system

Abstract

We provide the system. [Solution] A processing means that receives biometric data acquired by a facial recognition device and analyzes the biometric data as feature points, An authentication method that verifies the user's identity using analyzed feature points, A payment control means that associates the user's electronic payment account information with the authentication result obtained by this authentication means and automatically executes the payment via the electronic payment account, 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an office, it is necessary to provide a means to eliminate the complexity of requiring a physical device when an individual makes a payment and to complete the settlement quickly and smartly. In particular, it is required to save time during breaks or in busy situations at work. Furthermore, it is an issue to realize a more secure and reliable personal authentication that can cope with hacking and misrecognition in conventional authentication systems.

Means for Solving the Problems

[0005] This invention provides a system that performs highly accurate user authentication by analyzing biometric data acquired by a facial recognition device and extracting feature points. Specifically, it includes means for authenticating the user based on the analyzed feature points, thereby enabling the association of the authentication result with an electronic payment account. This association allows the user to complete payments automatically without manual operation. Furthermore, by integrating multiple recognition logs, the authentication accuracy can be further improved, and a means for notifying users of payment details can be added to promote secure use of the system.

[0006] A "face recognition device" is a device that recognizes a user's face using cameras and sensors and acquires biometric data.

[0007] "Biometric data" refers to data that includes facial feature points and other information used for biometric authentication.

[0008] "Feature points" are pieces of information that indicate a unique location or shape, and are used for facial recognition and identification.

[0009] "Analysis processing means" refers to means for processing acquired biological data and extracting feature points.

[0010] "Authentication methods" refer to means of verifying a user's identity based on analyzed feature points.

[0011] "Payment control means" refers to means for executing or controlling an electronic payment made by a user whose identity has been verified by an authentication means.

[0012] An "electronic payment account" is an account used by users to conduct financial transactions in digital format.

[0013] A "data integration method" is a means of integrating multiple facial recognition logs to achieve more reliable authentication.

[0014] "Notification means" refers to technical means for communicating payment details and authentication results to the user. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled 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.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled 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, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention provides an electronic payment system utilizing facial recognition technology. This system automatically identifies a user as they pass in front of a facial recognition device and completes the payment from their associated electronic payment account, providing convenience without requiring any physical operation within the office.

[0037] Specifically, this system works as follows: First, when a user registers with the system, a facial recognition device identifies the user's face and sends biometric data to the server. This data is analyzed as a series of feature points, and the server stores the analysis results in a database. The user registers personal information and electronic payment accounts with this system, thereby linking the facial recognition results with the payment account information.

[0038] When a facial recognition device installed in the office recognizes a user, the terminal sends the facial data to a server. The server verifies the received data and authenticates the user using feature points. If this authentication is successful, the server immediately executes the necessary payment via the associated electronic payment account using a payment control mechanism. This allows the user to complete the payment automatically without having to operate a physical device for payment.

[0039] Furthermore, integrating facial data obtained through multiple recognition rounds improves the accuracy of identity verification. In addition, once payment is complete, users receive an instant notification on their smartphone and can check the transaction details. Thus, payments can be completed quickly and securely simply by facing the recognition area. This system can be used efficiently even during breaks in work or when both hands are occupied.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device scans the user's face through a facial recognition device and acquires biometric data. This data includes facial feature points.

[0043] Step 2:

[0044] The device transmits the acquired facial biometric data to the server. This data transmission occurs in real time, and processing begins immediately.

[0045] Step 3:

[0046] The server analyzes the received biometric data and performs a process to extract facial feature points. This generates a dataset unique to each individual user.

[0047] Step 4:

[0048] The server compares the extracted feature points with the facial data of registered users stored in the database to perform identity verification. If authentication is successful, the server proceeds to the next step.

[0049] Step 5:

[0050] The server retrieves the user's electronic payment account information corresponding to the authentication result and immediately executes the payment process through the payment control mechanism.

[0051] Step 6:

[0052] The server verifies whether the payment was successful and sends a notification to the user's mobile device with the result. This notification includes a summary of the transaction.

[0053] Step 7:

[0054] Users can check notifications sent to their mobile devices to confirm that transactions have been successfully completed. This information provides users with peace of mind and improves the reliability of the system.

[0055] (Example 1)

[0056] 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."

[0057] Traditional electronic payment systems had the drawback of being inconvenient because users had to operate physical devices. Furthermore, the inability to perform authentication and transaction verification in real time meant there was a need for improvements in security and user experience.

[0058] 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.

[0059] In this invention, the server includes an information processing means that receives biometric information acquired by a facial information recognition device and analyzes the biometric information as feature points; an authentication means that authenticates an individual using the analyzed feature points; a payment management means that associates the individual's electronic payment information with the authentication result by the authentication means and automatically executes the payment via the electronic payment information; and a data storage means that stores the analysis data based on biometric characteristics. This makes it possible to complete payments automatically and securely without operating a physical device, and to immediately check the transaction history.

[0060] A "facial information recognition device" refers to hardware that acquires a user's biometric information and processes that information as digital data.

[0061] "Biometric information" refers to digital data, including facial features and attributes, used to identify individual users.

[0062] A "feature point" refers to a specific, identifiable point or pattern extracted from the biometric information of a face, and is used to recognize an individual.

[0063] "Information processing means" refers to software and processes for analyzing acquired biometric information and extracting and processing relevant feature points.

[0064] "Authentication means" refers to a mechanism for identifying an individual based on analyzed feature points and matching them with registered information.

[0065] "Payment management system" refers to a control system that automatically processes electronic payments based on authentication results.

[0066] "Electronic payment information" refers to account data, including payment information linked to a user.

[0067] "Data storage means" refers to databases and related systems for storing acquired and analyzed biometric and authentication data.

[0068] To implement this invention, a system is required in which a facial information recognition device, a server, a terminal, and a user work together. First, the facial information recognition device scans the user's face to acquire biometric information. This uses a camera-equipped device with image processing libraries such as OpenCV or Dlib implemented.

[0069] Upon receiving biometric information, the server analyzes the feature points using information processing tools. This analysis utilizes algorithms written in programming languages ​​such as Python. The analyzed feature points are stored in a database by data storage tools, preparing them for subsequent authentication processes.

[0070] The terminal works in conjunction with a facial recognition device installed in the office, capturing data when a user enters the recognition area and sending it to the server. The server verifies the received data using an authentication method and authenticates the individual by comparing it with registered information. If authentication is successful, an automatic payment is made based on electronic payment information using a payment management system.

[0071] As a concrete example, when a user purchases an item at the office cafe, this system automatically completes the payment by recognizing their face. This process allows users to make purchases smoothly without having to take out their wallet or smartphone.

[0072] Furthermore, after payment is completed, the server immediately notifies the user's smartphone of the transaction details. This feature allows users to check the transaction details on the spot, improving both security and convenience.

[0073] An example of a prompt using a generative AI model is: "Please describe an automated payment system using facial recognition within an office, focusing on the user experience. Please include specific examples of how users utilize the system and what benefits it offers." Using this prompt will clearly convey the system's operation and advantages.

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

[0075] Step 1:

[0076] The user stands in front of the facial recognition device, allowing it to acquire biometric information. During this process, the facial recognition device captures image data of the face via a camera and acquires this image as biometric information. Based on this input data, feature points are extracted and converted into a series of digital data.

[0077] Step 2:

[0078] The device transmits the acquired biometric information to the server. Since the transmitted data includes the coordinates and characteristics of facial feature points, the server receives this data and performs analysis using information processing tools. The server extracts feature points using a specific algorithm and prepares them for storage in a database.

[0079] Step 3:

[0080] The server authenticates the user through an authentication method based on the analyzed data. This authentication process verifies the user's identity by comparing it with previously registered data stored in the database. The input at this time is the coordinate information of the feature points, and the output is the authentication result.

[0081] Step 4:

[0082] If authentication is successful, the server activates the payment management system. This system uses the user's electronic payment information to execute payments in real time. The server generates the necessary transaction information and completes the payment process. As output, confirmation information of payment completion is obtained.

[0083] Step 5:

[0084] Once the payment is complete, the server sends a notification to the user's smartphone. The user receives this notification on their smartphone and can check the transaction details. In this process, the notification content generated by the server is the input, and the notification sent to the user is the output.

[0085] (Application Example 1)

[0086] 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."

[0087] Conventional facial recognition-based electronic payment systems have limitations in terms of convenience due to insufficient authentication accuracy and the need for users to perform special operations. Furthermore, there is a demand for quick and intuitive operation in the payment confirmation process.

[0088] 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.

[0089] In this invention, the server includes: information processing means for receiving biometric information acquired by a facial recognition device and analyzing the biometric information as feature points; authentication means for verifying the identity of the user using the analyzed feature points; payment control means for associating the user's electronic transaction account information with the authentication result by the authentication means and automatically executing a transaction through the electronic transaction account; device control means for enabling facial recognition and automated transactions by performing a predetermined viewing operation using a wearable device equipped with a shooting function; and display control means for displaying the completion of the transaction on the display device of the wearable device. This makes it possible for the user to perform highly accurate identity verification by facial recognition and rapid electronic payment while operating intuitively.

[0090] A "facial recognition device" is a device that acquires biometric information from a face and uses it to identify the user.

[0091] "Biometric information" refers to data that quantifies physical characteristics such as facial features as digital signals.

[0092] "Information processing means" refers to technology that has the function of analyzing received biological information and converting it into a data format as feature points.

[0093] "Feature points" are important data points extracted from facial biometric information and used for personal identification.

[0094] "Authentication methods" are technologies that use characteristic points to verify the identity of a user.

[0095] An "electronic trading account" refers to account information used by a user to conduct digital transactions.

[0096] "Payment control means" refers to technology that has the function of automatically executing transactions through the electronic transaction account of an authenticated user.

[0097] A "wearable device" is an electronic device that a user can wear and that has functions such as displaying various information and taking pictures.

[0098] "Device control means" refers to technology for performing facial recognition and electronic transactions in conjunction with the camera function of a wearable device.

[0099] "Display control means" refers to technology for visually communicating the completion of a transaction to the user on a wearable device.

[0100] In a system that realizes this invention, users can easily make payments using a wearable device (e.g., smart glasses) equipped with facial recognition technology.

[0101] The server first receives biometric information acquired through facial recognition equipment. This biometric information is analyzed as facial feature points, and the user is authenticated based on the analyzed data. The authentication method mainly utilizes facial recognition libraries such as OpenCV and is programmed using Python. If authentication is successful, the payment control mechanism automatically executes the transaction immediately via the associated electronic transaction account.

[0102] The wearable device, acting as the terminal, uses a camera to capture the user's face and takes a picture based on a predetermined viewing action. This device control means is implemented using a framework such as Flask and mediates communication between the server and the terminal. When the user performs the viewing action, a transaction completion notification is displayed on the wearable device's display. This notification is managed by a display control means, allowing the user to quickly grasp the transaction history.

[0103] For example, when a user selects an item in a store, they can simply turn their face towards the item through the smart glasses, and the purchase will be completed instantly, with a message like "A $3.50 coffee has been purchased" displayed on the glasses' screen. In this way, the everyday shopping experience can be improved while maintaining a high level of convenience and security.

[0104] An example of a prompt for a generated AI model is: "Design an automated payment system using facial recognition in stores with smart glasses. How does this system work, and how does it perform user authentication and payment?"

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The user uses the camera of a wearable device to visually confirm the direction of the product. During this process, biometric information of the user's face is acquired through the video captured by the device's camera. The input data is image data representing biometric information, which is then formatted into a format that can be analyzed in subsequent steps.

[0108] Step 2:

[0109] The device initiates face recognition processing on the acquired biometric information. Feature points are extracted using OpenCV and organized as data points. Here, the input is the image data obtained in step 1, and the output is the analyzed feature point data. This process provides the information necessary for individual recognition.

[0110] Step 3:

[0111] The terminal sends the extracted feature point data to the server. The server uses the received feature point data to perform user authentication and generates an authentication result. The input is the feature point data, and the output is the user authentication result. This result determines whether or not to proceed with the payment process.

[0112] Step 4:

[0113] If authentication is successful, the server automatically executes the transaction using the associated electronic transaction account. It verifies account information using payment control mechanisms and completes the transaction. The input is the authentication result, and the output is a payment completion notification. This completes the actual transaction.

[0114] Step 5:

[0115] The terminal displays the payment completion notification received from the server to the user. The user is immediately notified by visualizing the transaction completion message on the wearable device's display. The input is the payment completion notification, and the output is the displayed message. This allows the user to confirm the completion of the transaction.

