output is the smartphone's voice data.

[1123] Step 7:

[1124] The smartphone application uses speech recognition technology (Google Speech-to-Text API) to convert the voice response into text data and send it to the server. The input is the user's voice data and the output is text data. Specifically, the application analyzes the voice data, converts it into text, and sends that data to the server.

[1125] Step 8:

[1126] The server analyzes the received text data using a generative AI model and evaluates the precision and consistency of the user's answers. Based on the results of this analysis, the server evaluates the user's risk level. The input is text data, and the output is the result of the risk assessment. An example of a prompt is: "User's answer: Tokyo Disneyland. Please evaluate the risk level based on this answer."

[1127] Step 9:

[1128] If the server determines that the risk level is low, the user receives a notification to complete immigration procedures through self-service. If the risk level is high, an officer is notified and additional questions or inspections are carried out. The input is the result of the risk assessment, and the output is a notification of the completion of immigration procedures or a notification of escalation. Specifically, the server performs the appropriate action (self-service completion or officer notification) based on the risk assessment.

[1129] 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.

[1130] ---

[1131] This invention achieves efficient and accurate immigration screening by combining biometric information collection, question generation using generative AI, voice recognition technology, and an emotion engine. This system is composed of the following functional modules:

[1132] User Check-in and Identification

[1133] Biometric information collection and matching

[1134] When a user checks in at an airport terminal, the terminal collects the user's biometric information (e.g., facial photograph, fingerprint, iris, etc.), which is then sent to a server where it is matched with existing data in a database, comparing it with past immigration records and blacklists to confirm the user's identity.

[1135] Question Generation

[1136] Question creation using generative AI

[1137] The server is equipped with a generation AI that generates appropriate questions based on the user's attributes (such as nationality and purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[1138] Question and answer exchange

[1139] Speech synthesis and speech recognition

[1140] The device uses speech synthesis technology to present the generated questions to the user. When the user responds by voice, the device analyzes the response using speech recognition technology, converts it into text data, and sends it to the server.

[1141] Analysis by emotion engine

[1142] Emotional state analysis

[1143] The server includes an emotion engine that analyzes the user's emotional state from their voice and facial expressions. For example, it can detect emotions such as tension or anxiety by analyzing the tone of voice and facial expressions when the user answers.

[1144] Risk Assessment

[1145] Risk assessment based on response content and sentiment analysis

[1146] The server uses generative AI to assess risk based on the received text data and the analysis results of the emotion engine. For example, if a user answers "I want to go to Tokyo Disneyland," and the emotion engine recognizes an appropriate emotional response to that answer, it evaluates whether the response is consistent with the tourist purpose and whether it is a natural response.

[1147] Risk Classification

[1148] Classification by risk assessment

[1149] The user's risk is assessed based on the analysis results. If the risk is deemed low, the user is provided with a means to complete immigration procedures through self-service. On the other hand, if the risk is high, the server notifies the relevant person for escalation.

[1150] Escalation and Final Decision

[1151] Further review by personnel

[1152] If the issue is escalated, the person in charge will ask additional questions and confirm the details of the user and the risk assessment made by the generative AI and emotion engine. The person in charge will make a final decision, enter the result into the server, and notify the user.

[1153] Specific examples

[1154] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The terminal scans the user's passport and takes a photo of the user's face. The server uses this information to identify the user, and a generation AI generates questions such as "Where are you planning to visit?" and "How long will you be staying?", which are then presented to the user using speech synthesis technology. When the user answers "Tokyo Disneyland," the terminal uses voice recognition technology to analyze the answer and sends it to the server. At the same time, an emotion engine analyzes the user's tone of voice and facial expressions to detect signs of tension or anxiety. The server performs a risk assessment based on this information, and if the risk is low, the immigration process is completed, and the terminal notifies the user that "immigration processing has been completed." If the risk is deemed high, the case is escalated to a responsible official for additional screening.

[1155] This allows the system of the present invention to significantly improve the efficiency and accuracy of immigration inspections. In addition, by incorporating an emotion engine, the system can grasp the user's emotional state and perform more accurate risk assessments.

[1156] The processing flow will be explained below.

[1157] ---

[1158] Step 1:

[1159] Biometric collection and check-in

[1160] User: Upon arrival at the airport, scan your passport at the terminal and check in.

[1161] Terminal: Collects biometric information such as the user's face photo, fingerprint, and iris.

[1162] Terminal: Sends collected biometric information to the server.

[1163] Step 2:

[1164] User Identification

[1165] Server: The received biometric information is compared with a database, including past immigration records and blacklists, to verify the user's identity.

[1166] Step 3:

[1167] Question generation

[1168] Server: Based on the user's attributes (nationality, purpose of entry, etc.), the AI ​​generates appropriate questions. For example, it generates questions in Japanese and other languages, such as "Which tourist spots do you want to visit?" and "How many days do you plan to stay?"

[1169] Server: Sends the generated question to the terminal.

[1170] Step 4:

[1171] Posing the Question

[1172] Terminal: The generated question is presented to the user using speech synthesis technology. For example, it reads out in Japanese, "What tourist spots would you like to visit?"

[1173] User: Answers questions by voice.

[1174] Step 5:

[1175] Response collection and analysis

[1176] Terminal: Converts the user's response into text data using voice recognition technology.

[1177] Terminal: Sends the converted text data to the server.

[1178] Step 6:

[1179] Emotional state analysis

[1180] Server: Uses an emotion engine to analyze the user's emotional state (e.g., tension, anxiety, etc.) from their voice and facial expressions.

[1181] Step 7:

[1182] Risk assessment based on response content and sentiment analysis

[1183] Server: The generation AI performs a risk assessment based on the response content and the analysis results of the emotion engine. For example, if a user responds "I want to go to Tokyo Disneyland" and the emotion engine detects a natural emotional response, it evaluates whether the response matches the tourist purpose.

[1184] Step 8:

[1185] Risk Classification

[1186] Server: If the user is at low risk, it sends instructions to the terminal to complete the self-service. If the user is at high risk, it sends instructions for escalation.

[1187] Step 9:

[1188] Low risk actions

[1189] Terminal: Receives instructions from the server and notifies the user that "entry processing has been completed."

[1190] User: Complete immigration procedures.

[1191] Step 10:

[1192] High-risk escalation

[1193] Server: If the risk is deemed high, an escalation notice is sent to the appropriate person.

[1194] Person in charge: Asks additional questions or clarifies based on the escalated information.

[1195] Person in charge: Enters the final review results into the server and notifies the user.

[1196] ---

[1197] Through the above processing steps, the system of the present invention realizes efficient and accurate immigration inspection. In addition, by incorporating an emotion engine, it is possible to grasp the user's emotional state and perform more accurate risk assessment.

[1198] Example 2

[1199] 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."

[1200] Conventional immigration inspection systems take a lot of time to identify users and answer questions, and because they do not take the user's emotional state into account, risk assessment is often inaccurate. This results in problems such as reduced inspection efficiency and an increased risk of illegal entry. Therefore, a new system that can achieve efficient and accurate immigration inspection is needed.

[1201] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and verifying biometric information of a user, means including a generation AI for generating questions based on user attributes, means for presenting questions by voice and analyzing the user's answers using voice recognition technology, means for performing risk assessment based on the analyzed answers and emotion analysis results, and means for completing self-service if the risk is low and escalating if the risk is high. This enables highly accurate risk assessment that takes the user's emotional state into consideration, improving the efficiency and accuracy of immigration inspections.

[1202] "Biometric information" refers to information that indicates an individual's physical characteristics, such as a photograph of their face, fingerprints, or iris, for use in biometric authentication.

[1203] "Generative AI" is an artificial intelligence technology that automatically generates appropriate questions based on the user's attribute information.

[1204] "Speech recognition technology" is a technology that analyzes a user's voice and converts it into text data.

[1205] The "server" is a central computer system that centrally processes tasks such as matching biometric information, generating questions using AI, and assessing the risk of answers.

[1206] A "terminal" is a device that is directly operated by a user to collect biometric information, pose questions, and input answers.

[1207] An "emotion engine" is software or hardware that analyzes a user's voice and facial expressions and evaluates their emotional state.

[1208] "Risk assessment" is a process of assessing the risk of entry into a country on a three-point scale: "low," "medium," or "high" based on the user's responses and the results of sentiment analysis.

[1209] "Self-service" means that users can complete immigration procedures on their own.

[1210] "Escalation" is the process of requesting further review from a responsible party when a risk is deemed high.

[1211] The "person in charge" is the person in charge or the inspector who makes the final decision on entry permission.

