system

Abstract

We provide the system. [Solution] A means of acquiring information to obtain biometric authentication data, A data storage method for encrypting acquired biometric authentication data and saving it to a database, An authentication method for comparing stored biometric data with newly acquired data, A transaction execution method for executing a transaction upon successful authentication, A means of updating data to record transaction details and update user account information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, there is a problem that it becomes difficult to withdraw cash due to the loss or theft of a cash card or the loss during a disaster. As a result, users may experience financial anxiety or living difficulties, and there is a need for a more reliable and secure means of personal identification for cash transactions.

Means for Solving the Problems

[0005] This invention provides a system that eliminates the need for a cash card by utilizing biometric authentication data and performs identity verification based on biometric information (facial recognition, fingerprint, iris). Specifically, it includes an information acquisition means for acquiring, encrypting, and storing biometric authentication data, an authentication means for matching with the stored data, a transaction execution means for executing a transaction upon successful authentication and recording the transaction details, and a data update means. This enables cardless and secure financial transactions, alleviating user concerns.

[0006] "Biometric data" refers to information obtained as digital data of an individual's biological characteristics, and includes facial recognition, fingerprints, iris scans, and so on.

[0007] "Information acquisition means" refers to devices or technologies for collecting biometric authentication data from users.

[0008] "Data storage means" refers to a method or device for securely storing acquired biometric authentication data.

[0009] "Authentication method" refers to a function that verifies a person's identity by comparing acquired biometric authentication data with existing data.

[0010] "Transaction execution means" refers to the device or process that actually carries out a financial transaction when authentication is successful.

[0011] "Data update method" refers to a function that updates transaction details and user account information after a transaction is completed. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] As an embodiment for carrying out the present invention, a system for withdrawing cash without using a cash card using biometric authentication is described. This system includes a series of processes for acquiring, storing, and verifying the user's biometric information, and executing a financial transaction if authentication is successful.

[0034] First, users register their biometric authentication data with the system via their device. Specifically, the system obtains the user's biometric information using a facial recognition camera or fingerprint scanner. This information is encrypted in a secure format and sent to the server. The server stores the received data in a database and uses an AI model to train it for individual user identification. This enables highly accurate personal identification.

[0035] When using an ATM, the user undergoes facial recognition or fingerprint scanning again. At this time, the biometric information acquired by the terminal is immediately sent to the server and compared with information stored in advance. If this comparison is successful, the server sends an authentication success signal to the terminal and authorizes the user to make a transaction. After confirming the transaction input, the terminal dispenses the specified amount as cash, the transaction details are recorded on the server, and the user's account information is updated simultaneously.

[0036] As a concrete example, when a user withdraws cash using an ATM, the server verifies that the user's biometric information matches their registered information. If authentication is successful, the specified amount can be withdrawn smoothly. In this way, this system enables cardless cash withdrawals while ensuring security.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users are asked to access the system using their device and register their biometric information. Their face is scanned with a facial recognition camera, and their fingerprints are scanned with a fingerprint sensor. The resulting biometric data is encrypted and sent to the server.

[0040] Step 2:

[0041] The server stores the received biometric authentication data in a database. The stored data is then trained by an AI model to generate individual user profiles, which are used to identify users.

[0042] Step 3:

[0043] When a user uses an ATM, the terminal attempts to log in using biometric authentication. The terminal then uses the facial recognition camera and fingerprint sensor again to obtain the latest biometric information.

[0044] Step 4:

[0045] The device encrypts and transmits newly acquired biometric authentication data to the server in real time. The server then compares this data with the information stored in the database.

[0046] Step 5:

[0047] The server executes the matching process using an AI model. If the matching result shows a match that exceeds the threshold, the server sends an authentication success message to the terminal.

[0048] Step 6:

[0049] The user enters the withdrawal amount on the ATM screen. The server checks the user's account balance and verifies whether the specified amount can be withdrawn.

[0050] Step 7:

[0051] Once the transaction is approved, the terminal dispenses the specified amount as cash. The server logs the transaction details and updates the user's account information. This completes the transaction.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] There is a growing demand for a safe and fast way to withdraw cash without having to carry a cash card. Conventional technologies have had drawbacks such as the risk of card loss and the inaccuracy of biometric authentication. Therefore, the challenge is to provide users with a convenient transaction method while ensuring high-precision personal authentication and security.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes a device and means for acquiring biometric data, a data management device and means for securely transferring the acquired biometric data and storing it in a storage device, and a reliability verification device and means for comparing the stored biometric data with the reacquired biometric data. This enables secure, cardless cash withdrawals and high-precision personal identification through biometric authentication.

[0057] "Biometric data" refers to information acquired to identify individuals based on the shape and patterns of their faces, fingers, eyes, etc.

[0058] A "device" is a mechanical element or assembly of machines used to perform a specific function, such as acquiring, transferring, recording, or comparing biological data.

[0059] "Secure state" refers to a protected state in which data is safe from unauthorized access and tampering from external sources.

[0060] "Transfer" is the process of moving acquired data from one location to another.

[0061] A "storage device" is a medium or device that can store data and retrieve it as needed.

[0062] A "data management device" is a device used to store and manage acquired data so that it can be used when needed.

[0063] A "reliability verification device" is a device used to verify consistency and authenticity by comparing retained data with newly acquired data.

[0064] A "machine learning model" is an algorithm or statistical model that learns patterns from data and enables it to automatically perform specific tasks.

[0065] An "analytical device" is a device or software used to analyze data and extract information or patterns contained within it.

[0066] "Individual identification accuracy" is an indicator that represents the degree of the ability to accurately identify an individual in biometric authentication.

[0067] This system provides a way to withdraw cash without a cash card using biometric authentication. Users begin by registering their biometric data using a terminal. Devices such as facial recognition cameras and fingerprint scanners are used to acquire this biometric data. These devices capture the user's facial and finger features and transmit this information as digital data to the terminal. The terminal encrypts this data to maintain its security. Advanced encryption protocols such as TLS are employed to ensure data security.

[0068] The user's biometric information is encrypted and then transferred to the server. The server decrypts the data and stores it in a database. Furthermore, the server uses a generative AI model to learn from the acquired biometric data, enabling it to identify users with high accuracy. Machine learning algorithms allow the server to extract patterns from the acquired data, forming a foundation for strengthening the user authentication process.

[0069] When using an ATM, the user performs biometric authentication again at the terminal. At this time, the terminal immediately sends the newly acquired biometric data to the server, which compares this data with previously stored data. If authentication is successful, the terminal authorizes the transaction, and the specified amount is dispensed as cash. Furthermore, details of each transaction are recorded on the server, and the user's account information is updated to the latest state.

[0070] For example, if a user needs to withdraw money urgently, they can go to the nearest ATM and authenticate using facial recognition. If the system authenticates correctly, the user can receive cash without using a card. This process is quick and can be completed in seconds.

[0071] Examples of prompt messages include the following:

[0072] Please explain the system procedure for users to withdraw cash using facial recognition at an ATM.

[0073] "Could you please explain in detail the authentication process for cashless transactions using biometric information?"

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user registers biometric data on the device. Specifically, a facial recognition camera captures an image of the user's face, and a fingerprint scanner obtains a fingerprint. The data entered is then the user's facial and fingerprint information. The device encrypts this data and securely transmits it to the server. The output at this point is encrypted biometric data. Furthermore, the device displays a UI indicating to the user that the series of operations are complete.

[0077] Step 2:

[0078] The server receives encrypted data sent from the terminal, decrypts it, and stores it in a database. The input is encrypted biometric data, and the output is the biometric data stored in the database. The server also adds time and location information when recording the data, in preparation for future improvements in authentication accuracy.

[0079] Step 3:

[0080] The server analyzes stored biometric data using an AI model to train the model with user identification information. The input to this process is stored biometric data, and the output is an AI model with updated personal identification accuracy. The server monitors the model's learning progress and prepares an optimized model.

[0081] Step 4:

[0082] When a user approaches an ATM and withdraws cash, they undergo biometric authentication again. The user stands in front of the facial recognition camera and places their finger on the fingerprint scanner, allowing the terminal to acquire new biometric information. The input for this operation is real-time biometric data, and the output is the transmission of biometric data to the server.

[0083] Step 5:

[0084] The server receives newly transmitted biometric data and compares it to stored data. The input for the comparison is the new and stored biometric data, and the output is the authentication status. The server evaluates the degree of match and, if authentication is successful, returns a confirmation signal to the terminal.

[0085] Step 6:

[0086] Once the terminal confirms successful authentication, it authorizes the transaction. The user specifies the amount to withdraw on the screen. The input is the user's specified amount, and the output is the cash discharge. The terminal provides the user with the specified amount of cash by performing the discharge process using its internal mechanism.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In systems that allow cash withdrawals without using a cash card, there is a challenge in improving security and convenience by utilizing biometric authentication technology. In particular, there is a need to provide a secure and rapid authentication method for electronic payments and to strengthen the security of user transactions.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes information acquisition means for acquiring biometric authentication data, data storage means for encrypting the acquired biometric authentication data and storing it in a database, and authentication means for comparing the stored biometric authentication data with newly acquired data. This enables secure and rapid personal authentication and payment processing when a user makes an electronic payment.

[0092] "Biometric data" refers to information used to verify a person's identity using their physical characteristics, such as their face, fingerprints, and iris.

[0093] "Information acquisition means" refers to devices and technologies for collecting a user's physical characteristics and converting them into digital data.

[0094] "Data storage means" refers to databases and storage systems for securely storing acquired biometric authentication data.

[0095] "Authentication means" refers to a method or apparatus for comparing newly acquired biometric authentication data with data stored in an existing database to confirm a match.

[0096] A "transaction execution mechanism" is a mechanism for executing financial transactions or other requested operations when biometric authentication is successful.

[0097] "Data update means" refers to a method or process of updating a database to keep transaction details and user identification information up to date.

[0098] A "payment method" is a system or method for executing and completing transactions authorized based on biometric authentication.

[0099] The system for implementing this invention consists of users making electronic payments using biometric authentication via smartphones or other devices. This system operates by combining various hardware and software components to achieve highly secure cashless transactions. Specifically, it utilizes facial recognition cameras and fingerprint scanners to acquire the user's biometric information. The acquired data is encrypted via the information acquisition means and securely stored in a database. This prevents data leakage.

[0100] The system also uses authentication methods to compare newly acquired biometric information with pre-registered information. Here, computational models are used to analyze the data and achieve highly accurate personal identification. If biometric authentication is successful, the server approves the transaction through the payment method. Subsequently, transaction details are recorded by a data update mechanism, and the user's identification information is updated accordingly.

[0101] For example, when a user purchases concert tickets electronically, this system authenticates quickly and securely, completing the payment instantly. This facilitates cashless transactions, eliminating the need for users to carry physical cash cards.

[0102] Using a generative AI model, the system constantly strives to improve accuracy with new prompts and methods. Further applications can be explored using the prompt, "Explain specific ways to utilize this electronic payment platform and compare it with existing payment methods," as an example.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user operates the device to input biometric information. The device uses a facial recognition camera and fingerprint scanner to acquire the user's biometric authentication data. The input is the user's physical characteristics (image data), which is converted into digital data, encrypted, and output.

[0106] Step 2:

[0107] The device encrypts the acquired biometric authentication data and sends it to the server. Here, the device uses an encryption algorithm to protect the data and transmit it in order to maintain data security. The output is encrypted data.

[0108] Step 3:

[0109] The server stores the received data in the database. The server properly stores this data in the database in preparation for matching it with existing data. At this stage, the output is an updated database.

[0110] Step 4:

[0111] When a user attempts to conduct a transaction, they re-enter their biometric information into the terminal. The terminal then sends the newly acquired biometric authentication data to the server. This new biometric information, which is acquired again as input, is encrypted before transmission.

[0112] Step 5:

[0113] The server uses authentication methods to verify a match between stored biometric data and newly acquired data. During this process, a generated AI model is utilized to improve matching accuracy. The matching result is obtained as output.

[0114] Step 6:

[0115] If authentication is successful, the server executes the transaction using the transaction execution method. It approves the transaction details and initiates settlement processing. Depending on the choice of credit or debit, the actual financial transaction is performed. The output shows the approved transaction.

[0116] Step 7:

[0117] The server uses data update mechanisms to record transaction details and update user identification information. This includes updating user account information and saving transaction history. The output includes updated identification information and records.

[0118] Step 8:

[0119] The terminal notifies the user that the transaction is complete. For example, it displays a transaction success message on the terminal's screen and prints a receipt if necessary. The input in this case is a transaction completion signal from the server, and the output is visual or printed information for the user.

[0120] Through these processing steps, secure and rapid electronic payments using biometric authentication are realized. Maintaining this flow makes it possible to apply it to other uses. Other examples can be considered, such as prompting the user to "Explain specific ways to utilize the electronic payment platform using this system and compare it with existing payment methods."