[0116] 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.

[0117] This invention provides an electronic payment system that integrates facial recognition technology and emotion recognition technology. In this system, when a user provides biometric data through a facial recognition device, the emotion engine simultaneously analyzes the user's emotional state, thereby achieving advanced authentication that takes the user's emotions into account in addition to the usual identity verification process.

[0118] Specifically, the system works as follows: When a user stands in front of a facial recognition device, the terminal acquires the user's facial data and sends it to a server. This data includes biometric information and facial feature points. Simultaneously, the emotion engine analyzes the user's emotions. For example, it uses changes in facial expressions and subtle facial features to identify basic emotions such as joy, anger, and surprise.

[0119] The server analyzes the received biometric data and performs identity verification. If authentication is successful, the emotional state is then analyzed. If the emotion engine detects unnatural emotions, the server will consider it an emergency and may insert additional verification measures such as two-factor authentication. This further enhances security.

[0120] The payment control system verifies that identity verification and emotional state assessment are successful, and then completes the transaction via the user's electronic payment account. At the end of this process, the server automatically sends a transaction completion notification to the user's terminal.

[0121] As a concrete example, consider the process when a user purchases lunch at an office cafe. When the user arrives in front of the cafe's facial recognition device, the terminal simultaneously performs facial recognition and emotion analysis. If the user has a normal smile, the server proceeds smoothly with authentication and completes the payment. However, if the user is angry, the server requests additional authentication to enhance security. In this way, the present invention makes it possible to realize an advanced authentication system that combines facial recognition technology and emotion recognition technology.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] When a user stands in front of the facial recognition device, the terminal scans the user's face in real time, acquiring biometric data and facial feature points. At the same time, the emotion engine is also activated, analyzing the user's emotional state from their facial expressions.

[0125] Step 2:

[0126] The device transmits the acquired biometric data and emotion analysis results to the server. Data transmission is encrypted and conducted securely.

[0127] Step 3:

[0128] The server extracts facial feature points from the received biometric data and performs identity verification by comparing them with a registered database. Simultaneously, it evaluates the emotion analysis results to understand the user's emotional state.

[0129] Step 4:

[0130] The server also considers the sentiment analysis results if user authentication is successful. If no abnormalities are found in the emotional state, the normal authentication procedure proceeds. If abnormalities are found, additional authentication steps (e.g., a PIN code or two-factor authentication) may be requested.

[0131] Step 5:

[0132] After authentication and sentiment evaluation are complete, the server verifies the user's electronic payment account information and automatically executes the transaction via the payment control mechanism.

[0133] Step 6:

[0134] Once the payment is complete, the server notifies the user's mobile device of the payment details. The notification includes details such as the transaction name, amount, and location.

[0135] Step 7:

[0136] Users can check transaction notifications received on their mobile devices and confirm that all processes have been completed successfully. This notification is also important for verifying the security and reliability of the transaction.

[0137] (Example 2)

[0138] 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".

[0139] Conventional electronic payment systems lack sufficient measures to improve the accuracy of identity verification, resulting in a high risk of fraudulent use and authentication errors. Furthermore, transactions are prone to occurring without the user's intent, necessitating improved security. In particular, because payments proceed without considering the user's emotional state, authentication based on the user's true intentions is difficult.

[0140] 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.

[0141] In this invention, the server includes processing means, emotion analysis means, and security control means. This enables advanced identity verification by integrating user facial recognition and emotion analysis, and makes it possible to realize secure electronic payments that take into account the user's emotional state.

[0142] A "face recognition device" is a device that captures a user's face and acquires its feature points.

[0143] "Biometric data" refers to data that quantifies the user's facial features and is used for identity verification.

[0144] The "processing means" refers to the part that has the function of analyzing the biometric data received from the facial recognition device as feature points.

[0145] An "authentication method" is a mechanism for verifying a user's identity using analyzed feature points.

[0146] "Emotional analysis means" refers to a device or software that determines and analyzes a user's emotional state from their facial expressions.

[0147] The "safety control means" is a control unit that activates additional authentication means when unnatural emotions are detected by the emotion analysis means.

[0148] A "payment control mechanism" is a system that automatically executes a payment by associating the user's electronic payment account information with the authentication result from an authentication method.

[0149] This invention is an electronic payment system that combines facial recognition technology and emotion analysis technology, integrating user facial data and emotion data to perform highly accurate identity verification. The system mainly consists of a facial recognition device, an emotion analysis engine in the terminal, and authentication and payment control means located on the server.

[0150] When a user stands in front of a facial recognition device, the device captures the user's face with its camera and extracts feature points as biometric data. This facial recognition device includes a high-resolution digital camera and image processing software. The facial data is transmitted to a server using a secure communication protocol.

[0151] In parallel, the device runs an emotion analysis engine that analyzes the user's emotions from their face. This engine incorporates a facial recognition algorithm using computer vision and can identify basic emotions such as joy, anger, and surprise.

[0152] The server verifies the user's identity by comparing the received facial data with an existing user database. If successful, it checks the sentiment analysis results and requests additional authentication if defined unnatural emotions are detected. This process enables highly secure authentication.

[0153] The payment control system executes the payment after authentication is successful and the emotional state is determined to be normal. The payment is processed in real time and conducted through the electronic payment network. Once the transaction is complete, the server notifies the user of the transaction details by sending a transaction completion notification to the user's terminal.

[0154] For example, when a user purchases lunch at an office cafe, they simply stand in front of a facial recognition device and present a normal smile to the terminal to complete the payment quickly. If the user displays anger, the system will detect this as an anomaly and request additional authentication.

[0155] Examples of prompts to input into a generative AI model:

[0156] "Please explain each processing step of this system in detail. Please describe the flow of electronic payments using facial recognition and emotion analysis engines, including specific operations."

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The user stands in front of the facial recognition device.

[0160] The device captures the user's face with a camera and extracts feature points as biometric data. Specifically, it uses a digital camera to acquire an image of the user's face and extracts facial feature points as data from that image. This facial feature point data is obtained as the output of the device.

[0161] Step 2:

[0162] The terminal transmits the extracted facial feature point data to the server using a secure communication protocol.

[0163] The terminal uses encryption technologies such as SSL / TLS to ensure the secure transmission of biometric data. In this process, the input is facial feature point data, and the output is data wrapped in secure communication that reaches the server.

[0164] Step 3:

[0165] The server verifies the user's identity by comparing the received facial data with the user authentication database.

[0166] The server uses a database query to compare the received facial feature point data with existing personal data. Based on the query results, authentication is determined, and the authentication result is output.

[0167] Step 4:

[0168] If authentication is successful, the device runs an emotion engine that analyzes facial emotions.

[0169] The device's emotion analysis engine uses computer vision technology to identify emotions from facial images. Based on this analysis, it outputs emotional states such as joy or anger.

[0170] Step 5:

[0171] The server receives the results of the emotion analysis and checks whether the emotion is normal.

[0172] The server compares the received emotion data with a predefined normal range to determine whether or not abnormal emotions are present. As a result, the output indicates whether or not abnormal emotions are present and the determination result.

[0173] Step 6:

[0174] If an anomaly is detected, the server will request additional authentication methods.

[0175] The server uses secure control measures to implement two-factor authentication or additional question authentication, presenting the user with additional authentication steps. These steps require further input from the user.

[0176] Step 7:

[0177] The server executes the payment process after all authentications have been successful.

[0178] The server uses settlement control means to operate the user's electronic transaction account and execute the transaction. The settlement is completed via the settlement gateway, and the transaction completion result is output.

[0179] Step 8:

[0180] After the transaction is completed, the server sends a transaction completion notification to the user's terminal.

[0181] The server uses a notification system to send a message to the terminal, prompting the user to confirm the transaction. Upon receiving the notification, the terminal receives output that displays a transaction completion notice to the user.

[0182] (Application Example 2)

[0183] 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".

[0184] Conventional electronic payment systems rely solely on facial recognition for identity verification, making them vulnerable to unauthorized access and fraud from a security standpoint. Furthermore, they fail to consider the user's emotional state, making it difficult to detect signs of fraud beforehand, thus posing a challenge to improving security. Additionally, many personal authentication methods are simplistic, and there is a need to reduce the risk of incorrect authentication.

[0185] 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.

[0186] In this invention, the server includes processing means for receiving biometric data acquired by a facial recognition device and analyzing the biometric data as feature points; authentication means for verifying the identity of the user using the analyzed feature points; emotion analysis means for detecting the user's emotional state; determination means for requesting additional authentication based on the user's emotional state; and payment control means for associating the user's electronic payment account information with the authentication result from the authentication means and automatically executing the payment via the electronic payment account. This enables advanced authentication that utilizes emotional states in addition to identity verification based on facial recognition, making it possible to realize a safer and more reliable electronic payment system.

[0187] A "face recognition device" is a device equipped with technology that acquires the characteristics of a user's face as digital data, enabling individual identification.

[0188] "Biometric data" refers to data that represents the user's facial features and their unique physiological characteristics.

[0189] "Feature points" are a collection of measurable points that represent individual biological characteristics, such as the shape and arrangement of facial features.

[0190] "Authentication methods" refer to technical means of verifying whether or not a user is the person they declared based on acquired biometric data.

[0191] "Emotional analysis methods" are technologies that analyze and determine a user's emotional state from their facial expressions and subtle movements.

[0192] A "payment control mechanism" is a means that has the function of automatically controlling the completion of a transaction through an electronic payment account after user authentication is complete.

[0193] A "judgment tool" is a technology that uses emotion analysis results to determine whether an emotional state is normal or unnatural, and requests additional information as needed.

[0194] A "data integration method" is a technology that integrates data obtained from multiple events related to facial recognition and emotion analysis to improve the accuracy of authentication.

[0195] A "notification method" is a means of providing payment details to the user's device upon completion of payment, allowing them to check their transaction history.

[0196] This invention constructs an electronic payment system that integrates facial recognition technology and emotion analysis technology. The facial recognition device, emotion analysis means, and a complex authentication process implemented on the server play crucial roles in the system.

[0197] First, when a user stands in front of a facial recognition device, the device captures the user's face in real time. This process utilizes hardware such as a smartphone or a dedicated facial recognition device. The acquired facial biometric data is processed using a facial recognition library, such as OpenCV, and analyzed as facial feature points.

[0198] Simultaneously, the device uses emotion analysis tools to evaluate the user's emotions. This emotion analysis is performed using an emotion recognition engine, such as Microsoft® Azure® Face API. This analysis identifies the user's current emotional state and determines whether it is a natural state or not.

[0199] The server aggregates the results of facial recognition and emotion analysis to perform authentication. If the emotional state is deemed unnatural, a decision-making mechanism is activated to request additional authentication. Additional authentication further enhances the security of authentication by requiring the user to verify their identity through another means.

[0200] In this invention, a payment control means that integrates these processes plays a role in automatically completing electronic payments for users who have completed identity verification. Finally, the payment details are sent to the user's terminal via a notification means, allowing them to check the transaction history.

[0201] A concrete example would be when a user makes a payment at a specific restaurant. If the user is emotionally calm, authentication and payment will proceed smoothly. However, if the user is irritated or anxious, additional security checks will be performed. A prompt message based on this embodiment would be: "Analyze the emotional status from the facial image acquired by the smartphone camera and determine if it is a normal smile. Based on the determination result, approve the electronic payment service or perform additional authentication."

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user stands in front of the facial recognition device, and the device captures the user's face in real time. The input is a facial image obtained from the smartphone camera. This image data is analyzed using the OpenCV library to extract facial feature points. The output is facial feature point data.

[0205] Step 2:

[0206] The device performs facial recognition and simultaneously analyzes the user's emotional state. Based on the feature points of the facial image obtained as input, it analyzes emotions using the Microsoft Azure Face API. This results in the output of basic emotional states such as joy and anger.

[0207] Step 3:

[0208] The server receives feature point data and sentiment data transmitted from the terminal. It takes feature point data and sentiment data as input. The server first uses this data to activate authentication and verify the user's identity. The output is information indicating whether the identity verification was successful or not.

[0209] Step 4:

[0210] The server makes decisions based on the emotion analysis results. It uses data indicating whether the emotional state is normal or unnatural as input. If an unnatural emotional state is detected, the server requests additional authentication. The output is information indicating whether additional authentication is required.

[0211] Step 5:

[0212] If identity verification is successful and the emotional state is deemed acceptable, the server executes the electronic payment through the payment control mechanism. The inputs used are the success / failure status of the authentication and the user's electronic payment account information. The output is the payment completion status.