[1212] MODE FOR CARRYING OUT THE INVENTION

[1213] This invention is a system that combines biometric information collection, question generation using generative AI, voice recognition technology, and an emotion engine to efficiently and accurately conduct immigration inspections for users. In this system, the server and terminal work together to collect and authenticate the user's biometric information and perform risk assessment based on the generated questions.

[1214] Hardware and Software Configuration

[1215] Device:

[1216] It is equipped with a camera, fingerprint sensor, and iris recognition sensor to collect biometric information.

[1217] Includes a microphone and speaker for audio interface with the user.

[1218] It has a display that displays the user interface.

[1219] server:

[1220] It connects to the database and verifies the biometric information.

[1221] It is equipped with generative AI that generates questions based on the user's attributes.

[1222] Voice recognition technology is used to convert voice data into text.

[1223] An emotion engine is used to analyze the user's voice and facial expressions to assess their emotional state.

[1224] Data processing and calculation

[1225] When a user checks in at an airport terminal, the terminal collects biometric information such as a facial photo, fingerprints, and iris scan, and sends it to a server. The server compares the received biometric information with existing data in a database. Next, a generation AI generates questions based on the user's attribute information (for example, purpose of entry and nationality). These questions are presented to the user from the terminal using speech synthesis technology. When the user responds verbally, the terminal analyzes the response using speech recognition technology, converts it into text data, and sends it to the server. The server then uses an emotion engine to analyze the user's voice data and facial expressions to evaluate their emotional state. A risk assessment is performed based on this data, and depending on the result, the self-service is either completed or escalated.

[1226] Specific examples

[1227] For example, consider a foreign user entering Japan for tourism. The user scans their passport and takes a photo of their face at an airport terminal. The device's camera acts as a facial recognition sensor, and biometric information is collected using a fingerprint sensor and iris recognition sensor. This information is then sent to a server and compared against an existing database.

[1228] The generation AI confirms that the user is traveling and generates questions such as, "Which tourist spots do you want to visit?" and "How many days do you plan to stay?" These questions are presented to the user using voice synthesis technology. When the user answers, "I want to go to Tokyo Disneyland," the device analyzes the voice, converts it into text data, and sends it to the server.

[1229] The server then uses an emotion engine to analyze the user's tone of voice and facial expressions to assess their emotional state. Based on this information, the server performs a risk assessment, and if the risk is deemed low, the device notifies the user that entry processing has been completed. If the risk is deemed high, the process is escalated to a staff member who will ask additional questions and provide confirmation before making a final decision.

[1230] Prompt Sentence Examples

[1231] "If you are visiting Japan for tourism purposes, please tell us the specific places you will be visiting and the length of your stay."

[1232] In this way, this system improves the efficiency and accuracy of immigration inspections by combining biometric information matching technology, generative AI, voice recognition technology, and an emotion engine.

[1233] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1234] Program flow divided into processing steps

[1235] Step 1: Collect user biometric information

[1236] Specific actions

[1237] Users check in at airport terminals and provide a photo of their face, fingerprints, and iris information.

[1238] The terminal collects this biometric information, encrypts it, and sends it to the server.

[1239] Input and Output

[1240] Input: User's photo, fingerprint, and iris information.

[1241] Output: Encrypted biometric information is sent to the server.

[1242] Step 2: Verify biometric information

[1243] Specific actions

[1244] The server compares the received biometric information with existing data in a database.

[1245] Compare with past entry records and blacklists to find matching data.

[1246] Input and Output

[1247] Input: Encrypted biometric information.

[1248] Output: Matching results (matching rate and blacklist flags) are generated.

[1249] Step 3: Generate questions

[1250] Specific actions

[1251] The server uses a generation AI to generate appropriate questions based on the user's attribute information (purpose of entry, nationality, etc.).

[1252] The questions are translated into multiple languages ​​and sent to the device.

[1253] Input and Output

[1254] Input: User's demographic information (purpose of entry, nationality, etc.).

[1255] Output: The generated question (e.g., "Which tourist destination would you like to visit?").

[1256] Step 4: Posing the Question

[1257] Specific actions

[1258] The terminal presents the generated question to the user using voice synthesis technology.

[1259] The question is also displayed on the display so that the user can check it.

[1260] Input and Output

[1261] Input: The generated question and its translation.

[1262] Output: The question is presented to the user in audio and text form.

[1263] Step 5: Collect user responses

[1264] Specific actions

[1265] The user answers by voice into the microphone of the terminal.

[1266] The device receives the voice, analyzes it using voice recognition technology, and converts it into text data.

[1267] Input and Output

[1268] Input: The user's spoken response.

[1269] Output: The user's answer converted to text is sent to the server.

[1270] Step 6: Analyze emotional state

[1271] Specific actions

[1272] The server analyzes the received audio data and video feed with an emotion engine to assess the user's emotional state.

[1273] Detects emotions such as tension and anxiety and saves the evaluation results.

[1274] Input and Output

[1275] Input: User's voice data, video feed.

[1276] Output: Emotion analysis results (tension, anxiety, neutral, etc.) are generated.

[1277] Step 7: Risk Assessment

[1278] Specific actions

[1279] The server uses generative AI to perform risk assessment based on the user's responses and the results of sentiment analysis.

[1280] Risk levels are classified into three categories: "low," "medium," and "high."

[1281] Input and Output

[1282] Input: Translated user responses, sentiment analysis results.

[1283] Output: Risk level (low, medium, high).

[1284] Step 8: Determine risk treatment

[1285] Specific actions

[1286] Based on the risk assessment results, the server decides whether to continue or escalate the user's processing and sends instructions to the terminal.

[1287] If it's low risk, complete the self-service process. If it's high risk, escalate to a responsible person.

[1288] Input and Output

[1289] Input: Risk level.

[1290] Output: Action flag (self-service completed or escalated).

[1291] Step 9: Final decision and notification

[1292] Specific actions

[1293] The agent will review the escalated case and ask additional questions or clarify as needed.

[1294] The person in charge makes the final decision, enters the result into the server, and notifies the user from the server.

[1295] Input and Output

[1296] Input: Escalated user details, risk assessment results.

[1297] Output: Final assessment result (entry allowed or denied) and notification to the user.

[1298] Through these steps, the system achieves efficient and accurate immigration inspection.

[1299] (Application example 2)

[1300] 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."

[1301] Improving the efficiency and accuracy of access control is a key challenge for modern security systems. Current systems rely on biometric verification and simple questions to verify access, but do not consider the user's emotional state for risk assessment. This results in insufficient accuracy in detecting unauthorized access attempts, resulting in security risks. Furthermore, unclear escalation criteria result in a waste of human resources. Therefore, there is a need to improve security and efficiency by combining prompt generation based on the user's biometric information and attributes with emotion analysis and generative AI.

[1302] 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.

[1303] In this invention, the server includes means for collecting and verifying user biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting the questions by voice and analyzing the user's answers using voice recognition technology, means for analyzing the user's emotional state, means for reflecting the results of the emotion analysis in risk assessment, and means for the generation AI to create prompt sentences based on the user's attributes. This enables highly accurate risk assessment that takes the emotional state into account and appropriate escalation according to the situation.

[1304] A "user" is an individual using the system.

[1305] "Biometric information" is biological information used to identify an individual, such as a facial photograph, fingerprint, or retinal scan.

[1306] "Verification" is the process of comparing collected biometric information with an existing database to see if there is a match.

[1307] "Generative AI" is an algorithm that uses artificial intelligence to automatically generate questions and prompts based on user attribute information.

[1308] "Question generation" is the process of creating appropriate questions based on user attribute information.

[1309] "Speech recognition technology" is a technology for converting a user's voice into text data.

[1310] "Risk assessment" is the process of determining whether a user's behavior poses a security risk based on collected data and information.

[1311] "Self-service" is a means by which users can self-complete their own actions or procedures.

[1312] "Escalation" is the process of notifying a human reviewer when the system determines that a risk is high.

[1313] An "emotional state" is a psychological state that a user expresses through their voice and facial expressions.

[1314] "Emotion analysis" is a technology that determines a user's emotions based on collected audio and image data.

[1315] A "prompt sentence" is an instruction or question sentence created by generative AI based on the user's attributes and situation.

[1316] This invention is a system for efficiently and accurately managing access to facilities. This system collects user biometric information and combines voice recognition and emotion analysis to assess the risk of access and escalate the situation to the appropriate personnel as necessary.