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention provides a more secure and intuitive cashless card system by combining biometric authentication and emotion recognition. This system includes processes for acquiring, registering, and carefully managing the user's biometric information, as well as recognizing their emotional state in real time.

[0123] First, the user performs initial setup through the device. During this process, biometric data is obtained using a facial recognition camera and fingerprint scanner. This information is encrypted and securely stored on a server. Furthermore, an emotion engine enhances the ability to read emotions from the user's face. This data is analyzed by an AI model to form the user's emotional profile.

[0124] While using an ATM, the user interacts with the terminal to perform biometric and emotional data recognition. The terminal uses sensors to acquire this information and transmit it to a server. The server, in parallel with verifying biometric authentication, evaluates whether the user's emotions are unusual. Using an emotion engine, if a specific emotional state, such as tension or anxiety, is detected, a security assessment mechanism is activated, and a warning is issued if necessary.

[0125] A concrete example of this system is when a user is operating an ATM and the system detects unusually high levels of stress. In this case, the server analyzes the user's emotional changes and assesses the possibility of fraudulent transactions. Subsequently, the transaction may be temporarily suspended, or further identity verification may be required.

[0126] This invention adds a new security assessment based on emotion recognition to the conventional identity verification process that does not rely on cash cards, allowing users to use the system with even greater peace of mind. This makes it possible to achieve both convenience and security during financial transactions.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The user uses a device for initial registration, scanning their face and fingerprints to provide biometric data. The device receives this data, encrypts it, and sends it to the server.

[0130] Step 2:

[0131] The server securely stores the received biometric data in a database. Simultaneously, it analyzes the data using an AI model to form an individual user profile. It also creates a user's emotional profile using an emotion engine based on facial recognition data.

[0132] Step 3:

[0133] When a user uses an ATM, the terminal scans again, acquiring their face and fingerprints in real time. Furthermore, it analyzes the user's current emotional state through the facial recognition camera.

[0134] Step 4:

[0135] The device sends newly acquired biometric and emotional information to the server. The server compares this information with an existing database to verify biometric authentication.

[0136] Step 5:

[0137] The server analyzes emotional information obtained through the emotion engine and evaluates for any unusual emotions or signs of stress. If necessary, it performs a safety assessment.

[0138] Step 6:

[0139] The server, once it confirms that both biometric authentication and sentiment evaluation are successful, authorizes the terminal to execute the transaction. The authenticated user then enters the amount they wish to withdraw on the ATM screen.

[0140] Step 7:

[0141] The terminal, if withdrawal is permitted, dispenses the specified amount as cash. The server completes the process by recording the transaction details and updating the user's account information.

[0142] By evaluating the user's emotional state in this way, the system can provide even greater security in addition to conventional biometric authentication.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] In financial transactions that do not use cash cards, the challenge is to simultaneously improve security and convenience. In particular, it is necessary to prevent fraudulent transactions by utilizing emotion recognition in addition to biometric authentication. While conventional systems have some effect in acquiring and authenticating biometric information, they lack the function of monitoring the user's psychological state, so more comprehensive security measures are required.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes data acquisition means for acquiring biometric information, emotion evaluation means for sensing emotional states and evaluating the risk of fraudulent transactions, and analysis means for analyzing the acquired biometric and emotional information using an artificial intelligence model to enhance the accuracy of user individual recognition and safety evaluation. This enables a comprehensive analysis of the user's biometric information and emotional state, making safer transaction decisions possible.

[0148] "Biometric information" refers to human characteristic data that enables the biologically unique identification of an individual, including facial features, fingerprints, and iris scans.

[0149] "Data acquisition means" refers to technologies consisting of hardware and software for capturing and collecting biometric and emotional information.

[0150] A "secure storage device" is a storage medium that protects and stores acquired data, and has the function of protecting against unauthorized access by using encryption technology.

[0151] "Verification means" refers to a process and algorithm for comparing stored data with newly acquired data to determine the legitimacy of an individual.

[0152] "Emotional evaluation tools" are technologies that analyze a user's psychological state and determine whether that state differs from their normal state, utilizing artificial intelligence to analyze emotional characteristics.

[0153] "Emotion recognition" is a technology used to identify a user's emotions and psychological state, and it analyzes them based on facial expressions, voice, and other factors.

[0154] An "artificial intelligence model" is a program that has machine learning algorithms to analyze data and identify patterns, and can accurately determine the characteristics of user behavior and emotions.

[0155] "Fraudulent transactions" refer to financial transactions conducted with fraudulent intent that do not align with the normal purpose of use, and actions that are carried out against the user's will.

[0156] "Individual identification accuracy" refers to the ability to accurately identify a specific individual from all other individuals, and high-precision recognition is important for reducing the risk of misidentification.

[0157] This invention provides a safer and more convenient cashless transaction system by utilizing biometric authentication and emotion recognition technology. This system is constructed and implemented as follows.

[0158] The user first uses the device to provide biometric information. The device has a built-in facial recognition camera and fingerprint scanner, and biometric information is obtained through these devices. This information is encrypted using security technologies such as AES encryption before being transmitted from the device.

[0159] The server uses a database to receive encrypted biometric information and store it in a secure storage device. This data is later used in a matching process and forms the basis for identifying individual users.

[0160] Furthermore, the system can perform emotion recognition to evaluate emotional states. The terminal analyzes acquired facial video data and uses a generative AI model to identify the user's emotional state. This model has the ability to analyze changes in facial expressions and movements in real time, proactively detecting potentially fraudulent transactions.

[0161] For example, if a user attempts to make a transaction at an ATM and the system detects unusual signs of tension or anxiety, the server could detect this, temporarily suspend the transaction, and request additional identity verification procedures. These additional procedures would be communicated to the user's mobile device, thus ensuring user security.

[0162] An example of a prompt message would be: "Generate a description of a new security method using biometric authentication and emotion recognition at ATMs. In particular, please explain in detail how to analyze the user's emotion profile and how to evaluate fraudulent transactions."

[0163] In this way, the present invention has a configuration that reduces security risks in financial transactions and improves the user experience.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user registers their biometric information with the device. The device uses a facial recognition camera or fingerprint scanner to acquire facial images and fingerprint data. The device receives the entered biometric information and encrypts it using AES encryption technology. This process ensures the security of the registered biometric information. The encrypted data is then generated as output.

[0167] Step 2:

[0168] The device sends encrypted biometric information to the server. The transmitted data is input to the server, which stores the received data in a secure storage device. In this step, the server stores the biometric information in preparation for later authentication. The stored data is recorded as the output of the process.

[0169] Step 3:

[0170] When a user uses an ATM, they provide their biometric information to the terminal again. The terminal acquires this information in real time using a facial recognition camera and a fingerprint scanner. The terminal processes the entered information on the spot and outputs it as current biometric data.

[0171] Step 4:

[0172] The terminal re-encrypts the acquired biometric information and sends it to the server. The server receives this as input and compares it with the stored registration data. Based on this data, the server identifies the individual, and if the match is successful, an authentication success signal is output, allowing the transaction to be executed.

[0173] Step 5:

[0174] Simultaneously, the device processes video data from the face using an emotion recognition engine. A generative AI model receives this as input and analyzes the emotions. As a result, a specific emotional state is determined, and the user's emotional profile is output.

[0175] Step 6:

[0176] The server also checks the emotional profile upon successful matching, and if abnormal emotions (e.g., tension, anxiety) are detected, it assesses the transaction risk. If an anomaly is detected, the transaction is temporarily suspended or additional verification is requested, and the user is notified. This helps prevent fraudulent transactions.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] While conventional biometric authentication systems are effective for enhancing security, they have the problem of not considering the user's emotional state, making it impossible to completely eliminate the possibility of fraudulent transactions. Furthermore, systems based solely on biometric information may lack sufficient authentication accuracy or lead to misunderstandings due to user unfamiliarity. Therefore, there is a need for systems that provide a higher level of security by taking the user's emotional state into consideration.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes a data collection means for acquiring biometric information and an emotion recognition means for recognizing the user's emotional state; a data protection means for encrypting the acquired biometric information and emotion data and storing them in securely managed data storage; and a security enhancement means for detecting potential fraud based on emotion data acquired during the transaction process and ensuring secure transactions. By handling biometric information and emotion data in an integrated manner, it becomes possible to enhance the security and reliability of transactions and reduce the user's vulnerability to fraudulent transactions.

[0182] "Biometric information" refers to data that describes physical or behavioral characteristics used for individual user identification.

[0183] "Data collection means" refers to a device or program that functions to acquire biological information or emotional states.

[0184] "Emotion recognition means" refers to a technology that uses methods and techniques to infer and recognize an emotional state from a user's facial expressions and other characteristics, such as their facial features and expressions.

[0185] "Data protection measures" refer to technologies such as encryption that securely manage collected biometric and emotional data and prevent unauthorized access by third parties.

[0186] "Identification means" refers to a function that uses technology to identify a user by comparing acquired biometric information with information stored in a database.

[0187] "Action execution means" refers to a device or program that provides the functionality to execute a planned transaction or action upon successful authentication.

[0188] "Enhanced security measures" refer to technologies that detect abnormal user emotions or behavior during transactions, and take measures to mitigate the risk of fraud by temporarily suspending transactions as needed.

[0189] A "generative AI model" refers to an artificial intelligence program that learns patterns from large amounts of data and makes decisions based on new data.

[0190] A "prompt sentence" is a natural language input sentence that provides instructions to a generative AI model to solve a specific problem.

[0191] The system for realizing this application simultaneously acquires the user's biometric information and emotional state, and uses this information to ensure transaction security. The server analyzes the biometric and emotional information transmitted from the user's terminal in real time using software such as OpenCV and face_recognition. Specifically, it captures the user's face data using the camera installed in the device and verifies its match with the stored biometric data. It also analyzes the user's emotions using generative AI models such as EmotionRecognizer, and issues a warning if an emotional state different from normal is detected.

[0192] On the terminal side, when a user attempts to conduct a transaction, their biometric information is collected and emotion recognition is performed. This data is then encrypted and sent to the server. Based on this information, the server decides whether or not to proceed with the transaction, and if necessary, it may temporarily suspend the transaction or request additional identity verification.

[0193] As a concrete example, when a user makes a payment at a store, their smartphone performs facial recognition and fingerprint authentication. Simultaneously, the camera analyzes the user's facial expressions, and if EmotionRecognizer detects any unusual signs of anxiety, the user receives a notification. The user can then respond to the notification and proceed with the transaction while ensuring security.

[0194] An example of a prompt for a generated AI model is: "Describe the development of an application that recognizes a user's facial expression and emotions when they make an electronic payment, and then detects and responds to potential fraud." This makes it possible to build a transaction system that combines higher security and convenience.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The device acquires biometric information for user verification. Specifically, it activates the camera built into the device and captures the user's face. The face data is then mapped to feature points using the face_recognition library, and this is generated as biometric information. The input is the user's face image, and the output is digitized facial feature data.

[0198] Step 2:

[0199] The device uses EmotionRecognizer to recognize the user's emotional state. A facial image captured by the camera is input into an AI model, which analyzes the facial expression to determine the emotional state (e.g., joy, anxiety, surprise). The input is the user's facial image, and the output is the estimated emotion. The data output from the emotion engine is based on the user's emotional profile.

[0200] Step 3:

[0201] The terminal encrypts the acquired biometric and emotional information to ensure security and then transmits the data to the server. Before transmission, AES encryption is used to prevent unauthorized access to the data by third parties. The input consists of biometric and emotional information related to the transaction, and the output is encrypted data.

[0202] Step 4:

[0203] The server receives the transmitted encrypted data and decrypts it. Using the decrypted data, it performs a match check against existing biometric information stored in the database. Identity is confirmed through facial recognition. The input is encrypted biometric and emotional data, and the output is whether or not the authentication matches.

[0204] Step 5:

[0205] The server evaluates the user's mental state based on emotion recognition data. If the emotion is unusual, it issues a warning and requests a temporary suspension of the transaction or additional confirmation. This step involves the emotion engine acting as a security enhancement measure. The input is decoded emotion data, and the output is a decision regarding whether to continue the transaction.

[0206] Step 6:

[0207] The server will proceed with the transaction if all steps are completed successfully. After verifying that sentiment and biometric authentication are valid, the execution procedure begins. The input is the transaction data after authentication and security verification, and the output is the transaction completion status. This provides a high level of security throughout the entire system.

[0208] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0209] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0224] As an embodiment for carrying out the present invention, a system for withdrawing cash without using a cash card using biometric authentication is described. This system includes a series of processes for acquiring, storing, and verifying the user's biometric information, and executing a financial transaction if authentication is successful.