[0213] Step 6:

[0214] The server notifies the user terminal that the payment has been completed. The input is the payment completion status information. The transaction history is displayed on the user terminal for the user to review. The output consists of the payment completion notification and transaction history information.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention provides an electronic payment system utilizing facial recognition technology. This system automatically identifies a user as they pass in front of a facial recognition device and completes the payment from their associated electronic payment account, providing convenience without requiring any physical operation within the office.

[0232] Specifically, this system works as follows: First, when a user registers with the system, a facial recognition device identifies the user's face and sends biometric data to the server. This data is analyzed as a series of feature points, and the server stores the analysis results in a database. The user registers personal information and electronic payment accounts with this system, thereby linking the facial recognition results with the payment account information.

[0233] When a facial recognition device installed in the office recognizes a user, the terminal sends the facial data to a server. The server verifies the received data and authenticates the user using feature points. If this authentication is successful, the server immediately executes the necessary payment via the associated electronic payment account using a payment control mechanism. This allows the user to complete the payment automatically without having to operate a physical device for payment.

[0234] Furthermore, integrating facial data obtained through multiple recognition rounds improves the accuracy of identity verification. In addition, once payment is complete, users receive an instant notification on their smartphone and can check the transaction details. Thus, payments can be completed quickly and securely simply by facing the recognition area. This system can be used efficiently even during breaks in work or when both hands are occupied.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The device scans the user's face through a facial recognition device and acquires biometric data. This data includes facial feature points.

[0238] Step 2:

[0239] The device transmits the acquired facial biometric data to the server. This data transmission occurs in real time, and processing begins immediately.

[0240] Step 3:

[0241] The server analyzes the received biometric data and performs a process to extract facial feature points. This generates a dataset unique to each individual user.

[0242] Step 4:

[0243] The server compares the extracted feature points with the facial data of registered users stored in the database to perform identity verification. If authentication is successful, the server proceeds to the next step.

[0244] Step 5:

[0245] The server retrieves the user's electronic payment account information corresponding to the authentication result and immediately executes the payment process through the payment control mechanism.

[0246] Step 6:

[0247] The server verifies whether the payment was successful and sends a notification to the user's mobile device with the result. This notification includes a summary of the transaction.

[0248] Step 7:

[0249] Users can check notifications sent to their mobile devices to confirm that transactions have been successfully completed. This information provides users with peace of mind and improves the reliability of the system.

[0250] (Example 1)

[0251] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0252] Traditional electronic payment systems had the drawback of being inconvenient because users had to operate physical devices. Furthermore, the inability to perform authentication and transaction verification in real time meant there was a need for improvements in security and user experience.

[0253] 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.

[0254] In this invention, the server includes an information processing means that receives biometric information acquired by a facial information recognition device and analyzes the biometric information as feature points; an authentication means that authenticates an individual using the analyzed feature points; a payment management means that associates the individual's electronic payment information with the authentication result by the authentication means and automatically executes the payment via the electronic payment information; and a data storage means that stores the analysis data based on biometric characteristics. This makes it possible to complete payments automatically and securely without operating a physical device, and to immediately check the transaction history.

[0255] A "facial information recognition device" refers to hardware that acquires a user's biometric information and processes that information as digital data.

[0256] "Biometric information" refers to digital data, including facial features and attributes, used to identify individual users.

[0257] A "feature point" refers to a specific, identifiable point or pattern extracted from the biometric information of a face, and is used to recognize an individual.

[0258] "Information processing means" refers to software and processes for analyzing acquired biometric information and extracting and processing relevant feature points.

[0259] "Authentication means" refers to a mechanism for identifying an individual based on analyzed feature points and matching them with registered information.

[0260] "Payment management system" refers to a control system that automatically processes electronic payments based on authentication results.

[0261] "Electronic payment information" refers to account data, including payment information linked to a user.

[0262] "Data storage means" refers to databases and related systems for storing acquired and analyzed biometric and authentication data.

[0263] To implement this invention, a system is required in which a facial information recognition device, a server, a terminal, and a user work together. First, the facial information recognition device scans the user's face to acquire biometric information. This uses a camera-equipped device with image processing libraries such as OpenCV or Dlib implemented.

[0264] Upon receiving biometric information, the server analyzes the feature points using information processing tools. This analysis utilizes algorithms written in programming languages ​​such as Python. The analyzed feature points are stored in a database by data storage tools, preparing them for subsequent authentication processes.

[0265] The terminal works in conjunction with a facial recognition device installed in the office, capturing data when a user enters the recognition area and sending it to the server. The server verifies the received data using an authentication method and authenticates the individual by comparing it with registered information. If authentication is successful, an automatic payment is made based on electronic payment information using a payment management system.

[0266] As a concrete example, when a user purchases an item at the office cafe, this system automatically completes the payment by recognizing their face. This process allows users to make purchases smoothly without having to take out their wallet or smartphone.

[0267] Furthermore, after payment is completed, the server immediately notifies the user's smartphone of the transaction details. This feature allows users to check the transaction details on the spot, improving both security and convenience.

[0268] An example of a prompt using a generative AI model is: "Please describe an automated payment system using facial recognition within an office, focusing on the user experience. Please include specific examples of how users utilize the system and what benefits it offers." Using this prompt will clearly convey the system's operation and advantages.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The user stands in front of the facial recognition device, allowing it to acquire biometric information. During this process, the facial recognition device captures image data of the face via a camera and acquires this image as biometric information. Based on this input data, feature points are extracted and converted into a series of digital data.

[0272] Step 2:

[0273] The device transmits the acquired biometric information to the server. Since the transmitted data includes the coordinates and characteristics of facial feature points, the server receives this data and performs analysis using information processing tools. The server extracts feature points using a specific algorithm and prepares them for storage in a database.

[0274] Step 3:

[0275] The server authenticates the user through an authentication method based on the analyzed data. This authentication process verifies the user's identity by comparing it with previously registered data stored in the database. The input at this time is the coordinate information of the feature points, and the output is the authentication result.

[0276] Step 4:

[0277] If authentication is successful, the server activates the payment management system. This system uses the user's electronic payment information to execute payments in real time. The server generates the necessary transaction information and completes the payment process. As output, confirmation information of payment completion is obtained.

[0278] Step 5:

[0279] Once the payment is complete, the server sends a notification to the user's smartphone. The user receives this notification on their smartphone and can check the transaction details. In this process, the notification content generated by the server is the input, and the notification sent to the user is the output.

[0280] (Application Example 1)

[0281] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0282] Conventional facial recognition-based electronic payment systems have limitations in terms of convenience due to insufficient authentication accuracy and the need for users to perform special operations. Furthermore, there is a demand for quick and intuitive operation in the payment confirmation process.

[0283] 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.

[0284] In this invention, the server includes: information processing means for receiving biometric information acquired by a face recognition device and analyzing the biometric information as feature points; authentication means for verifying the user using the analyzed feature points; payment control means for associating the electronic transaction account information of the user with the authentication result by the authentication means and automatically executing a transaction via the electronic transaction account; device control means for enabling face recognition and automatic transactions by performing a predetermined visual recognition operation using a wearable device having a photographing function; and display control means for displaying the completion of the transaction on the display device of the wearable device. Thereby, it becomes possible for the user to perform highly accurate user verification by face recognition and quick electronic settlement while intuitively operating.

[0285] The "face recognition device" is a device that acquires biometric information of the face and is used for identifying the user.

[0286] The "biometric information" is data obtained by quantifying physical characteristics such as the face as digital signals.

[0287] The "information processing means" is a technology having a function of analyzing the received biometric information and converting it into a data format as feature points.

[0288] The "feature points" are important data points extracted from the biometric information of the face and used for personal identification.

[0289] The "authentication means" is a technology for verifying the identity of the user using the feature points.

[0290] The "electronic transaction account" refers to account information for the user to conduct digital transactions.

[0291] The "payment control means" is a technology having a function of automatically executing a transaction via the electronic transaction account of the authenticated user.

[0292] The "wearable device" is an electronic device that the user can wear and has functions for displaying various information and photographing.

[0293] "Device control means" refers to technology for performing facial recognition and electronic transactions in conjunction with the camera function of a wearable device.

[0294] "Display control means" refers to technology for visually communicating the completion of a transaction to the user on a wearable device.

[0295] In a system that realizes this invention, users can easily make payments using a wearable device (e.g., smart glasses) equipped with facial recognition technology.

[0296] The server first receives biometric information acquired through facial recognition equipment. This biometric information is analyzed as facial feature points, and the user is authenticated based on the analyzed data. The authentication method mainly utilizes facial recognition libraries such as OpenCV and is programmed using Python. If authentication is successful, the payment control mechanism automatically executes the transaction immediately via the associated electronic transaction account.

[0297] The wearable device, acting as the terminal, uses a camera to capture the user's face and takes a picture based on a predetermined viewing action. This device control means is implemented using a framework such as Flask and mediates communication between the server and the terminal. When the user performs the viewing action, a transaction completion notification is displayed on the wearable device's display. This notification is managed by a display control means, allowing the user to quickly grasp the transaction history.

[0298] For example, when a user selects an item in a store, they can simply turn their face towards the item through the smart glasses, and the purchase will be completed instantly, with a message like "A $3.50 coffee has been purchased" displayed on the glasses' screen. In this way, the everyday shopping experience can be improved while maintaining a high level of convenience and security.

[0299] Examples of prompt sentences for the generated AI model include "Please design an automatic payment system based on face recognition in a store using smart glasses. How does this system operate and how does it perform user authentication and payment?"

[0300] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0301] Step 1:

[0302] The user uses the camera of the wearable device to confirm the direction of the product while performing a visual recognition operation. At this time, biometric information of the face is obtained through the video captured by the camera of the device. The input data is image data as biometric information, and this is arranged in a form that can be analyzed in subsequent steps.

[0303] Step 2:

[0304] The terminal starts face recognition processing on the obtained biometric information. Feature points are extracted using OpenCV and arranged as data points. Here, the input is the image data obtained in Step 1, and the output is the analyzed feature point data. Through this process, information necessary for individual recognition is obtained.

[0305] Step 3:

[0306] The terminal transmits the extracted feature point data to the server. The server performs user authentication using the received feature point data and generates an authentication result. The input is the feature point data, and the output is the user authentication result. Based on this result, it is determined whether to proceed to the payment process.

[0307] Step 4:

[0308] When the authentication is successful, the server automatically executes a transaction using the associated electronic trading account. The account information is queried using payment control means to complete the transaction. The input is the authentication result, and the output is a payment completion notification. Thereby, the actual transaction is completed.

[0309] Step 5:

[0310] The terminal displays the payment completion notification received from the server to the user. The user is immediately notified by visualizing the transaction completion message on the wearable device's display. The input is the payment completion notification, and the output is the displayed message. This allows the user to confirm the completion of the transaction.

[0311] 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.

[0312] This invention provides an electronic payment system that integrates facial recognition technology and emotion recognition technology. In this system, when a user provides biometric data through a facial recognition device, the emotion engine simultaneously analyzes the user's emotional state, thereby achieving advanced authentication that takes the user's emotions into account in addition to the usual identity verification process.

[0313] Specifically, the system works as follows: When a user stands in front of a facial recognition device, the terminal acquires the user's facial data and sends it to a server. This data includes biometric information and facial feature points. Simultaneously, the emotion engine analyzes the user's emotions. For example, it uses changes in facial expressions and subtle facial features to identify basic emotions such as joy, anger, and surprise.

[0314] The server analyzes the received biometric data and performs identity verification. If authentication is successful, the emotional state is then analyzed. If the emotion engine detects unnatural emotions, the server will consider it an emergency and may insert additional verification measures such as two-factor authentication. This further enhances security.

[0315] The payment control system verifies that identity verification and emotional state assessment are successful, and then completes the transaction via the user's electronic payment account. At the end of this process, the server automatically sends a transaction completion notification to the user's terminal.

[0316] As a concrete example, consider the process when a user purchases lunch at an office cafe. When the user arrives in front of the cafe's facial recognition device, the terminal simultaneously performs facial recognition and emotion analysis. If the user has a normal smile, the server proceeds smoothly with authentication and completes the payment. However, if the user is angry, the server requests additional authentication to enhance security. In this way, the present invention makes it possible to realize an advanced authentication system that combines facial recognition technology and emotion recognition technology.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] When a user stands in front of the facial recognition device, the terminal scans the user's face in real time, acquiring biometric data and facial feature points. At the same time, the emotion engine is also activated, analyzing the user's emotional state from their facial expressions.