[1317] Component Description

[1318] The system consists of the following main components:

[1319] 1. Collection of Biometric Information

[1320] When a user enters a facility, biometric information such as a facial photograph and fingerprints is collected using a smartphone. Specifically, a facial photograph is taken using the smartphone's camera, and a fingerprint is acquired using the fingerprint sensor.

[1321] 2. Question Generation

[1322] The server is equipped with a generative AI that generates appropriate questions based on the user's attribute information (e.g., age, gender, nationality, purpose of visit, etc.). These questions are presented to the user via voice or text. The generated questions vary depending on the situation.

[1323] 3. Voice Recognition

[1324] When the user responds verbally, the smartphone's voice recognition technology is used to convert the voice data into text data, which is then sent to a server where it is analyzed.

[1325] 4. Emotion analysis

[1326] The server includes an emotion analysis engine that analyzes the user's emotional state from their voice and facial expressions. For example, it analyzes the tone of voice and facial expressions when the user answers to detect emotions such as tension or anxiety.

[1327] 5. Risk Assessment

[1328] The server performs a risk assessment based on the received text data and the results of sentiment analysis, using generative AI to determine whether the user's reaction is natural and consistent with the purpose of entering the facility.

[1329] 6. Escalation and Notification

[1330] If the risk assessment determines that the risk is low, the visitor can complete the self-service entry procedure and receive a notification on their smartphone. If the risk is high, the procedure is escalated to a staff member who will conduct additional checks and ask questions.

[1331] Processing example

[1332] For example:

[1333] When foreign tourists enter a facility, they take a photo of their face with their smartphone and scan their fingerprints. The server uses this information to verify their identity. Next, a generation AI automatically generates a question such as "Where is your tourist destination?" and presents it to the user aloud. If the user replies "Tokyo Disneyland," this is converted into text data using voice recognition technology and sent to the server. At the same time, an emotion analysis engine analyzes the user's tone of voice and facial expressions to detect signs of tension or anxiety. The server comprehensively evaluates this information and, if the risk is low, sends a notification to the smartphone stating, "Entry permitted." If the risk is high, a notification is sent to a responsible person to ask additional questions and conduct further confirmation.

[1334] Hardware and software used

[1335] The system uses the following hardware and software:

[1336] Smartphone: A device that collects biometric information and recognizes voice.

[1337] Server: A central management system that runs generative AI, sentiment analysis engines, and risk assessments.

[1338] Open source libraries:

[1339] OpenCV: Facial photo capture and analysis.

[1340] TensorFlow / Keras: Building a sentiment analysis engine.

[1341] SpeechRecognition: Speech recognition.

[1342] Hugging Face Transformers: Using generative AI models.

[1343] Prompt Sentence Examples

[1344] Here are some example prompts the generator AI might create:

[1345] "Generate appropriate questions for users with American nationality."

[1346] "Ask users who are visiting for sightseeing what kind of tourist spots they would like to visit."

[1347] This system enables highly accurate risk assessment that takes emotional states into account and appropriate escalation according to the situation, significantly improving the security and efficiency of access control.

[1348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1349] Step 1:

[1350] When a user enters a facility, the device (smartphone) collects the user's facial photograph and fingerprints. First, the smartphone's camera is activated and a photograph of the user's face is taken. This image is saved as a temporary file. Next, the fingerprint is scanned using the smartphone's fingerprint sensor and saved as digital data. This biometric information is sent to a server. The input is the user's facial photograph and fingerprint information, and the output is the matching result on the server.

[1351] Step 2:

[1352] The server matches the submitted biometric information with existing data in a database. Specifically, it compares the facial photo and fingerprint data against the existing database using a matching algorithm. This process verifies the user's identity. The input is the user's facial photo and fingerprint data, and the output is the matching result.

[1353] Step 3:

[1354] The generative AI model on the server generates appropriate questions based on the user's attribute information (e.g., age, gender, nationality, purpose of visit). The generated questions are sent to the terminal in the form of prompt sentences. Specifically, the generative AI takes user data as input and outputs the question as a prompt sentence. For example, a prompt sentence might be generated that reads, "Please ask users who are visiting for sightseeing what kind of tourist spots they would like to visit."

[1355] Step 4:

[1356] The device uses speech synthesis technology to present the generated question to the user aloud. The speech synthesis engine converts the text-based question into speech output, which is played through the smartphone's speaker. The user responds verbally. The input is the text of the generated question, and the output is the spoken question presented to the user.

[1357] Step 5:

[1358] When the user responds verbally, the device records the voice. The recorded voice data is analyzed by a voice recognition engine and converted into text data. Specifically, the smartphone's microphone captures the voice, and the voice data is analyzed by a voice recognition algorithm. The input is the user's voice data, and the output is text data that has been recognized by voice.

[1359] Step 6:

[1360] The server receives and analyzes the voice-recognized text data and the user's emotional state (e.g., tension, anxiety, excitement, etc.) provided by the emotion analysis engine. In emotion analysis, facial photos and voice data are processed as input to detect the user's emotional state from their tone of voice and facial expressions, and the results are passed to the risk assessment system. The inputs are the voice-recognized text data and emotional state data, and the output is the risk assessment result.

[1361] Step 7:

[1362] The server calculates the user's risk level based on the risk assessment results and decides on a response. If the risk is determined to be low, the self-service entry procedure is completed and a notification to that effect is sent to the terminal. If the risk is high, an escalation notification is sent to the person in charge, who will ask for additional confirmation or questions. The input is the risk assessment result, and the output is a self-service completion notification or an escalation notification.

[1363] 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.

[1364] 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.

[1365] 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.

[1366] [Fourth embodiment]

[1367] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1368] 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.

[1369] 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).

[1370] 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.

[1371] 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.

[1372] 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).

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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."

[1380] ---

[1381] The immigration inspection system of the present invention integrates several key modules and technologies to efficiently and accurately inspect users for immigration, including biometric information collection, question generation using generative AI, response analysis using voice recognition technology, risk assessment, and self-service or escalation measures.

[1382] Specifically, the immigration inspection system of the present invention is realized by the following functional modules.

[1383] User Check-in and Identification

[1384] Biometric information collection and matching

[1385] When a user checks in at an airport terminal, the terminal collects the user's biometric information (such as a photo of their face, fingerprints, or iris), which is then sent to a server where it is matched with existing data in a database. The user's identity is then confirmed by comparing it with past immigration records and blacklists.

[1386] Question Generation

[1387] Question creation using generative AI

[1388] The server is equipped with a generation AI that automatically generates appropriate questions based on the user's attributes (such as nationality or purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[1389] Question and answer exchange

[1390] Speech synthesis and speech recognition

[1391] The device uses speech synthesis technology to present the generated questions to the user. When the user responds by voice, the device analyzes the response using speech recognition technology, converts it into text data, and sends it to the server.

[1392] Risk Assessment

[1393] Analysis and evaluation of answers

[1394] The server analyzes the received text data using the AI ​​generator to evaluate the accuracy and consistency of the answer. For example, if a user answers "I want to go to Tokyo Disneyland," the server determines whether the answer is consistent with the tourist purpose and natural.

[1395] Risk Classification

[1396] The user's risk is assessed based on the analysis results. If the risk is determined to be low, the user can complete immigration procedures through self-service. On the other hand, if the risk is high, the server will escalate the process to the appropriate person depending on the level of urgency.

[1397] Escalation and Final Decision

[1398] Review by the person in charge

[1399] If the issue is escalated, the person in charge will ask additional questions and conduct further inspections based on the user's details and the risk assessment made by the generating AI. The person in charge will make a final decision, enter the result into the server, and notify the user.

[1400] Specific examples

[1401] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The terminal scans the user's passport and takes a photo of their face. The server uses this information to identify the user, and a generation AI generates questions such as "Where are you planning to visit?" and "How long will you be staying?", which are then presented to the user using voice synthesis technology. When the user answers "Tokyo Disneyland," voice recognition technology analyzes the answer, and the server performs a risk assessment. If the risk is low, the user automatically completes immigration procedures, and the terminal notifies the user that "immigration processing has been completed." If the risk is deemed high, the matter is escalated to a responsible official for additional screening.

[1402] As a result, the system of the present invention can significantly improve the efficiency and accuracy of immigration inspections.

[1403] The processing flow will be explained below.

[1404] ---

[1405] Step 1:

[1406] User check-in and biometric collection

[1407] Terminal: When the user arrives at the airport, they scan their passport at the terminal, which collects biometric information such as a photo, fingerprint, and iris.

[1408] Terminal: Sends collected biometric information to the server.