[0225] First, users register their biometric authentication data with the system via their device. Specifically, the system obtains the user's biometric information using a facial recognition camera or fingerprint scanner. This information is encrypted in a secure format and sent to the server. The server stores the received data in a database and uses an AI model to train it for individual user identification. This enables highly accurate personal identification.

[0226] When using an ATM, the user undergoes facial recognition or fingerprint scanning again. At this time, the biometric information acquired by the terminal is immediately sent to the server and compared with information stored in advance. If this comparison is successful, the server sends an authentication success signal to the terminal and authorizes the user to make a transaction. After confirming the transaction input, the terminal dispenses the specified amount as cash, the transaction details are recorded on the server, and the user's account information is updated simultaneously.

[0227] As a concrete example, when a user withdraws cash using an ATM, the server verifies that the user's biometric information matches their registered information. If authentication is successful, the specified amount can be withdrawn smoothly. In this way, this system enables cardless cash withdrawals while ensuring security.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] Users are asked to access the system using their device and register their biometric information. Their face is scanned with a facial recognition camera, and their fingerprints are scanned with a fingerprint sensor. The resulting biometric data is encrypted and sent to the server.

[0231] Step 2:

[0232] The server stores the received biometric authentication data in a database. The stored data is then trained by an AI model to generate individual user profiles, which are used to identify users.

[0233] Step 3:

[0234] When a user uses an ATM, the terminal attempts to log in using biometric authentication. The terminal then uses the facial recognition camera and fingerprint sensor again to obtain the latest biometric information.

[0235] Step 4:

[0236] The device encrypts and transmits newly acquired biometric authentication data to the server in real time. The server then compares this data with the information stored in the database.

[0237] Step 5:

[0238] The server executes the matching process using an AI model. If the matching result shows a match that exceeds the threshold, the server sends an authentication success message to the terminal.

[0239] Step 6:

[0240] The user enters the withdrawal amount on the ATM screen. The server checks the user's account balance and verifies whether the specified amount can be withdrawn.

[0241] Step 7:

[0242] Once the transaction is approved, the terminal dispenses the specified amount as cash. The server logs the transaction details and updates the user's account information. This completes the transaction.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0245] There is a growing demand for a safe and fast way to withdraw cash without having to carry a cash card. Conventional technologies have had drawbacks such as the risk of card loss and the inaccuracy of biometric authentication. Therefore, the challenge is to provide users with a convenient transaction method while ensuring high-precision personal authentication and security.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes a device and means for acquiring biometric data, a data management device and means for securely transferring the acquired biometric data and storing it in a storage device, and a reliability verification device and means for comparing the stored biometric data with the reacquired biometric data. This enables secure, cardless cash withdrawals and high-precision personal identification through biometric authentication.

[0248] "Biometric data" refers to information acquired to identify individuals based on the shape and patterns of their faces, fingers, eyes, etc.

[0249] A "device" is a mechanical element or assembly of machines used to perform a specific function, such as acquiring, transferring, recording, or comparing biological data.

[0250] "Secure state" refers to a protected state in which data is safe from unauthorized access and tampering from external sources.

[0251] "Transfer" is the process of moving acquired data from one location to another.

[0252] A "storage device" is a medium or device that can store data and retrieve it as needed.

[0253] A "data management device" is a device used to store and manage acquired data so that it can be used when needed.

[0254] A "reliability verification device" is a device used to verify consistency and authenticity by comparing retained data with newly acquired data.

[0255] A "machine learning model" is an algorithm or statistical model that learns patterns from data and enables it to automatically perform specific tasks.

[0256] An "analytical device" is a device or software used to analyze data and extract information or patterns contained within it.

[0257] "Individual identification accuracy" is an indicator that represents the degree of the ability to accurately identify an individual in biometric authentication.

[0258] This system provides a way to withdraw cash without a cash card using biometric authentication. Users begin by registering their biometric data using a terminal. Devices such as facial recognition cameras and fingerprint scanners are used to acquire this biometric data. These devices capture the user's facial and finger features and transmit this information as digital data to the terminal. The terminal encrypts this data to maintain its security. Advanced encryption protocols such as TLS are employed to ensure data security.

[0259] The user's biometric information is encrypted and then transferred to the server. The server decrypts the data and stores it in a database. Furthermore, the server uses a generative AI model to learn from the acquired biometric data, enabling it to identify users with high accuracy. Machine learning algorithms allow the server to extract patterns from the acquired data, forming a foundation for strengthening the user authentication process.

[0260] When using an ATM, the user performs biometric authentication again at the terminal. At this time, the terminal immediately sends the newly acquired biometric data to the server, which compares this data with previously stored data. If authentication is successful, the terminal authorizes the transaction, and the specified amount is dispensed as cash. Furthermore, details of each transaction are recorded on the server, and the user's account information is updated to the latest state.

[0261] For example, if a user needs to withdraw money urgently, they can go to the nearest ATM and authenticate using facial recognition. If the system authenticates correctly, the user can receive cash without using a card. This process is quick and can be completed in seconds.

[0262] Examples of prompt messages include the following:

[0263] Please explain the system procedure for users to withdraw cash using facial recognition at an ATM.

[0264] "Could you please explain in detail the authentication process for cashless transactions using biometric information?"

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] The user registers biometric data on the device. Specifically, a facial recognition camera captures an image of the user's face, and a fingerprint scanner obtains a fingerprint. The data entered is then the user's facial and fingerprint information. The device encrypts this data and securely transmits it to the server. The output at this point is encrypted biometric data. Furthermore, the device displays a UI indicating to the user that the series of operations are complete.

[0268] Step 2:

[0269] The server receives encrypted data sent from the terminal, decrypts it, and stores it in a database. The input is encrypted biometric data, and the output is the biometric data stored in the database. The server also adds time and location information when recording the data, in preparation for future improvements in authentication accuracy.

[0270] Step 3:

[0271] The server analyzes stored biometric data using an AI model to train the model with user identification information. The input to this process is stored biometric data, and the output is an AI model with updated personal identification accuracy. The server monitors the model's learning progress and prepares an optimized model.

[0272] Step 4:

[0273] When a user approaches an ATM and withdraws cash, they undergo biometric authentication again. The user stands in front of the facial recognition camera and places their finger on the fingerprint scanner, allowing the terminal to acquire new biometric information. The input for this operation is real-time biometric data, and the output is the transmission of biometric data to the server.

[0274] Step 5:

[0275] The server receives newly transmitted biometric data and compares it to stored data. The input for the comparison is the new and stored biometric data, and the output is the authentication status. The server evaluates the degree of match and, if authentication is successful, returns a confirmation signal to the terminal.

[0276] Step 6:

[0277] Once the terminal confirms successful authentication, it authorizes the transaction. The user specifies the amount to withdraw on the screen. The input is the user's specified amount, and the output is the cash discharge. The terminal provides the user with the specified amount of cash by performing the discharge process using its internal mechanism.

[0278] (Application Example 1)

[0279] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0280] In a system for withdrawing cash without using a cash card, there is an issue of improving safety and convenience by utilizing biometric authentication technology. In particular, it is necessary to provide a safe and rapid authentication means for electronic payment and strengthen the security of user transactions.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0282] In this invention, the server includes an information acquisition means for acquiring biometric authentication data, a data storage means for encrypting the acquired biometric authentication data and storing it in a database, and an authentication means for collating the stored biometric authentication data with newly acquired data. Thereby, when a user makes an electronic payment, safe and rapid personal authentication and settlement processing become possible.

[0283] "Biometric authentication data" is information for confirming the identity of a person using personal physical characteristics such as face, fingerprint, iris, etc.

[0284] "Information acquisition means" is a device or technology for collecting a user's physical characteristics and converting them into digital data.

[0285] "Data storage means" is a database or storage system for safely storing the acquired biometric authentication data.

[0286] "Authentication means" is a method or device for comparing newly acquired biometric authentication data with data stored in an existing database and confirming the match.

[0287] A "transaction execution mechanism" is a mechanism for executing financial transactions or other requested operations when biometric authentication is successful.

[0288] "Data update means" refers to a method or process of updating a database to keep transaction details and user identification information up to date.

[0289] A "payment method" is a system or method for executing and completing transactions authorized based on biometric authentication.

[0290] The system for implementing this invention consists of users making electronic payments using biometric authentication via smartphones or other devices. This system operates by combining various hardware and software components to achieve highly secure cashless transactions. Specifically, it utilizes facial recognition cameras and fingerprint scanners to acquire the user's biometric information. The acquired data is encrypted via the information acquisition means and securely stored in a database. This prevents data leakage.

[0291] The system also uses authentication methods to compare newly acquired biometric information with pre-registered information. Here, computational models are used to analyze the data and achieve highly accurate personal identification. If biometric authentication is successful, the server approves the transaction through the payment method. Subsequently, transaction details are recorded by a data update mechanism, and the user's identification information is updated accordingly.

[0292] For example, when a user purchases concert tickets electronically, this system authenticates quickly and securely, completing the payment instantly. This facilitates cashless transactions, eliminating the need for users to carry physical cash cards.

[0293] Using a generative AI model, the system constantly strives to improve accuracy with new prompts and methods. Further applications can be explored using the prompt, "Explain specific ways to utilize this electronic payment platform and compare it with existing payment methods," as an example.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user operates the device to input biometric information. The device uses a facial recognition camera and fingerprint scanner to acquire the user's biometric authentication data. The input is the user's physical characteristics (image data), which is converted into digital data, encrypted, and output.

[0297] Step 2:

[0298] The device encrypts the acquired biometric authentication data and sends it to the server. Here, the device uses an encryption algorithm to protect the data and transmit it in order to maintain data security. The output is encrypted data.

[0299] Step 3:

[0300] The server stores the received data in the database. The server properly stores this data in the database in preparation for matching it with existing data. At this stage, the output is an updated database.

[0301] Step 4:

[0302] When a user attempts to conduct a transaction, they re-enter their biometric information into the terminal. The terminal then sends the newly acquired biometric authentication data to the server. This new biometric information, which is acquired again as input, is encrypted before transmission.

[0303] Step 5:

[0304] The server uses authentication means to check and confirm the match between the saved biometric authentication data and the newly acquired data. In this process, the generated AI model is utilized to improve the matching accuracy. The matching result is obtained as the output.

[0305] Step 6:

[0306] If the authentication is successful, the server uses the transaction execution means to execute the transaction. It approves the transaction details and starts the settlement process. Depending on the selection of credit or debit, the actual financial transaction is carried out. The approved transaction is obtained as the output.

[0307] Step 7:

[0308] The server uses data update means to record the transaction details and update the user's identification information. This includes updating the user's account information and saving the transaction history. The updated identification information and records are obtained as the output.

[0309] Step 8:

[0310] The terminal notifies the user of the completion of the transaction. For example, it displays a transaction success message on the terminal's display and prints a receipt if necessary. The input at this time is the transaction completion signal from the server, and the output is the visual or printed information for the user.

[0311] Through these processing steps, a secure and fast electronic payment using biometric authentication is realized. By maintaining this flow, it can be applied to other uses. Other examples with prompt sentences such as "Explain the specific usage method of the electronic payment platform using this system and show the comparison with existing payment means." can also be considered.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0313] This invention provides a more secure and intuitive cashless card system by combining biometric authentication and emotion recognition. This system includes processes for acquiring, registering, and carefully managing the user's biometric information, as well as recognizing their emotional state in real time.

[0314] First, the user performs initial setup through the device. During this process, biometric data is obtained using a facial recognition camera and fingerprint scanner. This information is encrypted and securely stored on a server. Furthermore, an emotion engine enhances the ability to read emotions from the user's face. This data is analyzed by an AI model to form the user's emotional profile.

[0315] While using an ATM, the user interacts with the terminal to perform biometric and emotional data recognition. The terminal uses sensors to acquire this information and transmit it to a server. The server, in parallel with verifying biometric authentication, evaluates whether the user's emotions are unusual. Using an emotion engine, if a specific emotional state, such as tension or anxiety, is detected, a security assessment mechanism is activated, and a warning is issued if necessary.

[0316] A concrete example of this system is when a user is operating an ATM and the system detects unusually high levels of stress. In this case, the server analyzes the user's emotional changes and assesses the possibility of fraudulent transactions. Subsequently, the transaction may be temporarily suspended, or further identity verification may be required.

[0317] This invention adds a new security assessment based on emotion recognition to the conventional identity verification process that does not rely on cash cards, allowing users to use the system with even greater peace of mind. This makes it possible to achieve both convenience and security during financial transactions.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The user uses a device for initial registration, scanning their face and fingerprints to provide biometric data. The device receives this data, encrypts it, and sends it to the server.

[0321] Step 2:

[0322] The server securely stores the received biometric data in a database. Simultaneously, it analyzes the data using an AI model to form an individual user profile. It also creates a user's emotional profile using an emotion engine based on facial recognition data.