[0320] Step 2:

[0321] The device transmits the acquired biometric data and emotion analysis results to the server. Data transmission is encrypted and conducted securely.

[0322] Step 3:

[0323] The server extracts facial feature points from the received biometric data and performs identity verification by comparing them with a registered database. Simultaneously, it evaluates the emotion analysis results to understand the user's emotional state.

[0324] Step 4:

[0325] The server also considers the sentiment analysis results if user authentication is successful. If no abnormalities are found in the emotional state, the normal authentication procedure proceeds. If abnormalities are found, additional authentication steps (e.g., a PIN code or two-factor authentication) may be requested.

[0326] Step 5:

[0327] After authentication and sentiment evaluation are complete, the server verifies the user's electronic payment account information and automatically executes the transaction via the payment control mechanism.

[0328] Step 6:

[0329] Once the payment is complete, the server notifies the user's mobile device of the payment details. The notification includes details such as the transaction name, amount, and location.

[0330] Step 7:

[0331] Users can check transaction notifications received on their mobile devices and confirm that all processes have been completed successfully. This notification is also important for verifying the security and reliability of the transaction.

[0332] (Example 2)

[0333] 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".

[0334] Conventional electronic payment systems lack sufficient measures to improve the accuracy of identity verification, resulting in a high risk of fraudulent use and authentication errors. Furthermore, transactions are prone to occurring without the user's intent, necessitating improved security. In particular, because payments proceed without considering the user's emotional state, authentication based on the user's true intentions is difficult.

[0335] 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.

[0336] In this invention, the server includes processing means, emotion analysis means, and security control means. This enables advanced identity verification by integrating user facial recognition and emotion analysis, and makes it possible to realize secure electronic payments that take into account the user's emotional state.

[0337] A "face recognition device" is a device that captures a user's face and acquires its feature points.

[0338] "Biometric data" refers to data that quantifies the user's facial features and is used for identity verification.

[0339] The "processing means" refers to the part that has the function of analyzing the biometric data received from the facial recognition device as feature points.

[0340] An "authentication method" is a mechanism for verifying a user's identity using analyzed feature points.

[0341] "Emotional analysis means" refers to a device or software that determines and analyzes a user's emotional state from their facial expressions.

[0342] The "safety control means" is a control unit that activates additional authentication means when unnatural emotions are detected by the emotion analysis means.

[0343] A "payment control mechanism" is a system that automatically executes a payment by associating the user's electronic payment account information with the authentication result from an authentication method.

[0344] This invention is an electronic payment system that combines facial recognition technology and emotion analysis technology, integrating user facial data and emotion data to perform highly accurate identity verification. The system mainly consists of a facial recognition device, an emotion analysis engine in the terminal, and authentication and payment control means located on the server.

[0345] When a user stands in front of a facial recognition device, the device captures the user's face with its camera and extracts feature points as biometric data. This facial recognition device includes a high-resolution digital camera and image processing software. The facial data is transmitted to a server using a secure communication protocol.

[0346] In parallel, the device runs an emotion analysis engine that analyzes the user's emotions from their face. This engine incorporates a facial recognition algorithm using computer vision and can identify basic emotions such as joy, anger, and surprise.

[0347] The server verifies the user's identity by comparing the received facial data with an existing user database. If successful, it checks the sentiment analysis results and requests additional authentication if defined unnatural emotions are detected. This process enables highly secure authentication.

[0348] The payment control system executes the payment after authentication is successful and the emotional state is determined to be normal. The payment is processed in real time and conducted through the electronic payment network. Once the transaction is complete, the server notifies the user of the transaction details by sending a transaction completion notification to the user's terminal.

[0349] For example, when a user purchases lunch at an office cafe, they simply stand in front of a facial recognition device and present a normal smile to the terminal to complete the payment quickly. If the user displays anger, the system will detect this as an anomaly and request additional authentication.

[0350] Examples of prompts to input into a generative AI model:

[0351] "Please explain each processing step of this system in detail. Please describe the flow of electronic payments using facial recognition and emotion analysis engines, including specific operations."

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The user stands in front of the facial recognition device.

[0355] The device captures the user's face with a camera and extracts feature points as biometric data. Specifically, it uses a digital camera to acquire an image of the user's face and extracts facial feature points as data from that image. This facial feature point data is obtained as the output of the device.

[0356] Step 2:

[0357] The terminal transmits the extracted facial feature point data to the server using a secure communication protocol.

[0358] The terminal uses encryption technologies such as SSL / TLS to ensure the secure transmission of biometric data. In this process, the input is facial feature point data, and the output is data wrapped in secure communication that reaches the server.

[0359] Step 3:

[0360] The server verifies the user's identity by comparing the received facial data with the user authentication database.

[0361] The server uses a database query to compare the received facial feature point data with existing personal data. Based on the query results, authentication is determined, and the authentication result is output.

[0362] Step 4:

[0363] If authentication is successful, the device runs an emotion engine that analyzes facial emotions.

[0364] The device's emotion analysis engine uses computer vision technology to identify emotions from facial images. Based on this analysis, it outputs emotional states such as joy or anger.

[0365] Step 5:

[0366] The server receives the results of the emotion analysis and checks whether the emotion is normal.

[0367] The server compares the received emotion data with a predefined normal range to determine whether or not abnormal emotions are present. As a result, the output indicates whether or not abnormal emotions are present and the determination result.

[0368] Step 6:

[0369] If an anomaly is detected, the server will request additional authentication methods.

[0370] The server uses secure control measures to implement two-factor authentication or additional question authentication, presenting the user with additional authentication steps. These steps require further input from the user.

[0371] Step 7:

[0372] The server executes the payment process after all authentications have been successful.

[0373] The server uses settlement control means to operate the user's electronic transaction account and execute the transaction. The settlement is completed via the settlement gateway, and the transaction completion result is output.

[0374] Step 8:

[0375] After the transaction is completed, the server sends a transaction completion notification to the user's terminal.

[0376] The server uses a notification system to send a message to the terminal, prompting the user to confirm the transaction. Upon receiving the notification, the terminal receives output that displays a transaction completion notice to the user.

[0377] (Application Example 2)

[0378] 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."

[0379] Conventional electronic payment systems rely solely on facial recognition for identity verification, making them vulnerable to unauthorized access and fraud from a security standpoint. Furthermore, they fail to consider the user's emotional state, making it difficult to detect signs of fraud beforehand, thus posing a challenge to improving security. Additionally, many personal authentication methods are simplistic, and there is a need to reduce the risk of incorrect authentication.

[0380] 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.

[0381] In this invention, the server includes processing means for receiving biometric data acquired by a facial recognition device and analyzing the biometric data as feature points; authentication means for verifying the identity of the user using the analyzed feature points; emotion analysis means for detecting the user's emotional state; determination means for requesting additional authentication based on the user's emotional state; and payment control means for associating the user's electronic payment account information with the authentication result from the authentication means and automatically executing the payment via the electronic payment account. This enables advanced authentication that utilizes emotional states in addition to identity verification based on facial recognition, making it possible to realize a safer and more reliable electronic payment system.

[0382] A "face recognition device" is a device equipped with technology that acquires the characteristics of a user's face as digital data, enabling individual identification.

[0383] "Biometric data" refers to data that represents the user's facial features and their unique physiological characteristics.

[0384] "Feature points" are a collection of measurable points that represent individual biological characteristics, such as the shape and arrangement of facial features.

[0385] "Authentication methods" refer to technical means of verifying whether or not a user is the person they declared based on acquired biometric data.

[0386] "Emotional analysis methods" are technologies that analyze and determine a user's emotional state from their facial expressions and subtle movements.

[0387] A "payment control mechanism" is a means that has the function of automatically controlling the completion of a transaction through an electronic payment account after user authentication is complete.

[0388] A "judgment tool" is a technology that uses emotion analysis results to determine whether an emotional state is normal or unnatural, and requests additional information as needed.

[0389] A "data integration method" is a technology that integrates data obtained from multiple events related to facial recognition and emotion analysis to improve the accuracy of authentication.

[0390] A "notification method" is a means of providing payment details to the user's device upon completion of payment, allowing them to check their transaction history.

[0391] This invention constructs an electronic payment system that integrates facial recognition technology and emotion analysis technology. The facial recognition device, emotion analysis means, and a complex authentication process implemented on the server play crucial roles in the system.

[0392] First, when a user stands in front of a facial recognition device, the device captures the user's face in real time. This process utilizes hardware such as a smartphone or a dedicated facial recognition device. The acquired facial biometric data is processed using a facial recognition library, such as OpenCV, and analyzed as facial feature points.

[0393] Simultaneously, the device uses emotion analysis tools to evaluate the user's emotions. This emotion analysis is performed using an emotion recognition engine, such as the Microsoft Azure Face API. This analysis identifies the user's current emotional state and determines whether it is a natural state or not.

[0394] The server aggregates the results of facial recognition and emotion analysis to perform authentication. If the emotional state is deemed unnatural, a decision-making mechanism is activated to request additional authentication. Additional authentication further enhances the security of authentication by requiring the user to verify their identity through another means.

[0395] In this invention, a payment control means that integrates these processes plays a role in automatically completing electronic payments for users who have completed identity verification. Finally, the payment details are sent to the user's terminal via a notification means, allowing them to check the transaction history.

[0396] A concrete example would be when a user makes a payment at a specific restaurant. If the user is emotionally calm, authentication and payment will proceed smoothly. However, if the user is irritated or anxious, additional security checks will be performed. A prompt message based on this embodiment would be: "Analyze the emotional status from the facial image acquired by the smartphone camera and determine if it is a normal smile. Based on the determination result, approve the electronic payment service or perform additional authentication."

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The user stands in front of the facial recognition device, and the device captures the user's face in real time. The input is a facial image obtained from the smartphone camera. This image data is analyzed using the OpenCV library to extract facial feature points. The output is facial feature point data.

[0400] Step 2:

[0401] The device performs facial recognition and simultaneously analyzes the user's emotional state. Based on the feature points of the facial image obtained as input, it analyzes emotions using the Microsoft Azure Face API. This results in the output of basic emotional states such as joy and anger.

[0402] Step 3:

[0403] The server receives feature point data and sentiment data transmitted from the terminal. It takes feature point data and sentiment data as input. The server first uses this data to activate authentication and verify the user's identity. The output is information indicating whether the identity verification was successful or not.

[0404] Step 4:

[0405] The server makes decisions based on the emotion analysis results. It uses data indicating whether the emotional state is normal or unnatural as input. If an unnatural emotional state is detected, the server requests additional authentication. The output is information indicating whether additional authentication is required.

[0406] Step 5:

[0407] If identity verification is successful and the emotional state is deemed acceptable, the server executes the electronic payment through the payment control mechanism. The inputs used are the success / failure status of the authentication and the user's electronic payment account information. The output is the payment completion status.

[0408] Step 6:

[0409] The server notifies the user terminal that the payment has been completed. The input is the payment completion status information. The transaction history is displayed on the user terminal for the user to review. The output consists of the payment completion notification and transaction history information.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] [Third Embodiment]

[0414] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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".

[0426] This invention provides an electronic payment system utilizing facial recognition technology. This system automatically identifies a user as they pass in front of a facial recognition device and completes the payment from their associated electronic payment account, providing convenience without requiring any physical operation within the office.

[0427] Specifically, this system works as follows: First, when a user registers with the system, a facial recognition device identifies the user's face and sends biometric data to the server. This data is analyzed as a series of feature points, and the server stores the analysis results in a database. The user registers personal information and electronic payment accounts with this system, thereby linking the facial recognition results with the payment account information.

[0428] When a facial recognition device installed in the office recognizes a user, the terminal sends the facial data to a server. The server verifies the received data and authenticates the user using feature points. If this authentication is successful, the server immediately executes the necessary payment via the associated electronic payment account using a payment control mechanism. This allows the user to complete the payment automatically without having to operate a physical device for payment.

[0429] Furthermore, integrating facial data obtained through multiple recognition rounds improves the accuracy of identity verification. In addition, once payment is complete, users receive an instant notification on their smartphone and can check the transaction details. Thus, payments can be completed quickly and securely simply by facing the recognition area. This system can be used efficiently even during breaks in work or when both hands are occupied.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The device scans the user's face through a facial recognition device and acquires biometric data. This data includes facial feature points.