[1409] Step 2:

[1410] User Identification

[1411] Server: The received biometric information is compared with a database to verify the user's identity. It also compares the information with past immigration records and blacklists to make an initial assessment.

[1412] Step 3:

[1413] Question generation

[1414] Server: The generation AI generates appropriate questions based on the user's attributes (nationality, purpose of entry, etc.). For example, it generates questions in Japanese and other languages ​​such as "Which tourist spots do you want to visit?" and "How many days do you plan to stay?"

[1415] Server: Sends the generated question to the terminal.

[1416] Step 4:

[1417] Posing the Question

[1418] Terminal: Using speech synthesis technology, the generated question is presented to the user aloud. For example, it reads out in Japanese, "What tourist spots would you like to visit?"

[1419] User: Answers questions by voice.

[1420] Step 5:

[1421] Response collection and analysis

[1422] Terminal: The user's responses are analyzed using voice recognition technology and converted into text data.

[1423] Terminal: Sends the converted text data to the server.

[1424] Step 6:

[1425] Analysis and evaluation of answers

[1426] Server: The generation AI analyzes the received text data and evaluates the naturalness and consistency of the answers. For example, it evaluates whether the answer "Tokyo Disneyland" matches the tourist destination.

[1427] Step 7:

[1428] Risk Classification

[1429] Server: The generating AI performs a risk assessment of the responses and classifies entrants as low-risk or high-risk.

[1430] Step 8:

[1431] Low risk actions

[1432] Server: If the risk is determined to be low, notify the terminal that "entry processing has been completed."

[1433] Terminal: Upon receiving the notification, the user completes the immigration procedure.

[1434] Step 9:

[1435] High-risk escalation

[1436] Server: If the risk is deemed high, an escalation notice is sent to the appropriate person.

[1437] Person in charge: Asks additional questions or clarifies based on the escalated information.

[1438] Person in charge: Enters the final review results into the server and notifies the user.

[1439] ---

[1440] Through this processing step, the system of the present invention can conduct immigration inspections efficiently and fairly.

[1441] Example 1

[1442] 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."

[1443] Conventional immigration inspection systems require a lot of time and effort for manual passport inspections and questioning, resulting in problems with efficiency and accuracy. They also lack multilingual support and risk assessments often rely on subjective judgment. As a result, the risk of fraudulent entry and illegal residence can increase. To solve these problems, automation and highly accurate risk assessment are required.

[1444] 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.

[1445] In this invention, the server includes means for collecting and verifying a user's biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting questions by voice and analyzing the user's answers using voice recognition technology, means for evaluating the consistency and precision of the answers, means for allowing the user to complete the procedure self-service if the risk is low, means for escalating the process by sending information to an official if the risk is high, and means for notifying the user of the final decision. This significantly improves the efficiency and accuracy of immigration inspections and reduces the risk of fraudulent entry and illegal residence.

[1446] "Biometric information" is data that indicates the personal characteristics of a user, and includes physiological information such as a facial photograph, fingerprints, and iris.

[1447] "Attributes" are information related to a user, including nationality, purpose of entry, length of stay, and the like.

[1448] "Generative AI" is artificial intelligence that automatically generates appropriate questions based on the user's attribute information.

[1449] "Speech recognition technology" is a technology that converts a user's voice into text data and is used to analyze the voice response.

[1450] "Risk assessment" is the process of assessing a user's risk level based on collected and analyzed data.

[1451] "Self-service" refers to a means by which users can automatically complete a process themselves.

[1452] "Escalation" is a process in which, for high-risk users, the system sends information to a responsible person to prompt further review or action.

[1453] The "final decision" is the decision made by the person in charge after further questioning and inspection, and is notified to the user.

[1454] "Presenting in multiple languages" refers to a means by which the questions created by the generative AI can be communicated to the user in multiple languages.

[1455] "Evaluating consistency and elaboration" is the process of evaluating whether the answers are logical, consistent, and specific.

[1456] ---

[1457] The immigration inspection system of the present invention performs immigration inspection efficiently and accurately based on the user's biometric information. This system integrates biometric information collection, question generation using AI, answer analysis using voice recognition technology, risk assessment, self-service, and escalation methods.

[1458] User Check-in and Identification

[1459] Biometric information collection and matching

[1460] When a user goes to a check-in terminal at an airport, the terminal collects the user's biometric information (such as a photo of their face, fingerprints, or iris), for example, using the terminal's built-in camera and fingerprint sensor. This collected information is sent to a server and matched with existing data in a database. The server then verifies the user's identity by comparing it with past entry records and blacklists.

[1461] Question Generation

[1462] Question creation using generative AI

[1463] The server is equipped with a generative AI (for example, OpenAI's GPT-3) that automatically generates appropriate questions based on the user's attributes (for example, nationality or purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[1464] Question and answer exchange

[1465] Speech synthesis and speech recognition

[1466] The device uses voice synthesis technology (e.g., Google Cloud Text-to-Speech) to present the generated question to the user. When the user answers verbally, the device analyzes the answer using voice recognition technology (e.g., Google Cloud Speech-to-Text), converts it into text data, and sends it to the server.

[1467] Risk Assessment

[1468] Analysis and evaluation of answers

[1469] The server analyzes the received text data and uses a generative AI to evaluate the consistency and precision of the answer. For example, if a user answers "I want to go to Tokyo Disneyland," it determines whether the answer is consistent with the tourist purpose and sounds natural.

[1470] Risk Classification

[1471] The server evaluates the user's risk based on the analysis results. If the evaluation score meets certain criteria, it is determined to be low risk. If the risk is low, the user can complete immigration procedures self-service. If the risk is high, information is sent to an official for escalation.

[1472] Escalation and Final Decision

[1473] Review by the person in charge

[1474] If the issue is escalated, the person in charge will confirm the detailed information, ask additional questions, and conduct further inspections. The person in charge will make a final decision and enter the result into the server. The server will then notify the user of the final decision.

[1475] Specific examples

[1476] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The user scans their passport with a device, takes a photo of their face, and provides fingerprints. The device sends this biometric information to a server, which verifies the information. The server uses generative AI to generate questions such as "Where are your tourist destinations?" and "How long do you plan to stay?" and sends them to the device. The device presents the questions to the user using speech synthesis technology. When the user answers "Tokyo Disneyland," the device analyzes the answer using speech recognition technology, converts it into text data, and sends it to the server. The server analyzes the answer and performs a risk assessment. If the risk is low, the user automatically completes immigration procedures, and the device notifies them that "immigration processing has been completed." If the risk is determined to be high, the case is escalated to a responsible official for additional screening.

[1477] Prompt Sentence Examples

[1478] A scenario in which a user checks in to Japan for tourism purposes and plans to visit Tokyo Disneyland.

[1479] As a result, the system of the present invention can significantly improve the efficiency and accuracy of immigration inspections.

[1480] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1481] Processing Steps

[1482] Step 1:

[1483] Input A user goes to an airport check-in kiosk, scans their passport, takes a photo of their face, and provides their fingerprints.

[1484] concrete action

[1485] When a user inserts their passport into the terminal, the terminal's camera automatically takes a photo of the user's face and the fingerprint sensor captures their fingerprint.

[1486] Output Collected biometric information (e.g., facial photo, fingerprint, iris) is stored on the device and sent to a server for processing.

[1487] Step 2:

[1488] The input server receives the biometric information sent from the terminal.

[1489] concrete action

[1490] The server compares the biometric information with existing data in a database, using facial recognition and fingerprint matching algorithms to verify the user's identity.

[1491] Output The matching results include the user's identification information, past entry records, and blacklist matching results.

[1492] Step 3:

[1493] Input User identification information and attribute information (nationality, purpose of entry, etc.).

[1494] concrete action

[1495] The server uses a generative AI model (e.g., GPT-3) to generate appropriate questions based on the user's attributes.

[1496] Output The generated questions are generated in multiple languages ​​and sent to the terminal.

[1497] Step 4:

[1498] An input terminal receives the generated query from the server.

[1499] concrete action

[1500] The device uses speech synthesis technology (e.g., Google Cloud Text-to-Speech) to present questions to the user aloud.

[1501] Output The question is presented to the user aloud.

[1502] Step 5:

[1503] Input The user answers the question by voice.

[1504] concrete action

[1505] When the user responds, the device uses voice recognition technology (e.g., Google Cloud Speech-to-Text) to convert the voice into text data.

[1506] Output The answer converted into text data is sent from the terminal to the server.

[1507] Step 6:

[1508] The input server receives the text data of the user's answer.

[1509] concrete action

[1510] The server uses generative AI to evaluate the consistency and precision of the answers, for example, analyzing whether the answers match tourist objectives.