[0323] Step 3:

[0324] When a user uses an ATM, the terminal scans again, acquiring their face and fingerprints in real time. Furthermore, it analyzes the user's current emotional state through the facial recognition camera.

[0325] Step 4:

[0326] The device sends newly acquired biometric and emotional information to the server. The server compares this information with an existing database to verify biometric authentication.

[0327] Step 5:

[0328] The server analyzes emotional information obtained through the emotion engine and evaluates for any unusual emotions or signs of stress. If necessary, it performs a safety assessment.

[0329] Step 6:

[0330] The server, once it confirms that both biometric authentication and sentiment evaluation are successful, authorizes the terminal to execute the transaction. The authenticated user then enters the amount they wish to withdraw on the ATM screen.

[0331] Step 7:

[0332] The terminal, if withdrawal is permitted, dispenses the specified amount as cash. The server completes the process by recording the transaction details and updating the user's account information.

[0333] By evaluating the user's emotional state in this way, the system can provide even greater security in addition to conventional biometric authentication.

[0334] (Example 2)

[0335] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0336] In financial transactions that do not use cash cards, the challenge is to simultaneously improve security and convenience. In particular, it is necessary to prevent fraudulent transactions by utilizing emotion recognition in addition to biometric authentication. While conventional systems have some effect in acquiring and authenticating biometric information, they lack the function of monitoring the user's psychological state, so more comprehensive security measures are required.

[0337] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0338] In this invention, the server includes data acquisition means for acquiring biometric information, emotion evaluation means for sensing emotional states and evaluating the risk of fraudulent transactions, and analysis means for analyzing the acquired biometric and emotional information using an artificial intelligence model to enhance the accuracy of user individual recognition and safety evaluation. This enables a comprehensive analysis of the user's biometric information and emotional state, making safer transaction decisions possible.

[0339] "Biometric information" refers to human characteristic data that enables the biologically unique identification of an individual, including facial features, fingerprints, and iris scans.

[0340] "Data acquisition means" refers to technologies consisting of hardware and software for capturing and collecting biometric and emotional information.

[0341] A "secure storage device" is a storage medium that protects and stores acquired data, and has the function of protecting against unauthorized access by using encryption technology.

[0342] "Verification means" refers to a process and algorithm for comparing stored data with newly acquired data to determine the legitimacy of an individual.

[0343] "Emotional evaluation tools" are technologies that analyze a user's psychological state and determine whether that state differs from their normal state, utilizing artificial intelligence to analyze emotional characteristics.

[0344] "Emotion recognition" is a technology used to identify a user's emotions and psychological state, and it analyzes them based on facial expressions, voice, and other factors.

[0345] An "artificial intelligence model" is a program that has machine learning algorithms to analyze data and identify patterns, and can accurately determine the characteristics of user behavior and emotions.

[0346] "Fraudulent transactions" refer to financial transactions conducted with fraudulent intent that do not align with the normal purpose of use, and actions that are carried out against the user's will.

[0347] "Individual identification accuracy" refers to the ability to accurately identify a specific individual from all other individuals, and high-precision recognition is important for reducing the risk of misidentification.

[0348] This invention provides a safer and more convenient cashless transaction system by utilizing biometric authentication and emotion recognition technology. This system is constructed and implemented as follows.

[0349] The user first uses the device to provide biometric information. The device has a built-in facial recognition camera and fingerprint scanner, and biometric information is obtained through these devices. This information is encrypted using security technologies such as AES encryption before being transmitted from the device.

[0350] The server uses a database to receive encrypted biometric information and store it in a secure storage device. This data is later used in a matching process and forms the basis for identifying individual users.

[0351] Furthermore, the system can perform emotion recognition to evaluate emotional states. The terminal analyzes acquired facial video data and uses a generative AI model to identify the user's emotional state. This model has the ability to analyze changes in facial expressions and movements in real time, proactively detecting potentially fraudulent transactions.

[0352] For example, if a user attempts to make a transaction at an ATM and the system detects unusual signs of tension or anxiety, the server could detect this, temporarily suspend the transaction, and request additional identity verification procedures. These additional procedures would be communicated to the user's mobile device, thus ensuring user security.

[0353] An example of a prompt message would be: "Generate a description of a new security method using biometric authentication and emotion recognition at ATMs. In particular, please explain in detail how to analyze the user's emotion profile and how to evaluate fraudulent transactions."

[0354] In this way, the present invention has a configuration that reduces security risks in financial transactions and improves the user experience.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user registers their biometric information with the device. The device uses a facial recognition camera or fingerprint scanner to acquire facial images and fingerprint data. The device receives the entered biometric information and encrypts it using AES encryption technology. This process ensures the security of the registered biometric information. The encrypted data is then generated as output.

[0358] Step 2:

[0359] The device sends encrypted biometric information to the server. The transmitted data is input to the server, which stores the received data in a secure storage device. In this step, the server stores the biometric information in preparation for later authentication. The stored data is recorded as the output of the process.

[0360] Step 3:

[0361] When a user uses an ATM, they provide their biometric information to the terminal again. The terminal acquires this information in real time using a facial recognition camera and a fingerprint scanner. The terminal processes the entered information on the spot and outputs it as current biometric data.

[0362] Step 4:

[0363] The terminal re-encrypts the acquired biometric information and sends it to the server. The server receives this as input and compares it with the stored registration data. Based on this data, the server identifies the individual, and if the match is successful, an authentication success signal is output, allowing the transaction to be executed.

[0364] Step 5:

[0365] Simultaneously, the device processes video data from the face using an emotion recognition engine. A generative AI model receives this as input and analyzes the emotions. As a result, a specific emotional state is determined, and the user's emotional profile is output.

[0366] Step 6:

[0367] The server also checks the emotional profile upon successful matching, and if abnormal emotions (e.g., tension, anxiety) are detected, it assesses the transaction risk. If an anomaly is detected, the transaction is temporarily suspended or additional verification is requested, and the user is notified. This helps prevent fraudulent transactions.

[0368] (Application Example 2)

[0369] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0370] While conventional biometric authentication systems are effective for enhancing security, they have the problem of not considering the user's emotional state, making it impossible to completely eliminate the possibility of fraudulent transactions. Furthermore, systems based solely on biometric information may lack sufficient authentication accuracy or lead to misunderstandings due to user unfamiliarity. Therefore, there is a need for systems that provide a higher level of security by taking the user's emotional state into consideration.

[0371] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0372] In this invention, the server includes a data collection means for acquiring biometric information and an emotion recognition means for recognizing the user's emotional state; a data protection means for encrypting the acquired biometric information and emotion data and storing them in securely managed data storage; and a security enhancement means for detecting potential fraud based on emotion data acquired during the transaction process and ensuring secure transactions. By handling biometric information and emotion data in an integrated manner, it becomes possible to enhance the security and reliability of transactions and reduce the user's vulnerability to fraudulent transactions.

[0373] "Biometric information" refers to data that describes physical or behavioral characteristics used for individual user identification.

[0374] "Data collection means" refers to a device or program that functions to acquire biological information or emotional states.

[0375] "Emotion recognition means" refers to a technology that uses methods and techniques to infer and recognize an emotional state from a user's facial expressions and other characteristics, such as their facial features and expressions.

[0376] "Data protection measures" refer to technologies such as encryption that securely manage collected biometric and emotional data and prevent unauthorized access by third parties.

[0377] "Identification means" refers to a function that uses technology to identify a user by comparing acquired biometric information with information stored in a database.

[0378] "Action execution means" refers to a device or program that provides the functionality to execute a planned transaction or action upon successful authentication.

[0379] "Enhanced security measures" refer to technologies that detect abnormal user emotions or behavior during transactions, and take measures to mitigate the risk of fraud by temporarily suspending transactions as needed.

[0380] A "generative AI model" refers to an artificial intelligence program that learns patterns from large amounts of data and makes decisions based on new data.

[0381] A "prompt sentence" is a natural language input sentence that provides instructions to a generative AI model to solve a specific problem.

[0382] The system for realizing this application simultaneously acquires the user's biometric information and emotional state, and uses this information to ensure transaction security. The server analyzes the biometric and emotional information transmitted from the user's terminal in real time using software such as OpenCV and face_recognition. Specifically, it captures the user's face data using the camera installed in the device and verifies its match with the stored biometric data. It also analyzes the user's emotions using generative AI models such as EmotionRecognizer, and issues a warning if an emotional state different from normal is detected.

[0383] On the terminal side, when a user attempts to conduct a transaction, their biometric information is collected and emotion recognition is performed. This data is then encrypted and sent to the server. Based on this information, the server decides whether or not to proceed with the transaction, and if necessary, it may temporarily suspend the transaction or request additional identity verification.

[0384] As a concrete example, when a user makes a payment at a store, their smartphone performs facial recognition and fingerprint authentication. Simultaneously, the camera analyzes the user's facial expressions, and if EmotionRecognizer detects any unusual signs of anxiety, the user receives a notification. The user can then respond to the notification and proceed with the transaction while ensuring security.

[0385] An example of a prompt for a generated AI model is: "Describe the development of an application that recognizes a user's facial expression and emotions when they make an electronic payment, and then detects and responds to potential fraud." This makes it possible to build a transaction system that combines higher security and convenience.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The device acquires biometric information for user verification. Specifically, it activates the camera built into the device and captures the user's face. The face data is then mapped to feature points using the face_recognition library, and this is generated as biometric information. The input is the user's face image, and the output is digitized facial feature data.

[0389] Step 2:

[0390] The device uses EmotionRecognizer to recognize the user's emotional state. A facial image captured by the camera is input into an AI model, which analyzes the facial expression to determine the emotional state (e.g., joy, anxiety, surprise). The input is the user's facial image, and the output is the estimated emotion. The data output from the emotion engine is based on the user's emotional profile.

[0391] Step 3:

[0392] The terminal encrypts the acquired biometric and emotional information to ensure security and then transmits the data to the server. Before transmission, AES encryption is used to prevent unauthorized access to the data by third parties. The input consists of biometric and emotional information related to the transaction, and the output is encrypted data.

[0393] Step 4:

[0394] The server receives the transmitted encrypted data and decrypts it. Using the decrypted data, it performs a match check against existing biometric information stored in the database. Identity is confirmed through facial recognition. The input is encrypted biometric and emotional data, and the output is whether or not the authentication matches.

[0395] Step 5:

[0396] The server evaluates the user's mental state based on emotion recognition data. If the emotion is unusual, it issues a warning and requests a temporary suspension of the transaction or additional confirmation. This step involves the emotion engine acting as a security enhancement measure. The input is decoded emotion data, and the output is a decision regarding whether to continue the transaction.

[0397] Step 6:

[0398] The server will proceed with the transaction if all steps are completed successfully. After verifying that sentiment and biometric authentication are valid, the execution procedure begins. The input is the transaction data after authentication and security verification, and the output is the transaction completion status. This provides a high level of security throughout the entire system.

[0399] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0400] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0401] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0405] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0406] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0407] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0408] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0409] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0410] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0411] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0412] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0413] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0414] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0415] As an embodiment for carrying out the present invention, a system for withdrawing cash without using a cash card using biometric authentication is described. This system includes a series of processes for acquiring, storing, and verifying the user's biometric information, and executing a financial transaction if authentication is successful.

[0416] First, users register their biometric authentication data with the system via their device. Specifically, the system obtains the user's biometric information using a facial recognition camera or fingerprint scanner. This information is encrypted in a secure format and sent to the server. The server stores the received data in a database and uses an AI model to train it for individual user identification. This enables highly accurate personal identification.

[0417] When using an ATM, the user undergoes facial recognition or fingerprint scanning again. At this time, the biometric information acquired by the terminal is immediately sent to the server and compared with information stored in advance. If this comparison is successful, the server sends an authentication success signal to the terminal and authorizes the user to make a transaction. After confirming the transaction input, the terminal dispenses the specified amount as cash, the transaction details are recorded on the server, and the user's account information is updated simultaneously.

[0418] As a concrete example, when a user withdraws cash using an ATM, the server verifies that the user's biometric information matches their registered information. If authentication is successful, the specified amount can be withdrawn smoothly. In this way, this system enables cardless cash withdrawals while ensuring security.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] Users are asked to access the system using their device and register their biometric information. Their face is scanned with a facial recognition camera, and their fingerprints are scanned with a fingerprint sensor. The resulting biometric data is encrypted and sent to the server.

[0422] Step 2:

[0423] The server stores the received biometric authentication data in a database. The stored data is then trained by an AI model to generate individual user profiles, which are used to identify users.

[0424] Step 3:

[0425] When a user uses an ATM, the terminal attempts to log in using biometric authentication. The terminal then uses the facial recognition camera and fingerprint sensor again to obtain the latest biometric information.

[0426] Step 4:

[0427] The device encrypts and transmits newly acquired biometric authentication data to the server in real time. The server then compares this data with the information stored in the database.