[0433] Step 2:

[0434] The device transmits the acquired facial biometric data to the server. This data transmission occurs in real time, and processing begins immediately.

[0435] Step 3:

[0436] The server analyzes the received biometric data and performs a process to extract facial feature points. This generates a dataset unique to each individual user.

[0437] Step 4:

[0438] The server compares the extracted feature points with the facial data of registered users stored in the database to perform identity verification. If authentication is successful, the server proceeds to the next step.

[0439] Step 5:

[0440] The server retrieves the user's electronic payment account information corresponding to the authentication result and immediately executes the payment process through the payment control mechanism.

[0441] Step 6:

[0442] The server verifies whether the payment was successful and sends a notification to the user's mobile device with the result. This notification includes a summary of the transaction.

[0443] Step 7:

[0444] Users can check notifications sent to their mobile devices to confirm that transactions have been successfully completed. This information provides users with peace of mind and improves the reliability of the system.

[0445] (Example 1)

[0446] 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."

[0447] Traditional electronic payment systems had the drawback of being inconvenient because users had to operate physical devices. Furthermore, the inability to perform authentication and transaction verification in real time meant there was a need for improvements in security and user experience.

[0448] 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.

[0449] In this invention, the server includes an information processing means that receives biometric information acquired by a facial information recognition device and analyzes the biometric information as feature points; an authentication means that authenticates an individual using the analyzed feature points; a payment management means that associates the individual's electronic payment information with the authentication result by the authentication means and automatically executes the payment via the electronic payment information; and a data storage means that stores the analysis data based on biometric characteristics. This makes it possible to complete payments automatically and securely without operating a physical device, and to immediately check the transaction history.

[0450] A "facial information recognition device" refers to hardware that acquires a user's biometric information and processes that information as digital data.

[0451] "Biometric information" refers to digital data, including facial features and attributes, used to identify individual users.

[0452] A "feature point" refers to a specific, identifiable point or pattern extracted from the biometric information of a face, and is used to recognize an individual.

[0453] "Information processing means" refers to software and processes for analyzing acquired biometric information and extracting and processing relevant feature points.

[0454] "Authentication means" refers to a mechanism for identifying an individual based on analyzed feature points and matching them with registered information.

[0455] "Payment management system" refers to a control system that automatically processes electronic payments based on authentication results.

[0456] "Electronic payment information" refers to account data, including payment information linked to a user.

[0457] "Data storage means" refers to databases and related systems for storing acquired and analyzed biometric and authentication data.

[0458] To implement this invention, a system is required in which a facial information recognition device, a server, a terminal, and a user work together. First, the facial information recognition device scans the user's face to acquire biometric information. This uses a camera-equipped device with image processing libraries such as OpenCV or Dlib implemented.

[0459] Upon receiving biometric information, the server analyzes the feature points using information processing tools. This analysis utilizes algorithms written in programming languages ​​such as Python. The analyzed feature points are stored in a database by data storage tools, preparing them for subsequent authentication processes.

[0460] The terminal works in conjunction with a facial recognition device installed in the office, capturing data when a user enters the recognition area and sending it to the server. The server verifies the received data using an authentication method and authenticates the individual by comparing it with registered information. If authentication is successful, an automatic payment is made based on electronic payment information using a payment management system.

[0461] As a concrete example, when a user purchases an item at the office cafe, this system automatically completes the payment by recognizing their face. This process allows users to make purchases smoothly without having to take out their wallet or smartphone.

[0462] Furthermore, after payment is completed, the server immediately notifies the user's smartphone of the transaction details. This feature allows users to check the transaction details on the spot, improving both security and convenience.

[0463] An example of a prompt using a generative AI model is: "Please describe an automated payment system using facial recognition within an office, focusing on the user experience. Please include specific examples of how users utilize the system and what benefits it offers." Using this prompt will clearly convey the system's operation and advantages.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The user stands in front of the facial recognition device, allowing it to acquire biometric information. During this process, the facial recognition device captures image data of the face via a camera and acquires this image as biometric information. Based on this input data, feature points are extracted and converted into a series of digital data.

[0467] Step 2:

[0468] The device transmits the acquired biometric information to the server. Since the transmitted data includes the coordinates and characteristics of facial feature points, the server receives this data and performs analysis using information processing tools. The server extracts feature points using a specific algorithm and prepares them for storage in a database.

[0469] Step 3:

[0470] The server authenticates the user through an authentication method based on the analyzed data. This authentication process verifies the user's identity by comparing it with previously registered data stored in the database. The input at this time is the coordinate information of the feature points, and the output is the authentication result.

[0471] Step 4:

[0472] If authentication is successful, the server activates the payment management system. This system uses the user's electronic payment information to execute payments in real time. The server generates the necessary transaction information and completes the payment process. As output, confirmation information of payment completion is obtained.

[0473] Step 5:

[0474] Once the payment is complete, the server sends a notification to the user's smartphone. The user receives this notification on their smartphone and can check the transaction details. In this process, the notification content generated by the server is the input, and the notification sent to the user is the output.

[0475] (Application Example 1)

[0476] 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."

[0477] Conventional facial recognition-based electronic payment systems have limitations in terms of convenience due to insufficient authentication accuracy and the need for users to perform special operations. Furthermore, there is a demand for quick and intuitive operation in the payment confirmation process.

[0478] 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.

[0479] In this invention, the server includes: information processing means for receiving biometric information acquired by a facial recognition device and analyzing the biometric information as feature points; authentication means for verifying the identity of the user using the analyzed feature points; payment control means for associating the user's electronic transaction account information with the authentication result by the authentication means and automatically executing a transaction through the electronic transaction account; device control means for enabling facial recognition and automated transactions by performing a predetermined viewing operation using a wearable device equipped with a shooting function; and display control means for displaying the completion of the transaction on the display device of the wearable device. This makes it possible for the user to perform highly accurate identity verification by facial recognition and rapid electronic payment while operating intuitively.

[0480] A "facial recognition device" is a device that acquires biometric information from a face and uses it to identify the user.

[0481] "Biometric information" refers to data that quantifies physical characteristics such as facial features as digital signals.

[0482] "Information processing means" refers to technology that has the function of analyzing received biological information and converting it into a data format as feature points.

[0483] "Feature points" are important data points extracted from facial biometric information and used for personal identification.

[0484] "Authentication methods" are technologies that use characteristic points to verify the identity of a user.

[0485] An "electronic trading account" refers to account information used by a user to conduct digital transactions.

[0486] "Payment control means" refers to technology that has the function of automatically executing transactions through the electronic transaction account of an authenticated user.

[0487] A "wearable device" is an electronic device that a user can wear and that has functions such as displaying various information and taking pictures.

[0488] "Device control means" refers to technology for performing facial recognition and electronic transactions in conjunction with the camera function of a wearable device.

[0489] "Display control means" refers to technology for visually communicating the completion of a transaction to the user on a wearable device.

[0490] In a system that realizes this invention, users can easily make payments using a wearable device (e.g., smart glasses) equipped with facial recognition technology.

[0491] The server first receives biometric information acquired through facial recognition equipment. This biometric information is analyzed as facial feature points, and the user is authenticated based on the analyzed data. The authentication method mainly utilizes facial recognition libraries such as OpenCV and is programmed using Python. If authentication is successful, the payment control mechanism automatically executes the transaction immediately via the associated electronic transaction account.

[0492] The wearable device, acting as the terminal, uses a camera to capture the user's face and takes a picture based on a predetermined viewing action. This device control means is implemented using a framework such as Flask and mediates communication between the server and the terminal. When the user performs the viewing action, a transaction completion notification is displayed on the wearable device's display. This notification is managed by a display control means, allowing the user to quickly grasp the transaction history.

[0493] For example, when a user selects an item in a store, they can simply turn their face towards the item through the smart glasses, and the purchase will be completed instantly, with a message like "A $3.50 coffee has been purchased" displayed on the glasses' screen. In this way, the everyday shopping experience can be improved while maintaining a high level of convenience and security.

[0494] An example of a prompt for a generated AI model is: "Design an automated payment system using facial recognition in stores with smart glasses. How does this system work, and how does it perform user authentication and payment?"

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The user uses the camera of a wearable device to visually confirm the direction of the product. During this process, biometric information of the user's face is acquired through the video captured by the device's camera. The input data is image data representing biometric information, which is then formatted into a format that can be analyzed in subsequent steps.

[0498] Step 2:

[0499] The device initiates face recognition processing on the acquired biometric information. Feature points are extracted using OpenCV and organized as data points. Here, the input is the image data obtained in step 1, and the output is the analyzed feature point data. This process provides the information necessary for individual recognition.

[0500] Step 3:

[0501] The terminal sends the extracted feature point data to the server. The server uses the received feature point data to perform user authentication and generates an authentication result. The input is the feature point data, and the output is the user authentication result. This result determines whether or not to proceed with the payment process.

[0502] Step 4:

[0503] If authentication is successful, the server automatically executes the transaction using the associated electronic transaction account. It verifies account information using payment control mechanisms and completes the transaction. The input is the authentication result, and the output is a payment completion notification. This completes the actual transaction.

[0504] Step 5:

[0505] The terminal displays the payment completion notification received from the server to the user. The user is immediately notified by visualizing the transaction completion message on the wearable device's display. The input is the payment completion notification, and the output is the displayed message. This allows the user to confirm the completion of the transaction.

[0506] 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.

[0507] This invention provides an electronic payment system that integrates facial recognition technology and emotion recognition technology. In this system, when a user provides biometric data through a facial recognition device, the emotion engine simultaneously analyzes the user's emotional state, thereby achieving advanced authentication that takes the user's emotions into account in addition to the usual identity verification process.

[0508] Specifically, the system works as follows: When a user stands in front of a facial recognition device, the terminal acquires the user's facial data and sends it to a server. This data includes biometric information and facial feature points. Simultaneously, the emotion engine analyzes the user's emotions. For example, it uses changes in facial expressions and subtle facial features to identify basic emotions such as joy, anger, and surprise.

[0509] The server analyzes the received biometric data and performs identity verification. If authentication is successful, the emotional state is then analyzed. If the emotion engine detects unnatural emotions, the server will consider it an emergency and may insert additional verification measures such as two-factor authentication. This further enhances security.

[0510] The payment control system verifies that identity verification and emotional state assessment are successful, and then completes the transaction via the user's electronic payment account. At the end of this process, the server automatically sends a transaction completion notification to the user's terminal.

[0511] As a concrete example, consider the process when a user purchases lunch at an office cafe. When the user arrives in front of the cafe's facial recognition device, the terminal simultaneously performs facial recognition and emotion analysis. If the user has a normal smile, the server proceeds smoothly with authentication and completes the payment. However, if the user is angry, the server requests additional authentication to enhance security. In this way, the present invention makes it possible to realize an advanced authentication system that combines facial recognition technology and emotion recognition technology.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] When a user stands in front of the facial recognition device, the terminal scans the user's face in real time, acquiring biometric data and facial feature points. At the same time, the emotion engine is also activated, analyzing the user's emotional state from their facial expressions.

[0515] Step 2:

[0516] The device transmits the acquired biometric data and emotion analysis results to the server. Data transmission is encrypted and conducted securely.

[0517] Step 3:

[0518] The server extracts facial feature points from the received biometric data and performs identity verification by comparing them with a registered database. Simultaneously, it evaluates the emotion analysis results to understand the user's emotional state.

[0519] Step 4:

[0520] The server also considers the sentiment analysis results if user authentication is successful. If no abnormalities are found in the emotional state, the normal authentication procedure proceeds. If abnormalities are found, additional authentication steps (e.g., a PIN code or two-factor authentication) may be requested.

[0521] Step 5:

[0522] After authentication and sentiment evaluation are complete, the server verifies the user's electronic payment account information and automatically executes the transaction via the payment control mechanism.

[0523] Step 6:

[0524] Once the payment is complete, the server notifies the user's mobile device of the payment details. The notification includes details such as the transaction name, amount, and location.

[0525] Step 7:

[0526] Users can check transaction notifications received on their mobile devices and confirm that all processes have been completed successfully. This notification is also important for verifying the security and reliability of the transaction.

[0527] (Example 2)

[0528] 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."

[0529] Conventional electronic payment systems lack sufficient measures to improve the accuracy of identity verification, resulting in a high risk of fraudulent use and authentication errors. Furthermore, transactions are prone to occurring without the user's intent, necessitating improved security. In particular, because payments proceed without considering the user's emotional state, authentication based on the user's true intentions is difficult.

[0530] 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.