[1511] Output The answer analysis results in the user's risk assessment.

[1512] Step 7:

[1513] Input Answer analysis results and risk assessment.

[1514] concrete action

[1515] Based on the risk assessment, the server allows users to complete the procedure self-service if the risk is low, or sends an escalation notification to the responsible party if the risk is high.

[1516] Output The user is notified that immigration procedures have been completed, or an escalation notification is sent to the person in charge.

[1517] Step 8:

[1518] Input Escalated user details and risk assessment.

[1519] concrete action

[1520] The person in charge will review the details, ask additional questions or carry out additional tests as necessary, make a final decision, and enter the results into the server.

[1521] Output The final decision result is notified to the user via the server.

[1522] This allows the system of the present invention to achieve efficient and accurate immigration inspection.

[1523] (Application example 1)

[1524] 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."

[1525] In recent years, immigration inspections have become increasingly complex due to the large number of users involved, resulting in a demand for greater efficiency and accuracy. However, traditional immigration inspection systems often require manual inspections, consuming significant amounts of time and resources. Furthermore, there were challenges in generating appropriate questions for users who speak different languages ​​and analyzing their answers. Furthermore, there was no established method for accurately identifying high-risk users and promptly notifying immigration officials. This compromised the reliability and efficiency of the immigration inspection process.

[1526] 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.

[1527] In this invention, the server includes means for collecting and verifying a user's biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting questions aloud, analyzing the user's answers using voice recognition technology, and converting them into text data, means for conducting risk assessment based on the analyzed answers, and means for completing self-service if the risk is low, or escalating the process and notifying an official if the risk is high. This improves the efficiency and accuracy of immigration inspections. Furthermore, this system supports different languages ​​and supports biometric collection and answer input using smartphones, making it suitable for users with different backgrounds.

[1528] "User biometric information" refers to biometric information for identifying an individual, such as a facial photograph, fingerprint, or iris.

[1529] "Generative AI" is an artificial intelligence technology that automatically generates appropriate questions based on user attribute data.

[1530] "Speech recognition technology" is a technology that converts a user's voice into a digital signal and analyzes it as text data.

[1531] "Risk assessment" is a process of analyzing the user's answers and assessing the level of risk in immigration inspection.

[1532] "Escalation" is the process of automatically notifying high-risk users for further review.

[1533] "Self-service" is a system that allows users to automatically complete immigration procedures.

[1534] The "server" is a central computer system that analyzes the user's biometric information and response data and performs the necessary processing.

[1535] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[1536] The system that realizes this application integrates the following main modules and technologies: First, the user's smartphone is used. The user goes through immigration procedures using the smartphone application "Secure Travel Check-In."

[1537] 1. Collection and matching of biometric information

[1538] Hardware: Smartphone built-in camera, fingerprint sensor

[1539] Software: OpenCV, face_recognition library

[1540] Processing: When a user provides biometric information (face photo, fingerprint) to a smartphone application, the application collects it and sends it to a server, where it matches the collected information with existing data in a database.

[1541] 2. Question Generation

[1542] Software: OpenAI GPT-3 model

[1543] Processing: The server automatically generates appropriate questions using a generative AI model based on the user's attribute data (e.g., nationality and purpose of entry). The generated questions are then sent from the server to the smartphone application.

[1544] Example prompt: "The user is a tourist. Please generate questions suitable for immigration."

[1545] 3. Question and answer exchanges

[1546] Hardware: Smartphone microphone and speaker

[1547] Software: Google Speech-to-Text API

[1548] Processing: The smartphone application uses voice synthesis technology to present the questions sent from the server to the user. The user's voice response is converted into text data using voice recognition technology and sent to the server.

[1549] 4. Risk Assessment

[1550] Software: OpenAI GPT-3 model

[1551] Processing: The server analyzes the received text data using the generation AI and evaluates the accuracy and consistency of the answers. Based on the analysis results, the user's risk level is assessed.

[1552] Example prompt: "User answered: Tokyo Disneyland. Please rate the risk level based on this answer."

[1553] 5. Escalation and Final Decision

[1554] Processing: If the risk is low, the server allows the user to complete the immigration process on their own. If the risk is high, an escalation notification is sent to an officer who can conduct additional questioning or screening.

[1555] Specific examples

[1556] A foreign user wishes to enter Japan for tourism purposes using the smartphone application "Secure Travel Check-In." First, the user takes a photo of their face with their smartphone camera, scans their fingerprints, and provides them to the application. The server identifies the user based on this information, and a generative AI model generates questions such as "Where are your tourist destinations?" and "How long will you be staying?" These questions are presented to the user using speech synthesis technology. If the user answers "Tokyo Disneyland," speech recognition technology converts the answer into text data, and the server performs a risk assessment. If the risk is deemed low, the user automatically completes immigration procedures. However, if the risk is deemed high, an official is notified and additional screening is carried out.

[1557] In this way, the system of the invention significantly improves the efficiency and accuracy of immigration inspections.

[1558] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1559] Step 1:

[1560] A user launches a smartphone application and provides a facial photograph and fingerprint. At this time, biometric information is collected using the smartphone's camera and fingerprint sensor. This information is sent from the smartphone application to a server. The input is the user's facial photograph and fingerprint data, and the output is the biometric information sent to the server.

[1561] Step 2:

[1562] The server compares the received biometric information with existing data in a database. Here, it performs face recognition using OpenCV and the face_recognition library. It also performs fingerprint authentication. The input is biometric information, and the output is the user's identification result. Specifically, the server analyzes the face image and fingerprint to check whether they match.

[1563] Step 3:

[1564] The server uses a generative AI model (OpenAI GPT-3) to generate appropriate questions based on the user's attribute data (e.g., nationality and purpose of entry). The input is the user's attribute data, and the output is the generated question. An example of a prompt sentence: "The user is a tourist. Please generate questions appropriate for immigration inspection."

[1565] Step 4:

[1566] The server sends the generated question to the smartphone application. The input is the generated question, and the output is the question sent to the smartphone application. Specifically, the server sends the generated question to the application via an API.

[1567] Step 5:

[1568] The smartphone application uses speech synthesis technology to present the received questions to the user. The input is the question sent from the server, and the output is a voice message suggested to the user. Specifically, the application converts text into speech and transmits it to the user through the smartphone's speaker.

[1569] Step 6:

[1570] The user responds verbally, and the smartphone application's microphone receives the response. The input is the user's verbal response, and the output is the smartphone's voice data.

[1571] Step 7:

[1572] The smartphone application uses speech recognition technology (Google Speech-to-Text API) to convert the voice response into text data and send it to the server. The input is the user's voice data and the output is text data. Specifically, the application analyzes the voice data, converts it into text, and sends that data to the server.

[1573] Step 8:

[1574] The server analyzes the received text data using a generative AI model and evaluates the precision and consistency of the user's answers. Based on the results of this analysis, the server evaluates the user's risk level. The input is text data, and the output is the result of the risk assessment. An example of a prompt is: "User's answer: Tokyo Disneyland. Please evaluate the risk level based on this answer."

[1575] Step 9:

[1576] If the server determines that the risk level is low, the user receives a notification to complete immigration procedures through self-service. If the risk level is high, an officer is notified and additional questions or inspections are carried out. The input is the result of the risk assessment, and the output is a notification of the completion of immigration procedures or a notification of escalation. Specifically, the server performs the appropriate action (self-service completion or officer notification) based on the risk assessment.

[1577] 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.

[1578] ---

[1579] This invention achieves efficient and accurate immigration screening by combining biometric information collection, question generation using generative AI, voice recognition technology, and an emotion engine. This system is composed of the following functional modules:

[1580] User Check-in and Identification

[1581] Biometric information collection and matching

[1582] When a user checks in at an airport terminal, the terminal collects the user's biometric information (e.g., facial photograph, fingerprint, iris, etc.), which is then sent to a server where it is matched with existing data in a database, comparing it with past immigration records and blacklists to confirm the user's identity.

[1583] Question Generation

[1584] Question creation using generative AI

[1585] The server is equipped with a generation AI that generates appropriate questions based on the user's attributes (such as nationality and purpose of entry). These questions are created in multiple languages ​​and sent to the device. For example, for a user entering the country for tourism, questions such as "What tourist spots do you want to visit?" and "How many days do you plan to stay?" are generated.

[1586] Question and answer exchange

[1587] Speech synthesis and speech recognition

[1588] The device uses speech synthesis technology to present the generated questions to the user. When the user responds by voice, the device analyzes the response using speech recognition technology, converts it into text data, and sends it to the server.