[0428] Step 5:

[0429] The server executes the matching process using an AI model. If the matching result shows a match that exceeds the threshold, the server sends an authentication success message to the terminal.

[0430] Step 6:

[0431] The user enters the withdrawal amount on the ATM screen. The server checks the user's account balance and verifies whether the specified amount can be withdrawn.

[0432] Step 7:

[0433] Once the transaction is approved, the terminal dispenses the specified amount as cash. The server logs the transaction details and updates the user's account information. This completes the transaction.

[0434] (Example 1)

[0435] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0436] There is a growing demand for a safe and fast way to withdraw cash without having to carry a cash card. Conventional technologies have had drawbacks such as the risk of card loss and the inaccuracy of biometric authentication. Therefore, the challenge is to provide users with a convenient transaction method while ensuring high-precision personal authentication and security.

[0437] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0438] In this invention, the server includes a device and means for acquiring biometric data, a data management device and means for securely transferring the acquired biometric data and storing it in a storage device, and a reliability verification device and means for comparing the stored biometric data with the reacquired biometric data. This enables secure, cardless cash withdrawals and high-precision personal identification through biometric authentication.

[0439] "Biometric data" refers to information acquired to identify individuals based on the shape and patterns of their faces, fingers, eyes, etc.

[0440] A "device" is a mechanical element or assembly of machines used to perform a specific function, such as acquiring, transferring, recording, or comparing biological data.

[0441] "Secure state" refers to a protected state in which data is safe from unauthorized access and tampering from external sources.

[0442] "Transfer" is the process of moving acquired data from one location to another.

[0443] A "storage device" is a medium or device that can store data and retrieve it as needed.

[0444] A "data management device" is a device used to store and manage acquired data so that it can be used when needed.

[0445] A "reliability verification device" is a device used to verify consistency and authenticity by comparing retained data with newly acquired data.

[0446] A "machine learning model" is an algorithm or statistical model that learns patterns from data and enables it to automatically perform specific tasks.

[0447] An "analytical device" is a device or software used to analyze data and extract information or patterns contained within it.

[0448] "Individual identification accuracy" is an indicator that represents the degree of the ability to accurately identify an individual in biometric authentication.

[0449] This system provides a way to withdraw cash without a cash card using biometric authentication. Users begin by registering their biometric data using a terminal. Devices such as facial recognition cameras and fingerprint scanners are used to acquire this biometric data. These devices capture the user's facial and finger features and transmit this information as digital data to the terminal. The terminal encrypts this data to maintain its security. Advanced encryption protocols such as TLS are employed to ensure data security.

[0450] The user's biometric information is encrypted and then transferred to the server. The server decrypts the data and stores it in a database. Furthermore, the server uses a generative AI model to learn from the acquired biometric data, enabling it to identify users with high accuracy. Machine learning algorithms allow the server to extract patterns from the acquired data, forming a foundation for strengthening the user authentication process.

[0451] When using an ATM, the user performs biometric authentication again at the terminal. At this time, the terminal immediately sends the newly acquired biometric data to the server, which compares this data with previously stored data. If authentication is successful, the terminal authorizes the transaction, and the specified amount is dispensed as cash. Furthermore, details of each transaction are recorded on the server, and the user's account information is updated to the latest state.

[0452] For example, if a user needs to withdraw money urgently, they can go to the nearest ATM and authenticate using facial recognition. If the system authenticates correctly, the user can receive cash without using a card. This process is quick and can be completed in seconds.

[0453] Examples of prompt messages include the following:

[0454] Please explain the system procedure for users to withdraw cash using facial recognition at an ATM.

[0455] "Could you please explain in detail the authentication process for cashless transactions using biometric information?"

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The user registers biometric data on the device. Specifically, a facial recognition camera captures an image of the user's face, and a fingerprint scanner obtains a fingerprint. The data entered is then the user's facial and fingerprint information. The device encrypts this data and securely transmits it to the server. The output at this point is encrypted biometric data. Furthermore, the device displays a UI indicating to the user that the series of operations are complete.

[0459] Step 2:

[0460] The server receives encrypted data sent from the terminal, decrypts it, and stores it in a database. The input is encrypted biometric data, and the output is the biometric data stored in the database. The server also adds time and location information when recording the data, in preparation for future improvements in authentication accuracy.

[0461] Step 3:

[0462] The server analyzes stored biometric data using an AI model to train the model with user identification information. The input to this process is stored biometric data, and the output is an AI model with updated personal identification accuracy. The server monitors the model's learning progress and prepares an optimized model.

[0463] Step 4:

[0464] When a user approaches an ATM and withdraws cash, they undergo biometric authentication again. The user stands in front of the facial recognition camera and places their finger on the fingerprint scanner, allowing the terminal to acquire new biometric information. The input for this operation is real-time biometric data, and the output is the transmission of biometric data to the server.

[0465] Step 5:

[0466] The server receives newly transmitted biometric data and compares it to stored data. The input for the comparison is the new and stored biometric data, and the output is the authentication status. The server evaluates the degree of match and, if authentication is successful, returns a confirmation signal to the terminal.

[0467] Step 6:

[0468] Once the terminal confirms successful authentication, it authorizes the transaction. The user specifies the amount to withdraw on the screen. The input is the user's specified amount, and the output is the cash discharge. The terminal provides the user with the specified amount of cash by performing the discharge process using its internal mechanism.

[0469] (Application Example 1)

[0470] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0471] In systems that allow cash withdrawals without using a cash card, there is a challenge in improving security and convenience by utilizing biometric authentication technology. In particular, there is a need to provide a secure and rapid authentication method for electronic payments and to strengthen the security of user transactions.

[0472] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0473] In this invention, the server includes information acquisition means for acquiring biometric authentication data, data storage means for encrypting the acquired biometric authentication data and storing it in a database, and authentication means for comparing the stored biometric authentication data with newly acquired data. This enables secure and rapid personal authentication and payment processing when a user makes an electronic payment.

[0474] "Biometric data" refers to information used to verify a person's identity using their physical characteristics, such as their face, fingerprints, and iris.

[0475] "Information acquisition means" refers to devices and technologies for collecting a user's physical characteristics and converting them into digital data.

[0476] "Data storage means" refers to databases and storage systems for securely storing acquired biometric authentication data.

[0477] "Authentication means" refers to a method or apparatus for comparing newly acquired biometric authentication data with data stored in an existing database to confirm a match.

[0478] A "transaction execution mechanism" is a mechanism for executing financial transactions or other requested operations when biometric authentication is successful.

[0479] "Data update means" refers to a method or process of updating a database to keep transaction details and user identification information up to date.

[0480] A "payment method" is a system or method for executing and completing transactions authorized based on biometric authentication.

[0481] The system for implementing this invention consists of users making electronic payments using biometric authentication via smartphones or other devices. This system operates by combining various hardware and software components to achieve highly secure cashless transactions. Specifically, it utilizes facial recognition cameras and fingerprint scanners to acquire the user's biometric information. The acquired data is encrypted via the information acquisition means and securely stored in a database. This prevents data leakage.

[0482] The system also uses authentication methods to compare newly acquired biometric information with pre-registered information. Here, computational models are used to analyze the data and achieve highly accurate personal identification. If biometric authentication is successful, the server approves the transaction through the payment method. Subsequently, transaction details are recorded by a data update mechanism, and the user's identification information is updated accordingly.

[0483] For example, when a user purchases concert tickets electronically, this system authenticates quickly and securely, completing the payment instantly. This facilitates cashless transactions, eliminating the need for users to carry physical cash cards.

[0484] Using a generative AI model, the system constantly strives to improve accuracy with new prompts and methods. Further applications can be explored using the prompt, "Explain specific ways to utilize this electronic payment platform and compare it with existing payment methods," as an example.

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The user operates the device to input biometric information. The device uses a facial recognition camera and fingerprint scanner to acquire the user's biometric authentication data. The input is the user's physical characteristics (image data), which is converted into digital data, encrypted, and output.

[0488] Step 2:

[0489] The device encrypts the acquired biometric authentication data and sends it to the server. Here, the device uses an encryption algorithm to protect the data and transmit it in order to maintain data security. The output is encrypted data.

[0490] Step 3:

[0491] The server stores the received data in the database. The server properly stores this data in the database in preparation for matching it with existing data. At this stage, the output is an updated database.

[0492] Step 4:

[0493] When a user attempts to conduct a transaction, they re-enter their biometric information into the terminal. The terminal then sends the newly acquired biometric authentication data to the server. This new biometric information, which is acquired again as input, is encrypted before transmission.

[0494] Step 5:

[0495] The server uses authentication methods to verify a match between stored biometric data and newly acquired data. During this process, a generated AI model is utilized to improve matching accuracy. The matching result is obtained as output.

[0496] Step 6:

[0497] If authentication is successful, the server executes the transaction using the transaction execution method. It approves the transaction details and initiates settlement processing. Depending on the choice of credit or debit, the actual financial transaction is performed. The output shows the approved transaction.

[0498] Step 7:

[0499] The server uses data update mechanisms to record transaction details and update user identification information. This includes updating user account information and saving transaction history. The output includes updated identification information and records.

[0500] Step 8:

[0501] The terminal notifies the user that the transaction is complete. For example, it displays a transaction success message on the terminal's screen and prints a receipt if necessary. The input in this case is a transaction completion signal from the server, and the output is visual or printed information for the user.

[0502] Through these processing steps, secure and rapid electronic payments using biometric authentication are realized. Maintaining this flow makes it possible to apply it to other uses. Other examples can be considered, such as prompting the user to "Explain specific ways to utilize the electronic payment platform using this system and compare it with existing payment methods."

[0503] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0504] This invention provides a more secure and intuitive cashless card system by combining biometric authentication and emotion recognition. This system includes processes for acquiring, registering, and carefully managing the user's biometric information, as well as recognizing their emotional state in real time.

[0505] First, the user performs initial setup through the device. During this process, biometric data is obtained using a facial recognition camera and fingerprint scanner. This information is encrypted and securely stored on a server. Furthermore, an emotion engine enhances the ability to read emotions from the user's face. This data is analyzed by an AI model to form the user's emotional profile.

[0506] While using an ATM, the user interacts with the terminal to perform biometric and emotional data recognition. The terminal uses sensors to acquire this information and transmit it to a server. The server, in parallel with verifying biometric authentication, evaluates whether the user's emotions are unusual. Using an emotion engine, if a specific emotional state, such as tension or anxiety, is detected, a security assessment mechanism is activated, and a warning is issued if necessary.

[0507] A concrete example of this system is when a user is operating an ATM and the system detects unusually high levels of stress. In this case, the server analyzes the user's emotional changes and assesses the possibility of fraudulent transactions. Subsequently, the transaction may be temporarily suspended, or further identity verification may be required.

[0508] This invention adds a new security assessment based on emotion recognition to the conventional identity verification process that does not rely on cash cards, allowing users to use the system with even greater peace of mind. This makes it possible to achieve both convenience and security during financial transactions.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The user uses a device for initial registration, scanning their face and fingerprints to provide biometric data. The device receives this data, encrypts it, and sends it to the server.

[0512] Step 2:

[0513] The server securely stores the received biometric data in a database. Simultaneously, it analyzes the data using an AI model to form an individual user profile. It also creates a user's emotional profile using an emotion engine based on facial recognition data.

[0514] Step 3:

[0515] When a user uses an ATM, the terminal scans again, acquiring their face and fingerprints in real time. Furthermore, it analyzes the user's current emotional state through the facial recognition camera.

[0516] Step 4:

[0517] The device sends newly acquired biometric and emotional information to the server. The server compares this information with an existing database to verify biometric authentication.

[0518] Step 5:

[0519] The server analyzes emotional information obtained through the emotion engine and evaluates for any unusual emotions or signs of stress. If necessary, it performs a safety assessment.

[0520] Step 6:

[0521] The server, once it confirms that both biometric authentication and sentiment evaluation are successful, authorizes the terminal to execute the transaction. The authenticated user then enters the amount they wish to withdraw on the ATM screen.

[0522] Step 7:

[0523] The terminal, if withdrawal is permitted, dispenses the specified amount as cash. The server completes the process by recording the transaction details and updating the user's account information.

[0524] By evaluating the user's emotional state in this way, the system can provide even greater security in addition to conventional biometric authentication.

[0525] (Example 2)

[0526] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0527] In financial transactions that do not use cash cards, the challenge is to simultaneously improve security and convenience. In particular, it is necessary to prevent fraudulent transactions by utilizing emotion recognition in addition to biometric authentication. While conventional systems have some effect in acquiring and authenticating biometric information, they lack the function of monitoring the user's psychological state, so more comprehensive security measures are required.