[0531] In this invention, the server includes processing means, emotion analysis means, and security control means. This enables advanced identity verification by integrating user facial recognition and emotion analysis, and makes it possible to realize secure electronic payments that take into account the user's emotional state.

[0532] A "face recognition device" is a device that captures a user's face and acquires its feature points.

[0533] "Biometric data" refers to data that quantifies the user's facial features and is used for identity verification.

[0534] The "processing means" refers to the part that has the function of analyzing the biometric data received from the facial recognition device as feature points.

[0535] An "authentication method" is a mechanism for verifying a user's identity using analyzed feature points.

[0536] "Emotional analysis means" refers to a device or software that determines and analyzes a user's emotional state from their facial expressions.

[0537] The "safety control means" is a control unit that activates additional authentication means when unnatural emotions are detected by the emotion analysis means.

[0538] A "payment control mechanism" is a system that automatically executes a payment by associating the user's electronic payment account information with the authentication result from an authentication method.

[0539] This invention is an electronic payment system that combines facial recognition technology and emotion analysis technology, integrating user facial data and emotion data to perform highly accurate identity verification. The system mainly consists of a facial recognition device, an emotion analysis engine in the terminal, and authentication and payment control means located on the server.

[0540] When a user stands in front of a facial recognition device, the device captures the user's face with its camera and extracts feature points as biometric data. This facial recognition device includes a high-resolution digital camera and image processing software. The facial data is transmitted to a server using a secure communication protocol.

[0541] In parallel, the device runs an emotion analysis engine that analyzes the user's emotions from their face. This engine incorporates a facial recognition algorithm using computer vision and can identify basic emotions such as joy, anger, and surprise.

[0542] The server verifies the user's identity by comparing the received facial data with an existing user database. If successful, it checks the sentiment analysis results and requests additional authentication if defined unnatural emotions are detected. This process enables highly secure authentication.

[0543] The payment control system executes the payment after authentication is successful and the emotional state is determined to be normal. The payment is processed in real time and conducted through the electronic payment network. Once the transaction is complete, the server notifies the user of the transaction details by sending a transaction completion notification to the user's terminal.

[0544] For example, when a user purchases lunch at an office cafe, they simply stand in front of a facial recognition device and present a normal smile to the terminal to complete the payment quickly. If the user displays anger, the system will detect this as an anomaly and request additional authentication.

[0545] Examples of prompts to input into a generative AI model:

[0546] "Please explain each processing step of this system in detail. Please describe the flow of electronic payments using facial recognition and emotion analysis engines, including specific operations."

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The user stands in front of the facial recognition device.

[0550] The device captures the user's face with a camera and extracts feature points as biometric data. Specifically, it uses a digital camera to acquire an image of the user's face and extracts facial feature points as data from that image. This facial feature point data is obtained as the output of the device.

[0551] Step 2:

[0552] The terminal transmits the extracted facial feature point data to the server using a secure communication protocol.

[0553] The terminal uses encryption technologies such as SSL / TLS to ensure the secure transmission of biometric data. In this process, the input is facial feature point data, and the output is data wrapped in secure communication that reaches the server.

[0554] Step 3:

[0555] The server verifies the user's identity by comparing the received facial data with the user authentication database.

[0556] The server uses a database query to compare the received facial feature point data with existing personal data. Based on the query results, authentication is determined, and the authentication result is output.

[0557] Step 4:

[0558] If authentication is successful, the device runs an emotion engine that analyzes facial emotions.

[0559] The device's emotion analysis engine uses computer vision technology to identify emotions from facial images. Based on this analysis, it outputs emotional states such as joy or anger.

[0560] Step 5:

[0561] The server receives the results of the emotion analysis and checks whether the emotion is normal.

[0562] The server compares the received emotion data with a predefined normal range to determine whether or not abnormal emotions are present. As a result, the output indicates whether or not abnormal emotions are present and the determination result.

[0563] Step 6:

[0564] If an anomaly is detected, the server will request additional authentication methods.

[0565] The server uses secure control measures to implement two-factor authentication or additional question authentication, presenting the user with additional authentication steps. These steps require further input from the user.

[0566] Step 7:

[0567] The server executes the payment process after all authentications have been successful.

[0568] The server uses settlement control means to operate the user's electronic transaction account and execute the transaction. The settlement is completed via the settlement gateway, and the transaction completion result is output.

[0569] Step 8:

[0570] After the transaction is completed, the server sends a transaction completion notification to the user's terminal.

[0571] The server uses a notification system to send a message to the terminal, prompting the user to confirm the transaction. Upon receiving the notification, the terminal receives output that displays a transaction completion notice to the user.

[0572] (Application Example 2)

[0573] 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."

[0574] Conventional electronic payment systems rely solely on facial recognition for identity verification, making them vulnerable to unauthorized access and fraud from a security standpoint. Furthermore, they fail to consider the user's emotional state, making it difficult to detect signs of fraud beforehand, thus posing a challenge to improving security. Additionally, many personal authentication methods are simplistic, and there is a need to reduce the risk of incorrect authentication.

[0575] 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.

[0576] In this invention, the server includes processing means for receiving biometric data acquired by a facial recognition device and analyzing the biometric data as feature points; authentication means for verifying the identity of the user using the analyzed feature points; emotion analysis means for detecting the user's emotional state; determination means for requesting additional authentication based on the user's emotional state; and payment control means for associating the user's electronic payment account information with the authentication result from the authentication means and automatically executing the payment via the electronic payment account. This enables advanced authentication that utilizes emotional states in addition to identity verification based on facial recognition, making it possible to realize a safer and more reliable electronic payment system.

[0577] A "face recognition device" is a device equipped with technology that acquires the characteristics of a user's face as digital data, enabling individual identification.

[0578] "Biometric data" refers to data that represents the user's facial features and their unique physiological characteristics.

[0579] "Feature points" are a collection of measurable points that represent individual biological characteristics, such as the shape and arrangement of facial features.

[0580] "Authentication methods" refer to technical means of verifying whether or not a user is the person they declared based on acquired biometric data.

[0581] "Emotional analysis methods" are technologies that analyze and determine a user's emotional state from their facial expressions and subtle movements.

[0582] A "payment control mechanism" is a means that has the function of automatically controlling the completion of a transaction through an electronic payment account after user authentication is complete.

[0583] A "judgment tool" is a technology that uses emotion analysis results to determine whether an emotional state is normal or unnatural, and requests additional information as needed.

[0584] A "data integration method" is a technology that integrates data obtained from multiple events related to facial recognition and emotion analysis to improve the accuracy of authentication.

[0585] A "notification method" is a means of providing payment details to the user's device upon completion of payment, allowing them to check their transaction history.

[0586] This invention constructs an electronic payment system that integrates facial recognition technology and emotion analysis technology. The facial recognition device, emotion analysis means, and a complex authentication process implemented on the server play crucial roles in the system.

[0587] First, when a user stands in front of a facial recognition device, the device captures the user's face in real time. This process utilizes hardware such as a smartphone or a dedicated facial recognition device. The acquired facial biometric data is processed using a facial recognition library, such as OpenCV, and analyzed as facial feature points.

[0588] Simultaneously, the device uses emotion analysis tools to evaluate the user's emotions. This emotion analysis is performed using an emotion recognition engine, such as the Microsoft Azure Face API. This analysis identifies the user's current emotional state and determines whether it is a natural state or not.

[0589] The server aggregates the results of facial recognition and emotion analysis to perform authentication. If the emotional state is deemed unnatural, a decision-making mechanism is activated to request additional authentication. Additional authentication further enhances the security of authentication by requiring the user to verify their identity through another means.

[0590] In this invention, a payment control means that integrates these processes plays a role in automatically completing electronic payments for users who have completed identity verification. Finally, the payment details are sent to the user's terminal via a notification means, allowing them to check the transaction history.

[0591] A concrete example would be when a user makes a payment at a specific restaurant. If the user is emotionally calm, authentication and payment will proceed smoothly. However, if the user is irritated or anxious, additional security checks will be performed. A prompt message based on this embodiment would be: "Analyze the emotional status from the facial image acquired by the smartphone camera and determine if it is a normal smile. Based on the determination result, approve the electronic payment service or perform additional authentication."

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The user stands in front of the facial recognition device, and the device captures the user's face in real time. The input is a facial image obtained from the smartphone camera. This image data is analyzed using the OpenCV library to extract facial feature points. The output is facial feature point data.

[0595] Step 2:

[0596] The device performs facial recognition and simultaneously analyzes the user's emotional state. Based on the feature points of the facial image obtained as input, it analyzes emotions using the Microsoft Azure Face API. This results in the output of basic emotional states such as joy and anger.

[0597] Step 3:

[0598] The server receives feature point data and sentiment data transmitted from the terminal. It takes feature point data and sentiment data as input. The server first uses this data to activate authentication and verify the user's identity. The output is information indicating whether the identity verification was successful or not.

[0599] Step 4:

[0600] The server makes decisions based on the emotion analysis results. It uses data indicating whether the emotional state is normal or unnatural as input. If an unnatural emotional state is detected, the server requests additional authentication. The output is information indicating whether additional authentication is required.

[0601] Step 5:

[0602] If identity verification is successful and the emotional state is deemed acceptable, the server executes the electronic payment through the payment control mechanism. The inputs used are the success / failure status of the authentication and the user's electronic payment account information. The output is the payment completion status.

[0603] Step 6:

[0604] The server notifies the user terminal that the payment has been completed. The input is the payment completion status information. The transaction history is displayed on the user terminal for the user to review. The output consists of the payment completion notification and transaction history information.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] [Fourth Embodiment]

[0609] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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".

[0622] This invention provides an electronic payment system utilizing facial recognition technology. This system automatically identifies a user as they pass in front of a facial recognition device and completes the payment from their associated electronic payment account, providing convenience without requiring any physical operation within the office.

[0623] Specifically, this system works as follows: First, when a user registers with the system, a facial recognition device identifies the user's face and sends biometric data to the server. This data is analyzed as a series of feature points, and the server stores the analysis results in a database. The user registers personal information and electronic payment accounts with this system, thereby linking the facial recognition results with the payment account information.

[0624] When a facial recognition device installed in the office recognizes a user, the terminal sends the facial data to a server. The server verifies the received data and authenticates the user using feature points. If this authentication is successful, the server immediately executes the necessary payment via the associated electronic payment account using a payment control mechanism. This allows the user to complete the payment automatically without having to operate a physical device for payment.

[0625] Furthermore, integrating facial data obtained through multiple recognition rounds improves the accuracy of identity verification. In addition, once payment is complete, users receive an instant notification on their smartphone and can check the transaction details. Thus, payments can be completed quickly and securely simply by facing the recognition area. This system can be used efficiently even during breaks in work or when both hands are occupied.

[0626] The following describes the processing flow.

[0627] Step 1:

[0628] The device scans the user's face through a facial recognition device and acquires biometric data. This data includes facial feature points.

[0629] Step 2:

[0630] The device transmits the acquired facial biometric data to the server. This data transmission occurs in real time, and processing begins immediately.

[0631] Step 3:

[0632] The server analyzes the received biometric data and performs a process to extract facial feature points. This generates a dataset unique to each individual user.

[0633] Step 4:

[0634] The server compares the extracted feature points with the facial data of registered users stored in the database to perform identity verification. If authentication is successful, the server proceeds to the next step.

[0635] Step 5:

[0636] The server retrieves the user's electronic payment account information corresponding to the authentication result and immediately executes the payment process through the payment control mechanism.

[0637] Step 6:

[0638] The server verifies whether the payment was successful and sends a notification to the user's mobile device with the result. This notification includes a summary of the transaction.

[0639] Step 7:

[0640] Users can check notifications sent to their mobile devices to confirm that transactions have been successfully completed. This information provides users with peace of mind and improves the reliability of the system.

[0641] (Example 1)

[0642] 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".

[0643] Traditional electronic payment systems had the drawback of being inconvenient because users had to operate physical devices. Furthermore, the inability to perform authentication and transaction verification in real time meant there was a need for improvements in security and user experience.

[0644] 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.

[0645] In this invention, the server includes an information processing means that receives biometric information acquired by a facial information recognition device and analyzes the biometric information as feature points; an authentication means that authenticates an individual using the analyzed feature points; a payment management means that associates the individual's electronic payment information with the authentication result by the authentication means and automatically executes the payment via the electronic payment information; and a data storage means that stores the analysis data based on biometric characteristics. This makes it possible to complete payments automatically and securely without operating a physical device, and to immediately check the transaction history.