[1589] Analysis by emotion engine

[1590] Emotional state analysis

[1591] The server includes an emotion engine that analyzes the user's emotional state from their voice and facial expressions. For example, it can detect emotions such as tension or anxiety by analyzing the tone of voice and facial expressions when the user answers.

[1592] Risk Assessment

[1593] Risk assessment based on response content and sentiment analysis

[1594] The server uses generative AI to assess risk based on the received text data and the analysis results of the emotion engine. For example, if a user answers "I want to go to Tokyo Disneyland," and the emotion engine recognizes an appropriate emotional response to that answer, it evaluates whether the response is consistent with the tourist purpose and whether it is a natural response.

[1595] Risk Classification

[1596] Classification by risk assessment

[1597] The user's risk is assessed based on the analysis results. If the risk is deemed low, the user is provided with a means to complete immigration procedures through self-service. On the other hand, if the risk is high, the server notifies the relevant person for escalation.

[1598] Escalation and Final Decision

[1599] Further review by personnel

[1600] If the issue is escalated, the person in charge will ask additional questions and confirm the details of the user and the risk assessment made by the generative AI and emotion engine. The person in charge will make a final decision, enter the result into the server, and notify the user.

[1601] Specific examples

[1602] A foreign user wishes to enter Japan for tourism purposes and checks in at the airport. The terminal scans the user's passport and takes a photo of the user's face. The server uses this information to identify the user, and a generation AI generates questions such as "Where are you planning to visit?" and "How long will you be staying?", which are then presented to the user using speech synthesis technology. When the user answers "Tokyo Disneyland," the terminal uses voice recognition technology to analyze the answer and sends it to the server. At the same time, an emotion engine analyzes the user's tone of voice and facial expressions to detect signs of tension or anxiety. The server performs a risk assessment based on this information, and if the risk is low, the immigration process is completed, and the terminal notifies the user that "immigration processing has been completed." If the risk is deemed high, the case is escalated to a responsible official for additional screening.

[1603] This allows the system of the present invention to significantly improve the efficiency and accuracy of immigration inspections. In addition, by incorporating an emotion engine, the system can grasp the user's emotional state and perform more accurate risk assessments.

[1604] The processing flow will be explained below.

[1605] ---

[1606] Step 1:

[1607] Biometric collection and check-in

[1608] User: Upon arrival at the airport, scan your passport at the terminal and check in.

[1609] Terminal: Collects biometric information such as the user's face photo, fingerprint, and iris.

[1610] Terminal: Sends collected biometric information to the server.

[1611] Step 2:

[1612] User Identification

[1613] Server: The received biometric information is compared with a database, including past immigration records and blacklists, to verify the user's identity.

[1614] Step 3:

[1615] Question generation

[1616] Server: Based on the user's attributes (nationality, purpose of entry, etc.), the AI ​​generates appropriate questions. For example, it generates questions in Japanese and other languages, such as "Which tourist spots do you want to visit?" and "How many days do you plan to stay?"

[1617] Server: Sends the generated question to the terminal.

[1618] Step 4:

[1619] Posing the Question

[1620] Terminal: The generated question is presented to the user using speech synthesis technology. For example, it reads out in Japanese, "What tourist spots would you like to visit?"

[1621] User: Answers questions by voice.

[1622] Step 5:

[1623] Response collection and analysis

[1624] Terminal: Converts the user's response into text data using voice recognition technology.

[1625] Terminal: Sends the converted text data to the server.

[1626] Step 6:

[1627] Emotional state analysis

[1628] Server: Uses an emotion engine to analyze the user's emotional state (e.g., tension, anxiety, etc.) from their voice and facial expressions.

[1629] Step 7:

[1630] Risk assessment based on response content and sentiment analysis

[1631] Server: The generation AI performs a risk assessment based on the response content and the analysis results of the emotion engine. For example, if a user responds "I want to go to Tokyo Disneyland" and the emotion engine detects a natural emotional response, it evaluates whether the response matches the tourist purpose.

[1632] Step 8:

[1633] Risk Classification

[1634] Server: If the user is at low risk, it sends instructions to the terminal to complete the self-service. If the user is at high risk, it sends instructions for escalation.

[1635] Step 9:

[1636] Low risk actions

[1637] Terminal: Receives instructions from the server and notifies the user that "entry processing has been completed."

[1638] User: Complete immigration procedures.

[1639] Step 10:

[1640] High-risk escalation

[1641] Server: If the risk is deemed high, an escalation notice is sent to the appropriate person.

[1642] Person in charge: Asks additional questions or clarifies based on the escalated information.

[1643] Person in charge: Enters the final review results into the server and notifies the user.

[1644] ---

[1645] Through the above processing steps, the system of the present invention realizes efficient and accurate immigration inspection. In addition, by incorporating an emotion engine, it is possible to grasp the user's emotional state and perform more accurate risk assessment.

[1646] Example 2

[1647] 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."

[1648] Conventional immigration inspection systems take a lot of time to identify users and answer questions, and because they do not take the user's emotional state into account, risk assessment is often inaccurate. This results in problems such as reduced inspection efficiency and an increased risk of illegal entry. Therefore, a new system that can achieve efficient and accurate immigration inspection is needed.

[1649] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and verifying biometric information of a user, means including a generation AI for generating questions based on user attributes, means for presenting questions by voice and analyzing the user's answers using voice recognition technology, means for performing risk assessment based on the analyzed answers and emotion analysis results, and means for completing self-service if the risk is low and escalating if the risk is high. This enables highly accurate risk assessment that takes the user's emotional state into consideration, improving the efficiency and accuracy of immigration inspections.

[1650] "Biometric information" refers to information that indicates an individual's physical characteristics, such as a photograph of their face, fingerprints, or iris, for use in biometric authentication.

[1651] "Generative AI" is an artificial intelligence technology that automatically generates appropriate questions based on the user's attribute information.

[1652] "Speech recognition technology" is a technology that analyzes a user's voice and converts it into text data.

[1653] The "server" is a central computer system that centrally processes tasks such as matching biometric information, generating questions using AI, and assessing the risk of answers.

[1654] A "terminal" is a device that is directly operated by a user to collect biometric information, pose questions, and input answers.

[1655] An "emotion engine" is software or hardware that analyzes a user's voice and facial expressions and evaluates their emotional state.

[1656] "Risk assessment" is a process of assessing the risk of entry into a country on a three-point scale: "low," "medium," or "high" based on the user's responses and the results of sentiment analysis.

[1657] "Self-service" means that users can complete immigration procedures on their own.

[1658] "Escalation" is the process of requesting further review from a responsible party when a risk is deemed high.

[1659] The "person in charge" is the person in charge or the inspector who makes the final decision on entry permission.

[1660] MODE FOR CARRYING OUT THE INVENTION

[1661] This invention is a system that combines biometric information collection, question generation using generative AI, voice recognition technology, and an emotion engine to efficiently and accurately conduct immigration inspections for users. In this system, the server and terminal work together to collect and authenticate the user's biometric information and perform risk assessment based on the generated questions.

[1662] Hardware and Software Configuration

[1663] Device:

[1664] It is equipped with a camera, fingerprint sensor, and iris recognition sensor to collect biometric information.

[1665] Includes a microphone and speaker for audio interface with the user.

[1666] It has a display that displays the user interface.

[1667] server:

[1668] It connects to the database and verifies the biometric information.

[1669] It is equipped with generative AI that generates questions based on the user's attributes.

[1670] Voice recognition technology is used to convert voice data into text.

[1671] An emotion engine is used to analyze the user's voice and facial expressions to assess their emotional state.

[1672] Data processing and calculation

[1673] When a user checks in at an airport terminal, the terminal collects biometric information such as a facial photo, fingerprints, and iris scan, and sends it to a server. The server compares the received biometric information with existing data in a database. Next, a generation AI generates questions based on the user's attribute information (for example, purpose of entry and nationality). These questions are presented to the user from the terminal using speech synthesis technology. When the user responds verbally, the terminal analyzes the response using speech recognition technology, converts it into text data, and sends it to the server. The server then uses an emotion engine to analyze the user's voice data and facial expressions to evaluate their emotional state. A risk assessment is performed based on this data, and depending on the result, the self-service is either completed or escalated.

[1674] Specific examples

[1675] For example, consider a foreign user entering Japan for tourism. The user scans their passport and takes a photo of their face at an airport terminal. The device's camera acts as a facial recognition sensor, and biometric information is collected using a fingerprint sensor and iris recognition sensor. This information is then sent to a server and compared against an existing database.