[0528] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0529] In this invention, the server includes data acquisition means for acquiring biometric information, emotion evaluation means for sensing emotional states and evaluating the risk of fraudulent transactions, and analysis means for analyzing the acquired biometric and emotional information using an artificial intelligence model to enhance the accuracy of user individual recognition and safety evaluation. This enables a comprehensive analysis of the user's biometric information and emotional state, making safer transaction decisions possible.

[0530] "Biometric information" refers to human characteristic data that enables the biologically unique identification of an individual, including facial features, fingerprints, and iris scans.

[0531] "Data acquisition means" refers to technologies consisting of hardware and software for capturing and collecting biometric and emotional information.

[0532] A "secure storage device" is a storage medium that protects and stores acquired data, and has the function of protecting against unauthorized access by using encryption technology.

[0533] "Verification means" refers to a process and algorithm for comparing stored data with newly acquired data to determine the legitimacy of an individual.

[0534] "Emotional evaluation tools" are technologies that analyze a user's psychological state and determine whether that state differs from their normal state, utilizing artificial intelligence to analyze emotional characteristics.

[0535] "Emotion recognition" is a technology used to identify a user's emotions and psychological state, and it analyzes them based on facial expressions, voice, and other factors.

[0536] An "artificial intelligence model" is a program that has machine learning algorithms to analyze data and identify patterns, and can accurately determine the characteristics of user behavior and emotions.

[0537] "Fraudulent transactions" refer to financial transactions conducted with fraudulent intent that do not align with the normal purpose of use, and actions that are carried out against the user's will.

[0538] "Individual identification accuracy" refers to the ability to accurately identify a specific individual from all other individuals, and high-precision recognition is important for reducing the risk of misidentification.

[0539] This invention provides a safer and more convenient cashless transaction system by utilizing biometric authentication and emotion recognition technology. This system is constructed and implemented as follows.

[0540] The user first uses the device to provide biometric information. The device has a built-in facial recognition camera and fingerprint scanner, and biometric information is obtained through these devices. This information is encrypted using security technologies such as AES encryption before being transmitted from the device.

[0541] The server uses a database to receive encrypted biometric information and store it in a secure storage device. This data is later used in a matching process and forms the basis for identifying individual users.

[0542] Furthermore, the system can perform emotion recognition to evaluate emotional states. The terminal analyzes acquired facial video data and uses a generative AI model to identify the user's emotional state. This model has the ability to analyze changes in facial expressions and movements in real time, proactively detecting potentially fraudulent transactions.

[0543] For example, if a user attempts to make a transaction at an ATM and the system detects unusual signs of tension or anxiety, the server could detect this, temporarily suspend the transaction, and request additional identity verification procedures. These additional procedures would be communicated to the user's mobile device, thus ensuring user security.

[0544] An example of a prompt message would be: "Generate a description of a new security method using biometric authentication and emotion recognition at ATMs. In particular, please explain in detail how to analyze the user's emotion profile and how to evaluate fraudulent transactions."

[0545] In this way, the present invention has a configuration that reduces security risks in financial transactions and improves the user experience.

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The user registers their biometric information with the device. The device uses a facial recognition camera or fingerprint scanner to acquire facial images and fingerprint data. The device receives the entered biometric information and encrypts it using AES encryption technology. This process ensures the security of the registered biometric information. The encrypted data is then generated as output.

[0549] Step 2:

[0550] The device sends encrypted biometric information to the server. The transmitted data is input to the server, which stores the received data in a secure storage device. In this step, the server stores the biometric information in preparation for later authentication. The stored data is recorded as the output of the process.

[0551] Step 3:

[0552] When a user uses an ATM, they provide their biometric information to the terminal again. The terminal acquires this information in real time using a facial recognition camera and a fingerprint scanner. The terminal processes the entered information on the spot and outputs it as current biometric data.

[0553] Step 4:

[0554] The terminal re-encrypts the acquired biometric information and sends it to the server. The server receives this as input and compares it with the stored registration data. Based on this data, the server identifies the individual, and if the match is successful, an authentication success signal is output, allowing the transaction to be executed.

[0555] Step 5:

[0556] Simultaneously, the device processes video data from the face using an emotion recognition engine. A generative AI model receives this as input and analyzes the emotions. As a result, a specific emotional state is determined, and the user's emotional profile is output.

[0557] Step 6:

[0558] The server also checks the emotional profile upon successful matching, and if abnormal emotions (e.g., tension, anxiety) are detected, it assesses the transaction risk. If an anomaly is detected, the transaction is temporarily suspended or additional verification is requested, and the user is notified. This helps prevent fraudulent transactions.

[0559] (Application Example 2)

[0560] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0561] While conventional biometric authentication systems are effective for enhancing security, they have the problem of not considering the user's emotional state, making it impossible to completely eliminate the possibility of fraudulent transactions. Furthermore, systems based solely on biometric information may lack sufficient authentication accuracy or lead to misunderstandings due to user unfamiliarity. Therefore, there is a need for systems that provide a higher level of security by taking the user's emotional state into consideration.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0563] In this invention, the server includes a data collection means for acquiring biometric information and an emotion recognition means for recognizing the user's emotional state; a data protection means for encrypting the acquired biometric information and emotion data and storing them in securely managed data storage; and a security enhancement means for detecting potential fraud based on emotion data acquired during the transaction process and ensuring secure transactions. By handling biometric information and emotion data in an integrated manner, it becomes possible to enhance the security and reliability of transactions and reduce the user's vulnerability to fraudulent transactions.

[0564] "Biometric information" refers to data that describes physical or behavioral characteristics used for individual user identification.

[0565] "Data collection means" refers to a device or program that functions to acquire biological information or emotional states.

[0566] "Emotion recognition means" refers to a technology that uses methods and techniques to infer and recognize an emotional state from a user's facial expressions and other characteristics, such as their facial features and expressions.

[0567] "Data protection measures" refer to technologies such as encryption that securely manage collected biometric and emotional data and prevent unauthorized access by third parties.

[0568] "Identification means" refers to a function that uses technology to identify a user by comparing acquired biometric information with information stored in a database.

[0569] "Action execution means" refers to a device or program that provides the functionality to execute a planned transaction or action upon successful authentication.

[0570] "Enhanced security measures" refer to technologies that detect abnormal user emotions or behavior during transactions, and take measures to mitigate the risk of fraud by temporarily suspending transactions as needed.

[0571] A "generative AI model" refers to an artificial intelligence program that learns patterns from large amounts of data and makes decisions based on new data.

[0572] A "prompt sentence" is a natural language input sentence that provides instructions to a generative AI model to solve a specific problem.

[0573] The system for realizing this application simultaneously acquires the user's biometric information and emotional state, and uses this information to ensure transaction security. The server analyzes the biometric and emotional information transmitted from the user's terminal in real time using software such as OpenCV and face_recognition. Specifically, it captures the user's face data using the camera installed in the device and verifies its match with the stored biometric data. It also analyzes the user's emotions using generative AI models such as EmotionRecognizer, and issues a warning if an emotional state different from normal is detected.

[0574] On the terminal side, when a user attempts to conduct a transaction, their biometric information is collected and emotion recognition is performed. This data is then encrypted and sent to the server. Based on this information, the server decides whether or not to proceed with the transaction, and if necessary, it may temporarily suspend the transaction or request additional identity verification.

[0575] As a concrete example, when a user makes a payment at a store, their smartphone performs facial recognition and fingerprint authentication. Simultaneously, the camera analyzes the user's facial expressions, and if EmotionRecognizer detects any unusual signs of anxiety, the user receives a notification. The user can then respond to the notification and proceed with the transaction while ensuring security.

[0576] An example of a prompt for a generated AI model is: "Describe the development of an application that recognizes a user's facial expression and emotions when they make an electronic payment, and then detects and responds to potential fraud." This makes it possible to build a transaction system that combines higher security and convenience.

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The device acquires biometric information for user verification. Specifically, it activates the camera built into the device and captures the user's face. The face data is then mapped to feature points using the face_recognition library, and this is generated as biometric information. The input is the user's face image, and the output is digitized facial feature data.

[0580] Step 2:

[0581] The device uses EmotionRecognizer to recognize the user's emotional state. A facial image captured by the camera is input into an AI model, which analyzes the facial expression to determine the emotional state (e.g., joy, anxiety, surprise). The input is the user's facial image, and the output is the estimated emotion. The data output from the emotion engine is based on the user's emotional profile.

[0582] Step 3:

[0583] The terminal encrypts the acquired biometric and emotional information to ensure security and then transmits the data to the server. Before transmission, AES encryption is used to prevent unauthorized access to the data by third parties. The input consists of biometric and emotional information related to the transaction, and the output is encrypted data.

[0584] Step 4:

[0585] The server receives the transmitted encrypted data and decrypts it. Using the decrypted data, it performs a match check against existing biometric information stored in the database. Identity is confirmed through facial recognition. The input is encrypted biometric and emotional data, and the output is whether or not the authentication matches.

[0586] Step 5:

[0587] The server evaluates the user's mental state based on emotion recognition data. If the emotion is unusual, it issues a warning and requests a temporary suspension of the transaction or additional confirmation. This step involves the emotion engine acting as a security enhancement measure. The input is decoded emotion data, and the output is a decision regarding whether to continue the transaction.

[0588] Step 6:

[0589] The server will proceed with the transaction if all steps are completed successfully. After verifying that sentiment and biometric authentication are valid, the execution procedure begins. The input is the transaction data after authentication and security verification, and the output is the transaction completion status. This provides a high level of security throughout the entire system.

[0590] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0591] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0592] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0593] [Fourth Embodiment]

[0594] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0595] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0596] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0597] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0598] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0599] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0600] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0601] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0602] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0603] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0604] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0605] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0606] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0607] As an embodiment for carrying out the present invention, a system for withdrawing cash without using a cash card using biometric authentication is described. This system includes a series of processes for acquiring, storing, and verifying the user's biometric information, and executing a financial transaction if authentication is successful.

[0608] First, users register their biometric authentication data with the system via their device. Specifically, the system obtains the user's biometric information using a facial recognition camera or fingerprint scanner. This information is encrypted in a secure format and sent to the server. The server stores the received data in a database and uses an AI model to train it for individual user identification. This enables highly accurate personal identification.

[0609] When using an ATM, the user undergoes facial recognition or fingerprint scanning again. At this time, the biometric information acquired by the terminal is immediately sent to the server and compared with information stored in advance. If this comparison is successful, the server sends an authentication success signal to the terminal and authorizes the user to make a transaction. After confirming the transaction input, the terminal dispenses the specified amount as cash, the transaction details are recorded on the server, and the user's account information is updated simultaneously.

[0610] As a concrete example, when a user withdraws cash using an ATM, the server verifies that the user's biometric information matches their registered information. If authentication is successful, the specified amount can be withdrawn smoothly. In this way, this system enables cardless cash withdrawals while ensuring security.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] Users are asked to access the system using their device and register their biometric information. Their face is scanned with a facial recognition camera, and their fingerprints are scanned with a fingerprint sensor. The resulting biometric data is encrypted and sent to the server.

[0614] Step 2:

[0615] The server stores the received biometric authentication data in a database. The stored data is then trained by an AI model to generate individual user profiles, which are used to identify users.

[0616] Step 3:

[0617] When a user uses an ATM, the terminal attempts to log in using biometric authentication. The terminal then uses the facial recognition camera and fingerprint sensor again to obtain the latest biometric information.

[0618] Step 4:

[0619] The device encrypts and transmits newly acquired biometric authentication data to the server in real time. The server then compares this data with the information stored in the database.

[0620] Step 5:

[0621] The server executes the matching process using an AI model. If the matching result shows a match that exceeds the threshold, the server sends an authentication success message to the terminal.

[0622] Step 6:

[0623] The user enters the withdrawal amount on the ATM screen. The server checks the user's account balance and verifies whether the specified amount can be withdrawn.

[0624] Step 7:

[0625] Once the transaction is approved, the terminal dispenses the specified amount as cash. The server logs the transaction details and updates the user's account information. This completes the transaction.

[0626] (Example 1)

[0627] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] There is a growing demand for a safe and fast way to withdraw cash without having to carry a cash card. Conventional technologies have had drawbacks such as the risk of card loss and the inaccuracy of biometric authentication. Therefore, the challenge is to provide users with a convenient transaction method while ensuring high-precision personal authentication and security.

[0629] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0630] In this invention, the server includes a device and means for acquiring biometric data, a data management device and means for securely transferring the acquired biometric data and storing it in a storage device, and a reliability verification device and means for comparing the stored biometric data with the reacquired biometric data. This enables secure, cardless cash withdrawals and high-precision personal identification through biometric authentication.

[0631] "Biometric data" refers to information acquired to identify individuals based on the shape and patterns of their faces, fingers, eyes, etc.

[0632] A "device" is a mechanical element or assembly of machines used to perform a specific function, such as acquiring, transferring, recording, or comparing biological data.