[0646] A "facial information recognition device" refers to hardware that acquires a user's biometric information and processes that information as digital data.

[0647] "Biometric information" refers to digital data, including facial features and attributes, used to identify individual users.

[0648] A "feature point" refers to a specific, identifiable point or pattern extracted from the biometric information of a face, and is used to recognize an individual.

[0649] "Information processing means" refers to software and processes for analyzing acquired biometric information and extracting and processing relevant feature points.

[0650] "Authentication means" refers to a mechanism for identifying an individual based on analyzed feature points and matching them with registered information.

[0651] "Payment management system" refers to a control system that automatically processes electronic payments based on authentication results.

[0652] "Electronic payment information" refers to account data, including payment information linked to a user.

[0653] "Data storage means" refers to databases and related systems for storing acquired and analyzed biometric and authentication data.

[0654] To implement this invention, a system is required in which a facial information recognition device, a server, a terminal, and a user work together. First, the facial information recognition device scans the user's face to acquire biometric information. This uses a camera-equipped device with image processing libraries such as OpenCV or Dlib implemented.

[0655] Upon receiving biometric information, the server analyzes the feature points using information processing tools. This analysis utilizes algorithms written in programming languages ​​such as Python. The analyzed feature points are stored in a database by data storage tools, preparing them for subsequent authentication processes.

[0656] The terminal works in conjunction with a facial recognition device installed in the office, capturing data when a user enters the recognition area and sending it to the server. The server verifies the received data using an authentication method and authenticates the individual by comparing it with registered information. If authentication is successful, an automatic payment is made based on electronic payment information using a payment management system.

[0657] As a concrete example, when a user purchases an item at the office cafe, this system automatically completes the payment by recognizing their face. This process allows users to make purchases smoothly without having to take out their wallet or smartphone.

[0658] Furthermore, after payment is completed, the server immediately notifies the user's smartphone of the transaction details. This feature allows users to check the transaction details on the spot, improving both security and convenience.

[0659] An example of a prompt using a generative AI model is: "Please describe an automated payment system using facial recognition within an office, focusing on the user experience. Please include specific examples of how users utilize the system and what benefits it offers." Using this prompt will clearly convey the system's operation and advantages.

[0660] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0661] Step 1:

[0662] The user stands in front of the facial recognition device, allowing it to acquire biometric information. During this process, the facial recognition device captures image data of the face via a camera and acquires this image as biometric information. Based on this input data, feature points are extracted and converted into a series of digital data.

[0663] Step 2:

[0664] The device transmits the acquired biometric information to the server. Since the transmitted data includes the coordinates and characteristics of facial feature points, the server receives this data and performs analysis using information processing tools. The server extracts feature points using a specific algorithm and prepares them for storage in a database.

[0665] Step 3:

[0666] The server authenticates the user through an authentication method based on the analyzed data. This authentication process verifies the user's identity by comparing it with previously registered data stored in the database. The input at this time is the coordinate information of the feature points, and the output is the authentication result.

[0667] Step 4:

[0668] If authentication is successful, the server activates the payment management system. This system uses the user's electronic payment information to execute payments in real time. The server generates the necessary transaction information and completes the payment process. As output, confirmation information of payment completion is obtained.

[0669] Step 5:

[0670] Once the payment is complete, the server sends a notification to the user's smartphone. The user receives this notification on their smartphone and can check the transaction details. In this process, the notification content generated by the server is the input, and the notification sent to the user is the output.

[0671] (Application Example 1)

[0672] 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".

[0673] Conventional facial recognition-based electronic payment systems have limitations in terms of convenience due to insufficient authentication accuracy and the need for users to perform special operations. Furthermore, there is a demand for quick and intuitive operation in the payment confirmation process.

[0674] 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.

[0675] In this invention, the server includes: information processing means for receiving biometric information acquired by a facial recognition device and analyzing the biometric information as feature points; authentication means for verifying the identity of the user using the analyzed feature points; payment control means for associating the user's electronic transaction account information with the authentication result by the authentication means and automatically executing a transaction through the electronic transaction account; device control means for enabling facial recognition and automated transactions by performing a predetermined viewing operation using a wearable device equipped with a shooting function; and display control means for displaying the completion of the transaction on the display device of the wearable device. This makes it possible for the user to perform highly accurate identity verification by facial recognition and rapid electronic payment while operating intuitively.

[0676] A "facial recognition device" is a device that acquires biometric information from a face and uses it to identify the user.

[0677] "Biometric information" refers to data that quantifies physical characteristics such as facial features as digital signals.

[0678] "Information processing means" refers to technology that has the function of analyzing received biological information and converting it into a data format as feature points.

[0679] "Feature points" are important data points extracted from facial biometric information and used for personal identification.

[0680] "Authentication methods" are technologies that use characteristic points to verify the identity of a user.

[0681] An "electronic trading account" refers to account information used by a user to conduct digital transactions.

[0682] "Payment control means" refers to technology that has the function of automatically executing transactions through the electronic transaction account of an authenticated user.

[0683] A "wearable device" is an electronic device that a user can wear and that has functions such as displaying various information and taking pictures.

[0684] "Device control means" refers to technology for performing facial recognition and electronic transactions in conjunction with the camera function of a wearable device.

[0685] "Display control means" refers to technology for visually communicating the completion of a transaction to the user on a wearable device.

[0686] In a system that realizes this invention, users can easily make payments using a wearable device (e.g., smart glasses) equipped with facial recognition technology.

[0687] The server first receives biometric information acquired through facial recognition equipment. This biometric information is analyzed as facial feature points, and the user is authenticated based on the analyzed data. The authentication method mainly utilizes facial recognition libraries such as OpenCV and is programmed using Python. If authentication is successful, the payment control mechanism automatically executes the transaction immediately via the associated electronic transaction account.

[0688] The wearable device, acting as the terminal, uses a camera to capture the user's face and takes a picture based on a predetermined viewing action. This device control means is implemented using a framework such as Flask and mediates communication between the server and the terminal. When the user performs the viewing action, a transaction completion notification is displayed on the wearable device's display. This notification is managed by a display control means, allowing the user to quickly grasp the transaction history.

[0689] For example, when a user selects an item in a store, they can simply turn their face towards the item through the smart glasses, and the purchase will be completed instantly, with a message like "A $3.50 coffee has been purchased" displayed on the glasses' screen. In this way, the everyday shopping experience can be improved while maintaining a high level of convenience and security.

[0690] An example of a prompt for a generated AI model is: "Design an automated payment system using facial recognition in stores with smart glasses. How does this system work, and how does it perform user authentication and payment?"

[0691] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0692] Step 1:

[0693] The user uses the camera of a wearable device to visually confirm the direction of the product. During this process, biometric information of the user's face is acquired through the video captured by the device's camera. The input data is image data representing biometric information, which is then formatted into a format that can be analyzed in subsequent steps.

[0694] Step 2:

[0695] The device initiates face recognition processing on the acquired biometric information. Feature points are extracted using OpenCV and organized as data points. Here, the input is the image data obtained in step 1, and the output is the analyzed feature point data. This process provides the information necessary for individual recognition.

[0696] Step 3:

[0697] The terminal sends the extracted feature point data to the server. The server uses the received feature point data to perform user authentication and generates an authentication result. The input is the feature point data, and the output is the user authentication result. This result determines whether or not to proceed with the payment process.

[0698] Step 4:

[0699] If authentication is successful, the server automatically executes the transaction using the associated electronic transaction account. It verifies account information using payment control mechanisms and completes the transaction. The input is the authentication result, and the output is a payment completion notification. This completes the actual transaction.

[0700] Step 5:

[0701] The terminal displays the payment completion notification received from the server to the user. The user is immediately notified by visualizing the transaction completion message on the wearable device's display. The input is the payment completion notification, and the output is the displayed message. This allows the user to confirm the completion of the transaction.

[0702] 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.

[0703] This invention provides an electronic payment system that integrates facial recognition technology and emotion recognition technology. In this system, when a user provides biometric data through a facial recognition device, the emotion engine simultaneously analyzes the user's emotional state, thereby achieving advanced authentication that takes the user's emotions into account in addition to the usual identity verification process.

[0704] Specifically, the system works as follows: When a user stands in front of a facial recognition device, the terminal acquires the user's facial data and sends it to a server. This data includes biometric information and facial feature points. Simultaneously, the emotion engine analyzes the user's emotions. For example, it uses changes in facial expressions and subtle facial features to identify basic emotions such as joy, anger, and surprise.

[0705] The server analyzes the received biometric data and performs identity verification. If authentication is successful, the emotional state is then analyzed. If the emotion engine detects unnatural emotions, the server will consider it an emergency and may insert additional verification measures such as two-factor authentication. This further enhances security.

[0706] The payment control system verifies that identity verification and emotional state assessment are successful, and then completes the transaction via the user's electronic payment account. At the end of this process, the server automatically sends a transaction completion notification to the user's terminal.

[0707] As a concrete example, consider the process when a user purchases lunch at an office cafe. When the user arrives in front of the cafe's facial recognition device, the terminal simultaneously performs facial recognition and emotion analysis. If the user has a normal smile, the server proceeds smoothly with authentication and completes the payment. However, if the user is angry, the server requests additional authentication to enhance security. In this way, the present invention makes it possible to realize an advanced authentication system that combines facial recognition technology and emotion recognition technology.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] When a user stands in front of the facial recognition device, the terminal scans the user's face in real time, acquiring biometric data and facial feature points. At the same time, the emotion engine is also activated, analyzing the user's emotional state from their facial expressions.

[0711] Step 2:

[0712] The device transmits the acquired biometric data and emotion analysis results to the server. Data transmission is encrypted and conducted securely.

[0713] Step 3:

[0714] The server extracts facial feature points from the received biometric data and performs identity verification by comparing them with a registered database. Simultaneously, it evaluates the emotion analysis results to understand the user's emotional state.

[0715] Step 4:

[0716] The server also considers the sentiment analysis results if user authentication is successful. If no abnormalities are found in the emotional state, the normal authentication procedure proceeds. If abnormalities are found, additional authentication steps (e.g., a PIN code or two-factor authentication) may be requested.

[0717] Step 5:

[0718] After authentication and sentiment evaluation are complete, the server verifies the user's electronic payment account information and automatically executes the transaction via the payment control mechanism.

[0719] Step 6:

[0720] Once the payment is complete, the server notifies the user's mobile device of the payment details. The notification includes details such as the transaction name, amount, and location.

[0721] Step 7:

[0722] Users can check transaction notifications received on their mobile devices and confirm that all processes have been completed successfully. This notification is also important for verifying the security and reliability of the transaction.

[0723] (Example 2)

[0724] 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".

[0725] Conventional electronic payment systems lack sufficient measures to improve the accuracy of identity verification, resulting in a high risk of fraudulent use and authentication errors. Furthermore, transactions are prone to occurring without the user's intent, necessitating improved security. In particular, because payments proceed without considering the user's emotional state, authentication based on the user's true intentions is difficult.

[0726] 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.

[0727] In this invention, the server includes processing means, emotion analysis means, and security control means. This enables advanced identity verification by integrating user facial recognition and emotion analysis, and makes it possible to realize secure electronic payments that take into account the user's emotional state.

[0728] A "face recognition device" is a device that captures a user's face and acquires its feature points.

[0729] "Biometric data" refers to data that quantifies the user's facial features and is used for identity verification.

[0730] The "processing means" refers to the part that has the function of analyzing the biometric data received from the facial recognition device as feature points.

[0731] An "authentication method" is a mechanism for verifying a user's identity using analyzed feature points.

[0732] "Emotional analysis means" refers to a device or software that determines and analyzes a user's emotional state from their facial expressions.

[0733] The "safety control means" is a control unit that activates additional authentication means when unnatural emotions are detected by the emotion analysis means.

[0734] A "payment control mechanism" is a system that automatically executes a payment by associating the user's electronic payment account information with the authentication result from an authentication method.

[0735] This invention is an electronic payment system that combines facial recognition technology and emotion analysis technology, integrating user facial data and emotion data to perform highly accurate identity verification. The system mainly consists of a facial recognition device, an emotion analysis engine in the terminal, and authentication and payment control means located on the server.

[0736] When a user stands in front of a facial recognition device, the device captures the user's face with its camera and extracts feature points as biometric data. This facial recognition device includes a high-resolution digital camera and image processing software. The facial data is transmitted to a server using a secure communication protocol.

[0737] In parallel, the device runs an emotion analysis engine that analyzes the user's emotions from their face. This engine incorporates a facial recognition algorithm using computer vision and can identify basic emotions such as joy, anger, and surprise.