[1676] The generation AI confirms that the user is traveling and generates questions such as, "Which tourist spots do you want to visit?" and "How many days do you plan to stay?" These questions are presented to the user using voice synthesis technology. When the user answers, "I want to go to Tokyo Disneyland," the device analyzes the voice, converts it into text data, and sends it to the server.

[1677] The server then uses an emotion engine to analyze the user's tone of voice and facial expressions to assess their emotional state. Based on this information, the server performs a risk assessment, and if the risk is deemed low, the device notifies the user that entry processing has been completed. If the risk is deemed high, the process is escalated to a staff member who will ask additional questions and provide confirmation before making a final decision.

[1678] Prompt Sentence Examples

[1679] "If you are visiting Japan for tourism purposes, please tell us the specific places you will be visiting and the length of your stay."

[1680] In this way, this system improves the efficiency and accuracy of immigration inspections by combining biometric information matching technology, generative AI, voice recognition technology, and an emotion engine.

[1681] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1682] Program flow divided into processing steps

[1683] Step 1: Collect user biometric information

[1684] Specific actions

[1685] Users check in at airport terminals and provide a photo of their face, fingerprints, and iris information.

[1686] The terminal collects this biometric information, encrypts it, and sends it to the server.

[1687] Input and Output

[1688] Input: User's photo, fingerprint, and iris information.

[1689] Output: Encrypted biometric information is sent to the server.

[1690] Step 2: Verify biometric information

[1691] Specific actions

[1692] The server compares the received biometric information with existing data in a database.

[1693] Compare with past entry records and blacklists to find matching data.

[1694] Input and Output

[1695] Input: Encrypted biometric information.

[1696] Output: Matching results (matching rate and blacklist flags) are generated.

[1697] Step 3: Generate questions

[1698] Specific actions

[1699] The server uses a generation AI to generate appropriate questions based on the user's attribute information (purpose of entry, nationality, etc.).

[1700] The questions are translated into multiple languages ​​and sent to the device.

[1701] Input and Output

[1702] Input: User's demographic information (purpose of entry, nationality, etc.).

[1703] Output: The generated question (e.g., "Which tourist destination would you like to visit?").

[1704] Step 4: Posing the Question

[1705] Specific actions

[1706] The terminal presents the generated question to the user using voice synthesis technology.

[1707] The question is also displayed on the display so that the user can check it.

[1708] Input and Output

[1709] Input: The generated question and its translation.

[1710] Output: The question is presented to the user in audio and text form.

[1711] Step 5: Collect user responses

[1712] Specific actions

[1713] The user answers by voice into the microphone of the terminal.

[1714] The device receives the voice, analyzes it using voice recognition technology, and converts it into text data.

[1715] Input and Output

[1716] Input: The user's spoken response.

[1717] Output: The user's answer converted to text is sent to the server.

[1718] Step 6: Analyze emotional state

[1719] Specific actions

[1720] The server analyzes the received audio data and video feed with an emotion engine to assess the user's emotional state.

[1721] Detects emotions such as tension and anxiety and saves the evaluation results.

[1722] Input and Output

[1723] Input: User's voice data, video feed.

[1724] Output: Emotion analysis results (tension, anxiety, neutral, etc.) are generated.

[1725] Step 7: Risk Assessment

[1726] Specific actions

[1727] The server uses generative AI to perform risk assessment based on the user's responses and the results of sentiment analysis.

[1728] Risk levels are classified into three categories: "low," "medium," and "high."

[1729] Input and Output

[1730] Input: Translated user responses, sentiment analysis results.

[1731] Output: Risk level (low, medium, high).

[1732] Step 8: Determine risk treatment

[1733] Specific actions

[1734] Based on the risk assessment results, the server decides whether to continue or escalate the user's processing and sends instructions to the terminal.

[1735] If it's low risk, complete the self-service process. If it's high risk, escalate to a responsible person.

[1736] Input and Output

[1737] Input: Risk level.

[1738] Output: Action flag (self-service completed or escalated).

[1739] Step 9: Final decision and notification

[1740] Specific actions

[1741] The agent will review the escalated case and ask additional questions or clarify as needed.

[1742] The person in charge makes the final decision, enters the result into the server, and notifies the user from the server.

[1743] Input and Output

[1744] Input: Escalated user details, risk assessment results.

[1745] Output: Final assessment result (entry allowed or denied) and notification to the user.

[1746] Through these steps, the system achieves efficient and accurate immigration inspection.

[1747] (Application example 2)

[1748] 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."

[1749] Improving the efficiency and accuracy of access control is a key challenge for modern security systems. Current systems rely on biometric verification and simple questions to verify access, but do not consider the user's emotional state for risk assessment. This results in insufficient accuracy in detecting unauthorized access attempts, resulting in security risks. Furthermore, unclear escalation criteria result in a waste of human resources. Therefore, there is a need to improve security and efficiency by combining prompt generation based on the user's biometric information and attributes with emotion analysis and generative AI.

[1750] 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.

[1751] In this invention, the server includes means for collecting and verifying user biometric information, means including a generation AI for generating questions based on the user's attributes, means for presenting the questions by voice and analyzing the user's answers using voice recognition technology, means for analyzing the user's emotional state, means for reflecting the results of the emotion analysis in risk assessment, and means for the generation AI to create prompt sentences based on the user's attributes. This enables highly accurate risk assessment that takes the emotional state into account and appropriate escalation according to the situation.

[1752] A "user" is an individual using the system.

[1753] "Biometric information" is biological information used to identify an individual, such as a facial photograph, fingerprint, or retinal scan.

[1754] "Verification" is the process of comparing collected biometric information with an existing database to see if there is a match.

[1755] "Generative AI" is an algorithm that uses artificial intelligence to automatically generate questions and prompts based on user attribute information.

[1756] "Question generation" is the process of creating appropriate questions based on user attribute information.

[1757] "Speech recognition technology" is a technology for converting a user's voice into text data.

[1758] "Risk assessment" is the process of determining whether a user's behavior poses a security risk based on collected data and information.

[1759] "Self-service" is a means by which users can self-complete their own actions or procedures.

[1760] "Escalation" is the process of notifying a human reviewer when the system determines that a risk is high.

[1761] An "emotional state" is a psychological state that a user expresses through their voice and facial expressions.

[1762] "Emotion analysis" is a technology that determines a user's emotions based on collected audio and image data.

[1763] A "prompt sentence" is an instruction or question sentence created by generative AI based on the user's attributes and situation.

[1764] This invention is a system for efficiently and accurately managing access to facilities. This system collects user biometric information and combines voice recognition and emotion analysis to assess the risk of access and escalate the situation to the appropriate personnel as necessary.

[1765] Component Description

[1766] The system consists of the following main components:

[1767] 1. Collection of Biometric Information

[1768] When a user enters a facility, biometric information such as a facial photograph and fingerprints is collected using a smartphone. Specifically, a facial photograph is taken using the smartphone's camera, and a fingerprint is acquired using the fingerprint sensor.

[1769] 2. Question Generation

[1770] The server is equipped with a generative AI that generates appropriate questions based on the user's attribute information (e.g., age, gender, nationality, purpose of visit, etc.). These questions are presented to the user via voice or text. The generated questions vary depending on the situation.

[1771] 3. Voice Recognition

[1772] When the user responds verbally, the smartphone's voice recognition technology is used to convert the voice data into text data, which is then sent to a server where it is analyzed.

[1773] 4. Emotion analysis

[1774] The server includes an emotion analysis engine that analyzes the user's emotional state from their voice and facial expressions. For example, it analyzes the tone of voice and facial expressions when the user answers to detect emotions such as tension or anxiety.

[1775] 5. Risk Assessment

[1776] The server performs a risk assessment based on the received text data and the results of sentiment analysis, using generative AI to determine whether the user's reaction is natural and consistent with the purpose of entering the facility.

[1777] 6. Escalation and Notification

[1778] If the risk assessment determines that the risk is low, the visitor can complete the self-service entry procedure and receive a notification on their smartphone. If the risk is high, the procedure is escalated to a staff member who will conduct additional checks and ask questions.

[1779] Processing example

[1780] For example:

[1781] When foreign tourists enter a facility, they take a photo of their face with their smartphone and scan their fingerprints. The server uses this information to verify their identity. Next, a generation AI automatically generates a question such as "Where is your tourist destination?" and presents it to the user aloud. If the user replies "Tokyo Disneyland," this is converted into text data using voice recognition technology and sent to the server. At the same time, an emotion analysis engine analyzes the user's tone of voice and facial expressions to detect signs of tension or anxiety. The server comprehensively evaluates this information and, if the risk is low, sends a notification to the smartphone stating, "Entry permitted." If the risk is high, a notification is sent to a responsible person to ask additional questions and conduct further confirmation.