[0633] "Secure state" refers to a protected state in which data is safe from unauthorized access and tampering from external sources.

[0634] "Transfer" is the process of moving acquired data from one location to another.

[0635] A "storage device" is a medium or device that can store data and retrieve it as needed.

[0636] A "data management device" is a device used to store and manage acquired data so that it can be used when needed.

[0637] A "reliability verification device" is a device used to verify consistency and authenticity by comparing retained data with newly acquired data.

[0638] A "machine learning model" is an algorithm or statistical model that learns patterns from data and enables it to automatically perform specific tasks.

[0639] An "analytical device" is a device or software used to analyze data and extract information or patterns contained within it.

[0640] "Individual identification accuracy" is an indicator that represents the degree of the ability to accurately identify an individual in biometric authentication.

[0641] This system provides a way to withdraw cash without a cash card using biometric authentication. Users begin by registering their biometric data using a terminal. Devices such as facial recognition cameras and fingerprint scanners are used to acquire this biometric data. These devices capture the user's facial and finger features and transmit this information as digital data to the terminal. The terminal encrypts this data to maintain its security. Advanced encryption protocols such as TLS are employed to ensure data security.

[0642] The user's biometric information is encrypted and then transferred to the server. The server decrypts the data and stores it in a database. Furthermore, the server uses a generative AI model to learn from the acquired biometric data, enabling it to identify users with high accuracy. Machine learning algorithms allow the server to extract patterns from the acquired data, forming a foundation for strengthening the user authentication process.

[0643] When using an ATM, the user performs biometric authentication again at the terminal. At this time, the terminal immediately sends the newly acquired biometric data to the server, which compares this data with previously stored data. If authentication is successful, the terminal authorizes the transaction, and the specified amount is dispensed as cash. Furthermore, details of each transaction are recorded on the server, and the user's account information is updated to the latest state.

[0644] For example, if a user needs to withdraw money urgently, they can go to the nearest ATM and authenticate using facial recognition. If the system authenticates correctly, the user can receive cash without using a card. This process is quick and can be completed in seconds.

[0645] Examples of prompt messages include the following:

[0646] Please explain the system procedure for users to withdraw cash using facial recognition at an ATM.

[0647] "Could you please explain in detail the authentication process for cashless transactions using biometric information?"

[0648] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0649] Step 1:

[0650] The user registers biometric data on the device. Specifically, a facial recognition camera captures an image of the user's face, and a fingerprint scanner obtains a fingerprint. The data entered is then the user's facial and fingerprint information. The device encrypts this data and securely transmits it to the server. The output at this point is encrypted biometric data. Furthermore, the device displays a UI indicating to the user that the series of operations are complete.

[0651] Step 2:

[0652] The server receives encrypted data sent from the terminal, decrypts it, and stores it in a database. The input is encrypted biometric data, and the output is the biometric data stored in the database. The server also adds time and location information when recording the data, in preparation for future improvements in authentication accuracy.

[0653] Step 3:

[0654] The server analyzes stored biometric data using an AI model to train the model with user identification information. The input to this process is stored biometric data, and the output is an AI model with updated personal identification accuracy. The server monitors the model's learning progress and prepares an optimized model.

[0655] Step 4:

[0656] When a user approaches an ATM and withdraws cash, they undergo biometric authentication again. The user stands in front of the facial recognition camera and places their finger on the fingerprint scanner, allowing the terminal to acquire new biometric information. The input for this operation is real-time biometric data, and the output is the transmission of biometric data to the server.

[0657] Step 5:

[0658] The server receives newly transmitted biometric data and compares it to stored data. The input for the comparison is the new and stored biometric data, and the output is the authentication status. The server evaluates the degree of match and, if authentication is successful, returns a confirmation signal to the terminal.

[0659] Step 6:

[0660] Once the terminal confirms successful authentication, it authorizes the transaction. The user specifies the amount to withdraw on the screen. The input is the user's specified amount, and the output is the cash discharge. The terminal provides the user with the specified amount of cash by performing the discharge process using its internal mechanism.

[0661] (Application Example 1)

[0662] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0663] In systems that allow cash withdrawals without using a cash card, there is a challenge in improving security and convenience by utilizing biometric authentication technology. In particular, there is a need to provide a secure and rapid authentication method for electronic payments and to strengthen the security of user transactions.

[0664] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0665] In this invention, the server includes information acquisition means for acquiring biometric authentication data, data storage means for encrypting the acquired biometric authentication data and storing it in a database, and authentication means for comparing the stored biometric authentication data with newly acquired data. This enables secure and rapid personal authentication and payment processing when a user makes an electronic payment.

[0666] "Biometric data" refers to information used to verify a person's identity using their physical characteristics, such as their face, fingerprints, and iris.

[0667] "Information acquisition means" refers to devices and technologies for collecting a user's physical characteristics and converting them into digital data.

[0668] "Data storage means" refers to databases and storage systems for securely storing acquired biometric authentication data.

[0669] "Authentication means" refers to a method or apparatus for comparing newly acquired biometric authentication data with data stored in an existing database to confirm a match.

[0670] A "transaction execution mechanism" is a mechanism for executing financial transactions or other requested operations when biometric authentication is successful.

[0671] "Data update means" refers to a method or process of updating a database to keep transaction details and user identification information up to date.

[0672] A "payment method" is a system or method for executing and completing transactions authorized based on biometric authentication.

[0673] The system for implementing this invention consists of users making electronic payments using biometric authentication via smartphones or other devices. This system operates by combining various hardware and software components to achieve highly secure cashless transactions. Specifically, it utilizes facial recognition cameras and fingerprint scanners to acquire the user's biometric information. The acquired data is encrypted via the information acquisition means and securely stored in a database. This prevents data leakage.

[0674] The system also uses authentication methods to compare newly acquired biometric information with pre-registered information. Here, computational models are used to analyze the data and achieve highly accurate personal identification. If biometric authentication is successful, the server approves the transaction through the payment method. Subsequently, transaction details are recorded by a data update mechanism, and the user's identification information is updated accordingly.

[0675] For example, when a user purchases concert tickets electronically, this system authenticates quickly and securely, completing the payment instantly. This facilitates cashless transactions, eliminating the need for users to carry physical cash cards.

[0676] Using a generative AI model, the system constantly strives to improve accuracy with new prompts and methods. Further applications can be explored using the prompt, "Explain specific ways to utilize this electronic payment platform and compare it with existing payment methods," as an example.

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The user operates the device to input biometric information. The device uses a facial recognition camera and fingerprint scanner to acquire the user's biometric authentication data. The input is the user's physical characteristics (image data), which is converted into digital data, encrypted, and output.

[0680] Step 2:

[0681] The device encrypts the acquired biometric authentication data and sends it to the server. Here, the device uses an encryption algorithm to protect the data and transmit it in order to maintain data security. The output is encrypted data.

[0682] Step 3:

[0683] The server stores the received data in the database. The server properly stores this data in the database in preparation for matching it with existing data. At this stage, the output is an updated database.

[0684] Step 4:

[0685] When a user attempts to conduct a transaction, they re-enter their biometric information into the terminal. The terminal then sends the newly acquired biometric authentication data to the server. This new biometric information, which is acquired again as input, is encrypted before transmission.

[0686] Step 5:

[0687] The server uses authentication methods to verify a match between stored biometric data and newly acquired data. During this process, a generated AI model is utilized to improve matching accuracy. The matching result is obtained as output.

[0688] Step 6:

[0689] If authentication is successful, the server executes the transaction using the transaction execution method. It approves the transaction details and initiates settlement processing. Depending on the choice of credit or debit, the actual financial transaction is performed. The output shows the approved transaction.

[0690] Step 7:

[0691] The server uses data update mechanisms to record transaction details and update user identification information. This includes updating user account information and saving transaction history. The output includes updated identification information and records.

[0692] Step 8:

[0693] The terminal notifies the user that the transaction is complete. For example, it displays a transaction success message on the terminal's screen and prints a receipt if necessary. The input in this case is a transaction completion signal from the server, and the output is visual or printed information for the user.

[0694] Through these processing steps, secure and rapid electronic payments using biometric authentication are realized. Maintaining this flow makes it possible to apply it to other uses. Other examples can be considered, such as prompting the user to "Explain specific ways to utilize the electronic payment platform using this system and compare it with existing payment methods."

[0695] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0696] This invention provides a more secure and intuitive cashless card system by combining biometric authentication and emotion recognition. This system includes processes for acquiring, registering, and carefully managing the user's biometric information, as well as recognizing their emotional state in real time.

[0697] First, the user performs initial setup through the device. During this process, biometric data is obtained using a facial recognition camera and fingerprint scanner. This information is encrypted and securely stored on a server. Furthermore, an emotion engine enhances the ability to read emotions from the user's face. This data is analyzed by an AI model to form the user's emotional profile.

[0698] While using an ATM, the user interacts with the terminal to perform biometric and emotional data recognition. The terminal uses sensors to acquire this information and transmit it to a server. The server, in parallel with verifying biometric authentication, evaluates whether the user's emotions are unusual. Using an emotion engine, if a specific emotional state, such as tension or anxiety, is detected, a security assessment mechanism is activated, and a warning is issued if necessary.

[0699] A concrete example of this system is when a user is operating an ATM and the system detects unusually high levels of stress. In this case, the server analyzes the user's emotional changes and assesses the possibility of fraudulent transactions. Subsequently, the transaction may be temporarily suspended, or further identity verification may be required.

[0700] This invention adds a new security assessment based on emotion recognition to the conventional identity verification process that does not rely on cash cards, allowing users to use the system with even greater peace of mind. This makes it possible to achieve both convenience and security during financial transactions.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] The user uses a device for initial registration, scanning their face and fingerprints to provide biometric data. The device receives this data, encrypts it, and sends it to the server.

[0704] Step 2:

[0705] The server securely stores the received biometric data in a database. Simultaneously, it analyzes the data using an AI model to form an individual user profile. It also creates a user's emotional profile using an emotion engine based on facial recognition data.

[0706] Step 3:

[0707] When a user uses an ATM, the terminal scans again, acquiring their face and fingerprints in real time. Furthermore, it analyzes the user's current emotional state through the facial recognition camera.

[0708] Step 4:

[0709] The device sends newly acquired biometric and emotional information to the server. The server compares this information with an existing database to verify biometric authentication.

[0710] Step 5:

[0711] The server analyzes emotional information obtained through the emotion engine and evaluates for any unusual emotions or signs of stress. If necessary, it performs a safety assessment.

[0712] Step 6:

[0713] The server, once it confirms that both biometric authentication and sentiment evaluation are successful, authorizes the terminal to execute the transaction. The authenticated user then enters the amount they wish to withdraw on the ATM screen.

[0714] Step 7:

[0715] The terminal, if withdrawal is permitted, dispenses the specified amount as cash. The server completes the process by recording the transaction details and updating the user's account information.

[0716] By evaluating the user's emotional state in this way, the system can provide even greater security in addition to conventional biometric authentication.

[0717] (Example 2)

[0718] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] In financial transactions that do not use cash cards, the challenge is to simultaneously improve security and convenience. In particular, it is necessary to prevent fraudulent transactions by utilizing emotion recognition in addition to biometric authentication. While conventional systems have some effect in acquiring and authenticating biometric information, they lack the function of monitoring the user's psychological state, so more comprehensive security measures are required.

[0720] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0721] In this invention, the server includes data acquisition means for acquiring biometric information, emotion evaluation means for sensing emotional states and evaluating the risk of fraudulent transactions, and analysis means for analyzing the acquired biometric and emotional information using an artificial intelligence model to enhance the accuracy of user individual recognition and safety evaluation. This enables a comprehensive analysis of the user's biometric information and emotional state, making safer transaction decisions possible.

[0722] "Biometric information" refers to human characteristic data that enables the biologically unique identification of an individual, including facial features, fingerprints, and iris scans.

[0723] "Data acquisition means" refers to technologies consisting of hardware and software for capturing and collecting biometric and emotional information.

[0724] A "secure storage device" is a storage medium that protects and stores acquired data, and has the function of protecting against unauthorized access by using encryption technology.

[0725] "Verification means" refers to a process and algorithm for comparing stored data with newly acquired data to determine the legitimacy of an individual.

[0726] "Emotional evaluation tools" are technologies that analyze a user's psychological state and determine whether that state differs from their normal state, utilizing artificial intelligence to analyze emotional characteristics.

[0727] "Emotion recognition" is a technology used to identify a user's emotions and psychological state, and it analyzes them based on facial expressions, voice, and other factors.

[0728] An "artificial intelligence model" is a program that has machine learning algorithms to analyze data and identify patterns, and can accurately determine the characteristics of user behavior and emotions.

[0729] "Fraudulent transactions" refer to financial transactions conducted with fraudulent intent that do not align with the normal purpose of use, and actions that are carried out against the user's will.