[0738] The server verifies the user's identity by comparing the received facial data with an existing user database. If successful, it checks the sentiment analysis results and requests additional authentication if defined unnatural emotions are detected. This process enables highly secure authentication.

[0739] The payment control system executes the payment after authentication is successful and the emotional state is determined to be normal. The payment is processed in real time and conducted through the electronic payment network. Once the transaction is complete, the server notifies the user of the transaction details by sending a transaction completion notification to the user's terminal.

[0740] For example, when a user purchases lunch at an office cafe, they simply stand in front of a facial recognition device and present a normal smile to the terminal to complete the payment quickly. If the user displays anger, the system will detect this as an anomaly and request additional authentication.

[0741] Examples of prompts to input into a generative AI model:

[0742] "Please explain each processing step of this system in detail. Please describe the flow of electronic payments using facial recognition and emotion analysis engines, including specific operations."

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The user stands in front of the facial recognition device.

[0746] The device captures the user's face with a camera and extracts feature points as biometric data. Specifically, it uses a digital camera to acquire an image of the user's face and extracts facial feature points as data from that image. This facial feature point data is obtained as the output of the device.

[0747] Step 2:

[0748] The terminal transmits the extracted facial feature point data to the server using a secure communication protocol.

[0749] The terminal uses encryption technologies such as SSL / TLS to ensure the secure transmission of biometric data. In this process, the input is facial feature point data, and the output is data wrapped in secure communication that reaches the server.

[0750] Step 3:

[0751] The server verifies the user's identity by comparing the received facial data with the user authentication database.

[0752] The server uses a database query to compare the received facial feature point data with existing personal data. Based on the query results, authentication is determined, and the authentication result is output.

[0753] Step 4:

[0754] If authentication is successful, the device runs an emotion engine that analyzes facial emotions.

[0755] The device's emotion analysis engine uses computer vision technology to identify emotions from facial images. Based on this analysis, it outputs emotional states such as joy or anger.

[0756] Step 5:

[0757] The server receives the results of the emotion analysis and checks whether the emotion is normal.

[0758] The server compares the received emotion data with a predefined normal range to determine whether or not abnormal emotions are present. As a result, the output indicates whether or not abnormal emotions are present and the determination result.

[0759] Step 6:

[0760] If an anomaly is detected, the server will request additional authentication methods.

[0761] The server uses secure control measures to implement two-factor authentication or additional question authentication, presenting the user with additional authentication steps. These steps require further input from the user.

[0762] Step 7:

[0763] The server executes the payment process after all authentications have been successful.

[0764] The server uses settlement control means to operate the user's electronic transaction account and execute the transaction. The settlement is completed via the settlement gateway, and the transaction completion result is output.

[0765] Step 8:

[0766] After the transaction is completed, the server sends a transaction completion notification to the user's terminal.

[0767] The server uses a notification system to send a message to the terminal, prompting the user to confirm the transaction. Upon receiving the notification, the terminal receives output that displays a transaction completion notice to the user.

[0768] (Application Example 2)

[0769] 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".

[0770] Conventional electronic payment systems rely solely on facial recognition for identity verification, making them vulnerable to unauthorized access and fraud from a security standpoint. Furthermore, they fail to consider the user's emotional state, making it difficult to detect signs of fraud beforehand, thus posing a challenge to improving security. Additionally, many personal authentication methods are simplistic, and there is a need to reduce the risk of incorrect authentication.

[0771] 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.

[0772] In this invention, the server includes processing means for receiving biometric data acquired by a facial recognition device and analyzing the biometric data as feature points; authentication means for verifying the identity of the user using the analyzed feature points; emotion analysis means for detecting the user's emotional state; determination means for requesting additional authentication based on the user's emotional state; and payment control means for associating the user's electronic payment account information with the authentication result from the authentication means and automatically executing the payment via the electronic payment account. This enables advanced authentication that utilizes emotional states in addition to identity verification based on facial recognition, making it possible to realize a safer and more reliable electronic payment system.

[0773] A "face recognition device" is a device equipped with technology that acquires the characteristics of a user's face as digital data, enabling individual identification.

[0774] "Biometric data" refers to data that represents the user's facial features and their unique physiological characteristics.

[0775] "Feature points" are a collection of measurable points that represent individual biological characteristics, such as the shape and arrangement of facial features.

[0776] "Authentication methods" refer to technical means of verifying whether or not a user is the person they declared based on acquired biometric data.

[0777] "Emotional analysis methods" are technologies that analyze and determine a user's emotional state from their facial expressions and subtle movements.

[0778] A "payment control mechanism" is a means that has the function of automatically controlling the completion of a transaction through an electronic payment account after user authentication is complete.

[0779] A "judgment tool" is a technology that uses emotion analysis results to determine whether an emotional state is normal or unnatural, and requests additional information as needed.

[0780] A "data integration method" is a technology that integrates data obtained from multiple events related to facial recognition and emotion analysis to improve the accuracy of authentication.

[0781] A "notification method" is a means of providing payment details to the user's device upon completion of payment, allowing them to check their transaction history.

[0782] This invention constructs an electronic payment system that integrates facial recognition technology and emotion analysis technology. The facial recognition device, emotion analysis means, and a complex authentication process implemented on the server play crucial roles in the system.

[0783] First, when a user stands in front of a facial recognition device, the device captures the user's face in real time. This process utilizes hardware such as a smartphone or a dedicated facial recognition device. The acquired facial biometric data is processed using a facial recognition library, such as OpenCV, and analyzed as facial feature points.

[0784] Simultaneously, the device uses emotion analysis tools to evaluate the user's emotions. This emotion analysis is performed using an emotion recognition engine, such as the Microsoft Azure Face API. This analysis identifies the user's current emotional state and determines whether it is a natural state or not.

[0785] The server aggregates the results of facial recognition and emotion analysis to perform authentication. If the emotional state is deemed unnatural, a decision-making mechanism is activated to request additional authentication. Additional authentication further enhances the security of authentication by requiring the user to verify their identity through another means.

[0786] In this invention, a payment control means that integrates these processes plays a role in automatically completing electronic payments for users who have completed identity verification. Finally, the payment details are sent to the user's terminal via a notification means, allowing them to check the transaction history.

[0787] A concrete example would be when a user makes a payment at a specific restaurant. If the user is emotionally calm, authentication and payment will proceed smoothly. However, if the user is irritated or anxious, additional security checks will be performed. A prompt message based on this embodiment would be: "Analyze the emotional status from the facial image acquired by the smartphone camera and determine if it is a normal smile. Based on the determination result, approve the electronic payment service or perform additional authentication."

[0788] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0789] Step 1:

[0790] The user stands in front of the facial recognition device, and the device captures the user's face in real time. The input is a facial image obtained from the smartphone camera. This image data is analyzed using the OpenCV library to extract facial feature points. The output is facial feature point data.

[0791] Step 2:

[0792] The device performs facial recognition and simultaneously analyzes the user's emotional state. Based on the feature points of the facial image obtained as input, it analyzes emotions using the Microsoft Azure Face API. This results in the output of basic emotional states such as joy and anger.

[0793] Step 3:

[0794] The server receives feature point data and sentiment data transmitted from the terminal. It takes feature point data and sentiment data as input. The server first uses this data to activate authentication and verify the user's identity. The output is information indicating whether the identity verification was successful or not.

[0795] Step 4:

[0796] The server makes decisions based on the emotion analysis results. It uses data indicating whether the emotional state is normal or unnatural as input. If an unnatural emotional state is detected, the server requests additional authentication. The output is information indicating whether additional authentication is required.

[0797] Step 5:

[0798] If identity verification is successful and the emotional state is deemed acceptable, the server executes the electronic payment through the payment control mechanism. The inputs used are the success / failure status of the authentication and the user's electronic payment account information. The output is the payment completion status.

[0799] Step 6:

[0800] The server notifies the user terminal that the payment has been completed. The input is the payment completion status information. The transaction history is displayed on the user terminal for the user to review. The output consists of the payment completion notification and transaction history information.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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."

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] The following is further disclosed regarding the embodiments described above.

[0823] (Claim 1)

[0824] A processing means that receives biometric data acquired by a facial recognition device and analyzes the biometric data as feature points,

[0825] An authentication method that verifies the user's identity using analyzed feature points,

[0826] A payment control means that associates the user's electronic payment account information with the authentication result obtained by this authentication means and automatically executes the payment via the electronic payment account,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, comprising data integration means for integrating successful face recognition logs from multiple face recognition events to improve user authentication accuracy.

[0830] (Claim 3)

[0831] The system according to claim 1, further comprising a notification means for notifying the user terminal of the payment details upon completion of the payment, thereby enabling the user to check the transaction history.

[0832] "Example 1"

[0833] (Claim 1)

[0834] Information processing means that receives biometric information acquired by a facial information recognition device and analyzes said biometric information as feature points,

[0835] An authentication method that authenticates an individual using analyzed feature points,

[0836] A payment management means that associates an individual's electronic payment information with the authentication result obtained by this authentication means and automatically executes a payment via said electronic payment information,

[0837] A data storage method for storing analytical data based on biological characteristics,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, comprising information integration means for integrating records of successful facial recognition in multiple facial recognition events to improve the accuracy of individual identification.

[0841] (Claim 3)

[0842] The system according to claim 1, comprising a notification means for notifying a personal device of the payment details upon completion of the payment, thereby enabling the individual to check their transaction history.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] Information processing means that receives biometric information acquired by a facial recognition device and analyzes said biometric information as feature points,

[0846] An authentication method that uses analyzed feature points to verify the identity of the user,

[0847] A payment control means that associates the user's electronic transaction account information with the authentication result of this authentication means and automatically executes transactions through said electronic transaction account,

[0848] A device control means that enables face recognition and automated trading by performing a predetermined visual action using a wearable device equipped with a shooting function,

[0849] A display control means that displays the completion of the transaction on the display device of a wearable device,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, comprising data integration means for integrating successful face recognition logs from multiple face recognition events to improve the accuracy of user identity verification.

[0853] (Claim 3)

[0854] The system according to claim 1, further comprising a notification means for notifying the user's terminal of the details of the transaction upon completion of the transaction, thereby enabling the user to check the transaction history.

[0855] "Example 2 of combining an emotion engine"

[0856] (Claim 1)

[0857] A processing means that receives biometric data acquired by a facial recognition device and analyzes the biometric data as feature points,

[0858] An authentication method that verifies the user's identity using analyzed feature points,

[0859] A means of analyzing the emotional state of a user,

[0860] A security control means that activates an additional authentication means when an unnatural emotion is detected by the emotion analysis means,

[0861] A payment control means that associates the user's electronic payment account information with the authentication result obtained by this authentication means and automatically executes the payment via the electronic transaction,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, comprising data integration means for integrating success logs of face recognition and success logs of emotion analysis in multiple face recognition events to improve user authentication accuracy and emotion determination accuracy.

[0865] (Claim 3)

[0866] The system according to claim 1, further comprising a notification means for notifying the user's device of the transaction details upon completion of payment, thereby enabling the user to check the transaction history.

[0867] "Application example 2 of combining emotional engines"

[0868] (Claim 1)

[0869] A processing means that receives biometric data acquired by a facial recognition device and analyzes the biometric data as feature points,

[0870] An authentication method that verifies the user's identity using analyzed feature points,

[0871] A means for analyzing the emotional state of a user,

[0872] A decision-making mechanism that requests additional authentication based on the user's emotional state,

[0873] A payment control means that associates the user's electronic payment account information with the authentication result by an authentication means and automatically executes the payment via the electronic payment account,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, comprising data integration means for integrating face recognition success logs and emotion analysis data from multiple face recognition events to improve user authentication accuracy.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising a notification means that notifies the user terminal of the payment details upon completion of payment and enables the user to check the transaction history, including the sentiment analysis results. [Explanation of symbols]

[0879] 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 processing means that receives biometric data acquired by a facial recognition device and analyzes the biometric data as feature points, An authentication method that verifies the user's identity using analyzed feature points, A payment control means that associates the user's electronic payment account information with the authentication result obtained by this authentication means and automatically executes the payment via the electronic payment account, A system that includes this.

2. The system according to claim 1, comprising data integration means for integrating successful face recognition logs from multiple face recognition events to improve user authentication accuracy.

3. The system according to claim 1, further comprising a notification means for notifying the user terminal of the payment details upon completion of the payment, thereby enabling the user to check the transaction history.

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