[1782] Hardware and software used

[1783] The system uses the following hardware and software:

[1784] Smartphone: A device that collects biometric information and recognizes voice.

[1785] Server: A central management system that runs generative AI, sentiment analysis engines, and risk assessments.

[1786] Open source libraries:

[1787] OpenCV: Facial photo capture and analysis.

[1788] TensorFlow / Keras: Building a sentiment analysis engine.

[1789] SpeechRecognition: Speech recognition.

[1790] Hugging Face Transformers: Using generative AI models.

[1791] Prompt Sentence Examples

[1792] Here are some example prompts the generator AI might create:

[1793] "Generate appropriate questions for users with American nationality."

[1794] "Ask users who are visiting for sightseeing what kind of tourist spots they would like to visit."

[1795] This system enables highly accurate risk assessment that takes emotional states into account and appropriate escalation according to the situation, significantly improving the security and efficiency of access control.

[1796] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1797] Step 1:

[1798] When a user enters a facility, the device (smartphone) collects the user's facial photograph and fingerprints. First, the smartphone's camera is activated and a photograph of the user's face is taken. This image is saved as a temporary file. Next, the fingerprint is scanned using the smartphone's fingerprint sensor and saved as digital data. This biometric information is sent to a server. The input is the user's facial photograph and fingerprint information, and the output is the matching result on the server.

[1799] Step 2:

[1800] The server matches the submitted biometric information with existing data in a database. Specifically, it compares the facial photo and fingerprint data against the existing database using a matching algorithm. This process verifies the user's identity. The input is the user's facial photo and fingerprint data, and the output is the matching result.

[1801] Step 3:

[1802] The generative AI model on the server generates appropriate questions based on the user's attribute information (e.g., age, gender, nationality, purpose of visit). The generated questions are sent to the terminal in the form of prompt sentences. Specifically, the generative AI takes user data as input and outputs the question as a prompt sentence. For example, a prompt sentence might be generated that reads, "Please ask users who are visiting for sightseeing what kind of tourist spots they would like to visit."

[1803] Step 4:

[1804] The device uses speech synthesis technology to present the generated question to the user aloud. The speech synthesis engine converts the text-based question into speech output, which is played through the smartphone's speaker. The user responds verbally. The input is the text of the generated question, and the output is the spoken question presented to the user.

[1805] Step 5:

[1806] When the user responds verbally, the device records the voice. The recorded voice data is analyzed by a voice recognition engine and converted into text data. Specifically, the smartphone's microphone captures the voice, and the voice data is analyzed by a voice recognition algorithm. The input is the user's voice data, and the output is text data that has been recognized by voice.

[1807] Step 6:

[1808] The server receives and analyzes the voice-recognized text data and the user's emotional state (e.g., tension, anxiety, excitement, etc.) provided by the emotion analysis engine. In emotion analysis, facial photos and voice data are processed as input to detect the user's emotional state from their tone of voice and facial expressions, and the results are passed to the risk assessment system. The inputs are the voice-recognized text data and emotional state data, and the output is the risk assessment result.

[1809] Step 7:

[1810] The server calculates the user's risk level based on the risk assessment results and decides on a response. If the risk is determined to be low, the self-service entry procedure is completed and a notification to that effect is sent to the terminal. If the risk is high, an escalation notification is sent to the person in charge, who will ask for additional confirmation or questions. The input is the risk assessment result, and the output is a self-service completion notification or an escalation notification.

[1811] 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.

[1812] 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.

[1813] 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.

[1814] 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.

[1815] 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.

[1816] 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.

[1817] 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).

[1818] 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.

[1819] 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."

[1820] 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.

[1821] 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).

[1822] 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.

[1823] 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.

[1824] 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.

[1825] 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.

[1826] 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.

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] The following is further disclosed regarding the above embodiment.

[1833] ---

[1834] (Claim 1)

[1835] means for collecting and verifying biometric information of a user;

[1836] A means including a generation AI that generates questions based on user attributes;

[1837] A means for presenting a question by voice and analyzing a user's response using voice recognition technology;

[1838] A means for conducting a risk assessment based on the analyzed responses;

[1839] A means to complete self-service when risk is low and escalate when risk is high;

[1840] A system including:

[1841] (Claim 2)

[1842] 10. The system of claim 1, further comprising: means for presenting the generated question to the user in multiple languages.

[1843] (Claim 3)

[1844] 10. The system of claim 1, further comprising means for escalating high-risk users to personnel.

[1845] "Example 1"

[1846] (Claim 1)

[1847] means for collecting and verifying biometric information of a user;

[1848] A means including a generation AI that generates questions based on user attributes;

[1849] A means for presenting a question by voice and analyzing a user's response using voice recognition technology;

[1850] A means for conducting a risk assessment based on the analyzed responses;

[1851] A means for users to self-complete low-risk cases; and

[1852] If the risk is high, there is a way to send information to the person in charge and escalate the situation.

[1853] means for notifying the user of the final decision;

[1854] A system including:

[1855] (Claim 2)

[1856] 10. The system of claim 1, further comprising: means for presenting the generated question to the user in multiple languages.

[1857] (Claim 3)

[1858] 10. The system of claim 1, further comprising means for evaluating the consistency and elaboration of responses.

[1859] "Application Example 1"

[1860] (Claim 1)

[1861] means for collecting and verifying biometric information of a user;

[1862] A means including a generation AI that generates questions based on user attributes;

[1863] A means for presenting questions by voice, analyzing the user's answers using voice recognition technology, and converting them into text data;

[1864] A means for conducting a risk assessment based on the analyzed responses;

[1865] A means to complete self-service when risk is low and escalate when risk is high;

[1866] A means of notifying personnel when there is a high risk;

[1867] A system including:

[1868] (Claim 2)

[1869] 10. The system of claim 1, further comprising means for presenting the generated questions to the user in multiple languages ​​and evaluating the parsed answers using natural language processing techniques.

[1870] (Claim 3)

[1871] 10. The system of claim 1, further comprising means for enabling a user to provide biometric information through a smartphone application and to speak responses to generated questions.

[1872] "Example 2: Combining Emotion Engines"

[1873] (Claim 1)

[1874] means for collecting and verifying biometric information of a user;

[1875] A means including a generation AI that generates questions based on user attributes;

[1876] A means for presenting a question by voice and analyzing a user's response using voice recognition technology;

[1877] A means for performing risk assessment based on the analyzed answers and the sentiment analysis results;

[1878] A means to complete self-service when risk is low and escalate when risk is high;

[1879] A system including:

[1880] (Claim 2)

[1881] means for presenting the generated questions to the user in multiple languages;

[1882] 10. The system of claim 1, further comprising an emotion engine that analyzes the user's voice and facial expressions to assess their emotional state.

[1883] (Claim 3)

[1884] A means of escalating high-risk users to the appropriate personnel;

[1885] 10. The system of claim 1, further comprising means for a person to ask additional questions or make a final decision.

[1886] "Application example 2 when combining emotion engines"

[1887] (Claim 1)

[1888] means for collecting and verifying biometric information of a user;

[1889] A means including a generation AI that generates questions based on user attributes;

[1890] A means for presenting a question by voice and analyzing a user's response using voice recognition technology;

[1891] A means for conducting a risk assessment based on the analyzed responses;

[1892] A means to complete self-service when risk is low and escalate when risk is high;

[1893] means for analyzing the emotional state of a user;

[1894] A means of incorporating the results of sentiment analysis into risk assessment;

[1895] A means for the generative AI to create prompt sentences based on the user's attributes;

[1896] A system including:

[1897] (Claim 2)

[1898] 10. The system of claim 1, further comprising: means for presenting the generated question to the user in multiple languages.

[1899] (Claim 3)

[1900] 10. The system of claim 1, further comprising means for escalating high-risk users to personnel. [Explanation of symbols]

[1901] 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. means for collecting and verifying biometric information of a user; A means including a generation AI that generates questions based on user attributes; A means for presenting a question by voice and analyzing a user's response using voice recognition technology; A means for conducting a risk assessment based on the analyzed responses; A means to complete self-service when risk is low and escalate when risk is high; A system including:

2. The system of claim 1 further comprising means for presenting the generated question to the user in multiple languages.

3. The system of claim 1 , further comprising means for escalating high-risk users to personnel.

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