[0730] "Individual identification accuracy" refers to the ability to accurately identify a specific individual from all other individuals, and high-precision recognition is important for reducing the risk of misidentification.

[0731] This invention provides a safer and more convenient cashless transaction system by utilizing biometric authentication and emotion recognition technology. This system is constructed and implemented as follows.

[0732] The user first uses the device to provide biometric information. The device has a built-in facial recognition camera and fingerprint scanner, and biometric information is obtained through these devices. This information is encrypted using security technologies such as AES encryption before being transmitted from the device.

[0733] The server uses a database to receive encrypted biometric information and store it in a secure storage device. This data is later used in a matching process and forms the basis for identifying individual users.

[0734] Furthermore, the system can perform emotion recognition to evaluate emotional states. The terminal analyzes acquired facial video data and uses a generative AI model to identify the user's emotional state. This model has the ability to analyze changes in facial expressions and movements in real time, proactively detecting potentially fraudulent transactions.

[0735] For example, if a user attempts to make a transaction at an ATM and the system detects unusual signs of tension or anxiety, the server could detect this, temporarily suspend the transaction, and request additional identity verification procedures. These additional procedures would be communicated to the user's mobile device, thus ensuring user security.

[0736] An example of a prompt message would be: "Generate a description of a new security method using biometric authentication and emotion recognition at ATMs. In particular, please explain in detail how to analyze the user's emotion profile and how to evaluate fraudulent transactions."

[0737] In this way, the present invention has a configuration that reduces security risks in financial transactions and improves the user experience.

[0738] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0739] Step 1:

[0740] The user registers their biometric information with the device. The device uses a facial recognition camera or fingerprint scanner to acquire facial images and fingerprint data. The device receives the entered biometric information and encrypts it using AES encryption technology. This process ensures the security of the registered biometric information. The encrypted data is then generated as output.

[0741] Step 2:

[0742] The device sends encrypted biometric information to the server. The transmitted data is input to the server, which stores the received data in a secure storage device. In this step, the server stores the biometric information in preparation for later authentication. The stored data is recorded as the output of the process.

[0743] Step 3:

[0744] When a user uses an ATM, they provide their biometric information to the terminal again. The terminal acquires this information in real time using a facial recognition camera and a fingerprint scanner. The terminal processes the entered information on the spot and outputs it as current biometric data.

[0745] Step 4:

[0746] The terminal re-encrypts the acquired biometric information and sends it to the server. The server receives this as input and compares it with the stored registration data. Based on this data, the server identifies the individual, and if the match is successful, an authentication success signal is output, allowing the transaction to be executed.

[0747] Step 5:

[0748] Simultaneously, the device processes video data from the face using an emotion recognition engine. A generative AI model receives this as input and analyzes the emotions. As a result, a specific emotional state is determined, and the user's emotional profile is output.

[0749] Step 6:

[0750] The server also checks the emotional profile upon successful matching, and if abnormal emotions (e.g., tension, anxiety) are detected, it assesses the transaction risk. If an anomaly is detected, the transaction is temporarily suspended or additional verification is requested, and the user is notified. This helps prevent fraudulent transactions.

[0751] (Application Example 2)

[0752] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0753] While conventional biometric authentication systems are effective for enhancing security, they have the problem of not considering the user's emotional state, making it impossible to completely eliminate the possibility of fraudulent transactions. Furthermore, systems based solely on biometric information may lack sufficient authentication accuracy or lead to misunderstandings due to user unfamiliarity. Therefore, there is a need for systems that provide a higher level of security by taking the user's emotional state into consideration.

[0754] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0755] In this invention, the server includes a data collection means for acquiring biometric information and an emotion recognition means for recognizing the user's emotional state; a data protection means for encrypting the acquired biometric information and emotion data and storing them in securely managed data storage; and a security enhancement means for detecting potential fraud based on emotion data acquired during the transaction process and ensuring secure transactions. By handling biometric information and emotion data in an integrated manner, it becomes possible to enhance the security and reliability of transactions and reduce the user's vulnerability to fraudulent transactions.

[0756] "Biometric information" refers to data that describes physical or behavioral characteristics used for individual user identification.

[0757] "Data collection means" refers to a device or program that functions to acquire biological information or emotional states.

[0758] "Emotion recognition means" refers to a technology that uses methods and techniques to infer and recognize an emotional state from a user's facial expressions and other characteristics, such as their facial features and expressions.

[0759] "Data protection measures" refer to technologies such as encryption that securely manage collected biometric and emotional data and prevent unauthorized access by third parties.

[0760] "Identification means" refers to a function that uses technology to identify a user by comparing acquired biometric information with information stored in a database.

[0761] "Action execution means" refers to a device or program that provides the functionality to execute a planned transaction or action upon successful authentication.

[0762] "Enhanced security measures" refer to technologies that detect abnormal user emotions or behavior during transactions, and take measures to mitigate the risk of fraud by temporarily suspending transactions as needed.

[0763] A "generative AI model" refers to an artificial intelligence program that learns patterns from large amounts of data and makes decisions based on new data.

[0764] A "prompt sentence" is a natural language input sentence that provides instructions to a generative AI model to solve a specific problem.

[0765] The system for realizing this application simultaneously acquires the user's biometric information and emotional state, and uses this information to ensure transaction security. The server analyzes the biometric and emotional information transmitted from the user's terminal in real time using software such as OpenCV and face_recognition. Specifically, it captures the user's face data using the camera installed in the device and verifies its match with the stored biometric data. It also analyzes the user's emotions using generative AI models such as EmotionRecognizer, and issues a warning if an emotional state different from normal is detected.

[0766] On the terminal side, when a user attempts to conduct a transaction, their biometric information is collected and emotion recognition is performed. This data is then encrypted and sent to the server. Based on this information, the server decides whether or not to proceed with the transaction, and if necessary, it may temporarily suspend the transaction or request additional identity verification.

[0767] As a concrete example, when a user makes a payment at a store, their smartphone performs facial recognition and fingerprint authentication. Simultaneously, the camera analyzes the user's facial expressions, and if EmotionRecognizer detects any unusual signs of anxiety, the user receives a notification. The user can then respond to the notification and proceed with the transaction while ensuring security.

[0768] An example of a prompt for a generated AI model is: "Describe the development of an application that recognizes a user's facial expression and emotions when they make an electronic payment, and then detects and responds to potential fraud." This makes it possible to build a transaction system that combines higher security and convenience.

[0769] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0770] Step 1:

[0771] The device acquires biometric information for user verification. Specifically, it activates the camera built into the device and captures the user's face. The face data is then mapped to feature points using the face_recognition library, and this is generated as biometric information. The input is the user's face image, and the output is digitized facial feature data.

[0772] Step 2:

[0773] The device uses EmotionRecognizer to recognize the user's emotional state. A facial image captured by the camera is input into an AI model, which analyzes the facial expression to determine the emotional state (e.g., joy, anxiety, surprise). The input is the user's facial image, and the output is the estimated emotion. The data output from the emotion engine is based on the user's emotional profile.

[0774] Step 3:

[0775] The terminal encrypts the acquired biometric and emotional information to ensure security and then transmits the data to the server. Before transmission, AES encryption is used to prevent unauthorized access to the data by third parties. The input consists of biometric and emotional information related to the transaction, and the output is encrypted data.

[0776] Step 4:

[0777] The server receives the transmitted encrypted data and decrypts it. Using the decrypted data, it performs a match check against existing biometric information stored in the database. Identity is confirmed through facial recognition. The input is encrypted biometric and emotional data, and the output is whether or not the authentication matches.

[0778] Step 5:

[0779] The server evaluates the user's mental state based on emotion recognition data. If the emotion is unusual, it issues a warning and requests a temporary suspension of the transaction or additional confirmation. This step involves the emotion engine acting as a security enhancement measure. The input is decoded emotion data, and the output is a decision regarding whether to continue the transaction.

[0780] Step 6:

[0781] The server will proceed with the transaction if all steps are completed successfully. After verifying that sentiment and biometric authentication are valid, the execution procedure begins. The input is the transaction data after authentication and security verification, and the output is the transaction completion status. This provides a high level of security throughout the entire system.

[0782] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0783] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0784] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0785] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0786] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0787] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0788] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0789] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0790] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0791] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0792] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0793] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0794] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0795] 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.

[0796] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0797] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0798] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0799] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0800] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0801] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0802] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0803] The following is further disclosed regarding the embodiments described above.

[0804] (Claim 1)

[0805] A means of acquiring information to obtain biometric authentication data,

[0806] A data storage method for encrypting acquired biometric authentication data and saving it to a database,

[0807] An authentication method for comparing stored biometric data with newly acquired data,

[0808] A transaction execution method for executing a transaction upon successful authentication,

[0809] A means of updating data to record transaction details and update user account information,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, comprising information acquisition means for acquiring biometric authentication data, including facial recognition, fingerprint recognition, and iris recognition, in a variety of ways from different angles and under different conditions.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising an analysis means for analyzing acquired biometric authentication data with an artificial intelligence model to improve the accuracy of user identification.

[0815] "Example 1"

[0816] (Claim 1)

[0817] Devices and means for acquiring biological data,

[0818] A data management device and means for securely transferring acquired biometric data and storing it in a memory device.

[0819] A reliability verification device and means for comparing retained biometric data with reacquired biometric data,

[0820] A processing execution device and means for executing information processing when reliability verification is successful,

[0821] A data correction device and means for recording transaction information and updating user information,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, comprising a device for acquiring biometric data, including facial data, finger data, and eye data, in a variety of ways from different angles and under different conditions.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising an analytical device for analyzing acquired biometric data using a machine learning model to improve the accuracy of individual user identification.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] A means of acquiring information to obtain biometric authentication data,

[0830] A data storage method for encrypting acquired biometric authentication data and saving it to a database,

[0831] An authentication method for comparing stored biometric data with newly acquired data,

[0832] A transaction execution method for executing a transaction upon successful authentication,

[0833] A data update mechanism for recording transaction details and updating user identification information,

[0834] A payment method that uses the user's biometric information to ensure secure and rapid electronic payments,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, comprising means for acquiring biometric authentication data, including facial recognition, fingerprint recognition, and iris recognition, from various angles and under different conditions, and for utilizing this data in electronic transactions.

[0838] (Claim 3)

[0839] The system according to claim 1, comprising analytical means for analyzing acquired biometric authentication data using a computational model to improve the accuracy of individual user identification and enhance the security of electronic transactions.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] A data acquisition method for obtaining biometric information,

[0843] A storage means for encrypting acquired biometric information and storing it in a secure storage device,

[0844] A matching means for comparing stored biometric information with newly acquired information to perform individual identification,

[0845] A sentiment assessment tool for sensing emotional states and evaluating the risk of fraudulent transactions,

[0846] A means of updating information to record transaction details and update user account information,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, comprising data acquisition means capable of acquiring biometric information, including facial recognition, fingerprint recognition, and emotion recognition, in a variety of situations.

[0850] (Claim 3)

[0851] The system according to claim 1, comprising an analysis means for analyzing acquired biometric and emotional information using an artificial intelligence model to enhance the accuracy of user individual recognition and safety evaluation.

[0852] "Application example 2 when combining with an emotional engine"

[0853] (Claim 1)

[0854] By combining a data collection means for acquiring biometric information and an emotion recognition means for recognizing the user's emotional state, the means and

[0855] Data protection measures for encrypting acquired biometric and emotional data and storing it in securely managed data storage,

[0856] An identification means for recognizing stored biological and emotional data and evaluating its consistency with newly acquired data,

[0857] A means of taking action to conduct secure transactions upon successful authentication using both biometric and emotional methods,

[0858] Based on sentiment data acquired during the trading process, enhanced security measures are in place to detect potential fraud and, if necessary, suspend or review the transaction.

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, comprising information collection means capable of collecting facial recognition, fingerprint recognition, and other biometric data from various angles and under various environmental conditions, and further including processing for detecting changes in the user's emotions in real time.

[0862] (Claim 3)

[0863] The system according to claim 1, comprising analytical means for improving the accuracy of user authentication by analyzing collected biometric and emotional data with a generating AI model, constructing an anonymous emotional profile linked to an individual user. [Explanation of symbols]

[0864] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring information to obtain biometric authentication data, A data storage method for encrypting acquired biometric authentication data and saving it to a database, An authentication method for comparing stored biometric data with newly acquired data, A transaction execution method for executing a transaction upon successful authentication, A means of updating data to record transaction details and update user account information, A system that includes this.

2. The system according to claim 1, comprising information acquisition means for acquiring biometric authentication data, including facial recognition, fingerprint recognition, and iris recognition, in a variety of ways from different angles and under different conditions.

3. The system according to claim 1, further comprising an analysis means for analyzing acquired biometric authentication data with an artificial intelligence model to improve the accuracy of user identification.

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